Information system comprehensive operation and maintenance management platform method and system based on big data

Abstract

The invention relates to the technical field of operation and maintenance management, in particular to an information system comprehensive operation and maintenance management platform method and system based on big data, and the method comprises the steps: collecting system operation data of a specified enterprise information system in real time, storing and preprocessing the system operation data, and outputting a system operation data set; performing time axis calibration and fusion processing on the system operation data set, and outputting a multi-dimensional associated data set; establishing a dynamic association model based on the multi-dimensional association data set, monitoring an abnormal condition in real time, and outputting an early warning level; matching a candidate disposal scheme set based on the early warning level; based on a preset screening rule, screening an optimal processing scheme from the candidate processing scheme set and executing the optimal processing scheme; and outputting an execution effect evaluation result of the optimal disposal scheme, and feeding back the execution effect evaluation result to a preset big data platform. The method has the effects of discovering hidden dangers of the equipment in real time and automatically selecting the optimal solution.

Description

Technical Field

[0001] The present application relates to the field of operation and maintenance management technology, and in particular to a method and system for an integrated operation and maintenance management platform for an information system based on big data. Background Art

[0002] Traditional O&M models struggle to meet the stability and agility demands of enterprise IT systems due to the following core technical flaws: 1. Real-time detection of progressive anomalies: Traditional threshold monitoring (e.g., CPU > 80% alerts) cannot capture progressive failures such as memory leaks and service response degradation, resulting in delayed fault discovery and a wider impact. 2. Lack of cross-domain data correlation and analysis capabilities: Isolated data from servers, networks, and application systems lacks automated cross-domain correlation analysis, making manual root cause diagnosis time-consuming and leading to high MTTR (mean time to repair). 3. Bottlenecks in quantitative assessment of action decisions: Plan development relies on manual experience and lacks quantitative assessment of multiple dimensions such as business impact, cost, and complexity, leading to the risk of "dilemma," such as conflicts between service restart and business continuity. 4. Faulty closed-loop mechanisms for O&M optimization: Failure to form a feedback loop for action results leads to repeated occurrences of similar failures. Model iteration relies on manual annotation, resulting in long system self-optimization cycles and difficulty adapting to dynamic business needs.

[0003] Therefore, improvements are needed. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a method and system for an information system comprehensive operation and maintenance management platform based on big data.

[0005] The first object of the invention of this application is achieved through the following technical solutions:

[0006] A method for an information system integrated operation and maintenance management platform based on big data, comprising the steps of:

[0007] Collect system operation data of a specified enterprise information system in real time, store and pre-process the system operation data, and output the system operation data set;

[0008] Performing time axis calibration and fusion processing on the system operation data set to output a multi-dimensional correlation data set;

[0009] Establishing a dynamic correlation model based on the multi-dimensional correlation data set, monitoring abnormal situations in real time, and outputting warning levels;

[0010] Based on the warning level, matching a set of candidate disposal solutions;

[0011] Based on preset screening rules, selecting the optimal treatment solution from the candidate treatment solution set and executing the optimal treatment solution;

[0012] Output the execution effect evaluation result of the optimal disposal plan, and feed the execution effect evaluation result back to the preset big data platform.

[0013] In a preferred embodiment, the steps of collecting system operation data of a designated enterprise information system in real time, storing and preprocessing the system operation data, and outputting the system operation data set include:

[0014] Collect system operation data of designated enterprise information systems in real time through sensor collection, log file extraction, and API interface reception;

[0015] The system operation data includes server performance indicators and network equipment status data;

[0016] The service performance indicators include CPU utilization, memory usage, and disk space;

[0017] The network device status data includes network traffic, number of network connections, packet loss rate and application operation logs;

[0018] Based on the preset big data platform, the system operation data is stored and preprocessed, and the system operation data set is output.

[0019] In a preferred embodiment, the step of performing time axis calibration and fusion processing on the system operation data set to output a multi-dimensional correlation data set includes:

[0020] For the system operation data of two adjacent related nodes in the system operation data set, based on the preset data interpolation algorithm, the acquisition time deviation is eliminated and the system operation data is aligned;

[0021] Extracting temporal and spatial features from the aligned system operation data;

[0022] The time characteristics include the collection time, change trend and periodicity of system operation data;

[0023] The spatial characteristics include the source device of the system operation data, the device location, the device type, and the data association path;

[0024] The extracted temporal and spatial features are input into the pre-trained weighted spatiotemporal correlation matrix for fusion;

[0025] Based on the fusion results, multi-dimensional data fusion calculations are performed, including but not limited to equipment operation status fusion, service response delay fusion, and business health indicator fusion;

[0026] The results of the fusion calculation are used to generate a multi-dimensional associated data set according to a predefined data structure and format. The multi-dimensional data set includes a device dimension, a service dimension, a business dimension, and a time dimension.

[0027] In a preferred embodiment, the step of establishing a dynamic correlation model based on the multi-dimensional correlation data set, monitoring abnormal situations in real time, and outputting warning levels includes:

[0028] For the multi-dimensional correlation data set, a dynamic correlation model between hardware performance indicators and service response quality is established;

[0029] Based on the dynamic correlation model, hardware parameter anomalies are monitored in real time, and corresponding service response time degradation is detected simultaneously;

[0030] Once hardware parameter anomalies and service response time degradation are detected simultaneously, the corresponding warning level is output.

[0031] In a preferred embodiment, once hardware parameter anomalies and service response time degradation are detected simultaneously, the step of matching and outputting corresponding warning levels includes:

[0032] For hardware parameter abnormal values, calculate the deviation L1;

[0033] For service response time degradation, calculate the degradation rate L2;

[0034] Generate a dynamic score S based on the preset formula S = α·L1+β·L2, where α+β=1;

[0035] When S≥δ1 and δ1 is the first warning threshold, the first-level warning is matched;

[0036] When δ2≤S<δ1 and δ2 is the second warning threshold, the second-level warning is matched;

[0037] When δ3≤S<δ2 and δ3 is the third warning threshold, the third level warning is matched.

[0038] In a preferred embodiment, the step of matching a set of candidate treatment solutions based on the warning level includes:

[0039] Based on the treatment priority corresponding to the warning level, candidate solutions that meet the following conditions are screened from the preset historical case library:

[0040] The candidate solution type must match the current warning level to a degree that reaches or exceeds a preset similarity threshold, and the candidate solution's historical execution success rate must not be lower than a preset success rate threshold;

[0041] After the screening is completed, the top-N candidate solution sets that meet the conditions are output;

[0042] In the Top-N candidate solution set, for different warning levels, the preset differentiated disposal strategies are executed and the candidate solution set is output;

[0043] The preset differentiated handling strategies include:

[0044] When it is a Level 1 warning, match the emergency plan set that needs to be executed immediately;

[0045] When it is a Level 2 warning, match the optimization solution set that needs to be dealt with within a time limit;

[0046] When it is a level three warning, match the observation period disposal plan.

[0047] In a preferred embodiment, the step of selecting and executing the optimal treatment solution from the set of candidate treatment solutions based on preset screening rules includes:

[0048] Set the business impact dimension, resource cost dimension, operation complexity dimension, and historical success dimension, and assign preset weights W1, W2, W3, and W4 to each dimension;

[0049] After normalizing the scores of each dimension of each candidate solution, based on the preset formula (Dimension Rating k × weight W k ), calculate the comprehensive score;

[0050] The comprehensive scores are sorted from high to low, and the candidate solution with the highest comprehensive score is selected as the optimal disposal solution.

[0051] The second object of the invention of this application is achieved through the following technical solutions:

[0052] An information system integrated operation and maintenance management platform system based on big data, including:

[0053] Acquisition module: collects system operation data of specified enterprise information systems in real time, stores and pre-processes the system operation data, and outputs the system operation data set;

[0054] Fusion module: performs time axis calibration and fusion processing on the system operation data set, and outputs a multi-dimensional correlation data set;

[0055] Output module: establishes a dynamic correlation model based on the multi-dimensional correlation data set, monitors abnormal situations in real time, and outputs warning levels;

[0056] Matching module: matching candidate disposal solution sets based on the warning level;

[0057] Screening module: based on preset screening rules, screens the optimal treatment solution from the candidate treatment solution set and executes it;

[0058] Feedback module: outputs the execution effect evaluation result of the optimal disposal plan and feeds back the execution effect evaluation result to the preset big data platform.

[0059] The third object of the invention of this application is achieved through the following technical solutions:

[0060] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for an integrated operation and maintenance management platform for an information system based on big data are implemented.

[0061] The fourth object of the invention of this application is achieved through the following technical solutions:

[0062] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for an information system integrated operation and maintenance management platform based on big data.

[0063] In summary, this application includes at least one of the following beneficial technical effects:

[0064] The system uses a full-link automated process to achieve early warning and rapid handling of faults. Its technical solution can be broken down into the following steps: The system obtains the operating data of servers, network equipment and business systems (such as CPU load, network traffic, and application logs) in real time through distributed collection nodes, and uses streaming processing technology to clean, standardize the format, and screen for anomalies to ensure the quality and integrity of the input data, laying a solid foundation for subsequent analysis. In view of the differences in time bases of different devices, the data time series is unified through dynamic time alignment technology, and then the spatiotemporal correlation model is used to integrate multi-dimensional information such as device status, service performance, and business logic to generate a correlation data set that can reflect the overall operating status of the system, significantly improving the efficiency of tracing cross-domain faults. A dynamic correlation model is constructed based on fused data, and the potential correlation between hardware indicators and service quality is identified through machine learning algorithms. A dual-factor anomaly scoring mechanism (hardware parameter deviation + service response degradation rate) is combined to achieve quantitative warning, and a graded warning signal is output according to preset rules, transforming traditional passive responses into active predictions. When an alert is triggered, the system automatically matches candidate solutions from the historical case library, selects the optimal strategy through a multi-dimensional evaluation model (comprehensively considering factors such as business impact, execution cost, and operational risk), and executes it. Simultaneously, the results are fed back to the big data platform to drive iterative model optimization, forming a continuous improvement cycle of "monitoring-early warning-action-optimization." Through data-driven intelligent transformation, this solution has achieved a leap from single-point monitoring to global control, significantly improving the operational agility and self-healing capabilities of enterprise information systems, effectively reducing the incidence of failures and handling costs, and providing technical support for the stable operation of complex IT environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1This is a flowchart of an implementation of a method embodiment of an information system integrated operation and maintenance management platform based on big data in the present application;

[0066] Figure 2 This is a flowchart for implementing step S20 in an embodiment of a method for an information system integrated operation and maintenance management platform based on big data of the present application;

[0067] Figure 3 This is a flowchart for implementing step S30 in an embodiment of a method for an information system integrated operation and maintenance management platform based on big data of the present application;

[0068] Figure 4 This is a flowchart for implementing step S303 in an embodiment of a method for an information system integrated operation and maintenance management platform based on big data of the present application;

[0069] Figure 5 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0070] The following is combined with Figure 1-5 This application is further described in detail. In one embodiment, Figure 1 As shown, the present application discloses a method for an information system integrated operation and maintenance management platform based on big data, which specifically includes the following steps:

[0071] S10: collecting system operation data of a specified enterprise information system in real time, storing and preprocessing the system operation data, and outputting a system operation data set;

[0072] S20: performing time axis calibration and fusion processing on the system operation data set, and outputting a multi-dimensional correlation data set;

[0073] S30: Establishing a dynamic correlation model based on the multi-dimensional correlation data set, monitoring abnormal situations in real time, and outputting an early warning level;

[0074] S40: matching a set of candidate treatment solutions based on the warning level;

[0075] S50: Based on preset screening rules, select the optimal treatment solution from the candidate treatment solution set and execute it;

[0076] S60: Output the execution effect evaluation result of the optimal disposal plan, and feed back the execution effect evaluation result to the preset big data platform.

[0077] In this embodiment, early warning and rapid handling of faults are achieved through a full-link automated process. Its technical solution can be broken down into the following steps: the system obtains the operating data of servers, network devices and business systems (such as CPU load, network traffic, application logs) in real time through distributed collection nodes, and uses streaming processing technology to clean, standardize the format and screen for anomalies of the data to ensure the quality and integrity of the input data, laying a solid foundation for subsequent analysis. In view of the time base differences of different devices, the data time series is unified through dynamic time alignment technology, and then the spatiotemporal correlation model is used to integrate multi-dimensional information such as device status, service performance and business logic to generate a correlation data set that can reflect the overall operating status of the system, significantly improving the efficiency of tracing cross-domain faults. A dynamic correlation model is constructed based on fused data, and the potential correlation between hardware indicators and service quality is identified through machine learning algorithms. A two-factor anomaly scoring mechanism (hardware parameter deviation + service response degradation rate) is combined to achieve quantitative warning, and a graded warning signal is output according to preset rules, transforming traditional passive responses into active predictions. When an alert is triggered, the system automatically matches candidate solutions from the historical case library, selects the optimal strategy through a multi-dimensional evaluation model (comprehensively considering factors such as business impact, execution cost, and operational risk), and executes it. Simultaneously, the results are fed back to the big data platform to drive iterative model optimization, forming a continuous improvement cycle of "monitoring-early warning-action-optimization." Through data-driven intelligent transformation, this solution has achieved a leap from single-point monitoring to global control, significantly improving the operational agility and self-healing capabilities of enterprise information systems, effectively reducing the incidence of failures and handling costs, and providing technical support for the stable operation of complex IT environments.

[0078] Step S10 includes:

[0079] S101: Collect system operation data of a specified enterprise information system in real time through sensor collection, log file extraction, and API interface reception;

[0080] S102: The system operation data includes server performance indicators and network device status data;

[0081] S103: The service performance indicators include CPU utilization, memory usage, and disk space;

[0082] S104: The network device status data includes network traffic, number of network connections, packet loss rate, and application program operation logs;

[0083] S105: Based on the preset big data platform, the system operation data is stored and preprocessed, and the system operation data set is output.

[0084] In this embodiment, server performance (CPU / memory / disk) and network device status (traffic / packet loss rate, etc.) data are collected in real time through multi-source heterogeneous methods such as sensors, logs, and APIs, and distributed storage and preprocessing are performed on a big data platform. The core principle is to build a full-data collection pipeline, combine distributed storage technologies (such as Hadoop / Spark) to achieve high-throughput data processing, and eliminate noise through preprocessing operations such as data cleaning and format standardization. This achieves full-dimensional monitoring of enterprise IT resources and ensures data integrity; preprocessing can improve the efficiency of subsequent analysis, reduce the false alarm rate of anomaly detection, and provide a reliable data foundation for operation and maintenance decisions.

[0085] Figure 2 , step S20 includes:

[0086] S201: aligning the system operation data of two adjacent associated nodes in the system operation data set by eliminating the acquisition time deviation based on a preset data interpolation algorithm;

[0087] S202: Extracting temporal features and spatial features from the aligned system operation data;

[0088] S203: The time characteristics include the collection time, change trend, and periodicity of the system operation data;

[0089] S204: The spatial features include the source device, device location, device type, and data association path of the system operation data;

[0090] S205: Input the extracted temporal features and spatial features into a pre-trained weighted spatiotemporal correlation matrix for fusion;

[0091] S206: Based on the fusion results, perform multi-dimensional data fusion calculations, including but not limited to device operation status fusion, service response delay fusion, and business health indicator fusion;

[0092] S207: Generate a multi-dimensional associated data set based on the fusion calculation results according to a predefined data structure and format. The multi-dimensional data set includes a device dimension, a service dimension, a business dimension, and a time dimension.

[0093] In this embodiment, pre-set data interpolation algorithms (such as linear interpolation and spline interpolation) are used to align the time axes of multi-source data, eliminating collection bias. Based on spatiotemporal feature fusion techniques (such as LSTM time series models and graph neural networks), temporal features (periodicity / trend) and spatial features (device topology / association paths) are mapped to a weighted matrix to generate a four-dimensional correlation dataset of device-service-business-time. This breaks through the limitations of traditional single-dimensional monitoring and enables cross-device and cross-service correlation analysis. For example, disk I / O anomalies can be traced back to database response delays, providing structured data support for root cause identification.

[0094] Figure 3 , step S30 includes:

[0095] S301: Establishing a dynamic correlation model between hardware performance indicators and service response quality for the multi-dimensional correlation data set;

[0096] S302: Based on the dynamic correlation model, monitor hardware parameter anomalies in real time and simultaneously detect corresponding service response time degradation;

[0097] S303: Once hardware parameter anomalies and service response time degradation are detected simultaneously, the corresponding warning levels are matched and output.

[0098] In this embodiment, a hardware-service dynamic association model (such as a causal inference model based on random forest) is constructed to monitor the dynamic coupling relationship between hardware parameters (such as CPU utilization) and service response time (such as API call delay) in real time, laying the foundation for subsequent matching of warning levels.

[0099] Figure 4 , step S303 includes:

[0100] SB1: Calculate the deviation L1 for hardware parameter abnormal values;

[0101] SB2: Calculate the degradation rate L2 for service response time degradation;

[0102] SB3: Generate a dynamic score S based on the preset formula S = α·L1 + β·L2, where α+β=1;

[0103] SB4: When S ≥ δ1 and δ1 is the first warning threshold, match the first-level warning;

[0104] SB5: When δ2≤S<δ1 and δ2 is the second warning threshold, match the second-level warning;

[0105] SB6: When δ3≤S<δ2 and δ3 is the third warning threshold, match the third level warning.

[0106] In this embodiment, a dynamic score S is calculated by combining deviation (Z-Score standardization) and degradation rate (sliding window comparison), and combined with a threshold rule engine to output a three-level warning. This achieves a shift from "passive alerting" to "active prediction," enabling, for example, an early warning 30 minutes before a memory leak causes service downtime, shortening the mean time to detection (MTTD) to minutes.

[0107] Step S40 includes:

[0108] S401: Based on the treatment priority corresponding to the warning level, select candidate solutions that meet the following conditions from the preset historical case library:

[0109] S402: The candidate solution type must match the current warning level to a degree that reaches or exceeds a preset similarity threshold, and the candidate solution's historical execution success rate must not be lower than a preset success rate threshold;

[0110] S403: After the screening is completed, the top-N candidate solution set that meets the conditions is output;

[0111] S404: In the Top-N candidate solution set, for different warning levels, execute the preset differentiated disposal strategy and output the candidate solution set;

[0112] S405: The preset differentiated handling strategy includes:

[0113] S406: When it is a level 1 warning, match the emergency plan set that needs to be executed immediately;

[0114] S407: When it is a level 2 warning, match the optimization solution set that requires a deadline for disposal;

[0115] S408: When it is a level 3 warning, match the observation period disposal plan.

[0116] In this embodiment, the matching degree between the candidate solution type and the current warning level and the historical execution success rate of the candidate solution are calculated based on the preset historical case library. Only when the matching degree between the candidate solution type and the current warning level reaches or exceeds the preset similarity threshold, and the historical execution success rate of the candidate solution is not lower than the preset success rate threshold, can the conditions be met and the solution becomes a Top-N candidate solution. Combined with the warning level, the preset differentiated disposal strategy is triggered: when it is a level one warning, the emergency solution set that needs to be executed immediately is matched; when it is a level two warning, the optimization solution set that needs to be disposed within a time limit is matched; when it is a level three warning, the observation period disposal solution is matched.

[0117] Step S50 includes:

[0118] S501: Set the business impact dimension, resource cost dimension, operation complexity dimension and historical success dimension, and assign preset weights W1, W2, W3 and W4 to each dimension;

[0119] S502: After normalizing the scores of each dimension of each candidate solution, the default formula is used to calculate the score. (Dimension Rating k × weight W k ), calculate the comprehensive score;

[0120] S50: Sort the comprehensive scores from high to low, and select the candidate solution with the highest comprehensive score as the optimal disposal solution.

[0121] In this embodiment, the "multi-dimensional quantitative scoring + intelligent sorting" mechanism is used to solve the problem of "snap decisions" and low efficiency of traditional manual decision-making. The core logic is divided into three steps: like "weighing on a scale", four decision weights are set - business impact (such as the impact of the failure on the number of users and order volume, which has the highest weight), resource cost (if additional servers need to be purchased, points will be deducted), operational risk (such as restarting the core service may cause a short interruption), historical success rate (whether similar solutions have been effective in the past), and preset weights are assigned to ensure that key factors dominate the decision; the four-dimensional scores of each solution are "leveled" (such as uniformly mapping 0-100 points to the range of 0-1); using a preset formula (Dimension Rating k × weight W k ), generate the "health index" of each plan - comprehensive score, and finally sort out the best plan according to the comprehensive score.

[0122] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0123] In one embodiment, a big data-based information system integrated operation and maintenance management platform system is provided. The big data-based information system integrated operation and maintenance management platform system corresponds to the big data-based information system integrated operation and maintenance management platform method in the above embodiment. The big data-based information system integrated operation and maintenance management platform control system includes:

[0124] Acquisition module: collects system operation data of specified enterprise information systems in real time, stores and pre-processes the system operation data, and outputs the system operation data set;

[0125] Fusion module: performs time axis calibration and fusion processing on the system operation data set, and outputs a multi-dimensional correlation data set;

[0126] Output module: establishes a dynamic correlation model based on the multi-dimensional correlation data set, monitors abnormal situations in real time, and outputs warning levels;

[0127] Matching module: matching candidate disposal solution sets based on the warning level;

[0128] Screening module: based on preset screening rules, screens the optimal treatment solution from the candidate treatment solution set and executes it;

[0129] Feedback module: outputs the execution effect evaluation result of the optimal disposal plan and feeds back the execution effect evaluation result to the preset big data platform.

[0130] Optionally, also include:

[0131] The first acquisition module: collects system operation data of the specified enterprise information system in real time through sensor acquisition, log file extraction, and API interface reception;

[0132] The first module includes: the system operation data includes server performance indicators and network device status data;

[0133] The second module includes: the service performance indicators include CPU utilization, memory usage, and disk space;

[0134] The third module includes: the network device status data includes network traffic, number of network connections, packet loss rate and application running log;

[0135] The first output module: based on the preset big data platform, stores and preprocesses the system operation data, and outputs the system operation data set.

[0136] Optionally, also include:

[0137] Alignment module: For the system operation data of two adjacent related nodes in the system operation data set, based on the preset data interpolation algorithm, it eliminates the acquisition time deviation and aligns the system operation data;

[0138] Extraction module: extracts temporal and spatial features from the aligned system operation data;

[0139] The fifth module includes: the time characteristics include the collection time, change trend and periodicity of the system operation data;

[0140] The sixth module includes: the spatial characteristics include the source device, device location, device type, and data association path of the system operation data;

[0141] The first fusion module: the extracted temporal and spatial features are input into the pre-trained weighted spatiotemporal correlation matrix for fusion;

[0142] The first calculation module: Based on the fusion results, multi-dimensional data fusion calculation is performed, including but not limited to device operation status fusion, service response delay fusion, and business health indicator fusion;

[0143] The seventh module includes: generating a multi-dimensional related data set based on the results of the fusion calculation according to a predefined data structure and format, wherein the multi-dimensional data set includes device dimension, service dimension, business dimension, and time dimension.

[0144] Optionally, also include:

[0145] The first establishment module is to establish a dynamic association model between hardware performance indicators and service response quality for the multi-dimensional correlation data set;

[0146] The first monitoring module: based on the dynamic correlation model, monitors hardware parameter anomalies in real time and simultaneously detects corresponding service response time degradation;

[0147] The first matching module: once hardware parameter anomalies and service response time degradation are detected simultaneously, the matching output corresponds to the warning level.

[0148] Optionally, also include:

[0149] The second calculation module: calculates the deviation L1 for hardware parameter abnormal values;

[0150] The third calculation module: calculates the degradation rate L2 for the service response time degradation;

[0151] The first formula module generates a dynamic score S based on the preset formula S = α·L1+β·L2, where α+β=1;

[0152] Second matching module: when S≥δ1 and δ1 is the first warning threshold, match the first-level warning;

[0153] The third matching module: when δ2≤S<δ1 and δ2 is the second warning threshold, match the second warning;

[0154] The fifth matching module: when δ3≤S<δ2 and δ3 is the third warning threshold, match the third level warning.

[0155] Optionally, also include:

[0156] The first screening module: Based on the treatment priority corresponding to the warning level, the candidate solutions that meet the following conditions are screened from the preset historical case library:

[0157] First judgment module: The matching degree between the candidate solution type and the current warning level must reach or exceed the preset similarity threshold, and the historical execution success rate of the candidate solution must not be lower than the preset success rate threshold;

[0158] Second output module: After the screening is completed, the top-N candidate solution sets that meet the conditions are output;

[0159] The third output module: In the Top-N candidate solution set, for different warning levels, it executes the preset differentiated disposal strategy and outputs the candidate solution set;

[0160] The eighth including module: the preset differentiated handling strategy includes:

[0161] The sixth matching module: when it is a level one warning, it matches the emergency plan set that needs to be executed immediately;

[0162] The seventh matching module: when it is a level 2 warning, it matches the optimization solution set that needs to be dealt with within a time limit;

[0163] The eighth matching module: When it is a level three warning, match the observation period disposal plan.

[0164] Optionally, also include:

[0165] Setting module: Set business impact dimension, resource cost dimension, operation complexity dimension and historical success dimension, and assign preset weights W1, W2, W3 and W4 to each dimension;

[0166] The fifth calculation module: After normalizing the scores of each dimension of each candidate solution, the default formula is used to calculate the score. (Dimension Rating k × weight W k ), calculate the comprehensive score;

[0167] Sorting module: sort the comprehensive scores from high to low, and select the candidate solution with the highest comprehensive score as the optimal disposal solution.

[0168] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store system operation data sets. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for an information system integrated operation and maintenance management platform based on big data is implemented.

[0169] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for an integrated operation and maintenance management platform for an information system based on big data is implemented.

[0170] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for an information system integrated operation and maintenance management platform based on big data is provided.

[0171] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0172] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for an information system integrated operation and maintenance management platform based on big data, characterized in that: Including steps: Collect system operation data of a specified enterprise information system in real time, store and pre-process the system operation data, and output the system operation data set; Performing time axis calibration and fusion processing on the system operation data set to output a multi-dimensional correlation data set; Establishing a dynamic correlation model based on the multi-dimensional correlation data set, monitoring abnormal situations in real time, and outputting warning levels; Based on the warning level, matching a set of candidate disposal solutions; Based on preset screening rules, selecting the optimal treatment solution from the candidate treatment solution set and executing the optimal treatment solution; Output the execution effect evaluation result of the optimal disposal plan, and feed the execution effect evaluation result back to the preset big data platform.

2. The method of an information system integrated operation and maintenance management platform based on big data according to claim 1, characterized in that: The steps of collecting system operation data of a designated enterprise information system in real time, storing and preprocessing the system operation data, and outputting a system operation data set include: Collect system operation data of designated enterprise information systems in real time through sensor collection, log file extraction, and API interface reception; The system operation data includes server performance indicators and network equipment status data; The service performance indicators include CPU utilization, memory usage, and disk space; The network device status data includes network traffic, number of network connections, packet loss rate and application operation logs; Based on the preset big data platform, the system operation data is stored and preprocessed, and the system operation data set is output.

3. The method of an information system integrated operation and maintenance management platform based on big data according to claim 1, characterized in that: The step of performing time axis calibration and fusion processing on the system operation data set to output a multi-dimensional associated data set includes: For the system operation data of two adjacent related nodes in the system operation data set, based on the preset data interpolation algorithm, the acquisition time deviation is eliminated and the system operation data is aligned; Extracting temporal and spatial features from the aligned system operation data; The time characteristics include the collection time, change trend and periodicity of system operation data; The spatial characteristics include the source device of the system operation data, the device location, the device type, and the data association path; The extracted temporal and spatial features are input into the pre-trained weighted spatiotemporal correlation matrix for fusion; Based on the fusion results, multi-dimensional data fusion calculations are performed, including but not limited to equipment operation status fusion, service response delay fusion, and business health indicator fusion; The results of the fusion calculation are used to generate a multi-dimensional associated data set according to a predefined data structure and format. The multi-dimensional data set includes a device dimension, a service dimension, a business dimension, and a time dimension.

4. The method of an information system integrated operation and maintenance management platform based on big data according to claim 1, characterized in that: The step of establishing a dynamic correlation model based on the multi-dimensional correlation data set, monitoring abnormal situations in real time, and outputting warning levels includes: For the multi-dimensional correlation data set, a dynamic correlation model between hardware performance indicators and service response quality is established; Based on the dynamic correlation model, hardware parameter anomalies are monitored in real time, and corresponding service response time degradation is detected simultaneously; Once hardware parameter anomalies and service response time degradation are detected simultaneously, the corresponding warning level is output.

5. The method of an information system integrated operation and maintenance management platform based on big data according to claim 4, characterized in that: The step of matching and outputting corresponding warning levels upon simultaneous detection of hardware parameter anomalies and service response time degradation includes: For hardware parameter abnormal values, calculate the deviation L1; For service response time degradation, calculate the degradation rate L2; Generate a dynamic score S based on the preset formula S = α·L1+β·L2, where α+β=1; When S≥δ1 and δ1 is the first warning threshold, the first-level warning is matched; When δ2≤S<δ1 and δ2 is the second warning threshold, the second-level warning is matched; When δ3≤S<δ2 and δ3 is the third warning threshold, the third level warning is matched.

6. The method of an information system integrated operation and maintenance management platform based on big data according to claim 1, characterized in that: The step of matching a set of candidate disposal solutions based on the warning level includes: Based on the treatment priority corresponding to the warning level, candidate solutions that meet the following conditions are screened from the preset historical case library: The candidate solution type must match the current warning level to a degree that reaches or exceeds a preset similarity threshold, and the candidate solution's historical execution success rate must not be lower than a preset success rate threshold; After the screening is completed, the top-N candidate solution sets that meet the conditions are output; In the Top-N candidate solution set, for different warning levels, the preset differentiated disposal strategies are executed and the candidate solution set is output; The preset differentiated handling strategies include: When it is a Level 1 warning, match the emergency plan set that needs to be executed immediately; When it is a Level 2 warning, match the optimization solution set that needs to be dealt with within a time limit; When it is a level three warning, match the observation period disposal plan.

7. The method of an information system integrated operation and maintenance management platform based on big data according to claim 1, characterized in that: The step of screening the optimal treatment solution from the candidate treatment solution set based on the preset screening rules and executing the optimal treatment solution includes: Set the business impact dimension, resource cost dimension, operation complexity dimension, and historical success dimension, and assign preset weights W1, W2, W3, and W4 to each dimension; After normalizing the scores of each dimension of each candidate solution, based on the preset formula Calculate the comprehensive score; The comprehensive scores are sorted from high to low, and the candidate solution with the highest comprehensive score is selected as the optimal disposal solution.

8. An information system integrated operation and maintenance management platform system based on big data, characterized by: include: Acquisition module: collects system operation data of specified enterprise information systems in real time, stores and pre-processes the system operation data, and outputs the system operation data set; Fusion module: performs time axis calibration and fusion processing on the system operation data set, and outputs a multi-dimensional correlation data set; Output module: establishes a dynamic correlation model based on the multi-dimensional correlation data set, monitors abnormal situations in real time, and outputs warning levels; Matching module: matching candidate disposal solution sets based on the warning level; Screening module: based on preset screening rules, screens the optimal treatment solution from the candidate treatment solution set and executes it; Feedback module: outputs the execution effect evaluation result of the optimal disposal plan and feeds back the execution effect evaluation result to the preset big data platform.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a method for an information system integrated operation and maintenance management platform based on big data are implemented as described in claims 1-7.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for an information system integrated operation and maintenance management platform based on big data according to claims 1 to 7.

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