Intelligent terminal monitoring system and method based on edge computing

Abstract

The invention discloses an intelligent terminal monitoring system and method based on edge calculation, and relates to the technical field of terminal monitoring, and the method comprises the steps: calculating the important coefficient of a camera through obtaining the monitoring region data of a traffic monitoring camera corresponding to each fault edge calculation center; screening out a target edge calculation center capable of receiving data of the fault edge calculation center from the non-fault edge calculation centers; calculating an adaptation coefficient of the target edge calculation center and the fault edge calculation center; according to the adaptation coefficient and the importance coefficient, determining a final target edge calculation center of each fault edge calculation center, and transmitting traffic monitoring camera data to the center for analysis; and finally, the target edge calculation center analyzes the data by combining the important coefficient of the camera, and monitors whether the camera has a fault or not. The traffic monitoring camera data can be timely and efficiently processed, the edge computing center can reasonably distribute the load, and the stability and the accuracy of the monitoring system are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of terminal monitoring, and in particular to an intelligent terminal monitoring system and method based on edge computing. Background Art

[0002] With the acceleration of the urbanization process, real-time monitoring and management become particularly important; traditional traffic flow analysis relies on a centralized data processing architecture, which has a high latency. Especially when dealing with large-scale data and limited bandwidth, it is difficult to meet the real-time requirements. To address this challenge, edge computing is introduced, which can push data processing closer to the data source, enabling local analysis, reducing network latency, and improving response speed; in the application field of traffic monitoring, multiple edge computing centers are set up, and an edge-computing-based terminal monitoring system is set up in the edge computing center with the maximum computing power. The edge-computing-based terminal monitoring system allocates the terminals that need to be calculated and analyzed to each edge computing center. Each edge computing center, based on the terminals allocated by the edge-computing-based terminal monitoring system, such as traffic monitoring cameras, analyzes the allocated traffic monitoring cameras to determine whether there are any faults, and issues a reminder in a timely manner to ensure that the traffic monitoring cameras can collect correct image data, and identify key events such as traffic flow and vehicle speed based on the correct image data, so as to monitor the traffic conditions in real time; moreover, the edge-computing-based terminal monitoring system also has a redundant design and an emergency mechanism. For example, when a local edge computing center fails, the data of the traffic monitoring cameras in that area is transmitted to other edge computing centers for analysis, thus effectively balancing computing resources, reducing latency, and enhancing the response speed and processing capacity of the overall system, achieving efficient monitoring of traffic monitoring cameras.

[0003] For example, when a certain edge computing center fails, the edge-computing-based terminal monitoring system can, through analysis and judgment, select the most suitable edge center among other available edge computing centers to process the data of the terminal devices corresponding to the original faulty edge center; however, when multiple edge computing centers have problems, it is possible that one edge computing center is used as a backup for multiple faulty edge centers, which may lead to an overloaded load on this edge computing center, a decline in processing capacity, and even delays or overloaded operations. This approach that overly relies on a single backup center may affect the response speed and stability of the system, resulting in problems with the monitoring results of traffic monitoring cameras. Summary of the Invention

[0004] The object of the present invention is to solve the above-mentioned problems, and provide an intelligent terminal monitoring system and method based on edge computing.

[0005] In the first aspect of the implementation of the present invention, an intelligent terminal monitoring method based on edge computing is first proposed, and the method includes: Obtain the traffic area data monitored by each traffic monitoring camera, which is used to evaluate the complexity of the traffic area, and calculate the importance coefficient of the camera according to the traffic area data; Obtain the resource information of each non-faulty edge computing center. According to the resource information of each non-faulty edge computing center, screen out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center, which is called the target edge computing center; Obtain the distances and historical data transmission times between the target edge computing centers and each faulty edge computing center, and calculate the adaptation coefficients between each target edge computing center and each faulty edge computing center according to the distances and historical data transmission times; Determine the final target edge computing center selected by each faulty edge computing center according to the adaptation coefficient and the importance coefficient, and transfer the corresponding traffic monitoring camera data to the selected final target edge computing center for data analysis; The final target edge computing center analyzes the transmitted traffic monitoring camera data, and monitors whether the corresponding traffic monitoring camera is faulty in combination with the importance coefficient of the transmitted traffic monitoring camera.

[0006] Optionally, the steps for calculating the importance coefficient of the camera according to the traffic area data are as follows: Take the area captured by the traffic monitoring camera as the target area, obtain the total number of vehicles that have passed through the target area in the past preset time period, and obtain the driving time of each vehicle in the target area. Calculate the variance of the driving time according to the driving times of all vehicles in the target area, which is used as the vehicle speed fluctuation value of the target area; Obtain the vehicles that have had traffic accidents in the target area, and divide the number of vehicles that have had traffic accidents by the total number of vehicles that have passed through, to obtain the vehicle accident probability of the target area; Obtain the drivable area of the lanes in the target area, and divide the total number of vehicles that have passed through by the drivable area of the lanes, to obtain the vehicle density of the target area; Calculate the importance coefficient of the camera according to the vehicle speed fluctuation value, vehicle accident probability and vehicle density of the target area.

[0007] Optionally, the steps for calculating the importance coefficient of the camera according to the vehicle speed fluctuation value, vehicle accident probability and vehicle density of the target area are as follows: Take the vehicle speed fluctuation value, vehicle accident probability and vehicle density of the target area as the input items of the fuzzy rule, and take the importance coefficient of the camera as the output item; Fuzzify the input items, and convert the accurate input values into fuzzy sets; Define fuzzy rules according to the fuzzy sets of the input items, and map the input items to the output item; According to the fuzzy rules, reason about the input items to determine the fuzzy values of the output items; For each rule, calculate the membership degree of its antecedent and take the minimum value as the activation degree of the rule; Synthesize the results of all rules to calculate the fuzzy set of the output items; Convert the fuzzy values in the fuzzy set of the output items into exact values, and output the importance coefficient of the camera according to the result of defuzzification.

[0008] Optionally, the steps to screen out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center, which is called the target edge computing center, are as follows: Obtain the number of CPU cores and the clock frequency of each CPU of each non-faulty edge computing center, calculate the computing power value of the non-faulty edge computing center according to the number of CPU cores and the clock frequency of each CPU, and the calculation formula is: , where TR is the computing power value C of the non-faulty edge computing center i and F i are the number of the i-th CPU cores and the clock frequency respectively, and n is the total number of CPUs; Obtain the usage rate of each CPU of each non-faulty edge computing center, and calculate the average value of the usage rates of all CPUs as the load value of the non-faulty edge computing center; Calculate the computing power coefficient of each non-faulty edge computing center according to the computing power value and the load value, and the calculation formula is: , where UG is the computing power coefficient and TE is the load value; Compare the computing power coefficients of each non-faulty edge computing center with the preset computing power coefficient threshold. If the computing power coefficient is not less than the preset computing power coefficient threshold, each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center is called the target edge computing center.

[0009] Optionally, the steps to calculate the adaptation coefficient between each target edge computing center and each faulty edge computing center according to the distance and the historical data transmission times are as follows: Obtain the position coordinates of the target edge computing center and the faulty edge computing center, and calculate the shortest straight-line distance of signal transmission between the target edge computing center and the faulty edge computing center by calculating the Euclidean distance between two points: Calculate the signal transmission attenuation coefficient between the target edge computing center and the faulty edge computing center according to the shortest straight-line distance, and the calculation formula is: , where YT is the signal transmission attenuation coefficient, D is the shortest straight-line distance, A is a preset constant representing the basic value of path loss, and m is a preset path loss exponent with a value range of 2 - 3.5; Calculate the adaptation coefficients of each target edge computing center and each faulty edge computing center according to the signal transmission attenuation coefficient and the historical data transmission times between the target edge computing center and the faulty edge computing center.

[0010] Optionally, the steps for calculating the adaptation coefficients of each target edge computing center and each faulty edge computing center according to the signal transmission attenuation coefficient and the historical data transmission times between the target edge computing center and the faulty edge computing center are as follows: Obtain the total number of data transmissions and the total number of successful transmissions between the target edge computing center and the faulty edge computing center, and divide the total number of successful transmissions by the total number of data transmissions to obtain the transmission success rate. Obtain the time intervals between two adjacent transmissions, calculate the mean value of the transmission time intervals as the average transmission time interval, and perform normalization processing on the average transmission time interval to map it to the numerical interval of 0-1. Calculate the adaptation coefficient between the target edge computing center and the faulty edge computing center according to the signal transmission attenuation coefficient, the transmission success rate, and the normalized average transmission time interval. The calculation formula is: , where ED is the adaptation coefficient, YT, cv, and ku are the signal transmission attenuation coefficient, the transmission success rate, and the normalized average transmission time interval respectively, and a1, a2, and a3 are the preset proportionality coefficients of YT, cv, and ku respectively, and a1, a2, and a3 are all greater than 0.

[0011] Optionally, the steps for determining the final target edge computing center selected by each faulty edge computing center are as follows: After calculating the adaptation coefficients between each faulty edge computing center and each target edge computing center, for each faulty edge computing center, sort the adaptation coefficients between the faulty edge computing center and each target edge computing center in descending order to obtain the set of adaptation target edge computing centers corresponding to each faulty edge computing center. Take the target edge computing center with the largest set of adaptation target edge computing centers as the corresponding final target edge computing center. If a certain target edge computing center is simultaneously used as the final target edge computing center of multiple faulty edge computing centers, take this target edge computing center as the final target edge computing center of the faulty edge computing center with the largest importance coefficient.

[0012] Optionally, the steps for monitoring whether the corresponding traffic monitoring camera is faulty by combining the importance coefficient of the transmitted traffic monitoring camera are as follows: Extract frames from the video transmitted by the traffic monitoring camera to obtain several frames of images. For each frame of image, perform transformation through the Laplace operator, calculate the variance of the pixel values of the transformed image, and use the variance of the pixel values as the image clarity value of the corresponding image; add up the image clarity values of each frame of image as the video clarity index of the traffic monitoring camera. Obtain the actual frame rate of each frame of image, and calculate the difference between the actual frame rate and the preset minimum frame rate. When the difference is less than 0, mark the corresponding frame of image as an abnormal image; divide the number of abnormal images by the total number of images to obtain the image frame rate abnormality ratio. Obtain the health assessment index of the traffic monitoring camera based on the video clarity index, the image frame rate abnormality ratio, and the importance coefficient of the corresponding traffic monitoring camera, and monitor whether the corresponding traffic monitoring camera is faulty according to the health assessment index.

[0013] Optionally, the steps of obtaining the health assessment index of the traffic monitoring camera based on the video clarity index, the image frame rate abnormality ratio, and the importance coefficient of the corresponding traffic monitoring camera, and monitoring whether the corresponding traffic monitoring camera is faulty are as follows: Obtain the health assessment index of the traffic monitoring camera based on the video clarity index, the image frame rate abnormality ratio, and the importance coefficient of the corresponding traffic monitoring camera. The calculation formula is: , where FD is the health assessment index of the traffic monitoring camera, pl, uk, and tu are the video clarity index, the image frame rate abnormality ratio, and the importance coefficient of the corresponding traffic monitoring camera respectively, and b1 and b2 are the preset proportionality coefficients of the video clarity index and the image frame rate abnormality ratio respectively, and both b1 and b2 are greater than 0; Compare the health assessment index of the traffic monitoring camera with the preset health assessment index threshold. If the health index is not less than the preset health assessment index threshold, the corresponding traffic monitoring camera is not faulty; if it is less, the corresponding traffic monitoring camera is faulty.

[0014] In the second aspect of the implementation of the present invention, an intelligent terminal monitoring system based on edge computing is proposed. The system includes: Importance degree module: Obtain the traffic area data monitored by each traffic monitoring camera, used to evaluate the complexity of the traffic area, and calculate the importance coefficient of the camera according to the traffic area data; Screening module: Obtain the resource information of each non-faulty edge computing center, and screen out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center from the resource information of each non-faulty edge computing center, which is called the target edge computing center; Adaptation degree module: Obtain the distances and historical data transmission times between the target edge computing center and each faulty edge computing center, and calculate the adaptation coefficients between each target edge computing center and each faulty edge computing center according to the distances and historical data transmission times; Selection module: Determine the final target edge computing center selected by each faulty edge computing center according to the adaptation coefficient and the importance coefficient, and transfer the corresponding traffic monitoring camera data to the selected final target edge computing center for data analysis; Terminal monitoring module: The final target edge computing center analyzes the transmitted traffic monitoring camera data, and monitors whether the corresponding traffic monitoring camera is faulty in combination with the importance coefficient of the transmitted traffic monitoring camera.

[0015] Advantages of the present invention: The present invention proposes an intelligent terminal monitoring system and method based on edge computing. By obtaining the monitoring area data of traffic monitoring cameras corresponding to each faulty edge computing center, and calculating the importance coefficient of the cameras according to the area data to evaluate the importance degree of the cameras; obtaining the resource information of each non-faulty edge computing center, and screening out the target edge computing centers that can receive the data of the faulty edge computing centers; obtaining the distance information between the target edge computing centers and the faulty edge computing centers, calculating the adaptation coefficient, and evaluating the adaptation degree between them; according to the adaptation coefficient and the importance coefficient, determining the final target edge computing center of each faulty edge computing center, and transferring the traffic monitoring camera data to this center for analysis; the final target edge computing center analyzes the data in combination with the importance coefficient of the camera to monitor whether the camera is faulty; in this way, when multiple edge computing centers have problems and a certain edge computing center is backed up by multiple faulty edge centers, it can be appropriately allocated and calculated according to the actual situation, reducing the overloading of the backup edge computing center, ensuring its processing ability, and preventing delays or overloading situations, ensuring the response speed and stability of the entire edge computing-based terminal monitoring system, and making the monitoring results of traffic monitoring cameras correct. Description of the drawings

[0016] The present invention will be further described below with reference to the drawings.

[0017] Figure 1 It is a flowchart of an intelligent terminal monitoring method based on edge computing; Figure 2 It is a framework diagram of an intelligent terminal monitoring system based on edge computing. Specific implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The embodiment of the present invention provides an intelligent terminal monitoring method based on edge computing. Refer to Figure 1 , Figure 1 which is a flowchart of an intelligent terminal monitoring method based on edge computing provided by the embodiment of the present invention. The method includes the following steps: Obtain the traffic area data monitored by each traffic monitoring camera for evaluating the complexity of the traffic area, and calculate the importance coefficient of the camera according to the traffic area data; Obtain the resource information of each non-faulty edge computing center, and screen out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center from the resource information of each non-faulty edge computing center, which is called the target edge computing center; Obtain the distance and historical data transmission times between the target edge computing center and each faulty edge computing center, and calculate the adaptation coefficient between each target edge computing center and each faulty edge computing center according to the distance and historical data transmission times; Determine the final target edge computing center selected by each faulty edge computing center according to the adaptation coefficient and the importance coefficient, and transfer the corresponding traffic monitoring camera data to the selected final target edge computing center for data analysis; The final target edge computing center analyzes the transmitted traffic monitoring camera data, and monitors whether the corresponding traffic monitoring camera is faulty in combination with the importance coefficient of the transmitted traffic monitoring camera.

[0021] Based on the intelligent terminal monitoring method based on edge computing provided by the embodiment of the present invention, through the above method, when multiple edge computing centers have problems and a certain edge computing center is backed up by multiple faulty edge centers, it can be appropriately allocated and calculated according to the actual situation, reducing the overloading of the backup edge computing center, ensuring its processing ability, and preventing delays or overloading operations, ensuring the response speed and stability of the entire edge computing-based terminal monitoring system, and making the monitoring results of traffic monitoring cameras correct.

[0022] In one embodiment, traffic area data monitored by each traffic monitoring camera is obtained to evaluate the complexity of the traffic area, and the importance coefficient of the camera is calculated based on the traffic area data; the importance coefficient is used to evaluate the importance of each traffic monitoring camera of the faulty edge computing center; Among them, the steps of calculating the importance coefficient of the camera based on the traffic area data are as follows: Take the area captured by the traffic monitoring camera as the target area, obtain the total number of vehicles that have passed through the target area in the past preset time period, and obtain the travel time of each vehicle in the target area. Calculate the variance of the travel time based on the travel times of all vehicles in the target area as the vehicle speed fluctuation value of the target area; Obtain the vehicles that have had traffic accidents in the target area, and divide the number of vehicles that have had traffic accidents by the total number of vehicles that have passed through to obtain the vehicle accident probability of the target area; Obtain the drivable area of the lanes in the target area, and divide the total number of vehicles that have passed through by the drivable area of the lanes to obtain the vehicle density of the target area; Calculate the importance coefficient of the camera based on the vehicle speed fluctuation value, vehicle accident probability, and vehicle density of the target area.

[0023] It should be noted that in the above process of calculating the importance coefficient of the camera, the area captured by the traffic monitoring camera is used as the target area. The number of vehicles passing through and their travel times can be identified by an image processing algorithm to obtain the passing time of each vehicle, and combined with the timestamp of each vehicle, the total number of vehicles passing through the area within the preset time period and their respective travel times can be accurately calculated; these data are usually provided by a traffic monitoring system or a traffic flow analysis platform; vehicle speed information can be obtained by combining license plate recognition technology with spatio-temporal positioning data, or by estimating the real-time speed of vehicles by combining ground sensors with camera video streams; the variance of the vehicle speed reflects the fluctuation of traffic fluency in the area and can effectively reflect the congestion level and abnormal behavior; data on traffic accidents can be obtained from local relevant departments; the drivable area of the lanes can be directly obtained from the local traffic system; there can also be other acquisition methods, which are not specifically limited and elaborated here.

[0024] It should be noted that the greater the vehicle speed fluctuation value, vehicle accident probability, and vehicle density in the target area, the greater the importance coefficient of the corresponding camera and the greater the importance of the terminal device. The reason is as follows: When the vehicle speed fluctuation value, vehicle accident probability, and vehicle density in the target area are greater, it indicates that the traffic conditions in this area are more complex and dangerous. Therefore, more accurate and timely monitoring is required to ensure traffic safety and efficiency. A large vehicle speed fluctuation value usually means poor traffic flow in this area, possibly with severe congestion, frequent stops and accelerations, increasing the risk of traffic accidents. A high vehicle accident probability directly reflects a high frequency of traffic accidents in this area, indicating that the monitoring of this area is crucial for accident prevention and emergency response. A large vehicle density means greater traffic pressure in this area, which may lead to problems such as too small vehicle spacing and increased congestion, increasing the possibility of sudden traffic events. Since these factors directly affect traffic safety and smoothness, the camera needs to have higher monitoring accuracy and reaction speed to promptly capture abnormal changes, thereby effectively warning, handling traffic accidents, or optimizing traffic flow. Therefore, in areas where these factors are more significant, the importance coefficient of the camera will be calculated to be higher, reflecting the significance and urgency of the monitoring task in this area for the entire traffic management system. Consequently, the corresponding terminal device also appears to be more important and has higher monitoring value.

[0025] In one embodiment, the steps for calculating the importance coefficient of the camera based on the vehicle speed fluctuation value, vehicle accident probability, and vehicle density in the target area are as follows: Take the vehicle speed fluctuation value, vehicle accident probability, and vehicle density in the target area as the input items of the fuzzy rule, and take the importance coefficient of the camera as the output item; Fuzzify the input items, converting the precise input values into fuzzy sets; Define the fuzzy rules according to the fuzzy sets of the input items, mapping the input items to the output item; Based on the fuzzy rules, reason about the input items to determine the fuzzy value of the output item; For each rule, calculate the membership degree of its antecedent and take the minimum value as the activation degree of the rule; Synthesize the results of all rules to calculate the fuzzy set of the output item; Convert the fuzzy values in the fuzzy set of the output item into precise values, and output the importance coefficient of the camera according to the result of defuzzification.

[0026] It should be noted that the input and output items are defined as follows: Take the vehicle speed fluctuation value, vehicle accident probability, and vehicle density in the target area as the input items of the fuzzy rule, and the importance coefficient of the camera as the output item. Each input item and output item needs to clarify its fuzzification range and possible values.

[0027] Fuzzify the input items: Convert the exact numerical values of the input items (i.e., vehicle speed fluctuation value, vehicle accident probability, and vehicle density) into fuzzy sets. In this process, fuzzy methods such as triangular membership functions or Gaussian membership functions can be used to convert specific numerical values (such as vehicle speed fluctuation values, accident probabilities, etc.) into fuzzy sets. For example, the vehicle speed fluctuation value may be fuzzified into three levels: "low", "medium", and "high", and each level will have corresponding membership degree values.

[0028] Define fuzzy rules: Based on traffic management experience and expert opinions, define fuzzy rules to map the input items to the output items. An example of a fuzzy rule can be: "If the vehicle speed fluctuation value is high, the vehicle accident probability is high, and the vehicle density is high, then the importance coefficient of the camera is high." By setting the rules, the relationship between the input items and the output items is determined.

[0029] Fuzzy inference: According to the defined fuzzy rules, perform fuzzy inference. Map the fuzzy set of the input items to the fuzzy set of the output items through the inference method. At this time, use the inference method of fuzzy logic, such as the Mamdani fuzzy inference method, combined with the fuzzy set of the input items, to infer the fuzzy set of the importance coefficient of the camera.

[0030] Calculate the activation degree: For each rule, calculate the membership degree of its antecedent (i.e., the input items), and take the minimum value as the activation degree of this rule. This process ensures that the strength of the rule reflects the actual fuzzy degree of the input items. For example, when the vehicle speed fluctuation value is very high and the accident probability is relatively large, the activation degree of the rule will be stronger.

[0031] Combine the rule results: Combine the results of all fuzzy rules and calculate the fuzzy set of the output items. The activation degree of each rule will affect the membership degree of the output items. Combine the outputs of multiple rules through methods such as weighted average to obtain a comprehensive fuzzy output.

[0032] Defuzzify: Convert the fuzzy values in the combined output item fuzzy set into exact values. The defuzzification process uses a certain defuzzification algorithm (such as the centroid method) to obtain the exact value of the importance coefficient of the camera, thereby determining the final importance coefficient of each camera. This process provides specific numerical outputs for subsequent monitoring system decision-making.

[0033] Through this fuzzy inference method, it is possible to comprehensively consider the influences of different factors, flexibly evaluate the importance of the camera, and thus provide a basis for subsequent edge computing and resource allocation.

[0034] In one implementation manner, the benefits of calculating the importance coefficient of a camera based on the vehicle speed fluctuation value, vehicle accident probability, and vehicle density in the target area are as follows: The vehicle speed fluctuation value reveals the stability of the traffic flow, and the accident probability and vehicle density are directly related to the occurrence probability of traffic accidents and traffic pressure, and can reflect the complexity and danger of the area from multiple dimensions; Secondly, these factors are all the most critical indicators in traffic management, with strong practicality and prediction ability; By combining these three, a more comprehensive and effective monitoring and evaluation mechanism can be formed to ensure that the traffic monitoring system can give priority to high-risk areas, optimize resource allocation, and improve the overall traffic safety and emergency response ability.

[0035] In one embodiment, resource information of each non-faulty edge computing center is obtained, and based on the resource information of each non-faulty edge computing center, each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center is screened out and called the target edge computing center; Among them, the step of screening out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center based on the resource information of each non-faulty edge computing center and calling it the target edge computing center is as follows: Obtain the number of cores and clock frequency of each CPU of each non-faulty edge computing center, calculate the computing power value of the non-faulty edge computing center according to the number of cores and clock frequency of each CPU, and the calculation formula is: , where, TR is the computing power value of the non-faulty edge computing center, C i and F i are the number of cores and clock frequency of the i-th CPU respectively, and n is the total number of CPUs; Obtain the utilization rate of each CPU of each non-faulty edge computing center, and calculate the average value of the utilization rates of all CPUs as the load value of the non-faulty edge computing center; Calculate the computing power coefficient of each non-faulty edge computing center according to the computing power value and the load value, and the calculation formula is: , where, UG is the computing power coefficient, and TE is the load value; Screen out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center according to the computing power coefficient of each non-faulty edge computing center, and call it the target edge computing center.

[0036] It should be noted that when calculating the computing power coefficients of each non-faulty edge computing center, the data acquisition methods for the involved data are as follows: The number of cores and the clock frequency can be obtained through hardware monitoring tools (such as the lscpu command, system management interface, etc.), and usually directly extracted from the CPU specifications; the current usage rate of each CPU can be dynamically obtained through system monitoring tools (such as top, vmstat, htop, etc.) and will be reflected in the system resource management tool in real time. These data can help accurately evaluate the computing power coefficients of non-faulty edge computing centers.

[0037] It should be noted that when the number of cores of the non-faulty edge computing center is larger, the clock frequency is larger, and the CPU usage rate is smaller, the corresponding computing power coefficient of the non-faulty edge computing center is larger, and it can receive and analyze the traffic monitoring camera data of the faulty edge computing center. The reason is that: when the number of cores of the non-faulty edge computing center is larger, it means that the center has more computing units and can process more tasks simultaneously, improving the parallel computing ability; when the clock frequency is larger, each core can execute more instructions per unit time, and the computing speed is faster, which improves the processing ability of the center. On the other hand, the smaller the CPU usage rate, it means that the resources of this edge computing center have not been fully consumed, and there is still enough computing power and processing ability to carry more tasks, avoiding performance degradation caused by overload. Therefore, when these conditions are met simultaneously, the non-faulty edge computing center can process the traffic monitoring camera data of the faulty edge computing center more efficiently and stably, avoiding response delays or computing performance degradation caused by high loads, and thus being able to better perform data analysis, real-time monitoring, and rapid response to ensure the stability and efficiency of the overall system.

[0038] In one embodiment, the steps of screening out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center according to the computing power coefficients of each non-faulty edge computing center, which is called the target edge computing center, are as follows: Compare the computing power coefficients of each non-faulty edge computing center with a preset computing power coefficient threshold. If the computing power coefficient is not less than the preset computing power coefficient threshold, each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center is called the target edge computing center.

[0039] It should be noted that the preset computing power coefficient threshold is set by professionals according to the actual situation and is not specifically limited or elaborated here.

[0040] In one implementation manner, when screening edge computing centers that can receive traffic monitoring camera data from a faulty edge computing center, first compare the computing power coefficient of each non-faulty edge computing center with a preset computing power coefficient threshold. If the computing power coefficient of a certain non-faulty edge computing center is not less than the set threshold, it means that the center has sufficient computing power and resources to process additional traffic monitoring camera data. In this way, it can be ensured that the selected target edge computing center has sufficient processing power, avoiding calculation delays or overload phenomena caused by insufficient resources, and ensuring the real-time and accuracy of traffic data analysis. Therefore, the selected target edge computing centers are those that not only meet the computing power threshold requirements but also can efficiently process the data of the faulty edge computing center under the current load, thus maintaining the stability and efficient response of the system.

[0041] In one embodiment, obtain the distances and historical data transmission times between the target edge computing centers and each faulty edge computing center, and calculate the adaptation coefficients between each target edge computing center and each faulty edge computing center according to the distances and historical data transmission times; the adaptation coefficients are used to evaluate the adaptation degree between each target edge computing center and each faulty edge computing center; Among them, the steps of calculating the adaptation coefficients between each target edge computing center and each faulty edge computing center according to the distances and historical data transmission times are as follows: Obtain the position coordinates of the target edge computing center and the faulty edge computing center, and calculate the shortest straight-line distance of signal transmission between the target edge computing center and the faulty edge computing center by calculating the Euclidean distance between two points: Calculate the signal transmission attenuation coefficient between the target edge computing center and the faulty edge computing center according to the shortest straight-line distance, and the calculation formula is: , where YT is the signal transmission attenuation coefficient, D is the shortest straight-line distance, A is a preset constant representing the basic value of path loss, and m is a preset path loss exponent with a value range of 2 - 3.5; Calculate the adaptation coefficients between each target edge computing center and each faulty edge computing center according to the signal transmission attenuation coefficient and the historical data transmission times between the target edge computing center and the faulty edge computing center.

[0042] It should be noted that in the process of calculating the signal transmission attenuation coefficient, the data acquisition methods involved include the following aspects: First, the geographical location coordinates (i.e., longitude and latitude) of each target edge computing center and the faulty edge computing center need to be obtained, and these coordinates can be obtained through the GPS positioning system or the map API. The values of the path loss constant A and the path loss exponent m usually depend on the type of wireless signal propagation environment, and the specific value ranges vary according to different environments. For example, in an urban environment, due to the density of buildings and multiple reflections of signals, the path loss exponent nnn usually takes values between 2.7 and 3.5, and the path loss constant A is generally in the range of 50 to 100 dB. For a suburban environment, the signal propagation is relatively open, the value of m is generally 2.0 to 3.0, and the range of the path loss constant A is 40 to 80 dB. In open areas, since there is almost no building interference and the signal propagation is relatively straight, the path loss exponent m usually takes values between 1.8 and 2.2, and the path loss constant A will be lower, generally between 30 and 60 dB. These values are usually set through on-site measured data or through specific wireless communication standards (such as the ITU-R model); it can also be other acquisition methods, which are not specifically limited and elaborated here. It should be noted that when the signal transmission attenuation coefficient between the target edge computing center and the faulty edge computing center is larger, the corresponding adaptability between the target edge computing center and the faulty edge computing center is lower. At this time, if the traffic monitoring camera data of the faulty edge computing center is transmitted to the target edge computing center, it may cause monitoring errors at the target edge computing center. The reason is that: when the signal transmission attenuation coefficient between the target edge computing center and the faulty edge computing center is larger, it means that the signal loss is more serious during the transmission process, and the signal intensity will drop significantly. This is usually related to a longer transmission distance, complex environmental interference (such as buildings, terrain, etc.) or poor communication conditions (such as signal interference). Therefore, the data transmitted to the target edge computing center may be delayed, data may be lost or there may be errors, resulting in inaccurate data analysis results. If the attenuation coefficient is large, it means that the target edge computing center may not be able to effectively receive and process the monitoring data from the faulty edge computing center, thereby affecting the accuracy and reliability of the monitoring, and may lead to incorrect monitoring results at the target edge computing center and even make wrong decisions.

[0043] In one embodiment, the steps of calculating the adaptation coefficient of each target edge computing center and each faulty edge computing center according to the signal transmission attenuation coefficient and the historical data transmission times between the target edge computing center and the faulty edge computing center are as follows: Obtain the total number of data transmissions and the total number of successful transmissions between the target edge computing center and the faulty edge computing center, and divide the total number of successful transmissions by the total number of data transmissions to obtain the transmission success rate; Obtain the transmission time intervals of two adjacent times, calculate the mean value of the transmission time intervals as the average transmission time interval, and perform normalization processing on the average transmission time interval to map it to the numerical interval of 0 - 1; Calculate the adaptation coefficient between the target edge computing center and the faulty edge computing center according to the signal transmission attenuation coefficient, transmission success rate, and the normalized average transmission time interval. The calculation formula is: , where ED is the adaptation coefficient, YT, cv, and ku are the signal transmission attenuation coefficient, transmission success rate, and the normalized average transmission time interval respectively, and a1, a2, a3 are the preset proportional coefficients of YT, cv, and ku respectively, and a1, a2, a3 are all greater than 0.

[0044] It should be noted that a1, a2, a3 are set by professionals according to the actual situation. Generally, the sum of a1, a2, a3 is 1. For example, a1, a2, a3 can be 0.3, 0.3, 0.4 respectively, or other numbers, and there is no specific limitation; in addition, before calculating the adaptation coefficient, it is necessary to calculate after removing the units of the signal transmission attenuation coefficient, transmission success rate, and the normalized average transmission time interval.

[0045] It should be noted that all data transmission histories between the target edge computing center and the faulty edge computing center are obtained by monitoring and recording system logs, including the success and failure information of each transmission, so as to calculate the total number of data transmissions and the number of successful transmissions; by obtaining the timestamps of each data transmission, calculate the time intervals between adjacent transmissions, and then obtain the mean value of the transmission time intervals. Then, according to these time interval data, use the normalization method to map it to the 0 - 1 interval.

[0046] It should be noted that when the transmission success rate between the target edge computing center and the faulty edge computing center is smaller and the average transmission time interval is larger, the degree of adaptation between the corresponding target edge computing center and the faulty edge computing center is lower. At this time, if the traffic monitoring camera data of the faulty edge computing center is transmitted to the target edge computing center, it may cause monitoring errors at the target edge computing center. The reason is that when the transmission success rate between the target edge computing center and the faulty edge computing center is low and the average transmission time interval is large, it means that the data transmission stability is poor, and the network communication may be unstable or have a high latency, which directly affects the accuracy and timeliness of the data. A low data transmission success rate indicates that the probability of data loss or error during transmission is relatively large, resulting in incomplete or unreliable data received by the target edge computing center; while a large average transmission time interval means that the data transmission between the target edge computing center and the faulty edge computing center is not frequent, and there is less experience in data transmission. Therefore, in this case, transmitting the camera data of the faulty edge computing center to the target edge computing center may lead to mistakes or inaccuracies in the monitoring results, thus affecting the security and effectiveness of the overall system.

[0047] In one embodiment, the final target edge computing center selected by each faulty edge computing center is determined according to the adaptation coefficient and the importance coefficient, and the corresponding traffic monitoring camera data is transmitted to the selected target edge computing center for data analysis; Among them, the steps for determining the final target edge computing center selected by each faulty edge computing center are as follows: After calculating the adaptation coefficients between each faulty edge computing center and each target edge computing center, for each faulty edge computing center, sort the adaptation coefficients between the faulty edge computing center and each target edge computing center in descending order to obtain the set of adaptation target edge computing centers corresponding to each faulty edge computing center; Take the target edge computing center with the largest set of adaptation target edge computing centers as the corresponding final target edge computing center; If a certain target edge computing center serves as the final target edge computing center of multiple faulty edge computing centers at the same time, take this target edge computing center as the final target edge computing center of the faulty edge computing center with the largest importance coefficient.

[0048] It should be noted that, assuming there are three faulty edge computing centers: Faulty Center A, Faulty Center B, and Faulty Center C, and three target edge computing centers: Target Center X, Target Center Y, and Target Center Z. After calculation, the adaptation coefficients between each pair of faulty centers and target centers are as follows: The adaptation coefficient between Faulty Center A and Target Center X is 0.85, the adaptation coefficient between Faulty Center A and Target Center Y is 0.90, and the adaptation coefficient between Faulty Center A and Target Center Z is 0.80. The adaptation coefficient between Faulty Center B and Target Center X is 0.75, the adaptation coefficient between Faulty Center B and Target Center Y is 0.92, and the adaptation coefficient between Faulty Center B and Target Center Z is 0.88. The adaptation coefficient between Faulty Center C and Target Center X is 0.88, the adaptation coefficient between Faulty Center C and Target Center Y is 0.91, and the adaptation coefficient between Faulty Center C and Target Center Z is 0.85. According to the sorting of the adaptation coefficients, the following sets of adapted target edge computing centers for each faulty edge computing center are obtained: For Faulty Center A, the sorted set of adapted target edge computing centers is: [Target Center Y, Target Center X, Target Center Z], and the maximum adaptation coefficient is 0.90. Therefore, Faulty Center A selects Target Center Y as the final target edge computing center. For Faulty Center B, the sorted set of adapted target edge computing centers is: [Target Center Y, Target Center Z, Target Center X], and the maximum adaptation coefficient is 0.92. Therefore, Faulty Center B selects Target Center Y as the final target edge computing center. For Faulty Center C, the sorted set of adapted target edge computing centers is: [Target Center Y, Target Center X, Target Center Z], and the maximum adaptation coefficient is 0.91. Therefore, Faulty Center C selects Target Center Y as the final target edge computing center. At this time, it can be seen that Target Center Y is selected as the final target edge computing center by Faulty Centers A, B, and C. However, Target Center Y cannot process the data transmission of all faulty edge computing centers simultaneously. Therefore, Target Center Y needs to select the faulty edge computing center with the largest adaptation coefficient for data analysis. Assuming that in actual applications, the importance coefficient of Faulty Center B is the largest, then Target Center Y will select Faulty Center B as its final target edge computing center and receive the traffic monitoring camera data of Faulty Center B for analysis.

[0049] To sum up, the final data transmission arrangement is: Faulty Center A selects Target Center X for data transmission; Faulty Center B selects Target Center Y for data transmission; Faulty Center C selects Target Center Z for data transmission.

[0050] In one implementation manner, through sorting and selection based on the adaptation coefficient, it is possible to ensure the maximization of the adaptation degree between each faulty edge computing center and its corresponding target edge computing center, thereby improving the data transmission efficiency and accuracy. By preferentially selecting the target edge computing center with a larger adaptation coefficient, errors and delays that may occur during data transmission can be reduced, resource utilization can be optimized, and it is ensured that traffic monitoring camera data can be analyzed and processed more quickly and accurately. At the same time, when multiple faulty edge computing centers select the same target edge computing center, by considering the maximization selection of the importance coefficient, the priority of key faulty centers is further guaranteed, possible resource conflicts are avoided, and the overall stability and efficiency of the system are improved.

[0051] In one embodiment, the steps for the final target edge computing center to analyze the traffic monitoring camera data transmitted and monitor whether the corresponding traffic monitoring camera is faulty in combination with the importance coefficient of the transmitted traffic monitoring camera are as follows: Extract frames from the video transmitted by the traffic monitoring camera to obtain several frames of images. For each frame of image, perform a transformation through the Laplace operator, calculate the pixel value variance of the transformed image, and use the pixel value variance as the image clarity value of the corresponding image; add up the image clarity values of each frame of image as the video clarity index of the traffic monitoring camera. Obtain the actual frame rate of each frame of image, calculate the difference between the actual frame rate and the preset minimum frame rate. When the difference is less than 0, mark the corresponding frame of image as an abnormal image; divide the number of abnormal image frames by the total number of image frames to obtain the image frame rate abnormality ratio; obtain the health assessment index of the traffic monitoring camera based on the video clarity index, the image frame rate abnormality ratio, and the importance coefficient of the corresponding traffic monitoring camera, and monitor whether the corresponding traffic monitoring camera is faulty based on the health assessment index; the formula for calculating the health assessment index is: , where FD is the health assessment index of the traffic monitoring camera, pl, uk, and tu are the video clarity index, the image frame rate abnormality ratio, and the importance coefficient of the corresponding traffic monitoring camera respectively, and b1 and b2 are the preset proportionality coefficients of the video clarity index and the image frame rate abnormality ratio respectively, and both b1 and b2 are greater than 0; Compare the health assessment index of the traffic monitoring camera with the preset health assessment index threshold. If the health index is not less than the preset health assessment index threshold, the corresponding traffic monitoring camera is not faulty; if it is less, the corresponding traffic monitoring camera is faulty.

[0052] It should be noted that b1 and b2 are set by professionals according to the actual situation. Generally, the sum of b1 and b2 is 1. For example, b1 and b2 can be 0.4 and 0.6 respectively, or other numbers, which are not specifically limited; in addition, before calculating the health assessment index, it is necessary to calculate the video clarity index and the abnormal ratio of the image frame rate after removing the unit; in addition, the preset health assessment index threshold is set by professionals according to the actual situation, which is not specifically limited and elaborated.

[0053] It should be noted that the larger the video clarity index and the lower the abnormal ratio of the image frame rate of the traffic monitoring camera, the smaller the possibility of the traffic monitoring camera malfunctioning, indicating that the camera can stably output clear and detailed pictures, and the continuity and stability of the images are good. A high video clarity index indicates that the imaging quality of the camera is very good, and the lens and sensor are working properly; a low abnormal ratio of the image frame rate means that the camera continuously captures pictures at the normal working frequency without freezing or abnormal frame loss. Generally speaking, both of these two indicators are within the normal range, indicating that the camera can efficiently and accurately complete its functions when performing the monitoring task, so the possibility of malfunction is relatively small.

[0054] In one implementation, through the above method, the health assessment is combined with the importance coefficient of the traffic monitoring camera. When the importance coefficient is larger, the corresponding health assessment index decreases because the camera has a higher importance in the monitoring network. In order to ensure the monitoring stability and data accuracy of key areas, these high-importance cameras need to be evaluated more strictly; by improving the evaluation criteria for important cameras, the overall security and monitoring quality of the system can be better guaranteed, and the traffic management and safety guarantee of key areas can be avoided from being affected due to camera malfunctions.

[0055] Based on the same inventive concept, the embodiment of the present invention also provides an intelligent terminal monitoring system based on edge computing. See Figure 2 , Figure 2 is a framework diagram of an intelligent terminal monitoring system based on edge computing provided by an embodiment of the present invention. The system includes: Importance degree module: Obtain the traffic area data monitored by each traffic monitoring camera, used to evaluate the complexity of the traffic area, and calculate the importance coefficient of the camera according to the traffic area data; Screening module: Obtain the resource information of each non-faulty edge computing center, and screen out each edge computing center that can receive the traffic monitoring camera data of the faulty edge computing center from the resource information of each non-faulty edge computing center, which is called the target edge computing center; Adaptation degree module: Obtain the distances and historical data transmission times between the target edge computing center and each faulty edge computing center, and calculate the adaptation coefficients between each target edge computing center and each faulty edge computing center according to the distances and historical data transmission times; Selection module: Determine the final target edge computing center selected by each faulty edge computing center according to the adaptation coefficient and the importance coefficient, and transfer the corresponding traffic monitoring camera data to the selected final target edge computing center for data analysis; Terminal monitoring module: The final target edge computing center analyzes the transmitted traffic monitoring camera data, and monitors whether the corresponding traffic monitoring camera is faulty in combination with the importance coefficient of the transmitted traffic monitoring camera.

[0056] Based on the intelligent terminal monitoring system based on edge computing provided by the embodiment of the present invention, in the above manner, when multiple edge computing centers have problems and a certain edge computing center is backed up by multiple faulty edge centers, it can be appropriately allocated and calculated according to the actual situation, reducing the overloading of the backup edge computing center, ensuring its processing capacity, and preventing delays or overloading operations. It ensures the response speed and stability of the entire edge computing-based terminal monitoring system, making the monitoring results of traffic monitoring cameras correct.

[0057] The above has described an embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A smart terminal monitoring method based on edge computing, characterized in that: The following steps are involved: Obtaining the traffic area data monitored by each traffic monitoring camera to evaluate the complexity of the traffic area, and calculating the importance coefficient of the camera based on the traffic area data; Obtain resource information of each healthy edge computing center, and select edge computing centers that can receive traffic monitoring camera data of the failed edge computing center according to the resource information of each healthy edge computing center, which are called target edge computing centers; Obtain the distance and historical data transmission times between the target edge computing center and each fault edge computing center, and calculate the adaptation coefficient of each target edge computing center and each fault edge computing center according to the distance and historical data transmission times; Determine the final target edge computing center selected by each fault edge computing center according to the adaptation coefficient and the importance coefficient, and transmit the corresponding traffic monitoring camera data to the selected final target edge computing center for data analysis; The final target edge computing center analyzes the transmitted traffic monitoring camera data, and monitors whether the corresponding traffic monitoring camera is faulty based on the important coefficients of the transmitted traffic monitoring camera.

2. According to claim 1, a smart terminal monitoring method based on edge computing is characterized in that: The steps to calculate the importance coefficient of the camera based on the traffic area data are: The area captured by the traffic monitoring camera is taken as the target area, the total number of vehicles that have traveled in the target area in the past preset time period is obtained, and the travel time of each vehicle in the target area is obtained. The variance of the travel time is calculated based on the travel time of all vehicles in the target area as the speed fluctuation value of the target area; Obtain the vehicles involved in traffic accidents in the target area, and divide the number of vehicles involved in traffic accidents by the total number of vehicles that have traveled to obtain the vehicle accident probability in the target area; Obtain the drivable area of ​​the lane in the target area, and divide the total number of vehicles that have traveled by the drivable area of ​​the lane to obtain the vehicle density of the target area; The importance coefficient of the camera is calculated based on the vehicle speed fluctuation value, vehicle accident probability and vehicle density in the target area.

3. According to claim 2, a smart terminal monitoring method based on edge computing is characterized in that: The steps for calculating the important coefficients of the camera based on the speed fluctuation value, vehicle accident probability and vehicle density in the target area are: The speed fluctuation value, vehicle accident probability and vehicle density in the target area are used as the input items of the fuzzy rules, and the important coefficient of the camera is used as the output item; Fuzzify the input items and convert the exact input values ​​into fuzzy sets; Define fuzzy rules based on the fuzzy sets of input items to map input items to output items; According to the fuzzy rules, the input items are inferred to determine the fuzzy value of the output items; For each rule, calculate the membership of its predecessor and take the minimum value as the activation of the rule; Synthesize the results of all rules and calculate the fuzzy set of output items; The fuzzy values ​​in the output fuzzy set are converted into exact values, and the important coefficients of the camera are output according to the defuzzification results.

4. The method for monitoring an intelligent terminal based on edge computing according to claim 1, characterized in that: The steps to select the edge computing centers that can receive the traffic monitoring camera data of the faulty edge computing center, which are called the target edge computing centers, are as follows: The number of CPU cores and clock frequency of each intact edge computing center are obtained, and the computing capacity value of the intact edge computing center is calculated based on the number of CPU cores and clock frequency. The calculation formula is: , where TR is the computing capacity value of the edge computing center without faults, C i and F i are the number of CPU cores and clock frequency of the i-th CPU respectively, and n is the total number of CPUs; Obtain the usage rate of each CPU of each intact edge computing center, and calculate the average usage rate of all CPUs as the load value of the intact edge computing center; The computing capacity coefficient of each non-faulty edge computing center is calculated based on the computing capacity value and load value. The calculation formula is: , where UG is the computing capacity coefficient and TE is the load value; The computing capacity coefficient of each non-faulty edge computing center is compared with the preset computing capacity coefficient threshold. If the computing capacity coefficient is not less than the preset computing capacity coefficient threshold, the edge computing centers that receive the traffic monitoring camera data of the faulty edge computing center are called target edge computing centers.

5. The method for monitoring an intelligent terminal based on edge computing according to claim 1, characterized in that: The steps for calculating the adaptation coefficient of each target edge computing center and each fault edge computing center according to the distance and the number of historical data transmissions are as follows: Get the location coordinates of the target edge computing center and the fault edge computing center, and calculate the Euclidean distance between the two points to obtain the shortest straight-line distance for signal transmission between the target edge computing center and the fault edge computing center: The signal transmission attenuation coefficient of the target edge computing center and the fault edge computing center is calculated based on the shortest straight-line distance. The calculation formula is: , where YT is the signal transmission attenuation coefficient, D is the shortest straight-line distance, A is a preset constant representing the basic value of path loss, and m is a preset path loss index, ranging from 2 to 3.5; The adaptation coefficient of each target edge computing center and each fault edge computing center is calculated according to the signal transmission attenuation coefficient and historical data transmission times of the target edge computing center and the fault edge computing center.

6. The method for monitoring an intelligent terminal based on edge computing according to claim 5, characterized in that: The steps for calculating the adaptation coefficients of each target edge computing center and each fault edge computing center according to the signal transmission attenuation coefficient and the historical data transmission times of the target edge computing center and the fault edge computing center are as follows: Obtain the total number of data transmissions and the total number of successful transmissions between the target edge computing center and the faulty edge computing center, and divide the total number of successful transmissions by the total number of data transmissions to obtain the transmission success rate; Obtaining two adjacent transmission time intervals, and calculating the average of the transmission time intervals as the average transmission time interval, and normalizing the average transmission time interval and mapping it to a numerical range of 0-1; The adaptation coefficient between the target edge computing center and the fault edge computing center is calculated based on the signal transmission attenuation coefficient, the transmission success rate and the normalized average transmission time interval. The calculation formula is: , where ED is the adaptation coefficient, YT, cv and ku are the signal transmission attenuation coefficient, transmission success rate and the average transmission time interval after normalization, a1, a2 and a3 are the preset proportional coefficients of YT, cv and ku, and a1, a2 and a3 are all greater than 0.

7. The method for monitoring an intelligent terminal based on edge computing according to claim 1, characterized in that: The steps to determine the final target edge computing center selected by each faulty edge computing center are: After calculating the adaptation coefficients between each faulty edge computing center and each target edge computing center, for each faulty edge computing center, the adaptation coefficients between the faulty edge computing center and each target edge computing center are sorted in descending order to obtain a set of adapted target edge computing centers corresponding to each faulty edge computing center; The target edge computing center with the largest set of adapted target edge computing centers is taken as the corresponding final target edge computing center; If a target edge computing center is simultaneously used as the final target edge computing center of multiple faulty edge computing centers, the target edge computing center shall be used as the final target edge computing center of the faulty edge computing center with the largest importance coefficient.

8. The method for monitoring an intelligent terminal based on edge computing according to claim 1, characterized in that: The steps of monitoring whether the corresponding traffic monitoring camera is faulty in combination with the important coefficients of the transmitted traffic monitoring camera are: The video transmitted by the traffic monitoring camera is extracted to obtain several frames of images. For each frame of the image, the Laplace operator is used to change it, and the pixel value variance of the changed image is calculated, and the pixel value variance is used as the image clarity value of the corresponding image; The image clarity values ​​of each frame of the image are added together as the video clarity index of the traffic monitoring camera; Obtain the actual frame rate of each frame of the image, and calculate the difference between the actual frame rate and the preset minimum frame rate. When the difference is less than 0, the image of the corresponding frame is recorded as an abnormal image; divide the number of frames of the abnormal image by the total number of frames of the image to obtain the image frame rate abnormality ratio; The health assessment index of the traffic surveillance camera is obtained according to the video clarity index, the image frame rate abnormality ratio and the important coefficient of the corresponding traffic surveillance camera, and the corresponding traffic surveillance camera is monitored whether it is faulty according to the health assessment index.

9. The method for monitoring a smart terminal based on edge computing according to claim 8, characterized in that: The steps of obtaining the health assessment index of the traffic monitoring camera according to the video clarity index, the image frame rate abnormality ratio and the important coefficient of the corresponding traffic monitoring camera and monitoring whether the corresponding traffic monitoring camera is faulty according to the health assessment index are as follows: The health assessment index of the traffic surveillance camera is obtained according to the video clarity index, image frame rate abnormality ratio and the important coefficient of the corresponding traffic surveillance camera. The calculation formula is: , where FD is the health assessment index of the traffic monitoring camera, pl, uk, tu are the video clarity index, image frame rate abnormality ratio and the important coefficient of the corresponding traffic monitoring camera, b1 and b2 are the preset proportional coefficients of the video clarity index and image frame rate abnormality ratio, and b1 and b2 are both greater than 0; The health assessment index of the traffic monitoring camera is compared with the preset health assessment index threshold. If the health index is not less than the preset health index threshold, the corresponding traffic monitoring camera has no fault; if it is less than, the corresponding traffic monitoring camera has a fault.

10. An edge computing-based smart terminal monitoring system, used to implement an edge computing-based smart terminal monitoring method according to any one of claims 1 to 9, characterized in that: The system comprises: Importance module: obtains the traffic area data monitored by each traffic monitoring camera to evaluate the complexity of the traffic area and calculates the importance coefficient of the camera based on the traffic area data; Screening module: obtains resource information of all healthy edge computing centers, and based on the resource information of all healthy edge computing centers, screens out edge computing centers that can receive traffic monitoring camera data from the faulty edge computing centers, which are called target edge computing centers; Adaptation degree module: obtains the distance and historical data transmission times between the target edge computing center and each fault edge computing center, and calculates the adaptation coefficient of each target edge computing center and each fault edge computing center based on the distance and historical data transmission times; Selection module: Determine the final target edge computing center selected by each fault edge computing center according to the adaptation coefficient and the importance coefficient, and transmit the corresponding traffic monitoring camera data to the selected final target edge computing center for data analysis; Terminal monitoring module: The final target edge computing center analyzes the transmitted traffic monitoring camera data, and monitors whether the corresponding traffic monitoring camera is faulty based on the important coefficients of the transmitted traffic monitoring camera.

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