A linkage integrated operation and maintenance method, system, equipment and product based on big data

Abstract

The present invention belongs to the field of computer technology, and its purpose is to provide a linkage comprehensive operation and maintenance method, system, equipment and product based on big data. The real-time fault detection result in the present invention is obtained by real-time analysis of the current system status, so as to timely discover fault problems related to abnormal behaviors in the specified enterprise information system. The fault prediction result is obtained based on historical data analysis, so as to predict possible future faults in the specified enterprise information system. In the present invention, by fusing the real-time fault detection result and the fault prediction result, a comprehensive fault analysis result is obtained, which can realize the linkage comprehensive analysis of the system operation data of the specified enterprise information system, which is conducive to improving the accuracy and reliability of the fault analysis result, and performing linkage response to the specified enterprise information system, which is conducive to realizing the automatic processing of the specified enterprise information system, and the response speed is faster.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and specifically relates to a linkage comprehensive operation and maintenance method, system, equipment and product based on big data. Background Art

[0002] Enterprise information system refers to various information systems used in enterprises, such as management information systems, decision support systems, expert systems, various ERP (Enterprise Resource Planning) systems, and specialized systems for customer relationship management or human resource management. The stable operation of enterprise information systems is of great significance to the normal operation and decision-making of enterprises. Enterprise information systems are usually composed of a large number of hardware equipment, software applications and network facilities. They need to be continuously monitored, managed and maintained on a daily basis to ensure their normal operation and high availability.

[0003] In the prior art, traditional data collection, processing and analysis methods are usually used to implement the operation and maintenance of enterprise information systems. By collecting and entering data on the system operation status, such as server load and network traffic, pre-defined rules and thresholds are used for data analysis and anomaly detection. For example, thresholds are set to trigger alarms or alarms, and when the system fails or anomalies occur, the operation and maintenance personnel manually diagnose and handle the fault.

[0004] With the continuous development of computer technology, the architecture of enterprise information systems is becoming increasingly complex. In the process of using the existing technology, the inventors found that there are at least the following problems in the existing technology:

[0005] In the existing technology, rule-based analysis methods lack in-depth analysis and mining of data, and are prone to missed reports and false alarms, which affect the accuracy and reliability of the analysis results. In addition, in the existing technology, manual fault diagnosis and processing by operation and maintenance personnel cannot achieve timely processing and analysis of large amounts of real-time data, resulting in slow system response speed and inability to meet complex application scenarios with high real-time requirements. Summary of the invention

[0006] The present invention aims to solve the above-mentioned technical problems at least to a certain extent. The present invention provides a linkage comprehensive operation and maintenance method, system, equipment and product based on big data.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a linkage integrated operation and maintenance method based on big data, comprising:

[0009] Collect system operation data of designated enterprise information systems in real time, and store the system operation data in a preset big data platform;

[0010] Based on the big data platform, the big data analysis technology is used to perform real-time fault detection processing on the system operation data to obtain real-time fault detection results, and the real-time fault detection results are stored in the big data platform;

[0011] Acquire historical data stored in the big data platform, and construct a fault prediction model based on the historical data; wherein the historical data includes system operation data within a specified historical period and its corresponding real-time fault detection results;

[0012] Performing fault prediction processing on the system operation data based on the fault prediction model to obtain a fault prediction result;

[0013] Fusing the real-time fault detection result and the fault prediction result to obtain a comprehensive fault analysis result;

[0014] A linkage response is performed on the designated enterprise information system according to the comprehensive fault analysis result.

[0015] In a possible design, the system operation data is multi-source heterogeneous data; correspondingly, after real-time collection of the system operation data of the designated enterprise information system, the method further includes:

[0016] Integrating the system operation data to obtain integrated data;

[0017] The integrated data is preprocessed to obtain preprocessed data so as to store the preprocessed data in a preset big data platform.

[0018] In a possible design, historical data stored in the big data platform is obtained, and a fault prediction model is constructed based on the historical data, including:

[0019] Build an initial fault prediction model;

[0020] Dividing the historical data into a training set and a validation set;

[0021] The initial fault prediction model is trained based on the training set, the trained fault prediction model is verified and parameter tuning is performed based on the verification set, and the final fault prediction model is output after the verification passes.

[0022] In a possible design, the real-time fault detection result and the fault prediction result are fused to obtain a comprehensive fault analysis result, including:

[0023] Performing data quality assessment on the real-time fault detection result and the fault prediction result respectively to obtain quality assessment values ​​of the real-time fault detection result and the fault prediction result;

[0024] According to the quality evaluation value of the real-time fault detection result and the quality evaluation value of the fault prediction result, setting the weights of the real-time fault detection result and the fault prediction result;

[0025] According to the weight of the real-time fault detection result and the weight of the fault prediction result, weighted average processing is performed on the real-time fault detection result and the fault prediction result to obtain a comprehensive fault analysis result.

[0026] In a possible design, performing a linkage response on the designated enterprise information system according to the comprehensive fault analysis result includes:

[0027] Automatically perform operation and maintenance operations on the designated enterprise information system according to the comprehensive fault analysis results;

[0028] An alarm notification is issued according to the comprehensive fault analysis result.

[0029] In a possible design, after performing a linkage response to the designated enterprise information system according to the comprehensive fault analysis result, the method further includes:

[0030] Obtaining the operation and maintenance response result of the specified enterprise information system;

[0031] According to the comprehensive fault analysis result and the operation and maintenance response result, the fault prediction model is optimized to obtain an optimized fault prediction model, so as to perform subsequent fault prediction processing based on the optimized fault prediction model.

[0032] In a second aspect, the present invention provides a linkage integrated operation and maintenance system based on big data, which is used to implement the linkage integrated operation and maintenance method based on big data as described in any one of the above; the linkage integrated operation and maintenance system based on big data includes:

[0033] An operation data collection module is used to collect system operation data of a specified enterprise information system in real time and store the system operation data in a preset big data platform;

[0034] A real-time fault detection module, which is in communication connection with the operation data acquisition module, is used to perform fault detection processing on the system operation data in real time based on the big data platform using big data analysis technology, obtain real-time fault detection results, and store the real-time fault detection results in the big data platform;

[0035] A fault prediction module is connected to the operation data acquisition module for obtaining historical data stored in the big data platform and constructing a fault prediction model based on the historical data; wherein the historical data includes system operation data within a specified historical period and its corresponding real-time fault detection results; and is also used to perform fault prediction processing on the system operation data based on the fault prediction model to obtain a fault prediction result;

[0036] a data fusion module, which is in communication with the real-time fault detection module and the fault prediction module, respectively, and is used to fuse the real-time fault detection result and the fault prediction result to obtain a comprehensive fault analysis result;

[0037] The operation and maintenance response module is in communication with the data fusion module and is used for making a linkage response to the designated enterprise information system according to the comprehensive fault analysis result.

[0038] In a third aspect, the present invention provides an electronic device, comprising:

[0039] a memory for storing computer program instructions; and,

[0040] A processor is used to execute the computer program instructions to complete the operation of the big data-based integrated operation and maintenance method as described in any one of the above.

[0041] In a fourth aspect, the present invention provides a computer-readable storage product having instructions stored thereon. When the instructions are run on a computer, the integrated operation and maintenance method based on big data as described above is executed.

[0042] In a fifth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the big data-based integrated operation and maintenance method as described in any one of the above.

[0043] The beneficial effects of the present invention are:

[0044] The present invention discloses a linkage comprehensive operation and maintenance method, system, electronic device, computer readable storage product and computer program product based on big data, which can realize comprehensive monitoring and response to the operation status of enterprise information system, improve the operation and maintenance efficiency of enterprise information system, and provide strong support for the stable operation of complex enterprise information system. Specifically, in the implementation process of the present invention, the real-time fault detection result is obtained by real-time analysis of the current system state, so as to timely discover the fault problems related to abnormal behavior in the specified enterprise information system, so as to realize real-time monitoring and rapid response to the specified enterprise information system; the fault prediction result is obtained based on historical data analysis, so as to predict the possible future faults of the specified enterprise information system, so as to take preventive measures in advance, and the acquisition of the data is not carried out in real time; in the present invention, by fusing the real-time fault detection result and the fault prediction result, a comprehensive fault analysis result is obtained, which can realize the linkage comprehensive analysis of the system operation data of the specified enterprise information system, which is conducive to improving the accuracy and reliability of the fault analysis result, and the specified enterprise information system is linked to respond, which is conducive to realizing the automatic processing of the specified enterprise information system, and the response speed is faster.

[0045] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the linkage integrated operation and maintenance method based on big data in Example 1;

[0047] Figure 2 It is a module block diagram of the linkage integrated operation and maintenance system based on big data in Example 2;

[0048] Figure 3 This is a module block diagram of the electronic device in Example 3. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0050] Embodiment 1:

[0051] This embodiment discloses a linkage integrated operation and maintenance method based on big data, which can be executed by, but is not limited to, a computer device or a virtual machine with certain computing resources, such as a personal computer, a smart phone, a personal digital assistant or a wearable device, or by a virtual machine.

[0052] like Figure 1 As shown, a linkage integrated operation and maintenance method based on big data may include but is not limited to the following steps:

[0053] S1. Collect system operation data of designated enterprise information systems in real time, and store the system operation data in a preset big data platform; it should be noted that the system operation data includes server performance indicators, network device status data and application operation logs, wherein the server performance indicators include CPU (Central Processing Unit) utilization, memory usage and disk space, and the network device status data includes network traffic, number of network connections and packet loss rate. In the implementation process, the data can be collected by using sensors, extracted from log files or received from API (Application Programming Interface), which is not limited here; the big data platform can store and analyze massive data, which is conducive to improving the efficiency of data processing and analysis, and providing more in-depth data support for enterprise decision-making, thereby improving the operational efficiency and competitiveness of the enterprise. Specifically, the big data platform can use Hadoop (a big data framework based on distributed file storage system and programming model) or Spark (an analysis engine that supports multiple data sources and computing models) to use distributed storage and processing technology to realize the storage and real-time processing of massive data.

[0054] In this embodiment, the system operation data is multi-source heterogeneous data, that is, the system operation data is a data set from multiple data holders and different data types and structures; correspondingly, in step S1, after the system operation data of the designated enterprise information system is collected in real time, the method further includes:

[0055] Integrating the system operation data to obtain integrated data;

[0056] The integrated data is preprocessed to obtain preprocessed data so as to store the preprocessed data in a preset big data platform. Specifically, the preprocessing process includes data cleaning, missing value processing and outlier processing steps to ensure data quality and consistency.

[0057] In addition, in this embodiment, after obtaining the preprocessed data, the preprocessed data can be labeled, the variables and attributes of the preprocessed data can be extracted, and the variables and attributes can be analyzed. Finally, operation portrait data can be constructed based on the analysis results, and the operation portrait data can be stored in the big data platform to dynamically display the operation status of the specified enterprise information system.

[0058] S2. Based on the big data platform, big data analysis technology is used to perform real-time fault detection processing on the system operation data, such as data aggregation, feature extraction and anomaly detection processing, so as to obtain real-time fault detection results, and store the real-time fault detection results in the big data platform; in this embodiment, through real-time collection and big data analysis technology, the comprehensiveness and accuracy of the system operation data can be ensured, the risk of data errors and omissions can be reduced, and the data quality can be improved; the big data platform can use real-time data processing technology, such as Apache Kafka (a distributed data stream processing platform that can publish, subscribe, store and process data streams in real time) or Apache Flink (a stream processing system that supports batch and stream integration and exactly-once state consistency guarantee) to realize real-time fault detection and processing of the stored system operation data, so as to identify abnormal behavior and potential faults in the system in real time.

[0059] S3. Obtain the historical data stored in the big data platform, and construct a fault prediction model based on the historical data; wherein the historical data includes system operation data within a specified historical period and its corresponding real-time fault detection results.

[0060] In step S3, historical data stored in the big data platform is obtained, and a fault prediction model is constructed based on the historical data, including:

[0061] S301. Construct an initial fault prediction model; specifically, in this embodiment, a machine learning algorithm such as a support vector machine (SVM) or a random forest (Random Forest) is used to construct the initial fault prediction model, which is not limited here.

[0062] S302. Divide the historical data into a training set and a validation set;

[0063] S303. Train the initial fault prediction model based on the training set, verify and optimize the parameters of the trained fault prediction model based on the verification set, and output the final fault prediction model after verification to improve the accuracy and stability of the fault prediction model.

[0064] S4. Perform fault prediction processing on the system operation data based on the fault prediction model to obtain a fault prediction result. In this embodiment, the system operation data is input into the fault prediction model to implement fault prediction processing. The fault prediction model can detect potential fault problems of the designated enterprise information system in advance.

[0065] S5. The real-time fault detection result and the fault prediction result are integrated to obtain a comprehensive fault analysis result. It should be noted that after the real-time fault detection result and the fault prediction result are integrated, the comprehensive fault analysis result obtained is used to describe the current fault condition of the designated enterprise information system.

[0066] In the implementation process, the real-time fault detection result and the fault prediction result may be fused and processed by, but not limited to, a rule engine, a logical judgment or a weighted average method to obtain a comprehensive fault analysis result. Specifically, in step S5, the real-time fault detection result and the fault prediction result are fused and processed to obtain a comprehensive fault analysis result, including:

[0067] S501. Perform data quality assessment on the real-time fault detection result and the fault prediction result respectively to obtain quality assessment values ​​of the real-time fault detection result and the fault prediction result; specifically, in this embodiment, an anomaly detection algorithm may be used to perform data quality assessment on the real-time fault detection result. If the result deviates from the preset normal range, it indicates that the data quality of the real-time fault detection result is poor and its quality assessment value is low; the accuracy and recall rate of the fault prediction model are evaluated by cross-validation and model evaluation, and the data quality assessment of the fault prediction result is implemented according to the model performance index. If the model performance index value is high, it indicates that the quality assessment value of the fault prediction result is large. In addition, in this embodiment, before performing data quality assessment on the real-time fault detection result and the fault prediction result, the real-time fault detection result and the fault prediction result may be normalized in advance so as to map the two to the same numerical range.

[0068] S502. According to the quality assessment value of the real-time fault detection result and the quality assessment value of the fault prediction result, the weights of the real-time fault detection result and the fault prediction result are set; wherein, the higher the quality assessment value of the real-time fault detection result or the fault prediction result, the greater the corresponding weight; specifically, in this embodiment, the weights of the real-time fault detection result and the fault prediction result can also be obtained through but not limited to expert opinions, historical data analysis, cross-validation and evaluation, and subjective evaluation. In this embodiment, the quality assessment values ​​of the real-time fault detection result and the fault prediction result are obtained by performing data quality assessment on the real-time fault detection result and the fault prediction result, and finally the weights of the real-time fault detection result and the fault prediction result are set according to their quality assessment values, and the weight results are more objective.

[0069] S503. According to the weight of the real-time fault detection result and the weight of the fault prediction result, the real-time fault detection result and the fault prediction result are weighted averaged to obtain a comprehensive fault analysis result; wherein the comprehensive fault analysis result is: M=α*M1+(1-α)*M2; wherein M1 is the real-time fault detection result; α is the weight of the real-time fault detection result, α∈(0,1); M2 is the fault prediction result; and 1-α is the weight of the fault prediction result.

[0070] In this embodiment, a weighted average method is used to achieve the fusion processing of the real-time fault detection result and the fault prediction result, and the fusion process is simpler and more intuitive.

[0071] S6. Perform a linkage response on the designated enterprise information system according to the comprehensive fault analysis result.

[0072] In step S6, a linkage response is performed on the designated enterprise information system according to the comprehensive fault analysis result, including:

[0073] S601. Automatically perform operation and maintenance operations on the designated enterprise information system according to the comprehensive fault analysis results; wherein the operation and maintenance operations include automatically adjusting resource configuration, restarting services, and migrating virtual machines, so as to quickly restore the normal operation of the designated enterprise information system; in this embodiment, by adopting automated operation and maintenance operations, rapid response and processing of system failures can be achieved, reducing the need for manual intervention, reducing labor costs and management costs, and helping to improve system operation and maintenance efficiency and resource utilization.

[0074] S602. Issue an alarm notification based on the comprehensive fault analysis result, so as to promptly notify relevant personnel or teams for further processing and investigation.

[0075] In this embodiment, after performing a linkage response to the designated enterprise information system according to the comprehensive fault analysis result, the method further includes:

[0076] S7. Obtain the operation and maintenance response result of the specified enterprise information system;

[0077] S8. According to the comprehensive fault analysis result and the operation and maintenance response result, the fault prediction model is optimized to obtain an optimized fault prediction model, so as to perform subsequent fault prediction processing based on the optimized fault prediction model.

[0078] In this embodiment, the fault prediction model is optimized through the comprehensive fault analysis results and the operation and maintenance response results, which is conducive to continuously improving the effect and accuracy of the embodiment of the linked comprehensive analysis of system operation data.

[0079] This embodiment can realize comprehensive monitoring and response to the operation status of the enterprise information system, improve the operation and maintenance efficiency of the enterprise information system, and provide strong support for the stable operation of complex enterprise information systems. Specifically, during the implementation of this embodiment, the real-time fault detection result is obtained by real-time analysis of the current system status, so as to timely discover the fault problems related to abnormal behavior in the specified enterprise information system, so as to realize real-time monitoring and rapid response to the specified enterprise information system; the fault prediction result is obtained based on historical data analysis, so as to predict the possible future faults of the specified enterprise information system, so as to take preventive measures in advance, and the acquisition of this data is not carried out in real time; in this embodiment, by fusing the real-time fault detection result and the fault prediction result, a comprehensive fault analysis result is obtained, which can realize the linkage comprehensive analysis of the system operation data of the specified enterprise information system, which is conducive to improving the accuracy and reliability of the fault analysis result, and the specified enterprise information system is linked to respond, which is conducive to realizing the automatic processing of the specified enterprise information system, and the response speed is faster.

[0080] Embodiment 2:

[0081] This embodiment discloses a linkage integrated operation and maintenance system based on big data, which is used to implement the linkage integrated operation and maintenance method based on big data in embodiment 1; Figure 2 As shown, the linkage integrated operation and maintenance system based on big data includes:

[0082] An operation data collection module is used to collect system operation data of a specified enterprise information system in real time and store the system operation data in a preset big data platform;

[0083] A real-time fault detection module, which is in communication connection with the operation data acquisition module, is used to perform fault detection processing on the system operation data in real time based on the big data platform using big data analysis technology, obtain real-time fault detection results, and store the real-time fault detection results in the big data platform;

[0084] A fault prediction module is connected to the operation data acquisition module for obtaining historical data stored in the big data platform and constructing a fault prediction model based on the historical data; wherein the historical data includes system operation data within a specified historical period and its corresponding real-time fault detection results; and is also used to perform fault prediction processing on the system operation data based on the fault prediction model to obtain a fault prediction result;

[0085] a data fusion module, which is in communication with the real-time fault detection module and the fault prediction module, respectively, and is used to fuse the real-time fault detection result and the fault prediction result to obtain a comprehensive fault analysis result;

[0086] The operation and maintenance response module is in communication with the data fusion module and is used for making a linkage response to the designated enterprise information system according to the comprehensive fault analysis result.

[0087] It should be noted that the working process, working details and technical effects of the linkage integrated operation and maintenance system based on big data provided in this embodiment 2 can be found in embodiment 1 and will not be repeated here.

[0088] Embodiment 3:

[0089] Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer or a desktop computer. The electronic device may be referred to as a user terminal, a portable terminal or a desktop terminal, such as Figure 3 As shown, the electronic equipment includes:

[0090] a memory for storing computer program instructions; and,

[0091] A processor is used to execute the computer program instructions to complete the operation of the big data-based integrated operation and maintenance method as described in any one of Example 1.

[0092] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 301 may be implemented in at least one of the following hardware forms: DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0093] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the linkage integrated operation and maintenance method based on big data provided in Example 1 of the present application.

[0094] In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected via a bus or a signal line. Each peripheral device may be connected to the communication interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0095] The communication interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0096] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0097] The display screen 305 is used to display a UI (User Interface). The UI may include any combination of graphics, text, icons, and videos.

[0098] The power supply 306 is used to supply power to various components in the electronic device.

[0099] Embodiment 4:

[0100] Based on any one of embodiments 1 to 3, this embodiment discloses a computer-readable storage product, on which instructions are stored, and when the instructions are run on a computer, the linkage integrated operation and maintenance method based on big data as described in any one of embodiment 1 is executed. The computer-readable storage product refers to a carrier for storing data, which may include but is not limited to a computer-readable storage medium including a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick (MemoryStick), and the computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device.

[0101] Embodiment 5:

[0102] Based on any one of Examples 1 to 4, this embodiment discloses a computer program product, including a computer program or instructions, which, when executed by a computer, implements the big data-based integrated operation and maintenance method as described in any one of Example 1.

[0103] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A linkage integrated operation and maintenance method based on big data, characterized by: include: Collect system operation data of designated enterprise information systems in real time, and store the system operation data in a preset big data platform; Based on the big data platform, the big data analysis technology is used to perform real-time fault detection processing on the system operation data to obtain real-time fault detection results, and the real-time fault detection results are stored in the big data platform; Acquire historical data stored in the big data platform, and construct a fault prediction model based on the historical data; wherein the historical data includes system operation data within a specified historical period and its corresponding real-time fault detection results; Performing fault prediction processing on the system operation data based on the fault prediction model to obtain a fault prediction result; Fusing the real-time fault detection result and the fault prediction result to obtain a comprehensive fault analysis result; Performing a linkage response to the designated enterprise information system according to the comprehensive fault analysis result; The system operation data is multi-source heterogeneous data; correspondingly, after real-time collection of the system operation data of the designated enterprise information system, the method further includes: Integrating the system operation data to obtain integrated data; Preprocessing the integrated data to obtain preprocessed data, so as to store the preprocessed data in a preset big data platform; After obtaining the preprocessed data, the method further includes: Labeling the preprocessed data, extracting variables and attributes of the preprocessed data, analyzing the variables and attributes, and finally constructing operation profile data according to the analysis results, and then storing the operation profile data in the big data platform; The real-time fault detection result and the fault prediction result are fused to obtain a comprehensive fault analysis result, including: Performing data quality assessment on the real-time fault detection result and the fault prediction result respectively to obtain quality assessment values ​​of the real-time fault detection result and the fault prediction result; According to the quality evaluation value of the real-time fault detection result and the quality evaluation value of the fault prediction result, setting the weights of the real-time fault detection result and the fault prediction result; According to the weight of the real-time fault detection result and the weight of the fault prediction result, the real-time fault detection result and the fault prediction result are weighted averaged to obtain a comprehensive fault analysis result; wherein the comprehensive fault analysis result is: M=α*M1+(1-α)*M2; wherein M1 is the real-time fault detection result; α is the weight of the real-time fault detection result, α∈(0,1); M2 is the fault prediction result; and 1-α is the weight of the fault prediction result.

2. The method of integrated operation and maintenance based on big data according to claim 1, characterized in that: Acquiring historical data stored in the big data platform and constructing a fault prediction model based on the historical data includes: Build an initial fault prediction model; Dividing the historical data into a training set and a validation set; The initial fault prediction model is trained based on the training set, the trained fault prediction model is verified and parameter tuning is performed based on the verification set, and the final fault prediction model is output after the verification passes.

3. The method of integrated operation and maintenance based on big data according to claim 1, characterized in that: Performing a linkage response to the designated enterprise information system according to the comprehensive fault analysis result includes: Automatically perform operation and maintenance operations on the designated enterprise information system according to the comprehensive fault analysis results; An alarm notification is issued according to the comprehensive fault analysis result.

4. The method of integrated operation and maintenance based on big data according to claim 1, characterized in that: After performing a linkage response to the designated enterprise information system according to the comprehensive fault analysis result, the method further includes: Obtaining the operation and maintenance response result of the specified enterprise information system; According to the comprehensive fault analysis result and the operation and maintenance response result, the fault prediction model is optimized to obtain an optimized fault prediction model, so as to perform subsequent fault prediction processing based on the optimized fault prediction model.

5. A linkage integrated operation and maintenance system based on big data, characterized by: Used to implement the linkage integrated operation and maintenance method based on big data as described in any one of claims 1 to 4; the linkage integrated operation and maintenance system based on big data includes: An operation data collection module is used to collect system operation data of a specified enterprise information system in real time and store the system operation data in a preset big data platform; A real-time fault detection module, which is in communication connection with the operation data acquisition module, is used to perform fault detection processing on the system operation data in real time based on the big data platform using big data analysis technology, obtain real-time fault detection results, and store the real-time fault detection results in the big data platform; A fault prediction module is connected to the operation data acquisition module for obtaining historical data stored in the big data platform and constructing a fault prediction model based on the historical data; wherein the historical data includes system operation data within a specified historical period and its corresponding real-time fault detection results; and is also used to perform fault prediction processing on the system operation data based on the fault prediction model to obtain a fault prediction result; a data fusion module, which is in communication with the real-time fault detection module and the fault prediction module, respectively, and is used to fuse the real-time fault detection result and the fault prediction result to obtain a comprehensive fault analysis result; The operation and maintenance response module is in communication with the data fusion module and is used for making a linkage response to the designated enterprise information system according to the comprehensive fault analysis result.

6. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is used to execute the computer program instructions to complete the operation of the linkage integrated operation and maintenance method based on big data as described in any one of claims 1 to 4.

7. A computer readable storage product, characterized in that: The computer-readable storage product stores instructions, and when the instructions are executed on a computer, the linkage integrated operation and maintenance method based on big data as described in any one of claims 1 to 4 is executed.

8. A computer program product comprising a computer program or instructions, characterized in that: When the computer program or the instruction is executed by a computer, the computer program or the instruction implements the big data-based integrated operation and maintenance method as described in any one of claims 1 to 4.

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