Traffic situation real-time analysis system based on edge perception

Abstract

The invention discloses a traffic situation real-time analysis system based on edge perception. According to the invention, the data preprocessing sub-module of the edge computing module can perform real-time cleaning and formatting at the source of data generation, so that the amount of data transmitted to the central cloud is greatly reduced, and the pressure of network bandwidth is reduced. And the target detection and identification sub-module quickly processes the acquired image and sensor data by using the computing power of the edge node, so that the real-time detection and classification of vehicles and pedestrians are realized, the process is completed at the edge side, the response time is greatly shortened, and the quick response capability of the system to emergencies is improved. The traffic situation analysis sub-module analyzes traffic flow, speed and congestion conditions in real time in combination with the preprocessed data and detection results, timely and accurate data support is provided for traffic management and decision making, the data processing burden of the central cloud is effectively relieved, and the overall performance and efficiency of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic situation analysis, and specifically is a real-time traffic situation analysis system based on edge perception. Background Art

[0002] With the acceleration of urbanization and the popularization of transportation, traffic congestion is becoming increasingly serious, which puts higher requirements on urban traffic management. Existing traffic monitoring systems mostly rely on centralized data processing methods, which are difficult to meet the needs of real-time, efficient and intelligent traffic situation analysis. To this end, there is an urgent need for an intelligent traffic situation real-time analysis system that can realize rapid data collection, edge processing and central decision support. At present, the traffic monitoring system mainly includes data collection, data processing and analysis, information release and other links. In these links, the data acquisition module usually uses sensors, cameras and other equipment to collect traffic data.

[0003] However, the data processing and analysis of existing technologies are mostly carried out on central servers, resulting in data processing delays and the inability to achieve real-time traffic situation analysis. In addition, centralized data processing methods are prone to network congestion when facing large amounts of data, affecting the response speed and stability of the system. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time traffic situation analysis system based on edge perception in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a real-time traffic situation analysis system based on edge perception, the system comprising: a data acquisition module, an edge computing module, a central control module, a system management module, a system maintenance module and a user service module;

[0006] The edge computing module is internally provided with a data preprocessing submodule, a target detection and recognition submodule, a traffic situation analysis submodule, a result output and synchronization submodule, and a control and scheduling submodule;

[0007] The data acquisition module collects real-time traffic data through various sensors and cameras deployed in traffic scenarios, and transmits it to the edge computing module for preliminary processing and analysis; the edge computing module performs pre-processing, target detection and recognition, and traffic situation analysis on the data, and then synchronizes the processing results to the central control module. The central control module is responsible for integrating data from different edge nodes to perform global traffic management and decision support; the system management module monitors the operating status of the entire system, including data acquisition, edge computing, and central control links, to ensure stable and efficient operation of the system; the system maintenance module is responsible for regular inspection, fault diagnosis and repair of the system to ensure the continuous availability of the system; the user service module obtains traffic information through the central control module, provides users with real-time road conditions and travel advice services, and collects user feedback, which can be used by the system management module to optimize system performance.

[0008] In a preferred embodiment, the data acquisition module is the front end of the system, responsible for collecting raw data from traffic scenes; the specific operation content includes: cameras deployed at intersections and key sections continuously capture image video streams of vehicles and pedestrians; on-board radars and geomagnetic sensors monitor the speed, position and parking status of vehicles in real time; in addition, environmental sensors collect meteorological information, and these data are initially integrated through edge devices and transmitted to the central processing unit through an encrypted network.

[0009] In a preferred embodiment, the data preprocessing submodule performs denoising, scaling, and cropping operations on the image data to ensure that the image quality meets the requirements of subsequent processing; performs time synchronization, filtering, and normalization on the sensor data to eliminate errors and interference in the data acquisition process; in addition, the module also converts the data format to meet the input requirements of the target detection and recognition algorithm;

[0010] The target detection and recognition submodule uses the YOLO algorithm to input image data into a trained neural network model, and the model outputs the bounding box coordinates, category probability, and confidence of each target in the image; then, the non-maximum suppression (NMS) algorithm is used to filter out the best target detection results to achieve accurate recognition of vehicles and pedestrians.

[0011] The specific formula for calculating the bounding box coordinates is:

[0012] bx=σ(tx)+cx;

[0013] by=σ(ty)+cy;

[0014] bw=pw·etw;

[0015] bh = ph·eth;

[0016] Where tx, ty, tw, th are the predicted offsets, c_x, c_ycx, cy are the coordinates of the upper left corner of the grid, pw, ph are the width and height of the anchor, and σ is the Sigmoid function;

[0017] The confidence score is calculated as:

[0018] Conf=Pr(Object)·IOU(pred,truth);

[0019] Among them, Pr(Object) is the probability of the existence of the target, IOU(pred,truth) is the intersection and union ratio of the predicted bounding box and the true bounding box;

[0020] The formula for calculating the class probability is: Pr(Classi|Object)=softmax(classi);

[0021] Among them, softmax is a function used to calculate the probability of each category.

[0022] In a preferred embodiment, the traffic situation analysis submodule calculates the number of vehicles passing through a specific detection point per unit time to obtain traffic flow; estimates the average speed and driving distance of vehicles to evaluate traffic conditions; at the same time, the module also detects abnormal events and updates traffic situation information in real time;

[0023] The calculation formula is: Flow = Vehicles / T;

[0024] Where Flow represents the traffic flow vehicles / time; Vehicles represents the number of vehicles passing through the detection point within the time interval T; T represents the statistical time interval.

[0025] In a preferred embodiment, the result output and synchronization submodule is responsible for efficiently transmitting the analysis results to a central server or other edge nodes; including: compressing the analysis results to reduce the transmission bandwidth requirement; ensuring the reliability and security of data transmission through encryption and verification; and finally, synchronizing the compressed data to the target storage or processing system via a wired or wireless network for further analysis or real-time display.

[0026] In a preferred embodiment, the control and scheduling submodule is responsible for resource management and task scheduling of the entire system; specific operations include: dynamically allocating computing resources, including CPU, GPU and memory, according to the importance and urgency of the task; monitoring the system operation status, including load balancing, response time and fault recovery; at the same time, the module will also adjust the priority of detection and identification tasks according to the real-time traffic situation to ensure that key information can be processed and responded to in a timely manner.

[0027] In a preferred embodiment, the central control module serves as the center of the system and is responsible for directing and coordinating the operation of each subsystem. The specific operation contents include: receiving raw data from the data acquisition module and performing data quality checks, including verifying data integrity and removing invalid and erroneous data; scheduling computing resources for target detection and recognition according to the needs of traffic situation analysis, and classifying and counting vehicles and pedestrians in the image through deep learning algorithms; at the same time, the central control module is also responsible for formulating data collection strategies, adjusting the camera collection frequency according to traffic flow, and optimizing the sensor layout to cover blind spots, so as to adapt to different traffic management needs.

[0028] In a preferred embodiment, the system management module is responsible for the management of daily system operations; the specific operation contents include: real-time monitoring of the operating status of the data acquisition module and the central control module, including network connection, equipment power consumption, and data processing speed, to ensure stable operation of the system; managing user access rights, implementing identity authentication and data encryption measures, and ensuring data security; regularly evaluating system performance and optimizing system response speed by adjusting configuration parameters.

[0029] In a preferred embodiment, the system maintenance module focuses on the long-term reliability and maintenance of the system; specific operations include: formulating and maintaining equipment inspection plans, and regularly performing physical inspections and maintenance of data acquisition equipment; regularly updating and patching system software to fix known vulnerabilities and improve system performance and compatibility; when a system failure occurs, quickly intervening to diagnose and repair, including restarting the device, restoring data, and replacing hardware to ensure that the system resumes normal operation as soon as possible; at the same time, the module is also responsible for regular data backup and storage management to prevent data loss and ensure data traceability and recoverability.

[0030] In a preferred embodiment, the user service module displays real-time traffic conditions, traffic events, forecast information and travel suggestions to users through mobile applications or websites, so that users can understand traffic conditions in a timely manner; allows users to query historical traffic data to provide reference for travel, and helps users identify congestion patterns and choose the best travel time through data analysis; provides customized travel suggestions and route planning based on user preferences and travel habits.

[0031] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0032] 1. In the present invention, the data preprocessing submodule of the edge computing module can perform real-time cleaning and formatting at the source of data generation, which greatly reduces the amount of data transmitted to the central cloud and reduces the pressure on network bandwidth. The target detection and recognition submodule uses the computing power of the edge node to quickly process the collected images and sensor data, realizing real-time detection and classification of vehicles and pedestrians. This process is completed on the edge side, which greatly shortens the response time and improves the system's ability to respond quickly to emergencies. The traffic situation analysis submodule combines the preprocessed data and test results to perform real-time analysis of traffic flow, speed, and congestion, providing timely and accurate data support for traffic management and decision-making. The close cooperation of these three submodules enables the system to complete most of the computing tasks on the edge side, effectively reducing the data processing burden of the central cloud and improving the overall performance and efficiency of the system.

[0033] 2. In the present invention, the result output and synchronization submodule ensures that the analysis results of the edge computing module can be synchronized to the central cloud and other edge nodes in a timely manner, ensuring the real-time and consistency of system information. The control and scheduling submodule dynamically adjusts the computing resource allocation and task priority of the edge node according to the real-time traffic situation and system load, optimizes resource utilization, and ensures the priority processing of key tasks. This edge computing mode not only improves the flexibility and scalability of the system, but also enhances the robustness of the system through a distributed computing architecture, so that the system can maintain stable operation even when some nodes fail. In summary, the edge computing module significantly improves the real-time, accuracy and reliability of the real-time analysis system of traffic situation by performing efficient processing and analysis at the proximal end of data generation, providing strong technical support for intelligent traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is the overall system block diagram of the present invention;

[0035] Figure 2 This is a system block diagram of the edge computing module in the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] Reference Figure 1-2 ,

[0038] A real-time traffic situation analysis system based on edge perception, the system includes: a data acquisition module, an edge computing module, a central control module, a system management module, a system maintenance module and a user service module;

[0039] The edge computing module is internally configured with a data preprocessing submodule, a target detection and recognition submodule, a traffic situation analysis submodule, a result output and synchronization submodule, and a control and scheduling submodule;

[0040] The data acquisition module collects real-time traffic data through various sensors and cameras deployed in traffic scenes, and transmits it to the edge computing module for preliminary processing and analysis. After the edge computing module pre-processes the data, detects and identifies targets, and analyzes traffic trends, it synchronizes the processing results to the central control module. The central control module is responsible for integrating data from different edge nodes to perform global traffic management and decision support. The system management module monitors the operating status of the entire system, including data acquisition, edge computing, and central control, to ensure stable and efficient operation of the system. The system maintenance module is responsible for regular inspection, fault diagnosis, and repair of the system to ensure the continuous availability of the system. The user service module obtains traffic information through the central control module, provides users with real-time road conditions, travel suggestions, and other services, and collects user feedback, which can be used by the system management module to optimize system performance. The modules are closely connected through data flows, control instructions, and feedback information, forming a dynamically adjusted and self-optimizing intelligent transportation system.

[0041] The data acquisition module is the front end of the system, responsible for collecting raw data from traffic scenes. The specific operation content includes: cameras deployed at intersections and key sections continuously capture image video streams of vehicles and pedestrians; on-board radars and geomagnetic sensors monitor the speed, location and parking status of vehicles in real time; in addition, environmental sensors collect meteorological information such as rainfall, wind speed, etc. These data are initially integrated through edge devices and transmitted to the central processing unit through an encrypted network to ensure the real-time and security of the data.

[0042] The data preprocessing submodule performs preliminary processing on the raw data collected by the edge sensing device. The specific operations include: denoising, scaling, cropping and other operations on the image data to ensure that the image quality meets the requirements of subsequent processing; time synchronization, filtering and normalization of sensor data to eliminate errors and interference in the data collection process; in addition, this module will also convert the data format to meet the input requirements of the target detection and recognition algorithm.

[0043] The target detection and recognition submodule uses the YOLO algorithm to perform real-time analysis on pre-processed data. The specific operation includes: inputting image data into the trained neural network model, and the model outputs the bounding box coordinates, category probability and confidence of each target in the image; then, the non-maximum suppression (NMS) algorithm is used to select the best target detection results to achieve accurate recognition of vehicles, pedestrians and other traffic participants;

[0044] The specific formula for calculating the bounding box coordinates is:

[0045] bx=σ(tx)+cx;

[0046] by=σ(ty)+cy;

[0047] bw=pw·etw;

[0048] bh = ph·eth;

[0049] Where tx, ty, tw, th are the predicted offsets, c_x, c_ycx, cy are the coordinates of the upper left corner of the grid, pw, ph are the width and height of the anchor, and σ is the Sigmoid function;

[0050] The confidence score is calculated as:

[0051] Conf=Pr(Object)·IOU(pred,truth);

[0052] Among them, Pr(Object) is the probability of the existence of the target, and IOU(pred,truth) is the intersection and union ratio of the predicted bounding box and the true bounding box.

[0053] The formula for calculating the class probability is: Pr(Classi|Object)=softmax(classi);

[0054] Among them, softmax is a function used to calculate the probability of each category.

[0055] The traffic situation analysis submodule performs statistical analysis on traffic flow, speed, density and other parameters based on the target detection results. The specific operation contents include: calculating the number of vehicles passing through a specific detection point per unit time to obtain the traffic flow; estimating the average speed and driving distance of vehicles to evaluate the traffic conditions; at the same time, the module will also detect abnormal events (such as traffic accidents, congestion, etc.) and update the traffic situation information in real time;

[0056] The calculation formula is: Flow = Vehicles / T;

[0057] Flow represents the traffic flow (vehicles / time); Vehicles represents the number of vehicles passing through the detection point within the time interval T; T represents the statistical time interval

[0058] The result output and synchronization submodule is responsible for efficiently transmitting the analysis results to the central server or other edge nodes. The specific operation content includes: compressing the analysis results to reduce the transmission bandwidth requirements; ensuring the reliability and security of data transmission through encryption and verification; finally, synchronizing the compressed data to the target storage or processing system through a wired or wireless network for further analysis or real-time display

[0059] The control and scheduling submodule is responsible for resource management and task scheduling of the entire system. The specific operation contents include: dynamically allocating computing resources such as CPU, GPU and memory according to the importance and urgency of the task; monitoring the system operation status, including load balancing, response time and fault recovery; at the same time, the module will also adjust the priority of detection and identification tasks according to the real-time traffic situation to ensure that key information can be processed and responded to in a timely manner.

[0060] As the center of the system, the central control module is responsible for commanding and coordinating the operation of each subsystem. The specific operation contents include: receiving the raw data from the data acquisition module, performing data quality checks, such as verifying data integrity and removing invalid and erroneous data; dispatching computing resources for target detection and recognition according to the needs of traffic situation analysis, for example, classifying and counting vehicles and pedestrians in images through deep learning algorithms; at the same time, the central control module is also responsible for formulating data collection strategies, such as adjusting the camera collection frequency according to traffic flow, optimizing the sensor layout to cover blind spots, etc., to adapt to different traffic management needs. In addition, this module is also responsible for data distribution to ensure that key information can be quickly transmitted to relevant departments.

[0061] The system management module is responsible for the management of the daily operation of the system. The specific operation contents include: real-time monitoring of the operating status of the data acquisition module and the central control module, including network connection, equipment power consumption, data processing speed, etc., to ensure the stable operation of the system; managing user access rights, implementing identity authentication and data encryption measures to ensure data security; regularly evaluating system performance, and optimizing system response speed by adjusting configuration parameters, such as increasing cache, optimizing algorithms, etc.; in addition, the system management module is also responsible for log recording and error reporting, providing necessary information for system maintenance, and supporting the rapid location and elimination of faults.

[0062] The system maintenance module focuses on the long-term reliability and maintenance of the system. Specific operations include: formulating and maintaining equipment inspection plans, regularly performing physical inspections and maintenance on data acquisition equipment, such as cleaning lenses, calibrating sensors, and replacing faulty parts; regularly updating and patching system software to fix known vulnerabilities, improve system performance and compatibility; and quickly intervening to diagnose and repair system failures, including restarting equipment, restoring data, replacing hardware, etc., to ensure that the system resumes normal operation as soon as possible. At the same time, this module is also responsible for regular data backup and storage management to prevent data loss and ensure data traceability and recoverability.

[0063] The user service module displays real-time traffic conditions, traffic events, forecast information and travel suggestions to users through mobile applications or websites, so that users can understand traffic conditions in a timely manner; allows users to query historical traffic data to provide reference for travel, and helps users identify congestion patterns and choose the best travel time through data analysis; provides customized travel suggestions and route planning based on user preferences and travel habits, such as avoiding peak hours and choosing unobstructed sections of road; at the same time, the user service module also establishes a feedback mechanism to collect user opinions and suggestions for continuous improvement of system functions and user experience, and quickly responds to new needs and problems in traffic management through user feedback.

[0064] In the present invention, the data preprocessing submodule of the edge computing module can perform real-time cleaning and formatting at the source of data generation, greatly reducing the amount of data transmitted to the central cloud and reducing the pressure on network bandwidth. The target detection and recognition submodule uses the computing power of the edge node to quickly process the collected images and sensor data, realizing real-time detection and classification of vehicles and pedestrians. This process is completed on the edge side, greatly shortening the response time and improving the system's ability to respond quickly to emergencies. The traffic situation analysis submodule combines the preprocessed data and test results to perform real-time analysis of traffic flow, speed, and congestion, providing timely and accurate data support for traffic management and decision-making. The close cooperation of these three submodules enables the system to complete most of the computing tasks on the edge side, effectively reducing the data processing burden of the central cloud and improving the overall performance and efficiency of the system.

[0065] In the present invention, the result output and synchronization submodule ensures that the analysis results of the edge computing module can be synchronized to the central cloud and other edge nodes in a timely manner, ensuring the real-time and consistency of system information. The control and scheduling submodule dynamically adjusts the computing resource allocation and task priority of the edge node according to the real-time traffic situation and system load, optimizes resource utilization, and ensures the priority processing of key tasks. This edge computing mode not only improves the flexibility and scalability of the system, but also enhances the robustness of the system through a distributed computing architecture, so that the system can maintain stable operation even when some nodes fail. In summary, the edge computing module significantly improves the real-time, accuracy and reliability of the real-time analysis system of traffic situation by performing efficient processing and analysis at the proximal end of data generation, providing strong technical support for intelligent traffic management.

[0066] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time traffic situation analysis system based on edge perception, characterized by: The system includes: a data acquisition module, an edge computing module, a central control module, a system management module, a system maintenance module and a user service module; The edge computing module is internally provided with a data preprocessing submodule, a target detection and recognition submodule, a traffic situation analysis submodule, a result output and synchronization submodule, and a control and scheduling submodule; The data acquisition module collects real-time traffic data through various sensors and cameras deployed in traffic scenarios, and transmits it to the edge computing module for preliminary processing and analysis; the edge computing module performs pre-processing, target detection and recognition, and traffic situation analysis on the data, and then synchronizes the processing results to the central control module. The central control module is responsible for integrating data from different edge nodes to perform global traffic management and decision support; the system management module monitors the operating status of the entire system, including data acquisition, edge computing, and central control links, to ensure stable and efficient operation of the system; the system maintenance module is responsible for regular inspection, fault diagnosis and repair of the system to ensure the continuous availability of the system; the user service module obtains traffic information through the central control module, provides users with real-time road conditions and travel advice services, and collects user feedback, which can be used by the system management module to optimize system performance.

2. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The data acquisition module is the front end of the system, responsible for collecting raw data from traffic scenes; the specific operation content includes: cameras deployed at intersections and key sections continuously capture image video streams of vehicles and pedestrians; on-board radars and geomagnetic sensors monitor the speed, position and parking status of vehicles in real time; in addition, environmental sensors collect meteorological information, and these data are initially integrated through edge devices and transmitted to the central processing unit through an encrypted network.

3. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The data preprocessing submodule performs denoising, scaling, and cropping operations on the image data to ensure that the image quality meets the requirements of subsequent processing; Time synchronization, filtering and normalization of sensor data to eliminate errors and interference during data collection; In addition, the module will also convert the data format to meet the input requirements of the target detection and recognition algorithm; The target detection and recognition submodule uses the YOLO algorithm to input image data into a trained neural network model, and the model outputs the bounding box coordinates, category probability, and confidence of each target in the image; then, the non-maximum suppression (NMS) algorithm is used to filter out the best target detection results to achieve accurate recognition of vehicles and pedestrians. The specific formula for calculating the bounding box coordinates is: bx=σ(tx)+cx; by=σ(ty)+cy; bw=pw·etw; bh = ph·eth; Where tx, ty, tw, th are the predicted offsets, c_x, c_ycx, cy are the coordinates of the upper left corner of the grid, pw, ph are the width and height of the anchor, and σ is the Sigmoid function; The confidence score is calculated as: Conf=Pr(Object)·IOU(pred,truth); Among them, Pr(Object) is the probability of the existence of the target, IOU(pred,truth) is the intersection and union ratio of the predicted bounding box and the true bounding box; The formula for calculating the class probability is: Pr(Classi|Object)=softmax(classi); Among them, softmax is a function used to calculate the probability of each category.

4. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The traffic situation analysis submodule calculates the number of vehicles passing through a specific detection point per unit time to obtain traffic flow; estimates the average speed and driving distance of vehicles to evaluate traffic conditions; at the same time, the module also detects abnormal events and updates traffic situation information in real time; The calculation formula is: Flow = Vehicles / T; Where Flow represents the traffic flow vehicles / time; Vehicles represents the number of vehicles passing through the detection point within the time interval T; T represents the statistical time interval.

5. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The result output and synchronization submodule is responsible for efficiently transmitting the analysis results to the central server or other edge nodes; including: compressing the analysis results to reduce the transmission bandwidth requirement; ensuring the reliability and security of data transmission through encryption and verification; finally, synchronizing the compressed data to the target storage or processing system via a wired or wireless network for further analysis or real-time display.

6. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The control and scheduling submodule is responsible for the resource management and task scheduling of the entire system; specific operations include: dynamically allocating computing resources, including CPU, GPU and memory, according to the importance and urgency of the task; monitoring the system operation status, including load balancing, response time and fault recovery; at the same time, the module will also adjust the priority of detection and identification tasks according to the real-time traffic situation to ensure that key information can be processed and responded to in a timely manner.

7. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The central control module serves as the center of the system and is responsible for directing and coordinating the operation of each subsystem. Specific operations include: receiving raw data from the data acquisition module and performing data quality checks, including verifying data integrity and removing invalid and erroneous data; dispatching computing resources for target detection and recognition according to the needs of traffic situation analysis, and classifying and counting vehicles and pedestrians in the image through deep learning algorithms; at the same time, the central control module is also responsible for formulating data collection strategies, adjusting the camera collection frequency according to traffic flow, and optimizing the sensor layout to cover blind spots, so as to adapt to different traffic management needs.

8. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The system management module is responsible for the management of the daily operation of the system; the specific operation content includes: real-time monitoring of the operating status of the data acquisition module and the central control module, including network connection, equipment power consumption, and data processing speed, to ensure stable operation of the system; managing user access rights, implementing identity authentication and data encryption measures, and ensuring data security; regularly evaluating system performance and optimizing system response speed by adjusting configuration parameters.

9. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The system maintenance module focuses on the long-term reliability and maintenance of the system; specific operations include: formulating and maintaining equipment inspection plans, and regularly performing physical inspections and maintenance of data acquisition equipment; regularly updating and patching system software to fix known vulnerabilities and improve system performance and compatibility; when a system failure occurs, quickly intervene to diagnose and repair, including restarting the device, restoring data, and replacing hardware to ensure that the system resumes normal operation as soon as possible; at the same time, this module is also responsible for regular data backup and storage management to prevent data loss and ensure data traceability and recoverability.

10. The edge-sensing-based real-time traffic situation analysis system according to claim 1, characterized in that: The user service module displays real-time traffic conditions, traffic events, forecast information and travel suggestions to users through mobile applications or websites, so that users can understand traffic conditions in a timely manner; It allows users to query historical traffic data to provide reference for travel, and helps users identify congestion patterns and choose the best travel time through data analysis; it provides customized travel suggestions and route planning based on user preferences and travel habits.

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