An intelligent network self-healing method and system applied to a smart traffic system

Abstract

The application relates to the technical field of cloud computing, in particular to an intelligent network self-recovery method and system applied to a smart traffic system, which comprises the following steps: data collection, abnormality detection, fault diagnosis, automatic repair and self-learning optimization; the beneficial effects are that the intelligent network self-recovery method and system applied to the smart traffic system can immediately identify abnormal behaviors in the network and respond quickly through real-time monitoring and a quick abnormality detection mechanism, and the time for fault detection and response is greatly reduced; advanced fault diagnosis algorithms, including deep learning and pattern recognition technology, are used, and the system can accurately diagnose various complex faults, including unknown or novel fault modes.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, specifically to an intelligent network self-healing method and system applied to intelligent transportation systems. Background Technology

[0002] With the acceleration of urbanization, traffic congestion, traffic accidents, and environmental pollution have become increasingly prominent problems, becoming key factors restricting sustainable urban development. Intelligent Transportation Systems (ITS), as an important means to address these challenges, integrate advanced information technology, data communication and transmission technology, electronic sensing technology, control technology, and computer technology to achieve real-time monitoring, efficient management, and intelligent decision-making of the entire transportation system. The core of an ITS lies in its network infrastructure, which is responsible for collecting, processing, and transmitting large amounts of traffic data, including vehicle location, speed, traffic flow, traffic light status, and accident information. This data is crucial for traffic management centers; it needs to be transmitted to the control center in real time, accurately, and reliably for analysis and decision-making. However, existing network infrastructure faces numerous challenges:

[0003] Increased network complexity: With the continuous development of ITS, the network scale is expanding, the number of devices is increasing dramatically, and the network topology is becoming increasingly complex, which brings huge challenges to network management and maintenance.

[0004] Cybersecurity threats are escalating: cyberattack methods are constantly evolving, and security incidents such as hacker attacks, malware, and data breaches are occurring frequently, posing a serious threat to the cybersecurity of ITS.

[0005] Long fault recovery time: Traditional network maintenance mainly relies on manual monitoring and regular inspections. Once a fault occurs, it often takes a long time to detect and repair, leading to traffic service interruptions and affecting the efficiency of city operations.

[0006] High maintenance costs: As the network expands, the workload of maintenance personnel increases, requiring a significant investment of human, material, and financial resources for network maintenance and management.

[0007] Poor user experience: Frequent network failures have led to unstable traffic information services, preventing users from obtaining timely and accurate traffic information and affecting their travel experience. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent network self-healing method and system for use in intelligent transportation systems, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a self-healing method for intelligent networks applied to intelligent transportation systems, the method comprising the following steps:

[0010] Data acquisition involves collecting key data from the ITS network in real time, including vehicle traffic flow, traffic light status, traffic camera video streams, and network device logs.

[0011] Anomaly detection uses machine learning algorithms to analyze collected data in real time and identify behaviors that deviate from normal patterns.

[0012] Fault diagnosis combines fault diagnosis technology with deep learning algorithms to conduct in-depth fault cause analysis on detected anomalies;

[0013] Automatic repair executes corresponding repair strategies based on fault diagnosis results, including restarting services, resetting configurations, or isolating damaged devices;

[0014] Self-learning optimization continuously improves the fault detection and diagnosis algorithm by analyzing historical fault cases and repair results, thereby enhancing the system's self-healing capabilities.

[0015] Preferably, the specific data collection operations include:

[0016] Multi-source data integration capability, adaptable to various devices and system interfaces in the ITS network;

[0017] A real-time guarantee mechanism ensures that data is transmitted to the central processing system without delay;

[0018] Data preprocessing steps, including data cleaning and feature extraction, are used to improve the accuracy of anomaly detection.

[0019] The security design employs encryption technology to protect data transmission security.

[0020] Scalable design to accommodate future increases in data sources.

[0021] Preferably, the specific operations for anomaly detection include:

[0022] Suitable anomaly detection algorithm selection, such as isolated forest or autoencoder;

[0023] The model training process uses historical data to learn normal behavior patterns and identify abnormal behaviors;

[0024] The real-time monitoring function continuously analyzes the incoming data and immediately triggers an alarm once an anomaly is detected.

[0025] A reasonable threshold setting balances the false alarm rate and the false alarm rate;

[0026] A feedback mechanism is used to feed real-world failure cases back to the model for continuous optimization.

[0027] Preferably, the specific operations for fault diagnosis include:

[0028] The construction of the fault feature library includes feature descriptions of various known fault modes;

[0029] A real-time fault mode matching mechanism matches detected anomalies with a fault feature database;

[0030] Deep learning-assisted diagnostics function performs pattern recognition and cause inference for complex or unknown fault modes;

[0031] Interactive diagnostic features allow you to interact with your network administrator to obtain additional information or confirm diagnostic results.

[0032] The root cause analysis step involves in-depth analysis of the root cause of the failure and the generation of a detailed fault diagnosis report.

[0033] Preferred, the specific operations for automatic repair include:

[0034] A library of repair strategies for different fault types, including restarting services, resetting configurations, and isolating devices;

[0035] Automated script execution reduces manual intervention and improves repair speed;

[0036] For safety reasons, ensure that all repair procedures comply with safety regulations;

[0037] A rollback mechanism is in place to address situations where repair operations fail or cause new problems.

[0038] The self-learning optimization engine regularly updates its anomaly detection and fault diagnosis algorithms with new data and adjusts its repair strategies to improve overall system performance.

[0039] A self-healing intelligent network system for use in intelligent transportation systems includes a data acquisition module, an anomaly detection system, a fault diagnosis center, an automatic repair unit, a self-learning optimization engine, and a user interface, wherein:

[0040] The data acquisition module is used to collect key data from the intelligent transportation system (ITS) network in real time, including vehicle flow, traffic light status, traffic camera video streams and network device logs. It also has the capabilities of multi-source data integration, real-time performance assurance, data preprocessing, security design and scalability.

[0041] Anomaly detection systems utilize machine learning algorithms to analyze collected data in real time to identify behaviors that deviate from normal patterns. They also have functions such as algorithm selection, model training, real-time monitoring, threshold setting, and feedback mechanisms.

[0042] The fault diagnosis center integrates traditional fault diagnosis technologies and deep learning algorithms for real-time fault mode matching, deep learning-assisted diagnosis, interactive diagnosis, root cause analysis, and generation of diagnostic reports.

[0043] The automatic repair unit executes corresponding repair strategies based on the fault diagnosis results, including restarting services, resetting configurations, and isolating damaged devices. It also has the capabilities of a repair strategy library, automated execution, security considerations, rollback mechanisms, and repair feedback.

[0044] The self-learning optimization engine continuously optimizes fault detection and diagnosis algorithms by analyzing historical fault cases and repair results, thereby improving the system's self-healing capabilities. This includes data accumulation, algorithm optimization, strategy adjustment, and performance evaluation.

[0045] Preferably, the data acquisition module includes:

[0046] Develop adapters that interface with various devices and systems in the ITS network to achieve multi-source data integration;

[0047] Efficient data transmission protocols and buffering mechanisms are employed to ensure data real-time performance.

[0048] Design data cleaning and preprocessing algorithms to remove noise and outliers and extract useful features;

[0049] Encryption technology is used during data transmission to ensure data security;

[0050] When designing modules, consider the data sources that may be added in the future to ensure the scalability of the system.

[0051] Preferably, the anomaly detection system includes:

[0052] Select a suitable anomaly detection algorithm based on data characteristics and real-time requirements;

[0053] Historical data is used to train anomaly detection models, including learning normal behavior patterns and identifying abnormal behavior.

[0054] Deploy the model to the real-time monitoring system to continuously analyze the incoming data and set reasonable thresholds to balance the false alarm rate and the false negative rate;

[0055] Establish a feedback mechanism to use real-world failure cases for continuous training and optimization of the model.

[0056] Preferably, the fault diagnosis center includes:

[0057] Construct a fault feature library containing characteristics of various fault modes;

[0058] When the anomaly detection system detects an anomaly, it matches the anomaly characteristics with data in the fault characteristic database in real time.

[0059] For complex or unknown fault modes, deep learning algorithms are used for assisted diagnosis.

[0060] When necessary, interact with the network administrator through the user interface to obtain additional information to assist in diagnosis;

[0061] The system thoroughly analyzes the root cause of the fault and generates a detailed fault diagnosis report.

[0062] Preferred self-learning optimization engines include:

[0063] Collect data from each fault detection, diagnosis, and repair as training material for the optimization algorithm;

[0064] Regularly update anomaly detection and fault diagnosis algorithms with new data to improve their accuracy and efficiency;

[0065] Based on the feedback of the repair results, the strategies in the repair strategy library will be automatically adjusted;

[0066] Regularly evaluate the performance of the self-learning optimization engine to ensure its optimization effectiveness;

[0067] When necessary, allow network administrators to participate in policy adjustments and optimizations to incorporate more expertise.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] The intelligent network self-healing method and system proposed in this invention, applied to intelligent transportation systems, enables the system to immediately identify abnormal behaviors in the network and respond rapidly through real-time monitoring and a rapid anomaly detection mechanism, significantly reducing fault detection and response time. Utilizing advanced fault diagnosis algorithms, including deep learning and pattern recognition technologies, the system can accurately diagnose various complex faults, including unknown or novel fault modes. Automated fault handling processes reduce reliance on manual intervention, thereby lowering labor and time costs, especially as the scale of ITS networks continues to expand, resulting in significant cost savings. Integrated security measures and automatic isolation mechanisms can promptly identify and respond to network attacks, preventing malware and data leaks and improving the security of the entire ITS network. Rapid fault recovery and stable network services ensure users receive continuous and reliable traffic information and services, greatly enhancing the user's travel experience. The self-learning optimization engine enables the system to continuously learn from historical data, automatically optimizing fault detection and repair strategies, improving the system's adaptability to the ever-changing network environment. Attached Figure Description

[0070] Figure 1 This is a flowchart of the method of the present invention;

[0071] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the present invention clear and complete, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some, not all, embodiments of the present invention, and are merely illustrative of the embodiments of the present invention. They are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Example 1, please refer to Figure 1 This invention provides a technical solution: a self-healing method for intelligent networks applied to intelligent transportation systems, the method comprising the following steps:

[0074] Data acquisition involves real-time collection of key data from the ITS network, including vehicle traffic flow, traffic light status, traffic camera video streams, and network device logs; specific operations include:

[0075] Multi-source data integration capability, adaptable to various devices and system interfaces in the ITS network;

[0076] A real-time guarantee mechanism ensures that data is transmitted to the central processing system without delay;

[0077] Data preprocessing steps, including data cleaning and feature extraction, are used to improve the accuracy of anomaly detection.

[0078] The security design employs encryption technology to protect data transmission security.

[0079] Scalable design to accommodate future increases in data sources.

[0080] Anomaly detection utilizes machine learning algorithms to analyze collected data in real time and identify behaviors that deviate from normal patterns; specific operations include:

[0081] Suitable anomaly detection algorithm selection, such as isolated forest or autoencoder;

[0082] The model training process uses historical data to learn normal behavior patterns and identify abnormal behaviors;

[0083] The real-time monitoring function continuously analyzes the incoming data and immediately triggers an alarm once an anomaly is detected.

[0084] A reasonable threshold setting balances the false alarm rate and the false alarm rate;

[0085] A feedback mechanism is used to feed real-world failure cases back to the model for continuous optimization.

[0086] Fault diagnosis combines fault diagnosis techniques with deep learning algorithms to perform in-depth analysis of the root causes of detected anomalies; specific operations include:

[0087] The construction of the fault feature library includes feature descriptions of various known fault modes;

[0088] A real-time fault mode matching mechanism matches detected anomalies with a fault feature database;

[0089] Deep learning-assisted diagnostics function performs pattern recognition and cause inference for complex or unknown fault modes;

[0090] Interactive diagnostic features allow you to interact with your network administrator to obtain additional information or confirm diagnostic results.

[0091] The root cause analysis step involves in-depth analysis of the root cause of the failure and the generation of a detailed fault diagnosis report.

[0092] Automatic repair executes corresponding repair strategies based on fault diagnosis results, including restarting services, resetting configurations, or isolating damaged devices; specific operations include:

[0093] A library of repair strategies for different fault types, including restarting services, resetting configurations, and isolating devices;

[0094] Automated script execution reduces manual intervention and improves repair speed;

[0095] For safety reasons, ensure that all repair procedures comply with safety regulations;

[0096] A rollback mechanism is in place to address situations where repair operations fail or cause new problems.

[0097] The self-learning optimization engine regularly updates its anomaly detection and fault diagnosis algorithms with new data and adjusts its repair strategies to improve overall system performance.

[0098] Self-learning optimization continuously improves the fault detection and diagnosis algorithm by analyzing historical fault cases and repair results, thereby enhancing the system's self-healing capabilities.

[0099] Example 2, see attached document Figure 2 Based on Embodiment 1, an intelligent network self-healing system for intelligent transportation systems is proposed. The system includes a data acquisition module, an anomaly detection system, a fault diagnosis center, an automatic repair unit, a self-learning optimization engine, and a user interface.

[0100] 1. System Architecture Design

[0101] To achieve the above objectives, our proposed intelligent network self-healing technology solution includes the following key components: a data acquisition module, an anomaly detection system, a fault diagnosis center, an automatic repair unit, a self-learning optimization engine, and a user interface.

[0102] 2. Data Acquisition Module

[0103] This module is responsible for collecting key data from the ITS network in real time, including but not limited to vehicle traffic flow, traffic light status, traffic camera video streams, and network device logs. This data forms the basis for subsequent anomaly detection and fault diagnosis. Specific implementation details are as follows:

[0104] 2.1 Multi-source data integration: Develop adapters that can interface with various devices and systems in the ITS network, including traffic lights, surveillance cameras, vehicle sensors, etc., to achieve comprehensive data collection.

[0105] 2.2 Real-time performance guarantee: Efficient data transmission protocols and buffering mechanisms are adopted to ensure that data can be transmitted to the central processing system in real time, reflecting the network status without delay.

[0106] 2.3 Data Preprocessing: Design data cleaning and preprocessing algorithms to remove noise and outliers, and extract features useful for anomaly detection and fault diagnosis.

[0107] 2.4 Security Design: During the data acquisition process, encryption technology is used to protect the security of data transmission and prevent data from being intercepted or tampered with during transmission.

[0108] 2.5 Scalability: The module design needs to consider the data sources that may be added in the future to ensure the scalability of the system and adapt to the development and upgrading of the ITS network.

[0109] 3. Anomaly Detection System

[0110] Machine learning algorithms, such as random forests and support vector machines, are used to analyze the collected data in real time to identify behaviors that deviate from normal patterns. The anomaly detection system needs to be able to adapt to fluctuations in different traffic scenarios and accurately distinguish between genuine anomalies. Specific implementation details are as follows:

[0111] 3.1 Algorithm selection: Select a suitable anomaly detection algorithm based on data characteristics and real-time requirements, such as isolated forest or autoencoder.

[0112] 3.2 Model Training: The anomaly detection model is trained using historical data, including learning normal behavior patterns and identifying abnormal behavior.

[0113] 3.3 Real-time monitoring: Deploy the model to the real-time monitoring system to continuously analyze the incoming data, and trigger an alarm immediately once an anomaly is detected.

[0114] 3.4 Threshold setting: Set reasonable thresholds for anomaly detection to balance false alarm rate and false negative rate, and ensure the accuracy of anomaly detection.

[0115] 3.5 Feedback Mechanism: Establish a feedback mechanism to use real-world failure cases for continuous training and optimization of the model.

[0116] 4. Fault Diagnosis Center

[0117] Fault diagnosis is the core innovation of this technical solution. This center not only integrates traditional fault diagnosis techniques but also introduces deep learning and pattern recognition algorithms to achieve deeper fault cause analysis. The implementation modules include:

[0118] 4.1 Construction of Fault Feature Library

[0119] Build a comprehensive fault feature library containing feature descriptions of various known fault modes. These feature descriptions will serve as the basis for fault diagnosis.

[0120] 4.2 Real-time Fault Mode Matching

[0121] When the anomaly detection system detects an anomaly, the fault diagnosis center will match the anomaly characteristics with the data in the fault characteristic database in real time to quickly identify the possible fault type.

[0122] 4.3 Deep Learning-Assisted Diagnosis

[0123] For complex or unknown failure modes, the fault diagnosis center will use deep learning algorithms for assisted diagnosis. By training a neural network model to identify failure modes, the system can learn from historical failure cases and predict potential causes of failure.

[0124] 4.4 Interactive Diagnosis

[0125] In some cases, the system may need to interact with the network administrator to obtain additional information or confirm diagnostic results. This interactive diagnostic process can improve the accuracy of the diagnosis.

[0126] The implementation details are as follows:

[0127] Fault Feature Library: Construct a database containing features of various fault modes for matching with actual detected anomalies.

[0128] Deep learning assistance: For complex or unknown failure modes, deep learning algorithms are used for pattern recognition and cause inference.

[0129] Interactive diagnostics: When necessary, interact with the network administrator through the user interface to obtain additional information to assist in diagnosis.

[0130] Root cause analysis: It is not only about identifying the symptoms of a fault, but also about deeply analyzing the root cause of the fault to provide accurate guidance for repair.

[0131] Diagnostic Report: Generates a detailed fault diagnosis report, including information such as fault type, possible causes, and scope of impact.

[0132] 5. Automatic Repair Unit

[0133] The automatic repair unit executes corresponding repair strategies based on the fault causes provided by the fault diagnosis center. These strategies include restarting services, resetting configurations, and isolating damaged devices.

[0134] Repair strategy library: Establish a repair strategy library for different fault types, including restarting services, resetting configurations, and isolating devices.

[0135] Automated execution: Develop automated scripts to execute remediation strategies, reduce manual intervention, and improve remediation speed.

[0136] Safety considerations: During the repair process, ensure that all operations comply with safety standards to avoid introducing new safety risks.

[0137] Rollback mechanism: Design a rollback mechanism to deal with situations where the repair operation fails or causes new problems.

[0138] Repair Feedback: The repair results are fed back to the fault diagnosis center for further optimization of diagnosis and repair strategies.

[0139] 6. Self-learning optimization engine

[0140] The self-learning optimization engine is a key component for achieving self-learning and self-optimization. It continuously optimizes fault detection and diagnosis algorithms by analyzing historical fault cases and repair results, thereby improving the system's self-healing capabilities.

[0141] The implementation details are as follows:

[0142] Data accumulation: Collect data from each fault detection, diagnosis, and repair as training material for the optimization algorithm.

[0143] Algorithm optimization: Regularly update the anomaly detection and fault diagnosis algorithms with new data to improve their accuracy and efficiency.

[0144] Strategy Adjustment: Based on the feedback of the repair results, automatically adjust the strategies in the repair strategy library to adapt to new failure modes.

[0145] Performance evaluation: Regularly evaluate the performance of the self-learning optimization engine to ensure its optimization effectiveness.

[0146] User involvement: When necessary, allow network administrators to participate in the adjustment and optimization of policies to incorporate more expertise.

[0147] 7. User Interface

[0148] The user interface provides visual monitoring and reporting capabilities, enabling network administrators to understand network status, troubleshooting progress, and system optimization status in real time. Specific implementation details are as follows:

[0149] Real-time monitoring: Provides a real-time monitoring view of network status, including key performance indicators and anomaly alerts.

[0150] Fault Reporting: Generates and displays detailed reports on fault diagnosis and repair, making it easy for administrators to understand and record.

[0151] System Management: Provides an interface for system configuration and management, including setting anomaly detection thresholds and selecting repair strategies.

[0152] User interaction: Design a user-friendly interface to simplify user operations during fault diagnosis and repair.

[0153] Security design: Ensure the security of the user interface by employing authentication and authorization access control to prevent unauthorized access.

[0154] Through the collaborative work of the aforementioned modules, intelligent network self-healing technology enables comprehensive monitoring, rapid response, accurate diagnosis, and automatic repair of the ITS network, significantly improving network reliability and security, reducing maintenance costs, and providing a superior user experience. Simultaneously, the introduction of a self-learning optimization engine allows the system to continuously learn from experience, adapting to ever-changing network environments and business needs, achieving long-term high-efficiency operation.

[0155] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent network self-healing method applied to an intelligent transportation system, characterized in that: The method includes the following steps: Data collection, real-time collection of key data in the ITS network, including vehicle flow, signal light status, traffic camera video stream, and network device logs; Anomaly detection, real-time analysis of collected data using machine learning algorithms to identify behaviors deviating from normal patterns; Fault diagnosis, combining fault diagnosis techniques with deep learning algorithms for in-depth analysis of the causes of detected anomalies; Automatic repair, executing appropriate repair strategies based on fault diagnosis results, including restarting services, resetting configurations, or isolating damaged devices; Self-learning optimization, continuously optimizing fault detection and diagnosis algorithms by analyzing historical fault cases and repair results to improve system self-healing capabilities; Specific operations for anomaly detection include: Suitable anomaly detection algorithm selection, Isolation Forest or Autoencoder; Model training process, learning normal behavior patterns and anomaly behavior identification using historical data; Real-time monitoring function, continuously analyzing incoming data and triggering alerts immediately upon detecting anomalies; Reasonable threshold setting, balancing false positive rate and false negative rate; Feedback mechanism for feeding actual fault cases back to the model for continuous optimization; Specific operations for fault diagnosis include: Construction of a fault feature library containing feature descriptions of various known fault patterns; Real-time fault pattern matching mechanism, matching detected anomalies with the fault feature library; Deep learning assisted diagnosis function, pattern recognition and cause inference for complex or unknown fault patterns; Interactive diagnosis function, interacting with network administrators to obtain additional information or confirm diagnosis results; Root cause analysis step, in-depth analysis of the root cause of the fault and generation of detailed fault diagnosis reports; Specific operations for automatic repair include: Repair strategy library for different fault types, including restarting services, resetting configurations, and isolating devices; Automated execution scripts to reduce human intervention and improve repair speed; Safety considerations to ensure that all repair operations comply with safety specifications; Fallback mechanism to handle repair operation failures or new problems; 2.The intelligent network self-healing method applied to the intelligent transportation system according to claim 1, wherein: Self-learning optimization engine regularly updates anomaly detection and fault diagnosis algorithms using new data to adjust repair strategies and improve overall system performance. Specific operations for data collection include: Multi-source data integration capability to adapt to various device and system interfaces in the ITS network; Real-time assurance mechanism to ensure data is transmitted to the central processing system without delay; Data preprocessing steps, including data cleaning and feature extraction, to improve the accuracy of anomaly detection; Security design using encryption technology to protect data transmission security; 3.The intelligent network self-healing system applied to the intelligent transportation system according to any one of claims 1-2, characterized in that: Scalability design to accommodate future data sources. The system includes a data collection module, an anomaly detection system, a fault diagnosis center, an automatic repair unit, a self-learning optimization engine, and a user interface, wherein: The data collection module is used to collect key data in the ITS network in real time, including vehicle flow, signal light status, traffic camera video stream, and network device logs, and has the capabilities of multi-source data integration, real-time assurance, data preprocessing, security design, and scalability; Anomaly detection system utilizes machine learning algorithms to analyze collected data in real-time, identifying behaviors that deviate from normal patterns, and features algorithm selection, model training, real-time monitoring, threshold setting, and feedback mechanisms. Fault diagnosis center integrates traditional fault diagnosis techniques and deep learning algorithms for real-time fault pattern matching, deep learning-assisted diagnosis, interactive diagnosis, root cause analysis, and diagnostic report generation. Automatic repair unit executes appropriate repair strategies based on fault diagnosis results, including restarting services, resetting configurations, isolating damaged devices, and features a repair strategy library, automated execution, security considerations, rollback mechanisms, and repair feedback capabilities. Self-learning optimization engine continuously optimizes fault detection and diagnosis algorithms by analyzing historical fault cases and repair results, improving the system's self-healing capabilities, including data accumulation, algorithm optimization, strategy adjustment, and performance evaluation.

4. The intelligent network self-healing system applied to the intelligent transportation system according to claim 3, characterized in that: Data collection module includes: Develop adapters for various devices and systems in the ITS network to interface and integrate multi-source data. Use efficient data transmission protocols and buffering mechanisms to ensure real-time data transmission. Design data cleaning and preprocessing algorithms to remove noise and outliers and extract useful features. Use encryption techniques during data transmission to ensure data security. Consider future data sources when designing modules to ensure system scalability. 5.The intelligent network self-healing system applied to the intelligent transportation system according to claim 4, characterized in that: Anomaly detection system includes: Select appropriate anomaly detection algorithms based on data characteristics and real-time requirements. Train anomaly detection models using historical data, including learning normal behavior patterns and identifying abnormal behavior. Deploy models to real-time monitoring systems for continuous analysis of incoming data, and set reasonable thresholds to balance false positive and false negative rates. Establish a feedback mechanism to use actual fault cases for continuous model training and optimization. 6.The intelligent network self-healing system applied to the intelligent transportation system according to claim 4, characterized in that: Fault diagnosis center includes: Build a fault feature library containing various fault pattern characteristics. When the anomaly detection system detects anomalies, match the anomaly features with the data in the fault feature library in real time. For complex or unknown fault patterns, use deep learning algorithms for assisted diagnosis. Interact with network administrators through the user interface to obtain additional information to assist in diagnosis. In-depth analysis of the root cause of the fault and generation of detailed fault diagnosis reports. 7.The intelligent network self-healing system applied to the intelligent transportation system according to claim 5, wherein: Self-learning optimization engine includes: Collect data from each fault detection, diagnosis, and repair as training material for optimization algorithms. Regularly update anomaly detection and fault diagnosis algorithms with new data to improve their accuracy and efficiency. Adjust repair strategies in the strategy library automatically based on repair result feedback. Regularly evaluate the performance of the self-learning optimization engine to ensure its optimization effectiveness. Allow network administrators to participate in strategy adjustment and optimization to incorporate more professional knowledge.

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