Intelligent acquisition, analysis, prediction and automatic violation correction method and system based on public traffic violation big data

Abstract

The invention relates to an intelligent acquisition, analysis, prediction and automatic violation correction method and system based on public traffic violation big data, and belongs to the field of traffic informatization and intelligent management. The objective of the invention is to identify complex and diverse traffic illegal behaviors and analyze massive illegal data. The system comprises data acquisition, processing and storage, analysis and prediction, automatic violation correction and data security. Through multi-source data access, edge calculation and distributed storage, real-time acquisition and efficient processing of illegal behaviors are realized; deep analysis is carried out on the traffic violation data by using deep learning and big data technologies; and through real-time video stream analysis, tracking, recording and evidence obtaining are performed on suspected illegal behaviors. The system adopts a multi-level security protection mechanism, data privacy and security are ensured, and flexible expansion and multi-system collaboration are realized through modular design. Compared with a traditional system, the system has remarkable advantages in the aspects of data acquisition comprehensiveness, processing efficiency, prediction accuracy, violation correction automation, data security and the like.

Description

Technical Field

[0001] The present invention belongs to the field of traffic informatization and intelligent management, and specifically relates to a method and system for intelligent collection, analysis, prediction and automatic correction of public transportation violation big data. Background Art

[0002] With the continuous acceleration of urbanization, the types and number of traffic violations have increased year by year, bringing great challenges to urban traffic management. The existing traffic management system has exposed the following deficiencies when dealing with the huge amount of violation data: the front-end equipment has a single function. The current violation monitoring equipment is mostly single-function, such as speed monitoring, red light running capture, etc., which is difficult to adapt to the comprehensive identification needs of complex and diverse traffic violations. The data collection and processing efficiency is low. In the face of massive violation data, the traditional system relies on a large number of manual reviews for data collection and processing, which is inefficient and prone to processing delays, affecting the timeliness of traffic management. The equipment compatibility is poor. The widespread use of multi-brand and multi-model equipment in urban traffic management makes it difficult to achieve unified standards and efficient integration between systems, affecting the coordinated operation of the overall system. The ability to predict violations and early warning of accidents is insufficient. The existing system generally lacks the ability to deeply mine traffic data, making it difficult to predict the trend of road violations, early warning of potential accidents, and support the comprehensive improvement of traffic management efficiency. Data privacy and security issues are prominent. With the widespread application of big data technology, the privacy and security issues of traffic data have attracted more and more attention. How to achieve efficient use of data while ensuring data security has become a key issue. For example, the collection and analysis of large-scale data involves personal whereabouts and behavior data. If not handled properly, it may lead to privacy leakage or data abuse, which in turn may cause legal and social problems.

[0003] In recent years, intelligent collection, analysis, prediction and automatic correction methods and systems based on public transportation violation big data have played an important role in urban traffic management. By real-time collection and analysis of multi-source data such as vehicles, pedestrians, roads, and traffic lights, the system can: provide scientific accident warning and emergency handling support; improve the intelligent management of traffic facilities. For example, by analyzing historical and real-time data, the system can predict the trend of traffic flow changes. By analyzing historical and real-time traffic violation data, the system can predict the accident coefficient and traffic congestion coefficient of each road section, thereby optimizing the ratio of traffic police, reducing traffic congestion and improving traffic efficiency. However, these systems still face many challenges: data collection equipment may cause incomplete data due to failure or delay, affecting the timeliness and accuracy of system decision-making; in the process of massive data processing, the imperfection of privacy protection mechanism may lead to data leakage, limiting the further promotion and application of the system. Summary of the invention

[0004] The present invention provides a method and system for intelligent collection, analysis, prediction and automatic correction of public transportation violation big data, so as to solve the deficiencies of existing traffic management systems in data collection, processing, prediction and data security. By using big data analysis, deep learning and automatic control technology, efficient management and governance of traffic violations can be achieved. Through the present invention, efficient collection and processing of traffic violations, accurate prediction of violation trends, rapid warning of accidents and automatic correction of violations can be achieved, while fully protecting the privacy and security of data.

[0005] Specifically, it includes the following technical modules: Data collection module: used to collect illegal behaviors and related data in real time, and supports the access and compatibility expansion of multiple types of front-end devices. Data processing and storage module: realizes real-time processing, encrypted storage and distributed storage of illegal data to support high-concurrency system requirements. Data analysis and prediction module: based on big data and artificial intelligence technology, it conducts in-depth analysis and trend prediction of traffic violation data. Automatic correction module: realizes automatic identification and processing of illegal behaviors, including video analysis, image capture and generation of penalty recommendations. Data security and privacy protection module: through multi-level protection measures, ensures the security and privacy of data during collection, storage, transmission and processing. System compatibility and integration module: enhances the cross-platform compatibility and collaboration capabilities of the system, and supports flexible function expansion and module integration. Account authority module: through hierarchical authority management and access control, ensures the safe use of data and systems, and prevents data abuse and leakage.

[0006] Preferably, the data acquisition module includes: front-end device integration: support real-time collection of various types of violations, including speeding, running red lights, illegal parking, illegal honking, vehicle restrictions, etc.; support seamless access and compatibility expansion of various hardware devices (such as cameras, radars, sensors, etc.). Multi-source data access: integrate multi-source data such as traffic flow, weather, road conditions, etc. to build a complete traffic violation monitoring data pool. Edge computing capabilities: embed edge computing units in front-end devices to perform localized data processing, reduce data transmission delays and improve the overall efficiency of the system. Violation marking: automatically label the collected violation data to facilitate subsequent analysis and processing.

[0007] Preferably, the data processing and storage module includes: Real-time data processing: improve data processing efficiency and reduce system delays through data cleaning, formatting and index optimization technology. Encrypted storage mechanism: use AES256 or higher encryption algorithm to store illegal data to ensure data security. Distributed storage architecture: combined with Hadoop or MongoDB and other technologies, support high concurrency and high availability of big data storage needs. Redundant storage and disaster recovery solutions: provide data backup and recovery capabilities to prevent data loss due to accidents.

[0008] Preferably, the data analysis and prediction module includes: Illegal behavior pattern analysis: extracting characteristic patterns of illegal behaviors based on machine learning and deep learning algorithms. Trend prediction: predicting high-incidence areas and time periods of traffic violations through time series analysis, regression models and other technologies, and generating visual trend charts. Accident warning system: combining real-time traffic data with historical analysis results to quickly predict the possibility of accidents and send warning notifications. Situational analysis: analyzing the causes of illegal behaviors (such as weather factors, holidays, etc.) to provide a basis for formulating precise management strategies.

[0009] Preferably, the automatic data correction module is for violations that cannot be identified and captured by current front-end devices. We have invented an automatic violation correction method for video surveillance equipment, including: Real-time video analysis: By accessing video surveillance equipment, real-time or historical video streams are automatically analyzed to monitor vehicles suspected of violating the law, and pictures are captured once conditions for evidence collection are met. Intelligent reminders and penalty suggestions: Remind violators via text messages or push notifications, and generate intelligent penalty suggestions for reference by management departments. Dynamic induction system: Combined with traffic induction screens and signal light systems, traffic rules are dynamically adjusted to correct potential violations.

[0010] Preferably, in order to address data privacy and security issues, the present invention specially designs a data security and privacy protection module, which adopts multi-level and multi-dimensional protection measures to ensure the security and privacy of data during collection, transmission, storage and processing, including: Data encryption and desensitization: real-time encryption of data during the collection phase; desensitization technology is used for stored data to avoid leakage of personal privacy. Secure transmission protocol: Use SSL / TLS protocol to ensure the secure transmission of data in public networks. Permission management and access control: Set fine-grained permission control to ensure that data is accessible only to authorized users. Data security monitoring: Regularly perform vulnerability scans and security audits on the system to promptly patch potential risks. Federated learning technology: While achieving collaborative data analysis between different departments, avoid direct sharing of raw data.

[0011] Optionally, the system compatibility and integration modules include: Multi-device compatibility: support seamless integration of mainstream brand devices and unified access protocols. Cross-system collaboration: realize data docking and linkage with traffic management departments, traffic police systems and urban big data platforms. Modular architecture: the system design adopts a modular architecture to support flexible expansion and function upgrades.

[0012] Optionally, the account permission module includes: hierarchical permission management: define the scope of permission according to user roles to ensure hierarchical use of sensitive data. Operation log record: record every access and operation of the user in detail for easy tracking and auditing. Abnormal access warning: timely detect and intercept illegal access behavior through abnormal behavior detection.

[0013] Through the above technical scheme, the present invention provides a method and system for intelligent collection, analysis, prediction and automatic correction of public transportation violation big data. The system combines edge computing, big data analysis, deep learning and automatic control technology, which can not only efficiently collect and process traffic violation data, but also realize automatic correction of violations through accurate trend prediction and accident warning. At the same time, the present invention adopts a multi-level security protection mechanism to fully protect the privacy and security of data, and realizes the flexibility and scalability of the system through modular design, which is suitable for various traffic management scenarios. Compared with the prior art, the advantages of the present invention are: comprehensive data collection: support access and fusion of multi-source data, real-time monitoring of various violations, compatibility with various front-end devices, and improvement of the scope of application of the system. High processing efficiency: through edge computing and distributed storage technology, fast data processing and efficient storage are realized, and the load of the central server is reduced. Accurate prediction: using deep learning and big data analysis technology, it can accurately predict the high-incidence areas and time periods of violations, and effectively assist the management department in formulating preventive measures. Automatic correction: through video analysis technology and automatic control equipment, automatic identification and correction of violations are realized, reducing manual intervention. Data security and reliability: through encryption, desensitization, permission control and other multi-dimensional measures, the security and compliance of the data throughout its life cycle are ensured. Strong system flexibility: modular architecture design supports multi-functional expansion, facilitating seamless docking and collaboration with existing systems.

[0014] To sum up, the present invention has significant advantages in improving traffic management efficiency, reducing accident risks, optimizing illegal governance processes, etc., and can provide comprehensive and efficient technical support for smart transportation construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 : The present invention is an overall schematic diagram of a method and system for intelligent collection, analysis, prediction and automatic correction of public transportation violation big data.

[0016] Figure 2 : Schematic diagram of the structure of the data acquisition module.

[0017] Figure 3 : Schematic diagram of the structure and workflow of the data processing and storage module.

[0018] Figure 4 : Schematic diagram of the structure of the data analysis and prediction module.

[0019] Figure 5 : Schematic diagram of the structure and workflow of the automatic violation correction module.

[0020] Figure 6 : Schematic diagram of the structure of the data security and privacy protection module. DETAILED DESCRIPTION

[0021] To make the content, technical solutions and advantages of the embodiments of the present application clearer, the specific implementation methods of the embodiments of the present application are described in detail below in conjunction with the accompanying drawings. The described embodiments are part of the embodiments of the present application, not all of the embodiments.

[0022] Figure 1 It is an overall schematic diagram of a method and system for intelligent collection, analysis, prediction and automatic correction of public transportation violation big data in the present invention. In the present invention, the process of the method and system may include: front-end equipment, data collection, data transmission encryption, automatic correction, data processing and storage, data analysis and privacy protection, system compatibility and integration, and account permissions.

[0023] Figure 2 This is a structural diagram of the data acquisition module. The data acquisition module is the core input part of the system, which aims to collect violations and related data in real time and provide highly compatible and efficient multi-source data access. Through the connection between the front-end device and the data processing module, comprehensive perception and recording of traffic violations can be achieved.

[0024] The front-end device integrated data acquisition module integrates a variety of front-end devices and adopts standardized device access protocols (such as ONVIF, RTSP, FTP, etc.) to ensure seamless access and data collection of different types of devices. The main equipment includes: the camera is used to shoot real-time video streams and capture specific images of violations. It supports high-definition image quality and low-light imaging technology to ensure all-weather data collection during the day and night; millimeter-wave radar is used to detect the speed and position of the vehicle in real time. By combining with the camera, it can accurately monitor traffic violations such as speeding and illegal parking; environmental sensors (such as noise and temperature sensors) can provide auxiliary data when detecting specific violations, and further analyze and confirm the type of violations.

[0025] In the first embodiment, when the illegal parking capture camera captures an illegal behavior, it will upload the collected standard illegal parking violation data. The data transmission is divided into two steps: the capture camera uploads the data to the edge computing box and transmits it back to the background in JSON format. The background will match and encrypt the received raw data and wait for the next step of data combination verification; the camera will continuously capture 4 pictures of the same vehicle, namely the first picture, the second picture, the illegal picture and the close-up picture, and upload the 4 pictures to the edge computing box. The edge computing box will generate a standard four-in-one picture. The four-in-one picture refers to 4 pictures of the same vehicle captured continuously. The first picture (T0): Take the picture at the trigger time. The second picture (T0+3 minutes): Confirm whether the illegal state still exists. The third picture (T0+4 minutes): Fix the continuity of the illegal state. The fourth picture (T0+5 minutes): Close-up capture of key information of the vehicle (such as the license plate). And arrange and combine them in a 2x2 format to form an illegal evidence picture. The edge computing box will conduct a preliminary analysis of the image locally and mark the illegal behavior, then package it and upload it to the background, and match and verify it with the original data.

[0026] Specifically, the edge computing box will receive two types of data from the snapshot camera: raw data in JSON format (including license plate number, violation time, violation location, etc.). Four consecutively captured pictures. The received pictures and JSON data are cached locally to ensure data integrity. The edge computing box verifies the timestamps of the received pictures to ensure that the shooting time intervals of the four pictures meet the preset rules (such as 3 minutes and 1 minute).

[0027] Specifically, the picture combination rules are as follows: Upper left corner (first picture): record the overall picture of the moment when the illegal vehicle is triggered, including the surrounding environment and the vehicle's position. Upper right corner (secondary picture): confirm the existence of the vehicle again from different angles or distances. Lower left corner (illegal picture): confirm the continuous stop status of the vehicle. Lower right corner (close-up picture): zoom in to clearly capture the license plate, model, and color of the vehicle. The combination is arranged in a 2x2 grid to generate pictures with uniform resolution (such as 1920x1080 pixels), and lightweight compression is used to ensure upload efficiency.

[0028] Specifically, for local illegal behavior analysis, as an intelligent processing terminal, the edge computing box conducts preliminary analysis on the collected pictures and data. The analysis content includes: License plate recognition: By using the built-in OCR algorithm, the license plate number of the vehicle is extracted, and the extraction result is matched with the license plate information in the JSON data. Vehicle feature extraction: Analyze the appearance features of the vehicle (such as vehicle type, color) and verify their consistency. Illegal status confirmation: Based on the time interval and content changes of the four pictures, determine whether the vehicle meets the illegal status criteria (such as illegal parking for more than the set time). After completing the local analysis, the edge computing box marks the four-in-one picture as "illegal" or "abnormal". If the picture analysis result meets the illegal determination conditions, it is packaged and uploaded to the background; if the conditions are not met (such as blurred license plate, missing picture), it is marked as abnormal.

[0029] Specifically, for data packaging and uploading, the edge computing box conducts unified packaging processing on the analyzed data to generate a standardized data packet. The format of the data packet is as follows: { "car_id": "Yu A12345", "wf_time": "2024-12-12 14:30:00", "ip": "IP address of the illegal parking capture camera", "data_type": "WT", "files": "Binary picture content", "raw_data": ["Original JSON path"]} After the packaging is completed, the edge computing box encrypts the data packet (such as AES encryption) and uploads it to the background server through the HTTPS protocol.

[0030] Specifically, for combined verification and storage in the background, after receiving the data packet from the edge computing box, the background server performs the following processing steps: Data decryption and verification: Decrypt the data packet and verify its integrity and authenticity. This includes verifying whether the information such as the license plate number, illegal time, and illegal location in the JSON data is consistent with that in the four-in-one picture. Illegal status review: Further analyze the picture content through the background algorithm to confirm whether the vehicle actually has illegal behavior. Data storage: The background system stores the verified illegal records in the illegal record database and stores the four-in-one pictures in the evidence picture library.

[0031] Specifically, for the background algorithm picture content analysis method, the background is equipped with traffic illegal parking scanning software, which scans and verifies all types of pictures uploaded to the background server. Through algorithms such as hyperlpr3 and paddleocr, the pictures are re-recognized and verified. If the illegal behavior is confirmed to be established, it is marked as an "effective record" and stored in the database; if abnormalities are found (such as blurred pictures, missing data, etc.), it is marked as an "abnormal record".

[0032] Figure 5It is a schematic diagram of the structure and workflow of the automatic violation correction module. In the existing camera violation capture technology, the illegal behavior of taxis or online ride-hailing vehicles picking up and dropping off passengers at non-designated locations (such as the middle of the road, intersections, etc.) cannot be automatically identified and captured, and such behavior often has a great impact on traffic order and pedestrian safety. The present invention proposes a solution based on an automatic violation correction module, which combines a front-end surveillance camera or a high-altitude eagle-eye camera, and realizes accurate capture and evidence collection of new types of illegal behaviors through real-time video stream analysis and intelligent recognition technology.

[0033] Specifically, the structure and workflow of the automatic violation correction module include the following steps: Front-end monitoring deployment and data collection, by deploying surveillance cameras and high-altitude eagle-eye cameras in key areas (such as urban main roads, busy intersections, etc.), real-time / historical video stream data. These cameras support high-resolution imaging and night vision functions, and transmit data to the background analysis module through wireless networks. Real-time video stream analysis, the video stream analysis module uses artificial intelligence algorithms, deep learning models, and computer vision technology to analyze the collected video stream in real time. Identify the sudden deceleration, parking, and door opening of the vehicle. Determine whether the passengers are getting on and off the vehicle and the specific location of getting on and off the vehicle based on the action characteristics. Compare the vehicle stop with the set legal boarding and alighting areas (such as bus stops, designated boarding and alighting points) to determine whether there is a violation. Tracking and recording of suspected illegal behaviors, for vehicles suspected of violating the law, the system automatically turns on the behavior tracking function, records its parking time, boarding and alighting process, and the impact on surrounding traffic, and generates an analysis report. Evidence collection and violation confirmation: When the behavior of the vehicle meets the violation determination conditions, the system automatically captures the key images of the violation, including: environmental map, recording the specific location of the vehicle and the surrounding environment; dynamic process map, clearly capturing the process of passengers getting on and off the vehicle; close-up map, close-up capture of the vehicle license plate, model and passenger behavior. The system sends violation reminders to the owner in various forms (SMS, APP notifications), and feedbacks the processing results to the law enforcement department to achieve closed-loop management of the entire process. Data processing and storage: These image and video evidence are compressed and encrypted and uploaded to the backend server for storage, waiting for manual review.

[0034] Figure 3 The structure and workflow diagram of the data processing and storage module. The present invention proposes an efficient and reliable data processing and storage solution, which is suitable for the processing and storage needs of large-scale video data and illegal capture records. Through multiple links such as data collection, cleaning, encryption, and distributed storage, the data is managed securely and efficiently, while supporting subsequent data query and analysis.

[0035] Specifically, the system cleans the collected data and removes redundant and invalid data, such as blurred images and non-illegal records. The filtering algorithm ensures the quality and accuracy of the stored data. The data is formatted in a unified way: { "id": "Index number", / / unique index identifier generated by the system "car_id": "License plate number", / / The license plate number of the illegal vehicle "car_type": "Vehicle Type", / / Vehicle type (such as small car, large car, new energy small car, etc.) "wf_time": "Violation time", / / time when the violation occurred "sms_time": "SMS sending time", / / The time when the SMS notifying illegal behavior was sent "sms_result": "SMS sending result", / / Whether the SMS sending is successful "punish_time": "punishment time", / / execution time of the punishment behavior "punish_result": "Punishment Result", / / Punishment result (such as completed punishment or not completed punishment) "push_time": "Push time", / / Time when illegal data is pushed "push_result": "Push result", / / Whether the push result is successful "image": "Illegal evidence collection pictures", / / Pictures of illegal evidence collection "event_image": "Illegal event image", / / Panoramic image or video screenshot related to illegal events "sms_image": "SMS content image", / / Screenshot of SMS notification content "ip": "IP address of the camera that captures illegal photos", / / IP address of the camera responsible for capturing illegal photos "push_scxh": "Push serial number" / / The serial number of data push, for easy tracking and verification} The cleaned data is encrypted using the Advanced Encryption Standard (AES256) to ensure data security during storage and transmission and prevent data leakage or tampering. After data encryption is completed, distributed storage technology (Hadoop Distributed File System HDFS) is used to store data on multiple nodes (node ​​1, node 2, node 3...). The system has established a complete redundant storage mechanism to ensure multiple copies of data backup. The supporting disaster recovery solution supports rapid data recovery in the event of natural disasters or system failures, ensuring high reliability of data storage.

[0036] Figure 4 The implementation process of the data analysis and prediction module is demonstrated, aiming to provide more accurate traffic management and early warning support through in-depth analysis and trend prediction of traffic violation data.

[0037] Specifically, the illegal behavior pattern analysis algorithm, formula: in and are two samples, and n is the dimension of the feature. is the set of samples belonging to the kth cluster, is the center of the cluster. Calculate the distance from each sample to the cluster center and assign the sample to the nearest cluster center. Calculate the distance from each sample to the cluster center and assign the sample to the nearest cluster center. Update the center of each cluster and repeat steps 2 and 3 until the cluster center no longer changes.

[0038] Specifically, the trend prediction model algorithm formula is: in is the observed value at time t (e.g., the number of traffic violations). c is a constant term. is the autoregression (AR) coefficient, which indicates the impact of previous data on current data. is the moving average (MA) coefficient, which represents the impact of past errors. is the error term. Choose appropriate p (autoregressive order) and q (moving average order). Fit the ARIMA model and estimate the parameters. Predict future trends based on historical data.

[0039] Specifically, the scenario analysis algorithm, formula: Where Y is the prediction target (such as the probability of illegal behavior), X1, X2, …, Xn are influencing factors (such as temperature, time, etc.), β0, β1, …, βn are regression coefficients, and ϵ is the error term. Collect and process environmental data related to traffic violations (such as weather, holidays, etc.). Use regression analysis methods to build a model to evaluate the impact of different factors on illegal behavior. Predict the probability of illegal behavior in different scenarios based on the model.

[0040] In the data analysis and prediction module, a variety of machine learning and statistical analysis methods are used, such as K-means clustering, ARIMA time series, SVM support vector machine, regression analysis and other algorithms to analyze traffic violations, predict trends and conduct accident warnings. These algorithms can not only effectively analyze historical data, but also make dynamic predictions based on real-time data, greatly improving the intelligence and prediction capabilities of traffic management.

[0041] Figure 6 This is a schematic diagram of the structure of the data security and privacy protection module. The data security and privacy protection module is designed to ensure that all traffic data collected, stored and transmitted are fully protected to prevent unauthorized access and data leakage. This module uses multi-level security measures to ensure that the system's privacy protection complies with relevant laws and regulations and protects users' personal information from being abused.

[0042] Specifically, data transmission encryption (TLS / SSL) ensures the security of data during transmission and prevents data from being eavesdropped or tampered with during transmission. All data is encrypted and transmitted via the HTTPS protocol. The TLS / SSL encryption protocol establishes an encrypted channel between the client and the server to ensure the security of data transmission. Use the modern TLS encryption protocol (TLS 1.2) and use valid digital certificates to verify the identities of both parties in the communication.

[0043] Specifically, data storage encryption (AES encryption) ensures that sensitive data stored in the database, such as license plate numbers, illegal behavior information, etc., will not be illegally accessed. Sensitive data is encrypted using a symmetric encryption algorithm (AES-256). The management of encryption keys requires the use of a key management system (KMS) to ensure the security of the keys.

[0044] Specifically, access control (Role-based access control RBAC) limits user access to system resources and only allows users with appropriate permissions to access sensitive data. The role-based access control model (RBAC) is adopted to ensure that only authorized personnel can access specific resources by defining access rights for different user roles.

[0045] The above are only embodiments of the present application, and the present invention is not limited to the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected; the scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method and system for intelligent collection, analysis, prediction and automatic correction of public transportation violation big data, characterized in that: The method and system include: a data collection module: used for real-time collection of violations and related data, supporting access and compatibility expansion of multiple types of front-end devices; a data processing and storage module: used for real-time processing, encrypted storage and distributed storage of violation data to support high-concurrency system requirements; a data analysis and prediction module: based on big data and artificial intelligence technology, in-depth analysis and trend prediction of traffic violation data; an automatic violation correction module: used for automatic identification and processing of violations, including video analysis, image capture and generation of penalty recommendations; a data security and privacy protection module: through multi-level protection measures, ensuring the security and privacy of data during collection, storage, transmission and processing; a system compatibility and integration module: used to enhance the system's cross-platform compatibility and collaboration capabilities, supporting flexible functional expansion and module integration; an account authority module: through hierarchical authority management and access control, ensuring the safe use of data and systems and preventing data abuse and leakage.

2. The system according to claim 1, characterized in that The data collection module includes: front-end device integration: supporting real-time collection of various types of violations, including speeding, running red lights, illegal parking, illegal honking, vehicle restrictions, etc.; multi-source data access: integrating multi-source data such as traffic flow, weather, road conditions, etc. to build a complete traffic violation monitoring data pool; edge computing capabilities: equipping the front-end device with an edge computing box to perform localized data processing, reduce data transmission delays and improve the overall efficiency of the system; violation marking: automatically labeling the collected violation data to facilitate subsequent analysis and processing.

3. The edge computing capability according to claim 2, characterized in that: include: Edge computing box, used to receive illegal data and pictures uploaded by snapshot cameras; Four pictures of the same vehicle are captured continuously, including the first picture, the second picture, the violation picture and the close-up picture, and a four-in-one picture is generated; the picture combination rule is: the upper left corner is the first picture, the upper right corner is the second picture, the lower left corner is the violation picture, and the lower right corner is the close-up picture. The pictures are arranged in a 2x2 grid; localization analysis includes license plate recognition, vehicle feature extraction and violation status confirmation, and illegal behaviors are marked.

4. The system according to claim 3, characterized in that The generation process of the four-in-one picture includes: the first picture records the overall picture of the vehicle at the moment of triggering; the second picture confirms whether the vehicle is still in an illegal state after 3 minutes; the third picture fixes the continuity of the illegal state after 4 minutes; the fourth picture captures the vehicle's license plate, model and color close-up after 5 minutes; the picture resolution is unified, and lightweight compression is used to improve upload efficiency.

5. The system according to claim 1, characterized in that The data processing and storage module includes: real-time data processing: improving data processing efficiency and reducing system delays through data cleaning, formatting and index optimization technology; encrypted storage mechanism: using AES256 or higher-level encryption algorithms to store illegal data to ensure data security; distributed storage architecture: combining Hadoop or MongoDB and other technologies to support high-concurrency and high-availability big data storage needs; redundant storage and disaster recovery solutions: providing data backup and recovery capabilities to prevent data loss due to accidents.

6. The system according to claim 1, characterized in that The data analysis and prediction module includes: illegal behavior pattern analysis: based on machine learning and deep learning algorithms, characteristic patterns of illegal behaviors are extracted; trend prediction: through time series analysis, regression models and other technologies, high-incidence areas and time periods of traffic violations are predicted, and visual trend charts are generated; accident warning system: combining real-time traffic data with historical analysis results, the possibility of accidents is quickly predicted, and warning notifications are sent; situational analysis: analyzing the causes of illegal behaviors (such as weather factors, holidays, etc.) to provide a basis for formulating precise management strategies.

7. The system according to claim 6, characterized in that The background algorithms include: image content analysis method, based on hyperlpr3, paddleocr and other algorithms, secondary recognition and verification of images; review of license plate numbers, vehicle features and illegal status in images; if the image is blurred or data is missing, it is marked as an abnormal record and notified for manual review; illegal behavior pattern analysis algorithm: in and are two samples, n is the dimension of the feature, is the set of samples belonging to the kth cluster, is the center of the cluster, calculate the distance from each sample to the cluster center, and assign the sample to the nearest cluster center, calculate the distance from each sample to the cluster center, and assign the sample to the nearest cluster center, update the center of each cluster, repeat steps 2 and 3 until the cluster center no longer changes; Trend prediction model algorithm: in is the observed value at time t (such as the number of traffic violations), c is a constant term, is the autoregression (AR) coefficient, which indicates the impact of previous data on current data. is the moving average (MA) coefficient, which represents the impact of past errors, is the error term, select appropriate p (autoregressive order) and q (moving average order), fit the ARIMA model, estimate parameters, and predict future trends based on historical data; scenario analysis algorithm: Among them, Y is the prediction target (such as the probability of violation), X1, X2, …, Xn are influencing factors (such as temperature, time, etc.), β0, β1, …, βn are regression coefficients, ϵ is the error term, collect and process environmental data related to traffic violations (such as weather, holidays, etc.), use regression analysis methods to establish a model, evaluate the impact of different factors on violations, and predict the probability of violations in different scenarios based on the model.

8. The system according to claim 1, characterized in that The automatic violation correction module includes: real-time video analysis: by accessing video surveillance equipment, automatically analyzing real-time or historical video streams, monitoring vehicles suspected of violating laws, and taking pictures once the conditions for evidence collection are met; intelligent reminders and penalty suggestions: reminding violators through text messages or notification push, and generating intelligent penalty suggestions for reference by management departments; dynamic guidance system: combined with traffic guidance screens and signal light systems, dynamically adjusting traffic rules to correct potential violations.

9. The system according to claim 1, characterized in that The data security and privacy protection module includes: data encryption and desensitization: real-time encryption of data during the collection phase; use of desensitization technology for stored data to avoid leakage of personal privacy; secure transmission protocol: use of SSL / TLS protocol to ensure secure transmission of data in public networks; authority management and access control: setting fine-grained authority control to ensure that data is accessible only to authorized users; data security monitoring: regular vulnerability scanning and security audits of the system to promptly patch potential risks; federated learning technology: while achieving collaborative data analysis between different departments, avoid direct sharing of original data.

10. The system according to claim 1, characterized in that The system compatibility and integration modules include: multi-device compatibility: support seamless integration of mainstream brand devices and unified access protocols; cross-system collaboration: realize data docking and linkage with traffic management departments, traffic police systems and urban big data platforms; modular architecture: the system design adopts a modular architecture to support flexible expansion and functional upgrades.

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