Network line intelligent operation and maintenance monitoring management system and method

Abstract

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent operation and maintenance monitoring management of network lines, and in particular relates to an intelligent operation and maintenance monitoring management system and method for network lines. Background Art

[0002] Intelligent network line operation and maintenance monitoring and management is the process of using advanced technical means (such as big data, artificial intelligence, the Internet of Things, etc.) to comprehensively monitor, manage and troubleshoot network lines. Its goal is to improve the efficiency of network operation and maintenance through automated and intelligent means, reduce the need for human intervention, and ensure the stability, reliability and security of the network system. It monitors the status of network lines in real time 24 / 7, including multiple performance indicators such as traffic, delay, bandwidth utilization, and packet loss rate; predicts the operation status of network lines through intelligent analysis systems, identifies potential failure risks in advance, and automatically generates early warning notifications, which can reduce the impact caused by sudden failures; uses artificial intelligence and automation tools to quickly locate the root cause of network failures, and may automatically perform repair measures to reduce human intervention; uses big data analysis technology to conduct in-depth analysis of network traffic, load, etc., to help find bottlenecks and optimization points, so as to make reasonable resource allocation and network performance optimization; based on historical data and machine learning algorithms, the system can make intelligent decisions to optimize network operation paths, traffic load balancing, routing selection, etc., to ensure the optimal use of network resources; provides a visual monitoring interface, and administrators can intuitively view network status, performance indicators, fault records, etc., to facilitate management and decision-making.

[0003] However, although intelligent network line operation and maintenance monitoring and management has greatly improved network operation and maintenance efficiency and reliability, there are still some defects and challenges in actual application. The current automated repair technology is still limited to some relatively simple and standard faults. For complex network problems, automated repair methods may not be able to completely solve them and still require manual intervention. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent network line operation and maintenance monitoring management system and method, introduce a fault tree analysis and decision support system, analyze various fault causes through an intelligent system and automatically take repair measures to improve the efficiency and accuracy of the repair.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A network line intelligent operation and maintenance monitoring and management system, comprising:

[0007] Data collection module, used to collect operation data of various network devices through SNMP, NetFlow, and sFlow protocols;

[0008] The data processing and analysis module is used to perform real-time data analysis and processing through a real-time stream processing platform, identify network traffic anomalies, bandwidth bottlenecks, and equipment failures, use machine learning models to predict potential failures based on historical data, analyze network performance bottlenecks through AI, and generate optimization suggestions;

[0009] The fault management and prediction module combines AI with traditional threshold monitoring methods to generate real-time monitoring of various indicators in the network and perform intelligent fault detection. Based on intelligent algorithms, it quickly locates the source of faults, analyzes historical data through machine learning models, predicts upcoming network faults, and issues early warnings.

[0010] Performance optimization and resource scheduling module, which is used to dynamically adjust bandwidth allocation through real-time analysis of traffic load and network topology, monitor traffic distribution in real time, identify business traffic, network attacks or invalid traffic, and optimize network architecture;

[0011] Security management and protection module, which uses AI and machine learning algorithms to analyze network traffic, automatically detect DDoS attacks, virus propagation, data leakage security issues, and use intrusion detection systems, firewall security devices, and AI algorithms to identify and block abnormal behavior or attack traffic in real time;

[0012] Self-healing and automated repair module, which is used to automatically trigger the repair process when a network failure occurs, automatically adjust routing, load balancing, and bandwidth allocation. The system automatically optimizes the network topology based on real-time data and forecast information. When a major failure occurs, the system automatically starts the emergency response process according to predefined strategies;

[0013] The display and feedback module is used to display the topology of the entire network and the status of each node device in real time. The display panel displays the key performance indicators of the network in the form of charts and dashboards, collects users' network experience data, and feeds it back to the decision engine to optimize the system configuration.

[0014] A network line intelligent operation and maintenance monitoring management method, comprising:

[0015] Regularly collect network device information through protocols, use monitoring agents to collect resource data of physical servers, virtual machines, containers, and application servers, and integrate hardware sensors to monitor the computer room environment;

[0016] Use a distributed storage system to persistently store the collected data, and use a stream processing framework to process real-time data streams to generate instant network traffic analysis and fault detection data;

[0017] Based on the traditional threshold monitoring method, alarm thresholds are set, and machine learning algorithms are used to model network traffic. Based on historical fault data and trend analysis, time series analysis or machine learning models are used to predict potential network faults.

[0018] Monitor network traffic in real time through deep packet inspection and traffic analysis, identify high traffic peaks, bandwidth bottlenecks and potential performance issues, and intelligently schedule network resources based on network load and traffic demand;

[0019] Based on the prediction of network failures by machine learning models, when a failure or performance anomaly is detected, the intelligent decision engine automatically triggers the predefined self-healing strategy, performs repair operations through automated tools, automatically determines the fault area based on the nature of the fault, and enables backup resources for repair;

[0020] Use intrusion detection systems and traffic analysis tools, combined with machine learning and AI algorithms, to analyze malicious attacks, virus propagation, and DDoS attack security threats in network traffic in real time, regularly scan network devices and system security vulnerabilities, and deploy IPS systems to detect and block potential attack behaviors in real time;

[0021] The AI ​​engine analyzes historical and real-time data, provides optimization suggestions to network managers, and generates network topology diagrams, link status diagrams, and traffic usage visualizations on the visual dashboard. It automatically generates daily reports, performance reports, fault reports, and security reports based on monitoring data to provide management with a basis for decision-making.

[0022] As a preferred method, the network equipment information is collected regularly through the protocol, the resource data of the physical server, virtual machine, container and application server is collected by the monitoring agent, and the method of integrating the hardware sensor to monitor the computer room environment is as follows:

[0023] Regularly collect the status, traffic, bandwidth, delay, and packet loss information of routers, switches, and firewall network devices through SNMP, NetFlow, sFlow, and IPFIX protocols;

[0024] Use Zabbix and Prometheus monitoring agents to collect resource utilization, CPU, memory, disk IO, and network interface data for physical servers, virtual machines, containers, and application servers;

[0025] Integrated hardware temperature, humidity, and UPS battery status sensors monitor the computer room environment.

[0026] As a preferred method, a distributed storage system is used to persistently store the collected data, and a stream processing framework is used to process the real-time data stream to generate instant network traffic analysis and fault detection data:

[0027] Use Hadoop HDFS, Apache Cassandra, Elasticsearch, and Amazon S3 / Ceph distributed storage systems to persistently store the collected data;

[0028] Use Apache Kafka+Apache Flink / Apache Spark Streaming, Apache Kafka, Apache Flink, and Apache Spark Streaming stream processing frameworks to process data streams transmitted in real time from various data sources;

[0029] Receive real-time data streams from different data sources through the stream processing framework, including network traffic, device status, and log information data, clean and format the original data, and group and aggregate the stream data;

[0030] Real-time analysis of traffic data, calculation of bandwidth usage, traffic distribution, IP source / destination analysis, real-time calculation of network delay and packet loss performance indicators, and use of rule engines or machine learning models to detect network failures, performance bottlenecks, and abnormal traffic in real time;

[0031] After the real-time data stream is processed, the system generates the following two types of real-time data:

[0032] Traffic analysis data, including summary statistics of real-time network traffic, bandwidth usage, traffic trend graphs, packet size distribution, and source / destination IP address distribution;

[0033] Fault detection data, including network fault diagnosis results, abnormal event logs, performance bottleneck analysis, and alarm information.

[0034] As a preferred method, based on the traditional threshold monitoring method, the alarm threshold is set, the network traffic is modeled using a machine learning algorithm, and based on historical fault data and trend analysis, the method of using time series analysis or machine learning models to predict potential network faults is as follows:

[0035] Set thresholds based on historical data analysis or adopt dynamic threshold setting strategies to adjust thresholds based on changes in the network environment, seasonal changes, or load fluctuations;

[0036] Modeling is done based on traffic indicators, system indicators, historical fault data, and time characteristics, and machine learning models such as LSTM, GRU machine learning time series and decision trees, random forests, support vector machines, K-nearest neighbor supervised learning or clustering algorithms, and isolation forest unsupervised learning are used to predict potential network failures.

[0037] As a preferred method, the network traffic is monitored in real time through deep packet inspection and traffic analysis to identify high traffic peaks, bandwidth bottlenecks and potential performance issues. The method of intelligently scheduling network resources according to network load and traffic demand is as follows:

[0038] Through deep packet inspection and bandwidth usage monitoring, traffic pattern recognition, latency and packet loss analysis, and performance indicator tracking traffic analysis methods, the system intelligently identifies high traffic peaks and bandwidth bottlenecks;

[0039] Methods for identifying high traffic peaks and bandwidth bottlenecks include:

[0040] By quickly sampling and analyzing real-time traffic, short-term fluctuations in network traffic can be detected, traffic peaks can be identified, and early warnings can be taken;

[0041] Identify the location of bandwidth bottlenecks by analyzing link bandwidth utilization, traffic congestion, and the load of each network node;

[0042] By analyzing the load of different network nodes, it intelligently distributes traffic to network paths or devices with lower loads, and sets priorities for different traffic based on the application type or protocol identified by DPI;

[0043] When bandwidth bottleneck occurs, the system dynamically adjusts bandwidth according to demand, foresees traffic growth trends through traffic forecasting and analysis, and expands network resources in advance;

[0044] It uses reinforcement learning and supervised learning machine learning algorithms to intelligently adjust traffic allocation strategies and resource scheduling strategies based on historical traffic data and real-time traffic information. It combines time series analysis and historical data to predict changing trends in network traffic and perform traffic scheduling in advance.

[0045] As a preferred method, based on the prediction of network failures by machine learning models, when a failure or performance anomaly is detected, the intelligent decision engine automatically triggers a predefined self-healing strategy, performs repair operations through automated tools, automatically determines the fault area according to the nature of the fault, and enables backup resources for repair as follows:

[0046] Based on the output of the machine learning model, the intelligent decision engine determines whether the fault is hardware failure, link congestion, configuration error, or traffic anomaly. Based on the fault type, the decision engine selects a repair solution from the predefined self-healing strategy library.

[0047] Based on the selected repair plan, the intelligent decision engine triggers the automation tool. The automated repair includes traffic rerouting, enabling backup resources, automatic device restart or recovery, and failover.

[0048] By understanding the network topology and analyzing the performance data, the system automatically locates the fault area, and through intelligent analysis of the network topology, identifies the location of the fault and determines the root cause;

[0049] Regularly back up the configuration files and operating system images of network devices, and automatically restore to a known good configuration when a configuration error or device failure occurs.

[0050] As a preferred method, intrusion detection systems and traffic analysis tools are used, combined with machine learning and AI algorithms, to analyze malicious attacks, virus propagation, and DDoS attack security threats in network traffic in real time, regularly scan network devices and system security vulnerabilities, and deploy IPS systems. The methods for real-time detection and prevention of potential attack behaviors are as follows:

[0051] Through in-depth analysis of network traffic, it detects and identifies abnormal traffic patterns and potential malware propagation, unauthorized access, and port scanning attacks;

[0052] Collect network traffic data in real time through network devices and security sensors to generate a detailed view of network activity and analyze each packet in the traffic to identify potential malicious code, virus propagation or abnormal traffic patterns;

[0053] Unsupervised learning is used to detect abnormal patterns that are different from normal traffic behaviors and identify potential attack behaviors. Based on historical data and traffic characteristics, machine learning algorithms predict potential attack risks. AI algorithms automatically adjust network security protection measures by learning and optimizing security policies.

[0054] After detecting malicious traffic or attack behavior, the IPS system automatically takes defensive measures such as blocking malicious IP addresses, closing attack ports, or blocking abnormal traffic;

[0055] Scan network devices, operating systems, and applications for known vulnerabilities and assess their risk levels. After the scan is complete, the vulnerability scanning tool generates automatic repair suggestions;

[0056] Through traffic analysis tools and IDS / IPS systems, network traffic can be monitored in real time. Through the collaborative work of traffic analysis and IDS / IPS, signs of virus propagation or malware intrusion can be identified and the propagation path can be quickly cut off.

[0057] As a preferred method, the AI ​​engine is used to analyze historical and real-time data, provide optimization suggestions to network managers, and generate network topology diagrams, link status diagrams, and traffic usage visualizations on the visualization dashboard. Daily reports, performance reports, fault reports, and security reports are automatically generated based on monitoring data to provide decision-making basis to management:

[0058] The machine learning model is trained using historical data of traffic, link status, and fault logs to analyze network performance trends, traffic patterns, and potential bottlenecks. Based on the patterns of historical data, the AI ​​engine predicts future network demand and generates bandwidth allocation, traffic routing optimization, and equipment upgrade optimization suggestions for network managers.

[0059] Display the traffic usage of each network node or link through charts or real-time traffic graphs, and generate information for administrators to optimize bandwidth resources, identify traffic anomalies, and plan network capacity;

[0060] The AI ​​engine automatically generates optimization suggestions based on the analysis of real-time and historical data. The optimization suggestions include suggestions for expanding bandwidth or replacing faulty devices based on changes in network traffic and device status, and suggestions for strengthening firewall rules for certain nodes based on security reports.

[0061] By analyzing the trends of historical data, the AI ​​engine predicts future network needs. When AI detects network failures or anomalies, the system automatically generates response suggestions for adjusting routes, enabling redundant links, and repairing faulty equipment.

[0062] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an intelligent network line operation and maintenance monitoring management system and method as described above is implemented.

[0063] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, it implements, for example, a network line intelligent operation and maintenance monitoring management system and method.

[0064] The beneficial effects of the present invention are:

[0065] Through real-time data collection, traffic analysis, deep packet inspection and machine learning algorithms, the system can promptly detect network failures, performance bottlenecks and traffic anomalies, reduce the need for manual intervention, and significantly improve fault response speed; based on machine learning models and time series analysis, the system can model historical data and real-time data to accurately predict potential network failures and performance problems. This predictive capability can help operation and maintenance personnel take preventive measures in advance to avoid failures, thereby reducing downtime and improving network stability and reliability; by real-time monitoring of network traffic, identifying bandwidth bottlenecks and high traffic peaks, the system can intelligently dispatch network resources according to real-time traffic demand, optimize bandwidth utilization, and dynamically adjust traffic distribution according to network load. By combining AI and machine learning, it can analyze malicious attacks, virus propagation, DDoS attacks and other security threats in network traffic in real time, and take immediate protection measures (such as automatically blocking attack traffic, enabling IPS system, etc.) to effectively ensure network security, regularly scan network equipment and system security vulnerabilities, and deploy security patches in a timely manner to reduce potential risks caused by security vulnerabilities; the system displays network topology, link status, traffic usage and other information through a visual dashboard to help managers intuitively understand the network status, and automatically generate daily reports, performance reports, fault reports and security reports, which can provide comprehensive data support and decision-making basis for network managers and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The present invention is a flowchart of a network line intelligent operation and maintenance monitoring and management system. DETAILED DESCRIPTION

[0067] The principles and features of the present invention are described below, and the examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0069] Example

[0070] A network line intelligent operation and maintenance monitoring and management system, comprising:

[0071] Data collection module, used to collect operation data of various network devices through SNMP, NetFlow, and sFlow protocols;

[0072] The data processing and analysis module is used to perform real-time data analysis and processing through a real-time stream processing platform, identify network traffic anomalies, bandwidth bottlenecks, and equipment failures, use machine learning models to predict potential failures based on historical data, analyze network performance bottlenecks through AI, and generate optimization suggestions;

[0073] The fault management and prediction module combines AI with traditional threshold monitoring methods to generate real-time monitoring of various indicators in the network and perform intelligent fault detection. Based on intelligent algorithms, it quickly locates the source of faults, analyzes historical data through machine learning models, predicts upcoming network faults, and issues early warnings.

[0074] Performance optimization and resource scheduling module, which is used to dynamically adjust bandwidth allocation through real-time analysis of traffic load and network topology, monitor traffic distribution in real time, identify business traffic, network attacks or invalid traffic, and optimize network architecture;

[0075] Security management and protection module, which uses AI and machine learning algorithms to analyze network traffic, automatically detect DDoS attacks, virus propagation, data leakage security issues, and use intrusion detection systems, firewall security devices, and AI algorithms to identify and block abnormal behavior or attack traffic in real time;

[0076] Self-healing and automated repair module, which is used to automatically trigger the repair process when a network failure occurs, automatically adjust routing, load balancing, and bandwidth allocation. The system automatically optimizes the network topology based on real-time data and forecast information. When a major failure occurs, the system automatically starts the emergency response process according to predefined strategies;

[0077] The display and feedback module is used to display the topology of the entire network and the status of each node device in real time. The display panel displays the key performance indicators of the network in the form of charts and dashboards, collects users' network experience data, and feeds it back to the decision engine to optimize the system configuration.

[0078] Through data collection, real-time stream processing, AI analysis and other technologies, the system has a high degree of automation, which greatly reduces manual intervention and operation and maintenance pressure, and improves management efficiency. By combining traditional threshold monitoring and machine learning models, the system can timely warn of potential faults, predict and prevent faults, avoid network downtime, and reduce downtime. The system can dynamically adjust bandwidth allocation and network resources according to real-time traffic analysis to avoid performance bottlenecks and improve the performance and stability of the overall network. With the help of AI and machine learning, the system can automatically identify security threats such as DDoS attacks and virus transmission, and take real-time protection measures to effectively prevent security vulnerabilities. The automated repair module can respond quickly when a network failure occurs, and self-heal by automatically adjusting routing, load balancing, bandwidth allocation and other means, reducing manual intervention and improving network reliability. By displaying network topology, key performance indicators and other information in real time, administrators can fully understand the network status and quickly locate problems. At the same time, the optimization suggestions provided by the system help decision-makers make reasonable decisions and improve the overall intelligent level of network management. The system can not only protect the network through intrusion detection systems (IDS) and firewalls, but also analyze and block attack traffic in real time through AI technology, enhance the network's defense capabilities, and ensure the safe operation of the network.

[0079] A network line intelligent operation and maintenance monitoring management method, comprising:

[0080] Regularly collect network device information through protocols, use monitoring agents to collect resource data of physical servers, virtual machines, containers, and application servers, and integrate hardware sensors to monitor the computer room environment;

[0081] Use a distributed storage system to persistently store the collected data, and use a stream processing framework to process real-time data streams to generate instant network traffic analysis and fault detection data;

[0082] Based on the traditional threshold monitoring method, alarm thresholds are set, and machine learning algorithms are used to model network traffic. Based on historical fault data and trend analysis, time series analysis or machine learning models are used to predict potential network faults.

[0083] Monitor network traffic in real time through deep packet inspection and traffic analysis, identify high traffic peaks, bandwidth bottlenecks and potential performance issues, and intelligently schedule network resources based on network load and traffic demand;

[0084] Based on the prediction of network failures by machine learning models, when a failure or performance anomaly is detected, the intelligent decision engine automatically triggers the predefined self-healing strategy, performs repair operations through automated tools, automatically determines the fault area based on the nature of the fault, and enables backup resources for repair;

[0085] Use intrusion detection systems and traffic analysis tools, combined with machine learning and AI algorithms, to analyze malicious attacks, virus propagation, and DDoS attack security threats in network traffic in real time, regularly scan network devices and system security vulnerabilities, and deploy IPS systems to detect and block potential attack behaviors in real time;

[0086] The AI ​​engine analyzes historical and real-time data, provides optimization suggestions to network managers, and generates network topology diagrams, link status diagrams, and traffic usage visualizations on the visual dashboard. It automatically generates daily reports, performance reports, fault reports, and security reports based on monitoring data to provide management with a basis for decision-making.

[0087] Through the collection of network equipment information through protocols, the collection of server resource data by monitoring agents, and the monitoring of the computer room environment by hardware sensors, multi-dimensional data support is provided to ensure comprehensive monitoring; the use of distributed storage and stream processing frameworks supports the rapid processing and storage of large amounts of real-time data, effectively improving the speed and accuracy of network traffic analysis, fault detection, and resource optimization; combining traditional threshold monitoring with machine learning models for fault prediction, the intelligent decision engine automatically triggers repair operations, reducing fault response time and improving fault recovery speed; deep packet inspection and intelligent traffic scheduling can timely detect network bottlenecks, traffic peaks, and performance problems, ensure the reasonable allocation and use of network resources, and optimize network performance; using AI and machine learning algorithms for real-time traffic analysis, combined with the protection capabilities of intrusion detection systems and IPS, it can timely detect and prevent malicious attacks, virus transmission and other network security threats, and ensure network security; the real-time visual dashboard displays the network topology, link status, and traffic usage, helping network administrators quickly determine network conditions. The automatically generated reports provide detailed fault, security, and performance analysis, providing strong support for decision-making; the automatic generation of daily reports, performance reports, fault reports, and security reports reduces the workload of operation and maintenance personnel and improves work efficiency.

[0088] The method of regularly collecting network device information through protocols, using monitoring agents to collect resource data of physical servers, virtual machines, containers, and application servers, and integrating hardware sensors to monitor the computer room environment is as follows:

[0089] Regularly collect the status, traffic, bandwidth, delay, and packet loss information of routers, switches, and firewall network devices through SNMP, NetFlow, sFlow, and IPFIX protocols;

[0090] Use Zabbix and Prometheus monitoring agents to collect resource utilization, CPU, memory, disk IO, and network interface data for physical servers, virtual machines, containers, and application servers;

[0091] Integrated hardware temperature, humidity, and UPS battery status sensors monitor the computer room environment.

[0092] By collecting various status information of network devices through multiple protocols (SNMP, NetFlow, sFlow, IPFIX), we can fully understand the health status and performance of network devices; through the combination of Zabbix and Prometheus agents, we can fully cover the monitoring of physical servers, virtual machines, containers and application servers to ensure the rational use and optimization of IT infrastructure resources; integrated environmental sensors (such as temperature, humidity, UPS batteries, etc.) realize comprehensive monitoring of the computer room environment to ensure that the working environment of hardware facilities is in the best state, thereby avoiding hardware failures caused by environmental problems; using regular collection protocols and real-time monitoring tools (such as Prometheus), we can obtain the usage of equipment and resources in real time and provide instant data feedback for network and system administrators; monitoring results can be displayed through real-time dashboards to help managers quickly identify potential problems, such as network congestion, excessive server load, bandwidth bottlenecks, etc.; network traffic monitoring protocols (such as NetFlow, sFlow) can provide detailed analysis of traffic to help discover bandwidth bottlenecks, traffic anomalies and other problems.

[0093] The method of using a distributed storage system to persistently store the collected data and a stream processing framework to process the real-time data stream to generate instant network traffic analysis and fault detection data is as follows:

[0094] Use Hadoop HDFS, Apache Cassandra, Elasticsearch, and Amazon S3 / Ceph distributed storage systems to persistently store the collected data;

[0095] Use Apache Kafka+Apache Flink / Apache Spark Streaming, Apache Kafka, Apache Flink, and Apache Spark Streaming stream processing frameworks to process data streams transmitted in real time from various data sources;

[0096] Receive real-time data streams from different data sources through the stream processing framework, including network traffic, device status, and log information data, clean and format the original data, and group and aggregate the stream data;

[0097] Real-time analysis of traffic data, calculation of bandwidth usage, traffic distribution, IP source / destination analysis, real-time calculation of network delay and packet loss performance indicators, and use of rule engines or machine learning models to detect network failures, performance bottlenecks, and abnormal traffic in real time;

[0098] After the real-time data stream is processed, the system generates the following two types of real-time data:

[0099] Traffic analysis data, including summary statistics of real-time network traffic, bandwidth usage, traffic trend graphs, packet size distribution, and source / destination IP address distribution;

[0100] Fault detection data, including network fault diagnosis results, abnormal event logs, performance bottleneck analysis, and alarm information.

[0101] Distributed storage systems such as HDFS, Cassandra, and Elasticsearch can be used to scale storage capacity horizontally, support persistent storage of massive amounts of data, and meet the needs of large-scale network data. These distributed systems naturally support high availability and fault tolerance mechanisms, and can ensure data persistence and system stability in the face of hardware failures or network problems. Apache Kafka and Apache Flink / Spark Streaming stream processing frameworks can process real-time data streams with low latency, provide instant traffic analysis and fault detection results, support near-real-time data processing, and help administrators identify problems in a timely manner and reduce response time. Kafka can process large-scale, high-throughput data streams, while Flink and Spark Streaming can quickly and complexly process and analyze these data, ensuring the efficient performance of the system under large-scale streaming data. Real-time calculation of bandwidth usage, traffic distribution, and traffic trends can help identify bandwidth bottlenecks, traffic anomalies, and the rational allocation of network resources. Through data packet size and IP source / destination analysis, traffic analysis can be further refined, and key indicators such as network delay and packet loss rate can be calculated in real time to help identify network performance problems and adjust network resources in a timely manner to optimize performance; the use of rule engines and machine learning models can accurately detect potential faults, performance bottlenecks, and abnormal traffic in the network. Machine learning algorithms can automatically identify patterns and adapt to complex network environments, improving the accuracy of fault detection. Through real-time analysis and fault detection, the system can automatically generate alarm information and notify administrators. By integrating an automated response mechanism, human intervention can be reduced and fault recovery can be accelerated; through real-time traffic analysis data and fault detection data, the system can generate visual reports and dashboards to facilitate operation and maintenance personnel to monitor network conditions in real time.

[0102] Based on the traditional threshold monitoring method, the alarm threshold is set, the network traffic is modeled using machine learning algorithms, and the method of predicting potential network failures using time series analysis or machine learning models based on historical failure data and trend analysis is as follows:

[0103] Set thresholds based on historical data analysis or adopt dynamic threshold setting strategies to adjust thresholds based on changes in the network environment, seasonal changes, or load fluctuations;

[0104] Modeling is done based on traffic indicators, system indicators, historical fault data, and time characteristics, and machine learning models such as LSTM, GRU machine learning time series and decision trees, random forests, support vector machines, K-nearest neighbor supervised learning or clustering algorithms, and isolation forest unsupervised learning are used to predict potential network failures.

[0105] Traditional fixed thresholds may not be able to adapt to changes in the network environment (such as peak hours, load fluctuations, etc.). By dynamically adjusting the thresholds, the solution can adjust the alarm standards in real time according to different network conditions, avoiding the problem of too rigid threshold settings; the combination of dynamic thresholds and machine learning models can more accurately set reasonable alarm thresholds, reduce false alarms (frequent irrelevant alarms) and missed alarms (failure to detect faults in time); time series models such as LSTM and GRU can capture long-term trends and periodic characteristics in network traffic and system indicators, so potential network failures and abnormal conditions can be identified in advance. By combining the use of multiple machine learning algorithms (such as decision trees, random forests, SVM, etc.), network traffic and failure modes can be analyzed from different angles to further improve prediction accuracy and robustness; machine learning algorithms are very useful in automation It has advantages in fault detection and prediction. Once the model finds potential faults or abnormal conditions, it can automatically trigger an alarm and notify the operation and maintenance personnel. Compared with the traditional method of manually setting thresholds, it can reduce the subjectivity and errors of human judgment. As more data accumulates, the machine learning model can be continuously optimized and adjusted to improve the accuracy and adaptability of the prediction. The combination of dynamic thresholds and learning models allows the system to self-adjust and adapt to changes in the network environment. Through real-time monitoring and model-based predictions, the system can detect potential network failures in advance and issue alarms in advance. In this way, network administrators can take preventive measures before failures occur to avoid service interruptions or large-scale failures. Early detection of fault trends can help operation and maintenance personnel optimize network resources, avoid performance bottlenecks or excessive loads caused by peak periods, and improve network stability and reliability.

[0106] Through deep packet inspection and traffic analysis, network traffic is monitored in real time to identify high traffic peaks, bandwidth bottlenecks, and potential performance issues. Based on network load and traffic demand, network resources are intelligently scheduled as follows:

[0107] Through deep packet inspection and bandwidth usage monitoring, traffic pattern recognition, latency and packet loss analysis, and performance indicator tracking traffic analysis methods, the system intelligently identifies high traffic peaks and bandwidth bottlenecks;

[0108] Methods for identifying high traffic peaks and bandwidth bottlenecks include:

[0109] By quickly sampling and analyzing real-time traffic, short-term fluctuations in network traffic can be detected, traffic peaks can be identified, and early warnings can be taken;

[0110] Identify the location of bandwidth bottlenecks by analyzing link bandwidth utilization, traffic congestion, and the load of each network node;

[0111] By analyzing the load of different network nodes, it intelligently distributes traffic to network paths or devices with lower loads, and sets priorities for different traffic based on the application type or protocol identified by DPI;

[0112] When bandwidth bottleneck occurs, the system dynamically adjusts bandwidth according to demand, foresees traffic growth trends through traffic forecasting and analysis, and expands network resources in advance;

[0113] It uses reinforcement learning and supervised learning machine learning algorithms to intelligently adjust traffic allocation strategies and resource scheduling strategies based on historical traffic data and real-time traffic information. It combines time series analysis and historical data to predict changing trends in network traffic and perform traffic scheduling in advance.

[0114] By analyzing real-time traffic, the system can promptly identify peak values ​​in network traffic and issue early warnings, which helps network administrators take appropriate measures before traffic overloads to avoid performance degradation or service interruptions. When bandwidth bottlenecks occur, the system can dynamically adjust bandwidth allocation based on real-time demand, and by responding to traffic changes in real time, it avoids network congestion during high-load periods and ensures continuous stability of network performance. Intelligent traffic scheduling can automatically adjust traffic allocation based on network traffic analysis results to ensure load balancing for each device and link in the network, which can effectively avoid overloading of a certain part of the network and improve overall network performance. Through application layer protocols or traffic types identified based on DPI, the system can set different priorities for different traffic to ensure that It ensures that the traffic of key businesses will not be affected by other non-key businesses, thereby improving the network service quality (QoS); through traffic prediction and trend analysis, the system can foresee future changes in network load, so as to reserve resources in advance to ensure that the network can cope with traffic growth or bursts. Through accurate traffic prediction, the system avoids the waste of resources caused by over-reserving bandwidth, and only expands bandwidth when it is really needed, thereby reducing operating costs; through dynamic scheduling of network traffic, the system can avoid network overload, thereby reducing network latency and packet loss rate, and improving users' network experience. Based on traffic priority settings, key applications (such as video conferencing, voice calls, etc.) can obtain higher priority in the network, ensuring that their traffic is not interfered with by other traffic, thereby optimizing user experience.

[0115] Based on the prediction of network failures by machine learning models, when a failure or performance anomaly is detected, the intelligent decision engine automatically triggers the predefined self-healing strategy, performs repair operations through automated tools, automatically determines the fault area based on the nature of the fault, and enables backup resources for repair as follows:

[0116] Based on the output of the machine learning model, the intelligent decision engine determines whether the fault is hardware failure, link congestion, configuration error, or traffic anomaly. Based on the fault type, the decision engine selects a repair solution from the predefined self-healing strategy library.

[0117] Based on the selected repair plan, the intelligent decision engine triggers the automation tool. The automated repair includes traffic rerouting, enabling backup resources, automatic device restart or recovery, and failover.

[0118] By understanding the network topology and analyzing the performance data, the system automatically locates the fault area, and through intelligent analysis of the network topology, identifies the location of the fault and determines the root cause;

[0119] Regularly back up the configuration files and operating system images of network devices, and automatically restore to a known good configuration when a configuration error or device failure occurs.

[0120] Through automated tools and self-healing strategies, the system can respond quickly when a fault occurs and automatically perform repair operations, such as traffic rerouting and enabling backup resources, greatly shortening the fault recovery time. The intelligent decision engine, based on machine learning fault prediction and analysis, can automatically determine the fault type and select the appropriate repair plan, greatly reducing the reliance on manual intervention; the intelligent decision engine can detect potential network problems in advance based on prediction and real-time analysis, and maintain high network availability through automated repair, which helps to reduce system downtime and improve network stability; by enabling backup resources, such as spare links, servers or devices, the impact of single point failures on the network is avoided and business continuity is guaranteed; through the automated traffic rerouting function, when the link is congested or fails, the system can dynamically adjust the traffic path to ensure the stability and efficiency of traffic transmission; based on real-time network topology and performance data analysis, the system can intelligently adjust network resources, optimize network performance, and avoid bottlenecks; regularly back up the configuration files and operating system images of network devices, and automatically restore to a known good configuration when a configuration error occurs, avoiding the impact of human configuration errors.

[0121] Use intrusion detection systems and traffic analysis tools, combined with machine learning and AI algorithms, to analyze malicious attacks, virus propagation, and DDoS attack security threats in network traffic in real time, regularly scan network devices and system security vulnerabilities, deploy IPS systems, and detect and block potential attack behaviors in real time.

[0122] Through in-depth analysis of network traffic, it detects and identifies abnormal traffic patterns and potential malware propagation, unauthorized access, and port scanning attacks;

[0123] Collect network traffic data in real time through network devices and security sensors to generate a detailed view of network activity and analyze each packet in the traffic to identify potential malicious code, virus propagation or abnormal traffic patterns;

[0124] Unsupervised learning is used to detect abnormal patterns that are different from normal traffic behaviors and identify potential attack behaviors. Based on historical data and traffic characteristics, machine learning algorithms predict potential attack risks. AI algorithms automatically adjust network security protection measures by learning and optimizing security policies.

[0125] After detecting malicious traffic or attack behavior, the IPS system automatically takes defensive measures such as blocking malicious IP addresses, closing attack ports, or blocking abnormal traffic;

[0126] Scan network devices, operating systems, and applications for known vulnerabilities and assess their risk levels. After the scan is complete, the vulnerability scanning tool generates automatic repair suggestions;

[0127] Through traffic analysis tools and IDS / IPS systems, network traffic can be monitored in real time. Through the collaborative work of traffic analysis and IDS / IPS, signs of virus propagation or malware intrusion can be identified and the propagation path can be quickly cut off.

[0128] Through real-time analysis of network traffic, the system can instantly detect and block various types of attacks, such as virus propagation, DDoS attacks, port scanning, etc., effectively reducing the risk of network attacks. Combined with machine learning and AI algorithms, the system can continuously learn the changing trends of network traffic, automatically adjust defense strategies, respond to new and unknown security threats, and improve protection capabilities. When malicious traffic is detected, the IPS system can automatically respond and execute protective measures, such as blocking malicious IPs or closing attack ports, thereby greatly reducing the need for manual intervention and shortening response time. Vulnerability scanning tools can automatically detect and repair known vulnerabilities in the network, avoiding the tedious process of manual inspection and repair, and improving repair efficiency. The system automatically blocks malicious attacks, especially DDoS attacks and virus propagation. It can effectively reduce service interruptions and network failure time, maintain high business availability, and quickly cut off attack paths through the collaboration of traffic analysis tools and IDS / IPS, preventing attacks from spreading in the network and protecting core resources. By analyzing historical data and traffic characteristics using machine learning models, the system can identify potential attack risks and take defensive measures in advance. For example, machine learning algorithms can discover potential abnormal patterns in traffic and predict future attack trends, so as to respond in advance. Unsupervised learning can automatically detect unknown attack methods because it identifies anomalies based on changes in traffic behavior patterns, without relying on known attack characteristics.

[0129] The AI ​​engine analyzes historical and real-time data, provides optimization suggestions to network managers, and generates network topology diagrams, link status diagrams, and traffic usage visualizations on the visualization dashboard. It automatically generates daily reports, performance reports, fault reports, and security reports based on monitoring data to provide management with decision-making basis in the following ways:

[0130] The machine learning model is trained using historical data of traffic, link status, and fault logs to analyze network performance trends, traffic patterns, and potential bottlenecks. Based on the patterns of historical data, the AI ​​engine predicts future network demand and generates bandwidth allocation, traffic routing optimization, and equipment upgrade optimization suggestions for network managers.

[0131] Display the traffic usage of each network node or link through charts or real-time traffic graphs, and generate information for administrators to optimize bandwidth resources, identify traffic anomalies, and plan network capacity;

[0132] The AI ​​engine automatically generates optimization suggestions based on the analysis of real-time and historical data. The optimization suggestions include suggestions for expanding bandwidth or replacing faulty devices based on changes in network traffic and device status, and suggestions for strengthening firewall rules for certain nodes based on security reports.

[0133] By analyzing the trends of historical data, the AI ​​engine predicts future network needs. When AI detects network failures or anomalies, the system automatically generates response suggestions for adjusting routes, enabling redundant links, and repairing faulty equipment.

[0134] The AI ​​engine can optimize bandwidth and traffic routing based on real-time data such as traffic patterns, bandwidth usage, and network topology to avoid overloads, bottlenecks, and other problems, and improve network performance. Through real-time monitoring and analysis of device performance, the AI ​​engine can predict device failures and provide timely device upgrade suggestions, thereby avoiding network interruptions caused by device aging or failure. Through historical data trend analysis, the AI ​​engine can detect potential failure risks in advance and generate warnings to help managers take measures in advance to reduce the impact of network failures on the business. When a failure occurs, the system can automatically recommend or execute operations such as enabling redundant links, adjusting routes, or replacing equipment, reducing manual intervention and ensuring high network availability. Through a visual dashboard, network administrators can view network traffic, link status, device status, and other information in real time to quickly discover and solve problems. The chart and trend graphic display methods help managers understand network conditions more intuitively. The AI ​​system provides precise optimization suggestions based on data analysis, such as expanding bandwidth, Managers can make more scientific and reasonable decisions based on these suggestions, such as route adjustment, equipment replacement, etc. The AI ​​engine can automatically generate daily reports, performance reports, fault reports and security reports to help the network management team quickly understand the operating status of the network and save time for manual report generation. Through reports and optimization suggestions, management can obtain comprehensive information about network performance, capacity, equipment status, etc., to provide a basis for strategic decision-making. For example, management can understand network security threats based on security reports and strengthen the security of key nodes. The AI ​​engine is based on historical data and trend forecasts, and can accurately predict future network needs to help enterprises plan bandwidth, link and equipment expansion in advance to avoid insufficient resources when the business grows. By analyzing historical fault data, AI recommends adding redundant links or equipment to improve the network's disaster recovery capabilities and ensure that the business can continue to run in the event of a fault. The AI ​​engine can detect potential security threats and provide administrators with security recommendations based on traffic patterns and device status changes.

[0135] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an intelligent network line operation and maintenance monitoring management system and method as described above is implemented.

[0136] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent network line operation and maintenance monitoring management system and method as described above are implemented.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0139] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. A network line intelligent operation and maintenance monitoring and management system, characterized in that: Included are: Data collection module, used to collect operation data of various network devices through SNMP, NetFlow, and sFlow protocols; The data processing and analysis module is used to perform real-time data analysis and processing through a real-time stream processing platform, identify network traffic anomalies, bandwidth bottlenecks, and equipment failures, use machine learning models to predict potential failures based on historical data, analyze network performance bottlenecks through AI, and generate optimization suggestions; The fault management and prediction module combines AI with traditional threshold monitoring methods to generate real-time monitoring of various indicators in the network and perform intelligent fault detection. Based on intelligent algorithms, it can quickly locate the source of faults, analyze historical data through machine learning models, predict upcoming network faults, and issue early warnings. Performance optimization and resource scheduling module, which is used to dynamically adjust bandwidth allocation through real-time analysis of traffic load and network topology, monitor traffic distribution in real time, identify business traffic, network attacks or invalid traffic, and optimize network architecture; Security management and protection module, which uses AI and machine learning algorithms to analyze network traffic, automatically detect DDoS attacks, virus propagation, data leakage security issues, and use intrusion detection systems, firewall security devices, and AI algorithms to identify and block abnormal behavior or attack traffic in real time; Self-healing and automated repair module, which is used to automatically trigger the repair process when a network failure occurs, automatically adjust routing, load balancing, and bandwidth allocation. The system automatically optimizes the network topology based on real-time data and forecast information. When a major failure occurs, the system automatically starts the emergency response process according to predefined strategies; The display and feedback module is used to display the topology of the entire network and the status of each node device in real time. The display panel displays the key performance indicators of the network in the form of charts and dashboards, collects users' network experience data, and feeds it back to the decision engine to optimize the system configuration.

2. A network line intelligent operation and maintenance monitoring management method, characterized in that: Included are: Regularly collect network device information through protocols, use monitoring agents to collect resource data of physical servers, virtual machines, containers, and application servers, and integrate hardware sensors to monitor the computer room environment; Use distributed storage systems to persistently store collected data, use stream processing frameworks to process real-time data streams, and generate instant network traffic analysis and fault detection data; Based on the traditional threshold monitoring method, alarm thresholds are set, and machine learning algorithms are used to model network traffic. Based on historical fault data and trend analysis, time series analysis or machine learning models are used to predict potential network faults. Monitor network traffic in real time through deep packet inspection and traffic analysis, identify high traffic peaks, bandwidth bottlenecks and potential performance issues, and intelligently schedule network resources based on network load and traffic demand; Based on the prediction of network failures by machine learning models, when a failure or performance anomaly is detected, the intelligent decision engine automatically triggers the predefined self-healing strategy, performs repair operations through automated tools, automatically determines the fault area based on the nature of the fault, and enables backup resources for repair; Use intrusion detection systems and traffic analysis tools, combined with machine learning and AI algorithms, to analyze malicious attacks, virus propagation, and DDoS attack security threats in network traffic in real time, regularly scan network devices and system security vulnerabilities, and deploy IPS systems to detect and block potential attack behaviors in real time; The AI ​​engine analyzes historical and real-time data, provides optimization suggestions to network managers, and generates network topology diagrams, link status diagrams, and traffic usage visualizations on the visual dashboard. It automatically generates daily reports, performance reports, fault reports, and security reports based on monitoring data to provide management with a basis for decision-making.

3. The network line intelligent operation and maintenance monitoring management method according to claim 2 is characterized in that: The method of regularly collecting network device information through protocols, using monitoring agents to collect resource data of physical servers, virtual machines, containers, and application servers, and integrating hardware sensors to monitor the computer room environment is as follows: Regularly collect the status, traffic, bandwidth, delay, and packet loss information of routers, switches, and firewall network devices through SNMP, NetFlow, sFlow, and IPFIX protocols; Use Zabbix and Prometheus monitoring agents to collect resource utilization, CPU, memory, disk IO, and network interface data for physical servers, virtual machines, containers, and application servers; Integrated hardware temperature, humidity, and UPS battery status sensors monitor the computer room environment.

4. The network line intelligent operation and maintenance monitoring management method according to claim 3 is characterized in that: The method of using a distributed storage system to persistently store the collected data and a stream processing framework to process the real-time data stream to generate instant network traffic analysis and fault detection data is as follows: Use Hadoop HDFS, Apache Cassandra, Elasticsearch, and Amazon S3 / Ceph distributed storage systems to persistently store the collected data; Use Apache Kafka+Apache Flink / Apache Spark Streaming, Apache Kafka, ApacheFlink, and Apache Spark Streaming stream processing frameworks to process data streams transmitted in real time from various data sources; Receive real-time data streams from different data sources through the stream processing framework, including network traffic, device status, and log information data, clean and format the original data, and group and aggregate the stream data; Real-time analysis of traffic data, calculation of bandwidth usage, traffic distribution, IP source / destination analysis, real-time calculation of network delay and packet loss performance indicators, and use of rule engines or machine learning models to detect network failures, performance bottlenecks, and abnormal traffic in real time; After the real-time data stream is processed, the system generates the following two types of real-time data: Traffic analysis data, including summary statistics of real-time network traffic, bandwidth usage, traffic trend graphs, packet size distribution, and source / destination IP address distribution; Fault detection data, including network fault diagnosis results, abnormal event logs, performance bottleneck analysis, and alarm information.

5. The network line intelligent operation and maintenance monitoring management method according to claim 4 is characterized in that: Based on the traditional threshold monitoring method, the alarm threshold is set, the network traffic is modeled using machine learning algorithms, and the method of predicting potential network failures using time series analysis or machine learning models based on historical failure data and trend analysis is as follows: Set thresholds based on historical data analysis or adopt dynamic threshold setting strategies to adjust thresholds based on changes in the network environment, seasonal changes, or load fluctuations; Modeling is done based on traffic indicators, system indicators, historical fault data, and time characteristics, and machine learning models such as LSTM, GRU machine learning time series and decision trees, random forests, support vector machines, K-nearest neighbor supervised learning or clustering algorithms, and isolation forest unsupervised learning are used to predict potential network failures.

6. The network line intelligent operation and maintenance monitoring management method according to claim 5 is characterized in that: Through deep packet inspection and traffic analysis, network traffic is monitored in real time to identify high traffic peaks, bandwidth bottlenecks, and potential performance issues. Based on network load and traffic demand, network resources are intelligently scheduled as follows: Through deep packet inspection and bandwidth usage monitoring, traffic pattern recognition, latency and packet loss analysis, and performance indicator tracking traffic analysis methods, the system intelligently identifies high traffic peaks and bandwidth bottlenecks; Methods for identifying high traffic peaks and bandwidth bottlenecks include: By quickly sampling and analyzing real-time traffic, short-term fluctuations in network traffic can be detected, traffic peaks can be identified, and early warnings can be taken; Identify the location of bandwidth bottlenecks by analyzing link bandwidth utilization, traffic congestion, and the load of each network node; By analyzing the load of different network nodes, it intelligently distributes traffic to network paths or devices with lower loads, and sets priorities for different traffic based on the application type or protocol identified by DPI; When bandwidth bottleneck occurs, the system dynamically adjusts bandwidth according to demand, foresees traffic growth trends through traffic forecasting and analysis, and expands network resources in advance; It uses reinforcement learning and supervised learning machine learning algorithms to intelligently adjust traffic allocation strategies and resource scheduling strategies based on historical traffic data and real-time traffic information. It combines time series analysis and historical data to predict changing trends in network traffic and perform traffic scheduling in advance.

7. The network line intelligent operation and maintenance monitoring management method according to claim 6 is characterized in that: Based on the prediction of network failures by machine learning models, when a failure or performance anomaly is detected, the intelligent decision engine automatically triggers the predefined self-healing strategy, performs repair operations through automated tools, automatically determines the fault area based on the nature of the fault, and enables backup resources for repair as follows: Based on the output of the machine learning model, the intelligent decision engine determines whether the fault is hardware failure, link congestion, configuration error, or traffic anomaly. Based on the fault type, the decision engine selects a repair solution from the predefined self-healing strategy library. Based on the selected repair plan, the intelligent decision engine triggers the automation tool. The automated repair includes traffic rerouting, enabling backup resources, automatic device restart or recovery, and failover. By understanding the network topology and analyzing the performance data, the system automatically locates the fault area, and through intelligent analysis of the network topology, identifies the location of the fault and determines the root cause; Regularly back up the configuration files and operating system images of network devices, and automatically restore to a known good configuration when a configuration error or device failure occurs.

8. The network line intelligent operation and maintenance monitoring management method according to claim 7 is characterized in that: Use intrusion detection systems and traffic analysis tools, combined with machine learning and AI algorithms, to analyze malicious attacks, virus propagation, and DDoS attack security threats in network traffic in real time, regularly scan network devices and system security vulnerabilities, deploy IPS systems, and detect and block potential attack behaviors in real time. Through in-depth analysis of network traffic, it detects and identifies abnormal traffic patterns and potential malware propagation, unauthorized access, and port scanning attacks; Collect network traffic data in real time through network devices and security sensors to generate a detailed view of network activity and analyze each packet in the traffic to identify potential malicious code, virus propagation or abnormal traffic patterns; Unsupervised learning is used to detect abnormal patterns that are different from normal traffic behaviors and identify potential attack behaviors. Based on historical data and traffic characteristics, machine learning algorithms predict potential attack risks. AI algorithms automatically adjust network security protection measures by learning and optimizing security policies. After detecting malicious traffic or attack behavior, the IPS system automatically takes defensive measures such as blocking malicious IP addresses, closing attack ports, or blocking abnormal traffic; Scan network devices, operating systems, and applications for known vulnerabilities and assess their risk levels. After the scan is complete, the vulnerability scanning tool generates automatic repair suggestions; Through traffic analysis tools and IDS / IPS systems, network traffic can be monitored in real time. Through the collaborative work of traffic analysis and IDS / IPS, signs of virus propagation or malware intrusion can be identified and the propagation path can be quickly cut off.

9. The network line intelligent operation and maintenance monitoring management method according to claim 8 is characterized in that: The AI ​​engine analyzes historical and real-time data, provides optimization suggestions to network managers, and generates network topology diagrams, link status diagrams, and traffic usage visualizations on the visualization dashboard. It automatically generates daily reports, performance reports, fault reports, and security reports based on monitoring data to provide management with decision-making basis in the following ways: The machine learning model is trained using historical data of traffic, link status, and fault logs to analyze network performance trends, traffic patterns, and potential bottlenecks. Based on the patterns of historical data, the AI ​​engine predicts future network demand and generates bandwidth allocation, traffic routing optimization, and equipment upgrade optimization suggestions for network managers. Display the traffic usage of each network node or link through charts or real-time traffic graphs, and generate information for administrators to optimize bandwidth resources, identify traffic anomalies, and plan network capacity; The AI ​​engine automatically generates optimization suggestions based on the analysis of real-time and historical data. The optimization suggestions include suggestions for expanding bandwidth or replacing faulty devices based on changes in network traffic and device status, and suggestions for strengthening firewall rules for certain nodes based on security reports. By analyzing the trends of historical data, the AI ​​engine predicts future network needs. When AI detects network failures or anomalies, the system automatically generates response suggestions for adjusting routes, enabling redundant links, and repairing faulty equipment.

10. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a network line intelligent operation, maintenance, monitoring and management method as described in any one of claims 2 to 9.

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