Management method based on intelligent operation and maintenance of informatization system

Abstract

The invention discloses a management method based on intelligent operation and maintenance of an informatization system, and the method employs a management center. The management center is connected with a data acquisition module, a data processing and analysis module, a fault early warning module, an information extraction module, a decision support module, a big data module, a management area division module, a field monitoring module, a continuous monitoring optimization module and a field video acquisition module, and is used for acquiring a monitoring video on site by using a camera; the data acquisition module comprises an enterprise data acquisition unit and an enterprise data transmission unit; the enterprise data acquisition unit is used for acquiring enterprise information data and sending the enterprise information data to the enterprise data integration module through the enterprise data transmission unit to generate normal enterprise department information data; the data processing and analyzing module comprises a data processing unit and a data analyzing unit, and the data processing unit is used for processing normal enterprise information data; and powerful guarantee is provided for improving the service continuity, reducing the cost, improving the user experience and the like.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to a management method based on intelligent operation and maintenance of an information system. Background Art

[0002] With the deepening of the informatization construction process, the connection between informatization and enterprise business is becoming increasingly close, and the requirements for informatization infrastructure for business development are increasing day by day. At the same time, the scale of enterprise networks continues to expand, the number of software and hardware continues to grow, and the amount of information is increasing, which makes the dependence and requirements of enterprises on information systems continue to increase, and the informatization operation and maintenance management work faces huge pressure and challenges. Improving the efficiency of informatization operation and maintenance management and ensuring the stable, safe and efficient application of informatization infrastructure can provide effective guarantees for the development of various businesses of enterprises. With the development of technologies such as cloud computing, big data and artificial intelligence, the models and tools of informatization operation and maintenance are also constantly evolving, and will be more intelligent and automated in the future. Faced with complex operation and maintenance objects and data, the current informatization operation and maintenance system based on relational databases is increasingly difficult to meet monitoring requirements. Operation and maintenance objects such as container services and POD nodes are constantly generated and deleted in cloud environments. System log data and service call relationship chains are constantly added and adjusted during system operation. Relational databases are stretched when dealing with large amounts of dynamic and unstructured data.

[0003] In summary, the management method based on intelligent operation and maintenance of information system proposed in the present invention optimizes the processing capability of enterprise information operation and maintenance system for dynamic and unstructured operation and maintenance data, and provides strong support for the security and efficiency of enterprise intelligent operation and maintenance. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: the management center is connected to a data acquisition module, a data processing and analysis module, a fault warning module, an information extraction module, a decision support module, a big data module, a management area division module, an on-site monitoring module, a continuous monitoring optimization module and an on-site video acquisition module, which is used to collect monitoring videos on-site using a camera; the data acquisition module includes an enterprise data acquisition unit and an enterprise data transmission unit; the enterprise data acquisition unit is used to collect enterprise information data, and send the enterprise information data to the enterprise data integration module through the enterprise data transmission unit to generate normal enterprise department information data; the data processing and analysis module includes a data processing unit and a data analysis unit The data processing unit processes the normal enterprise information data to generate the enterprise information data, including: obtaining the enterprise department corresponding to the abnormal enterprise department information data according to the monitored abnormal enterprise department information data, re-collecting the enterprise department information data of the enterprise and department, and re-transmitting it through the corresponding enterprise data transmission unit until the normal enterprise department information data is obtained, and obtaining a plurality of normal enterprise department information data through a plurality of enterprise data transmission units; the data analysis unit analyzes the information data to generate the unified enterprise information data, including: setting the format recognition pool and the structure recognition pool in the enterprise information data analysis unit, and setting the standard format and the standard structure accordingly;

[0006] The fault warning module is used to monitor the performance and security status of IT infrastructure in real time, to issue warning signals based on the comprehensive system evaluation values ​​of each sub-area, to set up alarm mechanisms, to respond to system anomalies in a timely manner, to identify, analyze and resolve faults in a timely manner to minimize the impact on the business, and to establish a fault recording and analysis mechanism to prevent problems from recurring;

[0007] The information extraction module is used to extract management center data and fault information, timely change and configuration management based on data, standardize the change process of IT systems, and evaluate the impact of changes on existing services; maintain the configuration status of IT assets and services to ensure their correctness and consistency, and use the configuration management database to track system components and their relationships;

[0008] The decision support module will obtain analysis results, understand the production and operation status, identify problems and risks in a timely manner, provide scientific decision support for the company's senior management, and achieve long-term and stable development of the company;

[0009] The big data module collects necessary data to a central platform through various data sources including ERP system, CRM system and Internet data, obtains data sets, and performs preprocessing work on the data by filling missing values ​​and handling outliers. The data collected by the data acquisition module is preprocessed by the data analysis module, and then compared and analyzed with the data sets in the big data module; security assessment, monitoring and incident response are carried out to protect information assets; access control and data protection strategies are implemented to ensure information security;

[0010] The management area division module is used to identify the target enterprise as the target area, divide the target area into sub-areas by equal area division, and mark them as 1, 2...n in sequence; establish a regular backup mechanism for each sub-area to ensure data integrity and recoverability. Set up data backup and recovery functions for the system of each sub-area, formulate a disaster recovery plan, and ensure rapid service recovery in the event of a failure; the on-site monitoring module analyzes system performance based on the collected data, optimizes resource allocation, and improves system efficiency;

[0011] The continuous monitoring and optimization module continuously tracks the changes in the data of the data collection module, and timely adjusts the production plan and production process according to the data analysis results, gradually realizing the continuous optimization of the production process, using automated tools to perform daily operation and maintenance tasks, reducing manual operations and improving efficiency; using modern DevOps tool chains to promote the collaboration between development and operation and maintenance;

[0012] As a preferred solution of the management method based on intelligent operation and maintenance of information system described in the present invention, the management center also includes a daily management module, and the daily management module includes an emergency management unit. The emergency management unit starts the corresponding emergency response program, makes judgments and analyses, and determines whether there is a risk event due to the severity of the impact caused by the incident; the emergency response management unit collects risk notification content, notification status, warning level, notification time, start time, release time, and data information on the number of managed enterprises, and supports retrieval based on the content of the risk notification, notification status, and warning level.

[0013] As a preferred solution of the management method based on intelligent operation and maintenance of information system described in the present invention, the management area division module includes an information room and an operation and maintenance management inspection unit. The information room and the operation and maintenance management inspection unit are divided into regular inspections and special inspections. Regular inspections refer to regular inspections by operation and maintenance personnel to check the operating conditions of the computer room equipment, power supply system, network system, application system, and the temperature and humidity of the computer room, and to keep inspection records and report any abnormal situations in a timely manner; special inspections refer to arranging operation and maintenance personnel to conduct inspections when encountering bad weather, equipment abnormalities, or suspicious phenomena and major events during operation, and appropriately increasing the inspection frequency.

[0014] As a preferred solution of the management method based on intelligent operation and maintenance of information system described in the present invention, the field monitoring module models the spatial attributes, time attributes, environmental attributes, management attributes and real-time attributes within the regional division through the model library module.

[0015] As a preferred solution of the management method based on intelligent operation and maintenance of information system described in the present invention, it also includes jointly training and learning the multimodal data based on recurrent neural network to form a comprehensive anomaly detection model; combining adaptive learning rate and regularization technology to optimize the training process of the anomaly detection model and improve the generalization ability and stability of the anomaly detection model; using real-time abnormal data processing algorithm to dynamically analyze the multimodal data and identify abnormal data; the data processing and analysis module is configured to process the operation and maintenance data to form an operation and maintenance data source, and to warn of abnormal business data; the fault processing module is configured to obtain the operation and maintenance data with faults in the business system according to the results of the analysis and processing, and repair the faulty operation and maintenance data; set up backup and recovery of management center data, establish a regular backup mechanism, and ensure the integrity and recoverability of the data.

[0016] As a preferred solution of the management method based on intelligent operation and maintenance of information system described in the present invention, the data processing and analysis module includes data preprocessing, cleaning, processing and feature extraction of historical data, regular capacity planning, analysis of system performance, optimization of resource allocation, and improvement of system efficiency.

[0017] The beneficial effects of the present invention are: improving business continuity: ensuring the availability of IT services and supporting enterprise operations; reducing costs: improving operation and maintenance efficiency and reducing manpower and material costs by optimizing resources and automation; improving user experience: ensuring efficient and reliable services and improving end-user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0019] in:

[0020] Figure 1 A schematic diagram of the connection of a management center of a management method based on intelligent operation and maintenance of an information system disclosed in an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the process steps of a management method based on intelligent operation and maintenance of an information system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0023] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without making any creative work should fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Example 1

[0026] Reference Figure 1-2 As shown, a management method based on intelligent operation and maintenance of an information system includes a management center, which is connected to a data acquisition module, a data processing and analysis module, a fault warning module, an information extraction module, a decision support module, a big data module, a management area division module, an on-site monitoring module, and a continuous monitoring optimization module, and implements an on-site video acquisition module for collecting monitoring videos on-site using a camera; the data acquisition module includes an enterprise data acquisition unit and an enterprise data transmission unit; the enterprise data acquisition unit is used to collect enterprise information data, and send the enterprise information data to the enterprise data integration module through the enterprise data transmission unit to generate normal enterprise department information data; the data processing and analysis module includes a data processing unit and a data processing unit. According to the analysis unit, the data processing unit processes the normal enterprise information data, and the process of generating the enterprise information data includes: according to the monitored abnormal enterprise department information data, obtaining the enterprise department corresponding to the abnormal enterprise department information data, re-collecting the enterprise department information data of the enterprise and department, and re-transmitting it through the corresponding enterprise data transmission unit until the normal enterprise department information data is obtained, and a number of normal enterprise department information data obtained through a number of enterprise data transmission units; the data analysis unit analyzes the information data, and the process of generating the unified enterprise information data includes: setting the format recognition pool and the structure recognition pool in the enterprise information data analysis unit, and correspondingly setting the standard format and the standard structure;

[0027] The fault warning module is used to monitor the performance and security status of IT infrastructure in real time, to issue warning signals based on the comprehensive system evaluation values ​​of each sub-area, to set up alarm mechanisms, to respond to system anomalies in a timely manner, to identify, analyze and resolve faults in a timely manner to minimize the impact on the business, and to establish a fault recording and analysis mechanism to prevent problems from recurring;

[0028] The information extraction module is used to extract management center data and fault information, timely change and configuration management based on data, standardize the change process of IT systems, and evaluate the impact of changes on existing services. Standardize the change process of IT systems and evaluate the impact of changes on existing services; maintain the configuration status of IT assets and services to ensure their correctness and consistency, and use the configuration management database (CMDB) to track system components and their relationships;

[0029] The management area division module refers to a functional module designed to better manage and coordinate the work of various parts within an organization or system. It can be applied in different scenarios, such as geographic information system (GIS), urban planning, enterprise resource management, etc. Its main functions include:

[0030] Resource allocation and optimization: By rationally dividing management areas, human, material and financial resources can be allocated more effectively, ensuring that each area receives appropriate attention and support.

[0031] Task allocation and clear responsibilities: Breaking down large management goals into small, manageable tasks and clearly identifying the responsible persons for each area will help improve work efficiency and execution;

[0032] Refined management: formulate corresponding management strategies and measures according to the characteristics of different regions to achieve refined management and improve management effects;

[0033] Performance evaluation and incentives: Establish specific assessment indicators for each management area to facilitate performance evaluation, and implement reward or punishment measures accordingly to stimulate employee enthusiasm;

[0034] Information collection and analysis: collect data and information from each management area, conduct comprehensive analysis, and provide a basis for high-level decision-making;

[0035] Coordination and cooperation: Promote communication between different management areas, strengthen cross-departmental collaboration, and jointly solve problems;

[0036] Emergency response: In an emergency, resources can be quickly mobilized according to pre-set regional divisions to respond effectively;

[0037] Customer Service and Market Segmentation: In a business environment, customer groups can be divided based on geographic location or other criteria in order to better provide customized services and marketing strategies;

[0038] Policy implementation and supervision: In the field of public management, regional division helps to effectively implement and supervise policies and ensure that various policy measures can take root;

[0039] The big data module collects necessary data to a central platform through various data sources including Apache Hadoop system, Spark system and Internet data, obtains data sets, and performs preprocessing work on the data by filling missing values ​​and handling outliers. The data collected by the data acquisition module is preprocessed by the data analysis module, and then compared and analyzed with the data sets in the big data module; security assessment, monitoring and incident response are carried out to protect information assets; access control and data protection strategies are implemented to ensure information security.

[0040] Big data modules play a vital role in the field of modern information technology and are mainly used to process, analyze and interpret large-scale data sets. The following are some of the main roles of big data modules: Data storage: Big data technology is able to store large amounts of structured, semi-structured and unstructured data, which allows companies to save almost all data instead of selectively saving some data.

[0041] Efficient processing: By using distributed computing frameworks such as Apache Hadoop or Spark, big data technology can process data in parallel on multiple servers, thereby speeding up data processing;

[0042] Real-time analytics: Some big data solutions (such as Apache Storm or Kafka Streams) support stream processing, allowing real-time analysis of data streams, which is very important for application scenarios that require instant decision making;

[0043] Predictive analysis: Using machine learning algorithms and statistical models, big data can help companies conduct predictive analysis to better plan future strategies and development directions;

[0044] Business insights: Through in-depth analysis of large amounts of data, companies can gain valuable business insights, such as consumer behavior patterns, market trends, and other information;

[0045] Personalized services: Based on the user's historical behavior and preferences, big data analysis can be used to provide personalized services and recommendations to improve user experience;

[0046] Risk management: By analyzing historical data and identifying patterns, big data technology can help financial institutions and other industries identify potential risks and fraud;

[0047] Optimize operations: Industries such as manufacturing and logistics can use big data technology to optimize supply chain management, reduce waste, and improve production efficiency;

[0048] Assisting scientific research: In the field of scientific research, big data technology can help scientists process and analyze experimental data and accelerate the output of scientific research results;

[0049] In short, the big data module provides enterprises with powerful tools to manage and utilize data, helping them make more informed decisions and stay ahead in a highly competitive market environment. With the development of technology, the application scope of big data is also expanding.

[0050] The management area division module is used to identify the target enterprise as the target area, divide the target area into sub-areas by equal area division, and mark them as 1, 2...n in sequence; establish a regular backup mechanism for each sub-area to ensure the integrity and recoverability of the data. Set up data backup and recovery functions for the system of each sub-area, formulate a disaster recovery plan, and ensure rapid restoration of services in the event of a failure. The field monitoring module analyzes system performance based on the collected data, optimizes resource allocation, and improves system efficiency. The field monitoring module is a system specially designed to collect, process and transmit data in a field environment. Real-time data acquisition: Real-time acquisition of environmental or equipment status information through various sensors installed on site (such as temperature sensors, humidity sensors, pressure sensors, etc.).

[0051] Remote monitoring: Using wireless or wired communication technology, the data collected on site is transmitted to the central control system or cloud platform to achieve remote monitoring and management;

[0052] Fault warning and diagnosis: When the monitored data exceeds the preset safety range, the system will automatically issue an alarm to prompt the management personnel to take corresponding measures to prevent the fault from occurring;

[0053] Process optimization: Through the analysis of on-site data, bottlenecks or inefficient links in the production process can be identified, so that targeted improvements can be made to improve production efficiency;

[0054] Environmental and safety protection: In the field of environmental protection, it can monitor air quality, water pollution, etc.; in the field of industrial safety, it can be used to monitor the concentration of harmful gases, fire risk level, etc. to ensure the safety of personnel and equipment;

[0055] Asset management: By tracking the operating status of the equipment, maintenance personnel can better understand the health of the equipment, formulate reasonable maintenance plans, and extend the service life of the equipment;

[0056] Data-driven decision-making: The collected data can be used to analyze market trends, customer needs and other information, providing data support for corporate strategic planning and marketing strategies;

[0057] Automation and intelligence: Combining Internet of Things (IoT) technology and artificial intelligence algorithms to achieve automated control and intelligent decision-making, further improving management level;

[0058] The continuous monitoring and optimization module continuously tracks the changes in the data of the data collection module, and timely adjusts the production plan and production process according to the data analysis results, gradually realizing the continuous optimization of the production process, using automated tools to perform daily operation and maintenance tasks, reducing manual operations and improving efficiency; using modern DevOps tool chains to promote the collaboration between development and operation and maintenance;

[0059] The management center also includes a daily management module, which includes an emergency management unit. The emergency management unit starts the corresponding emergency response procedure, makes judgments and analyses, and determines whether there is a risk event due to the severity of the impact caused by the incident. The emergency response management unit collects risk notification content, notification status, warning level, notification time, start time, release time, and data information on the number of managed enterprises, and supports retrieval based on the content of the risk notification, notification status, and warning level. The management area division module includes an information room and operation and maintenance management inspection unit. The information room and operation and maintenance management inspection unit are divided into regular inspections and special inspections. Regular inspections refer to regular inspections by operation and maintenance personnel to check the operating conditions of the equipment, power supply system, network system, application system, and the temperature and humidity of the computer room, and keep inspection records and report any abnormalities in a timely manner. Special inspections refer to arranging operation and maintenance personnel to conduct inspections and appropriately increase the frequency of inspections when encountering bad weather, equipment abnormalities, or suspicious phenomena and major events during operation.

[0060] The multimodal data are jointly trained and learned based on a recurrent neural network to form a comprehensive anomaly detection model; the training process of the anomaly detection model is optimized by combining adaptive learning rate and regularization technology to improve the generalization ability and stability of the anomaly detection model; the multimodal data is dynamically analyzed by a real-time abnormal data processing algorithm to identify abnormal data; the data processing and analysis module is configured to process the operation and maintenance data to form an operation and maintenance data source, and to issue an early warning for abnormal business data; the fault handling module is configured to obtain the operation and maintenance data with faults in the business system according to the results of the analysis and processing, and to repair the faulty operation and maintenance data; the backup and recovery of the management center data is set up, and a regular backup mechanism is established to ensure the integrity and recoverability of the data.

[0061] Furthermore, the embodiment of the present invention utilizes a data fusion algorithm to fuse multimodal data, which is to perform linear weighted summation of the data collected by different sensors according to weights. The mathematical expression formula is as follows:

[0062]

[0063] Among them, C 融合 is the fused data, C i is the data collected by the i-th sensor, is the weight corresponding to the data collected by the i-th sensor, and N represents the number of sensors;

[0064] The fused multimodal data is then preprocessed, and the preprocessing at least includes denoising and normalization.

[0065] As an example, denoising can be performed by filtering the signal, and its mathematical expression is as follows:

[0066]

[0067] Among them, z filtered (p) is the signal after filtering, z(z) is the original signal, and Q is the filter window size.

[0068] The field monitoring module models the spatial attributes, time attributes, environmental attributes, management attributes, and real-time attributes within the regional division through the model library module.

[0069] Equipment attributes include: name, model, rated power and usage cycle;

[0070] Spatial attributes include: location, common areas, floors, and rooms;

[0071] Time attributes include: season and available time period;

[0072] Environmental attributes include: humidity, temperature, light intensity, and noise;

[0073] Management attributes include: users and managers;

[0074] Real-time attributes include: real-time power consumption, cumulative power consumption, and start and stop time.

[0075] The operation and maintenance management system includes: an architecture status module, an access management module, a policy management module, an alarm management module and a system management module. The operation and maintenance management system interacts with the server and network equipment of the enterprise intranet through the SNMP protocol to obtain the online status of the equipment and network traffic of the current information infrastructure.

[0076] According to the management method of intelligent operation and maintenance of an information system in accordance with an embodiment of the present application, the method includes the following steps: S1, obtaining a text description of a business requirement input by a user; S2, performing sentence-granular semantic encoding on the text description of the business requirement to obtain a sequence of sentence-granular semantic encoding feature vectors of the business requirement description; S3, performing sentence-inter-sentence semantic association encoding on the sequence of sentence-granular semantic encoding feature vectors of the business requirement description to obtain a sequence of sentence-inter-sentence semantic association encoding feature vectors of the business requirement description; S4, performing essential feature extraction on the sequence of sentence-inter-sentence semantic association encoding feature vectors of the business requirement description to obtain a business requirement semantic understanding feature vector; S5, determining the recommended type of the basic structure of the process based on the business requirement semantic understanding feature vector.

[0077] According to the process configuration system applied to the development and operation management platform of the embodiment of the present application, it is explained that it uses artificial intelligence technology based on deep learning to perform sentence-based semantic analysis on the business demand information input by the user, captures the sentence-based semantic features of the business demand information, and performs sentence-based semantic association encoding based on the semantic adjacency relationship between sentences of the business demand information, so as to fully mine the global semantic features of the business demand information, thereby intelligently recommending appropriate process basic structures according to the user's business needs. In this way, the operation and maintenance efficiency is effectively improved, the operation and maintenance costs are reduced, and a smarter and more efficient development and operation management platform is provided for enterprises.

[0078] Using various data mining algorithms and analysis techniques, valuable information and insights are extracted from the collected equipment data. The goal of data analysis and mining is to reveal patterns, associations, anomalies and other information in the data through processing and analysis of equipment data, and to provide support for decision-making and problem solving. Specifically, the following methods are included:

[0079] 1. Real-time monitoring: The system monitors device data in real time to quickly detect and respond to abnormal situations.

[0080] 2. Data association analysis: By analyzing the correlation between equipment data, the system can discover the mutual influence relationship between variables and their potential impact on equipment performance and failure.

[0081] 3. Anomaly detection: The system can use anomaly detection algorithms to identify anomalies in device data, including values, trends, or patterns of change that do not conform to expectations.

[0082] 4. Pattern recognition: Through data mining technology, the system can identify repetitive patterns, periodic patterns or other regular patterns in device data.

[0083] 5. Predictive analysis: Based on historical data and machine learning algorithms, the system can predict the future status and performance of the equipment.

[0084] Through data analysis and mining, the system can provide a comprehensive understanding of the equipment's operating status, help users identify potential problems, optimize equipment performance, and provide decision support for developing more effective maintenance plans, optimizing equipment scheduling and resource allocation, and improving equipment reliability and efficiency.

[0085] Fault diagnosis and prediction: Based on historical data and machine learning algorithms, the system can diagnose and predict equipment failures, help detect potential failures in advance, and take corresponding preventive measures to reduce equipment downtime and maintenance costs;

[0086] Using historical equipment data and machine learning algorithms to help identify equipment failures and predict possible failure conditions, the fault diagnosis process is divided into the following steps:

[0087] 1. Data collection and preparation: The system collects historical data of the equipment, including equipment operating status, sensor data, operation logs, etc., for training and establishing fault diagnosis models;

[0088] 2. Feature engineering: During the fault diagnosis process, useful features are extracted from the raw data, including key indicators, operating parameters, change trends, abnormal behaviors, etc. of the equipment, to better express the status and performance of the equipment to support subsequent fault diagnosis and predictive analysis.

[0089] 3. Model training and construction: The system uses algorithms such as classification algorithms, cluster analysis, decision trees, neural networks, etc. to train and model the extracted features to establish an accurate and reliable fault diagnosis model.

[0090] 4. Fault diagnosis and classification: After model training is completed, the system can use the model to perform fault diagnosis on existing equipment data and determine whether the equipment has faults by inputting real-time or historical data of the equipment.

[0091] Fault diagnosis and prediction is an important function in the intelligent equipment management system based on big data analysis. It provides the ability to quickly diagnose and predict equipment failures, helping enterprises reduce downtime and maintenance costs, and improve equipment reliability and operating efficiency.

[0092] Operation and maintenance management and optimization: The system can provide real-time monitoring of the operating status and performance of the equipment, and generate corresponding alarm and warning information to help operation and maintenance personnel deal with abnormal situations in a timely manner. The system can also provide statistics and analysis of equipment usage to provide decision support for optimal scheduling of equipment and reduction of energy consumption.

[0093] Through remote connection, the remote device, system or network is monitored, configured and controlled. Administrators or operation and maintenance personnel do not need to touch the device in person to manage and operate the device. The specific remote connection methods include: remote desktop protocol, virtual private network, remote terminal management tool, remote monitoring and alarm system and cloud management platform, which saves time and cost, improves efficiency, reduces business trips, responds to and solves problems quickly, and adopts appropriate security measures and encryption technology to prevent data leakage and unauthorized access.

[0094] In summary, information operation and maintenance is the core link to ensure the operation of enterprise information systems, including monitoring and alarm, fault management, change management, configuration management, security management, backup and recovery, performance optimization, automation and tools, improving business continuity: ensuring the availability of IT services and supporting enterprise operations; reducing costs: improving operation and maintenance efficiency and reducing manpower and material costs through optimizing resources and automation; improving user experience: ensuring efficient and reliable services and improving end-user satisfaction. It provides a strong guarantee for the security and efficiency of enterprise intelligent operation and maintenance.

[0095] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0096] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A management method based on intelligent operation and maintenance of an information system, including a management center, characterized in that: The management center is connected to the data acquisition module, data processing and analysis module, fault warning module, information extraction module, decision support module, big data module, management area division module, on-site monitoring module, continuous monitoring optimization module and on-site video acquisition module, and is used to collect monitoring videos on-site using cameras; The data collection module includes an enterprise data collection unit and an enterprise data transmission unit; the enterprise data collection unit is used to collect enterprise information data and send the enterprise information data to the enterprise data integration module through the enterprise data transmission unit to generate normal enterprise department information data; The data processing and analysis module includes a data processing unit and a data analysis unit, and the data processing unit processes normal enterprise information data; The process of generating enterprise information data, according to the abnormal enterprise department information data monitored, obtaining the enterprise department corresponding to the abnormal enterprise department information data, re-collecting the enterprise department information data, and re-transmitting it through the corresponding enterprise data transmission unit until normal enterprise department information data is obtained, and a number of normal enterprise department information data obtained through a number of enterprise data transmission units; the data analysis unit analyzes the information data to generate unified enterprise information data, setting a format recognition pool and a structure recognition pool in the enterprise information data analysis unit, and correspondingly setting a standard format and a standard structure; The fault warning module is used to monitor the performance and security status of IT infrastructure in real time, to issue warning signals based on the comprehensive system evaluation values ​​of each sub-area, to set up alarm mechanisms, to respond to system anomalies in a timely manner, to identify, analyze and resolve faults in a timely manner to minimize the impact on the business, and to establish a fault recording and analysis mechanism to prevent problems from recurring; The information extraction module is used to extract management center data and fault information, timely change and configuration management based on data, standardize the change process of IT systems, and evaluate the impact of changes on existing services; maintain the configuration status of IT assets and services to ensure their correctness and consistency, and use the configuration management database to track system components and their relationships; The decision support module will obtain analysis results, understand the production and operation status, identify problems and risks in a timely manner, provide scientific decision support for the company's senior management, and achieve long-term and stable development of the company; The big data module collects necessary data to a management center platform through various data sources including Apache Hadoop system, Spark system and Internet data, obtains data sets, and performs preprocessing work on the data by filling missing values ​​and handling outliers. The data collected by the data collection module is preprocessed by the data analysis module, and then compared and analyzed with the data sets in the big data module; security assessment, monitoring and incident response are carried out to protect information assets; access control and data protection strategies are implemented to ensure information security; The management area division module is used to determine the target enterprise as the target area, divide the target area into sub-areas by equal area division, and mark them as 1, 2...n in sequence; establish a regular backup mechanism for each sub-area to ensure data integrity and recoverability; set up data backup and recovery functions for the system of each sub-area, formulate a disaster recovery plan, and ensure rapid service recovery in the event of a failure; The field monitoring module analyzes system performance based on the collected data, optimizes resource allocation, and improves system efficiency; The continuous monitoring and optimization module constantly tracks changes in data from the data acquisition module, and timely adjusts production plans and production processes based on data analysis results, gradually achieving continuous optimization of production processes, using automated tools to perform daily operation and maintenance tasks, reducing manual operations and improving efficiency; using modern DevOps tool chains to promote collaboration between development and operation and maintenance.

2. The management method based on intelligent operation and maintenance of information system according to claim 1 is characterized by: The management center also includes a daily management module, which includes an emergency management unit. The emergency management unit starts the corresponding emergency response program, makes judgments and analyses, and determines whether there is a risk event due to the severity of the impact caused by the event; The emergency response management unit collects data information on risk notification content, notification status, warning level, notification time, start time, release time, and the number of managed enterprises, and supports retrieval based on risk notification content, notification status, and warning level.

3. The management method based on intelligent operation and maintenance of information system according to claim 1 is characterized by: The management area division module includes an information room and an operation and maintenance management inspection unit. The information room and operation and maintenance management inspection unit is divided into regular inspections and special inspections. Regular inspections refer to regular inspections by operation and maintenance personnel to check the operating conditions of the room equipment, power supply system, network system, application system, and the temperature and humidity of the room, and to keep inspection records and report any abnormal conditions in a timely manner. Special inspections refer to arranging operation and maintenance personnel to conduct inspections and increase the frequency of inspections when encountering bad weather, equipment abnormalities, or suspicious phenomena and major incidents during operation.

4. The management method based on intelligent operation and maintenance of information system according to claim 1 is characterized by: The field monitoring module models the spatial attributes, time attributes, environmental attributes, management attributes, and real-time attributes within the regional division through the model library module.

5. The management method based on intelligent operation and maintenance of information system according to claim 1 is characterized by: It also includes jointly training and learning multimodal data based on a recurrent neural network to form a comprehensive anomaly detection model; combining adaptive learning rate and regularization technology to optimize the training process of the anomaly detection model and improve the generalization ability and stability of the anomaly detection model; Use real-time abnormal data processing algorithms to dynamically analyze multimodal data and identify abnormal data; The data processing and analysis module is configured to process the operation and maintenance data, form an operation and maintenance data source, and issue an early warning for abnormal business data; The fault processing module is configured to obtain the operation and maintenance data of the fault in the business system according to the result of the analysis and processing, and perform repair processing on the faulty operation and maintenance data; Set up backup and recovery of management center data and establish a regular backup mechanism to ensure data integrity and recoverability.

6. The management method based on intelligent operation and maintenance of information system according to claim 1 is characterized by: The data processing and analysis module includes data preprocessing, cleaning, processing and feature extraction of historical data, regular capacity planning, analysis of system performance, optimization of resource allocation, and improvement of system efficiency.

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