Software and hardware integrated operation and maintenance monitoring management system
Patent Information
- Application Number
- CN202611029209.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-10
- Publication Date
- 2026-10-23
AI Technical Summary
资源管理不当：许多企业缺乏实时监测和预测能力，导致资源浪费或不足，影响业务的正常运行
本发明通过权限控制模块和安全管理模块的配合，达到在检测到异常行为时，自动调整权限并生成安全警告，从而实现层层防护，降低安全事件的发生率，通过虚拟管理模块与基础监控模块协同工作，达到根据实时监测数据预测资源需求并自动调整分配策略，提高资源使用效率，通过网络监控模块与数据恢复模块的配合，达到在网络异常情况下快速备份和恢复数据，从而提高业务连续性和稳定性，通过分析报告模块与应用管理模块相结合，达到基于用户行为生成个性化的性能监控报告，帮助决策者及时识别优化空间和决策依据，通过告警管理模块与智能聊天机器人的协同，达到实时回应用户疑问与告警信息，提高用户满意度，减少操作时间，通过自动运维模块与知识库模块结合，达到将最新的运维经验和解决方案反馈至知识库，确保运维人员得到持续的培训和信息支持，通过资产管理模块和虚拟管理模块配合，达到实时预测设备健康状态并结合历史数据自动调度维护，确保资源合理性和设备最佳运行状态，通过集成各个模块本软件硬件一体化运维监控管理系统实现了全面的智能化运维管理，能够动态监测和优化系统资源，确保数据安全和透明，同时提高用户体验和响应速度，从而提升整体运营效率、降低运行风险，最终助力企业实现更高效、稳定和安全的业务运作。
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Figure CN122963887A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated software and hardware operation and maintenance monitoring technology, and in particular to an integrated software and hardware operation and maintenance monitoring management system. Background Technology
[0002] In the process of digital transformation, modern enterprises face increasingly complex IT environments and data security challenges. With the widespread application of emerging technologies such as cloud computing, big data and the Internet of Things, enterprise operation and maintenance management has become increasingly cumbersome. Traditional operation and maintenance monitoring systems often struggle to respond to dynamic changes in real time and lack intelligent decision support, resulting in low resource utilization, increased security risks, and a decline in user experience.
[0003] Specifically, enterprises often face the following problems during operation and maintenance: Poor resource management: Many enterprises lack real-time monitoring and forecasting capabilities, leading to resource waste or shortages, which affects the normal operation of business.
[0004] Security risks: Traditional security management methods struggle to identify internal threats and unusual behavior, thereby increasing the risk of data breaches and security incidents.
[0005] Inefficient response: When a failure or anomaly occurs, the company lacks a rapid response mechanism, which leads to prolonged recovery time and affects business continuity.
[0006] Poor user experience: Users often do not receive timely and accurate feedback when facing technical problems, which reduces their satisfaction with IT services. Summary of the Invention
[0007] The purpose of this invention is to address the shortcomings of existing technologies. In the process of digital transformation of modern enterprises, they face increasingly complex IT environments and data security challenges. With the widespread application of emerging technologies such as cloud computing, big data, and the Internet of Things, enterprise operation and maintenance management has become increasingly cumbersome. Traditional operation and maintenance monitoring systems often struggle to respond to dynamic changes in real time and lack intelligent decision support, resulting in low resource utilization, increased security risks, and a decline in user experience. Therefore, this invention proposes an integrated software and hardware operation and maintenance monitoring management system.
[0008] To achieve the above objectives, the present invention adopts the following technical solution: The integrated software and hardware operation and maintenance monitoring management system includes: an access control module for analyzing user behavior based on machine learning and adjusting permission settings to ensure users can only access the system parts necessary for their work; a basic monitoring module for dynamically adjusting monitoring strategies based on real-time monitoring data and immediately enabling machine learning models for in-depth analysis when anomalies are detected; a network monitoring module for recording all monitoring data through distributed ledger technology to ensure data transparency and integrity and using adaptive algorithms to improve the accuracy of traffic anomaly detection; a data recovery module for fully automated management of user-specified multi-version data backups using blockchain technology to achieve precise data recovery rollback; a security management module for real-time monitoring and identification of internal security threats using social engineering recognition technology and achieving rapid repair through intelligent response algorithms; and a predictive resource allocation module for supporting predictive resource allocation, predicting future needs through historical data analysis, and automatically adjusting resource allocation strategies before resources become insufficient. The system includes: a virtual management module; an application management module that integrates sensing technology to analyze user behavior in real time, dynamically generate personalized performance monitoring indicators, and automatically issue alerts when abnormal user usage patterns are detected; an alarm management module that integrates an intelligent chatbot to enable real-time interaction with users using natural language, automatically answer frequently asked questions, collect user feedback, and intelligently optimize alarm content; an analysis report module that supports data visualization analysis and allows users to interactively view real-time monitoring data in a 3D view using augmented reality technology; an automatic operation and maintenance module that dynamically optimizes the scheduling of operation and maintenance tasks through real-time data feedback to improve the efficiency of system resource utilization; a knowledge base module that uses adaptive learning algorithms to continuously update documents and solution content to ensure that the information in the knowledge base is always consistent with the latest operation and maintenance practices; and an asset management module that combines artificial intelligence algorithms to make real-time asset health predictions and provide maintenance suggestions and repair scheduling based on the prediction results.
[0009] In one preferred implementation, the intelligent threshold setting of the basic monitoring module is based on global data analysis, forming data sharing among devices, and the sensing technology can dynamically adjust the monitoring parameters according to the actual business needs of the enterprise.
[0010] As a preferred implementation, the network monitoring module adopts a decentralized data processing architecture, which allows for multi-party collaborative monitoring.
[0011] In a preferred embodiment, the data recovery module employs a dual-version data backup function, which allows users to select data to be restored to a specific point in time.
[0012] As a preferred implementation, the intelligent chatbot can continuously learn user behavior, improve the accuracy of its responses, and update the FAQ content in a timely manner.
[0013] In one preferred embodiment, the asset health prediction uses sensor data analysis to update the device status in real time and proactively notify the user to perform maintenance.
[0014] As a preferred implementation, the access control module can generate personalized security warnings based on the user's historical behavior, and the social engineering recognition technology can identify possible abnormal behavior of internal personnel and generate security alerts in a timely manner.
[0015] In one preferred implementation, the access control module collects users' historical behavior data and uses machine learning to analyze user behavior, adjusting user permissions based on the analysis results. The basic monitoring module collects monitoring data in real time and sets intelligent thresholds based on global data analysis, forming a data sharing mechanism between devices. If an anomaly is detected, the machine learning model performs in-depth analysis. The network monitoring module constructs a decentralized data processing architecture, enabling multi-party collaborative monitoring, real-time sharing and analysis of data streams, and dynamically adjusts monitoring strategies based on network traffic. The data recovery module employs a dual-version data backup function, allowing users to choose a specific time point for data recovery. The security management module monitors the behavior of internal users in real time, uses social engineering recognition technology to analyze detected abnormal behavior, generates personalized security warnings, notifies relevant personnel for handling, and executes rapid remediation measures based on the severity of the event.
[0016] In one preferred implementation, the virtual management module collects and analyzes historical resource usage data, predicts future resource needs based on the data, the application management module analyzes user behavior data in real time and generates personalized performance monitoring indicators, and automatically issues an alarm when abnormal user usage patterns are detected, the alarm management module integrates an intelligent chatbot to interact with users in real time, automatically answers common questions and collects user feedback, and optimizes alarm content based on user feedback and monitoring data, and the analysis report module collects and processes real-time monitoring data, uses augmented reality technology to generate three-dimensional view analysis reports, and updates the analysis reports regularly for decision-makers to refer to.
[0017] In one preferred implementation, the automated operation and maintenance module continuously provides feedback on operational data, automatically optimizes operation and maintenance task scheduling, evaluates system resource utilization efficiency, and makes adjustments to optimize performance. It also periodically checks execution results and improves the scheduling algorithm. The knowledge base module uses an adaptive learning algorithm to automatically update document and solution content, maintain the latest information in the knowledge base, ensure synchronization with operation and maintenance practices, and periodically reviews the knowledge base content. The asset management module uses sensor data to analyze equipment status in real time, predicts maintenance needs based on equipment health status, and promptly notifies users.
[0018] The beneficial effects of this invention are as follows: This invention achieves layered protection and reduces the incidence of security incidents by coordinating an access control module and a security management module to automatically adjust permissions and generate security warnings upon detecting abnormal behavior. Through the collaboration of a virtual management module and a basic monitoring module, it predicts resource demands based on real-time monitoring data and automatically adjusts allocation strategies, improving resource utilization efficiency. The combination of a network monitoring module and a data recovery module enables rapid data backup and recovery in the event of network anomalies, thereby improving business continuity and stability. The integration of an analysis reporting module and an application management module generates personalized performance monitoring reports based on user behavior, helping decision-makers identify optimization opportunities and decision-making basis. Finally, the collaboration between an alarm management module and an intelligent chatbot enables real-time responses. This system addresses user questions and alarms, improving user satisfaction and reducing operation time. By combining an automated maintenance module with a knowledge base module, it feeds the latest maintenance experience and solutions back to the knowledge base, ensuring continuous training and information support for maintenance personnel. Through the asset management and virtual management modules, it predicts equipment health status in real time and automatically schedules maintenance based on historical data, ensuring resource rationality and optimal equipment operation. By integrating these modules, this integrated software and hardware maintenance monitoring and management system achieves comprehensive intelligent maintenance management. It can dynamically monitor and optimize system resources, ensure data security and transparency, and improve user experience and response speed, thereby enhancing overall operational efficiency, reducing operational risks, and ultimately helping enterprises achieve more efficient, stable, and secure business operations. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the integrated software and hardware operation and maintenance monitoring management system of the present invention. Detailed Implementation
[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0021] like Figure 1As shown, the integrated software and hardware operation and maintenance monitoring management system includes: an access control module for analyzing user behavior based on machine learning and adjusting permission settings to ensure users can only access the system parts they need for their work; a basic monitoring module for dynamically adjusting monitoring strategies based on real-time monitoring data and immediately enabling machine learning models for in-depth analysis when anomalies are detected; a network monitoring module for recording all monitoring data through distributed ledger technology to ensure data transparency and integrity and using adaptive algorithms to improve the accuracy of traffic anomaly detection; a data recovery module for fully automated management of user-specified multi-version data backups using blockchain technology to achieve precise data recovery rollback; a security management module for real-time monitoring and identification of internal security threats using social engineering recognition technology and achieving rapid repair through intelligent response algorithms; a virtual management module for supporting predictive resource allocation, predicting future needs through historical data analysis, and automatically adjusting resource allocation strategies before resources become insufficient; an application management module for integrating perception technology, analyzing user behavior in real time, dynamically generating personalized performance monitoring indicators, and automatically issuing alarms when abnormal user usage patterns are detected; an alarm management module for integrating intelligent chatbots to achieve real-time interaction with users using natural language, automatically answering frequently asked questions, collecting user feedback, and intelligently optimizing alarm content; and a data support module. The system includes: a visualization analysis module that allows users to interactively view real-time monitoring data in a 3D view using augmented reality technology; an automated maintenance module that dynamically optimizes the scheduling of maintenance tasks through real-time data feedback to improve system resource utilization; a knowledge base module that uses adaptive learning algorithms to continuously update documents and solutions, ensuring that the information in the knowledge base is always consistent with the latest maintenance practices; an asset management module that combines artificial intelligence algorithms to make real-time asset health predictions and provide maintenance suggestions and repair scheduling based on the prediction results; and a basic monitoring module with intelligent threshold settings. Based on global data analysis, data sharing is achieved between devices, and sensing technology can be tailored to the actual needs of the enterprise. The system dynamically adjusts monitoring parameters according to service requirements. The network monitoring module adopts a decentralized data processing architecture, which allows for multi-party collaborative monitoring. The data recovery module uses a dual-version data backup function, which allows users to choose to restore data to a specific point in time. The intelligent chatbot can continuously learn user behavior to improve the accuracy of responses and update FAQ content at any time. The asset health prediction uses sensor data analysis to update device status in real time and proactively notify users to perform maintenance. The access control module can generate personalized security warnings based on users' historical behavior. Social engineering recognition technology can identify possible abnormal behavior of internal personnel and generate security alerts in a timely manner. Through the above embodiments, the access control module analyzes user behavior based on machine learning to ensure that users can only access the system parts necessary for their work, thereby improving system security and reducing the risk of unauthorized access. The basic monitoring module monitors data in real time and dynamically adjusts monitoring strategies to promptly detect and respond to abnormal events, ensuring system stability and high availability. The network monitoring module uses distributed ledger technology to record monitoring data, improving data transparency and integrity, and ensuring traceability and sharing of data across different monitoring parties. The data recovery module provides dual-version data backup, allowing users to restore data to a specific point in time, enhancing the enterprise's ability to respond to data loss incidents. The security management module monitors and identifies internal security threats in real time, enabling rapid remediation of potential security vulnerabilities and improving the enterprise's overall security protection capabilities. The virtual management module supports predictive resource allocation, enabling proactive identification and fulfillment of resource needs, avoiding losses due to... Business interruptions caused by insufficient resources can be addressed by dynamically generating personalized performance monitoring indicators through the application management module, enabling timely identification of abnormal user usage patterns, thereby optimizing user experience and resource allocation. The alarm management module integrates an intelligent chatbot for real-time interaction, improving user response speed and the accuracy of information feedback. The analysis report module utilizes data visualization and augmented reality technology to interactively view real-time monitoring data in a 3D view, enhancing decision-makers' intuitive understanding of information. The automated operation and maintenance module provides real-time data feedback to dynamically optimize operation and maintenance task scheduling, improving system resource utilization efficiency and reducing operating costs. The knowledge base module uses adaptive learning algorithms to update documents and solutions, ensuring consistency between knowledge base information and the latest operation and maintenance practices, facilitating access and use by operation and maintenance personnel. The asset management module analyzes real-time equipment status, enabling proactive equipment maintenance, reducing failure rates, and extending equipment lifespan. The access control module collects users' historical behavior data and uses machine learning to analyze user behavior. Based on the analysis results, it adjusts user permissions. The basic monitoring module collects monitoring data in real time and sets intelligent thresholds based on global data analysis, forming a data sharing mechanism between devices. If an anomaly is detected, the machine learning model performs in-depth analysis. The network monitoring module builds a decentralized data processing architecture, enabling multi-party collaborative monitoring to achieve real-time sharing and analysis of data streams. It also dynamically adjusts monitoring strategies based on network traffic. The data recovery module uses a dual-version data backup function, allowing users to choose a specific time point for data recovery. The security management module monitors the behavior of internal users in real time, uses social engineering recognition technology to analyze detected abnormal behaviors, generates personalized security warnings, notifies relevant personnel for handling, and executes rapid remediation measures based on the severity of the incident. The virtual management module collects and analyzes historical resource usage data, predicts future resource needs based on the data, and the application management module analyzes user behavior data in real time and generates personalized performance monitoring indicators. When abnormal user usage patterns are detected, an alarm is automatically issued. The alarm management module integrates an intelligent chatbot to interact with users in real time, automatically answers common questions and collects user feedback. The alarm content is optimized based on user feedback and monitoring data. The analysis report module collects and processes real-time monitoring data, uses augmented reality technology to generate 3D view analysis reports, and updates the analysis reports regularly for decision-makers to refer to. The automated operation and maintenance module continuously provides feedback on operational data, automatically optimizes operation and maintenance task scheduling, evaluates system resource utilization efficiency, and makes adjustments to optimize performance. It also regularly checks execution results and improves scheduling algorithms. The knowledge base module uses adaptive learning algorithms to automatically update document and solution content, maintain the latest information in the knowledge base, ensure synchronization with operation and maintenance practices, and regularly reviews knowledge base content. The asset management module uses sensor data to analyze equipment status in real time, predicts maintenance needs based on equipment health status, and promptly notifies users. Through the above embodiments, the cooperation of the access control module and the security management module enables automatic adjustment of permissions and generation of security warnings when abnormal behavior is detected, thereby achieving layered protection and reducing the incidence of security incidents. The collaboration between the virtual management module and the basic monitoring module allows for the prediction of resource needs based on real-time monitoring data and automatic adjustment of allocation strategies, improving resource utilization efficiency. The cooperation between the network monitoring module and the data recovery module enables rapid backup and recovery of data in the event of network anomalies, thereby improving business continuity and stability. The combination of the analysis report module and the application management module enables the generation of personalized performance monitoring reports based on user behavior, helping decision-makers to promptly identify optimization opportunities and decision-making basis. The collaboration between the alarm management module and the intelligent chatbot enables real-time responses to user questions and alarm information, improving user satisfaction and reducing operation time. The combination of the automatic operation and maintenance module and the knowledge base module enables the feedback of the latest operation and maintenance experience and solutions to the knowledge base, ensuring continuous training and information support for operation and maintenance personnel. The cooperation between the asset management module and the virtual management module enables real-time prediction of equipment health status and automatic scheduling of maintenance based on historical data, ensuring resource rationality and optimal equipment operating status. By integrating various modules, this integrated software and hardware operation and maintenance monitoring management system achieves comprehensive intelligent operation and maintenance management. It can dynamically monitor and optimize system resources, ensure data security and transparency, and improve user experience and response speed, thereby improving overall operational efficiency, reducing operational risks, and ultimately helping enterprises achieve more efficient, stable and secure business operations.
[0022] Working principle: When this invention is in use, the access control module collects the user's historical operation records, roles and permission settings, uses machine learning algorithms to analyze user behavior patterns to identify their work needs, dynamically adjusts permissions based on the analysis results, ensures that users can only access necessary parts of the system, and automatically generates security alerts and notifies the administrator when permission abuse or abnormal behavior is detected. The basic monitoring module monitors system performance and user activity in real time, sets intelligent thresholds for various monitoring indicators through global data analysis, dynamically adjusts monitoring strategies based on real-time data to ensure monitoring accuracy, and enables machine learning models to perform in-depth analysis of detected anomalies. The network monitoring module is configured with a decentralized data processing architecture to achieve multi-party collaborative monitoring, collect network traffic data, analyze and process it, and use adaptive algorithms to improve the accuracy of traffic anomaly detection and identify potential threats in a timely manner. The data recovery module creates dual-version data backups, records system status and user data, allows users to select the data version to be restored to a specified point in time, performs data recovery operations based on user selection, and verifies integrity. The security management module monitors user behavior in real time, identifies potential internal security threats, applies relevant technologies to identify possible abnormal behaviors of internal personnel, automatically generates security alerts based on the identification results, and performs rapid remediation. The virtual management module analyzes historical data to identify resource demand trends, uses predictive algorithms to predict future resource needs, and automatically adjusts resource allocation strategies to meet demand before resources become insufficient. The application management module analyzes user behavior data in real time, dynamically generates personalized performance monitoring indicators based on the analysis results, and automatically issues an alarm when abnormal usage patterns are detected. The alarm management module integrates an intelligent chatbot that supports real-time interaction with users using natural language. By learning from user feedback, it continuously optimizes and updates the content of frequently asked questions (FAQs) and automatically replies to common questions, thereby improving the user experience. The analysis report module supports data visualization analysis, collects real-time monitoring data, generates 3D interactive views through augmented reality technology, and presents analysis reports to intuitively display monitoring and analysis results, making it easier for users to analyze. The automated operation and maintenance module collects current operation and maintenance task information through real-time data feedback, analyzes the data to optimize the scheduling of operation and maintenance tasks, ensures that system resources are used efficiently, and automatically adjusts the execution plan of operation and maintenance tasks to adapt to the dynamic environment. The knowledge base module uses adaptive learning algorithms to update document and solution content, regularly reviews the information in the knowledge base to ensure it is consistent with the latest operational practices, collects user feedback, and optimizes the information structure and content quality of the knowledge base.
[0023] The asset management module monitors and updates equipment status in real time through sensors, applies artificial intelligence algorithms to predict asset health, analyzes equipment conditions, and provides users with maintenance suggestions and repair scheduling information based on the prediction results.
[0024] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A software and hardware integrated operation and maintenance monitoring management system, characterized in that, This includes: an access control module for analyzing user behavior and adjusting permission settings based on machine learning to ensure users can only access the system parts necessary for their work; a basic monitoring module for dynamically adjusting monitoring strategies based on real-time monitoring data and immediately enabling machine learning models for in-depth analysis when anomalies are detected; a network monitoring module for recording all monitoring data through distributed ledger technology to ensure data transparency and integrity and using adaptive algorithms to improve the accuracy of traffic anomaly detection; a data recovery module for fully automated management of user-specified multi-version data backups using blockchain technology to achieve precise data recovery rollback; a security management module for real-time monitoring and identification of internal security threats using social engineering recognition technology and achieving rapid remediation through intelligent response algorithms; and a virtual management module for supporting predictive resource allocation, predicting future needs through historical data analysis, and automatically adjusting resource allocation strategies before resources become insufficient. This includes: an application management module that integrates sensing technology to analyze user behavior in real time, dynamically generate personalized performance monitoring indicators, and automatically issue alerts when abnormal user usage patterns are detected; an alarm management module that integrates an intelligent chatbot to enable real-time interaction with users using natural language, automatically answer frequently asked questions, collect user feedback, and intelligently optimize alarm content; an analysis report module that supports data visualization analysis and allows users to interactively view real-time monitoring data in a 3D view using augmented reality technology; an automated operation and maintenance module that dynamically optimizes the scheduling of operation and maintenance tasks through real-time data feedback to improve the efficiency of system resource utilization; a knowledge base module that uses adaptive learning algorithms to continuously update documents and solution content to ensure that the information in the knowledge base is always consistent with the latest operation and maintenance practices; and an asset management module that combines artificial intelligence algorithms to make real-time asset health predictions and provide maintenance suggestions and repair scheduling based on the prediction results.
2. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The intelligent threshold setting of the basic monitoring module is based on global data analysis, forming data sharing among devices. The sensing technology can dynamically adjust the monitoring parameters according to the actual business needs of the enterprise.
3. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The network monitoring module adopts a decentralized data processing architecture, which allows for multi-party collaborative monitoring.
4. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The data recovery module employs a dual-version data backup function, which allows users to select data to be restored to a specific point in time.
5. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The intelligent chatbot can continuously learn from user behavior, improve the accuracy of its responses, and update the FAQ content in a timely manner.
6. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The asset health prediction system analyzes sensor data to update equipment status in real time and proactively notify users to perform maintenance.
7. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The access control module can generate personalized security warnings based on users' historical behavior, and the social engineering recognition technology can identify possible abnormal behavior of internal personnel and generate security alerts in a timely manner.
8. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The access control module collects users' historical behavior data and uses machine learning to analyze user behavior. Based on the analysis results, it adjusts user permissions. The basic monitoring module collects monitoring data in real time and sets intelligent thresholds based on global data analysis, forming a data sharing mechanism between devices. If an anomaly is detected, the machine learning model performs in-depth analysis. The network monitoring module constructs a decentralized data processing architecture, enabling multi-party collaborative monitoring, real-time sharing and analysis of data streams, and dynamically adjusts monitoring strategies based on network traffic. The data recovery module employs a dual-version data backup function, allowing users to choose a specific time point for data recovery. The security management module monitors internal user behavior in real time, uses social engineering recognition technology to analyze detected abnormal behavior, generates personalized security warnings, notifies relevant personnel for handling, and executes rapid remediation measures based on the severity of the event.
9. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The virtual management module collects and analyzes historical resource usage data, predicts future resource needs based on the data, and the application management module analyzes user behavior data in real time and generates personalized performance monitoring indicators. When abnormal user usage patterns are detected, an alarm is automatically issued. The alarm management module integrates an intelligent chatbot to interact with users in real time, automatically answers common questions and collects user feedback, and optimizes alarm content based on user feedback and monitoring data. The analysis report module collects and processes real-time monitoring data, uses augmented reality technology to generate 3D view analysis reports, and updates the analysis reports regularly for decision-makers' reference.
10. The integrated software and hardware operation and maintenance monitoring management system according to claim 1, characterized in that, The automated operation and maintenance module continuously provides feedback on operational data, automatically optimizes operation and maintenance task scheduling, evaluates system resource utilization efficiency, and makes adjustments to optimize performance. It also periodically checks execution results and improves the scheduling algorithm. The knowledge base module uses an adaptive learning algorithm to automatically update document and solution content, maintain the latest information in the knowledge base, ensure synchronization with operation and maintenance practices, and periodically reviews the knowledge base content. The asset management module uses sensor data to analyze equipment status in real time, predicts maintenance needs based on equipment health status, and promptly notifies users.