WO2013075330A1

WO2013075330A1 - Method for accurately selecting point at wi-fi hotspot deployment planning stage, and model

Info

Publication number: WO2013075330A1
Application number: PCT/CN2011/082943
Authority: WO
Inventors: 任波; 郝应涛; 钱苏敏; 任翠红
Original assignee: 华为技术有限公司
Priority date: 2011-11-25
Filing date: 2011-11-25
Publication date: 2013-05-30
Also published as: CN102726089A

Abstract

A method for accurately selecting a point at a Wi-Fi hotspot deployment planning stage, and a model. The method includes: collecting statistics data of each candidate area within a statistics period; evaluating the statistics data of each candidate area according to a service class indicator to obtain a service evaluation score; evaluating the statistics data of each candidate area according to one or more of a user behaviour class and a terminal class to obtain a user behaviour evaluation score and/or terminal penetration rate evaluation score; performing summarization calculation according to the evaluation score of each class corresponding to each candidate area to obtain a heat value of each candidate area; and taking a candidate area the heat value of which is greater than a threshold value as a Wi-Fi hotspot area.

Description

Precise selection method and model of Wi-Fi hotspot deployment planning stage

Technical field

The present invention relates to the field of communications technologies, and in particular, to a precise method and model for selecting a Wi-Fi hotspot deployment planning stage.

Background technique

The rapid growth of data services by telecom operators has led to serious network pipelines and reduced investment income in network construction. As smart terminals continue to grow in popularity and the demand for user data services continues to grow, the burden on cellular networks will be further exacerbated. In this situation, WLAN (Wireless Local Area Networks) is widely recognized by mobile operators as an important means of offloading macro networks.

Wi-Fi (Wireless Fidelity) is a technology that wirelessly connects terminals such as personal computers and handheld devices (such as PDAs and terminals). It is the most widely used WLAN standard. The place where Wi-Fi can access the Internet is called a Wi-Fi hotspot. Accurate deployment of Wi-Fi hotspots is a problem that must be considered in the healthy development of the macro network and the normal operation of the WLAN.

In the prior art, there are usually two ways to determine whether a Wi-Fi hotspot needs to be deployed: one is to determine whether the cell is suitable for deploying a Wi-Fi hotspot by the data traffic level of the cell in the cellular network, and the other is to be geographicalized. The data traffic map is used to determine the latitude and longitude of the potential Wi-Fi hotspot location.

Regardless of the above method, only the traffic of the data service is considered, resulting in a large error in the judgment result, such as:

(1) In some areas, such as schools, the high demand for Internet access in student terminals leads to high statistics on data traffic in such communities. However, the student population uses low-end and mid-range terminals, and the proportion of Wi-Fi support is low. From the perspective of traffic distribution, such cells are not suitable for deploying Wi-Fi hotspots.

(2) From the perspective of operators' positioning of WLANs, it is generally expected that WLANs can offload low-value services in cellular networks. The so-called low-value business refers to services with high traffic but low revenue or excessive consumption of network resources. For example, the traffic of instant messaging services such as QQ is small, but the network resources are extremely high. This kind of service is the key diversion target of the WLAN network, and it cannot be judged by the high or low traffic. Summary of the invention The embodiments of the present invention provide a method and a model for accurately selecting a P-segment of a Wi-Fi hotspot deployment plan to improve the accuracy of Wi-Fi hotspot deployment.

In order to solve the above technical problem, the technical solution adopted by the embodiment of the present invention is:

A Wi-Fi hotspot deployment plan P-segment precise selection method, including:

Collecting statistical data of each candidate region in the statistical period;

The statistical data of each candidate area is evaluated according to the service type index, and the business evaluation score is obtained; and the statistical data of each candidate area is evaluated according to one or two types of the user behavior type indicator and the terminal type indicator, and the user is obtained. a behavior evaluation score or/and a terminal penetration assessment score; a summary calculation according to various evaluation scores corresponding to each candidate region, and obtaining a heat value for each candidate region;

A candidate area having a heat value greater than a set threshold is used as a Wi-Fi hotspot area.

A Wi-Fi hotspot deployment plan P-segment precise selection model, including:

a data collection unit, configured to collect statistics of each candidate area in the statistical period;

The business indicator evaluation unit evaluates the statistical data of each candidate area according to the service type indicator, and obtains a business evaluation score;

The other indicator evaluation unit is configured to evaluate the statistical data of each candidate region according to one or two types of the user behavior index and the terminal class index, and obtain a user behavior assessment score or/and a terminal penetration assessment score;

a calculation unit, configured to perform a summary calculation according to the various evaluation scores of the corresponding candidate regions obtained by the service indicator evaluation unit and the other indicator evaluation unit, to obtain a heat value of each candidate region;

The hotspot area determining unit is configured to use the candidate area whose heat value is greater than the set threshold as the Wi-Fi hotspot area i or .

The precise selection method and model of the Wi-Fi hotspot deployment planning stage in the embodiment of the present invention comprehensively evaluate one or two types of service class indicators, user behavior indicators and terminal class indicators in each candidate region. Calculate the heat value of each candidate area based on the evaluation result, so as to quickly and accurately identify and determine the optimal Wi-Fi deployment location. DRAWINGS In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below. Obviously, the drawings in the following description It is merely some embodiments of the present invention, and those skilled in the art can obtain other drawings according to the drawings without any creative work.

1 is a flowchart of a method for accurately selecting a segment of a Wi-Fi hotspot deployment plan P segment according to an embodiment of the present invention; FIG. 2 is a schematic structural diagram of a precise point selection model of a Wi-Fi hotspot deployment planning phase according to an embodiment of the present invention;

FIG. 3 is another schematic structural diagram of a precise point selection model in a Wi-Fi hotspot deployment planning phase according to an embodiment of the present invention.

detailed description

The embodiments of the present invention are further described in detail below in conjunction with the drawings and embodiments.

The Wi-Fi hotspot deployment plan of the Wi-Fi hotspot deployment plan P-segment precise selection method and model of the Wi-Fi hotspot deployment planning stage, the service type indicators for each candidate area, and the user behavior indicators or/and terminals The statistical indicators of the class indicators are comprehensively evaluated by the model, and the heat value of each candidate area is calculated according to the evaluation result, thereby quickly and accurately identifying and determining the optimal Wi-Fi deployment place.

As shown in FIG. 1 , it is a flowchart of a method for accurately selecting a Wi-Fi hotspot deployment planning stage according to an embodiment of the present invention, which includes the following steps:

Step 101: Collect statistics of each candidate area in the statistical period.

The above statistical period can be arbitrarily set, for example, three days to one week. The candidate area refers to a candidate area of the existing network cellular network, and may be an area of a cell level or a grid level (for example, a grid of 25 meters x 25 meters, 50 meters x 50 meters, 100 meters x 100 meters, etc.).

The foregoing statistics may include any one or more of the following: data service statistics, user operation or operation and maintenance statistics, different brand terminal statistics, and of course, other data may be included as needed, for the embodiment of the present invention Not limited.

Step 102: The statistical data of each candidate area is evaluated according to the service type indicator, and the business evaluation score is obtained.

In an actual application, different service type indicators may be set according to different services. For example, for a cell-level candidate area, the service type indicator may include any one or more of the following: Line resource utilization, busy time data service traffic, data traffic channel bearer efficiency, data traffic channel congestion rate, data traffic channel resource occupancy rate, low value service ratio; for the grid level candidate area, the service type indicator may include Any of the above indicators can be refined to any one or more of the grid levels.

It should be noted that, for each of the foregoing service type indicators, a threshold value needs to be set, and the value may be set by referring to the average value of the whole network, for example, it may be 1.2 times of the average value of the whole network, or may be based on the user. The expectations are set to suit the needs of different scenarios.

When the statistics of a candidate area are evaluated according to the service type indicator, the threshold value corresponding to the corresponding service type indicator in the statistical data of the candidate area may be first compared, if the threshold value is less than or equal to the corresponding threshold value, Then it is not considered, the business evaluation score is 0; if it is greater than the corresponding threshold, the evaluation score is calculated. The evaluation score is the ratio of the actual statistical data to the threshold value corresponding to the corresponding business type indicator multiplied by a certain weight. The weight of each indicator can be set according to actual needs. The greater the weight, the higher the importance of the indicator.

Step 103: Perform statistics on each candidate region according to one or two types of user behavior index and terminal class index, and obtain a user behavior assessment score or/and a terminal penetration assessment score.

For example, for a cell-level candidate area, the user behavior indicator may include any one or more of the following: proportion of high-end users, proportion of users using Wi-Fi data packages, proportion of users who frequently use Wi-Fi, use The average data traffic of users who do not support Wi-Fi terminals, the average data traffic of users who use Wi-Fi terminals, and the total PS (Packet Switch) traffic contributed by Wi-Fi-enabled terminals account for total cell traffic. The terminal class indicator may include any one or more of the following: a Wi-Fi-enabled terminal ratio, a star terminal ratio, and a data card ratio.

Similarly, for each of the above-mentioned service type indicators, a threshold value needs to be set, and the value can be set by referring to the average value of the whole network, for example, it can be 1.2 times of the average value of the whole network, or can be performed according to the expectation of the user. Set to suit different scenes.

When the statistical data of a candidate region is evaluated according to the user behavior index, the statistical data related to the user behavior in the statistical data of the candidate region may be first compared with the threshold value of the corresponding user behavior index, if less than Or equal to the corresponding user behavior class indicator corresponding to the gate The limit is not considered, the business evaluation score is 0; if it is greater than the corresponding threshold, the evaluation score is calculated. The evaluation score is the ratio of the actual statistical data to the corresponding threshold value multiplied by a certain weight.

The weight of each indicator can be set according to actual needs. The greater the weight, the higher the importance of the indicator.

Similarly, the process of evaluating the statistical data of a candidate region based on the terminal class index is similar to the above, and will not be described in detail herein.

Step 104: Perform a summary calculation according to each type of evaluation score corresponding to each candidate area, and obtain a heat value of each candidate area.

For each candidate region, after obtaining the evaluation scores corresponding to the candidate regions, the evaluation scores of the different types of index items may be weighted and averaged to obtain the heat value of the corresponding candidate region.

The weight of the evaluation score of each indicator item can be set according to actual needs. The weights corresponding to different indicator items can be the same or different. Of course, it can be positive or negative.

Step 105: A candidate area whose heat value is greater than the set threshold is used as a Wi-Fi hotspot area.

In addition, in order to facilitate the user's use, in the embodiment of the present invention, the Wi-Fi potential hotspot distribution map may be generated according to the heat value of each candidate region, thereby more intuitively reflecting the Wi-Fi potential hotspot distribution.

The following is a detailed description of the method for determining the popularity of candidate regions of different granularities in the Wi-Fi hotspot deployment planning phase of the embodiment of the present invention.

Example 1: Candidate area for cell granularity Indicators Indicator Threshold Determination Weight and score meter Indicator description

Classification Condition Calculation Service C1_0 (Radio resource utilization) T1_0=Whole network >= 5*(C1_0/T1_0) Radio resources analysis of different standards The average cell utilization rate is defined differently, and the value *1.2 is not mandatory.

Cl_l (Busy time data service flow Tl_l=full network >= 8*(C1_1/T1_1) can also be replaced by equivalent erl.

Value *1.2

Cl_2 (data traffic channel bears Tl_2=full network < 8*(T1_2/C1_2) data service load efficiency of different standards) The average channel bearer efficiency of the cell is different, such as the value *0.8 GSM can use the downlink throughput rate of the RLC layer, here is not Make a mandatory limit. Cl_3 (data traffic channel has Tl_3=full network>= 6*(C1_3/T1_3) data service rate of different standards) The average channel congestion rate of the cell is different, such as the value *1.2 GSM can use the TBF congestion rate, here is not mandatory limited.

Cl_4 (data service channel Tl_4 full network >= 10*(C1_4/T1_4) source data usage rate of different standards) The average channel resource occupancy rate of the cell is not defined as *1.2. For example, the WCDMA standard can use the code of the H carrier. Resource utilization, there is no mandatory limit here.

Cl_5 (low-value service ratio) Tl_5=full network >= 10*(C1_5/T1_5) Low-value service refers to multi-valued *1.2 services suitable for the average WLAN network in the cell, and refers to the video and download services of the Internet.

Cl_6 (proportion of certain types of services) Tl_6=full network>= 15*(C1_6/T1_6) refers to the business users who want to focus on certain types of services, such as value *1.2 QQ, C2_0 (high-end) User ratio) Τ2_0=Full network>= 5*(C2_0/T2_0) The definition of high-end users compares the behavior of the cell to the average flexibility, such as the high ARPU value with the analysis value *1.2 households.

C2_l (using Wi-Fi flow sleeve T2_l=full network >= 10*(C2_1/T2_1)

Proportion of users of meals)

Value *1.2

C2_2 (Τ2_2=When using Wi-Fi>=10*(C2_2/T2_2) Proportion of users who frequently use Wi-Fi) The average cell definition is flexible, for example, 30 values*1.2 days using Wi-Fi more than 5 times User

C2_3 (using mobile phone (not supported Τ2_3=full network >= 10*(C2_3/T2_3)

Average number of users of Wi-Fi)

According to the flow rate) *1.2

C2_4 (use mobile phone (support Τ2_4=full network >= 15*(C2_4/T2_4) no

Average number of users of Wi-Fi)

According to the flow rate) value *1.2 C2_5 (T2_5 with WIFI support = full network >= 10*(C2_5/T2_5) includes the total PS traffic contributed by the mobile phone, data card, and the total network traffic of all supported Wi-Fi zones, such as the average network size of the small cell.) Terminal terminal C3_0 with value *1.2 (Wi-Fi enabled handset Τ3_0=full network>= 10*(C3_0/T3_0) None

Permeation terminal ratio)

Rate score *1.2

Analysis

C3_l (star terminal ratio) T3_l=full network>= 10*(C3_1/T3_1) The definition of star terminal is relatively flexible. You can select one or several high-end mobile phones as star terminals according to the actual value of the current network *1.2. iPhone

C3_2 (data card ratio) Τ3_2=full network >= 10*(C3_2/T3_2)

Residential average

Value *1.2 Example 2: Candidate Region for Grid Granularity Index Indicator Threshold Judgment Weight and Scoring Indicator Description

Classification Condition Calculation Business Cl_l (Data Service Flow) Tl_l=All >= 8*(C1_1/T1_1) None

Analysis of the grid

Mean * 1.2

Cl_2 (data traffic channel has Tl_2=all>= 6*(C1_2/T1_2) data service rate of different standards) The flat channel congestion rate of the grid is different, such as the average value * 1.2 GSM can use the TBF congestion rate, do not do here Mandatory.

Cl_3 (low value business ratio) Tl_3=all >= 10*(C1_3/T1_3) Low value service refers to the average value of the flat WLAN network that is suitable for the grid. *1.2, which refers to the Internet video, download service, etc.

Cl_4 (proportion of certain types of business) Tl_4=all>= 15*(C1_4/T1_4) refers to the business of the targeted grid of the operator wishing to focus on a certain type of business, such as the average * 1.2 QQ The user in the C2JX grid is fixed with T2_0=all>= 10*(C2_0/T2_0). The fixed user is defined as the number of the number of behaviors. In the flat statistical period of the grid, the average value of the grid is analyzed by the average value of 1.2. More than a certain number of users.

C2_l (fixed user, high-end T2_l=all >= 5*(C2_1/T2_1) definition of high-end users comparison user ratio) The grid is flexible, such as high ARPU value with mean * 1.2 households.

C2_2 (in fixed users, use Τ2_2=all >= 10*(C2_2/T2_2)

Users of Wi-Fi data packages

Proportion) Mean * 1.2

C2_3 (of fixed users, often Τ2_3=all>= 10*(C2_3/T2_3) Proportion of users who use Wi-Fi frequently for Wi-Fi users) The flat definition of the grid is more flexible, for example, 30 averages * Wisdom within 1.2 days -Fi users over 5 times

C2_4 (for fixed users, use Τ2_3=all >= 10*(C2_4/T2_4)

Flat of the phone (not supported by Wi-Fi)

User's average data traffic) Mean * 1.2

C2_5 (for fixed users, use Τ2_5=all >= 15*(C2_5/T2_5)

Mobile phone (support Wi-Fi)

Average data flow of users) Mean * 1.2

C2_6 (terminals supporting WIFIΤ2_6=all>= 10*(C2_6/T2_6) including total mobile PS, data card, total PS traffic contributed on the raster grid, etc. All supported Wi-Fi total data traffic Terminals with a mean value of 1.2

Example) Terminal C3_0 (Wi-Fi enabled phone Τ3_0=All >= 10*(C3_0/T3_0) None

Permeation terminal ratio) grid flat

Rate average * 1.2

Analysis

C3_l (star terminal ratio) T3_l=all>= 10*(C3_1/T3_1) The definition of the star terminal is more flexible than the grid, and one or several high-end mobile phones can be selected as the star terminal according to the actual average value of the current network*1.2. Like the iPhone

C3_2 (data card scale) Τ3_2=all >= 10*(C3_2/T3_2)

Grid flat

Mean * 1.2 It should be noted that, in practical applications, in order to further ensure the accuracy of the determined Wi-Fi hotspot area, the heat value of each candidate area obtained in the above step 104 may be corrected, for example, the score integrity parameter is used as a correction. Coefficient, ie heat value* score integrity parameter = corrected heat value. Then, the Wi-Fi hotspot area is determined according to the heat value after the correction.

The above-mentioned principle of setting the score integrity parameter can be embodied by one or more aspects, such as: (1) Excluding the abnormal data hotspots caused by the extreme data service behavior of a few users, for example, if the top 5 of the community The user contributes 95% of the data traffic, and the score integrity parameter value is 0. Of course, the specific principles can be reasonably adjusted according to the actual situation of the network.

(2) The advantage of reflecting the comprehensive score of each indicator's classification dimension

For example, if there is at least one indicator score in the three categories of business class, user behavior class, and terminal penetration rate, the score integrity parameter value is 1; if none of the indicators in the class has no score, the score integrity The parameter value is 2/3; if there are no indicators in each of the two types of indicators, the score integrity parameter value is 1/3. Of course, the specific principles can be reasonably adjusted according to the actual situation of the network.

Of course, the heat value of each candidate region can be modified from other aspects, which is not limited in this embodiment of the present invention.

It can be seen that, in the Wi-Fi hotspot deployment planning stage of the embodiment of the present invention, the statistical data of each candidate area collected is based on one of a service indicator, a user behavior indicator, and a terminal penetration index. The two types are comprehensively evaluated, and the heat value of each candidate area is calculated according to the evaluation result, thereby quickly and accurately identifying and determining the optimal Wi-Fi deployment location.

It will be apparent to those skilled in the art from the above description of the embodiments that all or part of the steps of the above embodiments may be implemented by means of software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, may be embodied in the form of a software product, which may be stored in a storage medium such as a ROM/RAM or a disk. , an optical disk, etc., includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to perform the methods described in various embodiments of the present invention or portions of the embodiments.

Correspondingly, the embodiment of the present invention further provides a precise selection point model of a Wi-Fi hotspot deployment planning stage, as shown in FIG. 2, which is a structural schematic diagram of the model. In this embodiment, the model includes:

The data collection unit 201 is configured to collect statistical data of each candidate area in the statistical period; the service indicator evaluation unit 202 estimates the statistical data of each candidate area according to the service type indicator, and obtains a service evaluation score;

The other indicator evaluation unit 203 is configured to evaluate statistical data of each candidate region according to one or two types of user behavior index and terminal class index, and obtain a user behavior assessment score or/and a terminal penetration assessment score. ;

The calculating unit 204 is configured to perform a summary calculation on each type of evaluation scores corresponding to each candidate area obtained by the service indicator evaluation unit 202 and the other indicator evaluation unit 203, to obtain a heat value of each candidate area;

The hotspot area determining unit 205 is configured to use the candidate area whose heat value is greater than the set threshold as the Wi-Fi hotspot area i or .

The above statistical period can be arbitrarily set, for example, three days to one week. The candidate area refers to a candidate area of an existing network cellular network, and may be a cell level or a grid level area.

The above-mentioned various types of indicators may be determined according to factors such as the geographical location of the candidate area, the environment, and the like, and different indicator items are set for the candidate areas of different granularities. For details, refer to the Wi-Fi hotspot deployment plan P of the previous embodiment of the present invention. The description in the precise selection method of the segment will not be repeated here.

When the statistic data of the candidate area is evaluated according to the service type indicator, the service metrics evaluation unit 202 may first perform the threshold value corresponding to the service-related statistical data and the corresponding service type indicator in the statistical data of the candidate area. If the value is less than or equal to the corresponding threshold, it is not considered, and the service evaluation score is 0. If it is greater than the corresponding threshold, the evaluation score is calculated. The specific calculation method can be various, for example, it can be actual statistics. The ratio of the threshold value to the corresponding threshold value, or the difference between the actual statistical data and the corresponding threshold value, is not limited in this embodiment of the present invention.

Similarly, the process in which the other indicator evaluation unit 203 evaluates the statistical data of each candidate region according to one or both of the user behavior type index and the terminal type index is similar to the processing of the above-described business index evaluation unit 202, where No longer praise. The calculating unit 204 is specifically configured to perform weighted averaging on each evaluation score of each candidate region to obtain a heat value of the corresponding candidate region. The weight of the evaluation score of each indicator item can be set according to actual needs. The weights corresponding to different indicator items may be the same or different. Of course, it may be a positive number or a negative number.

It can be seen that, in the Wi-Fi hotspot deployment planning stage of the embodiment of the present invention, the statistical data of each candidate area collected is based on one of a service indicator, a user behavior indicator, and a terminal penetration index. The two are comprehensively evaluated, and the heat value of each candidate area is calculated according to the evaluation result, so that the optimal Wi-Fi deployment location can be quickly and accurately identified and determined.

As shown in FIG. 3, it is another structural schematic diagram of a precise point selection model in the Wi-Fi hotspot deployment planning stage according to the embodiment of the present invention.

Different from the embodiment shown in FIG. 2, in the embodiment, the model further includes: a distribution map generating unit 301, configured to generate a Wi-Fi hotspot distribution according to the heat value of each candidate region obtained by the calculating unit 204. Figure.

In this way, the distribution of Wi-Fi hotspots can be more intuitively reflected, which is convenient for users.

It is to be noted that the various embodiments in the present specification are described in a progressive manner, and the same similar parts between the various embodiments may be referred to each other, and each embodiment focuses on different embodiments from other embodiments. At the office. In particular, for the model embodiment, since it is basically similar to the method embodiment, it is described in a relatively simple manner, and the relevant parts can be referred to the description of the method embodiment. The apparatus and system embodiments described above are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located One place, or it can be distributed to multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment scheme. Those of ordinary skill in the art can understand and implement without any creative effort.

The above description is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Any modifications, equivalents, improvements, etc. made within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims

Rights request

A method for accurately selecting a Wi-Fi hotspot deployment planning stage, the method comprising: collecting statistical data of each candidate area in a statistical period;

2. The method according to claim 1, wherein the statistical data comprises any one or more of the following:

Data service statistics, user operations or operation and maintenance statistics, and different brand terminal statistics.

The method according to claim 1, wherein the granularity of the candidate area is a cell level;

The service type indicator includes any one or more of the following: radio resource utilization, busy hour data service traffic, data traffic channel bearer efficiency, data traffic channel congestion rate, data traffic channel resource occupancy rate, and low value service ratio;

The user behavior indicator includes any one or more of the following: the proportion of high-end users, the proportion of users using Wi-Fi data packages, the proportion of users who frequently use Wi-Fi, and the average of users who do not support Wi-Fi terminals. Data traffic, average data traffic using users supporting Wi-Fi terminals, total packet-switched traffic contributed by Wi-Fi-enabled terminals, and percentage of total cell traffic;

The terminal type indicator includes any one or more of the following: a ratio of a terminal supporting Wi-Fi, a proportion of a bright terminal, and a proportion of a data card.

The method according to claim 1, wherein the granularity of the candidate area is a grid level;

The service type indicator includes any one or more of the following: data service traffic, RRC congestion rate, and low value service ratio;

The user behavior indicator includes any one or more of the following: a fixed number of users in the grid, The proportion of high-end users in fixed users, the proportion of users who use Wi-Fi data packages in fixed users, the proportion of users who regularly use Wi-Fi in fixed users, the average data traffic of users who do not support Wi-Fi terminals in fixed users, The average data traffic of users using Wi-Fi enabled terminals, and the total packet-switched traffic contributed by Wi-Fi-enabled terminals to the total data traffic in the grid;

The method according to claim 1, wherein the summing calculation is performed according to each type of evaluation score corresponding to each candidate area, and the heat value of each candidate area is obtained:

The weighted averages of the various evaluation scores of each candidate region are respectively obtained, and the heat value of the corresponding candidate region is obtained.

The method according to any one of claims 1 to 5, wherein the method further comprises: generating a Wi-Fi hotspot profile according to the heat value of each candidate region.

7. A precise point selection model for a Wi-Fi hotspot deployment planning stage, the method comprising: a data collection unit, configured to collect statistical data of each candidate area in a statistical period;

8. The model according to claim 1, wherein the granularity of the candidate region is a cell level or a grid level.

9. The model of claim 1 wherein:

The calculating unit is specifically configured to perform weighted averaging on each type of evaluation scores of each candidate region to obtain a heat value of the corresponding candidate region.

The model according to any one of claims 6 to 9, wherein the model further comprises:

And a distribution map generating unit, configured to generate a Wi-Fi hotspot distribution map according to the heat value of each candidate region obtained by the calculating unit.