Video monitoring visualization effect evaluation system adaptive to sports place

Abstract

The invention relates to the technical field of video monitoring, in particular to a video monitoring visualization effect evaluation system adaptive to a sports place, which comprises a visualization effect evaluation center, a coverage analysis unit, a visual interference unit, an effect multi-dimensional acquisition unit, an expression persistence unit, an effect defect division unit and a rear-end visual unit. According to the method, analysis is performed preliminarily from the perspective of whether the target operation place has the monitoring blind area or not, so that monitoring comprehensiveness of the target operation place is ensured, health analysis is further performed on the monitoring equipment and the network in an information progressive mode, the influence of the network on the visual effect of the monitoring equipment is reduced, and the monitoring efficiency is improved. According to the method and the system, the multi-dimensional heterogeneous data is converted into the unified evaluation index to comprehensively evaluate the visualization effect of the monitoring video system, and the evaluation of the video monitoring visualization effect provides data support for the operation management of the target sports place, so that the improvement of the operation management efficiency is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of video monitoring technology, and in particular to a video monitoring visualization effect evaluation system adapted to sports places. BACKGROUND

[0002] With the development of society and the improvement of people's health consciousness, various sports places such as gymnasiums, fitness rooms, and outdoor sports grounds are used more and more frequently. The activities of people in sports places are intensive and the movement states are various, so the requirements for video monitoring systems are higher and higher. As an important part of the safety and protection system of sports places, the visualization effect of the video monitoring system directly affects the effectiveness of monitoring and the convenience of management. However, the current video monitoring visualization effect for sports places still has the following problems: the existing video monitoring visualization effect evaluation is mostly for ordinary scenes and does not fully consider the particularity of sports places, such as high-speed movement of people, complex light environment, and various types of movement, which leads to the fact that the evaluation results cannot accurately reflect the actual effect of video monitoring in sports places. At the same time, there is a lack of analysis of the equipment itself, the network, and the monitoring range of the monitoring end, which further affects the effectiveness of subsequent visualization effect evaluation, and it is difficult to analyze the stability and sustainability of the visualization effect and the degree of defects of the visualization effect, which further makes it difficult to manage the monitoring video system, resulting in poor and low management effect of the continuous and stable monitoring video visualization effect. In view of the above technical defects, a solution is proposed. SUMMARY

[0003] The purpose of the present application is to provide a video monitoring visualization effect evaluation system adapted to sports places to solve the above technical defects. The present application preliminarily analyzes whether there is a monitoring blind area in the target operating place to ensure the comprehensiveness of monitoring in the target operating place, and further analyzes the health of the monitoring equipment itself and the network through the information progressive way to reduce the influence of the network on the visualization effect of the monitoring equipment itself. At the same time, the multi-dimensional heterogeneous data is converted into a unified evaluation index to comprehensively evaluate the visualization effect of the monitoring video system. Through the evaluation of the video monitoring visualization effect, it is helpful to improve the operation and management efficiency. At the same time, the stability and sustainability of the visualization effect are analyzed, and the feedback information is reasonably adjusted to improve the stability and sustainability of the visualization effect. The analysis and feedback of the degree of defects of the visualization effect help to improve the visualization management effect of the monitoring video system.

[0004] The purpose of the application can be realized by the following technical solutions: a video monitoring visualization effect evaluation system suitable for sports venues, comprising a visualization effect evaluation center, a coverage analysis unit, a visual interference unit, an effect multidimensional acquisition unit, a performance persistence unit, an effect defect division unit, and a backend visual unit. The visualization effect evaluation center is used to call the effective monitoring area of the target operating site and the total planning monitoring area. The coverage analysis unit is used to determine whether there is a monitoring blind area in the target operating site, and obtain a comprehensive monitoring signal or a monitoring missing signal. The visual interference unit is used to analyze network feature data and state feature data to determine whether the monitoring device itself and the network are normal, and obtain a front-end interference signal or a front-end stable signal. The effect multidimensional acquisition unit is used to analyze visual monitoring video to determine whether the video monitoring visualization effect of the target sports venue meets the standard, obtain a comprehensive visualization effect score, and obtain a visual defect signal or a visual normal signal. The performance persistence unit is used to analyze high-speed follow-up frame loss rate and personnel count accuracy to determine whether the stability and persistence of the visualization effect are qualified, and obtain a continuous effect signal or a continuous deviation signal. The effect defect division unit is used to obtain visual effect defect degree report analysis on collected visual defect feature data, and divide the obtained visual defect score to obtain a corresponding visual defect feedback report.

[0005] Preferably, the analysis process of the coverage analysis unit is as follows: the effective monitoring area of the target operating site and the total planning monitoring area of the target operating site are collected, wherein the effective monitoring area represents the monitoring coverage area of the target operating site under the monitoring of the monitoring device, and the value obtained by subtracting the effective monitoring area from the total planning monitoring area is set as the monitoring coverage blind area. The monitoring coverage blind area is processed to obtain a comprehensive monitoring signal or a monitoring missing signal.

[0006] Preferably, the analysis process of the visual interference unit is as follows: the network feature data and the state feature data of the monitoring device in the target operating site within a time threshold are obtained, the network feature data and the state feature data are input into a pre-set network quality evaluation model and a device health score model respectively, the network quality score and the device health score output by the pre-set network quality evaluation model and the device health score model are obtained, and the network quality score and the device health score are processed to obtain a front-end interference signal or a front-end stable signal.

[0007] Preferably, the analysis process of the effect multidimensional acquisition unit is as follows: S1: set a monitoring period, collect the visual monitoring video of the target operating site in the monitoring period; S2: obtain the video display data of the visual terminal based on the visual monitoring video, generate an image quality evaluation report based on the video display data analysis, and obtain the image quality score of the visual terminal based on the image quality evaluation report; S3: obtain the target recognition performance score and dynamic response score of the visual terminal based on the visual monitoring video; S4: call the pre-set weight coefficients w1, w2 and w3 of the image quality score, target recognition performance score and dynamic response score, and set the value obtained by multiplying the image quality score × w1 + target recognition performance score × w2 + dynamic response score × w3 as the comprehensive visualization effect score; S5: and the comprehensive visualization effect score is judged: the visual defect signal or the visual normal signal is obtained.

[0008] Preferably, S31: the analysis process of the target recognition performance score is as follows: set the target recognition detection item, the target recognition detection item includes personnel / equipment recognition detection, multi-target tracking detection, obtain the detection score of each item in the target recognition detection item, and set the detection score of each item in the target recognition detection item as the performance detection score, and allocate the pre-set weight factor to each item in the target recognition detection item, and set the sum value of the performance detection score multiplied by the corresponding pre-set weight factor as the target recognition performance score.

[0009] Preferably, S32: the analysis process of the dynamic response score is as follows: The visual monitoring video is obtained through the pre-set blur estimation network to obtain the blur degree score, and the frame rate sequence f1, f2, f3……fn of continuous n frames is obtained based on the visual monitoring video, n is a natural number greater than zero, the standard deviation and mean value of the frame rate sequence are obtained, and the ratio between the standard deviation and the mean value of the frame rate sequence is set as the frame rate fluctuation coefficient; The blur degree score and the frame rate fluctuation coefficient are multiplied by the corresponding pre-set proportion coefficient respectively, and the sum value of the blur degree score and the frame rate fluctuation coefficient multiplied by the corresponding pre-set proportion coefficient is set as the dynamic response score.

[0010] Preferably, the analysis process of the performance persistence unit is as follows: a verification period is set, the verification period is divided into i sub-time periods, i is a natural number greater than zero, the high-speed follow-up frame loss rate and the personnel counting accuracy in each sub-time period are obtained, the change characteristic curve of the high-speed follow-up frame loss rate and the personnel counting accuracy is constructed based on the sub-time period sequence, the maximum peak value of the high-speed follow-up frame loss rate, the minimum valley value of the high-speed follow-up frame loss rate, the maximum peak value of the personnel counting accuracy and the minimum valley value of the personnel counting accuracy are obtained from the change characteristic curve of the high-speed follow-up frame loss rate and the personnel counting accuracy, the follow-up floating interval of the maximum peak value of the high-speed follow-up frame loss rate and the minimum valley value of the high-speed follow-up frame loss rate is constructed, the accuracy floating interval of the maximum peak value of the personnel counting accuracy and the minimum valley value of the personnel counting accuracy is constructed, and the follow-up floating interval and the accuracy floating interval are subjected to discrimination processing to obtain a persistence effect signal or a persistence deviation signal.

[0011] Preferably, the analysis process of the effect defect division unit is as follows: The visual defect characteristic data of the visual monitoring video in the monitoring period is obtained, the visual defect characteristic data includes the stall duration, the stall frequency, and the delay length, the visual defect scoring report is generated based on the visual defect characteristic data analysis, and the visual defect score of the visual monitoring video is obtained from the visual defect scoring report; Meanwhile, the comprehensive visual effect score corresponding to the visual defect signal is called, the sum value between the visual defect score and the comprehensive visual effect score is set as the visual defect comprehensive score, the visual defect comprehensive score is subjected to discrimination analysis to obtain a visual serious deviation or a visual moderate deviation or a visual slight deviation, and the corresponding visual defect feedback report is generated based on the visual serious deviation or the visual moderate deviation or the visual slight deviation.

[0012] The beneficial effects of the present application are as follows: The present application preliminarily analyzes whether there is a monitoring blind area in the target operating place, so as to reasonably manage and monitor the adjustment of the monitoring equipment of the target operating place, to ensure the comprehensiveness of the monitoring of the target operating place, and further analyze the health of the monitoring equipment itself and the network through the information progressive mode, to judge whether the monitoring equipment itself and the network are normal, which is helpful for management and reduction of the influence of the network on the visual effect of the monitoring equipment itself according to the feedback information; The application comprehensively evaluates the visual effect of the monitoring video system by converting multi-dimensional heterogeneous data into a unified evaluation index, and provides data support for the operation management of the sports place through the evaluation of the visual effect of the video monitoring, which helps to improve the operation management efficiency. Meanwhile, the stability and sustainability of the visual effect are analyzed, and the feedback information is reasonably adjusted to improve the stability and sustainability of the visual effect. The defect degree of the visual effect is analyzed and fed back, which helps to improve the visual management effect of the monitoring video system. BRIEF DESCRIPTION OF DRAWINGS

[0013] The application will be further described below with reference to the drawings; Fig. 1 is a system flowchart of the application; Fig. 2 is an analysis diagram of the second embodiment of the application. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0015] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another; Embodiment one: please refer to Figs. 1-2 As shown in the figure, the application is a video monitoring visual effect evaluation system suitable for sports places, which comprises a visual effect evaluation center, a coverage analysis unit, a visual interference unit, an effect multi-dimensional acquisition unit, a performance sustainability unit, an effect defect division unit, and a backend visual unit. The visual effect evaluation center is in bidirectional communication connection with the coverage analysis unit. The coverage analysis unit is in unidirectional communication connection with the visual interference unit. The visual interference unit is in unidirectional communication connection with the visual effect evaluation center. The visual effect evaluation center is in bidirectional communication connection with the effect multi-dimensional acquisition unit. The effect multi-dimensional acquisition unit is in bidirectional communication connection with the performance sustainability unit and the effect defect division unit. The effect multi-dimensional acquisition unit is in unidirectional communication connection with the backend visual unit. The visual effect evaluation center is used to call the effective monitoring area and the planned total monitoring area of the target sports place. The coverage analysis unit is used for monitoring blind area discrimination analysis on the effective monitoring area and the planned total monitoring area, to determine whether there is a monitoring blind area in the target operating site. The specific monitoring blind area discrimination analysis process is as follows: The effective monitoring area of the target operating site and the planned total monitoring area of the target operating site are collected. The effective monitoring area represents the monitoring coverage area of the target operating site under the monitoring of the monitoring device. The planned total monitoring area is subtracted from the effective monitoring area to obtain the monitoring coverage blind area, which is subjected to discrimination processing. If the monitoring coverage blind area is equal to zero, a monitoring comprehensive signal is generated. If the monitoring coverage blind area is not equal to zero, a monitoring missing signal is generated. The back-end visual unit is used to respond to the monitoring missing signal and immediately execute the corresponding preset operation, so as to reasonably manage and monitor the adjustment of the monitoring device of the target operating site, to ensure the comprehensiveness of the monitoring of the target operating site. When the monitoring comprehensive signal is generated, the visual interference unit is used for monitoring health risk assessment analysis on the collected network feature data and state feature data, to determine whether the monitoring device itself and the network are normal. The specific monitoring health risk assessment analysis process is as follows: The network feature data and the state feature data of the monitoring device in the target operating site within the time threshold are obtained. The network feature data includes network bandwidth, network delay rate, etc. The state feature data includes operating temperature, operating voltage, etc. The network feature data and the state feature data are respectively input into the pre-set network quality evaluation model and the device health score model. The network quality score and the device health score output by the pre-set network quality evaluation model and the device health score model are obtained, and the network quality score and the device health score are subjected to discrimination processing. If the network quality score is less than the pre-set network quality score threshold, or the device health score is less than the pre-set device health score threshold, a front-end interference signal is generated. If the network quality score is greater than or equal to the pre-set network quality score threshold, and the device health score is greater than or equal to the pre-set device health score threshold, a front-end stable signal is generated. The back-end visual unit is used to respond to the front-end interference signal and immediately execute the corresponding preset operation, so as to adjust the monitoring device itself or the network of the target operating site, to reduce the influence of the network on the monitoring device itself on the visual effect.

[0016] Embodiment two: when the front-end stable signal is generated: the effect multi-dimensional acquisition unit is used for visual effect multi-dimensional evaluation analysis on the collected visual monitoring video, to determine whether the video monitoring visualization effect of the target operating site meets the standard. The specific visual effect multi-dimensional evaluation analysis process is as follows: S1: set the monitoring period, and collect the visual monitoring video of the visual end of the target operating site within the monitoring period; S2: obtain video display data of the visual terminal based on the visual monitoring video, the video display data including definition, color restoration, peak signal-to-noise ratio, etc., generate an image quality evaluation report based on the video display data analysis, and obtain an image quality score of the visual terminal based on the image quality evaluation report; S3: obtain target recognition performance score and dynamic response score of the visual terminal based on the visual monitoring video; S31: the analysis process of the target recognition performance score is as follows: Set target recognition detection items, including personnel / equipment recognition detection, multi-target tracking detection, etc., obtain detection scores of each item in the target recognition detection items, set the detection scores of each item in the target recognition detection items as performance detection scores, assign a pre-set weight factor to each item in the target recognition detection items, and set the sum of the performance detection scores multiplied by the corresponding pre-set weight factors as the target recognition performance score; In the implementation of the present application, the related items are tested from the perspective of target recognition evaluation, and comprehensive quantitative analysis is performed based on the test results; S32: the analysis process of the dynamic response score is as follows: The visual monitoring video is passed through a pre-set blur estimation network (such as the blur branch of DeblurGANv2) to obtain a blur degree score, and a frame rate sequence f1, f2, f3……fn of n consecutive frames is obtained based on the visual monitoring video, n being a natural number greater than zero, the standard deviation and mean of the frame rate sequence are obtained, and the ratio between the standard deviation and the mean of the frame rate sequence is set as the frame rate fluctuation coefficient; The blur degree score and the frame rate fluctuation coefficient are multiplied by the corresponding pre-set proportion coefficients respectively, and the sum of the blur degree score and the frame rate fluctuation coefficient multiplied by the corresponding pre-set proportion coefficients is set as the dynamic response score; In the implementation of the present application, the video monitoring of target motion places (such as basketball courts, running tracks) needs to capture high-speed moving targets (such as athletes, sports equipment) in real time, at this time the “dynamic response ability” of the system directly affects the monitoring effectiveness; S4: call the pre-set weight coefficients w1, w2 and w3 of the image quality score, the target recognition performance score and the dynamic response score, and set the value obtained by multiplying the image quality score by w1, the target recognition performance score by w2 and the dynamic response score by w3 as the comprehensive visualization effect score; In the implementation of the present application, w1 represents the pre-set weight coefficient of the image quality score, w2 represents the pre-set weight coefficient of the target recognition performance score, and w3 represents the pre-set weight coefficient of the dynamic response score; S5: and the comprehensive visualization effect score is subjected to discrimination processing: generate a visual defect signal if the comprehensive visual effect score is less than a preset visual effect evaluation threshold value; generate a visual normal signal if the comprehensive visual effect score is greater than or equal to the preset visual effect evaluation threshold value; The backend visual unit is configured to immediately execute a preset warning operation corresponding to the visual defect signal or the visual normal signal in response to the visual defect signal or the visual normal signal, so as to manage the video monitoring system in a targeted manner and ensure that the video monitoring system can clearly and accurately capture personnel and events in the sports place, thereby providing strong support for security protection and emergency handling; In the implementation of the present application, the evaluation of the video monitoring visual effect provides data support for the operation and management of the sports place, such as personnel flow analysis and equipment maintenance plan development, thereby improving the operation and management efficiency.

[0017] In the third embodiment, when the visual normal signal is generated, the performance persistence unit is configured to perform information floating evaluation analysis on the collected high-speed follow-up frame loss rate and personnel counting accuracy, to determine whether the stability and persistence of the visual effect are qualified, and the specific information floating evaluation analysis process is as follows: The verification period is divided into i sub-time periods, i is a natural number greater than zero, the high-speed follow-up frame loss rate and the personnel counting accuracy in each sub-time period are obtained, the change characteristic curve of the high-speed follow-up frame loss rate and the personnel counting accuracy is constructed based on the sub-time period sequence, the maximum peak value of the high-speed follow-up frame loss rate, the minimum valley value of the high-speed follow-up frame loss rate, the maximum peak value of the personnel counting accuracy, and the minimum valley value of the personnel counting accuracy are obtained from the change characteristic curve of the high-speed follow-up frame loss rate and the personnel counting accuracy, the follow-up floating interval of the maximum peak value of the high-speed follow-up frame loss rate and the minimum valley value of the high-speed follow-up frame loss rate is constructed, and the accurate floating interval of the maximum peak value of the personnel counting accuracy and the minimum valley value of the personnel counting accuracy is constructed; The follow-up floating interval and the accurate floating interval are subjected to discrimination processing: generate a continuous effect signal if the follow-up floating interval is contained in a preset follow-up floating interval and the accurate floating interval is contained in a preset accurate floating interval; generate a continuous deviation signal if the follow-up floating interval is not contained in the preset follow-up floating interval or the accurate floating interval is not contained in the preset accurate floating interval; The backend visual unit is configured to immediately execute a preset warning operation corresponding to the continuous effect signal or the continuous deviation signal in response to the continuous effect signal or the continuous deviation signal, to intuitively understand whether the stability and persistence of the visual effect are qualified based on information feedback, so as to make reasonable adjustments to improve the stability and persistence of the visual effect; When the visual defect signal is generated, the effect defect classification unit is used for visual effect defect degree report acquisition analysis on the collected visual defect feature data, and the obtained visual defect score is classified to obtain the corresponding visual defect feedback report. The visual defect feature data of the visual monitoring video in the monitoring period is obtained, the visual defect feature data includes the stall time length, the stall number, the delay length, etc., the visual defect score report is generated based on the visual defect feature data analysis, and the visual defect score of the visual monitoring video is obtained from the visual defect score report; Meanwhile, the comprehensive visual effect score corresponding to the visual defect signal is called, the sum value between the visual defect score and the comprehensive visual effect score is set as the visual defect comprehensive score, and the visual defect comprehensive score is discriminated and analyzed: If the visual defect comprehensive score is less than the minimum value in the preset visual defect comprehensive score range, it is determined that the visual deviation is serious; If the visual defect comprehensive score belongs to the preset visual defect comprehensive score range, it is determined that the visual deviation is moderate; If the visual defect comprehensive score is greater than the maximum value in the preset visual defect comprehensive score range, it is determined that the visual deviation is slight; Based on the visual serious deviation or the visual moderate deviation or the visual slight deviation, the corresponding visual defect feedback report is generated, and the rear visual unit is used for responding to the visual defect feedback report, and the visual defect feedback report is displayed immediately, so that the defect degree of the visualization effect can be understood intuitively, the targeted defect management is facilitated, and the visual management effect of the monitoring video system is improved; As described above, the video monitoring visualization effect evaluation method suitable for sports places is as follows: step one: monitoring coverage blind area analysis of the target running place, obtaining monitoring comprehensive signal or monitoring missing signal, based on the monitoring comprehensive signal, the health of the target running place monitoring equipment itself and network is analyzed, and whether the monitoring equipment itself and network is normal is judged; Step two: based on the front-end stable signal, the visual monitoring video of the visual monitoring system of the target running place is analyzed, the comprehensive visual effect score is obtained, and the comprehensive visual effect score is analyzed, the visual defect signal or the visual normal signal is obtained; Step three, from the visual normal signal, the stability and sustainability of the visual effect are analyzed, whether the stability and sustainability of the visual effect are qualified is judged, the continuous effect signal or the continuous deviation signal is obtained, and from the visual defect signal, the visual effect defect degree is analyzed, and the visual defect feedback report is obtained. The application preliminarily analyzes whether there is a monitoring blind area in the target operating place, so as to reasonably manage and monitor the adjustment of the monitoring equipment of the target operating place, to ensure the comprehensiveness of the monitoring of the target operating place, and further analyze the health of the monitoring equipment itself and the network through the progressive way of information, to determine whether the monitoring equipment itself and the network are normal, which helps to manage and reduce the influence of the network on the visual effect of the monitoring equipment according to the feedback information, and to comprehensively evaluate the visual effect of the monitoring video system by converting multi-dimensional heterogeneous data into unified evaluation indexes, and to provide data support for the operation and management of the sports place through the evaluation of the visual effect of the video monitoring, which helps to improve the operation and management efficiency, and to analyze the stability and sustainability of the visual effect, and to reasonably adjust according to the feedback information, to improve the stability and sustainability of the visual effect, and to analyze and feedback the defect degree of the visual effect, which helps to improve the visual management effect of the monitoring video system.

[0018] The threshold is set for result comparison analysis, to determine whether it is good or bad, and the size of the threshold is determined by combining large model analysis of sample data and artificial experience to set the input storage, and can be appropriately adjusted by seasonal or rational influence conditions.

[0019] The size of the coefficient is a specific numerical value obtained by quantifying each parameter, for subsequent comparison, and the size of the coefficient depends on the amount of sample data and the corresponding operating coefficient preliminarily set by the person skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized numerical value.

[0020] The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the application within the technical range disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. A video surveillance visualization effect evaluation system adapted for sports venues, characterized in that, It includes a visualization effect evaluation center, coverage analysis unit, visual interference unit, multi-dimensional effect acquisition unit, performance persistence unit, effect defect classification unit, and backend visualization unit; Visualization Evaluation Center: Used to retrieve the effective monitoring area and planned total monitoring area of ​​the target operating site; Coverage analysis unit: used to determine whether there are blind spots in the target operating area, and to obtain comprehensive monitoring signals or signals with missing monitoring signals; Visual interference unit: used to analyze network characteristic data and status characteristic data to determine whether the monitoring equipment itself and the network are normal, and to obtain front-end interference signals or front-end stable signals; Multi-dimensional effect acquisition unit: used to determine whether the video surveillance visualization effect of the target motion site meets the standard through visual monitoring video analysis, obtain a comprehensive visualization effect score, and obtain visual defect signal or visual normal signal at the same time. Performance persistence unit: used to analyze the frame loss rate and personnel counting accuracy of high-speed tracking to determine whether the stability and persistence of the visualization effect is qualified, and to obtain the persistence effect signal or persistence deviation signal. Effect Defect Classification Unit: Used to obtain and analyze the visual effect defect degree report from the collected visual defect feature data, classify the obtained visual defect scores, and obtain the corresponding visual defect feedback report.

2. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 1, characterized in that, The analysis process of the coverage analysis unit is as follows: the effective monitoring area of ​​the target operating site and the planned total monitoring area of ​​the target operating site are collected. The effective monitoring area represents the monitoring coverage area of ​​the target operating site under the monitoring equipment. The value obtained by subtracting the effective monitoring area from the planned total monitoring area is set as the monitoring coverage blind area. The monitoring coverage blind area is then processed to obtain the full monitoring signal or the monitoring missing signal.

3. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 1, characterized in that, The analysis process of the visual interference unit is as follows: obtain the network characteristic data and status characteristic data of the monitoring equipment in the target operating location within the time threshold, input the network characteristic data and status characteristic data into the pre-set network quality assessment model and equipment health scoring model respectively, obtain the network quality score and equipment health score output by the pre-set network quality assessment model and equipment health scoring model, and perform discrimination processing on the network quality score and equipment health score to obtain the front-end interference signal or front-end stable signal.

4. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 1, characterized in that, The analysis process of the multi-dimensional effect acquisition unit is as follows: S1: Set the monitoring period and collect visual monitoring video from the visual terminal of the target operating location within the monitoring period; S2: Obtain video display data from the visual monitoring terminal based on the visual surveillance video, analyze the video display data to generate an image quality assessment report, and obtain the image quality score from the visual terminal based on the image quality assessment report; S3: Target recognition performance score and dynamic response score obtained from visual surveillance video at the visual end; S4: Retrieve the pre-set weighting coefficients w1, w2 and w3 of the image quality score, target recognition performance score and dynamic response score, and set the value obtained by image quality score × w1 + target recognition performance score × w2 + dynamic response score × w3 as the comprehensive visualization effect score. S5: And perform discrimination processing on the comprehensive visualization effect score: to obtain the visual defect signal or the visual normal signal.

5. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 4, characterized in that, It also includes S31: The analysis process of target recognition performance score is as follows: Set target recognition detection items, which include personnel / equipment recognition detection and multi-target tracking detection. Obtain the detection scores of each item in the target recognition detection items, set the detection scores of each item in the target recognition detection items as performance detection scores, assign pre-set weight factors to each item in the target recognition detection items, and set the sum of the performance detection scores and the corresponding pre-set weight factors as the target recognition performance score.

6. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 5, characterized in that, It also includes S32: The analysis process for dynamic response scoring is as follows: The visual surveillance video is passed through a pre-set fuzzy estimation network to obtain a fuzziness score. At the same time, a frame rate sequence f1, f2, f3...fn of n consecutive frames is obtained based on the visual surveillance video, where n is a natural number greater than zero. The standard deviation and mean of the frame rate sequence are obtained, and the ratio between the standard deviation and the mean of the frame rate sequence is set as the frame rate fluctuation coefficient. The blur score and frame rate fluctuation coefficient are multiplied by their respective preset scaling factors, and the sum of these products is set as the dynamic response score.

7. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 1, characterized in that, The analysis process of the performance persistence unit is as follows: A verification period is set, and this period is divided into i sub-periods, where i is a natural number greater than zero. The high-speed tracking frame loss rate and personnel counting accuracy are obtained within each sub-period. Based on the order of the sub-periods, characteristic curves of the changes in the high-speed tracking frame loss rate and personnel counting accuracy are constructed. From these characteristic curves, the maximum peak value, minimum valley value, maximum peak value, and minimum valley value of the high-speed tracking frame loss rate, as well as the personnel counting accuracy, are obtained. A tracking frame loss rate fluctuation range and a personnel counting accuracy accuracy accurate fluctuation range are constructed. The tracking frame loss rate fluctuation range and the accurate fluctuation range are then processed to obtain a persistent effect signal or a persistent deviation signal.

8. The video surveillance visualization effect evaluation system adapted for sports venues according to claim 1, characterized in that, The analysis process for the effect defect segmentation unit is as follows: The system acquires visual defect feature data of the visual surveillance video during the monitoring period. The visual defect feature data includes the duration of lag, the number of lags, and the delay length. Based on the analysis of the visual defect feature data, a visual defect scoring report is generated, and the visual defect score of the visual surveillance video is obtained from the visual defect scoring report. Simultaneously, the comprehensive visualization effect score corresponding to the visual defect signal is retrieved, and the sum between the visual defect score and the comprehensive visualization effect score is set as the comprehensive visual defect score. The comprehensive visual defect score is then subjected to discriminant analysis to obtain severe visual deviation, moderate visual deviation, or slight visual deviation. Based on the severe visual deviation, moderate visual deviation, or slight visual deviation, a corresponding visual defect feedback report is generated.

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