Intelligent security identification collaboration method and system based on AI monitoring

Abstract

The invention discloses an intelligent security identification cooperation method and system based on AI monitoring. The method comprises the following steps: S1, initializing various devices in a security system; s2, collecting and calculating real-time performance indexes of each device based on a peacock courtship algorithm in which a memory mechanism is introduced; s3, establishing a multi-level competition cooperation mechanism based on a memory mechanism; s4, when an abnormal event occurs in the low-level equipment, a complex event is transmitted to the high-level equipment for processing through competition; s5, dynamically adjusting the resource allocation strategy of each device according to the weighted performance index, and adjusting the resource allocation proportion through the weighted performance index by the security system according to the real-time performance and historical performance of the device; and S6, continuously monitoring the real-time performance of each device, and regularly updating the memory score of each device. According to the invention, intelligentization, self-adaptability and long-term optimization of resource allocation are realized, and the response speed, the resource utilization rate and the overall operation efficiency of an intelligent security system in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart security technology, and in particular to a smart security identification collaboration method and system based on AI monitoring. Background Art

[0002] In the existing technology, smart security systems are widely used in various scenarios, including public safety, traffic monitoring and enterprise security. They mainly rely on the collaborative work of cameras, sensors and central processing systems to complete the detection and response to abnormal events through real-time monitoring and data processing. However, existing smart security systems usually rely on preset rules and fixed resource allocation strategies to allocate equipment resources and schedule tasks, lacking flexibility and adaptability. In complex monitoring scenarios, the system's response speed and resource utilization are easily affected by fluctuations in equipment performance, resulting in unstable monitoring effects.

[0003] Existing technologies usually adopt a single-level resource allocation mechanism, and each device processes events according to a fixed priority. The traditional method cannot be dynamically adjusted according to different device performance and scenario requirements. Devices often have problems with uneven resource allocation when processing tasks. Inefficient devices may occupy a large amount of resources, while efficient devices cannot obtain sufficient resource support in a timely manner, which leads to low overall system operation efficiency. In addition, most existing abnormal event detection technologies are independent of the collaboration between devices. Devices cannot transfer tasks and share resources based on each other's performance and workload, which easily leads to resource waste and response delays when processing complex events.

[0004] On the other hand, when existing smart security systems are running for a long time, the historical performance of the equipment cannot be effectively recorded and utilized. The system cannot optimize resource allocation and task scheduling based on the long-term performance of the equipment. The system cannot effectively respond to dynamic changes in equipment performance. When certain equipment exhibits long-term stability or high efficiency, existing technologies do not give these devices more resources and task priority, reducing the system's adaptability and overall efficiency.

[0005] To sum up, the existing technology has the following main shortcomings: first, resource allocation and task scheduling lack flexibility, and it is difficult to make dynamic adjustments based on the real-time performance and historical performance of the device; second, the coordination ability between devices is poor, and effective multi-level competition and task transfer cannot be achieved, resulting in inefficient processing of complex events; finally, the system fails to make full use of the historical performance of the device and cannot optimize resource utilization and task allocation strategies under long-term operation. Summary of the invention

[0006] One purpose of the present invention is to propose a smart security identification collaborative method and system based on AI monitoring. The present invention realizes the intelligence, adaptability and long-term optimization of resource allocation, and improves the response speed, resource utilization and overall operation efficiency of the smart security system in a complex environment.

[0007] According to an embodiment of the present invention, a smart security identification and collaboration method based on AI monitoring includes the following steps: S1. Initialize various devices in the security system and set the initial resource allocation strategy and task assignment rules for each device; S2. Based on the peacock courtship algorithm with the memory mechanism introduced, the real-time performance indicators of each device are collected and calculated, and the weighted performance indicators of each device are calculated in combination with the memory score of the device; S3. Establish a multi-level competition and cooperation mechanism based on the memory mechanism. The low-level devices pre-process simple events through edge computing and compete according to their weighted performance indicators and historical performance. The competition results affect whether the low-level devices are qualified to handle complex tasks or whether to pass the tasks to high-level devices. The high-level devices decide whether to accept the tasks passed by the low-level devices according to the weighted performance indicators and historical performance of the low-level devices, and adjust the priority of the complex tasks based on the weighted performance. S4. When an abnormal event occurs in a low-level device, the complex event is transmitted to the high-level device for processing through competition. The high-level device determines the priority of each device according to the weighted performance index and memory score of the low-level device, and gives priority to the devices with excellent historical performance and weighted performance; S5. Dynamically adjust the resource allocation strategy of each device based on weighted performance indicators. The security system adjusts the resource allocation ratio based on the real-time and historical performance of the device through weighted performance indicators. When resources are limited, more resources are allocated to devices with higher weighted performance first, thereby optimizing the overall operating efficiency of the device. S6. Continuously monitor the real-time performance of each device, regularly update its memory score, and dynamically adjust the resource allocation strategy and task assignment rules of the security system based on weighted performance indicators, so that devices with long-term excellent performance continue to receive priority in subsequent task assignments.

[0008] Optionally, S1 includes the following sub-steps: S11, Initialize the surveillance camera ,sensor , edge computing devices and central processing system , where i, j, k, and l are the indexes of each device respectively; S12. Set the initial resource allocation strategy for surveillance cameras, sensors, edge computing devices, and central processing systems: ; in, The resource allocation ratio of the i-th camera, the j-th sensor, the k-th edge computing device, and the l-th central processing system, , and They are the processing capacity, response speed and abnormal event detection accuracy of each device. , , is the weight coefficient of the corresponding device, which is used to weight the device according to its importance in different scenarios. The cumulative term in the denominator represents the overall performance of all devices. S13. Set the task assignment rules for the device. The task assignment rules are based on the task load and task processing priority of the device. The monitoring cameras, sensors, edge computing devices and central processing systems are assigned tasks according to the task complexity and device performance: ; in, is the task priority of the device, Represents the complexity of the task, which depends on the task load of the current device and the processing power of the device , It is used to control the dynamics of task dispatching, so that complex tasks can be dispatched to devices with higher performance first when resources are limited; S14, establish an initial communication channel between devices to enable collaborative work between surveillance cameras, sensors, edge computing devices and the central processing system, and perform real-time updates and adjustments to initial resource allocation strategies and task dispatching rules through the communication channel.

[0009] Optionally, S2 includes the following sub-steps: S21. Collect the actual performance indicators of each security device, including processing capacity , Response speed and abnormal event detection accuracy ; S22. Initialize a memory score for each device to record the device's historical performance The initial memory score is set to the preset benchmark value of the security system. As the security system runs longer, the processing capacity of the device , Response speed and abnormal event detection accuracy Historical performance is gradually accumulated into memory scores: ; in, is the memory score of the previous iteration, and is the weight coefficient; S23. Calculate the weighted performance index of each device , including the real-time performance of the device combined with its memory score : ; in, , , , are the corresponding weight coefficients, which respectively control the impact of processing capacity, response speed, abnormal event detection accuracy and memory score on the comprehensive performance evaluation.

[0010] Optionally, S3 includes the following sub-steps: S31. Establish a multi-level competitive cooperation mechanism based on memory mechanism. Low-level devices include surveillance cameras, sensors and edge computing devices. Low-level devices pre-process simple events through edge computing. Low-level devices perform weighted performance indicators based on weighted performance indicators. and historical performance memory scores Participate in competition; S32. During the competition, low-level devices demonstrate their weighted performance indicators The lower-level device with the higher competition result has the opportunity to obtain the processing rights of complex tasks: ; in, The result of competition for low-level equipment; S33. According to the competition results Determine whether the low-level device is qualified to handle complex tasks. If the competition result is below the threshold set by the security system, the task will be passed to the high-level device; S34, high-level devices based on the tasks delivered by low-level devices and the weighted performance indicators of low-level devices And memory score Decide whether to accept the task and adjust the task priority of complex tasks : ; in, is the task complexity weight coefficient, Based on the time difference A function that controls the impact of the device's historical performance on the current task allocation. Used to introduce nonlinear cumulative effects of memory mechanisms, It is the task matching function of the device, which is used to measure whether the low-level device is suitable for processing the current task. It is the abnormal event detection accuracy of low-level devices, which is used to distinguish the ability of devices to handle complex tasks. is the response speed of the low-level device, through an exponential decay function Adjust task priorities; S35. After the competition is over, the security system updates the resource allocation strategy for low-level and high-level devices so that devices with outstanding performance can obtain priority resources and task processing permissions.

[0011] Optionally, S4 includes the following sub-steps: S41. When a low-level device detects an abnormal event, it transmits the complex event to the high-level device for processing through competition. The basis of competition is the weighted performance index and memory score of the low-level device. The low-level device calculates its competition result through the following formula: ; in, It's the time difference A function that reflects the impact of the device's historical performance on current competition; S42: The low-level device transmits the complex event to the high-level device with the highest competition result for processing. The high-level device determines the priority according to the weighted performance index and memory score of the low-level device, and gives priority to processing the events transmitted by the low-level device with excellent historical performance and higher weighted performance index; S43, Advanced equipment calculation task priority of complex events : ; in, is the task priority adjustment coefficient, Used to introduce nonlinear effects of memory scores, so that low-level devices that show stable history have higher priority in event delivery; S44, Advanced equipment based on calculated task priorities Determine the order in which events are processed, prioritize complex events transmitted from lower-level devices with better performance, and adjust event priorities based on the urgency of complex events; S45. After the processing is completed, the high-level device feeds back the processing results to the low-level device and updates the memory score of the low-level device. .

[0012] Optionally, S5 includes the following sub-steps: S51. The security system dynamically adjusts the resource allocation strategy of each device according to the weighted performance index of the device. The security system collects the performance data of the device in real time and the historical performance memory score of the device to calculate the weighted performance index of each device. S52. Based on the weighted performance index of the equipment Determine the resource allocation ratio for each device : ; in, To introduce nonlinear cumulative effects, adjust the growth rate of equipment performance, Reflects the impact of equipment processing capacity and detection accuracy on resource allocation ratio. and It will accelerate the growth rate of resource allocation, so that the equipment can obtain the corresponding resources when handling urgent or complex tasks. Used to standardize the resource allocation ratio of each device so that the total resource allocation ratio of all devices is 1; S53, security system based on the calculated resource allocation ratio When resources are limited, resources are allocated preferentially to devices with higher weighted performance indicators; S54. During the resource allocation process, the security system monitors the performance indicators and memory scores of the equipment in real time. When the real-time performance, response speed and detection accuracy of the equipment change, the security system dynamically updates the resource allocation ratio based on the weighted performance indicators; S55. Weighted performance indicators through regular evaluation of equipment and historical performance memory scores , the security system optimizes the resource allocation strategy so that devices with long-term excellent performance continue to receive priority resource allocation, while adjusting the resource allocation of inefficient devices in real time.

[0013] Optionally, the task complexity is a comprehensive performance score of the device calculated based on the weighted performance index and historical performance memory score of the device. , the comprehensive performance score is used to determine whether the device performs well: ; in, and are weight coefficients, which adjust the weighted performance index of the device and the impact of the historical performance memory score. Represents the overall performance score of the device over a period of time; Scoring based on task complexity and overall equipment performance , the security system introduces segmented thresholds: when When the device is judged to have excellent performance, it will be given priority to obtain the most complex tasks and resources; when When , the device obtains tasks and resources of medium complexity; when When, the device obtains low-complexity tasks and resources; is the threshold of the i-th complexity level.

[0014] An intelligent security identification and coordination system based on AI monitoring, including the following modules: The device initialization module is used to initialize various devices in the security system, including surveillance cameras, sensors, edge computing devices, and central processing systems, and to set the initial resource allocation strategy and task assignment rules for each device; Performance monitoring module, which is used to collect the performance indicators of the equipment in real time, including the equipment's processing capacity, response speed, and abnormal event detection accuracy, and record the equipment's historical performance memory score. The performance monitoring module combines real-time data and historical performance data to perform comprehensive calculations to generate weighted performance indicators; The task scheduling module uses the peacock courtship algorithm to conduct multi-level competition based on the weighted performance indicators and historical performance of the equipment to determine whether the low-level equipment is qualified to handle complex tasks, and transfers the complex tasks to high-level equipment for processing according to the competition results. The low-level equipment includes surveillance cameras, sensors, and edge computing devices, and the high-level equipment includes the central processing system; The resource allocation module is used to dynamically adjust the resource allocation strategy of each device based on the weighted performance indicators and historical performance memory scores of the device. The resource allocation module prioritizes more resources for devices with excellent performance when resources are limited, based on the performance of the device; Abnormal event processing module: when the low-level device detects an abnormal event, it transmits the complex event to the high-level device through competition. The high-level device adjusts the task priority according to the weighted performance index and memory score of the low-level device and handles the abnormal event; The memory update module is used to continuously monitor and update the device's historical performance memory score and dynamically adjust it according to the device's real-time performance. The memory update module optimizes the system's resource allocation and task dispatching strategies by recording the device's task completion status.

[0015] The beneficial effects of the present invention are: (1) The present invention introduces a memory mechanism that enables the system to accumulate memory scores based on the historical performance of the device. During the task processing of the device, not only its real-time performance is considered, but also the long-term stability and historical efficiency of the device are comprehensively evaluated. By combining weighted performance indicators and memory scores, the system can prioritize allocating more resources to devices with excellent historical performance, ensuring that the system can perform task scheduling more stably and efficiently in long-term operation. Compared with the traditional resource allocation method that relies on fixed rules, the adaptive mechanism of the present invention significantly improves the rationality of resource allocation and optimizes the overall operation efficiency of the device.

[0016] (2) The present invention realizes effective collaboration between low-level devices and high-level devices through a multi-level competition mechanism. Low-level devices participate in the competition based on their weighted performance indicators and decide whether to process complex events or pass them to high-level devices for processing. At the same time, high-level devices decide whether to accept tasks and adjust task priorities based on the weighted performance of low-level devices, thereby effectively avoiding waste of resources and ensuring that the system can respond quickly and schedule reasonably when faced with complex events, thereby improving the system's efficiency in handling abnormal events. In multi-task scenarios, the present invention optimizes task allocation between devices through competition and collaboration, thereby reducing response delays caused by unreasonable task allocation.

[0017] (3) The performance monitoring and memory update module of the present invention can continuously monitor the real-time performance and historical performance of the device, and by dynamically adjusting the resource allocation ratio and task priority, ensure that the system can make adaptive adjustments according to the performance changes of the device. In the case of fluctuations in device performance or changes in task complexity, the system dynamically adjusts the resource allocation strategy and task scheduling by updating the weighted performance indicators and memory scores in real time, so that the system can still maintain efficient operation under limited resources. Compared with the prior art, the dynamic adjustment mechanism of the present invention significantly enhances the flexibility of the system and its ability to cope with complex environments, ensuring the stability and security of the long-term operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a smart security identification collaborative method and system based on AI monitoring proposed by the present invention; Figure 2 This is a resource allocation and task scheduling flow chart based on the peacock courtship algorithm in a smart security identification collaborative method based on AI monitoring and a system proposed by the present invention; Figure 3 This is a schematic diagram of task allocation of a smart security identification collaboration method based on AI monitoring and a multi-level competitive collaboration mechanism in the system proposed by the present invention. DETAILED DESCRIPTION

[0019] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0020] refer to Figure 1-Figure 3 , a smart security identification and coordination method based on AI monitoring, comprising the following steps: S1. Initialize various devices in the security system and set the initial resource allocation strategy and task assignment rules for each device; S2. Based on the peacock courtship algorithm with the memory mechanism introduced, the real-time performance indicators of each device are collected and calculated, and the weighted performance indicators of each device are calculated in combination with the memory score of the device; S3. Establish a multi-level competition and cooperation mechanism based on the memory mechanism. The low-level devices pre-process simple events through edge computing and compete according to their weighted performance indicators and historical performance. The competition results affect whether the low-level devices are qualified to handle complex tasks or whether to pass the tasks to high-level devices. The high-level devices decide whether to accept the tasks passed by the low-level devices according to the weighted performance indicators and historical performance of the low-level devices, and adjust the priority of the complex tasks based on the weighted performance. S4. When an abnormal event occurs in a low-level device, the complex event is transmitted to the high-level device for processing through competition. The high-level device determines the priority of each device according to the weighted performance index and memory score of the low-level device, and gives priority to the devices with excellent historical performance and weighted performance; S5. Dynamically adjust the resource allocation strategy of each device based on weighted performance indicators. The security system adjusts the resource allocation ratio based on the real-time and historical performance of the device through weighted performance indicators. When resources are limited, more resources are allocated to devices with higher weighted performance first, thereby optimizing the overall operating efficiency of the device. S6. Continuously monitor the real-time performance of each device, regularly update its memory score, and dynamically adjust the resource allocation strategy and task assignment rules of the security system based on weighted performance indicators, so that devices with long-term excellent performance continue to receive priority in subsequent task assignments.

[0021] In this implementation, S1 includes the following sub-steps: S11, Initialize the surveillance camera ,sensor , edge computing devices and central processing system , where i, j, k, and l are the indexes of each device respectively; S12. Set the initial resource allocation strategy for surveillance cameras, sensors, edge computing devices, and central processing systems: ; in, The resource allocation ratio of the i-th camera, the j-th sensor, the k-th edge computing device, and the l-th central processing system, , and They are the processing capacity, response speed and abnormal event detection accuracy of each device. , , is the weight coefficient of the corresponding device, which is used to weight the device according to its importance in different scenarios. The cumulative term in the denominator represents the overall performance of all devices. S13. Set the task assignment rules for the device. The task assignment rules are based on the task load and task processing priority of the device. The monitoring cameras, sensors, edge computing devices and central processing systems are assigned tasks according to the task complexity and device performance: ; in, is the task priority of the device, Represents the complexity of the task, which depends on the task load of the current device and the processing power of the device , It is used to control the dynamics of task dispatching, so that complex tasks can be dispatched to devices with higher performance first when resources are limited; S14, establish an initial communication channel between devices to enable collaborative work between surveillance cameras, sensors, edge computing devices and the central processing system, and perform real-time updates and adjustments to initial resource allocation strategies and task dispatching rules through the communication channel.

[0022] In this implementation, S2 includes the following sub-steps: S21. Collect the actual performance indicators of each security device, including processing capacity , Response speed and abnormal event detection accuracy ; S22. Initialize a memory score for each device to record the device's historical performance The initial memory score is set to the preset benchmark value of the security system. As the security system runs longer, the processing capacity of the device , Response speed and abnormal event detection accuracy Historical performance is gradually accumulated into memory scores: ; in, is the memory score of the previous iteration, and is the weight coefficient; S23. Calculate the weighted performance index of each device , including the real-time performance of the device combined with its memory score : ; in, , , , are the corresponding weight coefficients, which respectively control the impact of processing capacity, response speed, abnormal event detection accuracy and memory score on the comprehensive performance evaluation.

[0023] In this implementation, S3 includes the following sub-steps: S31. Establish a multi-level competitive cooperation mechanism based on memory mechanism. Low-level devices include surveillance cameras, sensors and edge computing devices. Low-level devices pre-process simple events through edge computing. Low-level devices perform weighted performance indicators based on weighted performance indicators. and historical performance memory scores Participate in competition; S32. During the competition, low-level devices demonstrate their weighted performance indicators The lower-level device with the higher competition result has the opportunity to obtain the processing rights of complex tasks: ; in, The result of competition for low-level equipment; S33. According to the competition results Determine whether the low-level device is qualified to handle complex tasks. If the competition result is below the threshold set by the security system, the task will be passed to the high-level device; S34, high-level devices based on the tasks delivered by low-level devices and the weighted performance indicators of low-level devices And memory score Decide whether to accept the task and adjust the task priority of complex tasks : ; in, is the task complexity weight coefficient, Based on the time difference A function that controls the impact of the device's historical performance on the current task allocation. Used to introduce nonlinear cumulative effects of memory mechanisms, It is the task matching function of the device, which is used to measure whether the low-level device is suitable for processing the current task. It is the abnormal event detection accuracy of low-level devices, which is used to distinguish the ability of devices to handle complex tasks. is the response speed of the low-level device, through an exponential decay function Adjust task priorities; S35. After the competition is over, the security system updates the resource allocation strategy for low-level and high-level devices so that devices with outstanding performance can obtain priority resources and task processing permissions.

[0024] In this implementation, S4 includes the following sub-steps: S41. When a low-level device detects an abnormal event, it transmits the complex event to the high-level device for processing through competition. The basis of competition is the weighted performance index and memory score of the low-level device. The low-level device calculates its competition result through the following formula: ; in, It's the time difference A function that reflects the impact of the device's historical performance on current competition; S42: The low-level device transmits the complex event to the high-level device with the highest competition result for processing. The high-level device determines the priority according to the weighted performance index and memory score of the low-level device, and gives priority to processing the events transmitted by the low-level device with excellent historical performance and higher weighted performance index; S43, Advanced equipment calculation task priority of complex events : ; in, is the task priority adjustment coefficient, Used to introduce nonlinear effects of memory scores, so that low-level devices that show stable history have higher priority in event delivery; S44, Advanced equipment based on calculated task priorities Determine the order in which events are processed, prioritize complex events transmitted from lower-level devices with better performance, and adjust event priorities based on the urgency of complex events; S45. After the processing is completed, the high-level device feeds back the processing results to the low-level device and updates the memory score of the low-level device. .

[0025] In this implementation, S5 includes the following sub-steps: S51. The security system dynamically adjusts the resource allocation strategy of each device according to the weighted performance index of the device. The security system collects the performance data of the device in real time and the historical performance memory score of the device to calculate the weighted performance index of each device. S52. Based on the weighted performance index of the equipment Determine the resource allocation ratio for each device : ; in, To introduce nonlinear cumulative effects, adjust the growth rate of equipment performance, Reflects the impact of equipment processing capacity and detection accuracy on resource allocation ratio. and It will accelerate the growth rate of resource allocation, so that the equipment can obtain the corresponding resources when handling urgent or complex tasks. Used to standardize the resource allocation ratio of each device so that the total resource allocation ratio of all devices is 1; S53, security system based on the calculated resource allocation ratio When resources are limited, resources are allocated preferentially to devices with higher weighted performance indicators; S54. During the resource allocation process, the security system monitors the performance indicators and memory scores of the equipment in real time. When the real-time performance, response speed and detection accuracy of the equipment change, the security system dynamically updates the resource allocation ratio based on the weighted performance indicators; S55. Weighted performance indicators through regular evaluation of equipment and historical performance memory scores , the security system optimizes the resource allocation strategy so that devices with long-term excellent performance continue to receive priority resource allocation, while adjusting the resource allocation of inefficient devices in real time.

[0026] In this embodiment, the task complexity is the comprehensive performance score of the device calculated based on the weighted performance index and historical performance memory score of the device. , the comprehensive performance score is used to determine whether the device performs well: ; in, and are weight coefficients, which adjust the weighted performance index of the device and the impact of the historical performance memory score. Represents the overall performance score of the device over a period of time; Scoring based on task complexity and overall equipment performance , the security system introduces segmented thresholds: when When the device is judged to have excellent performance, it will be given priority to obtain the most complex tasks and resources; when When , the device obtains tasks and resources of medium complexity; when When, the device obtains low-complexity tasks and resources; is the threshold of the i-th complexity level.

[0027] An intelligent security identification and coordination system based on AI monitoring, including the following modules: The device initialization module is used to initialize various devices in the security system, including surveillance cameras, sensors, edge computing devices, and central processing systems, and to set the initial resource allocation strategy and task assignment rules for each device; Performance monitoring module, which is used to collect the performance indicators of the equipment in real time, including the equipment's processing capacity, response speed, and abnormal event detection accuracy, and record the equipment's historical performance memory score. The performance monitoring module combines real-time data and historical performance data to perform comprehensive calculations to generate weighted performance indicators; The task scheduling module uses the peacock courtship algorithm to conduct multi-level competition based on the weighted performance indicators and historical performance of the equipment to determine whether the low-level equipment is qualified to handle complex tasks, and transfers the complex tasks to high-level equipment for processing according to the competition results. The low-level equipment includes surveillance cameras, sensors, and edge computing devices, and the high-level equipment includes the central processing system; The resource allocation module is used to dynamically adjust the resource allocation strategy of each device based on the weighted performance indicators and historical performance memory scores of the device. The resource allocation module prioritizes more resources for devices with excellent performance when resources are limited, based on the performance of the device; Abnormal event processing module: when the low-level device detects an abnormal event, it transmits the complex event to the high-level device through competition. The high-level device adjusts the task priority according to the weighted performance index and memory score of the low-level device and handles the abnormal event; The memory update module is used to continuously monitor and update the device's historical performance memory score and dynamically adjust it according to the device's real-time performance. The memory update module optimizes the system's resource allocation and task dispatching strategies by recording the device's task completion status.

[0028] Example 1: In order to verify the actual application effect of the present invention in the intelligent security identification collaborative system, the experimenters selected two publicly available data sets, Stanford Drone Dataset (SDD) and DukeMTMC data set, which are used for high-traffic monitoring environments and complex abnormal behavior detection scenarios respectively. These two data sets provide different types of monitoring scenarios to test the performance improvement of the present invention in multi-level device collaboration, resource allocation and abnormal event detection.

[0029] The Stanford Drone Dataset (SDD) contains video data of large outdoor areas monitored from different angles, covering dynamic scenes of crowd behavior and vehicle activities. The experimenters selected a subset of the monitored shopping center area for the experiment. The data collection time was the peak period from 9 am to 9 pm every day, covering the whole year of 2020. The DukeMTMC dataset is mainly used for pedestrian trajectory analysis and abnormal behavior detection. It contains indoor and outdoor monitoring data obtained from multiple cameras to detect abnormal behavior in the crowd. The experimenters divided the two datasets into training and test sets in a ratio of 8:2, and respectively trained and tested the effect of the peacock courtship algorithm proposed in this invention in smart security recognition collaboration.

[0030] In this experiment, the pre-trained YOLOv5 model was used to extract target detection features in the video, including the location information and categories of pedestrians, vehicles and other dynamic targets. For these multimodal surveillance video data, the experimenters further used ResNet50 to extract visual features and combined them with the historical performance memory score of each camera for weighted calculation to adjust the resource allocation strategy of each camera. The historical performance of each device is based on its detection accuracy, response time and processing power in different scenarios. The historical data uses the performance in the past 30 days as the weight input into the memory module.

[0031] In the process of resource allocation and task scheduling, the experimenters compared the traditional fixed resource allocation method with the dynamic resource allocation strategy of the present invention. In the traditional method, the resource allocation of each camera and sensor is based on a preset fixed ratio, while the present invention dynamically adjusts resources through the peacock courtship algorithm, and determines the resource allocation priority based on the weighted performance indicators and historical performance of each device. In the experiment, the peacock courtship algorithm not only considers the real-time performance of the device, but also combines the performance of the device in past events, so that resources can be more effectively allocated to devices that perform well in complex environments.

[0032] During the experiment, the experimenter used the Adam optimizer for training with a learning rate of 0.0001, trained for 100 epochs, and set the mini-batch to 32. The experimenter used accuracy, response time, resource utilization, and average false alarm rate of event processing as the main evaluation indicators. In order to better evaluate the application effect of the present invention in complex scenarios, the experimenter introduced the human flow monitoring accuracy, abnormal event detection accuracy, and resource scheduling delay indicators in the StanfordDrone Dataset (SDD) dataset. The indicators can better reflect the performance of the system under the monitoring pressure during peak hours.

[0033] Table 1 Comparison between the proposed method and the traditional method on the SDD dataset

[0034] The experimental results on the SDD data set show that the peacock courtship algorithm of the present invention has obvious advantages in dealing with pedestrian flow monitoring and abnormal behavior detection. Compared with traditional methods, the response time is shortened by 61.9%, the accuracy of pedestrian flow monitoring is improved by about 14%, and the accuracy of abnormal event detection is also significantly improved by 17.6%. In addition, through the multi-level device collaboration mechanism and resource adaptive allocation strategy, the resource utilization rate is increased from 65% of the traditional method to 88.5%, which effectively reduces the waste of system resources. At the same time, the false alarm rate is reduced by 8.1 percentage points, indicating that the present invention is more robust when dealing with complex scenarios.

[0035] Table 2 Comparison between the proposed method and traditional methods on DukeMTMC dataset

[0036] In the experimental results of the DukeMTMC data set, the accuracy of abnormal behavior detection of the present invention reached 91.2%, which is nearly 17% higher than that of traditional methods. In addition, the response time of the system was shortened from 10.1 seconds of the traditional method to 4.7 seconds, which greatly improved the system's reaction speed to abnormal behavior. At the same time, the resource allocation optimization rate of the present invention was significantly higher than that of the traditional method, and the resource utilization rate was increased by 22.9 percentage points. In particular, when dealing with complex abnormal events, the average processing time was shortened by 54.2%, providing a more efficient solution for real-time security monitoring.

[0037] It can be seen from the results of Example 1 that the present invention significantly optimizes the resource allocation and task scheduling mechanism in complex scenarios, which not only improves the processing efficiency of the security system, but also reduces the false alarm rate and resource waste. In the experiment, the intelligent scheduling and competitive coordination mechanism of the present invention significantly improves the utilization rate of the equipment by combining weighted performance indicators and historical performance scores, ensuring that complex events can still be efficiently responded to under limited resources, fully verifying the feasibility and effectiveness of the present invention in practical applications.

[0038] The present invention introduces a memory mechanism, which enables the system to accumulate memory scores based on the historical performance of the device. During the task processing of the device, not only its real-time performance is considered, but also the long-term stability and historical efficiency of the device are comprehensively evaluated. Through the combination of weighted performance indicators and memory scores, the system can preferentially allocate more resources to devices with excellent historical performance, ensuring that the system can perform task scheduling more stably and efficiently in long-term operation. Compared with the traditional resource allocation method that relies on fixed rules, the adaptive mechanism of the present invention significantly improves the rationality of resource allocation and optimizes the overall operation efficiency of the device.

[0039] The present invention realizes effective collaboration between low-level devices and high-level devices through a multi-level competition mechanism. Low-level devices participate in competition based on their weighted performance indicators to decide whether to process complex events or pass them to high-level devices for processing. At the same time, high-level devices decide whether to accept tasks and adjust task priorities based on the weighted performance of low-level devices, thereby effectively avoiding waste of resources and ensuring that the system can respond quickly and schedule reasonably when faced with complex events, thereby improving the system's efficiency in handling abnormal events. In multi-task scenarios, the present invention optimizes task allocation between devices through competition and collaboration, thereby reducing response delays caused by unreasonable task allocation.

[0040] The performance monitoring and memory update module of the present invention can continuously monitor the real-time performance and historical performance of the device, and by dynamically adjusting the resource allocation ratio and task priority, it ensures that the system can adaptively adjust according to the performance changes of the device. In the case of device performance fluctuations or changes in task complexity, the system dynamically adjusts the resource allocation strategy and task scheduling by real-time updating of weighted performance indicators and memory scores, so that the system can still maintain efficient operation under limited resources. Compared with the prior art, the dynamic adjustment mechanism of the present invention significantly enhances the flexibility of the system and its ability to cope with complex environments, ensuring the stability and security of the long-term operation of the system.

[0041] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart security identification and collaboration method based on AI monitoring, characterized in that: The steps include: S1. Initialize various devices in the security system and set the initial resource allocation strategy and task assignment rules for each device; S2. Based on the peacock courtship algorithm with the memory mechanism introduced, the real-time performance indicators of each device are collected and calculated, and the weighted performance indicators of each device are calculated in combination with the memory score of the device; S3. Establish a multi-level competition and cooperation mechanism based on the memory mechanism. The low-level devices pre-process simple events through edge computing and compete according to their weighted performance indicators and historical performance. The competition results affect whether the low-level devices are qualified to handle complex tasks or whether to pass the tasks to high-level devices. The high-level devices decide whether to accept the tasks passed by the low-level devices according to the weighted performance indicators and historical performance of the low-level devices, and adjust the priority of the complex tasks based on the weighted performance. S4. When an abnormal event occurs in a low-level device, the complex event is transmitted to the high-level device for processing through competition. The high-level device determines the priority of each device according to the weighted performance index and memory score of the low-level device, and gives priority to the devices with excellent historical performance and weighted performance; S5. Dynamically adjust the resource allocation strategy of each device based on weighted performance indicators. The security system adjusts the resource allocation ratio based on the real-time and historical performance of the device through weighted performance indicators. When resources are limited, more resources are allocated to devices with higher weighted performance first, thereby optimizing the overall operating efficiency of the device. S6. Continuously monitor the real-time performance of each device, regularly update its memory score, and dynamically adjust the resource allocation strategy and task assignment rules of the security system based on weighted performance indicators, so that devices with long-term excellent performance continue to receive priority in subsequent task assignments.

2. According to claim 1, a smart security identification and coordination method based on AI monitoring is characterized in that: The S1 comprises the following sub-steps: S11, Initialize the surveillance camera ,sensor , edge computing devices and central processing system , where i, j, k, l are the indexes of each device respectively; S12. Set the initial resource allocation strategy for surveillance cameras, sensors, edge computing devices, and central processing systems: ； in, The resource allocation ratio of the i-th camera, the j-th sensor, the k-th edge computing device, and the l-th central processing system, , and They are the processing capacity, response speed and abnormal event detection accuracy of each device. , , is the weight coefficient of the corresponding device, which is used to weight the device according to its importance in different scenarios. The cumulative term in the denominator represents the overall performance of all devices. S13. Set the task assignment rules for the device. The task assignment rules are based on the task load and task processing priority of the device. The monitoring cameras, sensors, edge computing devices and central processing systems are assigned tasks according to the task complexity and device performance: ； in, is the task priority of the device, Represents the complexity of the task, which depends on the task load of the current device and the processing power of the device , It is used to control the dynamics of task dispatching, so that complex tasks can be dispatched to devices with higher performance first when resources are limited; S14, establish an initial communication channel between devices to enable collaborative work between surveillance cameras, sensors, edge computing devices and the central processing system, and perform real-time updates and adjustments to initial resource allocation strategies and task dispatching rules through the communication channel.

3. According to claim 1, the intelligent security identification and coordination method based on AI monitoring is characterized in that: The S2 comprises the following sub-steps: S21. Collect the actual performance indicators of each security device, including processing capacity , Response speed and abnormal event detection accuracy ; S22. Initialize a memory score for each device to record the device's historical performance The initial memory score is set to the preset benchmark value of the security system. As the security system runs longer, the processing capacity of the device , Response speed and abnormal event detection accuracy Historical performance is gradually accumulated into memory scores: ； in, is the memory score of the previous iteration, and is the weight coefficient; S23. Calculate the weighted performance index of each device , including the real-time performance of the device combined with its memory score : ； in, , , , are the corresponding weight coefficients, which respectively control the impact of processing capacity, response speed, abnormal event detection accuracy and memory score on the comprehensive performance evaluation.

4. According to the AI ​​monitoring-based intelligent security identification and coordination method of claim 1, it is characterized in that: The S3 comprises the following sub-steps: S31. Establish a multi-level competitive cooperation mechanism based on memory mechanism. Low-level devices include surveillance cameras, sensors and edge computing devices. Low-level devices pre-process simple events through edge computing. Low-level devices perform weighted performance indicators based on weighted performance indicators. and historical performance memory scores Participate in competition; S32. During the competition, low-level devices demonstrate their weighted performance indicators. The lower-level device with the higher competition result has the opportunity to obtain the processing rights of complex tasks: ； in, The result of competition for low-level equipment; S33. According to the competition results Determine whether the low-level device is qualified to handle complex tasks. If the competition result is below the threshold set by the security system, the task will be passed to the high-level device; S34, high-level devices based on the tasks delivered by low-level devices and the weighted performance indicators of low-level devices And memory score Decide whether to accept the task and adjust the task priority of complex tasks : ； in, is the task complexity weight coefficient, Based on the time difference A function that controls the impact of the device's historical performance on the current task allocation. Used to introduce nonlinear cumulative effects of memory mechanisms, It is the task matching function of the device, which is used to measure whether the low-level device is suitable for processing the current task. It is the abnormal event detection accuracy of low-level devices, which is used to distinguish the ability of devices to handle complex tasks. is the response speed of the low-level device, through an exponential decay function Adjust task priorities; S35. After the competition is over, the security system updates the resource allocation strategy for low-level and high-level devices so that devices with outstanding performance can obtain priority resources and task processing permissions.

5. According to claim 1, the intelligent security identification and coordination method based on AI monitoring is characterized in that: The S4 comprises the following sub-steps: S41. When a low-level device detects an abnormal event, it transmits the complex event to the high-level device for processing through competition. The basis of competition is the weighted performance index and memory score of the low-level device. The low-level device calculates its competition result through the following formula: ； in, It's the time difference A function that reflects the impact of the device's historical performance on current competition; S42: The low-level device transmits the complex event to the high-level device with the highest competition result for processing. The high-level device determines the priority according to the weighted performance index and memory score of the low-level device, and gives priority to processing the events transmitted by the low-level device with excellent historical performance and higher weighted performance index; S43, Advanced equipment calculation task priority of complex events : ； in, is the task priority adjustment coefficient, Used to introduce nonlinear effects of memory scores, so that low-level devices that show stable history have higher priority in event delivery; S44, Advanced equipment based on calculated task priorities Determine the order in which events are processed, prioritize complex events transmitted from lower-level devices with better performance, and adjust event priorities based on the urgency of complex events; S45. After the processing is completed, the high-level device feeds back the processing results to the low-level device and updates the memory score of the low-level device. .

6. The intelligent security identification and coordination method based on AI monitoring according to claim 1 is characterized in that: The S5 comprises the following sub-steps: S51. The security system dynamically adjusts the resource allocation strategy of each device according to the weighted performance index of the device. The security system collects the performance data of the device in real time and the historical performance memory score of the device to calculate the weighted performance index of each device. S52. Based on the weighted performance index of the equipment Determine the resource allocation ratio for each device : ； in, To introduce nonlinear cumulative effects, adjust the growth rate of equipment performance, Reflects the impact of equipment processing capacity and detection accuracy on resource allocation ratio. and It will accelerate the growth rate of resource allocation, so that the equipment can obtain the corresponding resources when handling urgent or complex tasks. Used to standardize the resource allocation ratio of each device so that the total resource allocation ratio of all devices is 1; S53, security system based on the calculated resource allocation ratio When resources are limited, resources are allocated preferentially to devices with higher weighted performance indicators; S54. During the resource allocation process, the security system monitors the performance indicators and memory scores of the equipment in real time. When the real-time performance, response speed and detection accuracy of the equipment change, the security system dynamically updates the resource allocation ratio based on the weighted performance indicators; S55. Weighted performance indicators through regular evaluation of equipment and historical performance memory scores , the security system optimizes the resource allocation strategy so that devices with long-term excellent performance continue to receive priority resource allocation, while adjusting the resource allocation of inefficient devices in real time.

7. The intelligent security identification and coordination method based on AI monitoring according to claim 1 is characterized in that: The task complexity is the comprehensive performance score of the device calculated based on the weighted performance index and historical performance memory score of the device. , the comprehensive performance score is used to determine whether the device performs well: ； in, and are weight coefficients, which adjust the weighted performance index of the device and the impact of the historical performance memory score. Represents the overall performance score of the device over a period of time; Scoring based on task complexity and overall equipment performance , the security system introduces segmented thresholds: when When the device is judged to have excellent performance, it will be given priority to obtain the most complex tasks and resources; when When , the device obtains tasks and resources of medium complexity; when When, the device obtains low-complexity tasks and resources; is the threshold of the i-th complexity level.

8. An intelligent security identification and coordination system based on AI monitoring, characterized in that: Includes the following modules: The device initialization module is used to initialize various devices in the security system, including surveillance cameras, sensors, edge computing devices, and central processing systems, and to set the initial resource allocation strategy and task assignment rules for each device; Performance monitoring module, which is used to collect the performance indicators of the equipment in real time, including the processing capacity, response speed, and abnormal event detection accuracy of the equipment, and record the historical performance memory score of the equipment. The performance monitoring module combines the real-time data and historical performance data to perform comprehensive calculations to generate weighted performance indicators; The task scheduling module uses the peacock courtship algorithm to conduct multi-level competition based on the weighted performance indicators and historical performance of the equipment to determine whether the low-level equipment is qualified to handle complex tasks, and transfers the complex tasks to high-level equipment for processing according to the competition results. The low-level equipment includes surveillance cameras, sensors, and edge computing devices, and the high-level equipment includes the central processing system; The resource allocation module is used to dynamically adjust the resource allocation strategy of each device based on the weighted performance indicators and historical performance memory scores of the device. The resource allocation module prioritizes more resources for devices with excellent performance when resources are limited, based on the performance of the device; Abnormal event processing module: when the low-level device detects an abnormal event, it transmits the complex event to the high-level device through competition. The high-level device adjusts the task priority according to the weighted performance index and memory score of the low-level device and handles the abnormal event; The memory update module is used to continuously monitor and update the device's historical performance memory score and dynamically adjust it according to the device's real-time performance. The memory update module optimizes the system's resource allocation and task dispatching strategies by recording the device's task completion status.

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