Intelligent network self-healing method and system applied to intelligent traffic system

Abstract

The invention relates to the technical field of cloud computing, in particular to an intelligent network self-healing method and system applied to an intelligent traffic system, and the method comprises the following steps: data collection, anomaly detection, fault diagnosis, automatic repair and self-learning optimization. The intelligent network self-healing method and the intelligent network self-healing system applied to the intelligent traffic system provided by the invention have the beneficial effects that the system can immediately identify abnormal behaviors in the network through real-time monitoring and a rapid anomaly detection mechanism, and rapidly respond, so that the fault detection and response time is greatly reduced; an advanced fault diagnosis algorithm including deep learning and pattern recognition technologies is utilized, and the system can accurately diagnose various complex faults including unknown or novel fault modes.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular to an intelligent network self-healing method and system applied to a smart transportation system. Background Art

[0002] With the acceleration of urbanization, problems such as traffic congestion, traffic accidents and environmental pollution have become increasingly prominent and have become key factors restricting the sustainable development of cities. As an important means to meet these challenges, Intelligent Transportation Systems (ITS) integrate advanced information technology, data communication 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 the intelligent transportation system lies in its network infrastructure, which is responsible for collecting, processing and transmitting a large amount of traffic data, including vehicle location, speed, flow, signal light status, accident information, etc. These data are crucial for the traffic management center, and they need to be transmitted to the control center in real time, accurately and reliably for analysis and decision-making. However, the existing network infrastructure faces many challenges:

[0003] Increased network complexity: With the continuous development of ITS, the scale of networks continues to expand, the number of devices increases dramatically, and the network topology becomes increasingly complex, which brings huge challenges to network management and maintenance.

[0004] Intensified cybersecurity threats: Cyber ​​attack methods are constantly escalating, and security incidents such as hacker attacks, malware, and data leaks are frequent, posing a serious threat to ITS network security.

[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 discover and repair it, resulting in traffic service interruptions and affecting urban operation efficiency.

[0006] High maintenance cost: As the network scale expands, the workload of maintenance personnel continues to increase, requiring a large amount of manpower, material and financial resources to maintain and manage the network.

[0007] Poor user experience: Due to frequent network failures, traffic information services are unstable, and users are unable to obtain timely and accurate traffic information, affecting their travel experience. Summary of the invention

[0008] The purpose of the present invention is to provide an intelligent network self-healing method and system applied to an intelligent transportation system to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: an intelligent network self-healing method applied to an intelligent transportation system, the method comprising the following steps:

[0010] Data collection: real-time collection of key data in the ITS network, including vehicle flow, signal light status, traffic camera video streams, and network device logs;

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

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

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

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

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

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

[0017] 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, to improve the accuracy of anomaly detection;

[0019] Security design, using encryption technology to protect data transmission security;

[0020] Designed for scalability to accommodate future additions of data sources.

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

[0022] Selection of suitable anomaly detection algorithms, such as Isolation Forest or Autoencoder;

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

[0024] Real-time monitoring function, which continuously analyzes the incoming data and immediately triggers an alarm if an anomaly is detected;

[0025] Reasonable threshold setting to balance the false positive rate and false negative rate;

[0026] Feedback mechanism to feed actual failure cases back to the model for continuous optimization.

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

[0028] Construction of a fault signature library, which contains the characteristic descriptions of various known fault modes;

[0029] Real-time fault pattern matching mechanism to match detected anomalies with the fault signature library;

[0030] Deep learning-assisted diagnosis function to perform pattern recognition and cause inference on complex or unknown failure modes;

[0031] Interactive diagnostic capabilities to interact with network administrators to obtain additional information or confirm diagnostic results;

[0032] The root cause analysis step deeply analyzes the root cause of the fault and generates a detailed fault diagnosis report.

[0033] Preferably, the specific operations of automatic repair include:

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

[0035] Automatically execute scripts to reduce manual intervention and increase repair speed;

[0036] Safety considerations ensure that all repair operations comply with safety regulations;

[0037] Fallback mechanism to deal with situations where repair operations fail or cause new problems;

[0038] The self-learning optimization engine regularly uses new data to update anomaly detection and fault diagnosis algorithms and adjust repair strategies to improve overall system performance.

[0039] An intelligent network self-healing system applied to a smart transportation system, the system comprising 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] Data collection module, which is used to collect key data in the intelligent transportation system (ITS) network in real time, including vehicle flow, signal light status, traffic camera video stream and network equipment log, and has the capabilities of multi-source data integration, real-time guarantee, data pre-processing, security design and scalability;

[0041] Anomaly detection systems use machine learning algorithms to analyze collected data in real time to identify behaviors that deviate from normal patterns, and have the functions of algorithm selection, model training, real-time monitoring, threshold setting, and feedback mechanisms;

[0042] Fault diagnosis center, which integrates traditional fault diagnosis technology and deep learning algorithms for real-time fault pattern matching, deep learning-assisted diagnosis, interactive diagnosis, root cause analysis, and generation of diagnostic reports;

[0043] The automatic repair unit executes the corresponding repair strategy according to the fault diagnosis results, including restarting services, resetting configurations, isolating damaged devices, etc., and has the capabilities of repair strategy library, automatic execution, security considerations, fallback mechanism and repair feedback;

[0044] The self-learning optimization engine continuously optimizes the fault detection and diagnosis algorithms by analyzing historical fault cases and repair results, and improves the system's self-healing capabilities, including 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] Adopt efficient data transmission protocol and buffer mechanism to ensure the real-time performance of data;

[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 future additions of data sources to ensure the scalability of the system.

[0051] Preferably, the anomaly detection system comprises:

[0052] Select appropriate anomaly detection algorithms based on data characteristics and real-time requirements;

[0053] Use historical data to train anomaly detection models, including learning normal behavior patterns and identifying abnormal behavior;

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

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

[0056] Preferably, the fault diagnosis center includes:

[0057] Build a fault feature library containing various fault mode features;

[0058] When the anomaly detection system finds an anomaly, it matches the anomaly features with the data in the fault feature library in real time;

[0059] For complex or unknown failure modes, use deep learning algorithms for auxiliary diagnosis;

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

[0061] In-depth analysis of the root cause of the failure and generation of detailed fault diagnosis reports.

[0062] Preferably, the self-learning optimization engine includes:

[0063] Collect data from every fault detection, diagnosis, and repair as training material for optimization algorithms;

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

[0065] Automatically adjust the strategies in the repair strategy library based on the feedback of the repair results;

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

[0067] When necessary, network administrators are allowed to participate in the adjustment and optimization of policies to incorporate more expertise.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The intelligent network self-healing method and system applied to the intelligent transportation system proposed in the present invention can immediately identify abnormal behavior in the network and respond quickly through real-time monitoring and rapid anomaly detection mechanism, thereby greatly reducing the time for fault detection and response; using advanced fault diagnosis algorithms, including deep learning and pattern recognition technology, the system can accurately diagnose various complex faults, including unknown or new fault modes; through automated fault handling processes, the reliance on manual intervention is reduced, thereby reducing labor costs and time costs, especially when the scale of ITS networks continues to expand, this cost saving is particularly significant; integrated security measures and automatic isolation mechanisms can timely identify and respond to network attacks, prevent malware and data leakage, and improve the security of the entire ITS network; rapid fault recovery and stable network services ensure that users can obtain continuous and reliable traffic information and services, greatly improving the user's travel experience; the self-learning optimization engine enables the system to continuously learn from historical data, automatically optimize fault detection and repair strategies, and improve the system's adaptability to adapt to the ever-changing network environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flow chart of the method of the present invention;

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

[0072] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] For example, see Figure 1 The present invention provides a technical solution: an intelligent network self-healing method applied to an intelligent transportation system, the method comprising the following steps:

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

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

[0076] 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, to improve the accuracy of anomaly detection;

[0078] Security design, using encryption technology to protect data transmission security;

[0079] Designed for scalability to accommodate future additions of data sources.

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

[0081] Selection of suitable anomaly detection algorithms, such as Isolation Forest or Autoencoder;

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

[0083] Real-time monitoring function, which continuously analyzes the incoming data and immediately triggers an alarm if an anomaly is detected;

[0084] Reasonable threshold setting to balance the false positive rate and false negative rate;

[0085] Feedback mechanism to feed actual failure cases back to the model for continuous optimization.

[0086] Fault diagnosis, combining fault diagnosis technology with deep learning algorithms, conducts in-depth fault cause analysis on detected anomalies; specific operations include:

[0087] Construction of a fault signature library, which contains the characteristic descriptions of various known fault modes;

[0088] Real-time fault pattern matching mechanism to match detected anomalies with the fault signature library;

[0089] Deep learning-assisted diagnosis function to perform pattern recognition and cause inference on complex or unknown failure modes;

[0090] Interactive diagnostic capabilities to interact with network administrators to obtain additional information or confirm diagnostic results;

[0091] The root cause analysis step deeply analyzes the root cause of the fault and generates a detailed fault diagnosis report.

[0092] Automatic repair: execute 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] Automatically execute scripts to reduce manual intervention and increase repair speed;

[0095] Safety considerations ensure that all repair operations comply with safety regulations;

[0096] Fallback mechanism to deal with situations where repair operations fail or cause new problems;

[0097] The self-learning optimization engine regularly uses new data to update anomaly detection and fault diagnosis algorithms and adjust repair strategies to improve overall system performance.

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

[0099] Embodiment 2, refer to the attached Figure 2 , based on the first embodiment, an intelligent network self-healing system applied to a smart transportation system is proposed, the system comprising 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, the intelligent network self-healing technology solution we proposed includes the following key components: data acquisition module, anomaly detection system, fault diagnosis center, automatic repair unit, self-learning optimization engine and user interface.

[0102] 2. Data acquisition module

[0103] This module is responsible for collecting key data in the ITS network in real time, including but not limited to vehicle flow, signal light status, traffic camera video stream, network device logs, etc. These data are the basis for subsequent anomaly detection and fault diagnosis. The 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 guarantee: Adopt efficient data transmission protocol and buffer mechanism to ensure that data can be transmitted to the central processing system in real time and reflect 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 collection 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: Module design needs to take into account possible future data sources to ensure system scalability to adapt to the development and upgrade of the ITS network.

[0109] 3. Anomaly Detection System

[0110] Using machine learning algorithms, such as random forests and support vector machines, the collected data is analyzed 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 true anomalies. The specific implementation details are as follows:

[0111] 3.1 Algorithm selection: According to the data characteristics and real-time requirements, select a suitable anomaly detection algorithm, such as isolation forest, autoencoder, etc.

[0112] 3.2 Model training: Use historical data to train the anomaly detection model, 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 if an anomaly is detected.

[0114] 3.4 Threshold setting: Reasonably set the threshold for anomaly detection to balance the false alarm rate and missed alarm rate to ensure the accuracy of anomaly detection.

[0115] 3.5 Feedback mechanism: Establish a feedback mechanism to use actual 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. The center not only integrates traditional fault diagnosis technology, but also introduces deep learning and pattern recognition algorithms to achieve a deeper level of fault cause analysis. The implementation modules include:

[0118] 4.1 Construction of fault feature library

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

[0120] 4.2 Real-time fault pattern matching

[0121] When the anomaly detection system finds an anomaly, the fault diagnosis center will match the anomaly characteristics with the data in the fault feature library 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 auxiliary diagnosis. By training neural network models to identify failure modes, the system can learn from historical failure cases and predict potential causes of failures.

[0124] 4.4 Interactive Diagnostics

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

[0126] The implementation details are as follows:

[0127] Fault feature library: Build a database containing various fault mode features for matching with the 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 diagnosis: When necessary, interact with the network administrator through the user interface to obtain additional information to assist diagnosis.

[0130] Root cause analysis: Not only identify the fault phenomenon, but also deeply analyze the root cause of the fault to provide accurate guidance for repair.

[0131] Diagnostic report: Generate a detailed fault diagnosis report, including fault type, possible cause, impact scope and other information.

[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, isolating damaged devices, etc.

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

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

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

[0137] Fallback mechanism: Design a fallback mechanism to deal with situations where repair operations fail or cause new problems.

[0138] Repair feedback: Feedback the repair results to the fault diagnosis center to further optimize the 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 anomaly detection and fault diagnosis algorithms with new data to improve their accuracy and efficiency.

[0144] Strategy adjustment: Based on the feedback from the repair results, the strategies in the repair strategy library are automatically adjusted to adapt to new failure modes.

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

[0146] User participation: Allow network administrators to participate in policy adjustment and optimization to incorporate more expertise when necessary.

[0147] 7. User Interface

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

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

[0150] Fault Report: Generates and displays detailed reports on fault diagnosis and repair for administrators to understand and record.

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

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

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

[0154] Through the collaborative work of the above modules, intelligent network self-healing technology can achieve comprehensive monitoring, rapid response, accurate diagnosis and automatic repair of ITS networks, significantly improve network reliability and security, reduce maintenance costs, and provide a good user experience. At the same time, the introduction of the self-learning optimization engine enables the system to continuously learn from experience, adapt to the ever-changing network environment and business needs, and achieve long-term efficient operation.

[0155] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention 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 comprises the following steps: Data collection: real-time collection of key data in the ITS network, including vehicle flow, signal light status, traffic camera video streams, and network device logs; Anomaly detection, which uses machine learning algorithms to analyze collected data in real time to identify behaviors that deviate from normal patterns; Fault diagnosis, combining fault diagnosis technology with deep learning algorithms to conduct in-depth fault cause analysis of detected anomalies; Automatic repair, executing corresponding repair strategies based on fault diagnosis results, including restarting services, resetting configurations, or isolating damaged devices; Self-learning optimization continuously optimizes fault detection and diagnosis algorithms by analyzing historical fault cases and repair results, thereby improving the system's self-healing capabilities.

2. The intelligent network self-healing method applied to the intelligent transportation system according to claim 1, characterized in that: The specific operations of data collection include: Multi-source data integration capability, adapting to various devices and system interfaces in the ITS network; Real-time guarantee mechanism ensures that 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; Designed for scalability to accommodate future additions of data sources.

3. The intelligent network self-healing method applied to an intelligent transportation system according to claim 1, characterized in that: The specific operations of anomaly detection include: Selection of suitable anomaly detection algorithms, such as Isolation Forest or Autoencoder; The model training process uses historical data to learn normal behavior patterns and identify abnormal behaviors; Real-time monitoring function, which continuously analyzes the incoming data and immediately triggers an alarm if an anomaly is detected; Reasonable threshold setting to balance the false positive rate and false negative rate; Feedback mechanism to feed actual failure cases back to the model for continuous optimization.

4. The intelligent network self-healing method applied to an intelligent transportation system according to claim 1, characterized in that: The specific operations of fault diagnosis include: Construction of a fault signature library, which contains the characteristic descriptions of various known fault modes; Real-time fault pattern matching mechanism to match detected anomalies with the fault signature library; Deep learning-assisted diagnosis function to perform pattern recognition and cause inference on complex or unknown failure modes; Interactive diagnostic capabilities to interact with network administrators to obtain additional information or confirm diagnostic results; The root cause analysis step deeply analyzes the root cause of the fault and generates a detailed fault diagnosis report.

5. The intelligent network self-healing method applied to an intelligent transportation system according to claim 1, characterized in that: The specific operations of automatic repair include: A library of repair strategies for different fault types, including restarting services, resetting configurations, and isolating devices; Automatically execute scripts to reduce manual intervention and increase repair speed; Safety considerations ensure that all repair operations comply with safety regulations; Fallback mechanism to deal with situations where repair operations fail or cause new problems; The self-learning optimization engine regularly uses new data to update anomaly detection and fault diagnosis algorithms and adjust repair strategies to improve overall system performance.

6. An intelligent network self-healing system applied to an intelligent transportation system according to the intelligent network self-healing method applied to an intelligent transportation system according to any one of claims 1 to 5, characterized in that: 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, wherein: Data collection module, which is used to collect key data in the intelligent transportation system (ITS) network in real time, including vehicle flow, signal light status, traffic camera video stream and network equipment log, and has the capabilities of multi-source data integration, real-time guarantee, data pre-processing, security design and scalability; Anomaly detection systems use machine learning algorithms to analyze collected data in real time to identify behaviors that deviate from normal patterns, and have the functions of algorithm selection, model training, real-time monitoring, threshold setting, and feedback mechanisms; Fault diagnosis center, which integrates traditional fault diagnosis technology and deep learning algorithms for real-time fault pattern matching, deep learning-assisted diagnosis, interactive diagnosis, root cause analysis, and generation of diagnostic reports; The automatic repair unit executes the corresponding repair strategy according to the fault diagnosis results, including restarting services, resetting configurations, isolating damaged devices, etc., and has the capabilities of repair strategy library, automatic execution, security considerations, fallback mechanism and repair feedback; The self-learning optimization engine continuously optimizes the fault detection and diagnosis algorithms by analyzing historical fault cases and repair results, and improves the system's self-healing capabilities, including data accumulation, algorithm optimization, strategy adjustment, and performance evaluation.

7. The intelligent network self-healing system applied to the intelligent transportation system according to claim 6, characterized in that: The data acquisition module includes: Develop adapters that interface with various devices and systems in the ITS network to achieve multi-source data integration; Adopt efficient data transmission protocol and buffer mechanism to ensure the real-time performance of data; Design data cleaning and preprocessing algorithms to remove noise and outliers and extract useful features; Encryption technology is used during data transmission to ensure data security; When designing modules, consider the data sources that will be added in the future to ensure the scalability of the system.

8. The intelligent network self-healing system applied to the intelligent transportation system according to claim 6, characterized in that: The anomaly detection system includes: Select appropriate anomaly detection algorithms based on data characteristics and real-time requirements; Use historical data to train anomaly detection models, including learning normal behavior patterns and identifying abnormal behavior; Deploy the model to the real-time monitoring system, continuously analyze the incoming data, and set reasonable thresholds to balance the false positive rate and false negative rate; Establish a feedback mechanism to use actual failure cases for continuous training and optimization of the model.

9. The intelligent network self-healing system applied to the intelligent transportation system according to claim 6, characterized in that: The troubleshooting center includes: Build a fault feature library containing various fault mode features; When the anomaly detection system finds an anomaly, it matches the anomaly features with the data in the fault feature library in real time; For complex or unknown failure modes, use deep learning algorithms for auxiliary diagnosis; When necessary, interact with the network administrator through the user interface to obtain additional information to assist in diagnosis; In-depth analysis of the root cause of the failure and generation of detailed fault diagnosis reports.

10. The intelligent network self-healing system applied to the intelligent transportation system according to claim 6, characterized in that: The self-learning optimization engine includes: Collect data from every 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; Automatically adjust the strategies in the repair strategy library based on the feedback of the repair results; Regularly evaluate the performance of the self-learning optimization engine to ensure its optimization effect; When necessary, network administrators are allowed to participate in the adjustment and optimization of policies to incorporate more expertise.

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