CN102013150A - System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation - Google Patents

System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation Download PDF

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CN102013150A
CN102013150A CN2010102979456A CN201010297945A CN102013150A CN 102013150 A CN102013150 A CN 102013150A CN 2010102979456 A CN2010102979456 A CN 2010102979456A CN 201010297945 A CN201010297945 A CN 201010297945A CN 102013150 A CN102013150 A CN 102013150A
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soil moisture
moisture content
rainfall
slope
geologic hazard
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CN102013150B (en
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汤一平
俞立
田贤园
宗明理
何熊熊
孙福顶
孟炎
叶良波
吴立娟
陈才国
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Zhejiang University of Technology ZJUT
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Abstract

The invention discloses a system for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation. The system comprises an automatic rainfall gauge, a soil moisture sensor, an omnibearing tilt sensor, an omnibearing vision sensor, an embedded system and a monitoring center computer, wherein the automatic rainfall gauge is used for monitoring rainfall and rainfall intensity of a geologic hazard point; the soil moisture sensor is used for monitoring moisture in soil at the geologic hazard point; the omnibearing tilt sensor is used for detecting earth surface deformation and internal deformation of the geologic hazard point; the omnibearing vision sensor is used for evaluating geologic hazard scale; the embedded system is used for wirelessly transmitting video and monitoring data; and the monitoring center computer is used for predicting and forecasting the geologic hazard and comprises a communication module, a data receiving module, a rainfall and rainfall intensity-based geologic hazard prediction module, a slope displacement-time curve-based geologic hazard prediction module, a soil moisture, rainfall and rainfall intensity-based geologic hazard prediction module and a soil moisture content and slope deformation-based geologic hazard prediction module. The system has the advantages of complete monitoring means, accurate prediction and forecast, high intelligence and on-line real time.

Description

Geological hazards prediction system based on raininess, slope soil moisture content and deformation quantity
Technical field
The present invention relates to a kind of geologic hazard monitoring and forecasting system based on technology such as the mechanics of materials, statics, dynamics, computer software and hardware, sensing detection.
Background technology
Because the polytrope of geologic media, randomness that the complicacy of geologic hazard genesis mechanism, space-time develop and process sudden, the mankind can't carry out prediction to geologic hazard at present effectively, and its basic reason is that the technological means that is adopted does not also possess the ability of perception environment effectively.
Learn from office of the national mitigation council, the first half of the year in 2010, China's disaster situation presents characteristics such as " great disaster frequently take place; casualty loss is huge ", this year 1 is to June, 19522 of geologic hazards take place in the whole nation, and 3514 people die in all kinds of disasteies, because of 2113.9 hundred million yuan of calamity direct economic losses.The most frequent, the disaster-stricken face of the generation of geologic hazard is the widest in the wherein various disasteies, loss is maximum.Compared to June with 2009 1, the geologic hazard generation same period in this year quantity growth nearly 10 times.
Geologic hazard has three characteristics.The one, multi-point and wide-ranging, based on landslide and avalanche.The 2nd, regional strong, scale is based on small-sized.The 3rd, based on the heavy showers initiation.
Aspect monitoring technology, the monitoring on avalanche, landslide mainly concentrates on surface deformation monitoring, underground deformation monitoring, is out of shape relevant physical quantity monitoring with the landslide avalanche, and concrete monitoring method is existing in " People's Republic of China's geological and mineral industry standard: avalanche, landslide, rubble flow monitoring standard " clearly to be stipulated.Except above routine monitoring method, relatively the method for widespread use also has TDR deformation monitoring, GPS deformation monitoring, radio network technique be applied to come down underground water and crack dynamic monitoring continuously automatically at present.The monitoring content of rubble flow then mainly is divided into formation condition (solid matter source, resource of water supply etc.) monitoring, motion conditions (dynamic key element that flows, dynamic factors and defeated move to dash become silted up etc.) monitoring, characteristic of fluid (material form and material resources chemical property etc.) monitoring, and concrete monitoring technology then mainly contains automatic monitoring methods such as earthquake sounds monitoring, the monitoring of mud position and impulsive force monitoring.
(number of patent application: 200710178762.0) disclose geological calamity rainfall monitoring prealarmer, this invention provides a kind of suitable geologic hazard meteorological early warning to Chinese invention patent, and is simple to operate, dustproof anti-blocking geological calamity rainfall monitoring prealarmer.It is characterized in that geologic hazard rainfall monitoring and early warning are organically combined, carry out the geologic hazard early warning according to the rainfall monitoring value of actual measurement.By liquid level reading observation rainfall value,, prevent by woven wire that foreign matter from entering and hold the rain mouth and blocking pipeline prevents that by jettison gear dust is residual by triangular-notch weir monitoring rainfall intensity.Chinese invention patent (number of patent application: 200910058195.4) disclose a kind of geologic hazard emergency monitoring prediction analysis method, by at the disposable radar responder of random even input or in the geologic hazard zone that forms of prediction, allow it on the face of land, move with landslide massif or rubble flow, flood etc.By adopting radar scanning technic, fast monitored directly perceived is the running orbit of radar responder relatively, in conjunction with place, the time of landform, the further analysis and judgement disaster formation of landforms situation, cooperate out of Memory such as rainfall amount, earthquake to draw basic cause, important informations such as the time period that territorial scope that disaster will influence and prediction take place, safe escape route.Chinese invention patent (number of patent application: 200910241585.5) disclose geologic hazard mass presdiction and disaster prevention multifunctional ruler and measuring method thereof, this invention comprises and fixing on the ground and with the measuring staff and the L type carriage of ground rigidity rotation, the top movable on the long limit of L type carriage has mounted first dip stick, its its length direction of minor face upper edge is provided with second dip stick, also is provided with compass on the L type carriage; The postpone length direction of measuring staff of the long limit of L type carriage reclines measuring, so that first dip stick is according to the change of measuring staff attitude and around the rotation that mounts of its upper end, second dip stick is measured the scale of the first dip stick bottom and its intersection location simultaneously.The present invention comes inverse to go out domatic inclination angle by measuring length, utilizes compass to measure the angle, fracture azimuth, measures the crack relative displacement with first dip stick and second dip stick.Chinese invention patent (number of patent application: 200910058196.9) disclose a kind of intelligent geological calamity synthetic monitoring system and multi-stage prediction analysis method, this system is a kind of capable of being combined as required, dismounting, be suitable for the open-air monitoring device of installing, after its installation combination is finished is a shaft, deep deformation information is in the system information analysis controlling unit under timing measurement and the terrestrial wireless emission slope land, in conjunction with adopting laser scanner, regularly continuous sweep obtains image in the monitored area, also set up multiple spot fixed point laser range finder simultaneously, selected object is carried out precision ranging.It is main monitoring and prediction foundation with the direct deformation displacement of the underground deep deformation of geologic hazard body and the face of land, change auxiliary parameter and risk factor in conjunction with physics and chemical fields, hazard forecasting is divided into preparation level, early warning level, forecast in early stage level, hazard forecasting level Four, just finishes monitoring, analysis, forecast function ground integratedly.
Above-mentioned several disclosed patented technologies exist the problem of the following aspects at least: monitoring method competitive list 1), thus the information when having caused the observation geologic hazard to take place is abundant inadequately; 2) monitoring means does not take into full account the cause-effect relationship that geologic hazard takes place, thereby has caused being difficult to hold the omen that geologic hazard takes place; 3) monitoring instrument that is adopted is relatively more expensive, thereby has caused being difficult to the extensive real-time and dynamic geological hazards prediction forecast of implementing; 4) do not adopt the formation mechanism of thought, the mechanics of materials, dynamics and static analytical geologic hazard of philosophy and the generation evolution process of whole geologic hazard as yet, thereby caused being difficult to explain well the problem such as sudden of the complicacy of geologic hazard genesis mechanism, randomness that space-time develops and process.The breakthrough of any one great natural science technology nearly all is to be based upon correct philosophical thinking to instruct on the basis, and it is guidance that the prediction of geologic hazard equally also needs philosophical thinking.
Summary of the invention
For overcome means competitive list one on the existing geologic hazard monitoring and forecasting forecasting procedure, the insufficient prediction model that causes of information is not accurate; The relation of not considering the chain of causation that geologic hazard takes place causes cause-effect relationship unintelligible; Monitoring instrument costliness, the more coarse geologic hazard situation that can only monitor some parts that causes of monitoring method are difficult to hold the essence of geologic hazard from the overall situation and whole angle; Mechanism and the whole process that takes place from geologic hazard do not decomposed fully, caused be difficult to hold at the prediction of macroscopic view, middle sight and microcosmic different angles and at the aspects such as monitoring of each material time point of disaster generating process main key core point, etc. some problems, the invention provides that a kind of monitoring means is comprehensive, prediction is accurate, the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity of intelligent degree height, real-time online.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of based on rainfall, the geological hazards prediction system of slope soil moisture content and deformation quantity comprises the automatic rain gage of the rainfall raininess that is used to monitor geologic hazard point, be used for monitoring the soil moisture sensor of geologic hazard point soil moisture, be used to detect the face of land of geologic hazard point and the omnibearing tilt sensor of inner deformation, be used to assess the omnibearing vision sensor of geologic hazard generation scale, the embedded system that is used for wireless transmission video and various Monitoring Data, be used to carry out Surveillance center's computing machine of geological hazards prediction forecast, be used for described omnibearing vision sensor, the solar powered unit of described soil moisture sensor and described automatic rain gage power supply; Described omnibearing vision sensor, described soil moisture sensor is connected with described embedded system with described automatic rain gage, described embedded system is connected with the Surveillance center computing machine by communication, described omnibearing vision sensor, described soil moisture sensor and described automatic rain gage are placed on the massif of geologic hazard origination point, described automatic rain gage, described omnibearing vision sensor and described embedded system are configured in the same vertical rod, and described soil moisture sensor is implanted near the depth of soils the described vertical rod;
Described omnibearing tilt sensor is configured on the massif of geologic hazard origination point, to form a power sensing net; Each omnibearing tilt sensor all has the code clerk that place position is buried in a reflection underground; Between each omnibearing tilt sensor and and described embedded system between adopt the mode of radio communication;
Described Surveillance center computing machine comprises:
Communication module is used for carrying out radio communication and various computer network communication based on the 3G wireless communication protocol with described embedded system, receives and transmits various control datas and Monitoring Data;
Data reception module, be used to receive the various Monitoring Data that send from described embedded system, described Monitoring Data comprises rainfall raininess data, soil moisture data, on-the-spot panoramic video data and the slope shape parameter certificate that has geographical location information; Simultaneously with these data with collecting location, be that the geographic position data of geologic hazard point is that major key leaves in the multimedia database; The geographic position data of geologic hazard point is named with the GPS locator data of this geologic hazard point; Implanting the geographic position data of the omnibearing tilt sensor in the slope names with the GPS locator data of burying underground a little;
Based on the geological hazards prediction module of rainfall raininess,, as shown in table 1 in order to adopt rainfall raininess and two kinds of Monitoring Data of slope deformation; The prerequisite that adopts this module to carry out effectively accurately prediction is to have understood fully that this geologic hazard point breaks out the critical rainfall amount of rubble flow, and table 1 is an early warning preventive measure corresponding tables under the different raininess situations between flush period:
Figure BSA00000291127500041
Table 1
In the table 1, the mm/d of unit of raininess, raininess below 25mm/d be in, light rain, raininess is a heavy rain between 25~49.9mm/d, raininess is a heavy rain between 50~100mm/d, it is extra torrential rain that raininess surpasses 100mm/d;
Carry out the geological hazards prediction module based on displacement of inclined plane-time curve, be used for utilizing the relation of the stressed and deformation of the mechanics of materials to predict, the slope of slope deformation curve utilizes grazing angle α iExpress, as shown in Equation (1),
α i = arctan ( T ( i ) - T ( i - 1 ) t i - t i - 1 ) = ΔT Δt - - - ( 1 )
In the formula (1), i (i=1,2,3 ..., n) be time series: α iBe the grazing angle of accumulation displacement T (i),
Figure BSA00000291127500052
Δ S (i) is a displacement of inclined plane variable quantity in a certain unit interval section; V is the rate of displacement of constant speed deformation stage; T (i) for after the conversion with the ordinate value of identical dimension of time; t iBe a certain monitoring moment;
Judge according to formula (1) result of calculation and by following condition,
Work as α iThe slope is in the initial deformation stage in the time of<45 °;
Work as α iThe slope is in the constant speed deformation stage during 45 ° of ≈;
45 °<α iBe first boost phase in the time of<80 °, send blue early warning;
80 °≤α iBe middle boost phase in the time of<85 °, send orange early warning;
α iFor facing the sliding stage, send red early warning in the time of 〉=85 °;
Geological hazards prediction module based on soil moisture content and rainfall raininess, in order to according to geologic hazard be controlled by sliding mass from gravity and shearing strength, the method of carrying out prediction according to water cut in the soil and deflection obtains prediction curve, as shown in Figure 6, wherein, K1 is the raininess-deformation geological hazards prediction curve of less soil moisture content, is that moisture in the soil reaches 1.5% as K1; K5 is the raininess-deformation geological hazards prediction curve of more soil moisture content, is that moisture in the soil reaches 11.5% as K5; Ordinate represents that rainfall raininess value predicted value, horizontal ordinate represent the deflection of sliding mass;
Can know from described prediction curve, for same deflection, uppermost curve K1 shows, bigger rainfall raininess threshold value just can cause geologic hazard and take place, there are K2, K3, three curves of K4 in the centre, and nethermost curve K5 shows that then less rainfall raininess threshold value will cause geologic hazard and take place; At first do a perpendicular line according to the deflection δ n that monitors in prediction curve, can obtain 5 points with 5 curve intersections, the water cut according to detected soil finds and immediate 2 points of this water cut, A2 and A3 then; Obtain 1 point on perpendicular line, An according to the mode of linear interpolation; The ordinate value of this An is exactly critical raininess prediction threshold value Yn, carries out different emergency measures according to the rainfall raininess value of weather forecast; Because the deflection δ n of sliding mass and the water cut An in the soil are constantly changing, the algorithm of prediction is continuous cycle calculations;
Judgment mode is that forecast raininess value Yf predicts that with present critical raininess threshold value Yn compares, and sends blue early warning when 50%Yn≤Yf<75%Yn, sends orange early warning when 75%Yn≤Yf<100%Yn, sends red early warning when Yn≤Yf.
As preferred a kind of scheme: described Surveillance center computing machine also comprises: based on the geological hazards prediction module of soil moisture content and slope deflection, be used to utilize the information such as rate of change of rate of change, slope deflection and the slope deflection of soil moisture content, soil moisture content; The variable quantity of displacement of inclined plane variable quantity and soil moisture content calculates with formula (2), (3);
Δδ(i)=(δ(i)-δ(i-1))/Δt (2)
ΔH(i)=(H(i)-H(i-1))/Δt (3)
Δ δ (i) is a displacement of inclined plane variable quantity in a certain unit interval section, δ (i) is existing monitoring displacement of inclined plane amount constantly, δ (i-1) is a last monitoring displacement of inclined plane amount constantly, Δ H (i) is the variable quantity of soil moisture content in a certain unit interval section, H (i) is existing monitoring soil moisture content constantly, H (i-1) is a last monitoring soil moisture content constantly, and Δ t is twice detection interval time constantly;
The stress of sliding mass and the computing method of stress rate are represented with formula (4);
σ(i)=w×sinα×(1+H(i))/D (4)
Δσ(i)=w×sinα×ΔH(i)/D
In the formula, σ (i) is the adaptability to changes of sliding mass, w * sin α/D is the adaptability to changes that is produced by the deadweight of sliding mass moisture-free, w is the moisture free deadweight of sliding mass, α is the angle on the slope at sliding mass place, D be sliding mass perpendicular to the minimum sectional area on the stress direction, w * sin α * H (i)/D is the adaptability to changes that is produced by the only moisture branch of sliding mass branch, Δ σ (i) be the variable quantity by the adaptability to changes of the only moisture branch of sliding mass branch generation;
The relation of representing soil body stress and strain with formula (5);
K1(i)=Δσ(i)/Δδ(i)=w×sinα×ΔH(i)/D×Δδ(i)=k×ΔH(i)/Δδ(i)?(5)
In the formula, k uses formula (6) expression near a constant,
k=w×sinα/D (6)
For the ease of calculating, we are rewritten into formula (5) form of formula (7);
K(i)=K1(i)/k=ΔH(i)/Δδ(i) (7)
Know that from the equation of formula (7) size of K (i) value depends primarily on the water cut the soil and the changing value of displacement of inclined plane amount; With the judgement of comparing of ratio K (i) value of the rate of change of stress and strain and several mechanics control threshold value, the main flow process of algorithm is as follows;
Step 1: read current soil moisture content and slope deflection data, these data are saved in the multimedia database;
Step 2: judge whether soil moisture content reaches 11.5%, and situation about not reaching forwards step 1 to;
Step 3: the soil moisture content and the slope deflection data that read the previous time, with the variation delta H (i) of formula (2), (3) calculating displacement of inclined plane variation delta δ (i) and soil moisture content, use the ratio K (i) of formula (7) calculating then based on the stress and strain of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content;
Step 4: compare according to the ratio K (i) of stress and strain and several mechanics control threshold values, if K (i) 〉=KV1 then judge the elastic deformation stage that is at present; If KV1>K (i) 〉=KV2 then judge and be in the plastic yield stage at present if the variation delta H of soil moisture content (i) 〉=KH1 sends orange early warning information, otherwise sends blue early warning information; If KV2>K (i) 〉=KV3 then judge is in plastic yield at present to the transition stage that destroys, at this moment send red early warning information; Forward step 1 to;
In the above-mentioned algorithm, KH1 is the control threshold value of the variable quantity of soil moisture content, and KV1, KV2, KV3 are respectively mechanics control threshold value, and satisfies the following KV1>KV2>KV3 that concerns.
As preferred another kind of scheme: described Surveillance center computing machine also comprises: the decision-making supplementary module, be used for the result of determination of above-mentioned 4 kinds of prediction module is carried out comprehensively, it is auxiliary to adopt average weighted mode to make a strategic decision, the weights coefficient of described geological hazards prediction module based on the rainfall raininess is respectively 0.5, the described weights coefficient that carries out the geological hazards prediction module based on displacement of inclined plane-time curve is respectively 1.05, the weights coefficient of described geological hazards prediction module based on soil moisture content and rainfall raininess is respectively 1.75, the weights coefficient of described geological hazards prediction module based on soil moisture content and slope deflection is respectively 1.2, quantized value with blue early warning is defined as 3 simultaneously, the quantized value of orange early warning is defined as 6, the quantized value of red early warning is defined as 9, calculate final comprehensive judged result with formula (8)
R=(R I×K I+R II×K II+R III×K III+R IV×K IV)/(K I+K II+K III+K IV) (8)
In the formula, R IAnd K IBe respectively described geological hazards prediction module and predict the outcome and weight coefficient R based on the rainfall raininess IIAnd K IIBeing respectively described carries out the geological hazards prediction module based on displacement of inclined plane-time curve and predicts the outcome and weight coefficient R IIIAnd K IIIBe respectively described geological hazards prediction module and predict the outcome and weight coefficient R based on soil moisture content and rainfall raininess IVAnd K IVBeing respectively described geological hazards prediction module based on soil moisture content and slope deflection predicts the outcome and weight coefficient, R is the final comprehensive result of calculation of judging, the numerical range of judging result of calculation is 0~9, final judge result of calculation with the early warning color, report, check on and sign and issue management and summarize with table 3, table 3 aid decision making and early warning signal are issued flow process:
Figure BSA00000291127500081
Table 3
Further, described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system adopt rainfall Event triggered mode, when rainy, activate described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system by automatic udometric contact switch, make them enter duty immediately; Rose in 72 hours after rain finishing, if the monitoring and forecasting forecast system does not take place under the geology disaster scenarios it and all monitoring devices all enter dormant state;
Described solar powered unit is made of solar energy photoelectric conversion plate, rechargeable battery and charging circuit, and the capacity of described rechargeable battery satisfies described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and the working time of described embedded system more than 120 hours.
The 3G wireless communications mode is adopted in message exchange between described embedded system and the described Surveillance center computing machine.
Described omnibearing tilt sensor comprises cone, conical conductive coil, insulated wire, following cone, lead, conduction hollow tubular, shell, less radio-frequency generating unit, power supply and mercury; The size of pyramid type conductive coil is big or small identical with last cone inside, coil by four big minor radius of difference in the conical conductive coil constitutes a cone with same center of circle vertical pile, promptly use loop A, coil B, coil C and coil D are superimposed as a taper shape, distance between each coil is a Δ, and constituting between each coil of conical conductive coil is not conducting, each coil all has an extension line to lead to the outside of cone, the pyramid type conductive coil is embedded in the cone, the distance of the edge of the pyramid type conductive coil after the embedding from the bottom of last cone is Δ, be welded on loop A respectively with 4 core insulation lines respectively, coil B, on the extension line of coil C and coil D, the epiconus place of following cone inserts the conduction hollow tubular, one section inside that enters down cone of conduction hollow tubular, insulated wire passes the inside of conduction hollow tubular and insulated wire is guided to down the outside of cone, inserting cementing agent makes conduction hollow tubular and following cone fix and seal, insulated wire keeps state of insulation with the inside and the conduction hollow tubular of following cone, adding mercury in the cone down, the capacity of mercury just in time fills up down cone, to go up cone and the following cone airtight space of formation that is fixed together then, with shell whole omnibearing tilt sensor will be fixed together at last; Last cone and following cone adopt the transparent plastic compacting to form, go between respectively on the shell with conduction hollow tubular lead A that is connected and the quad B that is connected with insulated wire, when omnibearing tilt sensor did not perceive, any core lead among lead A and the quad B did not communicate; When omnibearing tilt sensor has perceived inclination, some flow in the cone at the mercury that descends cone inside, and be embedded in cone in conical conductive coil in coil contact, when being 0.5 °, the angle of inclination contacts with loop A, when being 1 °, the angle of inclination contacts with loop A and coil B, when the angle of inclination is 1.5 ° and loop A, coil B and coil C contact, contact with all coil when the angle of inclination is 2 °, therefore all can make a certain core or multicore cable among lead A and the lead B communicate as long as the inclination of a certain degree appears in any one orientation; The method of judging the angle of inclination is: if communicate between lead A and the D coil just can judge inclination at this moment angle more than 2 ° or 2 °; If do not communicate between lead A and the D coil, between lead A and the coil C mutually the angle of general rule judgement inclination at this moment between 1.5 ° to 2 °; If do not communicate between lead A and D coil and the C coil, between lead A and the coil B mutually the angle of general rule judgement inclination at this moment between 1 ° to 1.5 °; If lead A only and between the loop A conducting judge that then the angle that at this moment tilts is between 0.5 ° to 1 °; If lead A and any coil not conducting judge that then the angle of inclination is below 0.5 °; Among the present invention with of the input of described 5 lines as the data acquisition end of described less radio-frequency generating unit.
Described less radio-frequency generating unit comprises MCU and radio transmitting and receiving chip, MCU is connected by spi bus with radio transmitting and receiving chip, the two constitutes wireless transport module, gives described embedded system with detected angle of inclination data transmission, and communication mode is the ZigBee technology; Described MCU is the STC89LE516AD single-chip microcomputer, is 8 single-chip microcomputers of 51 kernel enhancement mode, and is compatible fully with the IntelMCS51 series monolithic.STC89LE516AD has memory function on the abundant sheet, has 64KBFlash and 512 byte RAM.Singlechip is solidified with the ISP program, downloads by serial ports; Described lead A and described loop A, B, C, D all are linked into the parallel port of STC89LE516AD single-chip microcomputer, with the conducting between described lead A and the described loop A as exterior interrupt, only wake described less radio-frequency generating unit under the conducting situation between described lead A and the described loop A up, described less radio-frequency generating unit is in the park mode state when not tilting to take place;
Single-chip microcomputer at omnibearing tilt sensor comprises initialize routine module, trace routine module and launching procedure module, and the initialize routine module is handled single-chip microcomputer, radio frequency chip, SPI etc.; The trace routine module detects and judges the pitch angle, and detected pitch angle data and detection time are packed with the information such as code clerk that place position is buried in reflection underground; The launching procedure module is delivered to the output of radio frequency generation module with the packet of setting up by single-chip microcomputer SPI interface; Described embedded system receives the Monitoring Data that radio frequency generation module sends over from described omnibearing tilt sensor, and gives described Surveillance center computing machine with these data transmission.
Described automatic rain gage obtains the every day and the data of rainfall raininess per hour, and with data in real time be transferred to described Surveillance center computing machine.
Described soil moisture sensor adopts the condenser type soil moisture sensor, described soil moisture sensor vertically is inserted into the 10CM depths of spongiosa soil, rickle; Be in dormant state at ordinary times, when rainy, just trigger work, can obtain water cut data in the soil by the condenser type soil moisture sensor, and with these data in real time be transferred to described Surveillance center computing machine.
Technical conceive of the present invention is: crack this geological hazards prediction and forecast a global difficult problem, must form mechanism, carry out comprehensive analysis-by-synthesis from geologic hazard from the ubiquity and singularity relation, the principle from quantitative change to qualitative change that breed, develop and take place internal cause that evolution process sets about, takes place from geologic hazard and external cause relation, take place from geologic hazard of whole geologic hazard, from geologic hazard monitoring and forecasting forecast angle hold have ubiquity, the core key issue.
The philosophical thinking of quantitative change and qualitative change is for our enlightenment: 1) generation of geologic hazard is inevitable, and the human only thing that can do is how to take precautions against natural calamities, keep away calamity, mitigation and the disaster relief effectively; 2) will carry out prediction to geologic hazard and just must accurately hold degree when causing the geologic hazard qualitative change, the monitoring means of science and the degree when setting qualitative change exactly according to local concrete condition are crucial; 3) from the viewpoint of systematology, geologic hazard belongs to the critical open system of a kind of self-organization, and the identification of the critical conditions (self-organization critical characteristic) when qualitative change takes place is very crucial.
The philosophical thinking of internal cause and external cause relation is for our enlightenment: the element task of 1) investigating out geologic hazard point accurately is very important, must investigate further from situations such as geology, landforms, the hydrology, vegetation, soil, obtain related data, especially the landforms around the thickness of the angle on slope, spongiosa soil, rickle and scale and the slope; 2) external cause can cause that the variation of things matter, statistics also show sometimes, and greatly all because heavy showers causes, the early-warning and predicting work of carrying out heavy showers at the geologic hazard point is necessary and is highly effective in the geologic hazard of part; 3) the basis viewpoint that external causes become operative through internal causes, only existing more than the certain slope under spongiosa soil, the rickle situation, external influences such as heavy showers make the shearing resistance between spongiosa soil, the increase of rickle water cut and soil and the massif reduce, thereby caused the generation of geologic hazard, from this viewpoint, the size of the water cut in the soil of slope is directly related with the geologic hazard generation, and heavy showers and geologic hazard generation indirect correlation; Carrying out prediction with the water cut in the soil can be more accurate than the early-warning and predicting of heavy showers; 4) according to internal cause and the mutual viewpoint that transforms of external cause, earthquake generation, current hydraulic pressure change, historical geologic hazard generation scale and time or the like, all can change or affect the internal cause of geologic hazard point to a certain extent, must consider these factors during modeling, these factors need be summarized in the historical factor; In addition, prediction modelling well later is not unalterable, need revise according to actual conditions.
Fasten from the pass of ubiquity and singularity, philosophy is for our enlightenment: 1) though trend, the pattern of each geologic hazard point generation geologic hazard are similar, also need the concrete condition concrete analysis at certain geologic hazard point; 2) according to the formula of " characteristics of contradiction singularity=universality of contradiction+difference and things of the like description ", at first must extract the mathematical model of the universal law of describing the geologic hazard generation, the mathematical model of this part is geostationary; To revise mathematical model according to " characteristics of difference and things of the like description " then, to embody the singularity of each geologic hazard point; 3) internal cause of generation geologic hazard and external cause are constantly to change, internal cause and external cause during these change belongs to " characteristics of difference and things of the like description ", and therefore the prediction mathematical model of setting up also must adapt to the internal cause of generation geologic hazard and the variation of external cause; 4) prediction of geologic hazard must be held principal contradiction and the principal aspect of a contradiction, and the soil moisture content increase that the distortion of slope body and heavy showers cause is that the direct inducement of geologic hazard is exactly principal contradiction and the principal aspect of a contradiction that forms geologic hazard; The moisture of the distortion of monitoring slope body, sliding mass soil and rainfall raininess are the keys of carrying out the scientific forecasting forecast in real time.
Prediction must firmly be held the degree of rainfall raininess, the degree of soil moisture content and the degree of soil deformation accurately, exists apparent causal connection between their these three degree.As a geologic hazard point, the generation of geologic hazard mainly be controlled by sliding mass from gravity and shearing strength; As a geologic hazard zone, the generation of geologic hazard not only be controlled by sliding mass from gravity and shearing strength, a situation arises also to be controlled by the geologic hazard point of other direct neighbors, especially the geologic hazard situation that takes place of the top on slope; Therefore, also must fully take into account connecting each other between the chain of causation of geologic hazard, 1. from the slope body soil of the heavy showers of direct inducement → 2. moisture increase → 3. the deadweight increase and the shearing resistance between slope body and the massif of slope body reduce → 4. downward landing power and the shearing resistance of slope body be in any one little disturbance of the instability status of critical conditions → 5. slope body generation plastic yield → 6. → 7. will the weakest point (being in the point of critical conditions) cause the slope body of landslide landslide geologic hazard → 8. and massif take place the landslide of this point of destructive division → 9. come down cause adjacent slopes body unstability affect near on every side near the slope body of critical conditions, break out large-scale geologic hazard (being similar to the sandy beach model); The generating process and the chain of causation with geologic hazard among the present invention are decomposed into above-mentioned 9 nodes, in order to realize realizing accurately the prediction of geologic hazard, must set up corresponding mechanical model from each node on the chain of causation of extensive geology disaster generation, for the geological hazards prediction forecast of event-driven and model-driven is provided fundamental basis.
Beneficial effect of the present invention is: dispose system and command system by set up a kind of great geology calamity emergency based on the geological hazards prediction forecasting procedure of raininess, slope soil moisture content and deformation quantity, realize fast, efficient, science, dispose great geologic hazard in an orderly manner, reduce the loss that geologic hazard causes the mankind to greatest extent.Make the masses that threatened by geologic hazard can improve the ability of " self-identification, self-monitoring, self-forecast, self-precaution, oneself meet an urgent need and oneself's treatment "; Make government decision commander department can improve the level of " fast investigation, fast monitoring, qualitative, fast demonstration, decision-making and fast enforcement soon " soon.
Description of drawings
Fig. 1 is the internal and external reasons graph of a relation based on the geological hazards prediction forecast analysis geologic hazard generation of raininess, slope soil moisture content and deformation quantity;
The mechanical model that Fig. 2 takes place for geologic hazard;
Fig. 3 is the generation evolution process mechanical model of geologic hazard;
The several main observation station that Fig. 4 takes place for geologic hazard;
Fig. 5 puts the arrangement synoptic diagram of various sensors for geologic hazard;
Fig. 6 is the relation curve based on the geological hazards prediction of soil moisture content and rainfall raininess;
Fig. 7 is a kind of power sensing net design of graphics that is used to monitor geologic hazards such as landslide;
Fig. 8 is the power sensing net design of graphics of geologic hazards such as a kind of high precision monitor landslide;
Fig. 9 is a kind of synoptic diagram of automatic rainfall rainfall intensity recorder;
Figure 10 is a kind of synoptic diagram of plug-in type soil moisture sensor;
Figure 11 is a kind of synoptic diagram of omnibearing tilt sensor;
Figure 12 is the stress strain curve of the soil body;
Figure 13 is the geological hazards prediction software of forecasting system chart in Surveillance center's computing machine.
Embodiment
Below in conjunction with accompanying drawing the present invention is further described.
With reference to Fig. 1~Figure 13, a kind of based on rainfall, the geological hazards prediction system of slope soil moisture content and deformation quantity comprises the automatic rain gage of the rainfall raininess that is used to monitor geologic hazard point, be used for monitoring the soil moisture sensor of geologic hazard point soil moisture, be used to detect the face of land of geologic hazard point and the omnibearing tilt sensor of inner deformation, be used to assess the omnibearing vision sensor of geologic hazard generation scale, the embedded system that is used for wireless transmission video and various Monitoring Data, be used to carry out Surveillance center's computing machine of geological hazards prediction forecast, be used for described omnibearing vision sensor, the solar powered unit of described soil moisture sensor and described automatic rain gage power supply; Described omnibearing vision sensor, described soil moisture sensor is connected with described embedded system with described automatic rain gage, described embedded system is connected with the Surveillance center computing machine by communication, described omnibearing vision sensor, described soil moisture sensor and described automatic rain gage are placed in the zone on the landslide that is not easy on the massif of geologic hazard origination point to cave in, as more firm zone, massif basis, as shown in Figure 5, for near the settlement of convenient three sensors of line is configured in as far as possible, described automatic rain gage, described omnibearing vision sensor and described embedded system are configured in the same vertical rod; Described soil moisture sensor is implanted about 10CM depths near the soil of described vertical rod, and the soil of selection need be consistent with the soil on slope, so that can truly reflect the water cut in the soil of slope; Described omnibearing tilt sensor is configured in easy generation landslide and position, landslide according to certain distribution rule, to form a power sensing net, as Fig. 7, shown in Figure 8; Each omnibearing tilt sensor all has the code clerk that place position is buried in a reflection underground, just can know immediately the face of land or inner deformation have taken place in case the omnibearing tilt sensor of some code clerks is subjected to displacement deformation on which locus of massif; Between each omnibearing tilt sensor and and described embedded system between adopt the mode of radio communication, power sensing net just becomes the wireless sense network of a sensing deformation power ZigBee like this;
Communicate by letter often and the zone of electric power supply difficulty in the geologic hazard generation area, therefore need carry out the design of province's power consumption, self-powered and radio communication system; Because geologic hazard takes place all because heavy showers is brought out, so adopt rainfall Event triggered mode in the present invention, promptly only when rainy just start-up system carry out the monitoring and warning forecast, monitoring and forecasting forecast system at ordinary times, comprise that described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system all are in dormant state, can reduce the power consumption of system so greatly; When rainy, activate described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system by automatic udometric contact switch, make them enter duty immediately; Rose in 72 hours after rain finishing, if the monitoring and forecasting forecast system does not take place under the geology disaster scenarios it and all monitoring devices all enter dormant state, to realize economizing the design of power consumption; The power supply of described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system is provided by described solar powered unit, described solar powered unit is made of solar energy photoelectric conversion plate, rechargeable battery and charging circuit, and the capacity of described rechargeable battery need satisfy described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and the working time of described embedded system more than 120 hours; The 3G wireless communications mode is adopted in message exchange between described embedded system and the described Surveillance center computing machine;
For with the minimum more slope of senser element perception deformation information, adopt the omnibearing tilt sensor face of land, perception slope and internal modification simultaneously among the present invention, specific practice is may bury row's vertical rod underground on the ground of generation area in geologic hazards such as landslides, vertical rod adopts cement steel to make, the depth of burying of vertical rod is more than 0.5 meter, the upper end of vertical rod is from about 1 meter of slope, spacing between the vertical rod is 3~5 meters, as shown in Figure 7, connect with cable between each vertical rod, it is 2cm woollen goods wire rods that cable materials adopts diameter, be connected with weaving manner between cable and the cable, aperture area between the cable is not more than 0.25 square metre, omnibearing tilt sensor is placed in the middle vertical rod of each vertical rod,, considers to increase monitoring device if the distance of burying underground between each vertical rod surpasses more than 100 meters, promptly for the situation that a plurality of monitoring devices are arranged, the distance between each monitoring device is about 100 meters; Here be noted that power sensing net is not to causing rocking and tilting of vertical rod under the typhoon situation below 12 grades; When the face of land on slope and innerly power sensing net is produced acting force when deformation takes place, make monitoring device can monitor the inclination of vertical rod, omnibearing tilt sensor work at this moment passes to embedded system by wireless sense network with deformation data;
Because the face of land, perception slope and internal modification need a large amount of omnibearing tilt sensors that use, these omnibearing tilt sensors should satisfy can perception slope body the different face of land and inner deformation quantities, satisfy inclination sensor simultaneously again and be cheap, low-power consumption, requirement such as non-maintaining; Adopted many vertical rods to add the scheme of cable in the present invention, by cable each vertical rod is linked together, in case after being subjected to the external force of rubble flow, landslide generation on any cables or in any vertical rod, inclination in various degree will take place in all vertical rods, thereby make the omnibearing tilt sensor that is placed in the vertical rod perceive the inclination of vertical rod and trigger the transmission tilt data, its principle as shown in Figure 11; Described omnibearing tilt sensor comprises cone, conical conductive coil, insulated wire, cone, lead, conduction hollow tubular, shell, less radio-frequency generating unit, power supply and mercury constitute down; The size of pyramid type conductive coil is big or small identical with last cone inside, coil by four big minor radius of difference in the conical conductive coil constitutes a cone with same center of circle vertical pile, promptly use loop A, coil B, coil C and coil D are superimposed as a taper shape, distance between each coil is a Δ, and constituting between each coil of conical conductive coil is not conducting, each coil all has an extension line to lead to the outside of cone, the pyramid type conductive coil is embedded in the cone, the distance of the edge of the pyramid type conductive coil after the embedding from the bottom of last cone is Δ, be welded on loop A respectively with 4 core insulation lines respectively, coil B, on the extension line of coil C and coil D, the epiconus place of following cone inserts the conduction hollow tubular, one section inside that enters down cone of conduction hollow tubular, insulated wire passes the inside of conduction hollow tubular and insulated wire is guided to down the outside of cone, inserting cementing agent makes conduction hollow tubular and following cone fix and seal, insulated wire keeps state of insulation with the inside and the conduction hollow tubular of following cone, adding mercury in the cone down, the capacity of mercury just in time fills up down cone, to go up cone and the following cone airtight space of formation that is fixed together then, with shell whole omnibearing tilt sensor will be fixed together at last; Last cone and following cone adopt the transparent plastic compacting to form, go between respectively on the shell with conduction hollow tubular lead A that is connected and the quad B that is connected with insulated wire, when omnibearing tilt sensor did not perceive, any core lead among lead A and the quad B did not communicate; When omnibearing tilt sensor has perceived inclination, some flow in the cone at the mercury that descends cone inside, and be embedded in cone in conical conductive coil in coil contact, when being 0.5 °, the angle of inclination contacts with loop A, when being 1 °, the angle of inclination contacts with loop A and coil B, when the angle of inclination is 1.5 ° and loop A, coil B and coil C contact, contact with all coil when the angle of inclination is 2 °, therefore all can make a certain core or multicore cable among lead A and the lead B communicate as long as the inclination of a certain degree appears in any one orientation; The method of judging the angle of inclination is: if communicate between lead A and the D coil just can judge inclination at this moment angle more than 2 ° or 2 °; If do not communicate between lead A and the D coil, between lead A and the coil C mutually the angle of general rule judgement inclination at this moment between 1.5 ° to 2 °; If do not communicate between lead A and D coil and the C coil, between lead A and the coil B mutually the angle of general rule judgement inclination at this moment between 1 ° to 1.5 °; If lead A only and between the loop A conducting judge that then the angle that at this moment tilts is between 0.5 ° to 1 °; If lead A and any coil not conducting judge that then the angle of inclination is below 0.5 °; Among the present invention with of the input of these 5 lines as the data acquisition end of described less radio-frequency generating unit 9;
Described less radio-frequency generating unit mainly comprises MCU and radio transmitting and receiving chip, MCU is connected by spi bus with radio transmitting and receiving chip, the two constitutes wireless transport module, gives described embedded system with detected angle of inclination data transmission, and communication mode is the ZigBee technology; The ZigBee technology be a kind of closely, low complex degree, low-power consumption, low data rate, two-way wireless communication technology cheaply, mainly be suitable for control and remote control field automatically, can be embedded in the various device, support geographic positioning functionality simultaneously, be particularly useful for that use amount among the present invention is big, the layout scope is wide, power consumption requires low, the transmission geologic hazard point face of land that data are few and transmission range is near and the monitoring needs of internal modification;
Described MCU is the STC89LE516AD single-chip microcomputer, is 8 single-chip microcomputers of 51 kernel enhancement mode, and is compatible fully with the IntelMCS51 series monolithic.STC89LE516AD has memory function on the abundant sheet, has 64KBFlash and 512 byte RAM.Singlechip is solidified with the ISP program, downloads by serial ports; Described lead A and described loop A, B, C, D all are linked into the parallel port of STC89LE516AD single-chip microcomputer, with the conducting between described lead A and the described loop A as exterior interrupt, only wake described less radio-frequency generating unit under the conducting situation between described lead A and the described loop A up, described less radio-frequency generating unit is in the park mode state when not tilting to take place;
Described radio transmitting and receiving chip adopts CC2500, and it is a low cost, low-power consumption, high performance radio transmitting and receiving chip, and its working frequency range is the ISM band of 2.4GHz; Have good wireless receiving sensitivity and powerful antijamming capability; Only the stream of 0.9 μ A consumes when park mode, external interrupt or RTC energy waken system; The stream consumption that when standby mode, is less than 0.6 μ A, external interrupt energy waken system; Hardware supported CSMA/CA function; Voltage is 1.8~3.6V; Under transmission mode, when output power be-during 12dBm, current drain is 12mA.The receiver susceptibility of CC2500 is-101dBm (when 10kbps); Peak power output is 0dBm, and data rate can change between 1.2kbps~500kbps; The USART that has 2 powerful several groups of agreements of support, and 16 bit timers and 28 bit timers of 1 MAC timer, 1 routine;
Mainly comprised initialize routine, trace routine and launching procedure in the software of the single-chip microcomputer of omnibearing tilt sensor, initialize routine mainly is that single-chip microcomputer, radio frequency chip, SPI etc. are handled; Trace routine mainly detects and judges the pitch angle, and detected pitch angle data and detection time are packed with the information such as code clerk that place position is buried in reflection underground; Launching procedure is delivered to the output of radio frequency generation module with the packet of setting up by single-chip microcomputer SPI interface; Described embedded system receives the Monitoring Data that radio frequency generation module sends over from described omnibearing tilt sensor, and these data are transferred to described Surveillance center computing machine by 3G cordless communication network or computer communication network; Therefore, from the notion of network, several omnibearing tilt sensors and embedded system have constituted WLAN (wireless local area network), and embedded system and Surveillance center's computing machine have constituted wireless wide area network;
Described automatic rain gage employing Chinese invention patent application number is 200610154671.9 the intelligence testing apparatus for precipitation rain fall based on computer vision, can obtain the every day and the data of rainfall raininess per hour by this equipment, and with these data in real time be transferred to described Surveillance center computing machine; In general, automatic rain gage of configuration has satisfied the needs of geology disaster monitoring basically in 2 square kilometres zone, considers from economic angle, adopts an automatic rain gage at a geologic hazard point;
Described soil moisture sensor adopts the condenser type soil moisture sensor, adopts a kind of pointer soil moisture sensor among the present invention, described soil moisture sensor vertically is inserted into the 10CM depths of spongiosa soil, rickle; Be in dormant state at ordinary times, only when rainy, just trigger work, can obtain water cut data in the soil by this sensor, and with these data in real time be transferred to described Surveillance center computing machine; In general, pointer soil moisture sensor of configuration has satisfied the needs of geology disaster monitoring basically in 2 square kilometres zone, considers from economic angle, adopts a pointer soil moisture sensor at a geologic hazard point;
Described omnibearing vision sensor adopt the Chinese invention patent application number be 200610154827.3 based on the technology in the mud-stone flow disaster pick-up unit of omni-directional visual, on-the-spot panoramic picture in the time of obtaining the geologic hazard generation by this device, can assess the scale of geologic hazard generation and the harm that is caused objectively, the on-the-spot panoramic video data that omnibearing vision sensor is gathered are transferred to described Surveillance center computing machine in real time by described embedded system; In general, omnibearing vision sensor of configuration has satisfied the needs of geology disaster monitoring basically in 2 square kilometres zone, considers omnibearing vision sensor of configuration on the hillside of a geologic hazard point from economic angle;
Described embedded system, the wireless sense network by ZigBee reads the angle of inclination of described omnibearing tilt sensor and the data such as coding of described omnibearing tilt sensor; Read the monitoring soil moisture value of described soil moisture sensor by the A/D interface; Obtain described automatic udometric rainfall raininess monitor value by the analysis of image data that described omnibearing vision sensor obtained; Obtain the panoramic picture at geologic hazard origination point scene by described omnibearing vision sensor; Adopt 3G wireless communication transmissions Monitoring Data between described embedded system and the described Surveillance center computing machine;
Software in the described Surveillance center computing machine comprises: communication module, data reception module, based on the geological hazards prediction module of rainfall raininess, carry out geological hazards prediction module, the geological hazards prediction module based on soil moisture content and rainfall raininess, the geological hazards prediction module based on soil moisture content and slope deflection, decision-making supplementary module, information issuing module based on displacement of inclined plane-time curve;
Described communication module is used for carrying out radio communication and various computer network communication based on the 3G wireless communication protocol with described embedded system, receives and transmits various control datas and Monitoring Data;
Described data reception module, be used to receive the various Monitoring Data that send from described embedded system, described Monitoring Data comprises rainfall raininess data, soil moisture data, on-the-spot panoramic video data and the slope shape parameter certificate that has geographical location information; Simultaneously with these data with collecting location, be that the geographic position data of geologic hazard point is that major key leaves in the multimedia database; The geographic position data of geologic hazard point is named with the GPS locator data of this geologic hazard point; Implanting the geographic position data of the omnibearing tilt sensor in the slope names with the GPS locator data of burying underground a little;
Described geological hazards prediction module based on the rainfall raininess, mainly predict according to the data and the rainfall raininess data of slope deflection, Fig. 1 has shown the various internal and external reasons relations that geologic hazard takes place, because rainfall is the topmost external factor of bringing out the landslide, need to pay close attention to the slope deformation under the different rainfall rank situations, take different Disposal Measures, in this module, adopted two kinds of Monitoring Data of rainfall raininess and slope deformation, as shown in table 1; Though this Forecasting Methodology has tangible causalnexus, but need confirm with the slope shape variable, therefore it is very short to the time that the local resident takes refuge to carry out early-warning and predicting constantly at this, as shown in Figure 4, the prerequisite that adopts this module to carry out effectively accurately prediction is to have understood fully that this geologic hazard point breaks out the critical rainfall amount of rubble flow, the time of early-warning and predicting can be shifted to an earlier date like this, provide the sufficient time for keeping away calamity; Based on the geological hazards prediction of rainfall raininess come down to use on the geologic hazard generation chain of causation 1., the 8. monitoring of node;
Early warning preventive measure corresponding tables under the different raininess situations between table 1 flush period
Figure BSA00000291127500191
Describedly carry out the geological hazards prediction module based on displacement of inclined plane-time curve, be to utilize the relation of the stressed and deformation in the mechanics of materials to predict, material is stressed with relation deformation to be: along with the stressed material of stressed increase of material from elastic deformation, plastic yield to the process of destroying; In general, during elastic deformation, according to the Hooke theorem, distortion of materials speed almost is a constant; During plastic yield, distortion of materials speed increases gradually, and a distortion acceleration flex point can occur, as the t3 point of Fig. 4; Therefore according to the slope variation characteristics in each stage of slope deformation curve, can adopt mathematical method quantitatively to judge.The slope of slope deformation curve can utilize grazing angle α iExpress, as shown in Equation (1).
α i = arctan ( T ( i ) - T ( i - 1 ) t i - t i - 1 ) = ΔT Δt - - - ( 1 )
In the formula (1), i (i=1,2,3 ..., n) be time series: α iBe the grazing angle of accumulation displacement T (i), Δ S (i) is the interior displacement of inclined plane variable quantity of a certain unit interval section (generally adopting a monitoring periods, as 1 day, 1 week etc.); V is the rate of displacement of constant speed deformation stage; T (i) for after the conversion with the ordinate value of identical dimension of time; t iBe a certain monitoring moment.
Judge according to formula (1) result of calculation and by following condition,
Work as α iThe slope is in the initial deformation stage in the time of<45 °;
Work as α iThe slope is in the constant speed deformation stage during 45 ° of ≈;
45 °<α iBe first boost phase in the time of<80 °, send blue early warning;
80 °≤α iBe middle boost phase in the time of<85 °, send orange early warning;
α iFor facing the sliding stage, send red early warning in the time of 〉=85 °;
Grazing angle α when landslide and rubble flow take place iGenerally be about 89 °.
This Forecasting Methodology is owing to just used the information of slope distortion, and requirement can accurately monitor the process of slope deflection and distortion in real time, and in addition, the signal of the deflection on slope is smaller, be easy to introduce and disturb, and can be used as a kind of supplementary means of prediction; Based on displacement of inclined plane-time curve carry out geological hazards prediction come down to utilize on the geologic hazard generation chain of causation 5., 6., the 8. monitoring of node;
Described geological hazards prediction module based on soil moisture content and rainfall raininess, its main according to the generation that is geologic hazard mainly be controlled by sliding mass from gravity and shearing strength, as shown in Figure 2; What wherein the water cut in the soil promptly affected sliding mass affects the shearing strength of sliding mass again from gravity, when the moisture in the soil reaches 11.5%, sliding mass from gravity can increase by 11.5% simultaneously shearing force reduce rapidly; Obviously utilize water cut and deflection in the soil to carry out the precision that prediction can improve prediction significantly; If the water cut in the soil reach 11.5% and distortion be in boost phase just, that the probability of geologic hazard takes place is just very high for this geologic hazard point so, at this moment just can be used as early-warning and predicting information publishing point; If also have bigger rainfall raininess in the recent period, just should start urgent prediction scheme, the crowd that rapid evacuation may jeopardize in rainy or forecast at once; Carry out the method curve as shown in Figure 6 of prediction according to water cut in the soil and deflection, wherein K1 is the raininess-deformation geological hazards prediction curve of less soil moisture content, is that moisture in the soil reaches 1.5% as K1; K5 is the raininess-deformation geological hazards prediction curve of more soil moisture content, is that moisture in the soil reaches 11.5% as K5; Ordinate represents that rainfall raininess value predicted value, horizontal ordinate represent the deflection of sliding mass; Based on the geological hazards prediction of soil moisture content and rainfall raininess come down to utilize on the geologic hazard chain of causation 1., 2., 3., 4., 5., 6., the 8. monitoring of node;
Can know that from the curve of Fig. 6 for same deflection, uppermost curve K1 shows that bigger rainfall raininess threshold value just can cause geologic hazard and take place, nethermost curve K5 shows that then less rainfall raininess threshold value will cause geologic hazard and take place; A lot of geologic hazards take place can the above-mentioned prediction curve correctness of good explanation, the geologic hazard that causes as light rain after the snow melt and a light rain after a few days ago raining heavily; In this forecast model, the monitoring that to bring out external cause-raininess that geologic hazard takes place in time converts the internal cause monitoring soil from the soil moisture content of gravity and shearing strength that influences sliding mass to direct to, since the generation of geologic hazard mainly be controlled by sliding mass from gravity and shearing strength, hold this main key core problem and can improve precision of prediction effectively; The algorithm of prediction is as follows: at first do a perpendicular line according to the deflection δ n that monitors in Fig. 6, can obtain 5 points with 5 curve intersections, the water cut according to detected soil finds and immediate 2 points of this water cut, A2 and A3 then; Obtain 1 point on perpendicular line, An according to the mode of linear interpolation; The ordinate value of this An is exactly critical raininess prediction threshold value Yn, carries out different emergency measures according to the rainfall raininess value of weather forecast; Because the deflection δ n of sliding mass and the water cut An in the soil are constantly changing, the algorithm of prediction is continuous cycle calculations; Judgment mode is that forecast raininess value Yf predicts that with present critical raininess threshold value Yn compares, and sends blue early warning when 50%Yn≤Yf<75%Yn, sends orange early warning when 75%Yn≤Yf<100%Yn, sends red early warning when Yn≤Yf;
Described geological hazards prediction module based on soil moisture content and slope deflection, the description of the strain-stress relation of soil is the key problem of the various mechanical properties of the research soil body, as shown in figure 12, soil moisture content is a physical quantity directly related with the stress of soil, the slope deflection is again the amount directly related with the strain of soil, and the main mathematical model of the ess-strain of the soil body that adopts is predicted geologic hazard in this module; In this prediction module, utilize the information such as rate of change of rate of change, slope deflection and the slope deflection of soil moisture content, soil moisture content; The quick increase of the rate of change of soil moisture content must increase the possibility that geologic hazard takes place, the acceleration of the rate of change of same slope deflection also is the omen that geologic hazard takes place, viewpoint from mechanics, geologic hazard takes place must experience the elastic deformation of the soil body, stage such as plastic yield and destruction, to can make a prediction accurately in early days, just need hold the A-stage of the plastic yield of the soil body accurately, therefore this prediction module is a kind of Forecasting Methodology of utilizing static prediction and performance prediction to combine, so-called static prediction is mainly carried out analyses and prediction by the relation curve of ess-strain, and so-called performance prediction is then mainly predicted its development and change trend by the rate of change of ess-strain; Therefore, at first to calculate the rate of change of soil moisture content, the rate of change of slope deflection, comprehensively judge in conjunction with soil moisture content, the slope deflection of current this geologic hazard point that monitors then according to soil moisture content, the slope deflection of the soil moisture content that is recorded in this geologic hazard point in the multimedia database, slope deflection and current this geologic hazard point that monitors; Can obtain the relation curve of soil moisture content and slope deflection for each ground particle, the soil moisture content increase can cause that the deadweight of sliding mass increases, the sliding force size of sliding mass is calculated with the sine value of ramp angles and the product of sliding mass deadweight, and the variable quantity of displacement of inclined plane variable quantity and soil moisture content calculates with formula (2), (3);
Δδ(i)=(δ(i)-δ(i-1))/Δt (2)
ΔH(i)=(H(i)-H(i-1))/Δt (3)
Δ δ (i) is a displacement of inclined plane variable quantity in a certain unit interval section, δ (i) is existing monitoring displacement of inclined plane amount constantly, δ (i-1) is a last monitoring displacement of inclined plane amount constantly, Δ H (i) is the variable quantity of soil moisture content in a certain unit interval section, H (i) is existing monitoring soil moisture content constantly, H (i-1) is a last monitoring soil moisture content constantly, and Δ t was twice detection interval time constantly, such as 10 minutes, 1 hour and 1 day; Use Δ δ among the present invention 10min(i) represent 10 minutes displacement of inclined plane variable quantity of section sometime, Δ δ 1h(i) represent 1 hour displacement of inclined plane variable quantity of section sometime, Δ δ 1day(i) represent 1 day displacement of inclined plane variable quantity of section sometime, Δ H 10min(i) be the variable quantity of 10 minutes soil moisture contents of section sometime, Δ H 1h(i) be the variable quantity of 1 hour soil moisture content of section sometime, Δ H 1day(i) be the variable quantity of 1 day soil moisture content of section sometime;
The stress of sliding mass is that the product two parts by the sine that contains water inventory and value of slope of the sum of products sliding mass of the sine of the deadweight of sliding mass and value of slope are formed by stacking, and the former is determined by local geology and geomorphology environment, is geostationary; The latter is by the common decision of local geology and geomorphology environment and hydrometeorological condition, and mainly the variation by the water cut of soil in the sliding mass changes; The stress of sliding mass and the computing method of stress rate are represented with formula (4);
σ(i)=w×sinα×(1+H(i))/D (4)
Δσ(i)=w×sinα×ΔH(i)/D
In the formula, σ (i) is the adaptability to changes of sliding mass, w * sin α/D is the adaptability to changes that is produced by the deadweight of sliding mass moisture-free, w is the moisture free deadweight of sliding mass, α is the angle on the slope at sliding mass place, D be sliding mass perpendicular to the minimum sectional area on the stress direction, w * sin α * H (i)/D is the adaptability to changes that is produced by the only moisture branch of sliding mass branch, Δ σ (i) be the variable quantity by the adaptability to changes of the only moisture branch of sliding mass branch generation;
The relation of representing soil body stress and strain with formula (5);
K1(i)=Δσ(i)/Δδ(i)=w×sinα×ΔH(i)×Δδ(i)=k×ΔH(i)/Δδ(i)?(5)
In the formula, k uses formula (6) expression near a constant,
k=w×sinα/n (6)
For the ease of calculating, we are rewritten into formula (5) form of formula (7);
K(i)=K1(i)/k=ΔH(i)/Δδ(i) (7)
The calculated value of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content can select 10 minutes, 1 hour and 1 day to be the time sampling interval as required, for 10 minutes Δ δ 10min(i) and Δ H 10min(i), the undesired signal in the Monitoring Data considers to adopt the mode of single order digital low-pass filtering to eliminate internal disturbance and external disturbance disturbs;
Linear between the stress and strain in the material after solid material is stressed according to the Hooke theorem, as deflection among Figure 12 is the situation in δ interval below 1, basically be one more greatly on the occasion of constant, can know that from the equation of formula (7) size of K (i) value depends primarily on the water cut the soil and the changing value of displacement of inclined plane amount;
Along with presenting plasticity, stressed increase material changes, as deflection among Figure 12 is the situation in δ 1~δ 5 intervals, trend obviously appears reducing in K (i) value, the situation of zero or negative value appears approaching sometimes, therefore the principal character of soil body generation plastic yield that Here it is can utilize this feature to carry out the prediction of geologic hazard;
As shown in figure 12, when deflection reached δ 5 above situations, material destroyed, and initial geologic hazard origination point at this moment just occurred;
Simultaneously we also notice when the moisture in the soil reaches 11.5%, can the increasing by 11.5% while shearing force from gravity and reduce this phenomenon rapidly of sliding mass, and its stressed graph of a relation is as shown in Figure 2; In static prediction we with the water cut in the soil as the emphasis of paying close attention to, such as this point of the H2 in Figure 12 be exactly water cut be 11.5%; Then we control the threshold value judgement of comparing with ratio K (i) value of the rate of change of stress and strain and several mechanics, and the main flow process of algorithm is as follows;
Step 1: read current soil moisture content and slope deflection data, these data are saved in the multimedia database;
Step 2: judge whether soil moisture content reaches 11.5%, and situation about not reaching forwards step 1 to;
Step 3: the soil moisture content and the slope deflection data that read the previous time, with the variation delta H (i) of formula (2), (3) calculating displacement of inclined plane variation delta δ (i) and soil moisture content, use the ratio K (i) of formula (7) calculating then based on the stress and strain of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content;
Step 4: compare according to the ratio K (i) of stress and strain and several mechanics control threshold values, if K (i) 〉=KV1 then judge the elastic deformation stage that is at present; If KV1>K (i) 〉=KV2 then judge and be in the plastic yield stage at present if the variation delta H of soil moisture content (i) 〉=KH1 sends orange early warning information, otherwise sends blue early warning information; If KV2>K (i) 〉=KV3 then judge is in plastic yield at present to the transition stage that destroys, at this moment send red early warning information; Forward step 1 to;
KH1 is the control threshold value of the variable quantity of soil moisture content in the above-mentioned algorithm, and KV1, KV2, KV3 are respectively mechanics control threshold value, and satisfies the following KV1>KV2>KV3 that concerns;
Described geological hazards prediction module based on soil moisture content and slope deflection come down to utilize on the geologic hazard chain of causation 2., 5., 6., the 8. monitoring of node;
Described decision-making supplementary module, be used for above-mentioned multiple geological hazards prediction method is carried out comprehensively, above-mentioned four kinds of Forecasting Methodologies are carried out prediction from different aspects, as shown in figure 13, it is different that each prediction algorithm stresses face, be similar to the prediction judgement that a plurality of experts make, four kinds of prediction result can be consistent under certain conditions, but also different predicting the outcome can appear, therefore we have adopted the mode of information fusion, it is auxiliary promptly to adopt average weighted mode to make a strategic decision, and the node on the geologic hazard chain of causation considers, in general, the many more accuracy of predicting of considering of interstitial content will be high more, node location is high more by the back accuracy of predicting more, and the present invention has designed the weights that the supplementary table of making a strategic decision obtains each module according to this thought, and is as shown in table 2;
The weights coefficient of the various prediction module of table 2
Figure BSA00000291127500241
Described in the table 2 based on the geological hazards prediction module of rainfall raininess with the I type, describedly carry out the geological hazards prediction module based on displacement of inclined plane-time curve and represent with the IV type with III type, described geological hazards prediction module based on soil moisture content and slope deflection with II type, described geological hazards prediction module based on soil moisture content and rainfall raininess; Their weights coefficient is respectively 0.5,1.05,1.75 and 1.2, simultaneously we with the quantized value of blue early warning be defined as 3, the quantized value of orange early warning is defined as 6, the quantized value of red early warning is defined as 9, calculates final comprehensive judged result with formula (8),
R=(R I×K I+R II×K II+R III×K III+R IV×K IV)/(K I+K II+K III+K IV)(8)
In the formula, R IAnd K IBe respectively described geological hazards prediction module and predict the outcome and weight coefficient R based on the rainfall raininess IIAnd K IIBeing respectively described carries out the geological hazards prediction module based on displacement of inclined plane-time curve and predicts the outcome and weight coefficient R IIIAnd K IIIBe respectively described geological hazards prediction module and predict the outcome and weight coefficient R based on soil moisture content and rainfall raininess IVAnd K IVBeing respectively described geological hazards prediction module based on soil moisture content and slope deflection predicts the outcome and weight coefficient, R is the final comprehensive result of calculation of judging, the numerical range of judging result of calculation is 0~9, will finally judge among the present invention result of calculation with the early warning color, report, check on and sign and issue to manage and summarize with table 3;
Table 3 aid decision making and early warning signal issue flow process
Figure BSA00000291127500251
Fig. 3 is the sandy beach model that extensive geology disaster takes place, because the complicated geology of geologic hazard point, the ramp angles of each section massif is different, and the heavy black line among Fig. 3 is represented the gradient of each section of massif; Sliding mass 1 can increase the sliding force 2 of sliding mass 2 to lower slider, the downslide of sliding mass 3 equally also can reduce the shearing force 2 of sliding mass 2, so just formed a chain of causation, the generation of geologic hazard then often begins from the weakest place, all can influence the stable of its relevant up and down sliding mass in case gliding appears in the some sliding masses on the chain of causation, the generation of therefore large-scale geologic hazard must take place to slide from the localized landslip body.Because time and spatial stochastic that the complicacy of the geologic media of geologic hazard point can cause geologic hazard to take place; The tendency that will accurately and timely hold the geologic hazard generation must be monitored the generation of various mishaps, so in deformation of arranging each sliding mass of monitoring on the geologic hazard point and the relation of finding out between each sliding mass all is crucial, the spatial relationship that express between each sliding mass must adopt the GIS technology;
According to the geologic hazard influence degree, personnel's responsibilities at different levels in the early warning signal issue flow process: (1) person on duty thinks when reaching issue, change, releasing early warning signal condition, should in time report to the gaffer forecaster; Report content has comprised the spatial position data of the predicting the outcome of each prediction module, several fundamental surveillance data and prediction geologic hazard point; (2) gaffer's technology of being responsible for issuing, change, remove early warning signal is checked on, and proposes the issue suggestion, signs and issues or reports step by step according to all kinds of rank responsibilities of table 3; (3) after director on duty receives report, be responsible for holding a conference or consultation, propose the issue suggestion, sign and issue or report step by step according to all kinds of rank responsibilities of following table with the gaffer; (4) forecast is comprehensively issued suggestion after the department director receives report, signs and issues or reports step by step according to all kinds of rank responsibilities of table 3.Because the emergentness that geologic hazard takes place, what take place is greatly destructive, the early-warning and predicting that sends in the very first time of racing against time has crucial meaning to keeping away the calamity mitigation, early warning information need be delivered to the related personnel as soon as possible, any related personnel can both directly see the prediction address content that described decision-making supplementary module has been done by network as a reference, and the early warning signal that official makes need be by the issue flow process shown in the table 3;
Described information issuing module comprises being used for towards the information issue of decision-making related personnel's inside early warning signal issue flow process and being used for towards the information issue of the early-warning and predicting in the external world; The information issue is issued by the mode of network platform webpage, but related personnel at different levels can only see the content of own authority, in addition for the issue of other information of yellow early warning higher level, the mode by short message sends to the responsible person concerned, so that in time tackled processing in the very first time.

Claims (10)

1. one kind based on rainfall, the geological hazards prediction system of slope soil moisture content and deformation quantity is characterized in that: described prognoses system comprises the automatic rain gage of the rainfall raininess that is used to monitor geologic hazard point, be used for monitoring the soil moisture sensor of geologic hazard point soil moisture, be used to detect the face of land of geologic hazard point and the omnibearing tilt sensor of inner deformation, be used to assess the omnibearing vision sensor of geologic hazard generation scale, the embedded system that is used for wireless transmission video and various Monitoring Data, be used to carry out Surveillance center's computing machine of geological hazards prediction forecast, be used for described omnibearing vision sensor, the solar powered unit of described soil moisture sensor and described automatic rain gage power supply; Described omnibearing vision sensor, described soil moisture sensor is connected with described embedded system with described automatic rain gage, described embedded system is connected with the Surveillance center computing machine by communication, described omnibearing vision sensor, described soil moisture sensor and described automatic rain gage are placed on the massif of geologic hazard origination point, described automatic rain gage, described omnibearing vision sensor and described embedded system are configured in the same vertical rod, and described soil moisture sensor is implanted near the depth of soils the described vertical rod;
Described omnibearing tilt sensor is configured on the massif spongiosa soil of geologic hazard origination point, to form a power sensing net; Each omnibearing tilt sensor all has the code clerk that place position is buried in a reflection underground; Between each omnibearing tilt sensor and and described embedded system between adopt the mode of radio communication;
Described Surveillance center computing machine comprises:
Communication module is used for carrying out radio communication and various computer network communication based on the 3G wireless communication protocol with described embedded system, receives and transmits various control datas and Monitoring Data;
Data reception module, be used to receive the various Monitoring Data that send from described embedded system, described Monitoring Data comprises rainfall raininess data, soil moisture data, on-the-spot panoramic video data and the slope shape parameter certificate that has geographical location information; Simultaneously with these data with collecting location, be that the geographic position data of geologic hazard point is that major key leaves in the multimedia database; The geographic position data of geologic hazard point is named with the GPS locator data of this geologic hazard point; Implanting the geographic position data of the omnibearing tilt sensor in the slope names with the GPS locator data of burying underground a little;
Based on the geological hazards prediction module of rainfall raininess,, as shown in table 1 in order to adopt rainfall raininess and two kinds of Monitoring Data of slope deformation; The prerequisite that adopts this module to carry out effectively accurately prediction is to have understood fully that this geologic hazard point breaks out the critical rainfall amount of rubble flow, and table 1 is an early warning preventive measure corresponding tables under the different raininess situations between flush period:
Figure FSA00000291127400021
Table 1;
In the table 1, the mm/d of unit of raininess, raininess below 25mm/d be in, light rain, raininess is a heavy rain between 25~49.9mm/d, raininess is a heavy rain between 50~100mm/d, it is extra torrential rain that raininess surpasses 100mm/d;
Carry out the geological hazards prediction module based on displacement of inclined plane-time curve, be used for utilizing the relation of the stressed and deformation of the mechanics of materials to predict, the slope of slope deformation curve utilizes grazing angle α iExpress, as shown in Equation (1),
α i = arctan ( T ( i ) - T ( i - 1 ) t i - t i - 1 ) = ΔT Δt - - - ( 1 )
In the formula (1), i is a time series, i=1, and 2,3 ..., n: α iBe the grazing angle of accumulation displacement T (i),
Figure FSA00000291127400023
Δ S (i) is a displacement of inclined plane variable quantity in a certain unit interval section; V is the rate of displacement of constant speed deformation stage; T (i) for after the conversion with the ordinate value of identical dimension of time; t iBe a certain monitoring moment;
Judge according to formula (1) result of calculation and by following condition,
Work as α iThe slope is in the initial deformation stage in the time of<45 °;
Work as α iThe slope is in the constant speed deformation stage during 45 ° of ≈;
45 °<α iBe first boost phase in the time of<80 °, send blue early warning;
80 °≤α iBe middle boost phase in the time of<85 °, send orange early warning;
α iFor facing the sliding stage, send red early warning in the time of 〉=85 °;
Geological hazards prediction module based on soil moisture content and rainfall raininess, in order to according to geologic hazard be controlled by sliding mass from gravity and shearing strength, the method of carrying out prediction according to water cut in the soil and deflection obtains prediction curve, wherein, K1 is the raininess-deformation geological hazards prediction curve of less soil moisture content; There are K2, K3, three curves of K4 in the centre, and K5 is the raininess-deformation geological hazards prediction curve of more soil moisture content; Ordinate represents that rainfall raininess value predicted value, horizontal ordinate represent the deflection of sliding mass;
Can know from described prediction curve, for same deflection, uppermost curve K1 shows, bigger rainfall raininess threshold value just can cause geologic hazard and take place, there are K2, K3, three curves of K4 in the centre, and nethermost curve K5 shows that then less rainfall raininess threshold value will cause geologic hazard and take place; At first do a perpendicular line according to the deflection δ n that monitors in prediction curve, can obtain 5 points with 5 curve intersections, the water cut according to detected soil finds and immediate 2 points of this water cut, A2 and A3 then; Obtain 1 point on perpendicular line, An according to the mode of linear interpolation; The ordinate value of this An is exactly critical raininess prediction threshold value Yn, carries out different emergency measures according to the rainfall raininess value of weather forecast; Because the deflection δ n of sliding mass and the water cut An in the soil are constantly changing, the algorithm of prediction is continuous cycle calculations;
Judgment mode is that forecast raininess value Yf predicts that with present critical raininess threshold value Yn compares, and sends blue early warning when 50%Yn≤Yf<75%Yn, sends orange early warning when 75%Yn≤Yf<100%Yn, sends red early warning when Yn≤Yf.
2. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 is characterized in that: described Surveillance center computing machine also comprises:
Based on the geological hazards prediction module of soil moisture content and slope deflection, be used to utilize the information such as rate of change of rate of change, slope deflection and the slope deflection of soil moisture content, soil moisture content; The variable quantity of displacement of inclined plane variable quantity and soil moisture content calculates with formula (2), (3);
Δδ(i)=(δ(i)-δ(i-1))/Δt (2)
ΔH(i)=(H(i)-H(i-1))/Δt (3)
Δ δ (i) is a displacement of inclined plane variable quantity in a certain unit interval section, δ (i) is existing monitoring displacement of inclined plane amount constantly, δ (i-1) is a last monitoring displacement of inclined plane amount constantly, Δ H (i) is the variable quantity of soil moisture content in a certain unit interval section, H (i) is existing monitoring soil moisture content constantly, H (i-1) is a last monitoring soil moisture content constantly, and Δ t is twice detection interval time constantly;
The stress of sliding mass and the computing method of stress rate are represented with formula (4);
σ(i)=w×sinα×(1+H(i))/D (4)
Δσ(i)=w×sinα×ΔH(i)/D
In the formula, σ (i) is the adaptability to changes of sliding mass, w * sin α/D is the adaptability to changes that is produced by the deadweight of sliding mass moisture-free, w is the moisture free deadweight of sliding mass, α is the angle on the slope at sliding mass place, D be sliding mass perpendicular to the minimum sectional area on the stress direction, w * sin α * H (i)/D is the adaptability to changes that is produced by the only moisture branch of sliding mass branch, Δ σ (i) be the variable quantity by the adaptability to changes of the only moisture branch of sliding mass branch generation;
The relation of representing soil body stress and strain with formula (5);
K1(i)=Δσ(i)/Δδ(i)=w×sinα×ΔH(i)/D×Δδ(i)=k×ΔH(i)/Δδ(i)(5)
In the formula, k uses formula (6) expression near a constant,
k=w×sinα/D (6)
For the ease of calculating, we are rewritten into formula (5) form of formula (7);
K(i)=K1(i)/k=ΔH(i)/Δδ(i) (7)
Know that from the equation of formula (7) size of K (i) value depends primarily on the water cut the soil and the changing value of displacement of inclined plane amount; With the judgement of comparing of ratio K (i) value of the rate of change of stress and strain and several mechanics control threshold value, the main flow process of algorithm is as follows;
Step 1: read current soil moisture content and slope deflection data, these data are saved in the multimedia database;
Step 2: judge whether soil moisture content reaches 11.5%, and situation about not reaching forwards step 1 to;
Step 3: the soil moisture content and the slope deflection data that read the previous time, with the variation delta H (i) of formula (2), (3) calculating displacement of inclined plane variation delta δ (i) and soil moisture content, use the ratio K (i) of formula (7) calculating then based on the stress and strain of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content;
Step 4: compare according to the ratio K (i) of stress and strain and several mechanics control threshold values, if K (i) 〉=KV1 then judge the elastic deformation stage that is at present; If KV1>K (i) 〉=KV2 then judge and be in the plastic yield stage at present if the variation delta H of soil moisture content (i) 〉=KH1 sends orange early warning information, otherwise sends blue early warning information; If KV2>K (i) 〉=KV3 then judge is in plastic yield at present to the transition stage that destroys, at this moment send red early warning information; Forward step 1 to;
In the above-mentioned algorithm, KH1 is the control threshold value of the variable quantity of soil moisture content, and KV1, KV2, KV3 are respectively mechanics control threshold value, and satisfies the following KV1>KV2>KV3 that concerns.
3. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2 is characterized in that: described Surveillance center computing machine also comprises:
The decision-making supplementary module, be used for the result of determination of above-mentioned 4 kinds of prediction module is carried out comprehensively, it is auxiliary to adopt average weighted mode to make a strategic decision, the weights coefficient of described geological hazards prediction module based on the rainfall raininess is respectively 0.5, the described weights coefficient that carries out the geological hazards prediction module based on displacement of inclined plane-time curve is respectively 1.05, the weights coefficient of described geological hazards prediction module based on soil moisture content and rainfall raininess is respectively 1.75, the weights coefficient of described geological hazards prediction module based on soil moisture content and slope deflection is respectively 1.2, quantized value with blue early warning is defined as 3 simultaneously, the quantized value of orange early warning is defined as 6, the quantized value of red early warning is defined as 9, calculate final comprehensive judged result with formula (8)
R=(R I×K I+R II×K II+R III×K III+R IV×K IV)/(K I+K II+K III+K IV) (8)
In the formula, R IAnd K IBe respectively described geological hazards prediction module and predict the outcome and weight coefficient R based on the rainfall raininess IIAnd K IIBeing respectively described carries out the geological hazards prediction module based on displacement of inclined plane-time curve and predicts the outcome and weight coefficient R IIIAnd K IIIBe respectively described geological hazards prediction module and predict the outcome and weight coefficient R based on soil moisture content and rainfall raininess IVAnd K IVBeing respectively described geological hazards prediction module based on soil moisture content and slope deflection predicts the outcome and weight coefficient, R is the final comprehensive result of calculation of judging, the numerical range of judging result of calculation is 0~9, final judge result of calculation with the early warning color, report, check on and sign and issue management and summarize with table 3, table 3 aid decision making and early warning signal are issued flow process:
The result of calculation value Early warning face On duty The gaffer Director Be responsible for the deputy bureau director The chief Look The member 0~2.90 White Report Sign and issue 2.91~4.50 Blue Report Report/check on Sign and issue 4.51~6.00 Yellow Report Report/check on Report/check on Sign and issue 6.01~7.50 Orange Report Report/check on Report/check on Sign and issue 7.51~9.00 Red Report Report/check on Report/check on Report/check on Sign and issue
Table 3
4. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system adopt rainfall Event triggered mode, when rainy, activate described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and described embedded system by automatic udometric contact switch, make them enter duty immediately; Rose in 72 hours after rain finishing, if the monitoring and forecasting forecast system does not take place under the geology disaster scenarios it and all monitoring devices all enter dormant state.
5. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described solar powered unit is made of solar energy photoelectric conversion plate, rechargeable battery and charging circuit, and the capacity of described rechargeable battery satisfies described automatic rain gage, described omnibearing vision sensor, described soil moisture sensor and the working time of described embedded system more than 120 hours.
6. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2 is characterized in that: the 3G wireless communications mode is adopted in the message exchange between described embedded system and the described Surveillance center computing machine.
7. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2 is characterized in that: described omnibearing tilt sensor comprises cone, conical conductive coil, insulated wire, following cone, lead, conduction hollow tubular, shell, less radio-frequency generating unit, power supply and mercury; The size of pyramid type conductive coil is big or small identical with last cone inside, coil by four big minor radius of difference in the conical conductive coil constitutes a cone with same center of circle vertical pile, promptly use loop A, coil B, coil C and coil D are superimposed as a taper shape, distance between each coil is a Δ, and constituting between each coil of conical conductive coil is not conducting, each coil all has an extension line to lead to the outside of cone, the pyramid type conductive coil is embedded in the cone, the distance of the edge of the pyramid type conductive coil after the embedding from the bottom of last cone is Δ, be welded on loop A respectively with 4 core insulation lines respectively, coil B, on the extension line of coil C and coil D, the epiconus place of following cone 4 inserts the conduction hollow tubular, one section inside that enters down cone of conduction hollow tubular, insulated wire passes the inside of conduction hollow tubular and insulated wire is guided to down the outside of cone, inserting cementing agent makes conduction hollow tubular and following cone fix and seal, insulated wire keeps state of insulation with the inside and the conduction hollow tubular of following cone, adding mercury in the cone down, the capacity of mercury just in time fills up down cone, to go up cone and the following cone airtight space of formation that is fixed together then, with shell whole omnibearing tilt sensor will be fixed together at last; Last cone and following cone adopt the transparent plastic compacting to form, go between respectively on the shell with conduction hollow tubular lead A that is connected and the quad B that is connected with insulated wire, when omnibearing tilt sensor did not perceive, any core lead among lead A and the quad B did not communicate; When omnibearing tilt sensor has perceived inclination, some flow in the cone at the mercury that descends cone inside, and be embedded in cone in conical conductive coil in coil contact, when being 0.5 °, the angle of inclination contacts with loop A, when being 1 °, the angle of inclination contacts with loop A and coil B, when the angle of inclination is 1.5 ° and loop A, coil B and coil C contact, contact with all coil when the angle of inclination is 2 °, therefore all can make a certain core or multicore cable among lead A and the lead B communicate as long as the inclination of a certain degree appears in any one orientation; The method of judging the angle of inclination is: if communicate between lead A and the D coil just can judge inclination at this moment angle more than 2 ° or 2 °; If do not communicate between lead A and the D coil, between lead A and the coil C mutually the angle of general rule judgement inclination at this moment between 1.5 ° to 2 °; If do not communicate between lead A and D coil and the C coil, between lead A and the coil B mutually the angle of general rule judgement inclination at this moment between 1 ° to 1.5 °; If lead A only and between the loop A conducting judge that then the angle that at this moment tilts is between 0.5 ° to 1 °; If lead A and any coil not conducting judge that then the angle of inclination is below 0.5 °; Among the present invention with of the input of described 5 lines as the data acquisition end of described less radio-frequency generating unit.
8. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 7, it is characterized in that: described less radio-frequency generating unit comprises MCU and radio transmitting and receiving chip, MCU is connected by spi bus with radio transmitting and receiving chip, the two constitutes wireless transport module, give described embedded system with detected angle of inclination data transmission, communication mode is the ZigBee technology; Described MCU is the STC89LE516AD single-chip microcomputer, is 8 single-chip microcomputers of 51 kernel enhancement mode, and is compatible fully with the IntelMCS51 series monolithic.STC89LE516AD has memory function on the abundant sheet, has 64KBFlash and 512 byte RAM.Singlechip is solidified with the ISP program, downloads by serial ports; Described lead A and described loop A, B, C, D all are linked into the parallel port of STC89LE516AD single-chip microcomputer, with the conducting between described lead A and the described loop A as exterior interrupt, only wake described less radio-frequency generating unit under the conducting situation between described lead A and the described loop A up, described less radio-frequency generating unit is in the park mode state when not tilting to take place;
Single-chip microcomputer at omnibearing tilt sensor comprises initialize routine module, trace routine module and launching procedure module, and the initialize routine module is handled single-chip microcomputer, radio frequency chip, SPI etc.; The trace routine module detects and judges the pitch angle, and detected pitch angle data and detection time are packed with the information such as code clerk that place position is buried in reflection underground; The launching procedure module is delivered to the output of radio frequency generation module with the packet of setting up by single-chip microcomputer SPI interface; Described embedded system receives the Monitoring Data that radio frequency generation module sends over from described omnibearing tilt sensor, and gives described Surveillance center computing machine with these data transmission.
9. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described automatic rain gage obtains the every day and the data of rainfall raininess per hour, and with data in real time be transferred to described Surveillance center computing machine.
10. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described soil moisture sensor adopts the condenser type soil moisture sensor, described soil moisture sensor vertically is inserted into the 10CM depths of spongiosa soil, rickle; Be in dormant state at ordinary times, when rainy, just trigger work, can obtain water cut data in the soil by the condenser type soil moisture sensor, and with these data in real time be transferred to described Surveillance center computing machine.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0936589B1 (en) * 1998-02-17 2005-05-25 Mitsui Bussan Plant & Project Corp. Geographical displacement sensing unit
US7187277B2 (en) * 2003-05-28 2007-03-06 Nec Corporation Monitoring terminal device
CN101281678A (en) * 2008-05-23 2008-10-08 福州梦想现代科技有限公司 Automatic monitoring warning system for drawing triggering type landslide and rolling stone
CN101393269A (en) * 2008-11-06 2009-03-25 复旦大学 Method for monitoring geology by utilizing communication optical cable

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0936589B1 (en) * 1998-02-17 2005-05-25 Mitsui Bussan Plant & Project Corp. Geographical displacement sensing unit
US7187277B2 (en) * 2003-05-28 2007-03-06 Nec Corporation Monitoring terminal device
CN101281678A (en) * 2008-05-23 2008-10-08 福州梦想现代科技有限公司 Automatic monitoring warning system for drawing triggering type landslide and rolling stone
CN101393269A (en) * 2008-11-06 2009-03-25 复旦大学 Method for monitoring geology by utilizing communication optical cable

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
《中国优秀博士学位论文全文数据库》 20070821 张文君 滑坡灾害遥感动态特征监测及其预测分析研究 , 2 *

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