Intelligent operation and maintenance management and control system for information communication network

Abstract

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and more particularly, to an intelligent operation and maintenance management and control system for information communication networks. Background Art

[0002] With the rapid development of information and communication technologies, the continuous expansion of network scale, and the popularization of various network devices, traditional network operation and maintenance management methods are facing numerous challenges. Existing network operation and maintenance mainly rely on manual operations and management models with fixed rules, making it difficult to effectively cope with the increasingly complex network environment. Especially in cross-domain and multi-level network architectures, the processing speed and accuracy of information flows and data flows have become key factors restricting operation and maintenance efficiency. Tasks such as the health status monitoring of network devices and links, network performance optimization, fault prediction, and security protection can no longer be completed solely by manual inspections and simple preset rules.

[0003] Current network operation and maintenance systems generally have the following problems: First, traditional operation and maintenance models lack intelligent data collection and real-time monitoring capabilities, resulting in a lag in fault identification and repair speeds and an inability to quickly respond to emergencies; second, the management of network resources is often static, lacking a flexible scheduling mechanism and being unable to dynamically respond to different network environments and load conditions; third, the interoperability between systems is poor, and the collaborative management of cross-domain resources faces difficulties; finally, due to the lack of continuous optimization of management strategies, the operation and maintenance strategies cannot be adjusted according to long-term operation data and changing trends, leading to waste of system resources or potential security hazards.

[0004] To solve these problems, in recent years, network operation and maintenance systems have gradually developed towards intelligence and automation, using advanced technologies such as machine learning, artificial intelligence, and big data analysis to achieve functions such as network performance prediction, fault warning, and dynamic resource scheduling, gradually replacing traditional manual operation and maintenance methods. However, existing intelligent operation and maintenance systems still face a series of challenges such as strong heterogeneity in data collection, great difficulty in cross-domain resource management, and insufficient automated decision support, which affect the full implementation and effectiveness of intelligent operation and maintenance.

[0005] In summary, how to achieve intelligent scheduling of cross-domain network resources, real-time performance monitoring, dynamic fault warning, and security protection, and continuously optimize operation and maintenance strategies has become a technical problem that urgently needs to be solved. Summary of the Invention

[0006] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide an intelligent operation and maintenance management and control system for information communication networks, including the following modules:

[0007] The intelligent perception and data acquisition module is responsible for the real-time acquisition of multi-source network data, the dynamic monitoring of cross-domain device status, and the conversion of heterogeneous data into a unified standard;

[0008] The network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior recognition, and security threat monitoring to provide comprehensive network operation and maintenance insights;

[0009] The resource intelligent scheduling module realizes the dynamic allocation of cross-domain network resources. Through multi-dimensional optimization and real-time load balancing strategies, it ensures the efficient and secure utilization of resources;

[0010] The intelligent decision support module continuously optimizes operation and maintenance strategies based on machine learning, analyzes long-term operation trends, and provides suggestions for management decisions;

[0011] The open integration and collaboration module provides open API interfaces for integration with third-party systems, supports cross-domain collaborative management, and enhances the flexibility and scalability of the system.

[0012] Furthermore, the intelligent perception and data acquisition module includes the following components:

[0013] The data acquisition unit is responsible for real-time acquisition of network traffic, device status, and performance metric data from multiple data sources and supports data capture for different protocols;

[0014] The network device monitoring unit continuously monitors and acquires the operating status information of network devices to ensure the health and working status of multi-region and multi-level network devices are covered;

[0015] The data preprocessing unit performs preliminary processing on the collected raw data, including data cleaning, denoising, and verification;

[0016] The data standardization unit is responsible for data format conversion and unified encoding, mapping heterogeneous data from different sources to a standardized data model;

[0017] The time series synchronization unit synchronizes the timestamps of cross-domain data, eliminates latency deviation, and ensures the temporal accuracy of data analysis;

[0018] The data storage and management unit provides an efficient data storage, indexing, and retrieval mechanism, supporting long-term data archiving and fast querying.

[0019] Furthermore, the network intelligent analysis module includes the following components:

[0020] The network performance prediction unit predicts the performance metrics of devices and links based on historical data, identifies potential bottlenecks, and optimizes network planning;

[0021] The device health and risk assessment unit comprehensively analyzes the operating status, historical fault records, and network topology of the device to evaluate the device health and fault risk;

[0022] The traffic anomaly detection unit applies real-time traffic analysis technology to identify network traffic anomaly patterns and promptly warns of potential network faults and security risks;

[0023] The network security situation awareness unit continuously monitors the network security status, identifies potential threats and attack behaviors, and collaborates with the system-level security policy.

[0024] Furthermore, the resource intelligent scheduling module includes the following components:

[0025] The cross-domain resource management unit is responsible for the unified management of resources across multiple network domains to ensure the availability and scheduling consistency of resources in different network environments;

[0026] The load balancing scheduling unit implements dynamic load balancing strategies based on real-time network load conditions and traffic distribution, reasonably allocates tasks to each resource node, and avoids overload and bottlenecks;

[0027] The resource optimization decision-making unit comprehensively considers performance requirements, resource utilization, and load balancing factors to automatically generate the optimal resource allocation plan;

[0028] The security policy execution and control unit executes corresponding security policies based on the results of security threat identification, protects and isolates sensitive resources, and ensures no security hazards during resource sharing and dynamic scheduling.

[0029] Furthermore, comprehensively considering performance requirements, resource utilization, and load balancing factors, and automatically generating the optimal resource allocation plan includes the following steps:

[0030] Analyze historical load data through time series data mining and machine learning models to establish a resource demand prediction model to accurately predict future resource usage trends and effectively avoid resource waste or shortage problems;

[0031] Establish a quantitative evaluation system including performance indicators, resource utilization, and cost-benefit ratio to provide data support for resource allocation decisions and ensure the objectivity and quantifiability of decisions;

[0032] Implement a dynamic load balancing mechanism based on an adaptive algorithm to automatically adjust resource allocation strategies according to real-time load conditions, ensure load balancing of each node, and give full play to the overall performance of the cluster;

[0033] Rely on the automated operation and maintenance platform to realize the automatic deployment and execution of resource allocation strategies; at the same time, establish a real-time monitoring system to realize continuous tracking and rapid response to resource usage conditions, and ensure the effective implementation of the plan.

[0034] Furthermore, the intelligent decision-making support module includes the following components:

[0035] The trend analysis unit uses historical data and real-time data, through time series analysis and prediction models, to analyze the trends of overall operation and maintenance indicators, and identify potential performance degradation and resource bottlenecks;

[0036] The operation and maintenance process optimization unit continuously optimizes the overall operation and maintenance process based on machine learning algorithms and data analysis results to adapt to network environment changes and improve the overall network operation and maintenance efficiency and reliability;

[0037] The decision model evaluation unit evaluates the effects of different decision models through simulation and backtesting according to known business objectives and operation and maintenance requirements, and provides data support and risk assessment for actual decision-making;

[0038] The automated policy generation unit automatically generates operation and maintenance policies and makes dynamic adjustments according to the analysis results and optimization objectives to cope with different network conditions;

[0039] The intelligent recommendation and decision-making support unit provides intelligent decision-making suggestions based on operation and maintenance policies, assists in management decision-making, and supports the recommendation of personalized customization solutions;

[0040] The system security policy management unit is responsible for formulating and maintaining system-level security policies, uniformly managing the security rules of each module, and ensuring the consistency and effectiveness of security policies.

[0041] Furthermore, the open integration and collaboration module includes the following components:

[0042] The open API interface unit provides standardized and extensible API interfaces, supports seamless docking with external systems, and realizes two-way data flow and sharing;

[0043] The cross-domain communication adaptation unit realizes communication adaptation between different network domains, different protocols or platforms, and ensures cross-domain data interconnection and collaborative management;

[0044] The event and task collaboration unit realizes cross-domain event monitoring and task coordination, ensures that different systems can share event information and coordinate task processing, and improves collaboration efficiency;

[0045] The interface and integrated system monitoring unit monitors the running status of integrated interfaces and collaborative systems in real time, ensures interface availability and data consistency, and promptly discovers and processes exceptions occurring during the integration process;

[0046] The security authentication and access control unit is responsible for identity authentication, permission management and access control between systems, and ensures the security during the system opening process.

[0047] Furthermore, according to the analysis results and optimization objectives, automatically generate operation and maintenance strategies and dynamically adjust them to cope with different network conditions, including the following steps:

[0048] For network problems of different severities, formulate a corresponding hierarchical operation and maintenance strategy library in advance, and clarify the triggering conditions, execution steps, and expected effects;

[0049] According to the evaluation results of the current network conditions, automatically select the most suitable operation and maintenance strategy from the strategy library, and execute the corresponding configuration modifications and resource adjustments through automated tools to ensure that the strategy can quickly and accurately respond to network changes;

[0050] Evaluate the strategy effect after execution, analyze the improvement of key indicators, and adjust the strategy or switch to an alternative solution if necessary;

[0051] By accumulating experience in handling various network problems, continuously improve the strategy library, optimize the strategy matching algorithm, and improve the accuracy and efficiency of automated operation and maintenance.

[0052] Compared with the prior art, the present application has the following beneficial effects:

[0053] The present application realizes efficient and intelligent operation and maintenance management and control of cross-domain networks through intelligent perception, multi-source data fusion, resource dynamic scheduling, and machine learning optimization strategies, improving system flexibility, performance optimization, and security. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic structural diagram of an information communication network intelligent operation and maintenance management and control system disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0056] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] The embodiments described below by referring to the drawings and directional terms are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0058] As Figure 1As shown in the figure, an intelligent operation and maintenance management and control system for an information and communication network includes the following modules:

[0059] An intelligent perception and data acquisition module, responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain device status, and conversion of heterogeneous data into a unified standard;

[0060] A network intelligent analysis module, integrating network performance prediction, fault risk assessment, abnormal behavior recognition, and security threat monitoring, to provide comprehensive network operation and maintenance insights;

[0061] A resource intelligent scheduling module, realizing dynamic allocation of cross-domain network resources, and ensuring efficient and secure utilization of resources through multi-dimensional optimization and real-time load balancing strategies;

[0062] An intelligent decision-making support module, continuously optimizing operation and maintenance strategies based on machine learning, analyzing long-term operation trends, and providing suggestions for management decisions;

[0063] An open integration and collaboration module, providing open API interfaces for integration with third-party systems, supporting cross-domain collaborative management, and enhancing the flexibility and scalability of the system.

[0064] In this embodiment, the intelligent perception and data acquisition module is the core part of the intelligent operation and maintenance management and control system for an information and communication network. Its main responsibilities include real-time acquisition of multi-source network data, dynamic monitoring of cross-domain device status, and conversion of heterogeneous data into a unified standard. By deploying various sensors and probes in the network, data from different devices and different network layers can be captured in real time, including traffic data, protocol data, device status data, environmental monitoring data, etc. These data, after preliminary preprocessing and filtering, can provide a solid data foundation for subsequent analysis and decision-making. In addition, the intelligent perception and data acquisition module has high dynamic adaptability and flexibility, can monitor the operation status and performance of devices in the network in real time, and detect potential faults and abnormalities in a timely manner. By converting heterogeneous data into a unified standard, better data fusion and sharing can be achieved, thereby improving the overall network management and operation and maintenance efficiency.

[0065] In this embodiment, the network intelligent analysis module is responsible for deeply analyzing and processing the collected data to provide comprehensive network operation and maintenance insights. This module integrates a variety of advanced analysis technologies, including network performance prediction, fault risk assessment, abnormal behavior identification, and security threat monitoring. Through the analysis and modeling of historical data, it can accurately predict network performance, discover potential performance bottlenecks and fault points in advance, thereby effectively avoiding the occurrence of network problems. In addition, the network intelligent analysis module can also evaluate various risks existing in the network, including equipment failure risks, link failure risks, etc. By real-time monitoring of network traffic and identification of abnormal behaviors, it can quickly detect and respond to various abnormal situations to ensure the stable operation of the network. Security threat monitoring can timely discover potential security threats and attacks through the analysis of network traffic and behaviors to protect the security of the network.

[0066] In this embodiment, the main function of the resource intelligent scheduling module is to realize the dynamic allocation of cross-domain network resources to ensure the efficient and secure use of resources. This module adopts multi-dimensional optimization and real-time load balancing strategies and can intelligently adjust and allocate network resources according to the real-time operation status of the network and business requirements. Through the real-time monitoring of network traffic and equipment load, it can identify congestion points and bottlenecks in the network and timely adjust and optimize resources to ensure the efficient operation of the network. In addition, the resource intelligent scheduling module also has high flexibility and adaptability and can dynamically adjust the resource allocation strategy according to changes in the network environment and fluctuations in business requirements to maximize the utilization efficiency of resources. While ensuring the efficient use of resources, it can also guarantee the security of resource allocation and prevent the abuse and waste of resources.

[0067] In this embodiment, the intelligent decision support module continuously optimizes the operation and maintenance strategy through machine learning-based algorithms, analyzes long-term operation trends, and provides scientific suggestions for management decisions. This module can perform in-depth learning and mining on the historical data of the network, identify the rules and trends in network operation, and provide guidance for future operation and maintenance work. Through the analysis and modeling of data, it can discover potential problems and optimization spaces existing in the network, and put forward targeted operation and maintenance strategies and suggestions. In addition, the intelligent decision support module can also evaluate and feedback the operation effect of the network, continuously adjust and optimize the operation and maintenance strategy according to the actual operation situation, and improve the scientificity and effectiveness of operation and maintenance work. The application of this module can not only improve the efficiency and quality of network operation and maintenance, but also provide decision support for the management layer to help it formulate more scientific and reasonable management strategies.

[0068] In this embodiment, the open integration and collaboration module provides open API interfaces for integration with third-party systems, supports cross-domain collaborative management, and enhances the flexibility and scalability of the system. Through the open API interfaces, seamless docking can be achieved with other network management systems, operation and maintenance tools, and business systems, enabling data sharing and integration. This module can not only expand the functions of the system, but also improve the flexibility and adaptability of the system to meet different business scenarios and requirements. In terms of cross-domain collaborative management, it can achieve unified management and control of multiple networks and devices, improving the collaborative efficiency of operation and maintenance work and the overall management level. The application of the open integration and collaboration module can not only improve the flexibility and scalability of the system, but also facilitate future system upgrades and function expansions, ensuring the long-term stable operation of the system.

[0069] In summary, through the organic combination of the intelligent perception and data collection module, network intelligent analysis module, resource intelligent scheduling module, intelligent decision support module, and open integration and collaboration module, the intelligent operation and maintenance management and control system for information and communication networks realizes comprehensive monitoring, in-depth analysis, and efficient management of the network. Each module collaborates with each other in the system, jointly improving the operation efficiency and management level of the network, and ensuring the security and stability of the network.

[0070] Furthermore, the intelligent perception and data collection module includes the following components:

[0071] The data collection unit is responsible for real-time collection of network traffic, device status, and performance metric data from multiple data sources and supports data capture for different protocols;

[0072] The network device monitoring unit continuously monitors and collects the operating status information of network devices to ensure coverage of the health status and working conditions of multi-region and multi-level network devices;

[0073] The data preprocessing unit performs preliminary processing on the collected raw data, including data cleaning, denoising, and verification;

[0074] The data standardization unit is responsible for data format conversion and unified encoding, mapping heterogeneous data from different sources to a standardized data model;

[0075] The time series synchronization unit synchronizes the timestamps of cross-domain data to eliminate delay deviation and ensure the time series accuracy of data analysis;

[0076] The data storage and management unit provides an efficient data storage, indexing, and retrieval mechanism, supporting long-term archiving and fast query of data.

[0077] In summary, the intelligent perception and data acquisition module realizes the multi-source real-time acquisition, dynamic monitoring and efficient management of network traffic, device status and performance indicators through multiple components including a data acquisition unit, a network device monitoring unit, a data preprocessing unit, a data standardization unit, a timing synchronization unit, and a data storage and management unit. Through preliminary data cleaning, denoising, verification and standardization processing, this module not only eliminates data heterogeneity and latency deviation, but also supports long-term archiving and fast query through an efficient data storage and management mechanism, thus providing an accurate, standardized and timely data basis for subsequent intelligent analysis and decision-making.

[0078] Furthermore, the network intelligent analysis module includes the following components:

[0079] A network performance prediction unit that predicts the performance indicators of devices and links based on historical data, identifies potential bottlenecks, and optimizes network planning;

[0080] A device health and risk assessment unit that comprehensively analyzes the operating status of devices, historical fault records, and network topologies to evaluate device health and fault risks;

[0081] A traffic anomaly detection unit that applies real-time traffic analysis technology to identify network traffic anomaly patterns and timely warns of potential network faults and security risks;

[0082] A network security situation awareness unit that continuously monitors the network security status, identifies potential threats and attack behaviors, and collaborates with system-level security policies.

[0083] In summary, the network intelligent analysis module realizes the comprehensive prediction and evaluation of device performance, health status and risks, as well as the real-time detection and warning of network traffic anomalies and security threats by integrating a network performance prediction unit, a device health and risk assessment unit, a traffic anomaly detection unit, and a network security situation awareness unit. This module not only optimizes network planning, improves the stability and reliability of devices, but also ensures the security and stability of the network by timely identifying and responding to potential faults and security threats.

[0084] Furthermore, comprehensively analyzing the operating status of devices, historical fault records, and network topologies to evaluate device health and fault risks includes the following steps:

[0085] Analyze the collected device performance data to reflect the health status of the device;

[0086] Analyze the historical fault records of the device, including the fault occurrence frequency, fault duration, and fault type of the device, form a fault score of the device, and evaluate the long-term reliability of the device;

[0087] Evaluate the importance of devices in the network, identify core devices and edge devices, quantify the connectivity and data traffic of devices, and assign topology importance scores;

[0088] According to the operating status of the device, historical fault records, and network topology information, calculate the health of the device using the following formula: where R i (t) is the operating status index of device i at time t; H i (t) is the health of device i at time t, reflecting the overall health status of the device; F i (t) is the historical fault record of device i at time t, used to measure the fault risk of the device; F max is the maximum value of the historical fault records of all devices, used to normalize the impact of the fault records; T i is the importance of device i in the network topology; w1, w2, and w3 respectively represent the influence degrees of the device operating status, historical fault records, and network topology importance on the device health.

[0089] Based on the calculated device health H i (t), evaluate the fault risk of the device, expressed by the formula: R risk,i (t) = λ i ·(1 - H i (t))·(1 + α·F i (t)), where R risk,i (t) is the fault risk of device i at time t, representing the possibility of the device failing, based on the health and historical fault records; λ i is the failure rate of device i, reflecting the basic probability of the device failing; α is the influence coefficient of historical faults on the device fault risk, used to adjust the contribution of historical fault records to risk assessment.

[0090] According to the fault risk of each device, evaluate the fault risk of the entire network, expressed by the formula: where R network (t) is the fault risk of the entire network at time t, representing the health at the network level; N is the total number of devices in the network, used for calculating the average value when calculating the network fault risk.

[0091] In summary, by comprehensively analyzing the operating status, historical fault records, and network topology of the equipment, the equipment health and fault risks are evaluated through the following steps: First, analyze the collected equipment performance data to reflect the health status of the equipment; Second, analyze the historical fault records of the equipment, including the fault occurrence frequency, duration, and fault type, to form an equipment fault score, thereby evaluating its long-term reliability; Then, evaluate the importance of the equipment in the network, identify core equipment and edge equipment, quantify the connectivity and data traffic of the equipment, and assign a topology importance score; Calculate the equipment health through a formula, combining the equipment operating status, historical fault records, and network topology information to accurately reflect the overall health status of the equipment; Further, evaluate the fault risk of the equipment through a formula, comprehensively considering the health and historical fault records to predict the likelihood of the equipment failing; Finally, based on the fault risk of each equipment, evaluate the fault risk of the entire network, calculate the health at the network level, and ensure the safe and stable operation of the network. This process not only optimizes the management of equipment and networks but also improves the overall system's fault prevention and response capabilities.

[0092] Furthermore, the resource intelligent scheduling module includes the following components:

[0093] The cross-domain resource management unit is responsible for the unified management of resources across multiple network domains to ensure the availability and scheduling consistency of resources in different network environments;

[0094] The load balancing scheduling unit, based on the real-time network load conditions and traffic distribution, implements dynamic load balancing strategies to reasonably allocate tasks to each resource node, avoiding overload and bottlenecks;

[0095] The resource optimization decision-making unit comprehensively considers performance requirements, resource utilization, and load balancing factors to automatically generate an optimal resource allocation plan;

[0096] The security policy execution and control unit, based on the results of security threat identification, executes corresponding security policies to protect and isolate sensitive resources, ensuring no security risks during resource sharing and dynamic scheduling.

[0097] In summary, through the integration of the cross-domain resource management unit, load balancing scheduling unit, resource optimization decision-making unit, and security policy execution and control unit, the resource intelligent scheduling module realizes the unified management and dynamic scheduling of resources across multiple network domains. The cross-domain resource management unit ensures the availability and scheduling consistency of resources in different network environments, while the load balancing scheduling unit dynamically allocates tasks by real-time monitoring of network load conditions and traffic distribution, avoiding overload and bottlenecks. The resource optimization decision-making unit automatically generates the optimal resource allocation plan, comprehensively considering performance requirements, resource utilization rate, and load balancing factors to improve resource utilization efficiency. The security policy execution and control unit executes corresponding security policies based on the results of security threat identification, protects and isolates sensitive resources, and ensures that no security risks are generated during the resource sharing and dynamic scheduling process, thereby guaranteeing the efficient and secure utilization of network resources.

[0098] Furthermore, based on the real-time network load conditions and traffic distribution, implement dynamic load balancing strategies to reasonably allocate tasks to each resource node, avoiding overload and bottlenecks, including the following steps:

[0099] Based on the load condition L of each resource node p , calculate the average load L of all nodes avg : where M is the total number of nodes in the network;

[0100] According to the real-time traffic demand and the load condition of the nodes, calculate the weight W of each task allocated to each node pq : where T q is the load demand of task q; ∈ is a small constant used to avoid division by zero and ensure the stability of the calculation;

[0101] According to the bandwidth between nodes and the traffic demand of tasks, dynamically adjust the task allocation, which is expressed by the formula: where F pq is the traffic allocation impact of task q allocated to node p, indicating the impact of task traffic on the load and bandwidth of the node; C pk is the communication bandwidth between node p and node k, indicating the bandwidth capacity when transmitting data between the task and the node;

[0102] Check whether the load L of each node ′ p exceeds its maximum load threshold L max , if it exceeds, recalculate and adjust the task allocation to ensure that the load of each node is within a safe range, which is expressed by the formula: L ′ p = L p +∑q∈tasks(p) T q ≤L max where tasks(p) represents the set of all tasks to be executed on node p.

[0103] In summary, by implementing a dynamic load balancing strategy, the resource intelligent scheduling module can reasonably allocate tasks to each resource node based on real-time network load conditions and traffic distribution, avoiding overload and bottlenecks. The specific steps include: First, based on the load conditions of each resource node, calculate the average load of all nodes to ensure an even distribution of the overall load; Then, according to real-time traffic demands and node load conditions, calculate the weight of each task assigned to each node to ensure the rationality and stability of the allocation; Next, dynamically adjust the task allocation according to the bandwidth between nodes and task traffic demands to optimize the impact of traffic allocation; Finally, check whether the load of each node exceeds its maximum load threshold. If it exceeds, recalculate and adjust the task allocation to ensure that the load of all nodes is within a safe range. Through this series of steps, the system not only achieves efficient task allocation but also improves network resource utilization, ensuring the stability and efficient operation of the network.

[0104] Furthermore, comprehensively considering performance requirements, resource utilization, and load balancing factors, an optimal resource allocation plan is automatically generated, including the following steps:

[0105] Analyze historical load data through time series data mining and machine learning models to establish a resource demand prediction model to accurately predict future resource usage trends and effectively avoid resource waste or shortage problems;

[0106] Establish a quantitative evaluation system including performance indicators, resource utilization, and cost-benefit ratio to provide data support for resource allocation decisions and ensure the objectivity and quantifiability of decisions;

[0107] Implement a dynamic load balancing mechanism based on an adaptive algorithm to automatically adjust the resource allocation strategy according to real-time load conditions, ensuring load balance among nodes and giving full play to the overall performance of the cluster;

[0108] Rely on an automated operation and maintenance platform to achieve the automatic deployment and execution of resource allocation strategies; at the same time, establish a real-time monitoring system to continuously track and quickly respond to the resource usage status, ensuring the effective implementation of the plan.

[0109] In summary, by comprehensively considering performance requirements, resource utilization, and load balancing factors, the intelligent resource scheduling module automatically generates an optimal resource allocation plan, including the following steps: First, through time series data mining and machine learning models, analyze historical load data to establish a resource demand prediction model, accurately predict future resource usage trends, and effectively avoid resource waste or shortage problems; Second, establish a quantitative evaluation system including performance indicators, resource utilization, and cost-benefit ratio to provide data support for resource allocation decisions and ensure the objectivity and quantifiability of decisions; Then, implement a dynamic load balancing mechanism based on an adaptive algorithm to automatically adjust the resource allocation strategy according to the real-time load status, ensure load balancing of each node, and give full play to the overall performance of the cluster; Finally, relying on the automated operation and maintenance platform, realize the automatic deployment and execution of the resource allocation strategy, and establish a real-time monitoring system to continuously track and quickly respond to the resource usage status to ensure the effective implementation of the plan. Through the above steps, this module effectively improves resource utilization efficiency and ensures the efficient and stable operation of the network.

[0110] Furthermore, the intelligent decision support module includes the following components:

[0111] The trend analysis unit uses historical data and real-time data, through time series analysis and prediction models, to analyze the trends of overall operation and maintenance indicators and identify potential performance degradation and resource bottlenecks;

[0112] The operation and maintenance process optimization unit continuously optimizes the overall operation and maintenance process based on machine learning algorithms and data analysis results to adapt to changes in the network environment and improve the overall network operation and maintenance efficiency and reliability;

[0113] The decision model evaluation unit evaluates the effects of different decision models through simulation and backtesting according to known business goals and operation and maintenance requirements, and provides data support and risk assessment for actual decisions;

[0114] The automated policy generation unit automatically generates operation and maintenance policies and makes dynamic adjustments according to the analysis results and optimization goals to cope with different network conditions;

[0115] The intelligent recommendation and decision support unit provides intelligent decision-making suggestions based on operation and maintenance policies, assists management decisions, and supports personalized and customized solution recommendations;

[0116] The system security policy management unit is responsible for formulating and maintaining system-level security policies, uniformly managing the security rules of each module, and ensuring the consistency and effectiveness of security policies.

[0117] In summary, the intelligent decision-making support module realizes comprehensive intelligent operation and maintenance support by integrating the trend analysis unit, operation and maintenance process optimization unit, decision model evaluation unit, automated policy generation unit, intelligent recommendation and decision-making support unit, and system security policy management unit. This module uses historical data and real-time data for time series analysis and prediction to identify potential performance degradation and resource bottlenecks, thereby optimizing the overall operation and maintenance process and improving the efficiency and reliability of network operation and maintenance. By simulating and backtesting to evaluate the effects of different decision models, it provides data support and risk assessment for actual decision-making. At the same time, the automated policy generation unit generates operation and maintenance policies based on the analysis results and optimization goals and makes dynamic adjustments to cope with different network conditions. The intelligent recommendation and decision-making support unit provides intelligent decision-making suggestions to assist management decisions and supports personalized solution recommendations. The system security policy management unit is responsible for formulating and maintaining security policies to ensure the consistency and effectiveness of security rules. Generally speaking, this module significantly improves the operation and maintenance efficiency, decision-making scientificity, and security of the system.

[0118] Furthermore, the open integration and collaboration module includes the following components:

[0119] The open API interface unit provides standardized and extensible API interfaces to support seamless docking with external systems and achieve two-way data flow and sharing.

[0120] The cross-domain communication adaptation unit realizes communication adaptation between different network domains, different protocols, or platforms to ensure the interconnection and collaborative management of cross-domain data.

[0121] The event and task collaboration unit realizes cross-domain event monitoring and task coordination to ensure that different systems can share event information and coordinate task processing, improving collaboration efficiency.

[0122] The interface and integrated system monitoring unit monitors the running status of integrated interfaces and collaborative systems in real time to ensure interface availability, data consistency, and timely discovery and handling of anomalies during the integration process.

[0123] The security authentication and access control unit is responsible for identity authentication, permission management, and access control between systems to ensure security during system opening.

[0124] In summary, the open integration and collaboration module realizes seamless docking and efficient collaboration between systems by integrating the open API interface unit, cross-domain communication adaptation unit, event and task collaboration unit, interface and integration system monitoring unit, and security authentication and access control unit. The open API interface unit provides standardized and extensible API interfaces, supporting two-way data flow and sharing between external systems; the cross-domain communication adaptation unit ensures communication adaptation between different network domains, protocols, and platforms, realizing data interconnection; the event and task collaboration unit improves system collaboration efficiency through cross-domain event monitoring and task coordination; the interface and integration system monitoring unit monitors the system operation status in real time, ensures interface availability and data consistency, and timely processes abnormal situations; the security authentication and access control unit is responsible for identity authentication, permission management, and access control, ensuring the security during the system opening process. Through these components, the module improves the flexibility, scalability, and security of the system, and significantly improves the efficiency of cross-domain collaborative management.

[0125] Furthermore, according to the analysis results and optimization goals, operation and maintenance strategies are automatically generated and dynamically adjusted to cope with different network conditions, including the following steps:

[0126] For network problems of different severities, a corresponding hierarchical operation and maintenance strategy library is pre-established, and the triggering conditions, execution steps, and expected effects are clarified;

[0127] According to the evaluation results of the current network conditions, the most suitable operation and maintenance strategy is automatically selected from the strategy library, and the corresponding configuration modifications and resource adjustments are executed through automated tools to ensure that the strategy can quickly and accurately respond to network changes;

[0128] Evaluate the strategy effect after execution, analyze the improvement of key indicators, and adjust the strategy or switch to alternative solutions if necessary;

[0129] By accumulating experience in handling various network problems, continuously improve the strategy library, optimize the strategy matching algorithm, and improve the accuracy and efficiency of automated operation and maintenance.

[0130] In summary, the intelligent decision-making support module automatically generates and dynamically adjusts operation and maintenance strategies based on the analysis results and optimization objectives to cope with different network conditions. Its technical effects include the following aspects: First, for network problems of different severities, a hierarchical operation and maintenance strategy library is pre-established, clarifying the triggering conditions, execution steps, and expected effects to ensure the comprehensiveness and adaptability of the strategy library. Second, according to the evaluation results of the current network conditions, the most suitable operation and maintenance strategy is automatically selected from the strategy library, and configuration modifications and resource adjustments are executed through automated tools to ensure the rapid and accurate execution of the strategy. In addition, the system evaluates the strategy effect after execution, analyzes the improvement of key indicators, and adjusts the strategy or switches to alternative solutions when necessary to continuously optimize the accuracy of the operation and maintenance strategy. By accumulating experience in handling various network problems, continuously improving the strategy library and optimizing the strategy matching algorithm, the efficiency and accuracy of automated operation and maintenance are improved, ensuring the stable and efficient operation of the network.

[0131] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent operation and maintenance control system for information communication networks, characterized in that: Includes the following modules: Intelligent perception and data collection module, responsible for real-time collection of multi-source network data, dynamic monitoring of cross-domain device status, and conversion of heterogeneous data into a unified standard; The network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring to provide comprehensive network operation and maintenance insights; The resource intelligent scheduling module realizes the dynamic allocation of cross-domain network resources and ensures the efficient and safe use of resources through multi-dimensional optimization and real-time load balancing strategies; Intelligent decision support module, which continuously optimizes operation and maintenance strategies based on machine learning, analyzes long-term operation trends, and provides suggestions for management decisions; The open integration and collaboration module provides an open API interface for integration with third-party systems, supports cross-domain collaborative management, and improves the flexibility and scalability of the system.

2. According to claim 1, an intelligent operation and maintenance management system for information communication network is characterized in that: The intelligent perception and data acquisition module includes the following components: The data collection unit is responsible for collecting network traffic, device status and performance indicator data from multiple data sources in real time, and supports data capture of different protocols; The network equipment monitoring unit continuously monitors and collects the operating status information of network equipment to ensure the health and working status of multi-regional and multi-level network equipment; The data preprocessing unit performs preliminary processing on the collected raw data, including data cleaning, denoising and verification; Data standardization unit, responsible for data format conversion and unified coding, mapping heterogeneous data from different sources to standardized data models; The timing synchronization unit synchronizes the timestamps of cross-domain data to eliminate delay deviation and ensure the timing accuracy of data analysis; The data storage and management unit provides efficient data storage, indexing and retrieval mechanisms, supporting long-term archiving and fast query of data.

3. The intelligent operation and maintenance management system of information communication network according to claim 1 is characterized in that: The network intelligent analysis module includes the following components: The network performance prediction unit predicts the performance indicators of devices and links based on historical data, identifies potential bottlenecks, and optimizes network planning; Equipment health and risk assessment unit, which comprehensively analyzes the equipment's operating status, historical fault records, and network topology to assess equipment health and fault risks; Traffic anomaly detection unit, which uses real-time traffic analysis technology to identify abnormal network traffic patterns and promptly warn of potential network failures and security risks; The network security situation awareness unit continuously monitors the network security status, identifies potential threats and attack behaviors, and coordinates with system-level security policies.

4. The intelligent operation and maintenance management system of information communication network according to claim 1 is characterized in that: The resource intelligent scheduling module includes the following components: The cross-domain resource management unit is responsible for unified management of resources across multiple network domains to ensure the availability and scheduling consistency of resources in different network environments; The load balancing scheduling unit implements dynamic load balancing strategies based on real-time network load conditions and traffic distribution, and rationally distributes tasks to various resource nodes to avoid overload and bottlenecks; The resource optimization decision-making unit automatically generates the optimal resource allocation plan by comprehensively considering performance requirements, resource utilization, and load balancing factors; The security policy execution and control unit executes corresponding security policies based on the security threat identification results, protects and isolates sensitive resources, and ensures that no security risks arise during resource sharing and dynamic scheduling.

5. The intelligent operation and maintenance management system of information communication network according to claim 4 is characterized in that: Taking into account performance requirements, resource utilization, and load balancing factors, the optimal resource allocation plan is automatically generated, including the following steps: Analyze historical load data through time series data mining and machine learning models, and establish a resource demand forecasting model to accurately predict future resource usage trends and effectively avoid resource waste or shortage problems; Establish a quantitative evaluation system including performance indicators, resource utilization, and cost-benefit ratio to provide data support for resource allocation decisions and ensure the objectivity and quantifiability of decisions; Implement a dynamic load balancing mechanism based on an adaptive algorithm to automatically adjust resource allocation strategies according to real-time load conditions, ensure load balancing of each node, and give full play to the overall performance of the cluster; Relying on the automated operation and maintenance platform, the resource allocation strategy can be automatically deployed and executed. At the same time, a real-time monitoring system is established to continuously track and quickly respond to resource usage to ensure the effective implementation of the plan.

6. The intelligent operation and maintenance management system of information communication network according to claim 1 is characterized in that: The intelligent decision support module includes the following components: The trend analysis unit uses historical data and real-time data to analyze the overall operation and maintenance indicator trends through time series analysis and prediction models to identify potential performance degradation and resource bottlenecks; The operation and maintenance process optimization unit continuously optimizes the overall operation and maintenance process based on machine learning algorithms and data analysis results to adapt to changes in the network environment and improve the overall network operation and maintenance efficiency and reliability; The decision model evaluation unit evaluates the effects of different decision models through simulation and backtesting based on known business objectives and operation and maintenance requirements, and provides data support and risk assessment for actual decision-making; The automated policy generation unit automatically generates operation and maintenance policies and dynamically adjusts them to cope with different network conditions based on analysis results and optimization goals; Intelligent recommendation and decision support unit, which provides intelligent decision suggestions based on operation and maintenance strategies, assists management decisions, and supports personalized customized solution recommendations; The system security policy management unit is responsible for formulating and maintaining system-level security policies, uniformly managing the security rules of each module, and ensuring the consistency and effectiveness of the security policies.

7. The intelligent operation and maintenance management system of information communication network according to claim 1 is characterized in that: The open integration and collaboration module includes the following components: Open API interface unit, providing standardized and scalable API interface, supporting seamless connection with external systems, and realizing two-way flow and sharing of data; Cross-domain communication adaptation unit, which realizes communication adaptation between different network domains, different protocols or platforms, and ensures the intercommunication and collaborative management of cross-domain data; The event and task coordination unit realizes cross-domain event monitoring and task coordination, ensuring that different systems can share event information and coordinate task processing, thus improving collaboration efficiency; The interface and integrated system monitoring unit monitors the operating status of the integrated interface and collaborative system in real time to ensure interface availability and data consistency, and promptly detects and handles anomalies that occur during the integration process; The security authentication and access control unit is responsible for identity authentication, authority management and access control between systems to ensure security during the system opening process.

8. The intelligent operation and maintenance management system of information communication network according to claim 7, characterized in that: Based on the analysis results and optimization goals, the operation and maintenance strategy is automatically generated and dynamically adjusted to cope with different network conditions, including the following steps: For network problems of different severity, a corresponding hierarchical operation and maintenance strategy library is formulated in advance, and the triggering conditions, execution steps and expected effects are clearly defined; According to the current network status assessment results, the most suitable operation and maintenance strategy is automatically selected from the strategy library, and the corresponding configuration modification and resource adjustment are performed through automated tools to ensure that the strategy can respond to network changes quickly and accurately; Evaluate the effectiveness of the strategy after execution, analyze the improvement of key indicators, and adjust the strategy or switch to alternative plans when necessary; By accumulating experience in handling various network issues, we continuously improve the policy library, optimize the policy matching algorithm, and improve the accuracy and efficiency of automated operation and maintenance.

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