Operation and maintenance knowledge graph construction technology based on multi-source data fusion and semantic understanding

Abstract

The invention provides an operation and maintenance knowledge graph construction technology based on multi-source data fusion and semantic understanding. The technology aims at integrating operation and maintenance data from different sources and in different formats, and organic fusion of multi-source data is achieved through data extraction, cleaning, conversion and integration. And performing deep analysis on the fused data by utilizing a semantic understanding technology, extracting entities, attributes and relationships, and constructing an operation and maintenance knowledge graph. The atlas presents operation and maintenance knowledge in a structured mode, so that quick query, reasoning and analysis are facilitated, and powerful support is provided for operation and maintenance decisions. By implementing the technology, the intelligent level of operation and maintenance management is remarkably improved, and sharing and utilization of operation and maintenance knowledge are promoted.

Description

Technical Field

[0001] Multi-source data fusion: This area focuses on how to effectively extract, integrate, and cleanse data from multiple heterogeneous data sources to ensure data integrity and consistency. This includes, but is not limited to, the fusion of structured data (such as database records), semi-structured data (such as JSON and XML files), and unstructured data (such as log files and text files).

[0002] Semantic Understanding: Leveraging advanced technologies such as natural language processing (NLP) and machine learning, we conduct in-depth analysis of the fused data to extract semantic information such as entities and relationships. This helps computers understand and parse complex knowledge in the O&M field, providing a foundation for subsequent knowledge graph construction.

[0003] Knowledge graph construction: Based on the results of multi-source data fusion and semantic understanding, graph databases or knowledge graph construction tools are used to represent and organize operation and maintenance knowledge in a structured form. This includes defining entities, relationships, attributes, and establishing links and associations between entities, ultimately forming a complete operation and maintenance knowledge graph.

[0004] Operation and maintenance knowledge graph application: Apply the constructed operation and maintenance knowledge graph to actual operation and maintenance work, such as troubleshooting, performance monitoring, automated operation and maintenance, etc. The knowledge support provided by the knowledge graph can improve the efficiency and accuracy of operation and maintenance work. Background Art

[0005] The diversity and complexity of operations data: With the continuous expansion of enterprise IT systems, operations generate a vast amount of data. This data comes from diverse systems and tools, and is presented in a variety of formats and complex structures. This data includes structured data (such as database records), semi-structured data (such as log files and configuration files), and unstructured data (such as operations documents and fault reports). This diversity and complexity poses challenges to the management and utilization of operations knowledge.

[0006] Isolation and difficulty in sharing operational knowledge: Traditional operational knowledge management methods often lead to knowledge isolation, making it difficult to effectively share and reuse operational knowledge across different teams, systems, or projects. This reduces operational efficiency and increases the cost of troubleshooting and problem resolution.

[0007] The need for semantic understanding: Semantic understanding is necessary to extract valuable knowledge from operations and maintenance data. Traditional data processing methods struggle to directly understand the complex semantic information contained in operations and maintenance data, such as fault type, impact scope, and solution. Therefore, it is necessary to leverage technologies such as natural language processing and machine learning to deeply analyze operations and maintenance data.

[0008] The potential of knowledge graphs in operations and maintenance: As a structured knowledge representation, knowledge graphs can organize and manage operations and maintenance knowledge in a graphical form, clearly displaying the relationships and attributes between entities. By building an operations and maintenance knowledge graph, operations and maintenance knowledge can be easily queried, analyzed, and reasoned about, improving the intelligence of operations and maintenance work. Summary of the Invention

[0009] Data source integration: This invention first defines a unified data interface and protocol to collect operation and maintenance related structured, semi-structured and unstructured data from multiple heterogeneous data sources.

[0010] Data preprocessing: Cleaning, deduplication, and conversion of collected data ensures data quality and lays the foundation for subsequent semantic understanding and knowledge graph construction. This preprocessing process includes, but is not limited to, data format unification, outlier handling, and missing value filling.

[0011] Fusion algorithm: Use advanced data fusion algorithms and technologies to solve problems such as inconsistent formats and semantics between data from different sources, and achieve effective integration and unified representation of multi-source data.

[0012] Natural language processing: Using natural language processing (NLP) technology, we conduct in-depth analysis of unstructured text data such as operation and maintenance documents and fault reports to extract semantic information such as entities, relationships, and events.

[0013] Knowledge extraction: By combining the domain knowledge base and rule base, operational knowledge is extracted from preprocessed data to form a structured knowledge representation. The knowledge extraction process includes steps such as entity recognition, relationship extraction, and attribute assignment.

[0014] Semantic association: Based on the extracted entities and relationships, semantic associations between entities are established to form the preliminary framework of the operation and maintenance knowledge graph.

[0015] Knowledge graph model design: Based on the characteristics and needs of the operation and maintenance field, design the model architecture of the operation and maintenance knowledge graph, including entity types, relationship types, attribute definitions, etc.

[0016] Graph construction process: Develop a detailed knowledge graph construction process, including entity creation, relationship construction, attribute assignment, graph verification, and other steps. Through automated and semi-automated methods, the extracted operation and maintenance knowledge is organized and stored according to the knowledge graph model.

[0017] Graph Data Storage and Query: Utilize efficient data storage and query technologies, such as graph databases, to enable rapid access and intelligent retrieval of the operational knowledge graph. Graph databases fully utilize the relationship between nodes and edges to improve query efficiency and accuracy.

[0018] Application scenarios: Apply the constructed operation and maintenance knowledge graph to multiple operation and maintenance scenarios, such as troubleshooting, performance monitoring, and automated operation and maintenance. The knowledge support provided by the knowledge graph can improve the efficiency and accuracy of operation and maintenance work.

[0019] Continuous Optimization: Based on actual application, the operation and maintenance knowledge graph is regularly updated and maintained to ensure it remains in sync with the actual needs of operation and maintenance work. At the same time, algorithms and technical means such as data fusion, semantic understanding, and knowledge graph construction are continuously optimized to improve the construction quality and application effectiveness of the operation and maintenance knowledge graph.

[0020] Comprehensive integration of multi-source data: This solves the problems of isolated operation and maintenance data and inconsistent formats, and achieves effective integration and unified representation of multi-source data.

[0021] Deep semantic understanding: Improves the ability to extract useful knowledge from operation and maintenance data, and provides rich and accurate semantic information for the construction of operation and maintenance knowledge graphs.

[0022] Intelligent knowledge graph construction: The operation and maintenance knowledge graph is constructed through automated and semi-automated methods, which improves construction efficiency and accuracy.

[0023] Flexible and scalable: Supports customized development and optimization of the operation and maintenance knowledge graph based on actual needs to meet the operation and maintenance knowledge management needs in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS Attachment Figure 1 Data fusion flow chart: Description: This diagram illustrates the multi-source data fusion process, including key steps such as data extraction, cleaning, conversion, and integration. This flowchart visually demonstrates how to extract operational knowledge from multiple heterogeneous data sources and effectively integrate the data. Attachment Figure 2 Operation and maintenance knowledge graph structure diagram: Description: This diagram depicts the structure of the operations knowledge graph, including core elements such as entities, attributes, and relationships. It graphically demonstrates the relationships between entities in the knowledge graph and how relationships connect entities into an organic knowledge system. Attachment Figure 3 Schematic diagram of semantic understanding technology application: Description: This diagram illustrates the application of semantic understanding technology in the construction of an operations knowledge graph, including key steps such as entity recognition, relationship extraction, and knowledge fusion. This diagram illustrates how semantic understanding technology can be used to extract valuable knowledge from fused data and build a high-quality operations knowledge graph.

Claims

1. A technology for constructing an operation and maintenance knowledge graph based on multi-source data fusion and semantic understanding, characterized by: • Integrate multi-source operation and maintenance data and achieve data unification through pre-processing; • Use semantic understanding technology to analyze entities, attributes and relationships in data; • Build an operation and maintenance knowledge graph, which includes entity nodes, attributes and relationship edges; • Graphs support the query, reasoning, and application of operational knowledge.

2. This technology combines multi-source data fusion and semantic understanding to automatically construct an operation and maintenance knowledge graph, improve operation and maintenance management efficiency, and provide intelligent support for operation and maintenance decision-making.

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