A security monitoring storage information intelligent analysis management method and system

Abstract

The present invention discloses a method and system for intelligent analysis and management of security monitoring storage information, which relates to the technical field of intelligent analysis and management of security monitoring storage information, and includes the following steps: reasonably arranging monitoring cameras to ensure full coverage of key areas that require security monitoring, real-time acquisition of images and video data transmitted by monitoring cameras, and pre-processing and storing the acquired images. The present invention monitors the abnormal behavior analysis process in each time window in real time, and the system can perceive abnormal hidden dangers in the analysis process. By setting reference thresholds for the length of the queue to be processed or the image motion blur for comparison and analysis, the system can intelligently determine whether there are abnormal hidden dangers, and then perform corresponding processing. For the abnormalities found, the system adopts an intelligent repair strategy, which improves the real-time nature of abnormal behavior analysis and reduces potential security threats.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent analysis and management of security monitoring storage information, and in particular to a method and system for intelligent analysis and management of security monitoring storage information. Background Art

[0002] Realizing unified security resource management across the entire network through a management platform means using advanced information technology to integrate all security equipment such as surveillance cameras, intrusion detection systems, access control, etc. into a central management platform. This platform, through network connection, enables centralized monitoring, control and management of security equipment within the entire monitoring range, enabling enterprises to achieve unified scheduling and collaborative operations of security systems, improving overall security and response efficiency.

[0003] The intelligent analysis and management of security monitoring storage information means that on this unified management platform, the large amount of data generated by security monitoring is intelligently analyzed and managed. Through advanced data analysis algorithms and artificial intelligence technology, monitoring videos can be intelligently identified, anomaly detected, and behaviorally analyzed. This enables enterprises to monitor the production environment in real time, discover abnormal situations and take timely measures. At the same time, through the analysis of historical data, it provides prediction and prevention of potential risks. This intelligent analysis and management not only improves security, but also provides enterprises with more accurate and efficient production management and decision-making support.

[0004] The prior art has the following deficiencies:

[0005] When analyzing abnormal behavior through security monitoring and storage information, the existing technology is unable to control the abnormal situations of the entire analysis process. If there is a hidden danger of missing or erroneous analysis of abnormal behavior in the abnormal behavior analysis process, during this period, this may mean that potential security threats are not effectively prevented. For example, intruders may not be identified in time, resulting in unauthorized activities in restricted areas, thereby increasing the risk of theft, destruction or information leakage. Secondly, it may lead to manipulation or destruction of the production environment, which may cause accidents and damage equipment, production lines or raw materials, leading to production interruptions, equipment failures, and even endangering employee safety.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for intelligent analysis and management of security monitoring storage information. By real-time monitoring the abnormal behavior analysis process within each time window, the system can perceive the abnormal hidden dangers in the analysis process. By setting a reference threshold for the length of the queue to be processed or the image motion blur for comparison and analysis, the system can intelligently determine whether there are abnormal hidden dangers, and then perform corresponding processing. For the abnormalities found, the system adopts an intelligent repair strategy, which improves the real-time performance of the abnormal behavior analysis and reduces potential security threats, so as to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for intelligent analysis and management of security monitoring storage information, comprising the following steps:

[0009] Reasonably arrange surveillance cameras to ensure full coverage of key areas that require security monitoring, collect images and video data transmitted by surveillance cameras in real time, and pre-process and store the collected images;

[0010] The security monitoring storage information is divided into time windows, and the time in each time window is made equal. The process of abnormal behavior analysis through the security monitoring storage information in each time window is analyzed, and abnormal hidden dangers in the analysis process are intelligently perceived;

[0011] For each time window, when there is an abnormal hidden danger in the process of abnormal behavior analysis through security monitoring storage information, the actual duration of the camera's abnormal behavior analysis of the time window with abnormal hidden danger is obtained. After the acquisition is completed, the expected upper limit of the normal analysis duration of each time window of the camera is calculated according to the real-time requirements of video monitoring;

[0012] Compare and analyze the actual duration with the upper limit of the expected duration. Based on different comparison and analysis results, determine whether it is possible to continue to perform abnormal behavior analysis on the time window where abnormal risks exist through security monitoring storage information, thereby completing intelligent repair of occasional abnormal risks in abnormal behavior analysis.

[0013] Preferably, the process of analyzing abnormal behavior through security monitoring storage information in each time window is analyzed, and the process of intelligently sensing hidden dangers in the analysis process is as follows:

[0014] The real-time data processing information and image information are obtained when abnormal behavior analysis is performed through security monitoring storage information in each time window, wherein the real-time data processing information includes that the queue to be processed is too long, and the image information includes motion blur. After the data is acquired, the queue to be processed is too long and the motion blur are processed respectively to generate a queue to be processed length high index and an image motion blur index.

[0015] Preferably, the logic for obtaining the high index of the queue length to be processed is as follows:

[0016] In a fixed time window, obtain the real-time queue length for abnormal behavior analysis through security monitoring storage information, and represent the real-time queue length by function Queue(t) according to the time series;

[0017] The real-time queue length to be processed when abnormal behavior analysis is performed through security monitoring storage information is compared with the maximum queue length to be processed when abnormal behavior analysis is performed normally through security monitoring storage information, and the real-time high backlog rate index is calculated. The calculation expression is: In the formula, Queue 长度 Indicates the high index of the queue length to be processed, t1, t2] indicates the period when the real-time queue length to be processed is greater than the maximum queue length to be processed when abnormal behavior analysis is performed through security monitoring storage information, t1 <t2,Queue max Indicates the maximum length of the queue to be processed.

[0018] Preferably, the logic of obtaining the image motion blur index is as follows:

[0019] Within a fixed time window, obtain image information of the security monitoring area when performing abnormal behavior analysis through security monitoring storage information;

[0020] Choose to calculate blur in horizontal or vertical direction;

[0021] Construct a blur kernel to simulate the blur effect caused by motion. Motion blur is represented by a linear convolution operation, expressed as: blurred (x, y) = I original (x, y)*PSF(x, y), where * represents the convolution operation, I blurred (x, y) and I original (x, y) represents the blurred image and the original image respectively, PSF(x, y) represents the point spread function or blur kernel;

[0022] For horizontal or vertical motion blur, the calculation formula of the blur kernel is: PSF(x, y) = δ(xt·v, y), where δ(xt·v, y) represents the impulse function in the direction of motion, t is the blur time, and v is the motion speed;

[0023] The image motion blur index is calculated by the blur kernel width. The calculation expression is: In the formula, Indicates the blur kernel width, Blur 图像 Represents the image motion blur index, PSF(x, y)m represents the image motion blur value of a single frame image obtained within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information, m represents the sequential number of the image motion blur value of a single frame image obtained within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information, m=1, 2, 3, 4, ..., M, where M is a positive integer.

[0024] Preferably, the high index of queue length to be processed and the image motion blur index generated within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information are compared and analyzed with a preset reference threshold of the high index of queue length to be processed and a preset reference threshold of the image motion blur index, respectively. The results of the comparison and analysis are as follows:

[0025] If the high index of the queue length to be processed is greater than or equal to the reference threshold of the high index of the queue length to be processed, or the image motion blur index is greater than or equal to the reference threshold of the image motion blur index, the corresponding time window will be marked as an abnormal hidden danger time window; if the high index of the queue length to be processed is less than the reference threshold of the high index of the queue length to be processed and the image motion blur index is less than the reference threshold of the image motion blur index, the corresponding time window will be marked as a normal time window.

[0026] Preferably, in the context of abnormal behavior analysis, the actual duration refers to the time elapsed for each time window from the start of camera operation to the discovery of abnormal hidden dangers, abnormal behavior analysis, and determination of the abnormality. This includes the time period from the time the monitoring system starts recording video or image data, analyzing it through processing algorithms, to the final determination that there are abnormal hidden dangers.

[0027] Preferably, the expected upper limit of duration refers to the maximum allowed duration from the start of abnormal behavior analysis of security monitoring storage information to the final completion of the analysis within each time window. The setting of the expected upper limit of duration is based on business needs, system performance requirements and expectations for real-time performance.

[0028] Preferably, the actual duration is compared and analyzed with the upper limit of the expected duration, and according to different comparison and analysis results, it is determined whether it is possible to continue to perform abnormal behavior analysis on the time window with abnormal hidden dangers through security monitoring storage information as follows:

[0029] If the actual duration is greater than or equal to the upper limit of the expected duration, the corresponding duration window will be marked as a non-continuous hidden danger window, and an early warning will be issued for the non-continuous hidden danger window. When the actual duration is greater than the upper limit of the expected duration, it indicates that the real-time requirement is not met when performing abnormal behavior analysis through security monitoring storage information. When the actual duration is equal to the upper limit of the expected duration, it is impossible to continue to perform abnormal behavior analysis on the time window with abnormal hidden dangers through security monitoring storage information;

[0030] If the actual duration is less than the upper limit of the expected duration, the corresponding time window will be marked as a repairable hidden danger window, and the repairable hidden danger window will be re-analyzed for abnormal behavior of the time window with abnormal hidden dangers through the security monitoring storage information, and the duration of the re-analysis of abnormal behavior will be counted.

[0031] Preferably, the time for re-analyzing abnormal behavior is added to the actual time and then compared with the upper limit of the expected time. The result of the comparison analysis is as follows:

[0032] If the time for re-analyzing abnormal behavior plus the actual time is greater than or equal to the upper limit of the expected time, the corresponding time window is re-calibrated as a non-continuing hidden danger window, and an early warning prompt is given for the non-continuing hidden danger window. If the time for re-analyzing abnormal behavior plus the actual time is less than the upper limit of the expected time, but there is still a high index of the queue length to be processed that is greater than or equal to the reference threshold of the queue length to be processed or an image motion blur index that is greater than or equal to the reference threshold of the image motion blur index within the time window, then the above-mentioned operation of re-analyzing abnormal behavior for the time window with abnormal hidden dangers through security monitoring storage information is continued;

[0033] If the duration of the re-analysis of abnormal behavior plus the actual duration is less than the upper limit of the expected duration, but the high index of the queue length to be processed is less than the reference threshold of the high index of the queue length to be processed or the image motion blur index is less than the reference threshold of the image motion blur index within the time window, abnormal behavior analysis is performed on the next time window through the security monitoring storage information.

[0034] A security monitoring storage information intelligent analysis and management system, comprising a security monitoring information acquisition module, an abnormality perception module, a duration determination module, and a judgment and repair module;

[0035] The security monitoring information acquisition module collects the images and video data transmitted by the monitoring camera in real time, and stores the collected images after preprocessing;

[0036] The abnormal perception module divides the security monitoring storage information into time windows and makes the time in each time window equal. It analyzes the process of abnormal behavior analysis through the security monitoring storage information in each time window and intelligently perceives the abnormal hidden dangers in the analysis process.

[0037] The duration determination module, for each time window, when there is an abnormal hidden danger in the process of abnormal behavior analysis through security monitoring storage information, obtains the actual duration of the camera's abnormal behavior analysis of the time window with abnormal hidden dangers. After the acquisition is completed, the expected upper limit of the normal analysis duration of each time window of the camera is calculated according to the real-time requirements of video monitoring;

[0038] The judgment and repair module compares and analyzes the actual duration and the upper limit of the expected duration. Based on different comparison and analysis results, it determines whether it is possible to continue to perform abnormal behavior analysis on the time window where abnormal risks exist through security monitoring storage information, thereby completing intelligent repair of occasional abnormal risks in the abnormal behavior analysis.

[0039] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0040] This method monitors the abnormal behavior analysis process in each time window in real time. The system can perceive the abnormal hidden dangers in the analysis process. By setting reference thresholds for the length of the queue to be processed or the image motion blur, the system can intelligently determine whether there are abnormal hidden dangers and then take corresponding measures. For the abnormalities found, the system adopts intelligent repair strategies, which improves the real-time nature of abnormal behavior analysis and reduces potential security threats.

[0041] The present invention divides the security monitoring storage information into time windows and performs anomaly analysis in each window, so that the system can perceive and handle anomalies in a more detailed manner. This windowed analysis method helps to isolate anomalies in time, so that the system can more accurately locate the moment when the anomaly occurs, thereby taking targeted repair measures, thereby improving the accuracy and efficiency of the system;

[0042] The present invention compares the actual duration with the upper limit of the expected duration, so that the system can dynamically adjust the abnormal behavior analysis duration of the time window where abnormal hidden dangers exist. According to the comparison result, the system determines whether the analysis can continue, thereby realizing intelligent management of the analysis duration, which helps to respond flexibly in different situations and ensure that the system can effectively perform abnormal behavior analysis while meeting real-time requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 The present invention is a method flow chart of a security monitoring storage information intelligent analysis management method and system.

[0045] Figure 2 The present invention is a module schematic diagram of a security monitoring storage information intelligent analysis management method and system. DETAILED DESCRIPTION

[0046] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0047] The present invention provides Figure 1 A security monitoring storage information intelligent analysis and management method is shown, comprising the following steps:

[0048] Reasonably arrange surveillance cameras to ensure full coverage of key areas that require security monitoring, collect images and video data transmitted by surveillance cameras in real time, and pre-process and store the collected images;

[0049] Preprocessing the acquired images is a key step to improve the accuracy of subsequent analysis. The following are the main processes of image preprocessing and the role of image preprocessing:

[0050] Denoising: Removes noise from images, including random interference caused by sensors or communications. Denoising helps improve image quality and makes subsequent analysis more accurate.

[0051] Image enhancement: Use image enhancement techniques, such as contrast enhancement and brightness adjustment, to increase the clarity and contrast of the image, making abnormal behavior easier to identify.

[0052] Image normalization: Ensures that the brightness and color of the image are consistent, so that images collected by different cameras are consistent during the analysis process and reduce differences caused by different lighting conditions.

[0053] Resolution adjustment: Adjust the image resolution based on specific analysis requirements. In some cases, lowering the resolution can reduce computational complexity and increase processing speed, while in scenarios where more detailed information is required, the resolution can be increased.

[0054] Motion blur processing: For objects in motion, use motion blur processing technology to eliminate image blur caused by object movement and maintain the clarity of the object.

[0055] Color space conversion: Convert an image from one color space to another, such as from RGB to grayscale. This helps simplify the complexity of image analysis, especially in some specific application scenarios, such as person detection.

[0056] Edge detection: Emphasizes the edge structure in the image, helps extract the contour information of the object, and provides more features for the detection and identification of abnormal behavior.

[0057] Divide the security monitoring storage information into time windows and make the time in each time window equal. Analyze the process of abnormal behavior analysis through the security monitoring storage information in each time window, and intelligently perceive the abnormal hidden dangers in the analysis process (missed analysis or wrong analysis of abnormal behavior);

[0058] The process of analyzing abnormal behavior through security monitoring storage information in each time window is analyzed, and the process of intelligent perception and analysis of hidden dangers is as follows:

[0059] Acquire the real-time data processing information and image information when abnormal behavior analysis is performed through security monitoring storage information in each time window, wherein the real-time data processing information includes that the queue to be processed is too long (the queue to be processed here is the image data to be processed), and the image information includes motion blur. After the data is acquired, the queue to be processed is too long and the motion blur are processed respectively to generate a queue to be processed length high index and an image motion blur index;

[0060] When analyzing abnormal behaviors through security monitoring storage information, a high queue length may cause the abnormal behavior analysis process to miss or make incorrect analyses of abnormal behaviors. The reasons are as follows:

[0061] Processing delays lead to missed real-time events: When the length of the waiting queue is too high, it may increase the delay of the analysis process. Processing delays may cause the system to miss abnormal events that occur in real time, especially events that require timely response, such as intrusion detection or equipment failure.

[0062] Long queues lead to data lag: Long queues to be processed mean that there is a large backlog of data that needs to be processed during the analysis process. Long data backlogs may cause the system to analyze outdated data, thereby reducing the timeliness and accuracy of abnormal behavior.

[0063] System resource competition causes performance degradation: High queue lengths may cause system resource competition, reducing the computing performance of the analysis model. System resource competition may make the analysis process unstable, affect the accuracy of the model, and increase the risk of missing abnormal behavior analysis.

[0064] Data lag leads to incorrect analysis: Under high queue length, the system may not process in time, resulting in incorrect analysis of abnormal behavior. Data lag may cause the system to miss some important information in the abnormal behavior analysis, leading to wrong conclusions and decisions.

[0065] Data loss and incomplete analysis: If the length of the pending queue exceeds the system's processing capacity, some data may be discarded or analyzed incompletely. Data loss may mean that some key information is ignored, leading to incorrect analysis or omission of abnormal behavior.

[0066] Therefore, by using the security monitoring storage information to analyze the length of the queue to be processed, it is possible to intelligently perceive the abnormal hidden dangers that a high length of the queue to be processed may lead to missed analysis or incorrect analysis of abnormal behavior.

[0067] The logic for obtaining the high index of the queue length to be processed is as follows:

[0068] In a fixed time window, obtain the real-time queue length for abnormal behavior analysis through security monitoring storage information, and represent the real-time queue length by function Queue(t) according to the time series;

[0069] It should be noted that professional system monitoring tools are used to monitor the performance of the security monitoring system in real time. Such tools can provide real-time data on performance indicators such as queue length and processing speed. Common monitoring tools include Zabbix, Nagios, etc.

[0070] The real-time queue length to be processed when abnormal behavior analysis is performed through security monitoring storage information is compared with the maximum queue length to be processed when abnormal behavior analysis is performed normally through security monitoring storage information, and the real-time high backlog rate index is calculated. The calculation expression is: In the formula, Queue 长度Indicates the high index of the queue length to be processed. [t1, t2] indicates the period when the real-time queue length to be processed is greater than the maximum queue length to be processed when abnormal behavior analysis is performed through security monitoring storage information. t1 <t2,Queue max Indicates the maximum length of the queue to be processed.

[0071] It can be seen from the calculation formula of the high index of the queue length to be processed that the larger the performance value of the high index of the queue length to be processed generated within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information, the greater the abnormal risk of missing or erroneous analysis of abnormal behaviors when abnormal behavior analysis is performed through security monitoring storage information. Conversely, the smaller the abnormal risk of missing or erroneous analysis of abnormal behaviors when abnormal behavior analysis is performed through security monitoring storage information.

[0072] When analyzing abnormal behavior through security monitoring storage information, the motion blur of the image during the analysis process is high, which may cause some potential risks to the abnormal behavior analysis process. The specific reasons are as follows:

[0073] Difficulty in object recognition: High motion blur can cause the edges and details of objects to be blurred, making it difficult to clearly identify objects in the image. Abnormal behavior analysis usually relies on accurate recognition of target objects. If the motion blur is high, the system may not be able to correctly identify moving objects, resulting in missed analysis.

[0074] Inaccurate trajectory tracking: High motion blur may cause the target’s trajectory to be blurred, and the position of the target to be tracked becomes inaccurate. Abnormal behavior analysis usually requires tracking the target’s trajectory. If the ambiguity makes the trajectory inaccurate, it may lead to misjudgment or omission of abnormal behavior.

[0075] Feature extraction distortion: Motion blur may lead to inaccurate feature extraction in the image, and the position and shape of the feature points become blurred. Abnormal behavior analysis usually relies on the extraction and analysis of target features. Distorted feature extraction may cause errors in the analysis results and increase the possibility of missed analysis.

[0076] Increased false alarms: Motion blur may make normal motion appear abnormally blurry in the image, increasing the possibility of false alarms. The system may mistakenly interpret normal motion as abnormal behavior, resulting in false alarms and affecting system reliability.

[0077] Difficulty in behavioral analysis: High motion blur can cause missing or inaccurate information about the location and shape of the target in the image. Behavioral analysis requires accurate analysis of the target's motion trajectory and posture. When the image blur is high, this information may be unreliable, reducing the accuracy of behavioral analysis.

[0078] Therefore, by using the image motion blur when analyzing abnormal behavior through security monitoring storage information, it is possible to intelligently perceive the hidden dangers of abnormal behavior that may be caused by high image motion blur, which may lead to missed analysis or incorrect analysis of abnormal behavior.

[0079] The logic of obtaining the image motion blur index is as follows:

[0080] Within a fixed time window, obtain image information of the security monitoring area when performing abnormal behavior analysis through security monitoring storage information;

[0081] It should be noted that the image information here is a single frame image, which covers the motion trajectory of the object;

[0082] Choose to calculate blur in horizontal or vertical direction;

[0083] It should be noted that since motion blur is caused by the movement of the object on the image plane, choosing an appropriate motion direction is the key to calculating the blur. Usually, the horizontal or vertical direction is chosen.

[0084] A blur kernel (also called point spread function PSF) is constructed to simulate the blur effect caused by motion. Motion blur is represented by a linear convolution operation, expressed as: blurred (x, y) = I original (x, y)*PSF(x, y), where * represents the convolution operation, I blurred (x, y) and I original (x, y) represents the blurred image and the original image respectively, PSF(x, y) represents the point spread function or blur kernel;

[0085] For horizontal or vertical motion blur, the calculation formula of the blur kernel is: PSF(x, y) = δ(xt·v, y), where δ(xt·v, y) represents the impulse function in the direction of motion, t is the blur time, and v is the motion speed;

[0086] The image motion blur index is calculated by the blur kernel width (length), and the calculation expression is: In the formula, Indicates the blur kernel width (length), Blur 图像 Represents the image motion blur index, PSF(x, y) m represents the image motion blur value of a single frame image obtained within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information, m represents the sequential number of the image motion blur value of a single frame image obtained within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information, m=1, 2, 3, 4, ..., M, where M is a positive integer.

[0087] It can be seen from the calculation formula of the image motion blur index that the larger the performance value of the image motion blur index generated within a fixed time window when performing abnormal behavior analysis through security monitoring storage information, the greater the abnormal risk of missing or erroneous analysis of abnormal behaviors when performing abnormal behavior analysis through security monitoring storage information. Conversely, the smaller the abnormal risk of missing or erroneous analysis of abnormal behaviors when performing abnormal behavior analysis through security monitoring storage information.

[0088] The high queue length index and image motion blur index generated in a fixed time window when abnormal behavior analysis is performed through security monitoring storage information are compared and analyzed with the preset high queue length index reference threshold and the preset image motion blur index reference threshold, respectively. The results of the comparison and analysis are as follows:

[0089] If the high index of the queue length to be processed is greater than or equal to the reference threshold of the high index of the queue length to be processed, or the image motion blur index is greater than or equal to the reference threshold of the image motion blur index, the corresponding time window will be marked as an abnormal hidden danger time window; if the high index of the queue length to be processed is less than the reference threshold of the high index of the queue length to be processed and the image motion blur index is less than the reference threshold of the image motion blur index, the corresponding time window will be marked as a normal time window.

[0090] For each time window, when there is an abnormal hidden danger in the process of abnormal behavior analysis through security monitoring storage information, the actual duration of the camera's abnormal behavior analysis of the time window with abnormal hidden danger is obtained. After the acquisition is completed, the expected upper limit of the normal analysis duration of each time window of the camera is calculated according to the real-time requirements of video monitoring;

[0091] In the context of abnormal behavior analysis, the actual duration refers to the time for each time window, from the start of camera operation to the discovery of abnormal risks, abnormal behavior analysis, and determination of the abnormality. This includes the time period from the monitoring system starting to record video or image data, analyzing it through processing algorithms, to the final determination that there are abnormal risks.

[0092] The details are as follows:

[0093] The time point when the camera or monitoring system starts recording video or image data, which is the starting point of abnormal behavior analysis;

[0094] A key step in the abnormal behavior analysis process is to detect anomalies, which may involve motion detection, object recognition, matching of abnormal behavior models, etc. The actual duration includes the time from the start of operation to the first detection of anomalies;

[0095] For the discovered anomalies, the system may conduct a more in-depth analysis, such as feature extraction of abnormal behaviors, tracking of behavior trajectories, etc. The actual duration takes into account the entire abnormal behavior analysis process;

[0096] Ultimately, the system needs to determine the exact period when the abnormal behavior occurred, which may require a more detailed analysis of the context of the abnormal behavior, including the entire time from the start of the run to the determination of the abnormal period.

[0097] The expected upper limit of duration refers to the maximum allowed duration from the start of abnormal behavior analysis of security monitoring storage information to the final completion of the analysis within each time window. The setting of the expected upper limit of duration is based on business needs, system performance requirements and expectations for real-time performance.

[0098] The details are as follows:

[0099] First, we need to clearly define the concept of time window, which is the time period for each abnormal behavior analysis. This can be a fixed time interval, such as every ten minutes or every hour.

[0100] From the beginning of each time window, the system starts to analyze abnormal behavior, which may involve steps such as acquisition and preprocessing of image or video data.

[0101] Within the time window, the system executes the abnormal behavior analysis algorithm to identify and detect possible abnormal behaviors. This includes operations such as object recognition, behavior trajectory analysis, and feature extraction.

[0102] The expected duration upper limit is the maximum duration allowed for the entire abnormal behavior analysis process. This value is usually set based on business needs, system performance requirements, and expectations for real-time performance.

[0103] The upper limit of the expected duration is set to meet the system's real-time requirements. In some scenarios, it is necessary to respond to exceptions in a short time to prevent potential risks and security issues.

[0104] Compare and analyze the actual duration with the upper limit of the expected duration, and determine whether to continue to perform abnormal behavior analysis on the time window with abnormal hidden dangers through security monitoring storage information based on different comparison and analysis results, so as to complete the intelligent repair of occasional abnormal hidden dangers in abnormal behavior analysis;

[0105] The actual duration is compared with the upper limit of the expected duration. Based on different comparison and analysis results, it is determined whether the abnormal behavior analysis can be continued for the time window with abnormal hidden dangers through security monitoring storage information. The process is as follows:

[0106] If the actual duration is greater than or equal to the upper limit of the expected duration, the corresponding duration window will be marked as a non-continuous hidden danger window, and an early warning will be issued for the non-continuous hidden danger window. When the actual duration is greater than the upper limit of the expected duration, it indicates that the real-time requirement is not met when performing abnormal behavior analysis through security monitoring storage information. When the actual duration is equal to the upper limit of the expected duration, it is impossible to continue to perform abnormal behavior analysis on the time window with abnormal hidden dangers through security monitoring storage information;

[0107] If the actual duration is less than the upper limit of the expected duration, the corresponding duration window will be marked as a repairable hidden danger window, and the repairable hidden danger window will be re-analyzed for abnormal behavior of the time window with abnormal hidden dangers through the security monitoring storage information, and the duration of the re-analysis of abnormal behavior will be counted. The duration of the re-analysis of abnormal behavior plus the actual duration will be compared with the upper limit of the expected duration. The results of the comparison analysis are as follows:

[0108] If the time for re-analyzing abnormal behavior plus the actual time is greater than or equal to the upper limit of the expected time, the corresponding time window is re-calibrated as a non-continuing hidden danger window, and an early warning prompt is given for the non-continuing hidden danger window. If the time for re-analyzing abnormal behavior plus the actual time is less than the upper limit of the expected time, but there is still a high index of the queue length to be processed that is greater than or equal to the reference threshold of the queue length to be processed or an image motion blur index that is greater than or equal to the reference threshold of the image motion blur index within the time window, then the above-mentioned operation of re-analyzing abnormal behavior for the time window with abnormal hidden dangers through security monitoring storage information is continued;

[0109] If the time for re-analyzing abnormal behavior plus the actual time is less than the upper limit of the expected time, but the high index of the queue length to be processed is greater than or equal to the reference threshold of the high index of the queue length to be processed or the image motion blur index is greater than or equal to the reference threshold of the image motion blur index within the time window, abnormal behavior analysis is performed on the next time window through the security monitoring storage information. When the time for re-analyzing abnormal behavior plus the actual time is less than the upper limit of the expected time, but the high index of the queue length to be processed is less than the reference threshold of the high index of the queue length to be processed or the image motion blur index is less than the reference threshold of the image motion blur index within the time window, it indicates that the abnormal repair of the time window with abnormal hidden dangers is successful, which indicates that the time window with abnormal hidden dangers may be an occasional abnormal hidden danger. In this way, the occasional abnormal hidden dangers in the abnormal behavior analysis can be intelligently repaired.

[0110] This method monitors the abnormal behavior analysis process in each time window in real time. The system can perceive the abnormal hidden dangers in the analysis process. By setting reference thresholds for the length of the queue to be processed or the image motion blur, the system can intelligently determine whether there are abnormal hidden dangers and then take corresponding measures. For the abnormalities found, the system adopts intelligent repair strategies, which improves the real-time nature of abnormal behavior analysis and reduces potential security threats.

[0111] The present invention divides the security monitoring storage information into time windows and performs anomaly analysis in each window, so that the system can perceive and handle anomalies in a more detailed manner. This windowed analysis method helps to isolate anomalies in time, so that the system can more accurately locate the moment when the anomaly occurs, thereby taking targeted repair measures, thereby improving the accuracy and efficiency of the system;

[0112] The present invention compares the actual duration with the upper limit of the expected duration, so that the system can dynamically adjust the abnormal behavior analysis duration of the time window where abnormal hidden dangers exist. According to the comparison result, the system determines whether the analysis can continue, thereby realizing intelligent management of the analysis duration, which helps to respond flexibly in different situations and ensure that the system can effectively perform abnormal behavior analysis while meeting real-time requirements.

[0113] The present invention provides Figure 2 The security monitoring storage information intelligent analysis and management system shown includes a security monitoring information acquisition module, an abnormality perception module, a duration determination module, and a judgment and repair module;

[0114] The security monitoring information acquisition module collects the images and video data transmitted by the monitoring camera in real time, and stores the collected images after preprocessing;

[0115] The abnormal perception module divides the security monitoring storage information into time windows and makes the time in each time window equal. It analyzes the process of abnormal behavior analysis through the security monitoring storage information in each time window and intelligently perceives the abnormal hidden dangers in the analysis process.

[0116] The duration determination module, for each time window, when there is an abnormal hidden danger in the process of abnormal behavior analysis through security monitoring storage information, obtains the actual duration of the camera's abnormal behavior analysis of the time window with abnormal hidden dangers. After the acquisition is completed, the expected upper limit of the normal analysis duration of each time window of the camera is calculated according to the real-time requirements of video monitoring;

[0117] The judgment and repair module compares and analyzes the actual duration with the upper limit of the expected duration. Based on different comparison and analysis results, it is determined whether it is possible to continue to perform abnormal behavior analysis on the time window with abnormal hidden dangers through security monitoring storage information, thereby completing intelligent repair of occasional abnormal hidden dangers in abnormal behavior analysis;

[0118] An intelligent analysis and management method for security monitoring storage information provided by an embodiment of the present invention is implemented through the above-mentioned intelligent analysis and management system for security monitoring storage information. The specific methods and processes of an intelligent analysis and management system for security monitoring storage information are detailed in the embodiment of the above-mentioned intelligent analysis and management method for security monitoring storage information, which will not be repeated here.

[0119] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent analysis and management of security monitoring storage information, characterized in that: The following steps are involved: Reasonably arrange surveillance cameras to ensure full coverage of key areas that require security monitoring, collect images and video data transmitted by surveillance cameras in real time, and pre-process and store the collected images; The security monitoring storage information is divided into time windows, and the time in each time window is made equal. The process of abnormal behavior analysis through the security monitoring storage information in each time window is analyzed, and the abnormal hidden dangers of the abnormal behavior analysis process are intelligently perceived; For each time window, when there is an abnormal hidden danger in the process of abnormal behavior analysis through security monitoring storage information, the actual duration of the camera's abnormal behavior analysis of the time window with abnormal hidden danger is obtained. After the acquisition is completed, the expected upper limit of the normal analysis time of the camera for each time window is obtained according to the real-time requirements of video monitoring; Compare and analyze the actual duration with the upper limit of the expected duration, and determine whether to continue to perform abnormal behavior analysis on the time window with abnormal hidden dangers through security monitoring storage information based on different comparison and analysis results, so as to complete the intelligent repair of occasional abnormal hidden dangers in abnormal behavior analysis; Among them, the process of analyzing abnormal behavior through security monitoring storage information in each time window is analyzed, and the process of intelligently perceiving abnormal hidden dangers in the abnormal behavior analysis process is as follows: Obtain the real-time queue length and image information for abnormal behavior analysis through security monitoring storage information in each time window, and generate a high queue length index and image motion blur index based on the real-time queue length and image information; The high index of the queue length to be processed and the image motion blur index are compared and analyzed with the preset reference threshold of the high index of the queue length to be processed and the preset reference threshold of the image motion blur index, respectively. The results of the comparison and analysis are as follows: If the high index of the queue length to be processed is greater than or equal to the reference threshold of the high index of the queue length to be processed or the image motion blur index is greater than or equal to the reference threshold of the image motion blur index, the corresponding time window is marked as an abnormal hidden danger time window; if the high index of the queue length to be processed is less than the reference threshold of the high index of the queue length to be processed and the image motion blur index is less than the reference threshold of the image motion blur index, the corresponding time window is marked as a normal time window; Among them, the actual duration and the upper limit of the expected duration are compared and analyzed. According to different comparison and analysis results, it is determined whether it is possible to continue to analyze abnormal behavior in the time window with abnormal hidden dangers through security monitoring storage information as follows: If the actual duration is greater than or equal to the upper limit of the expected duration, the corresponding duration window will be marked as a non-continuing hidden danger window, and an early warning prompt will be issued for the non-continuing hidden danger window; If the actual duration is less than the upper limit of the expected duration, the corresponding time window will be marked as a repairable hidden danger window, and the repairable hidden danger window will be re-analyzed for abnormal behavior of the time window with abnormal hidden dangers through the security monitoring storage information, and the duration of the re-analysis of abnormal behavior will be counted; The time of re-analyzing abnormal behavior plus the actual time are compared with the upper limit of the expected time. The results of the comparison analysis are as follows: If the time of the abnormal behavior re-analysis plus the actual time is greater than or equal to the upper limit of the expected time, the corresponding time window is re-calibrated as a non-continuing hidden danger window, and an early warning prompt is issued for the non-continuing hidden danger window; If the time duration of the re-analysis of abnormal behavior plus the actual time duration is less than the upper limit of the expected time duration, but within the time window, there is still a high index of the queue length to be processed that is greater than or equal to the reference threshold of the high index of the queue length to be processed or an image motion blur index that is greater than or equal to the reference threshold of the image motion blur index, then continue to perform the above-mentioned operation of re-analyzing abnormal behavior for the time window with abnormal hidden dangers through the security monitoring storage information; If the duration of the re-analysis of abnormal behavior plus the actual duration is less than the upper limit of the expected duration, but the high index of the queue length to be processed is less than the reference threshold of the high index of the queue length to be processed or the image motion blur index is less than the reference threshold of the image motion blur index within the time window, abnormal behavior analysis is performed on the next time window through the security monitoring storage information.

2. A security monitoring storage information intelligent analysis and management method according to claim 1, characterized in that: The logic for obtaining the high index of the queue length to be processed is as follows: In a fixed time window, obtain the real-time queue length for abnormal behavior analysis through security monitoring storage information, and use the function to convert the real-time queue length into time series. To express; The real-time queue length to be processed when abnormal behavior analysis is performed through security monitoring storage information is compared with the maximum queue length to be processed when abnormal behavior analysis is performed normally through security monitoring storage information, and the high queue length index to be processed is calculated. The calculation expression is: , where Indicates that the length of the queue to be processed is too high. Indicates the period when the length of the real-time queue to be processed is greater than the maximum queue length to be processed when analyzing abnormal behavior through security monitoring storage information. , Indicates the maximum length of the queue to be processed.

3. The method for intelligent analysis and management of security monitoring storage information according to claim 1, characterized in that: The logic of obtaining the image motion blur index is as follows: Within a fixed time window, obtain image information of the security monitoring area when performing abnormal behavior analysis through security monitoring storage information; Constructing the point spread function To simulate the blur effect caused by motion, motion blur is expressed as: ,in, represents the convolution operation, and Represent the blurred image and the original image respectively; Calculate the image motion blur index, the calculation expression is: , where represents the image motion blur index, It represents the image motion blur value of a single frame image obtained within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information. m Indicates the sequential number of the image motion blur value of a single frame image obtained within a fixed time window when abnormal behavior analysis is performed through security monitoring storage information. m =1, 2, 3, 4, ..., M , M Is a positive integer.

4. A security monitoring storage information intelligent analysis and management system, used to implement a security monitoring storage information intelligent analysis and management method as described in any one of claims 1 to 3, characterized in that: It includes security monitoring information acquisition module, abnormality perception module, duration determination module and judgment and repair module; The security monitoring information acquisition module collects the image and video data transmitted by the monitoring camera in real time, and stores the collected images after preprocessing; The abnormal perception module divides the security monitoring storage information into time windows and makes the time in each time window equal. It analyzes the process of abnormal behavior analysis through the security monitoring storage information in each time window and intelligently perceives the abnormal hidden dangers in the abnormal behavior analysis process. The duration determination module obtains the actual duration of the abnormal behavior analysis of the time window with the abnormal potential when there is an abnormal hidden danger in the process of abnormal behavior analysis through the security monitoring storage information for each time window. After the acquisition is completed, the expected upper limit of the duration of the normal analysis of each time window by the camera is obtained according to the real-time requirements of the video monitoring; The judgment and repair module compares and analyzes the actual duration and the upper limit of the expected duration. Based on different comparison and analysis results, it determines whether it is possible to continue to perform abnormal behavior analysis on the time window where abnormal risks exist through security monitoring storage information, thereby completing intelligent repair of occasional abnormal risks in the abnormal behavior analysis.

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