Visual monitoring system and method for identity system

Abstract

The invention discloses a visual monitoring system and method for an identity system, and relates to the technical field of visual monitoring, and the system comprises a data collection module, an operation state analysis module and a state early warning module. The visual monitoring operation index of the campus is evaluated, when safety problems occur, management personnel can rapidly lock related personnel and action tracks thereof through backtracking data, the situation of equipment faults or performance reduction can be found in time, equipment maintenance and updating are arranged in advance, monitoring blind areas caused by the equipment problems are avoided, and the safety of the campus is improved. And stable operation of the monitoring system is ensured. The abnormal index of each device in the campus park is analyzed, the risk level of each abnormal device is judged, the devices can be maintained in a targeted manner, the high-risk devices are preferentially maintained, and the low-risk devices are regularly inspected and preventively maintained, so that the abrasion of the devices is reduced, the service life of the devices is prolonged, and the replacement cost of the devices is reduced; resources such as maintenance personnel, maintenance materials and maintenance time are reasonably distributed.

Description

Technical Field

[0001] The present invention relates to the technical field of visual monitoring, and particularly to a visual monitoring system and method for an identity system. Background Art

[0002] Driven by the digital wave, in fields such as education, business, and security, more and more attention is paid to the efficient management and security guarantee of personnel. The various drawbacks of traditional identity recognition and monitoring modes have increasingly highlighted the urgency of constructing a new visual monitoring system for the identity system. With the expansion of school scale and the intensification of personnel flow, the difficulty of campus security and personnel management has increased significantly. Therefore, it is necessary to analyze a visual monitoring system and method for an identity system.

[0003] The prior art, such as an invention patent application with publication number CN115601313A, discloses a visual monitoring and management system for a toughened glass production process. By specifically visually monitoring the roller quality, heating process, and bending process corresponding to the target toughened glass, if the roller quality, heating process, or bending process corresponding to the target toughened glass is in an abnormal state, corresponding processing is performed on the abnormal process, avoiding the occurrence of an abnormal state in a certain production process corresponding to the toughened glass, ensuring that the quality of the finished toughened glass is not affected, not only largely avoiding the problem of waste of raw materials for the finished toughened glass, but also reducing a certain amount of labor costs.

[0004] In the prior art, a visual monitoring system and method for an identity system can meet the basic requirements, but there are also some potential defects and challenges, which are specifically reflected in the following aspects: First, in the prior art, through the campus planning map, regional division is carried out to obtain each key area and each non-key area, and the attention to evaluating the visual monitoring operation index of the campus is not high. When a security problem occurs, the speed of locking relevant personnel and their movement trajectories is reduced. Due to the lack of analysis of personnel flow data in different regions, the rational arrangement of the patrol routes and times of security personnel is reduced, resulting in the unreasonable allocation of security resources, reducing the pertinence and efficiency of security work, affecting the discovery of equipment failures or performance degradation, increasing the problem of monitoring blind spots caused by equipment problems, and thus affecting the stable operation of the monitoring system.

[0005] Second, in the prior art, the analysis of the abnormal index of each device in the campus is lacking, which affects the judgment of the risk level based on the device abnormal index, and then affects the maintenance of the device, increasing the wear of the device, reducing the service life of the device, increasing the device replacement cost, increasing the problem of resource waste on low-risk devices, and reducing the utilization efficiency of maintenance resources. Summary of the Invention

[0006] The object of the present invention is to provide a visualization monitoring system and method for an identity system, which solves the problems existing in the background art.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a visualization monitoring system and method for an identity system, including: a data acquisition module, an operating state analysis module, and a status warning.

[0008] Data acquisition module: Through the campus planning map, regional division is carried out to obtain each key area and each non-key area, and through the installed visualization monitoring system for identification, the identity recognition data of each key area and non-key area in the campus is obtained, and the visualization monitoring operation index of the campus is evaluated.

[0009] Operating state analysis module: Based on the obtained visualization monitoring operation index of the campus, it is judged whether the equipment is operating abnormally. If abnormal, the abnormal state data of each equipment is collected.

[0010] Status warning: Based on the obtained abnormal state data of each equipment, the abnormal index of each equipment in the campus is analyzed, the risk level of each abnormal equipment is judged, and risk handling and feedback are carried out.

[0011] Further, for the evaluation of the visualization monitoring operation index of the campus, the specific analysis method is: Based on the obtained identity recognition data of each key area and non-key recognition data in the campus, and then analyze the visualization monitoring operation index K of each key area in the campus u and the visualization monitoring operation index L of each non-key area in the campus y , to evaluate the visualization monitoring operation index of the campus, the specific calculation formula is: where, u represents the number of the key area, u = 1, 2,..., r, r represents the number of key areas, y represents the number of the non-key area, y = 1, 2,..., t, and t represents the number of non-key areas.

[0012] Further, for the identity recognition data of each key area and non-key recognition data in the campus, the identity recognition data of each key area includes: the deviation value of each person's walking route, the entry duration and departure duration of each time period, and the identity recognition data of each non-key area includes: the accurate value of personnel matching of each equipment and the data transmission duration.

[0013] Further, the specific analysis method for the visual monitoring operation index of each key area in the campus park is as follows: Based on the obtained deviation values of each personnel's walking route and the entry and exit durations of each time period in each key area of the campus park, the difference between the entry duration and the exit duration of each time period in each key area of the campus park is calculated to obtain the stay duration of each time period in each key area of the campus park. And the reference deviation value of each personnel's walking route in each key area of the campus park and the reference stay duration of each time period in each key area of the campus park are extracted from the database to analyze the visual monitoring operation index of each key area in the campus park. The specific calculation formula is: where s ud ' represents the reference deviation value of the d-th personnel's walking route in the u-th key area of the campus park, s ud represents the deviation value of the d-th personnel's walking route in the u-th key area of the campus park, d represents the personnel number, d = 1, 2,..., v, v represents the number of personnel, c um ' is the reference stay duration of the m-th time period in the u-th key area of the campus park, c um is the stay duration of the m-th time period in the u-th key area of the campus park, m represents the time period number, m = 1, 2,..., n, n represents the number of time periods.

[0014] Further, the specific analysis method for the visual monitoring operation index of each non-key area in the campus park is as follows: Based on the obtained accurate matching value of personnel for each device and the data transmission duration in each non-key area of the campus park, and the reference accurate matching value of personnel for each device and the reference data transmission duration in each non-key area of the campus park are extracted from the database to analyze the visual monitoring operation index of each non-key area in the campus park. The specific calculation formula is: where z yf represents the accurate matching value of personnel for the f-th device in the y-th non-key area of the campus park, z yf ' represents the reference accurate matching value of personnel for the f-th device in the y-th non-key area of the campus park, x yf ' represents the reference data transmission duration for the f-th device in the y-th non-key area of the campus park, x yf represents the data transmission duration for the f-th device in the y-th non-key area of the campus park, f represents the device number, f = 1, 2,..., q, q represents the number of devices.

[0015] Further, it is determined whether the device is operating abnormally. If it is abnormal, the abnormal state data of each device is collected. The specific analysis method is as follows: Based on the obtained visual monitoring operation index of the campus park, the visual monitoring operation index threshold of the campus park is extracted from the database, and the visual monitoring operation index of the campus park is compared with the visual monitoring operation index threshold of the campus park. If the visual monitoring operation index of the campus park is less than the visual monitoring operation index threshold of the campus park, it indicates that the visual monitoring equipment of the campus park is operating abnormally. Then, each abnormal device in the campus park is screened, and the abnormal state data of each device in the campus park is collected through the monitoring system.

[0016] Further, for the abnormal index of each device in the campus park, the specific analysis method is as follows: Based on the obtained abnormal state data of each device in the campus park, where the abnormal state data includes: camera focus clarity, aperture magnification value and reduction value, external temperature, number of external cracks, image transmission quantity and reception quantity. The difference between the aperture magnification value and reduction value of each device in the campus park is processed to obtain the aperture adjustment difference of each device in the campus park. The difference between the image transmission quantity and reception quantity of each device in the campus park is processed to obtain the image transmission difference of each device in the campus park. The camera focus clarity reference range, aperture adjustment difference reference range, external temperature reference range, number of external cracks reference range, and image transmission reference difference range of the devices in the campus park are extracted from the database to analyze the abnormal index of each device in the campus park. The specific calculation formula is: p il represents the l-th abnormal state data of the i-th device in the campus park, p l ' represents the reference range of the l-th abnormal state data of the devices in the campus park, l ∈ [1, 5].

[0017] Further, for the risk level of each abnormal device, the specific analysis method is as follows: Based on the obtained abnormal index of each device in the campus park, the mean value of the abnormal index of each device in the campus park is processed to obtain the mean value of the abnormal index of the devices in the campus park. The difference between the abnormal index of each device in the campus park and the mean value of the abnormal index of the devices in the campus park is processed to obtain the mean value difference of the abnormal index of each device in the campus park. The mean value difference of the abnormal index of each device in the campus park is compared with the predefined safe range of the mean value difference of the abnormal index of the devices in the campus park. If the mean value difference of the abnormal index of a certain device in the campus park is not within the safe range of the mean value difference of the abnormal index of the devices in the campus park, then the device in the campus park is recorded as a first-level abnormal device; otherwise, it is recorded as a second-level abnormal device.

[0018] Furthermore, for the risk handling and feedback, the specific analysis method is as follows: Based on the obtained first-level abnormal devices, arrange professional technicians to inspect and repair the first-level abnormal devices, prioritize the handling of the first-level abnormal devices, increase the monitoring frequency and intensity of the first-level devices, and generate a report for feedback.

[0019] Based on the obtained second-level abnormal devices, perform optimization and adjustment on the second-level abnormal devices, and generate a report for feedback.

[0020] The second aspect of the present invention provides a method for implementing the above identity system visualization monitoring system, which is characterized by including: Step 1, data collection: Through the campus planning map, conduct area division to obtain each key area and each non-key area, and through the installed visualization monitoring system for identification, obtain the identity recognition data of each key area and non-key area in the campus, and evaluate the visualization monitoring operation index of the campus.

[0021] Step 2, operating status analysis: Based on the obtained visualization monitoring operation index of the campus, determine whether the device operation is abnormal. If it is abnormal, collect the abnormal status data of each device.

[0022] Step 3, status warning: Based on the obtained abnormal status data of each device, analyze the abnormal index of each device in the campus, determine the risk level of each abnormal device, perform risk handling and feedback.

[0023] The beneficial effects of the present invention are as follows: In the data collection module: Through the campus planning map, conduct area division to obtain each key area and each non-key area, and through the installed visualization monitoring system for identification, obtain the identity recognition data of each key area and non-key area in the campus, and evaluate the visualization monitoring operation index of the campus. When a security problem occurs, the management personnel can quickly lock the relevant personnel and their movement trajectories by tracing the data, providing strong support for accident investigation and handling. By analyzing the personnel flow data in different areas, the campus management department can master the personnel flow situation in each area at different time periods, and then reasonably arrange the patrol routes and times of security personnel, realizing the precise allocation of security resources, improving the pertinence and efficiency of security work. The management department can timely discover equipment failures or performance degradation, arrange equipment maintenance and updates in advance, avoid monitoring blind spots caused by equipment problems, and ensure the stable operation of the monitoring system.

[0024] In status early warning, based on the obtained abnormal status data of each device, analyze the abnormal indexes of each device in the campus park, judge the risk levels of the abnormal devices, conduct risk handling and feedback. Judging the risk levels according to the device abnormal indexes can targetedly maintain and repair the devices. For high-risk devices, prioritize the arrangement of repairs and allocate high-quality resources for processing first, avoiding wasting resources on low-risk devices, improving the utilization efficiency of maintenance resources, and conducting regular inspections and preventive maintenance on low-risk devices, which helps reduce device wear, extend the service life of the devices, lower the device replacement cost, and reasonably allocate resources such as maintenance personnel, maintenance materials, and maintenance time. Brief Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0026] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0027] Figure 2 It is a schematic diagram of the implementation step flow of the method of the present invention. Detailed Embodiments

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0029] Refer to Figure 1 As shown, the present invention provides an identity system visualization monitoring system and method, including: a data collection module, an operating status analysis module, and a status early warning.

[0030] It should be noted that the data collection module is connected to the operating status analysis module, the operating status analysis module is connected to the status early warning, the data collection module is connected to the database, the operating status analysis module is connected to the database, and the status early warning is connected to the database.

[0031] It should be noted that the database is used to store the reference values of the walking route offsets of each person in each key area of the campus park, the reference residence durations of each time period in each key area of the campus park, the accurate reference values of personnel matching of each device in each non-key area of the campus park and the reference data transmission durations, the threshold values of the visual monitoring operation indexes of the campus park, the reference intervals of the clear focus degrees of the cameras of the devices in the campus park, the reference intervals of the aperture adjustment differences, the reference intervals of the external surface temperatures, the reference intervals of the numbers of external surface cracks, and the reference difference intervals of image transmissions, the average threshold values of the device anomaly indexes of the campus park, and the safety intervals of the average differences of the device anomaly indexes of the campus park.

[0032] Data acquisition module: Through the campus park planning map, regional division is carried out to obtain each key area and each non-key area, and through the installed visual monitoring system, identification is carried out to obtain the identity recognition data of each key area and the identity recognition data of each non-key area in the campus park, and the visual monitoring operation index of the campus park is evaluated.

[0033] In the above embodiment, for the evaluation of the visual monitoring operation index of the campus park, the specific analysis method is as follows: Based on the obtained identity recognition data of each key area and non-key recognition data of the campus park, the visual monitoring operation index K of each key area in the campus park is further analyzed u and the visual monitoring operation index L of each non-key area in the campus park y , and for the evaluation of the visual monitoring operation index of the campus park, the specific calculation formula is: where u represents the number of the key area, u = 1, 2,..., r, r represents the number of key areas, y represents the number of the non-key area, y = 1, 2,..., t, and t represents the number of non-key areas.

[0034] In the above embodiment, for the identity recognition data of each key area and non-key recognition data of the campus park, the identity recognition data of each key area includes: the walking route offset values of each person, the entry duration and departure duration of each time period, and the identity recognition data of each non-key area includes: the accurate value of personnel matching of each device and the data transmission duration.

[0035] In the above embodiments, the specific analysis method for the visual monitoring operation index of each key area in the campus park is as follows: Based on the obtained deviation values of the walking routes of each person in each key area of the campus park and the entry duration and departure duration in each time period, the difference between the entry duration and the departure duration in each time period of each key area in the campus park is calculated to obtain the stay duration in each time period of each key area in the campus park. And the reference deviation value of the walking route of each person in each key area of the campus park and the reference stay duration in each time period of each key area in the campus park are extracted from the database to analyze the visual monitoring operation index of each key area in the campus park. The specific calculation formula is as follows: Among them, s ud ' represents the reference deviation value of the walking route of the d-th person in the u-th key area of the campus park, and s ud represents the deviation value of the walking route of the d-th person in the u-th key area of the campus park. d represents the number of the person, d = 1, 2,..., v, and v represents the number of people. c um ' is the reference stay duration in the m-th time period of the u-th key area of the campus park, and c um is the stay duration in the m-th time period of the u-th key area of the campus park. m represents the number of the time period, m = 1, 2,..., n, and n represents the number of time periods.

[0036] In the above embodiments, the specific analysis method for the visual monitoring operation index of each non-key area in the campus park is as follows: Based on the obtained accurate matching value of personnel and data transmission duration of each device in each non-key area of the campus park, and the reference accurate matching value of personnel and data transmission reference duration of each device in each non-key area of the campus park are extracted from the database to analyze the visual monitoring operation index of each non-key area in the campus park. The specific calculation formula is as follows: Among them, z yf represents the accurate matching value of personnel of the f-th device in the y-th non-key area of the campus park, and z yf ' represents the reference accurate matching value of personnel of the f-th device in the y-th non-key area of the campus park, and x yf ' represents the data transmission reference duration of the f-th device in the y-th non-key area of the campus park, and x yf represents the data transmission duration of the f-th device in the y-th non-key area of the campus park. f represents the number of the device, f = 1, 2,..., q, and q represents the number of devices.

[0037] In the data acquisition module: Through the campus planning map, regional division is carried out to obtain each key area and each non-key area, and through the installed visual monitoring system for identification, the identity recognition data of each key area and non-key area in the campus is obtained. The visual monitoring operation index of the campus is evaluated. When a security problem occurs, the management personnel can quickly lock in the relevant personnel and their movement trajectories by tracing the data, providing strong support for accident investigation and handling. By analyzing the personnel flow data in different areas, the campus management department can master the personnel flow in each area at different time periods, and then reasonably arrange the patrol routes and times of security personnel to achieve the precise allocation of security resources, improve the pertinence and efficiency of security work. The management department can timely detect equipment failures or performance degradation, arrange equipment maintenance and updates in advance, avoid monitoring blind spots caused by equipment problems, and ensure the stable operation of the monitoring system.

[0038] Operation status analysis module: Based on the obtained visual monitoring operation index of the campus, judge whether the equipment is operating abnormally. If it is abnormal, collect the abnormal status data of each equipment.

[0039] In the above embodiment, the method for judging whether the equipment is operating abnormally and collecting the abnormal status data of each equipment if it is abnormal is as follows: Based on the obtained visual monitoring operation index of the campus, extract the visual monitoring operation index threshold of the campus from the database, and compare the visual monitoring operation index of the campus with the visual monitoring operation index threshold of the campus. If the visual monitoring operation index of the campus is less than the visual monitoring operation index threshold of the campus, it indicates that the visual monitoring equipment in the campus is operating abnormally. Then, screen each abnormal equipment in the campus, and collect the abnormal status data of each equipment in the campus through the monitoring system.

[0040] Status warning: Based on the obtained abnormal status data of each equipment, analyze the abnormal index of each equipment in the campus, judge the risk level of each abnormal equipment, and perform risk handling and feedback.

[0041] In the above embodiments, the specific analysis method for the anomaly index of each device in the campus park is as follows: Based on the obtained anomaly status data of each device in the campus park, where the anomaly status data includes: camera focus clarity, aperture magnification value and reduction value, external temperature, number of external cracks, image transmission quantity and reception quantity, perform a difference operation on the aperture magnification value and reduction value of each device in the campus park to obtain the aperture adjustment difference of each device in the campus park, and perform a difference operation on the image transmission quantity and reception quantity of each device in the campus park to obtain the image transmission difference of each device in the campus park. Then, extract the camera focus clarity reference range, aperture adjustment difference reference range, external temperature reference range, number of external cracks reference range, and image transmission reference difference range of the devices in the campus park from the database, and analyze the anomaly index of each device in the campus park. The specific calculation formula is as follows: p il represents the l-th anomaly status data of the i-th device in the campus park, and p l ' represents the reference range of the l-th anomaly status data of the devices in the campus park, where l ∈ [1, 5].

[0042] In the above embodiments, the specific analysis method for determining the risk level of each anomalous device is as follows: Based on the obtained anomaly index of each device in the campus park, perform an average operation on the anomaly index of each device in the campus park to obtain the average anomaly index of the devices in the campus park, and perform a difference operation on the anomaly index of each device in the campus park and the average anomaly index of the devices in the campus park to obtain the difference between the average anomaly index of each device in the campus park and the average anomaly index. Compare the difference between the average anomaly index of each device in the campus park and the predefined safe range of the difference between the average anomaly indices of the devices in the campus park. If the difference between the average anomaly index of a certain device in the campus park is not within the safe range of the difference between the average anomaly indices of the devices in the campus park, then mark this device in the campus park as a first-level anomalous device; otherwise, mark it as a second-level anomalous device.

[0043] In the above embodiments, the specific analysis method for performing risk handling and feedback is as follows: Based on the obtained first-level anomalous devices, arrange professional technicians to inspect and repair the first-level anomalous devices, prioritize the handling of the first-level anomalous devices, increase the monitoring frequency and intensity for the first-level devices, and generate a report for feedback.

[0044] Based on the obtained second-level anomalous devices, perform optimization and adjustment on the second-level anomalous devices, and generate a report for feedback.

[0045] It should be noted that for the second-level anomalous devices, perform optimization and adjustment, for example, adjust the parameter settings of the devices, clean the sensors or lenses of the devices, optimize the network configuration, etc., to improve the performance and stability of the devices and reduce the risk of the anomaly index rising.

[0046] In the status warning, based on the obtained abnormal status data of each device, analyze the abnormal index of each device in the campus park, judge the risk level of each abnormal device, conduct risk handling and feedback. According to the abnormal index of the device to judge the risk level, the device can be maintained and repaired in a targeted manner. For high-risk devices, repair is arranged first, and high-quality resources are preferentially allocated for processing to avoid wasting resources on low-risk devices, improve the utilization efficiency of repair resources, conduct regular inspections and preventive maintenance on low-risk devices, which helps to reduce the wear of the devices, extend the service life of the devices, reduce the device replacement cost, and reasonably allocate resources such as maintenance personnel, maintenance materials and maintenance time.

[0047] Refer to Figure 2 As shown, the present invention provides a method for a visualization monitoring system of an identity system, which is characterized in that it includes: Step 1, data collection: Through the campus park planning map, perform area division to obtain each key area and each non-key area, and through the installed visualization monitoring system for identification, obtain the identity recognition data of each key area and non-key area in the campus park, and evaluate the visualization monitoring operation index of the campus park.

[0048] Step 2, operating status analysis: Based on the obtained visualization monitoring operation index of the campus park, judge whether the device is operating abnormally. If it is abnormal, collect the abnormal status data of each device.

[0049] Step 3, status warning: Based on the obtained abnormal status data of each device, analyze the abnormal index of each device in the campus park, judge the risk level of each abnormal device, conduct risk handling and feedback.

[0050] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A visual monitoring system and method for an identity system, characterized in that: include: Data collection module: The campus planning map is used to divide the area into key areas and non-key areas, and the identification is carried out through the installed visual monitoring system to obtain the identity identification data of each key area and non-key area of ​​the campus, and evaluate the visual monitoring operation index of the campus; Operation status analysis module: Based on the visual monitoring operation index of the campus, it determines whether the equipment is operating abnormally. If it is abnormal, it collects the abnormal status data of each device; Status warning: Based on the abnormal status data of each device, analyze the abnormal index of each device in the campus, determine the risk level of each abnormal device, perform risk processing and provide feedback.

2. The identity system visual monitoring system and method according to claim 1, characterized in that: The specific analysis method for evaluating the visual monitoring operation index of the campus is as follows: Based on the obtained identification data of each key area of ​​the campus and the non-key area, the visual monitoring operation index K of each key area of ​​the campus is analyzed. u and the visual monitoring operation index L of non-key areas of the campus y , evaluate the visual monitoring operation index of the campus, and the specific calculation formula is: Wherein, u represents the number of the key area, u=1, 2, ..., r, r represents the number of the key areas, y represents the number of the non-key areas, y=1, 2, ..., t, t represents the number of the non-key areas.

3. The identity system visual monitoring system and method according to claim 2, characterized in that: The campus includes key area identification data and non-key area identification data. The key area identification data include: the walking route deviation value of each person, the entry time and the exit time of each time period. The non-key area identification data include: the personnel matching accuracy value and data transmission time of each device.

4. The identity system visual monitoring system and method according to claim 2, characterized in that: The specific analysis method of the visual monitoring operation index of each key area of ​​the campus is as follows: Based on the obtained walking route deviation values ​​of each person in each key area of ​​the campus park and the entry and exit times of each time period, the entry and exit times of each time period of each key area of ​​the campus park are subtracted to obtain the stay time of each time period of each key area of ​​the campus park, and the walking route deviation reference values ​​of each person in each key area of ​​the campus park and the stay reference time of each time period of each key area of ​​the campus park are extracted from the database to analyze the visual monitoring operation index of each key area of ​​the campus park. The specific calculation formula is: Among them, s ud ' represents the reference value of the walking route deviation of the dth person in the uth key area of ​​the campus, s ud It is represented as the walking route deviation value of the dth person in the uth key area of ​​the campus, d represents the number of the person, d = 1, 2, ..., v, v represents the number of people, c um ' is the reference duration of stay in the mth time period of the uth key area of ​​the campus, c um is the length of stay in the mth time period of the uth key area of ​​the campus, m represents the number of the time period, m=1,2,...,n, and n represents the number of time periods.

5. The identity system visual monitoring system and method according to claim 2, characterized in that: The specific analysis method of the visual monitoring operation index of each non-key area of ​​the campus is as follows: Based on the obtained personnel matching accuracy value and data transmission duration of each device in each non-key area of ​​the campus, the personnel matching accuracy reference value and data transmission reference duration of each device in each non-key area of ​​the campus are extracted from the database to analyze the visual monitoring operation index of each non-key area of ​​the campus. The specific calculation formula is: Among them, z yf It is represented as the personnel matching accuracy value of the fth device in the yth non-key area of ​​the campus, z yf ' represents the accurate reference value of personnel matching for the fth device in the yth non-key area of ​​the campus, x yf ' represents the data transmission reference duration of the fth device in the yth non-key area of ​​the campus, x yf It is represented as the data transmission duration of the f-th device in the y-th non-key area of ​​the campus, where f is the device number, f=1, 2, ..., q, and q is the number of devices.

6. The identity system visual monitoring system and method according to claim 1, characterized in that: The method of judging whether the equipment is working abnormally or not, and collecting abnormal status data of each equipment if abnormal, is as follows: Based on the obtained visualization monitoring operation index of the campus park, the visualization monitoring operation index threshold of the campus park is extracted from the database, and the visualization monitoring operation index of the campus park is compared with the visualization monitoring operation index threshold of the campus park. If the visualization monitoring operation index of the campus park is less than the visualization monitoring operation index threshold of the campus park, it means that the visualization monitoring equipment of the campus park is operating abnormally, and then the abnormal equipment in the campus park is screened, and the abnormal status data of each equipment in the campus park is obtained through the monitoring system.

7. The identity system visual monitoring system and method according to claim 1, characterized in that: The specific analysis method of the abnormal index of each device in the campus is as follows: Based on the abnormal status data of each device in the campus park, the abnormal status data includes: camera focus clarity, aperture magnification value and reduction value, surface temperature, number of surface cracks, image transmission number and reception number, the aperture magnification value and reduction value of each device in the campus park are subjected to difference processing to obtain the aperture adjustment difference of each device in the campus park, and the image transmission number and reception number of each device in the campus park are subjected to difference processing to obtain the image transmission difference of each device in the campus park, and the camera focus clarity reference interval, aperture adjustment difference reference interval, surface temperature reference interval, surface crack number reference interval and image transmission reference difference interval of the devices in the campus park are extracted from the database to analyze the abnormal index of each device in the campus park, and the specific calculation formula is as follows: p il It is represented as the lth abnormal status data of the i-th device in the campus, p l ' represents the lth abnormal state data reference interval of the equipment in the campus, l∈[1,5].

8. The identity system visual monitoring system and method according to claim 1, characterized in that: The specific analysis method for determining the risk level of each abnormal device is as follows: Based on the obtained abnormal index of each device in the campus park, the abnormal index of each device in the campus park is averaged to obtain the average of the abnormal index of the devices in the campus park, and the abnormal index of each device in the campus park is differenced with the average of the abnormal index of the devices in the campus park to obtain the difference of the mean of the abnormal index of each device in the campus park, and the difference of the mean of the abnormal index of each device in the campus park is compared with the predefined safety interval of the mean difference of the abnormal index of the devices in the campus park. If the mean difference of the abnormal index of a device in the campus park is not within the safety interval of the mean difference of the abnormal index of the devices in the campus park, the device in the campus park is recorded as a first-level abnormal device, otherwise, it is recorded as a second-level abnormal device.

9. The identity system visual monitoring system and method according to claim 8, characterized in that: The specific analysis method for risk processing and feedback is as follows: Based on the first-level abnormal equipment obtained, arrange professional technicians to inspect and repair the first-level abnormal equipment, give priority to the first-level abnormal equipment, increase the monitoring frequency and intensity of the first-level equipment, and generate reports for feedback; Based on the obtained second-level abnormal equipment, the second-level abnormal equipment is optimized and adjusted, and a report is generated for feedback.

10. A method for executing an identity system visual monitoring system according to any one of claims 1 to 9, characterized in that: include: Step 1: Data collection: The campus planning map is used to divide the area into key areas and non-key areas, and the visual monitoring system is used to identify the key areas and non-key areas of the campus, and the visual monitoring operation index of the campus is evaluated; Step 2: Operation status analysis: Based on the visual monitoring operation index of the campus, determine whether the equipment is operating abnormally. If abnormal, collect abnormal status data of each device; Step 3: Status warning: Based on the abnormal status data of each device, analyze the abnormal index of each device in the campus, determine the risk level of each abnormal device, perform risk processing and provide feedback.

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