WO2017020501A1 - Fatigue monitoring and early-warning system and method based on digital helmet - Google Patents

Abstract

A fatigue monitoring and early-warning system and method based on a digital helmet. The method comprises: collecting a brain-wave signal of a user by using an electroencephalograph collector (11) integrated on a digital helmet (1), and analyzing a mental fatigue coefficient of the user according to the rhythmic change of the brain-wave signal (S11); capturing an ocular image of the user by using a camera (12) integrated on the digital helmet (1), and analyzing a physiological fatigue coefficient of the user according to the ocular image (S12); calculating the fatigue degree of the user according to the mental fatigue coefficient and the physiological fatigue coefficient of the user (S13); generating alarm information comprising the fatigue degree of the user when the fatigue degree of the user exceeds a preset value of the user in the normal case, and sending the alarm information to a monitoring center platform (2) by using a communications network (3), to perform early-warning on the fatigue state of the user (S14, S15). The system and method are used for a digital battlefield, and can monitor the fatigue state of soldiers in real time during combat readiness or task execution of an army, and perform early-warning soldiers in mental fatigue or physiological fatigue.

Description

 Digital helmet-based fatigue monitoring and early warning system and method

Technical field

The invention relates to the technical field of human physiological psychology detection, in particular to a fatigue monitoring and early warning system and method based on a digital helmet.

Background technique

Human fatigue is a physiological and psychological phenomenon caused by excessive brain and physical activity, excessive mental stimulation or some monotonous activity. Human fatigue can be divided into two types: physical fatigue and mental fatigue. In particular, military work fatigue refers to the fatigue caused by military labor, training and combat under the special environmental conditions of the troops or soldiers. In military labor and training, due to the high labor intensity and long time, the muscles are repeatedly contracted and the energy consumption is large, resulting in physical fatigue. Because of the high concentration of attention, mental stress, and mental fatigue, the training ability is reduced, training The results have dropped. At the same time, with the rapid development of mechanization and informatization, the military operations have also undergone major changes. In a changing battlefield or mission environment, changes in the fatigue status of key position commanders can affect an action, a battle, or even a war. Therefore, it is of great significance to monitor the fatigue of the majority of officers and soldiers during the war or mission.

At present, the single-person digital helmet system is known as the second brain of the vast number of officers and men, and its function is getting stronger and stronger, playing an increasingly important role in the war. The individual digital helmet system is mainly divided into head protection system (including impact protection, nuclear biochemical protection, laser protection, etc.), human-computer interaction system (including call module, video module, etc.), detection and sensing system (including infrared thermal imaging, Laser detection, etc.). However, the existing single-person digital helmet system cannot monitor the fatigue situation of the soldiers during wartime or missions in real time. Therefore, the war preparation command center may not be able to grasp the soldiers immediately and cause battlefield decision-making mistakes, thus affecting the army's combat readiness or when performing tasks. The combat effectiveness of the soldiers.

Summary of the invention

The main object of the present invention is to provide a digital helmet-based fatigue monitoring and early warning system and method, aiming at solving the technical problem that the existing single-person digital helmet system cannot monitor and warn the soldiers in the event of military readiness or when performing tasks. .

To achieve the above object, the present invention provides a digital helmet-based fatigue monitoring and early warning system, the system comprising: a mental fatigue monitoring module for collecting a user's brain wave signal through an electroencephalogram collector integrated on a digitized helmet And analyzing a user's psychological fatigue coefficient according to the rhythmic change of the brain wave signal; the physiological fatigue monitoring module is configured to take a user's eye image through a camera integrated on the digitized helmet, and from the eye image The physiological fatigue coefficient of the user is analyzed; the fatigue calculation module is configured to calculate the fatigue degree of the user according to the psychological fatigue coefficient and the physiological fatigue coefficient of the user, and determine whether the fatigue degree exceeds a preset of the user under normal conditions. The alarm module is configured to generate an alarm message including user fatigue when the fatigue exceeds a preset value of the user under normal conditions, and send the alarm information to the monitoring center platform through the communication network. Early warning of the user's fatigue.

Preferably, when the fatigue exceeds a preset value of the user under normal conditions, the alarm module is further configured to control an alarm integrated on the digitized helmet to generate an alarm sound.

Further, the psychological fatigue monitoring module analyzes the user's psychological fatigue coefficient according to the rhythmic change of the brain wave signal, including: preprocessing the brain wave signal collected by the brain power collector to extract the interference signal; using the wavelet packet The decomposition method extracts the characteristic band alpha wave, delta wave, theta wave and beta wave of brain wave; the band energy proportional value method is used to analyze the rhythmic changes of the characteristic band alpha wave, delta wave, theta wave and beta wave. The psychological fatigue factor of the person.

Further, the physiological fatigue monitoring module analyzes the physiological fatigue coefficient of the user from the eye image, including: identifying the eye movement of the user from the eye image taken by the camera; using Perclos measurement principle analysis The eye movement of the user obtains the physiological fatigue coefficient of the user.

Further, the fatigue calculation module calculates the user's fatigue degree according to the user's psychological fatigue coefficient and the physiological fatigue coefficient, including: pre-defining the user's psychological fatigue coefficient and the physiological fatigue coefficient weight ratio; according to the predefined weighting The ratio weights the user's mental fatigue coefficient and physiological fatigue coefficient to obtain the user's fatigue.

In order to achieve the above object of the present invention, the present invention also provides a fatigue monitoring and early warning method based on a digital helmet, the method comprising the steps of: The brain wave signal of the user is collected by an EEG collector integrated on the digitized helmet, and the user's psychological fatigue coefficient is analyzed according to the rhythmic change of the brain wave signal; the user is taken by the camera integrated in the digital helmet The eye image, and analyzing the physiological fatigue coefficient of the user from the eye image; calculating the user's fatigue degree according to the user's psychological fatigue coefficient and the physiological fatigue coefficient; determining whether the fatigue degree exceeds the user's normal situation Setting a value; when the fatigue degree exceeds a preset value of the user under normal conditions, generating an alarm message including user fatigue degree, and transmitting the alarm information to the monitoring center platform to the user through the communication network The situation is early warning.

Preferably, the digital helmet-based fatigue monitoring and early warning method further comprises the step of: controlling an alarm integrated on the digitized helmet to generate an alarm sound when the fatigue level exceeds a preset value of the user under normal conditions.

Further, the step of analyzing the user's psychological fatigue coefficient according to the rhythmic change of the brain wave signal comprises: pre-processing the brain wave signal collected by the brain power collector to extract the interference signal; using the wavelet packet decomposition method Extracting the characteristic band alpha wave, delta wave, theta wave, and beta wave of the brain wave; analyzing the rhythmic changes of the characteristic band alpha wave, delta wave, theta wave, and beta wave by using the band energy proportional value method to obtain the user's Psychological fatigue coefficient.

Further, the step of analyzing a physiological fatigue coefficient of the user from the eye image includes: identifying an eye movement of the user from an eye image taken by the camera; analyzing the method by using a Perclos measurement principle The user's eye movements get the user's physiological fatigue coefficient.

Further, the step of calculating the user's fatigue degree according to the user's psychological fatigue coefficient and the physiological fatigue coefficient includes: predefining a weighted ratio of the user's psychological fatigue coefficient and the physiological fatigue coefficient; according to a predefined weighting ratio The user's mental fatigue coefficient and physiological fatigue coefficient are weighted to obtain the user's fatigue.

Compared with the prior art, the digital helmet-based fatigue monitoring and early warning method and the method can simultaneously monitor the user's fatigue condition and the physiological appearance by monitoring the user's psychological fatigue degree and physiological fatigue degree simultaneously. Fatigue users are alerted. In particular, the present invention is applied to a digital battlefield, and is capable of accurately and accurately monitoring the fatigue condition of soldiers during military readiness or when performing tasks in real time. Warnings are given to soldiers with physical fatigue and soldiers with mild mental fatigue. At the same time, for soldiers with severe mental fatigue, report to the commander, perform job deployment, and avoid performing important battle operations, thus ensuring the smooth completion of combat missions and avoiding The various losses caused by the excessive fatigue of the soldiers. At the end of the task, the soldiers were mentally fatigued and the corresponding treatment plans were formulated for the moderate and severe mental fatigue soldiers to help the soldiers recover their mental fatigue and physical fatigue, thus improving the cohesiveness and combat effectiveness of the army.

DRAWINGS

1 is a schematic diagram of an application environment of a preferred embodiment of a digital helmet-based fatigue monitoring and early warning system according to the present invention.

2 is a schematic structural view of a preferred embodiment of the digitized helmet of FIG. 1.

3 is a functional block diagram of a preferred embodiment of the fatigue monitoring and early warning system of FIG. 1.

4 is a flow chart of a first preferred embodiment of the digital helmet-based fatigue monitoring and early warning method of the present invention.

Figure 5 is a flow chart of a second preferred embodiment of the digital helmet based fatigue monitoring and early warning method of the present invention.

The implementation, functional features, and advantages of the present invention will be further described in conjunction with the embodiments.

detailed description

It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The digitized helmet of the present invention can be worn by any user who needs to use the digitized helmet. The term "users" as used in the present invention include, but are not limited to, those who use digital helmets for combat commanders and soldiers, and those who use digital helmets in high-intensity and high-risk operations (such as aerial work) in combat or performing combat readiness tasks, Competitive athletes who use digital helmets for high-intensity intense sports and staff who need to use the digital helmet of the present invention. The embodiments of the present invention are only described in detail by taking the soldiers in the army preparation or when performing the tasks as an example.

FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of the digital helmet-based fatigue monitoring and early warning system of the present invention. In the present embodiment, the fatigue monitoring and early warning system 10 is installed and operated on a digitized helmet 1 worn on a soldier's head. When the digitized helmet 1 is not opened by a soldier, the digitized helmet 1 is in a dormant state, which is a low-radiation, low-power, non-working mode, and thus does not affect the health of the soldier.

The digital helmet 1 is wirelessly communicated with the monitoring center platform 2 installed in the combat read command through the communication network 3. The communication network 3 is a remote wireless communication network, including but not limited to a wireless transmission network such as a GSM network, a GPRS network, or a CDMA. The monitoring center platform 2 can be a monitor, a computer device, a server, or the like.

The fatigue monitoring and early warning system 10 can accurately monitor the fatigue condition of the soldier during the military readiness or the execution of the task in real time, and issue an early warning to the monitoring center platform 2 through the communication network 3 according to the fatigue condition of the soldier, so that the combat read command can be immediately Master the degree of fatigue of the soldiers, thus providing a basis for the combat read command to make battlefield decisions, and can improve the combat effectiveness of the military during the military readiness or when performing tasks.

2 is a schematic structural view of a preferred embodiment of the digitized helmet 1 of FIG. 1. In the preferred embodiment, the digitized helmet 1 includes, but is not limited to, a fatigue monitoring and early warning system 10, a brain wave collector 11, a camera 12, an alarm 13, a memory 14, and a microprocessor 15. The brain wave collector 11, the camera 12, the alarm 13, and the memory 14 are electrically connected to the microprocessor 15, and can exchange information with the fatigue monitoring and early warning system 10 through the microprocessor 15.

The brain wave collector 11 is a sensor for detecting a user's brain wave, and is used for detecting a soldier's brain wave signal from a soldier's brain wearing the digitized helmet 1, the brain power collector 11 The soldier's brain wave signal was collected from the area above the forehead of the soldier's head.

The camera 12 can be an imaging instrument or a small image capturing device for monitoring and acquiring an eye image of a soldier. In the present embodiment, two cameras 12 may be mounted on the left and right goggles of the digitized helmet 1, or a camera 12 may be mounted in the middle of the goggles of the digitized helmet 1.

The alarm device 13 can be a woofer or a speaker, which is used for soldiers to sound an alarm in high fatigue to seek help from surrounding soldiers. The memory 14 can be a read only memory ROM, an electrically erasable memory EEPROM, or a flash memory FLASH or the like. The microprocessor 15 can be a microcontroller (MCU), a data processing chip, or an information processing unit having data processing functions.

3 is a functional block diagram of a preferred embodiment of the fatigue monitoring and early warning system 10 of FIG. In the preferred embodiment, the fatigue monitoring and early warning system 10 includes, but is not limited to, a mental fatigue monitoring module 101, a physiological fatigue monitoring module 102, a fatigue calculation module 103, and an alarm module 104. The module referred to in the embodiment of the present invention refers to a series of computer program instruction segments that can be executed by the microprocessor 15 of the digitized helmet 1 and that can perform a fixed function, which is stored in the memory 14 of the digitized helmet 1 . in.

The mental fatigue monitoring module 101 is configured to collect the brain wave signal of the soldier through the brain electrical collector 11 integrated on the digitized helmet 1, and analyze the mental fatigue coefficient of the soldier according to the rhythmic change of the soldier's brain wave signal.

In the present embodiment, the mental fatigue monitoring module 101 uses an electroencephalogram (EEG) to reflect the brainwave signal of the soldier, and changes the rhythm of the brain wave, such as the rhythmic changes of the alpha wave, the delta wave, the theta wave, and the beta wave. To analyze the nervousness and psychological fatigue of the soldiers as the psychological fatigue coefficient of the soldiers. The delta wave frequency ranges from 1 to 4 Hz, and the healthy person can observe the wave while sleeping; the wave of the theta frequency ranges from 4 to 8 Hz, and the wave occurs when the soldier is in a sleepy state; the alpha wave, the frequency range is 8 -13 Hz, the band is related to whether the mood of the person is calm and easy, and whether the feeling is relaxed or not. The anxiety and tension will inhibit the alpha wave, and disappear when the human body thinks about the problem or is stimulated; the beta wave, the frequency range is 14-30Hz, the brain The cortex appears when excited, usually caused by depression and tension. When the force is applied to the human body, the alpha rhythm decreases or disappears, and the beta rhythm strengthens; as time increases, the load increases, theta wave appears, theta wave indicates inhibition of the cerebral cortex; when the force activity ends, The elimination of the load, the slow wave gradually disappears, the beta rhythm is gradually reduced, and the alpha rhythm is restored. If theta rhythm and delta rhythm increase in the EEG signal, and the beta rhythm decreases, it means that it is in a state of fatigue sleepiness.

In this embodiment, after the EEG collector 11 collects the brain wave signal of the soldier, the mental fatigue monitoring module 101 performs pre-processing of the brain wave signal to extract the interference signal, and extracts the characteristic band of the brain wave by using the wavelet packet decomposition method. For example, delta wave, theta wave, alpha wave, beta wave. In this embodiment, a normalized energy relative monitoring method can be adopted, that is, a frequency band energy ratio method (Frequency) is adopted. Band Energy Ratio, FBER), analyze the characteristic band delta wave, theta wave, alpha wave, beta wave to get the degree of tension and psychological fatigue of the soldier. The energy ratio method of the band includes: 1) When the alpha wave is dominant, FBERa is equal to 0.8 to 1 unit. Between, awake; 2) when FBER(beta)>FBER(alpha) and FBER (alpha+beta)>FBER(theta+delta) is in a state of tension; 3) when FBER(alpha+beta)<FBER(theta+delta) is in a state of fatigue. Among them, theta wave and belt wave have the greatest influence on the evaluation of the characteristic structure of normal human wakefulness and mental fatigue. With the deepening of mental fatigue, the energy of theta wave in EEG signal increases, the energy of belta wave decreases; the alpha wave is the comfortable state of the human body. Wave; beta wave indicates stress; Theta wave indicates fatigue; delta wave is the normal band in sleep; in the state of mental fatigue, theta wave energy increases; beta wave energy decreases.

The physiological fatigue monitoring module 102 is configured to take an image of a soldier's eye through a camera 12 integrated on the digitized helmet 1 and analyze the physiological fatigue coefficient of the soldier from the eye image. In the present embodiment, the physiological fatigue monitoring module 102 can analyze the eye movement from the eye image taken by the camera 12, such as the eye blink frequency, the eyelid average closing time, the eyelid average opening time, the left and right pupil diameters, and the eyeball rotation. Speed, eyes, etc., and the soldier's eye movements were analyzed using the Perclos measurement principle to obtain the soldier's physiological fatigue coefficient. In the case of complex and variable external environment and information interference of digital individual soldier system, it will affect the accuracy of EEK monitoring soldiers' physiological fatigue and mental fatigue results; using eye monitoring can assist EEG to monitor physiological fatigue, and at the same time help To confirm the degree of psychological fatigue of the soldiers in the awake state. Under the awake state of the soldiers, eye fatigue monitoring can be used as a standard for physiological fatigue monitoring, and together with EEG monitoring psychology to determine the degree of physical fatigue of soldiers.

According to the Perclos measurement principle, in accordance with the Perclos measurement principle, the fatigue detection formula is used in combination with the pupil area in the eye image taken by the camera 12: f=∑[open(t)]/N, where f is the total number of frames per eye. Closed ratio, that is, the proportion of frames is used instead of the specific time ratio, for example, 30 seconds is taken as one unit. N is the total number of frames per unit time. Open(t) is whether the human eye in the current frame image is closed. If open(t)=0, it is blinking; if open(t)=1, it is closed. If the unit time, f is greater than 0.4, the soldier is considered to be in a state of fatigue. If the closed eye continues to close the eye for more than 3 seconds, the soldier is considered to be dozing. If the total number of closed eyes is more than 37 frames, the soldier is considered to be in a state of fatigue. If the average number of blinks is greater than 10-15 times per minute, the standard soldier may be in a state of fatigue sleepiness.

The fatigue calculation module 103 is configured to calculate the fatigue of the soldier according to the psychological fatigue coefficient and the physiological fatigue coefficient of the soldier, and determine whether the fatigue of the soldier exceeds a preset value of the soldier under normal conditions. In the present embodiment, the fatigue calculation module 103 can calculate the fatigue of the soldier according to a weighted ratio of the predefined psychological fatigue coefficient and the physiological fatigue coefficient. For example, it can be defined that the soldier's psychological fatigue factor accounts for 60% of the soldier's fatigue, and defines the soldier's physiological fatigue coefficient as a proportion of 40% of the soldier's fatigue, and then the psychological fatigue coefficient and physical fatigue according to a predefined weighted ratio. The coefficients are weighted to obtain the fatigue of the soldiers.

The alarm module 104 is configured to generate an alarm message including the fatigue of the soldier when the fatigue of the soldier exceeds a preset value of the soldier under normal conditions, and send the alarm information to the monitoring center through the wireless communication network 3. Platform 2 provides early warning of the fatigue of the soldiers, so that the combat readiness command can instantly grasp the fatigue level of the soldiers, thus providing a basis for the combat read command to make battlefield decisions.

The alarm module 104 is also used to control the alarm 13 integrated on the digitized helmet 1 to generate an alarm sound to seek help from surrounding soldiers. For soldiers with severe mental fatigue, report the commander, perform job deployment, and avoid mental fatigue. Soldiers perform important battle operations, thereby enhancing their morale and cohesion.

In order to achieve the above object of the present invention, the present invention also provides a fatigue monitoring and early warning method based on a digital helmet, which can accurately and accurately monitor the fatigue condition of soldiers during military readiness or when performing tasks, for soldiers with physical fatigue and mildness. Psychologically fatigued soldiers are warning.

As shown in FIG. 4, it is a flow chart of a first preferred embodiment of the digital helmet-based fatigue monitoring and early warning method of the present invention. 1 , 2 and 3, in the first preferred embodiment, the fatigue monitoring and early warning method is applied to a digital helmet 1 worn by a soldier, and the method includes, but is not limited to, steps S11 to S15. :

Step S11, collecting the brain wave signal of the soldier through an EEG collector integrated on the digitized helmet, and analyzing the psychological fatigue coefficient of the soldier according to the rhythmic change of the brain wave signal;

Specifically, the mental fatigue monitoring module 101 uses an electroencephalogram (EEG) to reflect the brain wave signal of the soldier, and analyzes the rhythmic changes of the brain wave, such as the rhythmic changes of the alpha wave, the delta wave, the theta wave, and the beta wave. A mental fatigue factor indicating the degree of tension and psychological fatigue of the soldier. After the brain electricity collector 11 collects the brain wave signal of the soldier, the mental fatigue monitoring module 101 performs pre-processing of the brain wave signal to extract the interference signal, and extracts the characteristic band of the brain wave by using the wavelet packet decomposition method, delta wave, theta wave, Alpha wave, beta wave. As described above, the present embodiment can use the band energy proportional value method (FBER) to analyze the soldier's degree of stress and the degree of psychological fatigue as the soldier's psychological fatigue coefficient.

Step S12, ingesting an image of the soldier's eye through an imaging device integrated on the digitized helmet, and analyzing the physiological fatigue coefficient of the soldier from the eye image;

Specifically, the physiological fatigue monitoring module 102 takes in the image of the soldier's eye through the camera 12 integrated on the digitized helmet 1, and analyzes the soldier's physiological fatigue coefficient from the eye image. When the camera 12 takes the eye image of the soldier, the physiological fatigue monitoring module 102 can analyze the eye movement from the eye image taken by the camera 12, such as blink frequency, eyelid average closing time, eyelid average opening time, left and right pupils. The diameter, the rotation speed of the eyeball, the eye's slight vision, etc., were analyzed by Perclos measurement principle to obtain the soldier's physiological fatigue coefficient.

Step S13, calculating the fatigue degree of the soldier according to the psychological fatigue coefficient and the physiological fatigue coefficient of the soldier;

Specifically, the fatigue calculation module 103 calculates the fatigue of the soldier based on the mental fatigue coefficient and the physiological fatigue coefficient of the soldier. In the present embodiment, the fatigue calculation module 103 can calculate the fatigue of the soldier according to a weighted ratio of the predefined psychological fatigue coefficient and the physiological fatigue coefficient. For example, it can be defined that the soldier's psychological fatigue factor accounts for 60% of the soldier's fatigue, and defines the soldier's physiological fatigue coefficient as a proportion of 40% of the soldier's fatigue, and then the psychological fatigue coefficient and physical fatigue according to a predefined weighted ratio. The coefficient is weighted to obtain the fatigue of the soldier.

Step S14, determining whether the fatigue degree exceeds a preset value of the soldier under normal conditions;

Specifically, the fatigue calculation module 103 determines whether the fatigue of the soldier exceeds a preset value of the soldier under normal conditions. If the fatigue of the soldier does not exceed the preset value of the soldier under normal conditions, the process proceeds to step S11; if the fatigue of the soldier exceeds the preset value of the soldier under normal conditions, the process proceeds to step S15.

In step S15, an alarm message containing the fatigue of the soldier is generated, and the alarm information is sent to the monitoring center platform through the wireless communication network to warn the fatigue of the soldier.

Specifically, the alarm module 104 generates an alarm message including the fatigue of the soldier and sends the alarm information to the monitoring center platform 2 through the wireless communication network 3 to alert the soldier's fatigue condition, so that the combat read command can immediately grasp the soldiers. The degree of fatigue provides a basis for the battle preparation headquarters to make battlefield decisions.

As shown in FIG. 5, it is a flow chart of a second preferred embodiment of the digital helmet-based fatigue monitoring and early warning method of the present invention. In a second preferred embodiment, the method further comprises a step S16 after the step S15, in addition to all the method steps (i.e., steps S11 to S15) of the first preferred embodiment shown in FIG.

Step S16, controlling an alarm integrated on the digitized helmet to generate an alarm sound;

Specifically, the alarm module 104 controls the alarm 13 integrated on the digitized helmet 1 to generate an alarm sound to seek assistance from surrounding soldiers. For soldiers with severe mental fatigue, report to the commander, perform job deployment, and avoid letting the mentally fatigued soldiers perform important. Battle position.

The digital helmet-based fatigue monitoring and early warning system and method provided by the invention can accurately and accurately monitor the fatigue condition of the soldiers during military readiness or when performing tasks, and provide early warning to soldiers with physiological fatigue and soldiers with mild mental fatigue. For soldiers with severe psychological fatigue, report to the commander, carry out job deployment, avoid performing important battle operations, thus ensuring the smooth completion of combat missions and avoiding various losses caused by excessive fatigue of soldiers. At the end of the task, the EEG focuses on monitoring the psychological fatigue of the soldiers, and develops corresponding treatment plans for the moderate and severe mental fatigue soldiers to help the soldiers restore psychological fatigue and physical fatigue, thereby improving the cohesiveness and combat effectiveness of the army.

The above is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and equivalent structural or equivalent functional changes made by the description of the present invention and the accompanying drawings may be directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of the present invention.

Claims (13)

1.  A digital helmet-based fatigue monitoring and early warning method, characterized in that the method comprises the steps of:

   The brain wave signal of the user is collected by an EEG collector integrated on the digitized helmet, and the user's psychological fatigue coefficient is analyzed according to the rhythmic change of the brain wave signal;

   The user's eye image is taken by a camera integrated on the digitized helmet, and the user's physiological fatigue coefficient is analyzed from the eye image;

   Calculating the user's fatigue degree according to the user's psychological fatigue coefficient and physiological fatigue coefficient;

   Determining whether the fatigue degree exceeds a preset value of a normal condition of the user;

   When the fatigue degree exceeds a preset value of the user under normal conditions, an alarm information including user fatigue is generated, and the alarm information is sent to the monitoring center platform through the communication network to alert the user of the fatigue condition. .
2. The digital helmet-based fatigue monitoring and early warning method according to claim 1, wherein the step of analyzing the user's psychological fatigue coefficient according to the rhythmic change of the brain wave signal comprises:

   Performing pre-processing of the brain wave signal collected by the EEG collector to remove the interference signal;

   The wavelet wave packet decomposition method is used to extract the characteristic band alpha wave, delta wave, theta wave and beta wave of brain wave;

   Using the band energy ratio method to analyze the rhythmic changes of the characteristic band alpha wave, delta wave, theta wave, and beta wave, the psychological fatigue coefficient of the user is obtained.
3. The digital helmet-based fatigue monitoring and early warning method according to claim 1, wherein the step of analyzing a physiological fatigue coefficient of the user from the image of the eye comprises:

   Identifying an eye movement of the user from an eye image taken by the camera;

   The user's eye movement is analyzed using the Perclos measurement principle to obtain the user's physiological fatigue coefficient.
4. The digital helmet-based fatigue monitoring and early warning method according to claim 1, wherein the step of calculating the user's fatigue according to the user's psychological fatigue coefficient and the physiological fatigue coefficient comprises:

   Pre-defining the weighted ratio of the user's psychological fatigue coefficient and physiological fatigue coefficient;

   The user's fatigue factor is obtained by weighting the user's mental fatigue coefficient and physiological fatigue coefficient according to a predefined weighting ratio.
5. The digital helmet-based fatigue monitoring and early warning method according to claim 1, wherein the method further comprises the steps of:

   The alarm integrated on the digitized helmet generates an alarm when the fatigue exceeds a preset value under normal conditions of the user.
6. The digital helmet-based fatigue monitoring and early warning method according to claim 5, wherein the step of analyzing the user's psychological fatigue coefficient according to the rhythmic change of the brain wave signal comprises:

   Performing pre-processing of the brain wave signal collected by the EEG collector to remove the interference signal;

   The wavelet wave packet decomposition method is used to extract the characteristic band alpha wave, delta wave, theta wave and beta wave of brain wave;

   Using the band energy ratio method to analyze the rhythmic changes of the characteristic band alpha wave, delta wave, theta wave, and beta wave, the psychological fatigue coefficient of the user is obtained.
7. The digitized helmet-based fatigue monitoring and early warning method according to claim 5, wherein the step of analyzing a physiological fatigue coefficient of the user from the image of the eye comprises:

   Identifying an eye movement of the user from an eye image taken by the camera;

   The user's eye movement is analyzed using the Perclos measurement principle to obtain the user's physiological fatigue coefficient.
8. The digital helmet-based fatigue monitoring and early warning method according to claim 5, wherein the step of calculating the user's fatigue according to the user's psychological fatigue coefficient and the physiological fatigue coefficient comprises:

   Pre-defining the weighted ratio of the user's psychological fatigue coefficient and physiological fatigue coefficient;

   The user's fatigue factor is obtained by weighting the user's mental fatigue coefficient and physiological fatigue coefficient according to a predefined weighting ratio.
9. A digital helmet-based fatigue monitoring and early warning system, characterized in that the system comprises:

   a psychological fatigue monitoring module, configured to collect a brain wave signal of a user through an EEG collector integrated on the digitized helmet, and analyze a user's psychological fatigue coefficient according to a rhythmic change of the brain wave signal;

   a physiological fatigue monitoring module, configured to ingest a user's eye image through a camera integrated on the digitized helmet, and analyze a physiological fatigue coefficient of the user from the eye image;

   a fatigue calculation module, configured to calculate a user's fatigue degree according to a user's psychological fatigue coefficient and a physiological fatigue coefficient, and determine whether the fatigue degree exceeds a preset value of the user under normal conditions;

   An alarm module, configured to generate an alarm message including user fatigue when the fatigue exceeds a preset value of the user under normal conditions, and send the alarm information to the monitoring center platform to the user through the communication network Early warning of fatigue.
10. The digital helmet-based fatigue monitoring and early warning system according to claim 9, wherein said alarm module is further used for controlling integration in digitization when said fatigue degree exceeds a preset value of a user under normal conditions. The alarm on the helmet produces an alarm.
11. The digital helmet-based fatigue monitoring and early warning method according to claim 10, wherein the psychological fatigue monitoring module analyzes the user's psychological fatigue coefficient according to the rhythmic change of the brain wave signal, including:

    Performing pre-processing of the brain wave signal collected by the EEG collector to remove the interference signal;

    The wavelet wave packet decomposition method is used to extract the characteristic band alpha wave, delta wave, theta wave and beta wave of brain wave;

    Using the band energy ratio method to analyze the rhythmic changes of the characteristic band alpha wave, delta wave, theta wave, and beta wave, the psychological fatigue coefficient of the user is obtained.
12. The digital helmet-based fatigue monitoring and early warning system according to claim 10, wherein the physiological fatigue monitoring module analyzes the physiological fatigue coefficient of the user from the image of the eye, including:

    Identifying an eye movement of the user from an eye image taken by the camera;

    The user's eye movement is analyzed using the Perclos measurement principle to obtain the user's physiological fatigue coefficient.
13. The digital helmet-based fatigue monitoring and early warning system according to claim 10, wherein the fatigue calculation module calculates the user's fatigue according to the user's psychological fatigue coefficient and the physiological fatigue coefficient, including:

    Pre-defining the weighted ratio of the user's psychological fatigue coefficient and physiological fatigue coefficient;

    The user's fatigue factor is obtained by weighting the user's mental fatigue coefficient and physiological fatigue coefficient according to a predefined weighting ratio.