Highway electromechanical terminal cooperative control system based on edge computing

Abstract

The application belongs to the technical field of expressway management and control, and particularly relates to an expressway electromechanical terminal cooperative control system based on edge computing, which comprises a multi-source data sensing and fusion module, an edge intelligent analysis and decision module, a terminal cooperative control execution module, a safety protection management module and a remote monitoring center; the original data on the expressway are collected by the multi-source data sensing and fusion module and subjected to fusion processing, the fused data are subjected to real-time analysis and processing by means of edge computing, and an intelligent algorithm is used to generate corresponding control strategies and decision instructions; the control instructions are sent to corresponding electromechanical terminal equipment and the execution status thereof is monitored; the safety protection module safeguards the network security and data security of the system, effectively improves the operation efficiency and safety of the expressway, and provides strong support for intelligent management of the expressway.

Description

Edge computing-based highway electromechanical terminal collaborative control system Technical Field

[0001] This invention relates to the field of highway management and control technology, specifically a highway electromechanical terminal collaborative control system based on edge computing. Background Technology

[0002] Highway electromechanical terminals are a general term for various devices that are at the end of the highway electromechanical system and directly perform functions or collect information. They cover multiple systems such as traffic monitoring, communication, and power supply and distribution, providing support for traffic management decisions and improving operation management and service levels.

[0003] Currently, the electromechanical system of highways contains many independent terminal devices, such as traffic lights, surveillance cameras and variable message signs. These devices are usually managed by their own independent control systems, lacking an effective coordination mechanism.

[0004] In existing technologies, although some systems attempt to achieve device collaboration through centralized cloud computing, they suffer from large data transmission latency, high network bandwidth requirements, low system reliability, and limited data processing and analysis capabilities. These limitations make it difficult to meet the complex and ever-changing operational needs of highways, and they cannot effectively monitor system safety or reasonably assess the collaborative control status, which is not conducive to improving the operational efficiency and safety of highways.

[0005] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a highway electromechanical terminal collaborative control system based on edge computing, which solves the problems that existing technologies cannot meet the complex and ever-changing operational needs of highways, cannot effectively supervise system safety and reasonably judge the collaborative control status, thus hindering the improvement of highway operation efficiency and safety, and has a low level of intelligence.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The highway electromechanical terminal collaborative control system based on edge computing includes a multi-source data sensing and fusion module, an edge intelligent analysis and decision-making module, a terminal collaborative control execution module, a safety protection module, and a remote monitoring center. The multi-source data sensing and fusion module collects various raw data from the highway, fuses the heterogeneous data from multiple sources, and sends the fused data to the edge intelligent analysis and decision-making module. Based on edge computing capabilities, the edge intelligent analysis and decision-making module performs real-time analysis and processing of the fused data and uses intelligent algorithms to generate corresponding control strategies and decision commands.

[0009] The terminal collaborative control execution module sends the control commands generated by the edge intelligent analysis and decision-making module to the corresponding electromechanical terminal devices, monitors the execution status of the electromechanical terminal devices, and sends the execution information of the electromechanical terminal devices to the remote monitoring center; the security protection module is used to ensure the network security and data security of the system, and sends the system security protection log information to the remote monitoring center.

[0010] Furthermore, the multi-source data perception and fusion module collects multi-source data, including traffic flow, vehicle speed, and weather information, in real time through a sensor network deployed at various key locations on the highway. It uses data cleaning algorithms to remove noise and abnormal data, and employs data fusion technology to associate and integrate data from different sensors and data sources to form a unified data format and semantic representation.

[0011] Furthermore, the edge intelligent analysis and decision-making module receives data from the multi-source data perception and fusion module, and uses machine learning and deep learning algorithms to perform in-depth mining and analysis of the data; based on the analysis results and in combination with preset rules and models, it generates control strategies for different electromechanical terminal devices.

[0012] Furthermore, the security protection module employs a variety of security protection technologies, including firewalls, intrusion detection systems, and data encryption, to provide comprehensive security protection for the system. During data transmission, data is encrypted, and the system's security status is monitored in real time to detect and prevent intrusion attacks.

[0013] Furthermore, the security protection module communicates with the protection risk decision module. The security protection decision module sends the system security protection log information to the protection risk decision module. The protection risk decision module analyzes the protection risk of the system during the detection period, generates a low-risk protection signal or a high-risk protection signal through analysis, and sends the low-risk protection signal or the high-risk protection signal to the remote monitoring center. When the remote monitoring center receives the high-risk protection signal, it issues a corresponding warning.

[0014] Furthermore, the specific analysis process of the risk protection decision-making module includes:

[0015] The system obtains the total number of intrusions or attacks that the system has been subjected to during the detection period and marks them as intrusion detection values. The intrusion detection values ​​are compared with preset intrusion detection thresholds. If the intrusion detection value exceeds the preset intrusion detection threshold, a high-risk protection signal is generated.

[0016] If the intrusion detection value does not exceed the preset intrusion detection threshold, the intrusion failure value is calculated by comparing the number of times the intrusion attack was not prevented during the detection period with the intrusion detection value. The intrusion failure value is then compared with the preset intrusion failure threshold. If the intrusion failure value exceeds the preset intrusion failure threshold, a high-risk protection signal is generated.

[0017] Furthermore, if the intrusion failure value does not exceed the preset intrusion failure threshold, then when the intrusion attack behavior cannot be stopped, the intrusion attack duration of the corresponding intrusion attack behavior is collected, and the intrusion attack duration of all intrusion attacks within the detection period is summed to obtain the intrusion time measurement value.

[0018] The protection risk decision value is calculated by weighting and summing the attack detection value, attack failure value, and attack time measurement value. The protection risk decision value is then compared with a preset protection risk decision threshold. If the protection risk decision value exceeds the preset protection risk decision threshold, a high-risk protection signal is generated; if the protection risk decision value does not exceed the preset protection risk decision threshold, a low-risk protection signal is generated.

[0019] Furthermore, the terminal collaborative control execution module communicates with the collaborative cooperation hazard assessment module. The collaborative cooperation hazard assessment module marks the corresponding electromechanical terminal equipment as assessment object i, where i is a natural number greater than 1. The terminal collaborative control execution module sends the execution information of all electromechanical terminal equipment to the collaborative cooperation hazard assessment module. The collaborative cooperation hazard assessment module analyzes the operational performance of assessment object i and comprehensively evaluates the collaborative cooperation performance of all electromechanical terminal equipment. Based on this, it generates a normal collaborative cooperation signal or a normal collaborative cooperation signal and sends the normal collaborative cooperation signal or a normal collaborative cooperation signal to the remote monitoring center. When the remote monitoring center receives the abnormal collaborative cooperation signal, it issues a corresponding warning.

[0020] Furthermore, the specific analysis process of the collaborative hazard assessment module includes:

[0021] Several evaluation periods are set within the detection period. If the electromechanical terminal equipment fails to execute the received control command correctly within the corresponding evaluation period, the corresponding evaluation period is marked as an abnormal period of cooperative control. The number of abnormal periods of cooperative control within the detection period is obtained and the ratio is calculated with the total number of evaluation periods to obtain the cooperative control anomaly value. The cooperative control anomaly value is compared with the preset cooperative control anomaly threshold. If the cooperative control anomaly value exceeds the preset cooperative control anomaly threshold, a cooperative cooperation anomaly signal is generated.

[0022] Furthermore, if the collaborative control anomaly value does not exceed the preset collaborative control anomaly threshold, the number of times the evaluation object i failed to correctly execute the corresponding control command during the detection period is obtained and marked as the non-execution detection value. The non-execution detection value is compared with the corresponding preset non-execution detection threshold. If the non-execution detection value exceeds the preset non-execution detection threshold, the evaluation object i is marked as a non-excellent object.

[0023] If the non-compliance detection value does not exceed the preset non-compliance detection threshold, the operating parameters that need to be monitored for the evaluation object i are obtained, the real-time data of the corresponding operating parameters are collected, and the real-time data is compared with the corresponding preset data requirements. If the corresponding real-time data does not meet the corresponding preset data requirements, the corresponding operating parameter is determined to be in an unsafe state. The total duration of the corresponding operating parameter in an unsafe state during the detection period is obtained and marked as the parameter risk time characteristic value. The number of times the single duration of the corresponding operating parameter in an unsafe state exceeds the corresponding preset single duration threshold during the detection period is marked as the parameter risk frequency characteristic value. The maximum value of the single duration of the corresponding operating parameter in an unsafe state during the detection period is marked as the parameter risk time amplitude measurement value.

[0024] The parameter operation characteristic value is obtained by weighted summation of the parameter risk time characteristic value, the parameter risk frequency characteristic value, and the parameter risk time amplitude measurement value. The parameter operation characteristic value is compared with the corresponding preset parameter operation characteristic threshold. If the parameter operation characteristic value exceeds the corresponding preset parameter operation characteristic threshold, the corresponding operation parameter is marked as a control optimization parameter.

[0025] If the evaluation object i has control optimization parameters during the detection period, then the evaluation object i is marked as a non-excellent object; if the evaluation object i does not have control optimization parameters during the detection period, then the ratio of the parameter operation characteristic value of the corresponding operation parameter to the corresponding preset parameter operation characteristic threshold is calculated to obtain the parameter operation ratio value of the corresponding operation parameter. Each operation parameter is pre-set to correspond to a set of preset weight values. The parameter ratio value of the corresponding operation parameter is multiplied by the corresponding preset weight value to obtain the parameter evaluation value.

[0026] The parameter evaluation values ​​of all the operating parameters that need to be monitored for the evaluation object i are obtained and summed to obtain the operating analysis value. The operating analysis value is compared with the preset operating analysis threshold. If the operating analysis value exceeds the preset operating analysis threshold, the evaluation object i is marked as a non-excellent object. If there is a non-excellent object during the detection period, an abnormal cooperation signal is generated. If there is no non-excellent object during the detection period, a normal cooperation signal is generated.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. In this invention, various raw data from highways are collected and fused. Edge computing is used to analyze and process the fused data in real time and intelligent algorithms are used to generate corresponding control strategies and decision commands. The control commands are sent to the corresponding electromechanical terminal equipment and their execution status is monitored. The network security and data security of the system are guaranteed, which effectively improves the operating efficiency and safety of highways and provides strong support for intelligent management of highways.

[0029] 2. In this invention, the protection risk decision module analyzes the protection risk of the system during the detection period. When a high-risk protection signal is generated, the system safety measures are optimized and subsequent monitoring and management are strengthened to avoid system paralysis and adverse effects on the normal operation of the highway. Furthermore, by comprehensively evaluating the collaborative performance of all electromechanical terminal equipment, effective control of all electromechanical terminal equipment is ensured, thereby further improving the operating efficiency and safety of the highway and demonstrating a high level of intelligence. Attached Figure Description

[0030] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0031] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0032] Figure 2 is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1: As shown in Figure 1, the highway electromechanical terminal collaborative control system based on edge computing proposed in this invention includes a multi-source data perception and fusion module, an edge intelligent analysis and decision-making module, a terminal collaborative control execution module, a security protection module, and a remote monitoring center; it should be noted that highway electromechanical terminal equipment includes traffic lights, variable message signs, etc.

[0035] The multi-source data perception and fusion module collects various raw data from the highway, performs fusion processing on the multi-source heterogeneous data, and sends the fused data to the edge intelligent analysis and decision-making module. This enables the module to comprehensively and accurately obtain highway operation status information, providing a reliable basis for subsequent intelligent analysis and decision control, and improving the accuracy and reliability of decision control.

[0036] Specifically, firstly, the multi-source data perception and fusion module collects multi-source data such as traffic flow, vehicle speed, and weather information in real time through a sensor network deployed at various key locations on the highway. Then, it uses data cleaning algorithms to remove noise and abnormal data to ensure data quality. Next, it employs data fusion technology to associate and integrate data from different sensors and data sources to form a unified data format and semantic representation. For example, fusing traffic flow data with video surveillance data can more accurately determine road congestion.

[0037] The edge intelligent analysis and decision-making module, based on edge computing capabilities, performs real-time analysis and processing of fused data. It uses intelligent algorithms to generate corresponding control strategies and decision commands and sends them to the terminal collaborative control execution module and remote monitoring center. It can process data quickly at the edge, reduce the time delay of data transmission to the cloud, realize real-time decision-making and control, and the application of intelligent algorithms improves the scientificity and accuracy of decision-making, effectively improving the operating efficiency of highways.

[0038] Specifically, the edge intelligent analysis and decision-making module receives data from the multi-source data perception and fusion module, and uses machine learning and deep learning algorithms to deeply mine and analyze the data. For example, by analyzing historical traffic data and real-time traffic data, it predicts the trend of traffic flow changes in the future. Based on the analysis results and combined with preset rules and models, it generates control strategies for different electromechanical terminal equipment, such as adjusting the timing of traffic lights and updating the display content of variable message signs.

[0039] The terminal collaborative control execution module sends the control commands generated by the edge intelligent analysis and decision-making module to the corresponding electromechanical terminal devices, monitors the execution status of the electromechanical terminal devices, and sends the execution information of the electromechanical terminal devices to the remote monitoring center. For example, when a traffic light receives a command to adjust its timing, the terminal collaborative control execution module monitors whether the traffic light switches according to the command and feeds back the switching result to the upper-level module. This achieves precise control and collaborative operation of the electromechanical terminal devices, ensures the effective execution of control commands, and improves the reliability and stability of the system.

[0040] The security protection module is used to ensure the network security and data security of the system, and sends the system security protection log information to the remote monitoring center, which significantly improves the security and reliability of the system and helps protect the system's data and operation from external threats, thereby ensuring the stable operation of the highway electromechanical terminal collaborative control system.

[0041] It should be noted that the security protection module employs multiple security technologies, such as firewalls, intrusion detection systems, and data encryption, to provide comprehensive security protection for the system. During data transmission, data is encrypted to prevent it from being stolen or tampered with during transmission, and the system's security status is monitored in real time to promptly detect and prevent intrusion attacks. For example, when an illegal IP address attempts to access the system, the firewall will automatically block the access request and record relevant log information.

[0042] Example 2: As shown in Figure 2, the difference between this example and Example 1 is that the security protection module is connected to the protection risk decision module. The security protection decision module sends the system security protection log information to the protection risk decision module. The protection risk decision module analyzes the protection risk of the system during the detection period and generates a low-risk protection signal or a high-risk protection signal through analysis.

[0043] Furthermore, it sends low-risk or high-risk protection signals to the remote monitoring center. Upon receiving a high-risk protection signal, the remote monitoring center issues a corresponding warning to remind management personnel to strengthen system security measures, optimize subsequent monitoring and management, ensure stable system operation and data security, and avoid system paralysis that could adversely affect the normal operation of the highway. It demonstrates a high level of intelligence. The specific analysis process of the risk protection decision module is as follows:

[0044] The system obtains the total number of intrusions or attacks that the system has been subjected to during the detection period and marks them as intrusion detection values. The intrusion detection values ​​are compared with preset intrusion detection thresholds. If the intrusion detection value exceeds the preset intrusion detection threshold, it indicates that the system has a high risk of protection during the detection period and that system security protection supervision and optimization need to be strengthened. In this case, a high-risk protection signal is generated.

[0045] If the intrusion detection value does not exceed the preset intrusion detection threshold, the intrusion failure value is calculated by comparing the number of times the intrusion attack was not prevented during the detection period with the intrusion detection value. The intrusion failure value is then compared with the preset intrusion failure threshold. If the intrusion failure value exceeds the preset intrusion failure threshold, it indicates that the system has a high protection risk during the detection period, and a high protection risk signal is generated.

[0046] Furthermore, if the intrusion failure value does not exceed the preset intrusion failure threshold, the intrusion duration of the corresponding intrusion attack behavior is collected when the intrusion attack behavior cannot be stopped. The larger the intrusion attack duration value, the more untimely the response to the corresponding intrusion attack behavior is and the more serious the damage to the system. The intrusion time measurement value is obtained by summing up all the intrusion attack durations within the detection period.

[0047] The protection risk decision value is obtained by weighted summation of the attack detection value, attack failure value, and attack duration measurement value. Specifically, each of the attack detection value, attack failure value, and attack duration measurement value is assigned a corresponding preset weight coefficient, and then each of these values ​​is multiplied by its respective preset weight coefficient. The sum of these three products is then marked as the protection risk decision value. It should be noted that the larger the protection risk decision value, the higher the overall protection risk of the system during the detection period.

[0048] The protection risk decision value is compared with the preset protection risk decision threshold. If the protection risk decision value exceeds the preset protection risk decision threshold, it indicates that the overall protection risk of the system is relatively high during the detection period, and a high protection risk signal is generated. If the protection risk decision value does not exceed the preset protection risk decision threshold, it indicates that the overall protection risk of the system is relatively low during the detection period, and a low protection risk signal is generated.

[0049] Example 3: As shown in Figure 2, the difference between this example and Examples 1 and 2 is that the terminal collaborative control execution module is connected to the collaborative cooperation hazard assessment module. The collaborative cooperation hazard assessment module marks the corresponding electromechanical terminal equipment as assessment object i, where i is a natural number greater than 1. The terminal collaborative control execution module sends the execution information of all electromechanical terminal equipment to the collaborative cooperation hazard assessment module. The collaborative cooperation hazard assessment module analyzes the operating performance of assessment object i and comprehensively evaluates the collaborative cooperation performance of all electromechanical terminal equipment, thereby generating a normal collaborative cooperation signal or an abnormal collaborative cooperation signal.

[0050] Furthermore, normal or abnormal coordination signals are sent to the remote monitoring center. Upon receiving an abnormal coordination signal, the remote monitoring center issues a corresponding warning to remind management personnel to promptly inspect, repair, or replace the relevant electromechanical terminal equipment. This ensures the safe and stable operation of all electromechanical terminal equipment on the highway, guarantees effective control of all electromechanical terminal equipment, and further improves the highway's operational efficiency and safety. The specific analysis process of the coordination hazard assessment module is as follows:

[0051] Several evaluation periods are set within the detection period. If the electromechanical terminal equipment fails to execute the received control command correctly within the corresponding evaluation period, the corresponding evaluation period is marked as an abnormal period of collaborative control. The number of abnormal periods of collaborative control within the detection period is obtained and the ratio is calculated with the total number of evaluation periods to obtain the collaborative control anomaly value. The collaborative control anomaly value is then compared with the preset collaborative control anomaly threshold.

[0052] If the collaborative control anomaly value exceeds the preset collaborative control anomaly threshold, it indicates that there is a significant hidden danger in the collaborative control of all electromechanical terminal equipment on the highway during the detection period, and timely targeted improvement measures are required, thus generating a collaborative control anomaly signal.

[0053] Furthermore, if the collaborative control anomaly value does not exceed the preset collaborative control anomaly threshold, the number of times the evaluation object i failed to correctly execute the corresponding control command during the detection period is obtained and marked as the non-execution detection value. The non-execution detection value is compared with the corresponding preset non-execution detection threshold. If the non-execution detection value exceeds the preset non-execution detection threshold, it indicates that the command execution performance of the evaluation object i is poor during the detection period, and the evaluation object i is marked as a non-excellent object.

[0054] If the non-compliance detection value does not exceed the preset non-compliance detection threshold, the operating parameters (including temperature, current and other parameters) that need to be monitored for the evaluation object i are obtained, the real-time data of the corresponding operating parameters are collected, and the real-time data is compared with the corresponding preset data requirements. If the corresponding real-time data does not meet the corresponding preset data requirements, that is, the corresponding operating parameter has a deviation, then the corresponding operating parameter is judged to be in an unsafe state.

[0055] The total duration of the corresponding operating parameter being in an unsafe state during the detection period is obtained and marked as the parameter risk time feature value. The number of times the single duration of the corresponding operating parameter being in an unsafe state during the detection period exceeds the corresponding preset single duration threshold is marked as the parameter risk frequency feature value. The maximum value of the single duration of the corresponding operating parameter being in an unsafe state during the detection period is marked as the parameter risk time amplitude measurement value.

[0056] The parameter operation characteristic value is obtained by weighted summation of the parameter risk time characteristic value, parameter risk frequency characteristic value, and parameter risk time amplitude measurement value. Specifically, the parameter risk time characteristic value, parameter risk frequency characteristic value, and parameter risk time amplitude measurement value are assigned corresponding preset weight coefficients, and the parameter risk time characteristic value, parameter risk frequency characteristic value, and parameter risk time amplitude measurement value are multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the parameter operation characteristic value.

[0057] It should be noted that the larger the value of the parameter operating characteristic value, the worse the overall control performance of the corresponding operating parameter for the evaluation object i during the detection period. The parameter operating characteristic value is compared with the corresponding preset parameter operating characteristic threshold. If the parameter operating characteristic value exceeds the corresponding preset parameter operating characteristic threshold, it indicates that the overall control performance of the corresponding operating parameter for the evaluation object i during the detection period is poor. In this case, the corresponding operating parameter is marked as the control optimization parameter.

[0058] If the evaluation object i has control optimization parameters during the testing period, it indicates that the operation risk of the evaluation object i is high during the testing period, and the evaluation object i is marked as a non-excellent object; if the evaluation object i does not have control optimization parameters during the testing period, the ratio of the parameter operation characteristic value of the corresponding operation parameter to the corresponding preset parameter operation characteristic threshold is calculated to obtain the parameter operation ratio value of the corresponding operation parameter. Each operation parameter is pre-set to correspond to a set of preset weight values ​​greater than zero. The more important the stable operation of the corresponding operation parameter is, the larger the value of the preset weight value that matches it.

[0059] The parameter ratio value of the corresponding operating parameter is multiplied by the corresponding preset weight value to obtain the parameter evaluation value. The parameter evaluation values ​​of all operating parameters that need to be monitored for the evaluation object i are obtained and summed to obtain the operating analysis value. The operating analysis value is compared with the preset operating analysis threshold. If the operating analysis value exceeds the preset operating analysis threshold, it indicates that the operating risk of the evaluation object i is high during the detection period. Then, the evaluation object i is marked as a non-excellent object.

[0060] If there are substandard objects during the testing period, it indicates that there are significant potential risks to the coordinated control of all electromechanical terminal equipment on the highway during the testing period, requiring timely and targeted improvement measures, thus generating an abnormal coordination signal; if there are no substandard objects during the testing period, it indicates that there are relatively few potential risks to the coordinated control of all electromechanical terminal equipment on the highway during the testing period, thus generating a normal coordination signal.

[0061] The working principle of this invention is as follows: In use, the multi-source data sensing and fusion module collects various raw data from the highway and performs fusion processing. The edge intelligent analysis and decision-making module uses edge computing to perform real-time analysis and processing of the fused data and uses intelligent algorithms to generate corresponding control strategies and decision commands. The terminal collaborative control execution module sends the control commands to the corresponding electromechanical terminal equipment and monitors their execution status. The security protection module ensures the network security and data security of the system. This invention helps to solve the problems of large data transmission latency, high network bandwidth requirements, low system reliability, and limited data processing and analysis capabilities in existing technologies. It can meet the complex and ever-changing operational needs of highways, and through the edge computing architecture and the synergistic effect of various modules, it effectively improves the operational efficiency and safety of highways, providing strong support for intelligent highway management.

[0062] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0063] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A highway electromechanical terminal collaborative control system based on edge computing, characterized in that, It includes a multi-source data perception and fusion module, an edge intelligent analysis and decision-making module, a terminal collaborative control and execution module, a security protection module, and a remote monitoring center; the multi-source data perception and fusion module collects various raw data on the highway and performs fusion processing on the multi-source heterogeneous data; the edge intelligent analysis and decision-making module, based on edge computing capabilities, performs real-time analysis and processing on the fused data, and uses intelligent algorithms to generate corresponding control strategies and decision commands; The terminal collaborative control execution module sends the control commands generated by the edge intelligent analysis and decision-making module to the corresponding electromechanical terminal devices, monitors the execution status of the electromechanical terminal devices, and sends the execution information of the electromechanical terminal devices to the remote monitoring center. The security protection module is used to ensure the network security and data security of the system, and sends the system security protection log information to the remote monitoring center. The terminal collaborative control execution module communicates with the collaborative cooperation hidden danger assessment module, which marks the corresponding electromechanical terminal devices as assessment objects i, where i is a natural number greater than 1. The collaborative cooperation hidden danger assessment module analyzes the operating performance of assessment object i and comprehensively evaluates the collaborative cooperation performance of all electromechanical terminal devices, and sends the normal collaborative cooperation signal or the abnormal collaborative cooperation signal to the remote monitoring center. The specific analysis process of the collaborative cooperation hidden danger assessment module includes: setting several assessment periods within the detection period; if there are electromechanical terminal devices that do not correctly execute the received control commands within the corresponding assessment period, the corresponding assessment period is marked as an abnormal collaborative control period. The number of abnormal control periods during the detection period is obtained and the ratio is calculated with the total number during the evaluation period to obtain the control anomaly value. If the control anomaly value exceeds the preset control anomaly threshold, a control anomaly signal is generated; if the control anomaly value does not exceed the preset control anomaly threshold, a control anomaly signal is generated when there are non-excellent objects during the detection period; otherwise, a control normal signal is generated.

2. The highway electromechanical terminal collaborative control system based on edge computing according to claim 1, characterized in that, The multi-source data sensing and fusion module collects multi-source data in real time through a sensor network deployed at various key locations on the highway; it uses data cleaning algorithms to remove noise and abnormal data, and employs data fusion technology to correlate and integrate data from different sensors and data sources.

3. The highway electromechanical terminal collaborative control system based on edge computing according to claim 1, characterized in that, The edge intelligent analysis and decision-making module receives data from the multi-source data perception and fusion module, and uses machine learning and deep learning algorithms to deeply mine and analyze the data; based on the analysis results and in combination with preset rules and models, it generates control strategies for different electromechanical terminal devices.

4. The highway electromechanical terminal collaborative control system based on edge computing according to claim 1, characterized in that, The security protection module employs multiple security protection technologies to provide comprehensive security protection for the system. During data transmission, it encrypts the data and monitors the system's security status in real time, detecting and preventing intrusion attacks.

5. The highway electromechanical terminal collaborative control system based on edge computing according to claim 1, characterized in that, The security protection module communicates with the protection risk decision module. The security protection decision module sends the system security protection log information to the protection risk decision module. The protection risk decision module analyzes the protection risk of the system during the detection period and sends the protection low risk signal or protection high risk signal to the remote monitoring center.

6. The highway electromechanical terminal collaborative control system based on edge computing according to claim 5, characterized in that, The specific analysis process of the protection risk decision module includes: obtaining the total number of intrusions or attacks on the system during the detection period and marking it as an intrusion detection value; if the intrusion detection value exceeds the preset intrusion detection threshold, a protection high-risk signal is generated; if the intrusion detection value does not exceed the preset intrusion detection threshold, the ratio of the number of intrusion attacks that could not be prevented during the detection period to the intrusion detection value is calculated to obtain an intrusion failure value; if the intrusion failure value exceeds the preset intrusion failure threshold, a protection high-risk signal is generated.

7. The highway electromechanical terminal collaborative control system based on edge computing according to claim 6, characterized in that, If the attack failure value does not exceed the preset attack failure threshold, the protection risk decision value is calculated by weighting and summing the attack detection value, attack failure value and attack time measurement value. If the protection risk decision value exceeds the preset protection risk decision threshold, a high protection risk signal is generated. If the protection risk decision value does not exceed the preset protection risk decision threshold, a low protection risk signal is generated.

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