WO2021232567A1 - Ai technology-based smart operation and maintenance knowledge analysis method - Google Patents
Ai technology-based smart operation and maintenance knowledge analysis method Download PDFInfo
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- WO2021232567A1 WO2021232567A1 PCT/CN2020/102157 CN2020102157W WO2021232567A1 WO 2021232567 A1 WO2021232567 A1 WO 2021232567A1 CN 2020102157 W CN2020102157 W CN 2020102157W WO 2021232567 A1 WO2021232567 A1 WO 2021232567A1
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- 238000004458 analytical method Methods 0.000 title claims abstract description 62
- 238000012423 maintenance Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 claims abstract description 15
- 238000001514 detection method Methods 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 13
- 230000008713 feedback mechanism Effects 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000011897 real-time detection Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/243—Natural language query formulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
Definitions
- the invention relates to the technical field of operation and maintenance management, in particular to a method for analyzing intelligent operation and maintenance knowledge based on AI technology.
- Operation and maintenance usually refers to the Internet operation and maintenance, which belongs to the technical department. It is essentially the operation and maintenance of each stage of the life cycle of the network, server, and service. It has reached a consensus and acceptable state in terms of cost, stability, and efficiency.
- the current operation The maintenance method is performed by the operation and maintenance personnel after discovering the abnormal operation and resource consumption of the service, and then manually analyze and deal with it.
- In the operation and maintenance management there are often many repeated faults, and these faults can be dealt with through a fixed solution. Manually handling these repetitive faults each time greatly wastes the time of operation and maintenance personnel and reduces the processing efficiency. For this reason, we propose a smart operation and maintenance knowledge analysis method based on AI technology to solve the above problems.
- the purpose of the present invention is to provide a smart operation and maintenance knowledge analysis method based on AI technology to solve the problems raised in the background art.
- an AI technology-based intelligent operation and maintenance knowledge analysis method including the following steps:
- the AI analysis engine generates a fault handling plan, and feeds the handling plan back to the knowledge base, enriching knowledge base data;
- the execution unit executes the operation and maintenance process according to the fault handling plan
- step S1 the monitoring system continuously collects real-time load data of the device through collection devices such as sensors, and judges whether the device is overloaded according to the device load threshold.
- the knowledge base includes a fault database, a solution database, a collection module, and a judgment module.
- the fault data in the fault database corresponds to the solution data in the solution database, so
- the collection module is used to collect the fault information and solutions processed by the operation and maintenance personnel in the past, and the judgment module determines whether the identified fault data is consistent with the implementation of the solution.
- the AI analysis engine is connected to a manual intervention system, and the manual intervention system includes a display and an input unit for detecting the rationality of the processing plan generated by the AI analysis engine , And can actively input processing instructions through the input unit or make a selection of multiple processing schemes.
- the AI analysis engine includes an analysis unit model for analyzing the fault data and selecting a processing plan based on the data stored in the knowledge base.
- the method for generating the analysis unit model is as follows: constructing AI Learning framework, using historical manual operation and maintenance data as the training set of the AI learning framework to train the machine learning framework to obtain the analysis unit model, and use the parsed solution text and the answer corresponding to the input text before analysis as the AI learning.
- the training set of the framework trains the AI learning framework and improves the analytical unit model.
- the answer is corrected according to the statistical probability and the feedback mechanism.
- the number of times the answer is selected is obtained, and the answer is determined.
- the answer that has been selected the most times is the standard answer, which is stored in the analytical unit model and the knowledge base.
- the AI analysis engine further includes a storage unit and an update training unit, the storage unit is used to store the parsed input text and answers corresponding to the fault data, and the update training unit is used to use the parsed input text The input text and answers of the analytic unit model are trained.
- the present invention has the beneficial effects of establishing an AI analysis engine and a knowledge base, collecting the operation and maintenance data of the past operation and maintenance personnel through the analytical unit model, and performing fault data and processing solutions through the knowledge base.
- the AI analysis engine analyzes the fault information, and then selects the corresponding processing plan from the database according to the analysis result, which greatly saves the workload of the operation and maintenance personnel, and the processing when repeated faults occur Efficiency is improved.
- FIG. 1 is a flowchart of the present invention
- Figure 2 is a block diagram of the knowledge base structure in the present invention.
- an AI technology-based intelligent operation and maintenance knowledge analysis method including the following steps:
- the AI analysis engine generates a fault handling plan, and feeds the handling plan back to the knowledge base, enriching knowledge base data;
- the execution unit executes the operation and maintenance process according to the fault handling plan
- step S1 the monitoring system continuously collects real-time load data of the device through collection devices such as sensors, and judges whether the device is overloaded according to the device load threshold.
- the knowledge base includes a fault database, a solution database, a collection module, and a judgment module.
- the fault data in the fault database corresponds to the solution data in the solution database, and the collection module is used to compare
- the operation and maintenance personnel collects the fault information and plans that have been processed in the past, and the judgment module judges whether the identified fault data is consistent with the plan implementation.
- the AI analysis engine is connected to the manual intervention system.
- the manual intervention system includes a display and an input unit for detecting the rationality of the processing scheme generated by the AI analysis engine, and can pass The input unit actively inputs processing instructions or selects the best for multiple processing schemes.
- the AI analysis engine includes an analysis unit model for analyzing the fault data and selecting a processing plan based on the data stored in the knowledge base.
- the method for generating the analysis unit model is as follows: construct an AI learning framework to Historical manual operation and maintenance data is used as the training set of the AI learning framework to train the machine learning framework, and the analytical unit model is obtained, and the parsed plan text and the answer corresponding to the input text before the analysis are used as the training set of the AI learning framework to AI
- the learning framework is trained to improve the analytical unit model.
- a preferred implementation case is that after the analytical unit model parses the answer, the answer is corrected according to the statistical probability and feedback mechanism.
- the number of times the answer is selected is obtained, and the number of times the answer is selected is determined to be the most.
- the answer is the standard answer, which is stored in the analytical unit model and the knowledge base.
- the AI analysis engine further includes a storage unit and an update training unit.
- the storage unit is used to store the parsed input text and answers corresponding to the fault data
- the update training unit is used to use the parsed input text and answer pairs. Analyze the unit model for training.
- the invention collects the operation and maintenance data of the past operation and maintenance personnel through the analysis unit model by establishing an AI analysis engine and a knowledge base, and corresponds the fault data and the processing plan through the knowledge base, so that when repeated faults occur, the AI
- the analysis engine analyzes the fault information, and selects the corresponding processing plan from the database according to the analysis result, which greatly saves the workload of operation and maintenance personnel and improves the processing efficiency when repeated faults occur.
Abstract
Description
Claims (7)
- 一种基于AI技术的智慧运维知识分析方法,其特征在于,包括如下步骤:A method for analyzing intelligent operation and maintenance knowledge based on AI technology, which is characterized in that it includes the following steps:S1、通过检测系统实时检测设备运行状态和负载数据,当设备运行发生故障或负载过大时,将故障信号收集并传输至AI分析引擎;S1. Real-time detection of equipment operating status and load data through the detection system. When equipment fails or the load is too large, the fault signal is collected and transmitted to the AI analysis engine;S2、通过AI分析引擎对故障信号进行解析分析,并根据解析结果与知识库内的运维数据相匹配;S2. Analyze the fault signal through the AI analysis engine, and match the analysis result with the operation and maintenance data in the knowledge base;S3、AI分析引擎生成故障处理方案,并将处理方案反向回馈至知识库,丰富知识库数据;S3. The AI analysis engine generates a fault handling plan, and feeds the handling plan back to the knowledge base to enrich the data of the knowledge base;S4、执行单元根据故障处理方案执行运维流程;S4. The execution unit executes the operation and maintenance process according to the fault handling plan;S5、执行运维流程后生成运维报告。S5. Generate an operation and maintenance report after executing the operation and maintenance process.
- 根据权利要求1所述的一种基于AI技术的智慧运维知识分析方法,其特征在于:步骤S1中,所述监测系统通过传感器等采集设备不间断收集设备实时的负载数据,并根据设备负载阈值判断设备是否超负载运行。The intelligent operation and maintenance knowledge analysis method based on AI technology according to claim 1, characterized in that: in step S1, the monitoring system continuously collects real-time load data of the equipment through collection equipment such as sensors, and according to the equipment load The threshold value determines whether the device is overloaded.
- 根据权利要求1所述的一种基于AI技术的智慧运维知识分析方法,其特征在于:步骤S2和步骤S3中,所述知识库包括故障数据库、方案数据库、收集模块和判断模块,所述故障数据库内的故障数据与方案数据库内的方案数据相互对应,所述收集模块用于对运维人员过往处理的故障信息和方案进行收集,所述判断模块判断识别故障数据与方案实施是否一致。An AI technology-based knowledge analysis method for intelligent operation and maintenance according to claim 1, characterized in that: in step S2 and step S3, the knowledge base includes a fault database, a solution database, a collection module, and a judgment module. The fault data in the fault database corresponds to the plan data in the plan database. The collection module is used to collect the fault information and plans processed by the operation and maintenance personnel in the past, and the judgment module judges whether the identified fault data is consistent with the plan implementation.
- 根据权利要求1所述的一种基于AI技术的智慧运维知识分析方法,其特征在于:步骤S2和步骤S3中,所述AI分析引擎连接人工介入系统,所述人工介入系统包括显示器和输入单元,用于对AI分析引擎生成的处理方案进行合理性进行检测,并可通过输入单元主动输入处理指令或对多个处理方案进行择优选择。The AI technology-based intelligent operation and maintenance knowledge analysis method according to claim 1, wherein in step S2 and step S3, the AI analysis engine is connected to an artificial intervention system, and the artificial intervention system includes a display and an input The unit is used to check the rationality of the processing plan generated by the AI analysis engine, and can actively input processing instructions through the input unit or make a selection of multiple processing plans.
- 根据权利要求1所述的一种基于AI技术的智慧运维知识分析方法,其特征在于:步骤S2中,所述AI分析引擎包括解析单元模型,用于对故障数据进行解析并根据知识库内存储数据选取处理方案,所述解析单元模型生成方法如下:构建AI学习框架,以历史人工运维数据作为AI学习框架的训练集对所述机器学习框架进行训练,得到解析单元模型,并以解析后的方案文本和与解析前的输入文本对应的答案作为AI学习框架的训练集对AI学习框架进行训练,提高解析单元模型。An AI technology-based knowledge analysis method for smart operation and maintenance according to claim 1, wherein in step S2, the AI analysis engine includes an analysis unit model for analyzing fault data and analyzing fault data according to the knowledge base The storage data selection processing scheme, the analytical unit model generation method is as follows: construct an AI learning framework, use historical manual operation and maintenance data as the training set of the AI learning framework to train the machine learning framework to obtain the analytical unit model, and analyze The latter program text and the answer corresponding to the input text before the analysis are used as the training set of the AI learning framework to train the AI learning framework and improve the analysis unit model.
- 根据权利要求5所述的一种基于AI技术的智慧运维知识分析方法,其特征在于:所述解析单元模型解析得到答案之后,根据统计概率和反馈机制校正答案,当所述解析后的文本输入得出多个答案时,获取答案被选择的次数,确定所述被选择的次数最多的答案为标准答案,存于解析单元模型和知识库中。The AI technology-based intelligent operation and maintenance knowledge analysis method according to claim 5, characterized in that: after the analysis unit model parses and obtains the answer, the answer is corrected according to the statistical probability and the feedback mechanism, and when the parsed text When multiple answers are obtained by input, the number of times the answer is selected is obtained, and the answer with the most selected times is determined to be the standard answer, which is stored in the analysis unit model and the knowledge base.
- 根据权利要求5所述的一种基于AI技术的智慧运维知识分析方法,其特征在于:所述AI分析引擎还包括存储单元和更新训练单元,所述存储单元用于存储故障数据对应的解析后的输入文本和答案,所述更新训练单元用于利用解析后的输入文本和答案对解析单元模型进行训练。The intelligent operation and maintenance knowledge analysis method based on AI technology according to claim 5, characterized in that: the AI analysis engine further comprises a storage unit and an update training unit, and the storage unit is used to store analysis corresponding to the fault data After the input text and answer, the update training unit is used to train the parsing unit model by using the parsed input text and answer.
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