Anti-theft monitoring and early warning method based on video remote monitoring

Abstract

The invention belongs to the technical field of anti-theft monitoring and early warning, and particularly relates to an anti-theft monitoring and early warning method based on video remote monitoring. Comprising the steps of collecting a real-time monitoring video of a corresponding target area, judging whether a theft risk exists in the corresponding target area or not, triggering an alarm when judging that the theft risk exists, and analyzing a theft hidden danger degree of the corresponding target area; the anti-theft risk is judged through video monitoring and automatic identification, the anti-theft safety performance of the area needing to be monitored can be guaranteed, the inspection area and the monitoring area are determined by analyzing the theft hidden danger degree of the corresponding target area, the follow-up arrangement of adaptive management modes for different sub-areas is facilitated, and the anti-theft safety performance of the area needing to be monitored is improved. The inspection performance of the corresponding inspection area is analyzed through the inspection tracking module, inspection supervision and inspection personnel training of the corresponding inspection area are enhanced in time, the anti-theft management and control performance of the area needing to be monitored is further guaranteed, and the intelligent degree is high.

Description

Technical Field

[0001] The invention relates to the technical field of anti-theft monitoring and early warning, and in particular to an anti-theft monitoring and early warning method based on video remote monitoring. Background Art

[0002] With the development of science and technology and the progress of society, people's demand for security protection is growing, especially how to prevent or reduce the risk of theft of items or property. Video remote monitoring technology has been widely used in the security field in recent years, enhancing the anti-theft performance of the corresponding areas;

[0003] However, it is currently difficult to conduct a comprehensive assessment of the theft risks in different sub-areas of the corresponding area and to reasonably plan subsequent anti-theft control plans. It is also impossible to reasonably analyze the inspection performance of the sub-areas that need to be inspected and provide accurate feedback. It is also impossible to comprehensively detect the operating status of each monitoring device and issue timely warnings, which is not conducive to ensuring the anti-theft security performance of the corresponding area and reducing the workload of management personnel. The degree of intelligence is low;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an anti-theft monitoring and early warning method based on video remote monitoring, which solves the problem that the prior art is difficult to conduct a comprehensive assessment of the theft risks in different sub-areas in the corresponding area and to reasonably plan subsequent anti-theft control plans, and is unable to reasonably analyze and accurately feedback the inspection performance of the sub-areas that need to be inspected, as well as being unable to comprehensively detect the operating conditions of each monitoring device and issue timely early warnings, which is not conducive to ensuring the anti-theft safety performance of the corresponding area.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The anti-theft monitoring and early warning method based on video remote monitoring includes the following steps:

[0008] Step 1: The monitoring management platform obtains the area to be monitored, divides the area to be monitored into several sub-areas, and marks the corresponding sub-areas as target areas;

[0009] Step 2: The video monitoring module monitors all target areas through cameras, collects real-time monitoring videos of the corresponding target areas, and sends the real-time monitoring videos to the video processing and analysis module through the monitoring management platform;

[0010] Step 3: The video processing and analysis module receives the real-time surveillance video, analyzes the video image in real time through an intelligent algorithm, identifies the moving target, and determines whether there is a theft risk in the corresponding target area based on the behavioral characteristics of the moving target;

[0011] Step 4: If it is determined that there is a risk of theft, the anti-theft warning module automatically triggers an alarm to warn the scene, and transmits the alarm information to the remote monitoring center through the monitoring management platform. The remote monitoring center monitors and directs the situation on site in real time;

[0012] Step 5: The theft hazard assessment module analyzes the degree of theft hazard in the corresponding target area during the detection period, generates a theft high hazard signal or a theft low hazard signal through the analysis, and sends the theft high hazard signal or the theft low hazard signal to the remote monitoring center via the monitoring management platform.

[0013] Furthermore, in step three, the specific analysis and judgment process of judging whether there is a theft risk in the corresponding target area based on the behavioral characteristics of the moving target is as follows:

[0014] Image preprocessing: noise reduction is performed on the received video images, and brightness and contrast are enhanced according to the ambient lighting conditions, and image jitter caused by camera jitter or environmental interference is compensated;

[0015] Moving target detection: Establish and update the background model to distinguish the static background from the dynamic target in the image, and extract the moving target in the foreground by comparing the current image with the background model; use machine learning or deep learning algorithms to classify the extracted moving targets and identify different types of targets;

[0016] Target behavior analysis: Track the identified moving target in consecutive frames to generate its motion trajectory, extract key behavior features from the corresponding moving target's motion trajectory, compare the extracted behavior features with the preset normal behavior pattern, and determine whether the target has abnormal behavior;

[0017] Theft risk assessment: The target's behavior is contextually analyzed in combination with the target's behavioral characteristics and scene information, and the theft risk of the corresponding target is scored based on the contextual analysis results; one or more risk thresholds are set in advance, and when the target's theft risk score exceeds a certain threshold, it is considered that there is a potential theft risk.

[0018] Furthermore, in step 5, the specific analysis process of the theft risk assessment module is as follows:

[0019] Set the detection cycle to L1 days. When the number of days reaches L1 days, divide the detection cycle into several detection periods. Through the theft detection analysis by period, mark the corresponding detection period as an abnormal theft period or a safe period. Obtain the number of abnormal theft periods in the corresponding target area within the detection cycle and mark it as the abnormal theft detection value. Mark the number of abnormal theft periods between two adjacent groups of safe periods as the abnormal theft duration value. Calculate the average of all abnormal theft duration values ​​to obtain the abnormal theft duration value.

[0020] And the total number of times the alarm information is generated in the corresponding target area within the detection period is obtained and marked as the theft alarm value, the theft potential risk assessment value is obtained by numerically calculating the abnormal theft detection value, the abnormal theft condition value and the theft alarm value, and the theft potential risk assessment value is numerically compared with the preset theft potential risk assessment threshold value. If the theft potential risk assessment value exceeds the preset theft potential risk assessment threshold value, a theft high potential risk signal is generated for the corresponding target area; if the theft potential risk assessment value does not exceed the preset theft potential risk assessment threshold value, a theft low potential risk signal is generated for the corresponding target area.

[0021] Furthermore, the specific analysis process of theft detection and analysis by time period is as follows:

[0022] The number of times the corresponding target area generates alarm information in the corresponding detection period is collected and marked as the period alarm value, and the period alarm value is numerically compared with the preset period alarm threshold. If the period alarm value exceeds the preset period alarm threshold, the corresponding detection period is marked as an abnormal theft period; if the period alarm value does not exceed the preset period alarm threshold, the corresponding detection period is marked as a safe period.

[0023] Furthermore, when the monitoring and management platform receives a high-risk theft signal, it marks the corresponding target area as an inspection area; when it receives a low-risk theft signal, it marks the corresponding target area as a monitoring area; and the monitoring and management platform is communicated with the inspection tracking module, and the monitoring and management platform sends the inspection area to the inspection tracking module, which analyzes the inspection performance of the corresponding inspection area within the next detection cycle, generates an inspection pass signal or an inspection abnormality signal through the analysis, and sends the inspection pass signal or inspection abnormality signal of the corresponding inspection area to the remote monitoring center via the monitoring and management platform.

[0024] Furthermore, the specific operation process of the inspection and tracking module includes:

[0025] The total number of inspections for the corresponding inspection area within the detection period is obtained and marked as the total inspection frequency value. Through inspection monitoring and analysis, it is determined whether the corresponding inspection process is a poor meter inspection process. The number of poor meter inspection processes for the corresponding inspection area within the detection period is obtained and the ratio is calculated with the total inspection frequency value to obtain the poor inspection value.

[0026] And calculate the time difference between the start time of the corresponding inspection process and the end time of the adjacent previous inspection process to obtain the inspection time difference, compare the inspection time difference with the corresponding preset inspection time difference threshold, if the inspection time difference exceeds the preset inspection time difference threshold, then mark the corresponding inspection time difference as the inspection time difference, obtain the number of inspection time differences within the detection period and calculate the ratio of it to the number of inspection time differences to obtain the inspection isolation value;

[0027] The inspection tracking value is obtained by numerically calculating the total inspection frequency value, the inspection isolation value and the inspection poor condition value, and the inspection tracking value is numerically compared with the corresponding preset inspection tracking threshold. If the inspection tracking value exceeds the preset inspection tracking threshold, an inspection abnormality signal is generated for the corresponding inspection area; if the inspection tracking value does not exceed the preset inspection tracking threshold, a patrol qualified signal is generated for the corresponding inspection area.

[0028] Furthermore, the specific analysis process of inspection monitoring analysis is as follows:

[0029] After the corresponding inspection process is completed, the actual inspection track of the corresponding inspection process is obtained, and the actual inspection track is compared with the pre-set standard inspection track, and the track length that has not been inspected in the standard inspection track of the corresponding inspection process is marked as the uninspected track value, and the uninspected track value is numerically compared with the corresponding preset uninspected track threshold. If the uninspected track value exceeds the preset uninspected track threshold, the corresponding inspection process is marked as a bad table inspection process;

[0030] If the unpatrol track value does not exceed the preset unpatrol track threshold, the real-time patrol speed in the corresponding patrol process is collected, and the real-time patrol speed is compared with the corresponding preset patrol speed range. If the real-time patrol speed is not within the corresponding preset patrol speed range, it is determined that the corresponding moment is in the patrol speed abnormal state; the duration of the patrol speed abnormal state in the corresponding patrol process is collected and the ratio is calculated with the total duration of the patrol to obtain the speed abnormal time ratio, and the deviation value of the total duration of the patrol compared to the pre-set standard patrol duration is marked as the patrol time deviation value;

[0031] The ratio of the unpatrolled track value to the corresponding preset unpatrolled track threshold is marked as the unpatrolled track occupancy value, and the inspection evaluation value is obtained by numerically calculating the unpatrolled track occupancy value, the speed difference time occupancy value and the inspection time deviation value. The inspection evaluation value is numerically compared with the preset inspection evaluation threshold. If the inspection evaluation value exceeds the preset inspection evaluation threshold, the corresponding inspection process is marked as a poor inspection process.

[0032] Furthermore, the monitoring management platform is connected in communication with the monitoring equipment supervision module, and the monitoring equipment supervision module obtains all monitoring equipment in the area to be monitored, monitors the corresponding monitoring equipment, collects the real-time temperature at several positions in the corresponding monitoring equipment, calculates the average of the real-time temperatures at all positions to obtain the monitoring temperature detection value, and collects the vibration data and noise data generated by the corresponding monitoring equipment and marks them as the monitoring vibration detection value and the monitoring noise detection value, respectively;

[0033] The delay data and clarity data of the video images collected by the corresponding monitoring equipment are obtained and marked as monitoring delay value and monitoring clarity value respectively. The monitoring abnormal detection value is obtained by numerically calculating the monitoring temperature detection value, monitoring vibration detection value, monitoring noise detection value, monitoring delay value and monitoring clarity value. The monitoring abnormal detection value is numerically compared with the preset monitoring abnormal detection threshold. If the monitoring abnormal detection value exceeds the preset monitoring abnormal detection threshold, a monitoring abnormal signal of the corresponding monitoring equipment is generated; if the monitoring abnormal detection value does not exceed the preset monitoring abnormal detection threshold, a monitoring normal signal of the corresponding monitoring equipment is generated, and the monitoring abnormal signal or monitoring normal signal of the corresponding monitoring equipment is sent to the remote monitoring center via the monitoring management platform.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. In the present invention, the real-time monitoring video of the corresponding target area is collected by the video monitoring module, and the video processing and analysis module determines whether there is a theft risk in the corresponding target area. If it is determined that there is a theft risk, an alarm is automatically triggered, which is conducive to ensuring the anti-theft safety performance of the required monitoring area, and the theft hazard assessment module analyzes the degree of theft hazard of the corresponding target area during the detection period to determine the inspection area and the monitoring area, which is convenient for arranging suitable management modes for different sub-areas in the future, ensuring the regional security of the required monitoring area, and improving the anti-theft performance of the required monitoring area, with a high degree of intelligence;

[0036] 2. In the present invention, the inspection performance of the corresponding inspection area is analyzed through the inspection tracking module, and an inspection qualified signal or an inspection abnormal signal is generated through the analysis. When the inspection abnormal signal is generated, the inspection supervision and inspection personnel training of the corresponding inspection area are strengthened to ensure the subsequent inspection effect and improve the regional anti-theft performance. The operating conditions of all monitoring equipment are analyzed through the monitoring equipment supervision module, and the corresponding monitoring equipment is adjusted, inspected and repaired in time to ensure the safe operation of all monitoring equipment, which is conducive to further ensuring the anti-theft control performance of the required monitoring area. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0038] Figure 1 This is a flow chart of the method of Embodiment 1 of the present invention;

[0039] Figure 2 This is a system block diagram of Embodiment 1 of the present invention;

[0040] Figure 3 This is a system block diagram of Embodiment 2 of the present invention;

[0041] Figure 4 This is a system block diagram of Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0043] Embodiment 1: Figure 1-2 As shown, the anti-theft monitoring and early warning method based on video remote monitoring proposed by the present invention includes the following steps:

[0044] Step 1: The monitoring management platform obtains the area to be monitored, divides the area to be monitored into several sub-areas, and marks the corresponding sub-areas as target areas.

[0045] Step 2: The video monitoring module monitors all target areas through cameras, collects real-time monitoring videos of the corresponding target areas, and sends the real-time monitoring videos to the video processing and analysis module through the monitoring management platform.

[0046] Step 3: The video processing and analysis module receives the real-time surveillance video, analyzes the video image in real time through intelligent algorithms, identifies moving targets (such as human bodies and vehicles), and determines whether there is a theft risk in the corresponding target area based on the behavioral characteristics of the moving targets. The specific analysis and judgment process is as follows:

[0047] Image preprocessing: Since there may be noise during video transmission, the received video images are first subjected to noise reduction processing to improve the accuracy of subsequent analysis. The video images are then enhanced in terms of brightness and contrast according to the ambient lighting conditions. Image jitter caused by camera jitter or environmental interference is compensated to maintain image stability.

[0048] Moving target detection: Establish and update the background model to distinguish the static background from the dynamic target in the image, and extract the moving target in the foreground by comparing the current image with the background model; use machine learning or deep learning algorithms to classify the extracted moving targets and identify different types of targets such as human bodies and vehicles;

[0049] Target behavior analysis: Track the identified moving target in consecutive frames to generate its motion trajectory, extract key behavior features from the corresponding moving target's motion trajectory, such as speed, acceleration, direction change, etc., compare the extracted behavior features with the preset normal behavior pattern, and determine whether the target has abnormal behavior, such as long-term wandering, sudden acceleration or change of direction, etc.;

[0050] Theft risk assessment: The target's behavior is contextually analyzed in combination with its behavioral characteristics and other information in the scene (such as time, weather, lighting conditions, etc.), and the theft risk of the corresponding mobile target is scored based on the results of the contextual analysis. The score can be weighted based on multiple factors, such as the type of target (human or vehicle), the degree of abnormality of the behavior, the distance from sensitive areas, etc.; one or more risk thresholds are set in advance. When the target's theft risk score exceeds a certain threshold, it is considered that there is a potential theft risk.

[0051] Step 4: If it is determined that there is a risk of theft, the anti-theft warning module automatically triggers an alarm to warn the site, and transmits the alarm information to the remote monitoring center through the monitoring management platform. The remote monitoring center monitors and directs the on-site situation in real time.

[0052] Step 5: The theft hazard assessment module analyzes the degree of theft hazard in the corresponding target area during the detection period, generates a high theft hazard signal or a low theft hazard signal through the analysis, and sends the high theft hazard signal or the low theft hazard signal to the remote monitoring center via the monitoring management platform; the specific analysis process is as follows:

[0053] The detection period is set to be L1 days, preferably, L1 days is thirty-five days; when the number of days reaches L1 days, the detection period is divided into several detection periods, and the corresponding detection period is marked as an abnormal theft period or a safe period through the theft detection analysis by period, specifically: the number of times the corresponding target area generates alarm information in the corresponding detection period is collected and marked as a period alarm value, and the period alarm value is numerically compared with the preset period alarm threshold. If the period alarm value exceeds the preset period alarm threshold, it indicates that the theft risk of the target area in the corresponding detection period is high, and the corresponding detection period is marked as an abnormal theft period; if the period alarm value does not exceed the preset period alarm threshold, it indicates that the theft risk of the target area in the corresponding detection period is low, and the corresponding detection period is marked as a safe period;

[0054] The number of abnormal theft periods in the corresponding target area within the detection period is obtained and marked as the abnormal theft detection value, and the number of abnormal theft periods between two adjacent groups of safe periods is marked as the abnormal theft duration value, and the average of all abnormal theft duration values ​​is calculated to obtain the abnormal theft duration value, and the total number of alarm information generated in the corresponding target area within the detection period is obtained and marked as the theft alarm value;

[0055] By formula The theft detection value QS, the theft condition value QF and the theft alarm value QR are numerically calculated to obtain the theft hidden danger assessment value QP, wherein wq1, wq2 and wq3 are preset proportional coefficients, wq2>wq1>wq3>0; and the larger the theft hidden danger assessment value QP is, the greater the theft risk of the corresponding target area within the detection period is, and the higher the difficulty of theft prevention management for the corresponding target area is in general;

[0056] The theft hazard assessment value QP is numerically compared with the preset theft hazard assessment threshold. If the theft hazard assessment value QP exceeds the preset theft hazard assessment threshold, it indicates that the anti-theft management difficulty for the corresponding target area is generally high, and a theft high hazard signal for the corresponding target area is generated; if the theft hazard assessment value QP does not exceed the preset theft hazard assessment threshold, it indicates that the anti-theft management difficulty for the corresponding target area is generally low, and a theft low hazard signal for the corresponding target area is generated.

[0057] Furthermore, when the monitoring and management platform receives a high-risk theft signal, it will mark the corresponding target area as an inspection area. When it receives a low-risk theft signal, it will mark the corresponding target area as a monitoring area, and send the marking information of the corresponding target area to the remote monitoring center. Subsequently, not only will it be necessary to conduct video surveillance of the inspection area, but it will also be necessary to arrange corresponding patrol personnel to patrol the inspection area. For the monitoring area, it is only necessary to conduct video surveillance of the corresponding area through monitoring equipment, which will facilitate the subsequent arrangement of appropriate management modes for different sub-areas, ensure the regional security of the required monitoring area, and improve the anti-theft performance of the required monitoring area.

[0058] Embodiment 2: Figure 3 As shown, the difference between this embodiment and embodiment 1 is that the monitoring management platform is connected to the inspection tracking module in communication, the monitoring management platform sends the inspection area to the inspection tracking module, and the inspection tracking module analyzes the inspection performance of the corresponding inspection area in the next detection cycle, generates an inspection qualified signal or an inspection abnormality signal through analysis, and sends the inspection qualified signal or the inspection abnormality signal of the corresponding inspection area to the remote monitoring center via the monitoring management platform. When the inspection abnormality signal is received, the inspection supervision of the corresponding inspection area is strengthened, and the corresponding inspection personnel are trained and educated to ensure the subsequent inspection effect and improve the regional anti-theft performance; the specific operation process of the inspection tracking module is as follows:

[0059] The total number of inspections for the corresponding inspection area within the detection cycle is obtained and marked as the total inspection frequency value, and the inspection monitoring and analysis is used to determine whether the corresponding inspection process is a poor inspection process, specifically: after the corresponding inspection process is completed, the actual inspection trajectory of the corresponding inspection process is obtained, and the actual inspection trajectory is compared with the pre-set standard inspection trajectory, and the length of the trajectory that has not been inspected in the standard inspection trajectory of the corresponding inspection process is marked as a non-inspected trajectory value, and the non-inspected trajectory value is numerically compared with the corresponding preset non-inspected trajectory threshold. If the non-inspected trajectory value exceeds the preset non-inspected trajectory threshold, it indicates that the inspection effect of the corresponding inspection process is poor, and the corresponding inspection process is marked as a poor inspection process;

[0060] If the unpatrol track value does not exceed the preset unpatrol track threshold, the real-time patrol speed in the corresponding patrol process is collected, and the real-time patrol speed is compared with the corresponding preset patrol speed range. If the real-time patrol speed is not within the corresponding preset patrol speed range, it is determined that the corresponding moment is in the patrol speed abnormal state; the duration of the patrol speed abnormal state in the corresponding patrol process is collected and the ratio is calculated with the total duration of the patrol to obtain the speed abnormal time ratio, and the deviation value of the total duration of the patrol compared to the pre-set standard patrol duration is marked as the patrol time deviation value;

[0061] And the ratio of the unpatrolled track value to the corresponding preset unpatrolled track threshold is marked as the unpatrolled track occupancy value, and the unpatrolled track occupancy value HF, the speed difference time occupancy value HS and the inspection time deviation value HK are numerically calculated by the formula HX=ey1*HF+ey2*HS+ey3*HK to obtain the inspection evaluation value HX, where ey1, ey2, and ey3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the inspection evaluation value HX, the worse the inspection effect of the corresponding inspection process;

[0062] The inspection evaluation value HX is numerically compared with the preset inspection evaluation threshold. If the inspection evaluation value HX exceeds the preset inspection evaluation threshold, it indicates that the inspection effect of the corresponding inspection process is poor, and the corresponding inspection process is marked as a poor meter inspection process; the number of poor meter inspection processes for the corresponding inspection area within the detection period is obtained and the ratio is calculated with the total inspection frequency value to obtain the inspection poor value;

[0063] And calculate the time difference between the start time of the corresponding inspection process and the end time of the adjacent previous inspection process to obtain the inspection time difference, compare the inspection time difference with the corresponding preset inspection time difference threshold, if the inspection time difference exceeds the preset inspection time difference threshold, then mark the corresponding inspection time difference as the inspection time difference, obtain the number of inspection time differences within the detection period and calculate the ratio of it to the number of inspection time differences to obtain the inspection isolation value;

[0064] By formula The inspection total frequency value TY, the inspection isolation value TW and the inspection poor condition value TP are numerically calculated to obtain the inspection tracking value TF, wherein fp1, fp2 and fp3 are preset proportional coefficients, and the values ​​of fp1, fp2 and fp3 are all positive numbers; and the larger the value of the inspection tracking value TF is, the worse the inspection performance for the corresponding inspection area is overall.

[0065] The inspection tracking value TF is numerically compared with the corresponding preset inspection tracking threshold. If the inspection tracking value TF exceeds the preset inspection tracking threshold, it indicates that the inspection performance of the corresponding inspection area is generally poor, and an inspection abnormality signal is generated for the corresponding inspection area; if the inspection tracking value TF does not exceed the preset inspection tracking threshold, it indicates that the inspection performance of the corresponding inspection area is generally good, and a patrol qualified signal is generated for the corresponding inspection area.

[0066] Embodiment 3: Figure 4 As shown, the difference between this embodiment and embodiment 1 and embodiment 2 is that the monitoring management platform is connected in communication with the monitoring device supervision module, the monitoring device supervision module obtains all monitoring devices in the area to be monitored, monitors the corresponding monitoring devices, collects the real-time temperatures at several positions in the corresponding monitoring devices, calculates the average of the real-time temperatures at all positions to obtain the monitoring temperature detection value, and collects the vibration data (i.e., vibration amplitude) and noise data (i.e., noise decibel value) generated by the corresponding monitoring devices and marks them as monitoring vibration detection value and monitoring noise detection value respectively; obtains the delay data and clarity data of the video image collected by the corresponding monitoring device and marks them as monitoring delay value and monitoring clarity value respectively;

[0067] The monitoring abnormality detection value YP is obtained by numerically calculating the monitoring temperature detection value YW, the monitoring vibration detection value YN, the monitoring noise detection value YK, the monitoring delay value YS and the monitoring clarity value YF through the formula YP=a1*YW+a2*YN+a3*YK+a4*YS+a5 / (YF+0.826), wherein a1, a2, a3, a4, and a5 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the monitoring abnormality detection value YP is, the worse the current operating condition of the corresponding monitoring equipment is, and the more timely the corresponding monitoring equipment needs to be inspected, repaired, and other treatment measures are required;

[0068] The monitoring abnormal detection value YP is numerically compared with the preset monitoring abnormal detection threshold. If the monitoring abnormal detection value YP exceeds the preset monitoring abnormal detection threshold, it indicates that the current operating condition of the corresponding monitoring device is poor, and a monitoring abnormal signal of the corresponding monitoring device is generated; if the monitoring abnormal detection value YP does not exceed the preset monitoring abnormal detection threshold, it indicates that the current operating condition of the corresponding monitoring device is good, and a monitoring normal signal of the corresponding monitoring device is generated, and the monitoring abnormal signal or the monitoring normal signal of the corresponding monitoring device is sent to the remote monitoring center via the monitoring management platform. When the remote monitoring center receives the monitoring abnormal signal, it issues a corresponding early warning, and promptly adjusts, inspects and repairs the corresponding monitoring equipment to ensure the safe operation of all monitoring equipment, which is conducive to further ensuring the anti-theft control performance of the required monitoring area.

[0069] The working principle of the present invention is as follows: when in use, the area to be monitored is divided into several sub-areas through the monitoring management platform and the corresponding sub-areas are marked as target areas. The video monitoring module collects real-time monitoring videos of the corresponding target areas. The video processing and analysis module determines whether there is a theft risk in the corresponding target area based on the behavioral characteristics of the mobile target. If it is determined that there is a theft risk, an alarm is automatically triggered, which is beneficial to ensure the anti-theft safety performance of the required monitoring area. The theft hazard assessment module analyzes the degree of theft hazard of the corresponding target area during the detection period, and generates a high theft hazard signal or a low theft hazard signal through analysis, which facilitates the subsequent arrangement of appropriate management modes for different sub-areas, ensures the regional security of the required monitoring area, improves the anti-theft performance of the required monitoring area, and has a high degree of intelligence.

[0070] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. 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 contents 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 technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The anti-theft monitoring and early warning method based on video remote monitoring is characterized by: The following steps are involved: Step 1: The monitoring management platform obtains the area to be monitored, divides the area to be monitored into several sub-areas, and marks the corresponding sub-areas as target areas; Step 2: The video monitoring module monitors all target areas through cameras, collects real-time monitoring videos of the corresponding target areas, and sends the real-time monitoring videos to the video processing and analysis module through the monitoring management platform; Step 3: The video processing and analysis module receives the real-time surveillance video, analyzes the video image in real time through an intelligent algorithm, identifies the moving target, and determines whether there is a theft risk in the corresponding target area based on the behavioral characteristics of the moving target; Step 4: If it is determined that there is a risk of theft, the anti-theft warning module automatically triggers an alarm to warn the scene, and transmits the alarm information to the remote monitoring center through the monitoring management platform. The remote monitoring center monitors and directs the situation on site in real time; Step 5: The theft hazard assessment module analyzes the degree of theft hazard in the corresponding target area during the detection period, generates a high theft hazard signal or a low theft hazard signal through the analysis, and sends the high theft hazard signal or the low theft hazard signal to the remote monitoring center via the monitoring management platform; The specific analysis process of the theft risk assessment module is as follows: Set the detection cycle to L1 days. When the number of days reaches L1 days, divide the detection cycle into several detection periods. Through the theft detection analysis by period, mark the corresponding detection period as an abnormal theft period or a safe period. Obtain the number of abnormal theft periods in the corresponding target area within the detection cycle and mark it as the abnormal theft detection value. Mark the number of abnormal theft periods between two adjacent groups of safe periods as the abnormal theft duration value. Calculate the average of all abnormal theft duration values ​​to obtain the abnormal theft duration value. and obtaining the total number of alarm information generated in the corresponding target area within the detection cycle and marking it as the theft alarm value, and obtaining the theft risk assessment value by numerically calculating the abnormal theft detection value, the abnormal theft condition value and the theft alarm value. If the theft risk assessment value exceeds the preset theft risk assessment threshold, a theft high risk signal of the corresponding target area is generated; if the theft risk assessment value does not exceed the preset theft risk assessment threshold, a theft low risk signal of the corresponding target area is generated; The specific analysis process of theft detection and analysis by time period is as follows: The number of times the corresponding target area generates alarm information in the corresponding detection period is collected and marked as the period alarm value. If the period alarm value exceeds the preset period alarm threshold, the corresponding detection period is marked as the abnormal theft period; If the period alarm value does not exceed the preset period alarm threshold, the corresponding detection period is marked as a safe period.

2. The anti-theft monitoring and early warning method based on video remote monitoring according to claim 1 is characterized in that: In step 3, the specific analysis and judgment process of judging whether there is a theft risk in the corresponding target area based on the behavioral characteristics of the moving target is as follows: Image preprocessing: noise reduction is performed on the received video images, and brightness and contrast are enhanced according to the ambient lighting conditions, and image jitter caused by camera jitter or environmental interference is compensated; Moving target detection: Establish and update the background model to distinguish the static background from the dynamic target in the image, and extract the moving target in the foreground by comparing the current image with the background model; Use machine learning or deep learning algorithms to classify the extracted moving targets and identify different types of targets; Target behavior analysis: Track the identified moving target in consecutive frames to generate its motion trajectory, extract key behavior features from the corresponding moving target's motion trajectory, compare the extracted behavior features with the preset normal behavior pattern, and determine whether the target has abnormal behavior; Theft risk assessment: Combine the behavioral characteristics of the mobile target with the scene information to conduct contextual analysis of the target's behavior, and score the theft risk of the corresponding mobile target based on the results of the contextual analysis; One or more risk thresholds are set in advance. When the theft risk score of a target exceeds a certain threshold, it is considered that there is a potential theft risk.

3. The anti-theft monitoring and early warning method based on video remote monitoring according to claim 1 is characterized in that: When the monitoring and management platform receives a high-risk theft signal, it marks the corresponding target area as an inspection area; when it receives a low-risk theft signal, it marks the corresponding target area as a monitoring area; and the monitoring and management platform is communicated with the inspection tracking module, and the monitoring and management platform sends the inspection area to the inspection tracking module. The inspection tracking module will analyze the inspection performance of the corresponding inspection area in the next detection cycle, generate an inspection pass signal or an inspection abnormality signal through the analysis, and send the inspection pass signal or inspection abnormality signal of the corresponding inspection area to the remote monitoring center via the monitoring and management platform.

4. The anti-theft monitoring and early warning method based on video remote monitoring according to claim 3 is characterized in that: The specific operation process of the inspection and tracking module includes: The total number of inspections for the corresponding inspection area within the detection period is obtained and marked as the total inspection frequency value. Through inspection monitoring and analysis, it is determined whether the corresponding inspection process is a poor meter inspection process. The number of poor meter inspection processes for the corresponding inspection area within the detection period is obtained and the ratio is calculated with the total inspection frequency value to obtain the poor inspection value. And the time difference between the start time of the corresponding inspection process and the end time of the previous inspection process is calculated to obtain the inspection time difference. If the inspection time difference exceeds the preset inspection time difference threshold, the corresponding inspection time difference is marked as the inspection time difference, and the number of inspection time differences within the detection period is obtained and the ratio is calculated with the number of inspection time differences to obtain the inspection isolation value; The inspection tracking value is obtained by numerically calculating the total inspection frequency value, the inspection isolation value and the inspection poor condition value. If the inspection tracking value exceeds the preset inspection tracking threshold, an inspection abnormality signal of the corresponding inspection area is generated; if the inspection tracking value does not exceed the preset inspection tracking threshold, an inspection qualified signal of the corresponding inspection area is generated.

5. The anti-theft monitoring and early warning method based on video remote monitoring according to claim 4 is characterized in that: The specific analysis process of inspection monitoring analysis is as follows: After the corresponding inspection process is completed, if the unpatrolled track value exceeds the preset unpatrolled track threshold, the corresponding inspection process is marked as a bad meter inspection process; If the unpatrol track value does not exceed the preset unpatrol track threshold, the real-time patrol speed in the corresponding patrol process is collected, and the real-time patrol speed is compared with the corresponding preset patrol speed range. If the real-time patrol speed is not within the corresponding preset patrol speed range, it is determined that the corresponding moment is in the patrol speed abnormal state; The time duration of the inspection speed abnormality during the corresponding inspection process is collected and the ratio is calculated with the total inspection time duration to obtain the speed abnormality time percentage value, and the deviation value of the total inspection time duration compared with the pre-set standard inspection time duration is marked as the inspection time deviation value; The ratio of the unpatrolled track value to the corresponding preset unpatrolled track threshold is marked as the unpatrolled track occupancy value, and the inspection evaluation value is obtained by numerically calculating the unpatrolled track occupancy value, the speed difference time occupancy value and the inspection time deviation value. If the inspection evaluation value exceeds the preset inspection evaluation threshold, the corresponding inspection process is marked as a poor inspection process.

6. The anti-theft monitoring and early warning method based on video remote monitoring according to claim 1 is characterized in that: The monitoring management platform is communicated with the monitoring equipment supervision module. The monitoring equipment supervision module obtains all the monitoring equipment in the area to be monitored, and monitors the corresponding monitoring equipment. The monitoring abnormal detection value is obtained by numerically calculating the monitoring temperature detection value, the monitoring vibration detection value, the monitoring noise detection value, the monitoring delay value and the monitoring clarity value. If the monitoring abnormal detection value exceeds the preset monitoring abnormal detection threshold, a monitoring abnormal signal of the corresponding monitoring equipment is generated; if the monitoring abnormal detection value does not exceed the preset monitoring abnormal detection threshold, a monitoring normal signal of the corresponding monitoring equipment is generated, and the monitoring abnormal signal or the monitoring normal signal of the corresponding monitoring equipment is sent to the remote monitoring center via the monitoring management platform.

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