Intelligent operation and maintenance optimization method based on semantic enhanced knowledge graph

Abstract

The invention belongs to the technical field of data operation and maintenance, and particularly relates to an intelligent operation and maintenance optimization method based on a semantic enhanced knowledge graph, and the intelligent operation and maintenance optimization method comprises the following specific steps: 1, constructing the semantic enhanced knowledge graph: widely collecting original data from various operation and maintenance systems, and carrying out cleaning, conversion and standardization processing to obtain a semantic enhanced knowledge graph; by means of the operation and maintenance optimization method, semantics can be analyzed and reasoned, problem description and command instructions input by operation and maintenance personnel can be accurately analyzed, a traditional rule engine and a modern logical reasoning tool are integrated, and the operation and maintenance efficiency is improved. According to the operation and maintenance optimization method, misunderstanding or misrecognition is avoided when complex sentence structures or terminologies are processed, data can be updated regularly, and the collected new data is used for replacing previous stored old data, so that the quality of the operation and maintenance optimization method is ensured.

Description

Technical Field

[0001] The present invention relates to the field of data operation and maintenance technology, and specifically to an intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph. Background Art

[0002] The intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph is a method that combines natural language processing (NLP), machine learning and knowledge graph technology to improve IT operation and maintenance management efficiency by structuring, associating and understanding unstructured data. This method can not only automatically process and analyze a large amount of operation and maintenance data, but also improve the accuracy of problem diagnosis and response speed by introducing domain knowledge;

[0003] However, the existing operation and maintenance optimization methods still have the following technical problems:

[0004] Current optimization methods have limited semantic understanding capabilities, and may lead to misunderstandings or misidentifications when dealing with complex sentence structures or professional terms, affecting the accurate conversion of problem descriptions. Data cannot be updated frequently, and the operation and maintenance system has a long update cycle, making it impossible to replace old data within the system.

[0005] To this end, an intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph is proposed to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph to solve the problems raised in the above background technology.

[0007] To achieve the above purpose, the present invention provides the following technical solution: an intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph, the specific steps of the intelligent operation and maintenance optimization method are as follows:

[0008] Step 1: Build a semantically enhanced knowledge graph: Collect raw data from various operation and maintenance systems, clean, convert and standardize them to ensure the quality of subsequent analysis. Use named entity recognition technology to automatically extract key entities in the text and map them to standard terms through a synonym library or external API. Establish a relationship network between entities and define a set of ontology models that meet industry standards to describe the concept system and their interrelationships in the operation and maintenance field. Continuously enrich and improve the ontology content according to actual needs to cover more business scenarios.

[0009] Step 2: Entity recognition: Use natural language processing technology to automatically extract key entities in the text, map the extracted entities to standard terms through a synonym library or external API, establish a relationship network between entities, determine the relationship type between entities based on rules or machine learning algorithms, and form a knowledge structure;

[0010] Step 3: Ontology modeling and expansion: Based on industry standards and specific business needs, design an ontology model that describes the concept system and its interrelationships in the field of operation and maintenance, integrate expert experience, documentation and public resources, continuously enrich and improve the ontology content, cover more business scenarios, and adjust the ontology model in a timely manner with the development of new technologies and changes in business processes to maintain its timeliness and applicability;

[0011] Step 4: Semantic parsing and reasoning engine: A powerful NLU model is trained through deep learning algorithms to accurately parse the problem descriptions and command instructions entered by operation and maintenance personnel. It integrates traditional rule engines with modern logical reasoning tools, makes inferences and judgments based on existing knowledge graphs, and derives causes and solutions. It supports uncertainty and probabilistic reasoning and can deal with fuzzy situations in complex scenarios.

[0012] Step 5: Intelligent diagnosis and predictive maintenance: Use cluster analysis and classification algorithms to mine historical failure cases, find out common failure modes and their characteristics, combine with real-time monitoring data to quickly locate the current problem, use time series prediction models or reinforcement learning frameworks to predict potential risk factors in the future, take preventive measures in advance, and reduce the probability of emergencies;

[0013] Step 6: Visualization and decision support: Design intuitive and easy-to-understand visualization components to display key indicators and trend analysis results in a graphical way, support user-defined views, meet personalized needs, provide personalized resource recommendations and service suggestions based on user operation behaviors and preferences, build a virtual environment, simulate different types of emergencies and their development processes, evaluate the effectiveness of existing emergency plans, and improve emergency response capabilities through repeated practice and improvement;

[0014] Step 7. Continuous update and improvement: Regularly conduct quantitative evaluation of the system's operating performance, collect user feedback, identify existing problems and improvement suggestions, and update and replace old data.

[0015] Preferably, in step 1, the sources of the original data include log files, monitoring platforms, and work order systems, and the collected data are processed in batches and in real-time streams.

[0016] Preferably, in step 2, the key entities in the text include server name, IP address, and error code.

[0017] Preferably, in step 4, the causal link tracing technology and network model are used to deeply explore the deep-seated causes behind the problem to avoid staying on the surface phenomenon.

[0018] Preferably, in step six, the visualization components include charts and maps.

[0019] Preferably, in step seven, based on the evaluation results and user feedback, the model parameters are continuously optimized and the algorithm configuration is adjusted to ensure that the performance of the system is continuously improved, and the lessons learned from each successful response to an emergency are collated and saved.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] This operation and maintenance optimization method can parse and reason about semantics, and can accurately parse the problem descriptions and command instructions entered by operation and maintenance personnel. It integrates traditional rule engines with modern logical reasoning tools to ensure that there will be no misunderstandings or misidentifications when processing complex sentence structures or professional terms. It can also update data regularly, replacing old data stored in the past with newly collected data, thereby ensuring the quality of the operation and maintenance optimization method. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flow chart of the steps of this method. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0025] Example:

[0026] See also Figure 1 , the present invention provides a technical solution:

[0027] An intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph, the specific steps of the intelligent operation and maintenance optimization method are as follows:

[0028] Step 1: Build a semantically enhanced knowledge graph: Collect raw data from various operation and maintenance systems, clean, convert and standardize them to ensure the quality of subsequent analysis. Use named entity recognition technology to automatically extract key entities in the text and map them to standard terms through a synonym library or external API. Establish a relationship network between entities and define a set of ontology models that meet industry standards to describe the concept system and their interrelationships in the operation and maintenance field. Continuously enrich and improve the ontology content according to actual needs to cover more business scenarios.

[0029] Step 2: Entity recognition: Use natural language processing technology to automatically extract key entities in the text, map the extracted entities to standard terms through a synonym library or external API, establish a relationship network between entities, determine the relationship type between entities based on rules or machine learning algorithms, and form a knowledge structure;

[0030] Step 3: Ontology modeling and expansion: Based on industry standards and specific business needs, design an ontology model that describes the concept system and its interrelationships in the field of operation and maintenance, integrate expert experience, documentation and public resources, continuously enrich and improve the ontology content, cover more business scenarios, and adjust the ontology model in a timely manner with the development of new technologies and changes in business processes to maintain its timeliness and applicability;

[0031] Step 4: Semantic parsing and reasoning engine: A powerful NLU model is trained through deep learning algorithms to accurately parse the problem descriptions and command instructions entered by operation and maintenance personnel. It integrates traditional rule engines with modern logical reasoning tools, makes inferences and judgments based on existing knowledge graphs, and derives causes and solutions. It supports uncertainty and probabilistic reasoning and can deal with fuzzy situations in complex scenarios.

[0032] Step 5: Intelligent diagnosis and predictive maintenance: Use cluster analysis and classification algorithms to mine historical failure cases, find out common failure modes and their characteristics, combine with real-time monitoring data to quickly locate the current problem, use time series prediction models or reinforcement learning frameworks to predict potential risk factors in the future, take preventive measures in advance, and reduce the probability of emergencies;

[0033] Step 6: Visualization and decision support: Design intuitive and easy-to-understand visualization components to display key indicators and trend analysis results in a graphical way, support user-defined views, meet personalized needs, provide personalized resource recommendations and service suggestions based on user operation behaviors and preferences, build a virtual environment, simulate different types of emergencies and their development processes, evaluate the effectiveness of existing emergency plans, and improve emergency response capabilities through repeated practice and improvement;

[0034] Step 7. Continuous update and improvement: Regularly conduct quantitative evaluation of the system's operating performance, collect user feedback, identify existing problems and improvement suggestions, and update and replace old data.

[0035] In the step 1, the sources of the original data include log files, monitoring platforms, and work order systems, and the collected data can be processed in batches or in real-time.

[0036] In step 2, the key entities in the text include server name, IP address, and error code.

[0037] In step 4, through causal link tracing technology and network models, we can deeply explore the deep-seated causes behind the problem to avoid staying on the surface phenomenon.

[0038] In step six, the visualization components include charts and maps.

[0039] In step seven, based on the evaluation results and user feedback, the model parameters are continuously optimized and the algorithm configuration is adjusted to ensure that the system performance is continuously improved, and the lessons learned from each successful response to an emergency are collated and saved.

[0040] The intelligent operation and maintenance optimization system can achieve automation in many aspects, including:

[0041] Automation of data collection and preprocessing: The system can automatically capture and integrate data from various operation and maintenance systems without manual operation. It uses preset rules and algorithms to automatically clean, remove duplicates, fill in missing values, and format the collected data to ensure data quality and consistency.

[0042] Entity recognition and link automation: Through the trained natural language processing model, the system can automatically extract key entities from unstructured text and normalize them into standard terms. Based on rules or machine learning algorithms, the system can automatically determine the relationship type between entities (such as "belongs to", "affects", "depends on", etc.) to form a preliminary knowledge structure without relying on a large amount of manual annotation;

[0043] Ontology modeling and extension automation: As new technologies develop and business processes change, the system can automatically adjust the ontology model through built-in learning mechanisms to maintain its timeliness and applicability; for example, when new concepts or terms are detected, they are automatically incorporated into the knowledge system;

[0044] Semantic parsing and automated reasoning: The fully trained NLU model enables the system to automatically parse the problem descriptions, commands and other natural language expressions entered by the operation and maintenance personnel, and convert them into structured query statements, supporting multiple rounds of dialogue to obtain more detailed information. The integrated traditional rule engine and modern logical reasoning tools can automatically perform reasoning and judgment based on the existing knowledge graph, and deduce possible causes and solutions; it also has the corresponding processing capabilities for uncertain and probabilistic situations;

[0045] Intelligent diagnosis and predictive maintenance automation: Use statistical methods such as cluster analysis and classification algorithms to mine historical failure cases and find common failure modes and their characteristics; combined with real-time monitoring data, the system can quickly locate the current problem. Using time series prediction models (such as ARIMA, LSTM, etc.) or reinforcement learning frameworks, the system can predict potential risk factors in the future and automatically generate preventive measures to remind administrators to take action in advance.

[0046] Automation of visualization and decision support: Based on user needs and preferences, the system can automatically generate intuitive and easy-to-understand visualization components to graphically display key indicators and trend analysis results. Based on user operating behaviors and preferences, the system can automatically provide personalized resource recommendations and service suggestions. For example, when encountering specific types of problems, it automatically pushes relevant documents, tutorials or expert opinions, builds a virtual environment, simulates different types of emergencies and their development processes, and evaluates the effectiveness of existing emergency plans. Through repeated practice and improvement, emergency response capabilities are improved.

[0047] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the attached claims rather than the above description. Therefore, it is intended to include all changes within the meaning and scope of the equivalent elements of the claims in the present invention, and any figure marks in the claims should not be regarded as limiting the claims involved.

[0048] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph, characterized in that: The specific steps of the intelligent operation and maintenance optimization method are as follows: Step 1: Build a semantically enhanced knowledge graph: Collect raw data from various operation and maintenance systems, clean, convert and standardize them to ensure the quality of subsequent analysis. Use named entity recognition technology to automatically extract key entities in the text and map them to standard terms through a synonym library or external API. Establish a relationship network between entities and define a set of ontology models that meet industry standards to describe the concept system and their interrelationships in the operation and maintenance field. Continuously enrich and improve the ontology content according to actual needs to cover more business scenarios. Step 2: Entity recognition: Use natural language processing technology to automatically extract key entities in the text, map the extracted entities to standard terms through a synonym library or external API, establish a relationship network between entities, determine the relationship type between entities based on rules or machine learning algorithms, and form a knowledge structure; Step 3: Ontology modeling and expansion: Based on industry standards and specific business needs, design an ontology model that describes the concept system and its interrelationships in the field of operation and maintenance, integrate expert experience, documentation and public resources, continuously enrich and improve the ontology content, cover more business scenarios, and adjust the ontology model in a timely manner with the development of new technologies and changes in business processes to maintain its timeliness and applicability; Step 4: Semantic parsing and reasoning engine: A powerful NLU model is trained through deep learning algorithms to accurately parse the problem descriptions and command instructions entered by operation and maintenance personnel. It integrates traditional rule engines with modern logical reasoning tools, makes inferences and judgments based on existing knowledge graphs, and derives causes and solutions. It supports uncertainty and probabilistic reasoning and can deal with fuzzy situations in complex scenarios. Step 5: Intelligent diagnosis and predictive maintenance: Use cluster analysis and classification algorithms to mine historical failure cases, find out common failure modes and their characteristics, combine with real-time monitoring data to quickly locate the current problem, use time series prediction models or reinforcement learning frameworks to predict potential risk factors in the future, take preventive measures in advance, and reduce the probability of emergencies; Step 6: Visualization and decision support: Design intuitive and easy-to-understand visualization components to display key indicators and trend analysis results in a graphical way, support user-defined views, meet personalized needs, provide personalized resource recommendations and service suggestions based on user operation behaviors and preferences, build a virtual environment, simulate different types of emergencies and their development processes, evaluate the effectiveness of existing emergency plans, and improve emergency response capabilities through repeated practice and improvement; Step 7. Continuous update and improvement: Regularly conduct quantitative evaluation of the system's operating performance, collect user feedback, identify existing problems and improvement suggestions, and update and replace old data.

2. According to claim 1, an intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph is characterized in that: In the step 1, the sources of the original data include log files, monitoring platforms, and work order systems, and the collected data can be processed in batches or in real-time.

3. According to claim 1, the intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph is characterized by: In step 2, the key entities in the text include server name, IP address, and error code.

4. The intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph according to claim 1 is characterized in that: In step 4, through causal link tracing technology and network models, we can deeply explore the deep-seated causes behind the problem to avoid staying on the surface phenomenon.

5. The intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph according to claim 1, characterized in that: In step six, the visualization components include charts and maps.

6. The intelligent operation and maintenance optimization method based on semantically enhanced knowledge graph according to claim 1, characterized in that: In step seven, based on the evaluation results and user feedback, the model parameters are continuously optimized and the algorithm configuration is adjusted to ensure that the system performance is continuously improved, and the lessons learned from each successful response to an emergency are collated and saved.

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