Big data-based camp management security risk assessment system and method

Abstract

The invention discloses a big data-based camp management safety risk assessment system and method, relates to the technical field of camp management, and solves the problem that the current camp management often depends on manual patrol to guarantee the safety of a camp and cannot monitor all areas in the camp in real time. Comprising a data acquisition module, a behavior analysis module, a security detection module, a video detection module and a digital twinning module, the digital twinning module is used for constructing a digital twinning model of a camp, the security detection module is used for performing real-time security detection on an area enclosed by an enclosing wall in the camp, and a risk assessment video of the detection area is obtained through detection; the video detection module is used for detecting a risk assessment video of a detection area in the camp to obtain a risk assessment picture of the detection area in the camp through detection, and the behavior analysis module is used for analyzing behaviors of triggering personnel in the camp to generate alarm signals of different grades and sending the alarm signals to the alarm module. According to the invention, risk assessment of camp safety management is realized based on big data.

Description

Technical Field

[0001] The present invention belongs to the technical field of camp management, and specifically relates to a camp management safety risk assessment system and method based on big data. Background Art

[0002] Camp management refers to the comprehensive management and operation of camps for specific purposes (such as military camps, factory parks, scientific research bases, etc.) to ensure the safe, efficient and orderly operation of personnel, facilities, equipment, resources, etc. Camp management includes security assurance, facility maintenance, resource scheduling, personnel management, emergency response and other aspects. Through digital and automated management methods, such as video surveillance, personnel positioning, access control, security systems, etc., the management efficiency and safety of the camps are improved. At the same time, camp management also pays attention to environmental protection, traffic scheduling, emergency response and other aspects to ensure smooth operation inside and outside the camps.

[0003] In the existing technology, the existing camp management often relies on manual patrols to ensure the safety of the camp, and it is impossible to monitor all key areas in the camp in real time. In addition, the existing camp management cannot monitor the behavior of all personnel in the camp in real time, resulting in an inability to efficiently respond to various emergencies; To this end, the present invention proposes a camp management safety risk assessment system and method based on big data. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a camp management safety risk assessment system and method based on big data.

[0005] The technical problems to be solved by the present invention are: How to achieve risk assessment of camp safety management based on big data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a camp management security risk assessment system based on big data, including a data acquisition module, a behavior analysis module, a security detection module, a video detection module, a digital twin module, an alarm module, a display module and a database; the digital twin module is used to construct a digital twin model of the camp, and send the digital twin model of the camp to the display module; The data acquisition module is used to collect detection videos of the camp and send the detection videos to the security detection module; the security detection module is used to perform real-time security detection on the area surrounded by the fence in the camp, and the risk assessment video of the detection area obtained by the detection is sent to the video detection module; The video detection module is used to detect the risk assessment video of the detection area in the camp, and the risk assessment picture of the detection area in the camp is sent to the behavior analysis module, and the face image corresponding to the triggering person is sent to the database, and the database is used to send the corresponding historical behavior data to the behavior analysis module based on the face image; The database is also used to store preset connection diagrams of key points of human postures corresponding to different violations and send them to the behavior analysis module; the behavior analysis module is used to analyze the behaviors of the triggered personnel in the camp, and generate alarm signals of different levels through analysis and send them to the alarm module and display module. The display module is used to highlight the detection area in the digital twin model according to the alarm signals of different levels, and the alarm module is used to issue an alarm after receiving alarm signals of different levels.

[0007] As a further solution of the present invention, the construction process of the digital twin model is specifically as follows: With the geometric center of the camp as the origin O, the north direction as the positive direction of the Y axis, the east direction as the positive direction of the X axis, and the plane perpendicular to the X axis and the Y axis as the positive direction of the Z axis, the three-dimensional rectangular coordinate system OXYZ of the camp is constructed; The digital twin data of the camp is obtained, and then the digital twin data of the camp is denoised and standardized to obtain the standard digital twin data of the camp; The standard digital twin data of the camp are constructed one by one in the three-dimensional rectangular coordinate system OXYZ of the camp to obtain a digital twin model of the camp.

[0008] As a further solution of the present invention, the working process of the security detection module includes: The camp is divided into multiple detection areas according to the fence, and each detection area in the camp is labeled and numbered to obtain the area number of the detection area in the camp; Construct a detection area corresponding to the detection area; Obtain the deployment time data of all detection areas in the camp. When the distance between any person and the corresponding wall of the detection area is greater than the detection distance, no operation will be performed.

[0009] As a further solution of the present invention, the detection area includes an all-weather detection area and a timed detection area. The detection area of ​​the all-weather detection area is constructed with a first detection distance, and the detection area of ​​the timed detection area is constructed with a second detection distance. The first detection distance is greater than the second detection distance.

[0010] As a further solution of the present invention, the working process of the security detection module also includes: When the distance between any person and the corresponding wall of the detection area is less than or equal to the detection distance, and the current time is the disarming time interval, no operation will be performed; When the distance between any person and the fence corresponding to the detection area is less than or equal to the detection distance, and the current time is the deployment time interval and the continuous stay time exceeds the time threshold, the person is determined to have entered the detection area of ​​the detection area, triggering the real-time security detection of the camp, and the person who triggered the real-time security detection is recorded as the triggering person; the continuous stay time is the continuous time that the distance between the person and the fence corresponding to the detection area is less than or equal to the detection distance; The area number and trigger time node of the detection area that triggers real-time security detection are recorded. According to the area number, a fixed time before and after the trigger time node of the detection area is used as the interception point. According to the interception point, the detection video is intercepted to obtain the risk assessment video of the corresponding detection area.

[0011] As a further solution of the present invention, the working process of the video detection module is as follows: The risk assessment video of the detection area in the camp is extracted frame by frame to obtain multiple risk assessment pictures, and the risk assessment pictures are denoised to obtain denoised risk assessment pictures of the detection area in the camp; Perform face detection on the risk assessment image to obtain the face image of the corresponding triggering person who triggers the real-time security detection.

[0012] As a further solution of the present invention, the historical behavior data is the number of violations of persons corresponding to different facial images.

[0013] As a further solution of the present invention, the analysis process of the behavior analysis module is specifically as follows: Adjust the risk assessment images of the detection area in the camp to a standard size; this is to unify the size of continuous frame images; Identify the key points of human posture corresponding to the human figure outline in the risk assessment picture, and connect the identified key points of human posture to obtain a real-time connection diagram of the key points of human posture corresponding to the human figure outline in the risk assessment; Compare the real-time connection diagram with the preset connection diagram one by one; If the real-time connection diagram does not match all preset connection diagrams, the next real-time connection diagram is compared with the preset connection diagrams one by one until the real-time connection diagram comparison is completed and all real-time connection diagrams do not match all preset connection diagrams, then no operation is performed; If the real-time connection diagram matches any preset connection diagram, the historical behavior data of the triggering person is obtained to obtain the number of violations of the triggering person. If the number of violations is less than the comparison number threshold, a third-level alarm signal, a second-level alarm signal or a first-level alarm signal is generated.

[0014] As a further solution of the present invention, the alarm intensity of the first-level alarm signal is higher than the alarm intensity of the second-level alarm signal, and the alarm intensity of the second-level alarm signal is higher than the alarm intensity of the third-level alarm signal.

[0015] The present invention also provides: a camp management safety risk assessment method based on big data, the method is as follows: Step 1: Collect the inspection video of the camp, and conduct real-time safety inspection of the area surrounded by the fence in the camp based on the inspection video to obtain a risk assessment video of the inspection area; Step 2: Detect the risk assessment video of the detection area in the camp, obtain the risk assessment picture of the detection area in the camp, and trigger the facial image of the person corresponding to the person; Step 3: Obtain the corresponding historical behavior data based on the face image, and obtain the preset connection diagram of the key points of human posture corresponding to different violations; Step 4: Analyze the behaviors of the triggering personnel in the camp by combining historical behavior data and preset connection diagrams, and generate alarm signals of different levels; Step 5: Highlight the detection area in the digital twin model according to the different levels of alarm signals and issue an alarm when receiving different levels of alarm signals.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention first collects the detection video of the camp, and combines the detection video to perform real-time security detection on the area enclosed by the wall in the camp, so as to detect and obtain a risk assessment video of the detection area, and then detects the risk assessment video of the detection area in the camp to obtain a risk assessment picture of the detection area in the camp, and simultaneously obtains the facial image corresponding to the triggering person, and obtains the corresponding historical behavior data based on the facial image, and simultaneously obtains the preset connection diagram of the key points of the human body posture corresponding to different violations, and analyzes the behavior of the triggering person in the camp in combination with the historical behavior data and the preset connection diagram, and generates alarm signals of different levels, and highlights the detection area in the digital twin model according to the alarm signals of different levels and issues an alarm when receiving alarm signals of different levels. The present invention realizes risk assessment of camp safety management based on big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 is the overall system block diagram of the present invention; Figure 2 It is a schematic diagram of the detection area in the present invention; Figure 3 It is a preset connection diagram of key points of human body posture in the present invention; Figure 4 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1-Figure 3 As shown, the technical solution provided by the present invention is: a camp management security risk assessment system based on big data, which is used to detect the behavior of personnel in the camp and monitor the possible security risks in various areas of the camp, including a data acquisition module, a behavior analysis module, a security detection module, a video detection module, a digital twin module, an alarm module, a display module and a database; In this embodiment, the digital twin module is used to construct a digital twin model of the camp, and the construction process is as follows: With the geometric center of the camp as the origin O, the north direction as the positive direction of the Y axis, the east direction as the positive direction of the X axis, and the plane perpendicular to the X axis and the Y axis as the positive direction of the Z axis, the three-dimensional rectangular coordinate system OXYZ of the camp is constructed; The digital twin data of the camp is obtained, and then the digital twin data of the camp is denoised and standardized to obtain the standard digital twin data of the camp; The digital twin data of the camp includes the 3D structure diagram, longitude and latitude, altitude of the buildings in the camp, and the road parameters of the camp. It should be specifically explained that standardizing the denoised digital twin data specifically includes: unifying the denoised digital twin data to a standard scale, unifying the units of length, width and height in the 3D structure diagram of the building to meters, unifying the slope in the road parameters to radians, etc.; The standard digital twin data of the camp are constructed one by one in the three-dimensional rectangular coordinate system OXYZ of the camp to obtain the digital twin model of the camp; Specifically, the standard digital twin data of the camp is converted into coordinates in the three-dimensional rectangular coordinate system of the camp through a conversion formula, and then the coordinates are sequentially input into the three-dimensional rectangular coordinate system of the camp to construct a digital twin model of the camp; wherein the conversion formula is used to convert any longitude and latitude in the standard digital twin data of the camp into corresponding coordinates in the three-dimensional rectangular coordinate system OXYZ of the camp, which is an existing formula; The digital twin module sends the digital twin model of the camp to the display module.

[0021] Furthermore, the data acquisition module is used to collect detection videos of the camp and send the detection videos to the security detection module; in specific implementation, the data acquisition module is specifically a high-definition camera deployed in the camp and an API interface deployed in the computer equipment.

[0022] In this embodiment, the security detection module is used to perform real-time security detection on the area surrounded by the fence in the camp. The detection process is as follows: like Figure 2 As shown, the camp is divided into multiple detection areas according to the fence, and each detection area in the camp is labeled and numbered to obtain the area number of the detection area in the camp; wherein the detection area includes an all-weather detection area and a timed detection area, the detection area of ​​the all-weather detection area is constructed with a first detection distance, and the detection area of ​​the timed detection area is constructed with a second detection distance, and the first detection distance is greater than the second detection distance; In specific implementation, the all-weather detection area is the area where the core facilities are located in the camp, and the scheduled detection area is the office area of ​​the camp; Obtain the deployment time data of all detection areas in the camp. When the distance between any person and the corresponding wall of the detection area is greater than the detection distance, no operation will be performed; When the distance between any person and the corresponding wall of the detection area is less than or equal to the detection distance, and the current time is the disarming time interval, no operation will be performed; When the distance between any person and the fence corresponding to the detection area is less than or equal to the detection distance, and the current time is the deployment time interval and the continuous stay time exceeds the time threshold, the person is determined to have entered the detection area of ​​the detection area, triggering the real-time security detection of the camp, and the person who triggered the real-time security detection is recorded as the triggering person; the continuous stay time is the continuous time that the distance between the person and the fence corresponding to the detection area is less than or equal to the detection distance; Among them, the deployment time data is the deployment time interval and disarming time interval of all detection areas in the camp. In practice, the minimum endpoint value of the deployment time interval is used as the initial deployment time node, and a fixed time is added upward as the deployment delay time. When there is manual deployment of the corresponding fence of the detection area in the camp, the deployment delay time is set to avoid false alarms caused by personnel failing to exit the detection area in time during the deployment process; Record the area number and trigger time node of the detection area that triggers the real-time security detection, and use the fixed time length of the detection area before and after the trigger time node as the interception point according to the area number, and intercept the detection video according to the interception point to obtain the risk assessment video of the corresponding detection area; in practice, the fixed time length can be 10 seconds before and after the trigger time node, and the 10 seconds before and after the trigger time node are used as the interception point to intercept the risk assessment video; The security detection module sends the risk assessment video of the detection area to the video detection module.

[0023] In this embodiment, the video detection module is used to detect the risk assessment video of the detection area in the camp, and the detection process is as follows: The risk assessment video of the detection area in the camp is extracted frame by frame to obtain multiple risk assessment pictures, and the risk assessment pictures are denoised to obtain denoised risk assessment pictures of the detection area in the camp; Perform face detection on the risk assessment image through the face detection model to obtain the face image of the corresponding triggering person that triggers the real-time security detection; Specifically, the denoised detection image is input into the trained face detection model, and then the face detection model is compared with the face images saved in the database to obtain the face image with the highest similarity. The face image with the highest similarity is the triggering person for triggering real-time security detection. The video detection module sends the risk assessment picture of the detection area in the camp to the behavior analysis module, and the video detection module sends the facial image corresponding to the triggering person to the database, which is used to store the facial images of different people and the historical behavior data corresponding to the different facial images. The database sends the corresponding historical behavior data to the behavior analysis module based on the facial image; Among them, the historical behavior data is the number of violations committed by people corresponding to different facial images, including snatching weapons, attacking others, fighting, etc.

[0024] In this embodiment, the database is also used to store a preset connection diagram of key points of human postures corresponding to different violations, and the database sends the preset connection diagram of key points of human postures corresponding to different violations to the behavior analysis module; Among them, Figure 3 As shown, there are fifteen key points of human posture, including one chest point (point 3), one spine point (point 7), two ankle points (left ankle and right ankle, point 14 and point 15), two knee points (left knee and right knee, point 12 and point 13), two shoulder points (left shoulder and right shoulder, point 2 and point 4), one head point (point 1), two hip points (left hip and right hip, point 10 and point 11), two elbow points (left elbow and right elbow, point 5 and point 6), and two wrist points (left wrist and right wrist, point 8 and point 9).

[0025] In this embodiment, the behavior analysis module is used to analyze the behavior of the triggering personnel in the camp, and the analysis process is as follows: Adjust the risk assessment images of the detection area in the camp to a standard size; this is to unify the size of continuous frame images; Identify the key points of human posture corresponding to the human figure outline in the risk assessment picture, and connect the identified key points of human posture to obtain a real-time connection diagram of the key points of human posture corresponding to the human figure outline in the risk assessment; In practice, continuous frame images can be input into a trained deep learning model for detection, and the deep learning model can recognize the human outline in the risk assessment image; Compare the real-time connection diagram with the preset connection diagram one by one; If the real-time connection diagram does not match all preset connection diagrams, the next real-time connection diagram is compared with the preset connection diagrams one by one until the real-time connection diagram comparison is completed and all real-time connection diagrams do not match all preset connection diagrams, then no operation is performed; If the real-time connection graph matches any preset connection graph, the historical behavior data of the triggering person is obtained to obtain the number of violations of the triggering person; When the number of violations is less than or equal to the first number threshold, a third-level alarm signal is generated; when the number of violations is greater than the first number threshold and less than or equal to the second number threshold, a second-level alarm signal is generated; when the number of violations is greater than the second number threshold, a first-level alarm signal is generated; wherein the first number threshold is less than the second number threshold, the alarm intensity of the first-level alarm signal is higher than the alarm intensity of the second-level alarm signal, and the alarm intensity of the second-level alarm signal is higher than the alarm intensity of the third-level alarm signal; The behavior analysis module sends alarm signals of different levels to the alarm module and the display module. The display module is used to highlight the detection area in the digital twin model according to the alarm signals of different levels. The alarm module is used to sound an alarm after receiving alarm signals of different levels. Specifically, the alarm module is specifically set in the camp or in the broadcasting equipment within the detection area.

[0026] This embodiment adopts the above-mentioned technical solution. First, the detection video of the camp is collected, and the area enclosed by the wall in the camp is detected in real time in combination with the detection video, so as to obtain a risk assessment video of the detection area. Then, the risk assessment video of the detection area in the camp is detected to obtain a risk assessment picture of the detection area in the camp. At the same time, the facial image corresponding to the triggering person is taken, and the corresponding historical behavior data is obtained based on the facial image. At the same time, a preset connection diagram of the key points of human postures corresponding to different violations is obtained, and the behavior of the triggering person in the camp is analyzed in combination with the historical behavior data and the preset connection diagram. The analysis generates alarm signals of different levels, and the detection area is highlighted in the digital twin model according to the alarm signals of different levels, and an alarm is issued when the alarm signals of different levels are received, so as to realize the risk assessment of the security management of the camp.

[0027] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0028] Embodiment 2: Figure 4 As shown, based on another concept of the same invention, a camp management safety risk assessment method based on big data is now proposed, comprising the following steps: Step 1: Collect the inspection video of the camp, and conduct real-time safety inspection of the area surrounded by the fence in the camp based on the inspection video to obtain a risk assessment video of the inspection area; Step 2: Detect the risk assessment video of the detection area in the camp, obtain the risk assessment picture of the detection area in the camp, and trigger the facial image of the person corresponding to the person; Step 3: Obtain the corresponding historical behavior data based on the face image, and obtain the preset connection diagram of the key points of human posture corresponding to different violations; Step 4: Analyze the behaviors of the triggering personnel in the camp by combining historical behavior data and preset connection diagrams, and generate alarm signals of different levels; Step 5: Highlight the detection area in the digital twin model according to the different levels of alarm signals and issue an alarm when receiving different levels of alarm signals.

[0029] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The camp management safety risk assessment system based on big data is characterized by: It includes a data acquisition module, a behavior analysis module, a security detection module, a video detection module, a digital twin module, an alarm module, a display module and a database; the digital twin module is used to construct a digital twin model of the camp, and send the digital twin model of the camp to the display module; the data acquisition module is used to collect detection videos of the camp, and send the detection videos to the security detection module; the security detection module is used to perform real-time security detection on the area enclosed by the wall in the camp, and the risk assessment video of the detection area obtained by detection is sent to the video detection module; the video detection module is used to detect the risk assessment video of the detection area in the camp, and the risk assessment picture of the detection area in the camp is sent to the behavior analysis module, and the facial image corresponding to the triggering person is sent to the database, and the database is used to send the corresponding historical behavior data to the behavior analysis module based on the facial image; The database is also used to store preset connection diagrams of key points of human postures corresponding to different violations and send them to the behavior analysis module; the behavior analysis module is used to analyze the behaviors of the triggered personnel in the camp, and generate alarm signals of different levels through analysis and send them to the alarm module and display module. The display module is used to highlight the detection area in the digital twin model according to the alarm signals of different levels, and the alarm module is used to issue an alarm after receiving alarm signals of different levels.

2. The camp management safety risk assessment system based on big data according to claim 1 is characterized in that: The construction process of the digital twin model is as follows: With the geometric center of the camp as the origin O, the north direction as the positive direction of the Y axis, the east direction as the positive direction of the X axis, and the plane perpendicular to the X axis and the Y axis as the positive direction of the Z axis, the three-dimensional rectangular coordinate system OXYZ of the camp is constructed; The digital twin data of the camp is obtained, and then the digital twin data of the camp is denoised and standardized to obtain the standard digital twin data of the camp; The standard digital twin data of the camp are constructed one by one in the three-dimensional rectangular coordinate system OXYZ of the camp to obtain a digital twin model of the camp.

3. The camp management safety risk assessment system based on big data according to claim 2 is characterized in that: The working process of the security detection module includes: The camp is divided into multiple detection areas according to the fence, and each detection area in the camp is labeled and numbered to obtain the area number of the detection area in the camp; Construct a detection area corresponding to the detection area; Obtain deployment time data for all detection areas in the camp; When the distance between any person and the corresponding wall of the detection area is greater than the detection distance, no operation will be performed.

4. The camp management safety risk assessment system based on big data according to claim 3 is characterized in that: The detection area includes an all-weather detection area and a scheduled detection area. The detection area of ​​the all-weather detection area is constructed with a first detection distance, and the detection area of ​​the scheduled detection area is constructed with a second detection distance. The first detection distance is greater than the second detection distance.

5. The camp management safety risk assessment system based on big data according to claim 3 is characterized in that: The working process of the security detection module also includes: When the distance between any person and the corresponding wall of the detection area is less than or equal to the detection distance, and the current time is the disarming time interval, no operation will be performed; When the distance between any person and the corresponding wall of the detection area is less than or equal to the detection distance, and the current time is the deployment time interval and the continuous stay time exceeds the time threshold, the real-time security detection of the camp is triggered, and the person who triggers the real-time security detection is recorded as the triggering person; the continuous stay time is the continuous time that the distance between the person and the corresponding wall of the detection area is less than or equal to the detection distance; The area number and trigger time node of the detection area that triggers real-time security detection are recorded. According to the area number, a fixed time before and after the trigger time node of the detection area is used as the interception point. According to the interception point, the detection video is intercepted to obtain the risk assessment video of the corresponding detection area.

6. The camp management safety risk assessment system based on big data according to claim 5 is characterized in that: The working process of the video detection module is as follows: The risk assessment video of the detection area in the camp is extracted frame by frame to obtain multiple risk assessment pictures, and the risk assessment pictures are denoised to obtain denoised risk assessment pictures of the detection area in the camp; Perform face detection on the risk assessment image to obtain the face image of the corresponding triggering person who triggers the real-time security detection.

7. The camp management safety risk assessment system based on big data according to claim 6 is characterized in that: The historical behavior data is the number of violations committed by people corresponding to different facial images.

8. The camp management safety risk assessment system based on big data according to claim 7 is characterized in that: The behavior analysis module is used to analyze the behavior of triggering personnel in the camp. The analysis process is as follows: Adjust the risk assessment images of the detection area in the camp to a standard size; this is to unify the size of continuous frame images; Identify the key points of human posture corresponding to the human figure outline in the risk assessment picture, and connect the identified key points of human posture to obtain a real-time connection diagram of the key points of human posture corresponding to the human figure outline in the risk assessment; Compare the real-time connection diagram with the preset connection diagram one by one; If the real-time connection diagram does not match all preset connection diagrams, the next real-time connection diagram is compared with the preset connection diagrams one by one until the real-time connection diagram comparison is completed and all real-time connection diagrams do not match all preset connection diagrams, then no operation is performed; If the real-time connection diagram matches any preset connection diagram, the historical behavior data of the triggering person is obtained to obtain the number of violations of the triggering person. If the number of violations is less than the comparison number threshold, a third-level alarm signal, a second-level alarm signal or a first-level alarm signal is generated.

9. The camp management safety risk assessment system based on big data according to claim 8 is characterized in that: The alarm intensity of the first-level alarm signal is higher than the alarm intensity of the second-level alarm signal, and the alarm intensity of the second-level alarm signal is higher than the alarm intensity of the third-level alarm signal.

10. A camp management safety risk assessment method based on big data, characterized in that: Using the camp management safety risk assessment system based on big data as described in any one of claims 1 to 9, the method is as follows: Step 1: Collect the inspection video of the camp, and conduct real-time safety inspection of the area surrounded by the fence in the camp based on the inspection video to obtain a risk assessment video of the inspection area; Step 2: Detect the risk assessment video of the detection area in the camp, obtain the risk assessment picture of the detection area in the camp, and trigger the facial image of the person corresponding to the person; Step 3: Obtain the corresponding historical behavior data based on the face image, and obtain the preset connection diagram of the key points of human posture corresponding to different violations; Step 4: Analyze the behaviors of the triggering personnel in the camp by combining historical behavior data and preset connection diagrams, and generate alarm signals of different levels; Step 5: Highlight the detection area in the digital twin model according to the different levels of alarm signals and issue an alarm when receiving different levels of alarm signals.

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