Intelligent monitoring protection system based on Internet

Abstract

The invention relates to the technical field of closed circuit television systems, in particular to an internet-based intelligent monitoring protection system, which comprises a distributed monitoring unit, a data analysis unit, a decision support unit, a transmission optimization unit, a hardware adaptation unit and a cloud collaboration unit. According to the invention, through the distributed architecture design, in combination with the adaptive link switching module, the multi-source data fusion module, the dynamic rule updating module and the like, efficient collaboration and intelligent decision support are realized, the integration, real-time performance and intelligent level of the system are improved, the deployment flexibility and maintenance convenience are enhanced, and the system is suitable for various complex monitoring scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of closed-circuit television systems, and particularly to an intelligent monitoring and protection system based on the Internet. Background Art

[0002] In the prior art, in order to meet the intelligent monitoring requirements in the fields of traffic management, public safety, and industrial automation, etc., a monitoring system based on the Internet is usually adopted. However, the existing intelligent monitoring and protection systems have certain limitations in terms of integration, real-time performance, and intelligence level, which affect the overall performance of the system and the user experience.

[0003] The patent with the publication number CN109756702B in the prior art proposes an integration method, a gateway, and a system for a closed-circuit television system, which realizes unified data transmission and integration by decoupling the SDK of the closed-circuit television system from external systems. However, this technical solution focuses on the integration efficiency and data transmission security between systems, and fails to fully meet the large-scale distributed monitoring requirements based on the Internet. Its architecture design has high requirements for hardware and network environments, which may increase the deployment and maintenance costs. At the same time, this technical solution has limited support for intelligent analysis, and there is still room for improvement in real-time data analysis and decision support. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent monitoring and protection system based on the Internet, which solves the technical problems of the prior art in terms of insufficient integration, real-time performance, and intelligence level through a distributed architecture design, combined with real-time data processing and decision support.

[0005] According to one aspect of the present invention, there is provided an intelligent monitoring and protection system based on the Internet, which system includes a processor, and further includes a distributed monitoring unit, a data analysis unit, a decision support unit, a transmission optimization unit, a hardware adaptation unit, and a cloud collaboration unit that are data-connected to the processor; the distributed monitoring unit is respectively connected to the data analysis unit, the decision support unit, the transmission optimization unit, the hardware adaptation unit, and the cloud collaboration unit through signal interfaces; wherein: The distributed monitoring unit is used for collecting and preliminarily processing monitoring data; The data analysis unit is used for analyzing the collected data and detecting anomalies; The decision support unit is used for generating countermeasures and executing relevant instructions; The transmission optimization unit is used for dynamically adjusting the data transmission priority and compressing data; The hardware adaptation unit is used for unifying the communication protocols of different devices; The cloud collaboration unit is used for storing historical data and providing computing resources.

[0006] In some embodiments, the distributed monitoring unit at least includes monitoring nodes and a coordination controller; each of the monitoring nodes includes an image acquisition device, an environment perception device, and a local operation module; the image acquisition device is arranged at the front end of the monitoring node and is used for acquiring video stream information; the environment perception device is installed on the side of the monitoring node and is used for collecting environmental parameters such as temperature and humidity; the local operation module is embedded inside the monitoring node and is connected to the image acquisition device and the environment perception device through a data bus, and is used for preliminarily processing the collected data; the coordination controller is installed at the central node of the monitoring and protection system, communicates with each monitoring node through the Internet, receives and integrates the data uploaded by each node, and coordinates the working states of each node.

[0007] In some embodiments, the data analysis unit at least includes a data acquisition module, a data preprocessing module, and an event detection module; the data acquisition module obtains video streams and environmental parameters from the distributed monitoring unit; the data preprocessing module denoises, formats, and marks timestamps for the received data; the event detection module, based on the preprocessed data, identifies abnormal events through an algorithm model and transmits the results to the decision support unit.

[0008] In some embodiments, the decision support unit at least includes a policy generation module and a rule storage module; the policy generation module generates response strategies according to the abnormal event information provided by the event detection module, combines the rule library and historical data in the rule storage module, and sends instructions through a signal transceiver; the rule storage module stores response rules in various scenarios and supports dynamic updates.

[0009] In some embodiments, the transmission optimization unit at least includes a bandwidth allocation module and a data compression module; the bandwidth allocation module dynamically adjusts the data transmission priorities of each monitoring node according to the current network conditions; the data compression module performs lossless or lossy compression on video streams and environmental parameters to reduce the bandwidth occupancy required for data transmission.

[0010] In some embodiments, the hardware adaptation unit at least includes a device driver module and a protocol conversion module; the device driver module is used to connect the image acquisition device and the environment perception device; the protocol conversion module unifies the communication protocols of different devices into a standard protocol to ensure compatibility and interoperability between devices.

[0011] In some embodiments, the cloud collaboration unit at least includes a data storage module and a computing resource module; the data storage module is used to store the historical data uploaded by the distributed monitoring unit and the analysis results generated by the data analysis unit; the computing resource module provides high-performance computing resources for supporting the complex algorithm operations of the decision support unit.

[0012] In some embodiments, the distributed monitoring unit further includes an adaptive link switching module; the adaptive link switching module includes a link monitor, a link switching controller, and a backup communication channel; the link monitor is connected to the main communication link through a signal interface to monitor the status of the main communication link in real time; when a fault occurs in the main communication link, the link switching controller automatically switches to the backup communication channel to ensure that the communication between the monitoring node and the coordination controller is not interrupted; the backup communication channel supports 4G / 5G wireless networks.

[0013] In some embodiments, the data analysis unit further includes a multi-source data fusion module; the multi-source data fusion module includes a data synchronization unit, a feature extraction unit, and a comprehensive operation unit; the data synchronization unit performs time synchronization and spatial alignment on the video streams and environmental parameters from different monitoring nodes; the feature extraction unit extracts key features from the aligned data; the comprehensive operation unit performs weighted fusion on the extracted features to generate a comprehensive analysis result.

[0014] In some embodiments, the decision support unit further includes a dynamic rule update module; the dynamic rule update module includes a rule learning unit and a rule evaluation unit; the rule learning unit generates new response rules through machine learning algorithms based on historical data and the output results of the real-time data analysis unit; the rule evaluation unit performs simulation tests on the newly generated rules, evaluates their effectiveness and reliability, and feeds back the evaluation results to the rule storage module.

[0015] Compared with the prior art, the present invention has the following beneficial effects: Through the distributed architecture design, combined with functional modules such as adaptive link switching, multi-source data fusion, and dynamic rule update, it can not only collect and analyze multi-source data in real time, but also quickly generate response strategies and dynamically adjust resource allocation, thus significantly improving the intelligent level and emergency response ability of traffic management; at the same time, the adaptive link switching module can quickly switch to the backup communication channel when a fault occurs in the main communication link, significantly improving the reliability and stability of the system; the multi-source data fusion module generates a comprehensive analysis result by performing time synchronization, spatial alignment, and feature fusion on multi-source data, improving the accuracy and comprehensiveness of data analysis; the dynamic rule update module generates new response rules through machine learning algorithms and performs simulation tests to ensure the effectiveness and reliability of the rules, improving the ability of intelligent decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 is the overall schematic diagram of the monitoring and protection system of the present invention; Figure 2 is the schematic diagram of the distributed monitoring unit of the monitoring and protection system of the present invention; Figure 3 is the schematic diagram of the data analysis unit of the monitoring and protection system of the present invention; Figure 4 is the schematic diagram of the decision support unit of the monitoring and protection system of the present invention; Figure 5 is the schematic diagram of the transmission optimization unit of the monitoring and protection system of the present invention; Figure 6 is the schematic diagram of the hardware adaptation unit of the monitoring and protection system of the present invention; Figure 7 is the schematic diagram of the cloud collaboration unit of the monitoring and protection system of the present invention. Detailed implementation manners

[0018] The following will combine the drawings in the embodiments of the present invention Figure 1-7 and clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0019] The present invention provides an Internet-based intelligent monitoring and protection system, which includes a distributed monitoring unit, a data analysis unit, a decision support unit, a transmission optimization unit, a hardware adaptation unit, and a cloud collaboration unit. The aforementioned units are connected by signals and interact with data through the Internet to complete the entire system operation process. Embodiment

[0020] Figure 1Schematic diagram of the Internet-based intelligent monitoring and protection system provided by the embodiments of the present invention. The system includes a processor, which can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0021] The following, in conjunction with the attached Figure 2 - attached Figure 7 .

[0022] Furthermore, the distributed monitoring unit is the core data acquisition part of this system, including multiple monitoring nodes and a coordination controller. Each monitoring node contains an image acquisition device, an environmental perception device, and a local operation module. The image acquisition device is arranged at the front end of the monitoring node and is used to obtain video stream information; the environmental perception device is installed on the side of the monitoring node and is used to collect environmental parameters such as temperature and humidity; the local operation module is embedded inside the monitoring node and is respectively connected to the image acquisition device and the environmental perception device through a data bus, and is used to perform preliminary processing on the collected data. The coordination controller is located at the central node of the monitoring system, communicates with each monitoring node through the Internet, receives and integrates the data uploaded by each node, and coordinates the working status of each node. The signal connection between the monitoring node and the coordination controller is realized through the Internet to ensure efficient data transmission.

[0023] Furthermore, the data analysis unit is connected to the distributed monitoring unit through the Internet and is used to receive the video stream and environmental parameter data uploaded by the distributed monitoring unit. The data analysis unit includes a data acquisition module, a data preprocessing module, and an event detection module. After the data acquisition module obtains the video stream and environmental parameters from the distributed monitoring unit, it transmits the data to the data preprocessing module. The data preprocessing module performs denoising, formatting, and timestamp marking on the received data to ensure the consistency and availability of the data. The preprocessed data is transmitted to the event detection module, and the event detection module analyzes the data based on the algorithm model, identifies abnormal events, and transmits the results to the decision support unit. The data analysis unit is connected to the decision support unit through a signal interface to ensure fast data transmission.

[0024] Furthermore, the decision support unit includes a policy generation module and a rule storage module. The policy generation module receives abnormal event information from the data analysis unit, and combines the rule library and historical data in the rule storage module to generate response policies. The rule storage module stores response rules for various scenarios and supports dynamic updates. The policy generation module sends the generated response policies to the relevant execution modules through a signal transceiver to guide subsequent operations. The decision support unit is also connected to the cloud collaboration unit through a signal interface to call cloud computing resources to complete complex algorithm operations.

[0025] Furthermore, the data transmission optimization unit includes a bandwidth allocation module and a data compression module. The bandwidth allocation module dynamically adjusts the data transmission priorities of each monitoring node according to the current network conditions to ensure that important data can be transmitted first. The data compression module performs lossless or lossy compression on video streams and environmental parameters to reduce the bandwidth occupancy required for data transmission. The data transmission optimization unit is connected to the distributed monitoring unit through a signal interface to adjust the data transmission policy in real time, thereby improving the overall efficiency of the system.

[0026] Furthermore, the hardware adaptation unit includes a device driver module and a protocol conversion module. The device driver module is used to connect image acquisition devices and environmental perception devices of different brands and models to ensure that these devices can work properly. The protocol conversion module unifies the communication protocols of different devices into a standard protocol to ensure the compatibility and interoperability between devices. The hardware adaptation unit is connected to the distributed monitoring unit through a signal interface to ensure seamless cooperation of the devices in the monitoring node.

[0027] Furthermore, the cloud collaboration unit includes a data storage module and a computing resource module. The data storage module is used to store the historical data uploaded by the distributed monitoring unit and the analysis results generated by the data analysis unit to ensure that the data can be stored for a long time and called at any time. The computing resource module provides high-performance computing resources to support the complex algorithm operations of the decision support unit. The cloud collaboration unit is connected to the distributed monitoring unit, the data analysis unit, and the decision support unit through the Internet to ensure the efficient flow of data and computing resources within the system.

[0028] It can be understood that during the specific operation process, the monitoring nodes of the distributed monitoring unit collect video streams and environmental parameters through the image acquisition device and the environmental perception device, and perform preliminary processing through the local operation module. The processed data is uploaded to the coordination controller through the Internet. After the coordination controller integrates the data, it transmits the data to the data analysis unit. After the data acquisition module of the data analysis unit receives the data, it performs denoising, formatting, and timestamp marking processing through the data preprocessing module, and then transfers the data to the event detection module. The event detection module identifies abnormal events based on the algorithm model and transmits the results to the decision support unit. The policy generation module of the decision support unit combines the rule library and historical data in the rule storage module to generate response strategies and sends instructions to the relevant execution modules through the signal transceiver. At the same time, the data transmission optimization unit dynamically adjusts the data transmission priority according to the current network conditions and compresses the data through the data compression module to reduce bandwidth occupancy. The hardware adaptation unit ensures that the devices in the monitoring nodes can work properly and achieve interoperability through the device driver module and the protocol conversion module. The cloud collaboration unit provides storage and computing support through the data storage module and the computing resource module to ensure the efficient operation of the system.

[0029] In some preferred embodiments, the distributed monitoring unit further includes an adaptive link switching module, which includes a link monitor, a link switching controller, and a backup communication channel. The link monitor is connected to the main communication link through a signal interface and monitors the status of the main communication link in real time. When the main communication link fails, the link switching controller automatically switches to the backup communication channel to ensure that the communication between the monitoring node and the coordination controller is not interrupted. The backup communication channel supports 4G / 5G wireless networks or satellite communications, and the response time of the link switching controller is 1 second.

[0030] In some preferred embodiments, the data analysis unit further includes a multi-source data fusion module, which includes a data synchronization unit, a feature extraction unit, and a comprehensive operation unit. The data synchronization unit performs time synchronization and spatial alignment on the video streams and environmental parameters from different monitoring nodes. The feature extraction unit extracts key features from the aligned data, and the comprehensive operation unit performs weighted fusion on the extracted features to generate a comprehensive analysis result. The calculation accuracy of the comprehensive operation unit is 99.9%, and the response time is 0.2 seconds.

[0031] In some preferred embodiments, the decision support unit further includes a dynamic rule update module, which includes a rule learning unit and a rule evaluation unit. The rule learning unit generates new response rules through machine learning algorithms based on historical data and the output results of the data analysis unit. The rule evaluation unit conducts simulation tests on the newly generated rules, evaluates their effectiveness and reliability, and feeds back the evaluation results to the rule storage module. The training cycle of the rule learning unit is 1 hour, and the simulation test time of the rule evaluation unit is 5 minutes.

[0032] In some preferred embodiments, the data transmission optimization unit further includes a cache management module, which includes a cache allocation unit and a cache cleaning unit. The cache allocation unit dynamically allocates cache space according to the data transmission requirements of each monitoring node. The cache cleaning unit regularly clears expired or low-priority data to release cache space. The maximum cache capacity of the cache allocation unit is 1TB, and the cleaning cycle of the cache cleaning unit is 10 minutes.

[0033] In some preferred embodiments, the hardware adaptation unit further includes a device health monitoring module, which includes a status acquisition unit and a fault prediction unit. The status acquisition unit collects the working status data of the image acquisition device and the environment perception device in real time. The fault prediction unit judges the possible faults of the device based on the collected data through a prediction model and sends the prediction results to the coordination controller. The sampling frequency of the status acquisition unit is 1Hz, and the prediction accuracy of the fault prediction unit is 95%.

[0034] In some preferred embodiments, the cloud collaboration unit further includes an elastic resource scheduling module, which includes a resource monitoring unit and a resource allocation unit. The resource monitoring unit monitors the resource usage of the computing resource module in real time. The resource allocation unit dynamically allocates computing resources according to the current task requirements to ensure the efficient execution of tasks. The monitoring accuracy of the resource monitoring unit is ±1%, and the allocation response time of the resource allocation unit is 0.5 seconds.

[0035] Through the above specific embodiments, the present invention realizes the efficient operation of the intelligent monitoring and protection system based on the Internet. In order to better enable relevant personnel in the technical field to fully understand and implement the present invention, the following supplements the specific implementation principles of the present invention in combination with a specific application scenario.

[0036] In the intelligent transportation management system of a certain city, the intelligent monitoring and protection system of the present invention is deployed at multiple key intersections and sections for real-time monitoring of traffic flow, identification of abnormal events, and provision of decision support. The following are the specific steps and their principle explanations in the actual operation of the system.

[0037] First, multiple monitoring nodes of the distributed monitoring unit are distributed at major intersections and sections in the city. The image acquisition device of each monitoring node obtains real-time video stream information through a high-definition camera, and the environmental perception device collects environmental parameters such as temperature, humidity, and light intensity through sensors. These data are preliminarily processed by the local computing module, such as extracting frames from the video stream and filtering the environmental parameters, to reduce data redundancy and noise interference. Subsequently, the processed data is uploaded to the coordination controller via the Internet. The coordination controller integrates the data from multiple monitoring nodes and adjusts the working status of each node according to global requirements, such as dynamically allocating computing resources or adjusting the acquisition frequency.

[0038] Secondly, the data analysis unit receives data from the distributed monitoring unit. The data acquisition module transmits the video stream and environmental parameters to the data preprocessing module, where the video stream undergoes denoising and formatting processes to ensure data consistency; the environmental parameters are timestamped for subsequent analysis. The preprocessed data is passed to the event detection module, which analyzes the data through algorithm models. For example, a deep learning-based object detection algorithm is used to identify vehicle congestion or abnormal pedestrian behavior in the video stream, and environmental parameters are combined to determine whether there are potential risks caused by weather conditions. Once an abnormal event is detected, the result is quickly transmitted to the decision support unit.

[0039] After receiving the abnormal event information, the policy generation module of the decision support unit generates response policies in combination with the rule library and historical data in the rule storage module. For example, when severe congestion is detected on a certain section, the policy generation module will call the rules regarding traffic guidance in the rule library and generate a specific guidance plan in combination with similar scenarios in historical data. The generated policies are sent to the relevant execution modules through the signal transceiver, such as adjusting the traffic light timing or notifying nearby traffic management personnel to go to the scene for handling. At the same time, the decision support unit is also connected to the cloud collaboration unit through the signal interface and calls the high-performance computing resources in the cloud to complete complex algorithm operations, such as comprehensively analyzing multi-source data to optimize the decision-making plan.

[0040] During this process, the transmission optimization unit dynamically adjusts the data transmission priority according to the current network conditions. For example, when the network bandwidth is tight, the bandwidth allocation module gives priority to transmitting high-priority data (such as the video stream related to abnormal events), while low-priority data (such as regular environmental parameters) is transmitted later. In addition, the data compression module performs lossless compression on the video stream to reduce bandwidth occupancy, thereby ensuring that important data can be transmitted to the cloud collaboration unit in a timely manner.

[0041] The hardware adaptation unit ensures the normal operation of image acquisition devices and environmental perception devices of different brands and models through the device driver module and the protocol conversion module. For example, some monitoring nodes may use cameras and sensors from different manufacturers. The protocol conversion module unifies the communication protocols of these devices into a standard protocol, thus achieving compatibility and interoperability between devices. In addition, the status acquisition unit of the device health monitoring module collects the working status data of the device in real time at a frequency of 1 Hz. The fault prediction unit judges the possible faults of the device through a prediction model based on the collected data and sends the prediction results to the coordination controller for timely maintenance measures.

[0042] The data storage module of the cloud collaboration unit stores the historical data uploaded by the distributed monitoring unit and the analysis results generated by the data analysis unit, ensuring that the data can be stored for a long time and called at any time. For example, historical traffic flow data can be used to analyze the changing trends of traffic patterns and provide reference for future traffic planning. The computing resource module provides high-performance computing resources to support the complex algorithm operations of the decision support unit, such as fusing and analyzing multi-source data to generate more accurate decision-making schemes. The resource monitoring unit of the elastic resource scheduling module monitors the usage of computing resources in real time, and the resource allocation unit dynamically allocates computing resources according to the current task requirements, thus ensuring the efficient execution of tasks.

[0043] In addition, the adaptive link switching module monitors the status of the main communication link in real time through the link monitor. For example, when the main communication link of a certain monitoring node is interrupted due to a network fault, the link switching controller automatically switches to the backup communication channel (such as 4G / 5G wireless network or satellite communication) within 1 second to ensure the uninterrupted communication between the monitoring node and the coordination controller. The data synchronization unit of the multi-source data fusion module synchronizes the time and aligns the space of the video streams and environmental parameters from different monitoring nodes. The feature extraction unit extracts key features from the aligned data, and the comprehensive operation unit performs weighted fusion on the extracted features to generate a comprehensive analysis result. For example, by fusing and analyzing the video streams of multiple monitoring nodes, the scope and degree of traffic congestion can be judged more accurately.

[0044] The rule learning unit of the dynamic rule update module generates new response rules through machine learning algorithms based on the historical data and the output results of the real-time data analysis unit. For example, for the traffic flow change law under a certain specific weather condition, the rule learning unit generates corresponding traffic guidance rules and conducts simulation tests through the rule evaluation unit to evaluate their effectiveness. The cache allocation unit of the cache management module dynamically allocates cache space according to the data transmission requirements of each monitoring node, and the cache cleaning unit regularly clears expired or low-priority data to release the cache space and improve the system efficiency.

[0045] The foregoing has shown and described the basic principles, main features and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic features of the present invention, the present invention can be implemented in other specific forms. Therefore, in whatever aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced by the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0046] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An Internet-based intelligent monitoring and protection system, the system includes a processor, characterized in that, It further includes a distributed monitoring unit, a data analysis unit, a decision support unit, a transmission optimization unit, a hardware adaptation unit, and a cloud collaboration unit that are data-connected to the processor; the distributed monitoring unit is respectively connected to the data analysis unit, the decision support unit, the transmission optimization unit, the hardware adaptation unit, and the cloud collaboration unit through signal interfaces; where: The distributed monitoring unit is used to collect and preliminarily process monitoring data; The data analysis unit is used to analyze the collected data and detect anomalies; The decision support unit is used to generate response strategies and execute relevant instructions; The transmission optimization unit is used to dynamically adjust the data transmission priority and compress data; The hardware adaptation unit is used to unify the communication protocols of different devices; The cloud collaboration unit is used to store historical data and provide computing resources.

2. The system according to claim 1, wherein The distributed monitoring unit at least includes monitoring nodes and a coordination controller; each monitoring node includes an image acquisition device, an environmental perception device, and a local operation module; the image acquisition device is arranged at the front end of the monitoring node and is used to obtain video stream information; the environmental perception device is installed on the side of the monitoring node and is used to collect environmental parameters such as temperature and humidity; the local operation module is embedded inside the monitoring node and is connected to the image acquisition device and the environmental perception device through a data bus, and is used to preliminarily process the collected data; the coordination controller is installed at the central node of the monitoring and protection system, communicates with each monitoring node through the Internet, receives and integrates the data uploaded by each node, and coordinates the working states of each node.

3. The system according to claim 1, wherein The data analysis unit at least includes a data acquisition module, a data preprocessing module, and an event detection module; the data acquisition module obtains the video stream and environmental parameters from the distributed monitoring unit; the data preprocessing module denoises, formats, and timestamp-marks the received data; the event detection module, based on the preprocessed data, identifies abnormal events through an algorithm model and transmits the results to the decision support unit.

4. The system according to claim 1, wherein The decision support unit at least includes a strategy generation module and a rule storage module; the strategy generation module generates response strategies according to the abnormal event information provided by the event detection module, combines the rule library and historical data in the rule storage module, and sends instructions through a signal transceiver; the rule storage module stores response rules in multiple scenarios and supports dynamic updates.

5. The system according to claim 2, characterized in that, The transmission optimization unit at least includes a bandwidth allocation module and a data compression module; the bandwidth allocation module dynamically adjusts the data transmission priority of each monitoring node according to the current network condition; the data compression module performs lossless or lossy compression on the video stream and environmental parameters to reduce the bandwidth occupancy required for data transmission.

6. The system according to claim 2, wherein The hardware adaptation unit at least includes a device driver module and a protocol conversion module; the device driver module is used to connect the image acquisition device and the environmental perception device; the protocol conversion module unifies the communication protocols of different devices into a standard protocol to ensure compatibility and interoperability between devices.

7. The system according to claim 1, characterized in that, The cloud collaboration unit at least includes a data storage module and a computing resource module; the data storage module is used to store the historical data uploaded by the distributed monitoring unit and the analysis results generated by the data analysis unit; the computing resource module provides high-performance computing resources for supporting the complex algorithm operations of the decision support unit.

8. The system according to claim 2, wherein The distributed monitoring unit further includes an adaptive link switching module; the adaptive link switching module includes a link monitor, a link switching controller, and a standby communication channel; the link monitor is connected to the main communication link through a signal interface to monitor the status of the main communication link in real time; when the main communication link fails, the link switching controller automatically switches to the standby communication channel to ensure that the communication between the monitoring node and the coordination controller is not interrupted; the standby communication channel supports 4G / 5G wireless networks.

9. The system according to claim 2, wherein The data analysis unit further includes a multi-source data fusion module; the multi-source data fusion module includes a data synchronization unit, a feature extraction unit, and a comprehensive operation unit; the data synchronization unit performs time synchronization and spatial alignment on the video streams and environmental parameters from different monitoring nodes. The feature extraction unit extracts key features from the aligned data. The comprehensive operation unit performs weighted fusion on the extracted features to generate a comprehensive analysis result.

10. The system according to claim 4, wherein The decision support unit further includes a dynamic rule update module; the dynamic rule update module includes a rule learning unit and a rule evaluation unit; the rule learning unit generates new response rules through machine learning algorithms based on the historical data and the output results of the real-time data analysis unit; the rule evaluation unit conducts simulation tests on the newly generated rules, evaluates their effectiveness and reliability, and feeds back the evaluation results to the rule storage module.

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