A railway field map construction method and system

Abstract

This invention provides a method and system for constructing a knowledge graph in the railway field. The method includes: constructing a railway construction energy consumption graph based on an ontology approach; building a knowledge graph ontology model; mapping the ontology model to entities to generate graph data; constructing the graph based on a natural language processing algorithm; selecting a dataset; creating a list of railway construction energy consumption control factors; and constructing a construction energy conservation knowledge graph based on the control factor list. This invention, through systematic analysis and integration of railway construction energy consumption data, combined with natural language processing technology and a rigorous ontology construction process, can construct a highly accurate, comprehensive, and practical construction energy conservation knowledge graph, providing strong data support and decision-making assistance for the field of railway construction energy conservation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of knowledge graph construction, and particularly relates to a railway field graph construction method and system. BACKGROUND

[0002] As a national important infrastructure and national economy artery, the railway plays an irreplaceable overall supporting role in social economy and cultural development.

[0003] Railway construction energy saving and carbon reduction work is an important part of railway construction process, which directly affects the overall environmental protection effect. In the environment of advocating green development, energy saving and emission reduction, it occupies a very important position in the field of civil engineering. However, the energy saving and carbon reduction measures of engineering construction are often affected and restricted by many factors such as natural conditions, technical factors and economic conditions of the region. For example, in the complex mountainous area, the implementation of energy saving and carbon reduction measures is greatly affected by factors such as adverse geology, sudden mud gushing, climate difference, geographical location and lack of infrastructure, making it difficult to implement energy saving and carbon reduction measures. The implementation of railway construction energy saving and carbon reduction is essentially to achieve the balance between engineering construction and control factors, and to obtain the optimal solution under the condition of meeting all environmental protection, economic and construction period restrictions. Therefore, the treatment of control factors directly affects the implementation difficulty of construction scheme, construction cost and maintenance cost in the later period, which is also a difficult problem that needs to be solved in the work of railway construction energy saving and carbon reduction.

[0004] However, the current problems of railway construction energy saving and carbon reduction are low efficiency of manual data processing, low data quality and accuracy. The field of railway construction energy consumption involves a large amount of structured and unstructured data, and the data often has problems of data quality and accuracy, such as incorrect labeling, missing information and inconsistent data format. This leads to errors and incompleteness of entities and relationships in the graph, affecting the accuracy of subsequent analysis and decision-making.

[0005] Therefore, there is an urgent need for a railway field graph construction method and system to solve the problems in the prior art. SUMMARY

[0006] The present application provides a railway field graph construction method and system to solve the problem of low efficiency of manual data processing, low data quality and accuracy in the prior art.

[0007] In one aspect, the present application provides a railway field graph construction method, comprising:

[0008] A railway field graph construction method, characterized in that it comprises:

[0009] A railway construction energy consumption map is constructed based on an ontology approach. A knowledge graph ontology model is built, and the graph ontology model is mapped to entities to generate graph data, including:

[0010] S1. Obtain energy consumption data for railway construction, analyze the data resources, divide them into structured data and heterogeneous data, and construct a knowledge graph ontology model;

[0011] S2, based on the correspondence between entities in the ontology model and the relational database, maps the attributes of the ontology model to the fields in the database to obtain the data mapping relationship;

[0012] S3, based on data mapping relationships, extracts data into the graph database, generates graph data, and obtains a data graph;

[0013] A knowledge graph for energy conservation in railway construction is constructed based on natural language processing algorithms. A dataset is selected, a list of control factors for energy consumption in railway construction is created, and a knowledge graph for energy conservation in construction is constructed based on the list of control factors.

[0014] According to the railway domain knowledge graph construction method provided by the present invention, the steps of acquiring railway construction energy consumption data, analyzing data resources, dividing them into structured data and text data, and constructing a knowledge graph ontology model include:

[0015] Identify structured data in the mechanical energy consumption data that conforms to the constructed ontology data pattern, and determine the first ontology based on the constructed ontology data pattern and the structured data;

[0016] Based on the heterogeneous data in the mechanical energy consumption data that does not conform to the constructed ontology data pattern, an ontology data pattern is extracted, and a second ontology is determined according to the extracted ontology data pattern and the heterogeneous data.

[0017] By merging the first ontology and the second ontology, we obtain the knowledge graph ontology model.

[0018] According to the railway domain knowledge graph construction method provided by the present invention, the step of merging the first ontology and the second ontology to obtain a knowledge graph ontology model includes:

[0019] Based on the structure, concepts, and relationships of the first and second ontologies, the similarities and differences between them are analyzed to determine the parts that need to be merged and generate analysis results.

[0020] Mapping rules are established based on the analysis results, and matching is performed.

[0021] During the mapping process, naming conflicts and semantic inconsistencies are resolved, and the merged ontology is ensured to be semantically and logically consistent.

[0022] Based on the mapping rules, the concepts, attributes, and relationships in the two ontology are integrated to form a comprehensive ontology model. During the integration process, duplicate concepts and relationships are filtered out and normalized and standardized to obtain the knowledge graph ontology model.

[0023] The merged ontology model is validated to ensure that the merged knowledge graph ontology model meets the expected logical and semantic requirements.

[0024] According to the railway domain mapping method provided by the present invention, the step of mapping ontology model attributes and database fields based on the correspondence between entities in the ontology model and the relational database to obtain data mapping relationships includes:

[0025] Analyze the concepts, attributes, and relationships in the ontology model to determine the attributes that need to be mapped to the relational database, and understand their data types and constraints;

[0026] Review the structure and table design of the target relational database to ensure that the database has appropriate entities and relationships to store the information in the ontology model;

[0027] Establish corresponding relationships based on the attributes of the ontology model and the fields of the relational database;

[0028] Perform data type matching, constraint matching, multi-value attribute processing, and relationship establishment on relational databases. Organize the data mapping relationships into documents and record the correspondence between ontology model attributes and relational database fields.

[0029] According to the railway mapping method provided by the present invention, the step of extracting data into a graph database based on data mapping relationships, generating graph data, and obtaining a data graph includes:

[0030] Based on the data mapping relationship, extract the corresponding data from the relational database and convert the format of the extracted data;

[0031] Based on the data mapping relationship, create nodes and edges in the graph database;

[0032] The transformed data is imported into a graph database, and indexes are created for the nodes and edges in the graph database.

[0033] According to the railway field knowledge graph construction method provided by the present invention, the step of constructing the knowledge graph based on natural language algorithms, selecting a dataset, creating a list of railway construction energy consumption control factors, and constructing a construction energy conservation knowledge graph based on the list of control factors includes:

[0034] Based on historical railway construction and machinery configuration experience, data related to railway machinery energy consumption is extracted as the basic data for constructing a construction energy-saving knowledge graph, and a dataset is generated.

[0035] Summarize and organize the historical factors that affect railway construction efficiency and machinery configuration and their corresponding countermeasures to form a list of factors for controlling railway construction energy consumption.

[0036] The control factors and their corresponding measures in the control factor list are quantified.

[0037] The dataset is parsed using natural language processing techniques to extract entities, attributes, and relationships related to the control factor list.

[0038] The extracted information is organized into a structured knowledge graph of energy conservation in construction.

[0039] Users can query the construction energy-saving knowledge graph based on the list of railway construction energy consumption control factors to obtain guidance and suggestions related to railway construction and machinery configuration.

[0040] According to the railway field knowledge graph construction method provided by the present invention, the step of organizing the extracted information into a structured energy-saving knowledge graph includes:

[0041] Code the control factors on the list of energy consumption control factors in railway construction.

[0042] The quantitative values ​​of the control measures are made dimensionless, and a knowledge template of the control factors and their corresponding control measures is constructed.

[0043] The control factors, treatment measures, and quantitative values ​​are classified into levels, and entities are extracted from them. Relationships between entities are established based on the classified levels, and a knowledge ontology for the control factor treatment domain is constructed based on knowledge templates.

[0044] All control factors and control measures in the domain knowledge ontology are assigned unique search terms, and a search term sequence list is constructed.

[0045] On the other hand, the present invention also provides a railway mapping system, comprising:

[0046] Ontology construction module: Constructs a railway construction energy consumption map based on ontology, builds a knowledge graph ontology model, and generates graph data by mapping the graph ontology model with entities;

[0047] Natural Language Construction Module: Constructs a knowledge graph based on natural language algorithms, selects a dataset, creates a list of energy consumption control factors for railway construction, and constructs a knowledge graph for energy conservation in construction based on the list of control factors.

[0048] According to the railway domain mapping system provided by the present invention, the ontology construction module includes:

[0049] Construction Unit: Acquire railway machinery energy consumption data, analyze data resources, and construct a knowledge graph ontology model;

[0050] Mapping Unit: Based on the correspondence between entities in the ontology model and the relational database, the ontology model attributes and database fields are mapped to obtain the data mapping relationship;

[0051] Extraction Unit: Based on the data mapping relationship, the data is extracted into the graph database to generate graph data and obtain the data graph.

[0052] This invention provides a method and system for constructing a knowledge graph in the railway field. The method includes constructing a railway construction energy consumption graph based on an ontology approach, constructing a knowledge graph ontology model, mapping the graph ontology model with entities to generate graph data, constructing the graph based on a natural language algorithm approach, selecting a dataset, creating a list of railway construction energy consumption control factors, and constructing a construction energy conservation knowledge graph based on the list of control factors.

[0053] The railway domain mapping method has the following significant advantages:

[0054] 1. Comprehensive and detailed data analysis and modeling: By distinguishing between structured and heterogeneous (or unstructured) data, this method can analyze railway construction energy consumption data in more detail, ensuring that all types of data are fully utilized, thereby constructing a more complete and accurate knowledge graph ontology model.

[0055] 2. Flexible ontology model construction and integration: By identifying structured data that conforms to predefined ontology patterns and extracting new ontology patterns from heterogeneous data, this method can not only utilize existing knowledge frameworks, but also extend and adapt to newly emerging data patterns, enabling the knowledge graph to evolve dynamically and adapt to the development and changes in the railway industry.

[0056] 3. Efficient data mapping and graph generation: Based on the mapping mechanism between ontology models and relational databases, this method can effectively transform data resources into graph data while ensuring data consistency and integrity, facilitating subsequent querying and analysis.

[0057] 4. Natural Language Processing Enhanced Knowledge Graph Construction: By combining natural language processing technology, this method can automatically parse text data, extract key information, and construct knowledge graphs, greatly improving the automation of data processing and reducing the need for manual intervention.

[0058] 5. Enhance decision support capabilities: By creating a list of energy consumption control factors for railway construction and a construction energy-saving knowledge graph built based on this list, this method can provide guidance and suggestions for railway construction and machinery configuration based on historical data, thereby improving the scientific nature and efficiency of decision-making.

[0059] 6. Standardization and normalization: Standardization and normalization processes were implemented during the construction of the ontology model and data mapping to ensure the consistency and readability of the knowledge graph, facilitating sharing and interoperability across systems.

[0060] 7. Verifiability and Reliability: Finally, by verifying the merged knowledge graph ontology model, the correctness of its logic and semantics was ensured, enhancing the reliability and effectiveness of the graph and providing a solid foundation for knowledge management and application in the railway field.

[0061] In summary, this method, by systematically analyzing and integrating railway construction energy consumption data, combined with natural language processing technology and a rigorous ontology construction process, can construct a highly accurate, comprehensive, and practical knowledge graph, providing strong data support and decision-making assistance for the railway industry. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0063] Figure 1 This is a flowchart of a railway atlas construction method provided by an embodiment of the present invention;

[0064] Figure 2 This is a structural diagram of a railway mapping system provided in an embodiment of the present invention;

[0065] Figure 3 This is a flowchart illustrating the construction of a knowledge graph ontology model for a railway-related knowledge graph construction system, as provided in an embodiment of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0067] Figure 1 This is a flowchart of a railway mapping method provided by an embodiment of the present invention.

[0068] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for constructing a railway atlas, which mainly includes the following steps:

[0069] 101. Construct a railway construction energy consumption map based on ontology, build a knowledge graph ontology model, and generate map data by mapping the graph ontology model with entities.

[0070] S1. Obtain energy consumption data for railway construction, analyze the data resources, divide them into structured data and heterogeneous data, and construct a knowledge graph ontology model;

[0071] S2, based on the correspondence between entities in the ontology model and the relational database, maps the attributes of the ontology model to the fields in the database to obtain the data mapping relationship;

[0072] S3, based on data mapping relationships, extracts data into the graph database, generates graph data, and obtains a data graph;

[0073] Among them, such as Figure 3 As shown, railway machinery energy consumption data is acquired, and the data resources are analyzed, dividing them into structured data and heterogeneous data. A knowledge graph ontology model is constructed, including: identifying structured data in the machinery energy consumption data that conforms to the constructed ontology data pattern; determining a first ontology based on the constructed ontology data pattern and the structured data; extracting ontology data patterns from heterogeneous data in the machinery energy consumption data that do not conform to the constructed ontology data pattern; determining a second ontology based on the extracted ontology data pattern and the heterogeneous data; and merging the first and second ontology to obtain the knowledge graph ontology model.

[0074] The first ontology is an initial version of the ontology determined based on the constructed ontology data pattern and the matching structured data. It is an ontology model created based on existing knowledge structures and identified and classified data. The first ontology can be seen as a preliminary representation of existing knowledge, serving as the starting point for constructing a knowledge graph.

[0075] The second ontology is determined based on a new ontology data pattern extracted from heterogeneous data derived from mechanical energy consumption data that does not match the existing ontology data pattern. In other words, the second ontology is an ontology model created based on data that cannot be described by existing ontology data patterns, i.e., representing text data.

[0076] This means that a second ontology can capture domain knowledge or concepts that could not be represented by a first ontology. By analyzing and mining heterogeneous data, new concepts, relationships, and attributes can be discovered, thereby enriching and expanding the original ontology model.

[0077] By merging the first and second ontology, a more complete and comprehensive knowledge graph ontology model is obtained, which includes descriptions of various concepts, relationships, and attributes obtained from structured and heterogeneous data. This comprehensive ontology model can be used to more comprehensively understand and represent knowledge in related domains.

[0078] The process of merging the first and second ontologies to obtain a knowledge graph ontology model includes: gaining a detailed understanding of the structure, concepts, and relationships of the first and second ontologies, determining their similarities and differences, and identifying the parts that need to be merged; establishing mapping rules to map common concepts and relationships between the two ontologies; resolving naming conflicts and semantic inconsistencies during the mapping process, and ensuring that the merged ontology is semantically and logically consistent; integrating the concepts, attributes, and relationships from the two ontologies according to the mapping rules to form a comprehensive ontology model, filtering out duplicate concepts and relationships during the integration process, and performing normalization and standardization to obtain the knowledge graph ontology model; and validating the merged ontology model to ensure that it meets the expected logical and semantic requirements.

[0079] Based on the correspondence between the ontology model and entities in the relational database, the ontology model attributes and database fields are mapped to obtain data mapping relationships. This includes: analyzing the concepts, attributes, and relationships in the ontology model, determining the attributes that need to be mapped to the relational database, and understanding their data types and constraints; reviewing the structure and table design of the target relational database to ensure that the database has appropriate entities and relationships to store the information in the ontology model; establishing correspondence relationships based on the attributes of the ontology model and the fields of the relational database; performing data type matching, constraint matching, multi-valued attribute processing, and relationship establishment on the relational database; and organizing the data mapping relationships into a document to record the correspondence between the ontology model attributes and the relational database fields.

[0080] Data type matching refers to ensuring that the data types of attributes and fields match between the ontology model and the relational database. For example, mapping text attributes in the ontology model to character fields in the relational database.

[0081] Constraint matching refers to ensuring that if the ontology model defines constraints on attributes (such as minimum value, maximum value, uniqueness, etc.), the corresponding fields in the relational database also have corresponding constraints.

[0082] Multi-valued attribute handling refers to the practice of using additional tables or splitting fields to store multi-valued attributes if the attributes in the ontology model allow multiple values, but the table structure in the relational database cannot directly represent the multi-valued attributes.

[0083] Relationship establishment refers to the process of creating corresponding tables and foreign key constraints in a relational database to represent relationships if they exist in the ontology model.

[0084] Based on data mapping relationships, data is extracted into a graph database to generate graph data and obtain a data graph. This includes: extracting relevant data from a relational database according to the data mapping relationships; converting the extracted data into a format that conforms to the data model of the graph database; creating nodes and edges in the graph database according to the data mapping relationships to construct the basic structure of the data graph; importing the converted data into the graph database; and indexing the nodes and edges in the graph database to speed up the indexing process.

[0085] This process involves extracting relevant data from a relational database based on data mapping relationships. By identifying and understanding the relationships and dependencies between data, data relevant to the graph database is selectively extracted from the relational database. The extracted data is then formatted to conform to the graph database's data model. Due to the different data models between relational and graph databases, the extracted data needs appropriate transformation and adjustment to fit the graph database's data model (e.g., nodes and edges). Based on the data mapping relationships, nodes and edges are created in the graph database to construct the basic structure of the data graph. Nodes represent entities or concepts, and edges represent relationships or connections between entities. The transformed data is then imported into the graph database. The format-transformed data is loaded into the graph database to create the data graph. This involves inserting node and edge information into the corresponding data structures of the graph database.

[0086] Indexing nodes and edges in a graph database can speed up the indexing process. To improve the efficiency of data graph retrieval, indexes can be created for nodes and edges in the graph database. Indexes can quickly locate and access relevant nodes and edges based on specific attributes or labels, accelerating data query and navigation operations.

[0087] 102. Construct a knowledge graph based on natural language processing algorithms, select a dataset, create a list of energy consumption control factors for railway construction, and construct a knowledge graph for energy conservation in construction based on the list of control factors.

[0088] A knowledge graph for energy conservation in railway construction is constructed based on natural language processing algorithms. A dataset is selected, a list of energy consumption control factors for railway construction is created, and a knowledge graph for energy conservation in construction is built based on this list, including:

[0089] Based on historical railway construction and machinery configuration experience, data related to railway machinery energy consumption is extracted as the basic data for constructing a knowledge graph, and a dataset is generated.

[0090] Summarize and organize the historical factors that affect railway construction efficiency and machinery configuration and their corresponding countermeasures to form a list of factors for controlling railway construction energy consumption.

[0091] The control factors and their corresponding measures in the control factor list are quantified.

[0092] The dataset was parsed using natural language processing technology to extract entities, attributes, and relationships related to the list of energy consumption control factors for railway construction.

[0093] The extracted information is organized into a structured knowledge graph;

[0094] Users can query the construction energy-saving knowledge graph based on the list of railway construction energy consumption control factors to obtain guidance and suggestions related to railway construction and machinery configuration.

[0095] This includes leveraging historical construction and machinery configuration experience to aid in the analysis of railway machinery energy consumption data. Historical construction and machinery configuration experience can include past machinery planning and optimization results, as well as expert knowledge in related fields. This experience can help us better understand the patterns and trends in railway machinery energy consumption data and provide targeted suggestions and decision support.

[0096] A knowledge graph of energy conservation in railway construction is constructed based on a list of energy consumption control factors, including:

[0097] The control factors on the list of energy consumption control factors in railway construction are coded; the quantitative values ​​of the treatment measures are processed to be dimensionless, and a knowledge template of the control factors and their corresponding treatment measures is constructed; the control factors, treatment measures and quantitative values ​​are classified into levels and entities are extracted from them. Relationships between entities are established based on the classified levels, and a knowledge ontology of the control factor treatment domain is constructed based on the knowledge template; unique search terms are set for all control factors and treatment measures in the control factor treatment domain knowledge ontology, and a search term sequence list is constructed.

[0098] The coding of control factors on the list of energy consumption control factors in railway construction involves transforming these factors into a unified identifier or number to facilitate processing and management within the system. The purpose of coding is to more easily represent and reference control factors, as well as to enable their classification, association, and querying.

[0099] Based on the same general inventive concept, this invention also protects a railway domain map construction system. The railway domain map construction system provided by this invention will be described below. The railway domain map construction system described below and the railway domain map construction method described above can be referred to in correspondence.

[0100] like Figure 2As shown in the figure, an embodiment of the present invention provides a railway mapping system, which mainly includes the following modules:

[0101] Ontology construction module: Constructs a railway construction energy consumption map based on ontology, builds a knowledge graph ontology model, and generates graph data by mapping the graph ontology model with entities;

[0102] Natural Language Construction Module: Constructs a knowledge graph based on natural language algorithms, selects a dataset, creates a list of energy consumption control factors for railway construction, and constructs a knowledge graph based on the list of control factors.

[0103] The ontology construction module includes:

[0104] Construction Unit: Acquire railway machinery energy consumption data, analyze data resources, and construct a knowledge graph ontology model;

[0105] Mapping Unit: Based on the correspondence between entities in the ontology model and the relational database, the ontology model attributes and database fields are mapped to obtain the data mapping relationship;

[0106] Extraction Unit: Based on the data mapping relationship, the data is extracted into the graph database to generate graph data and obtain the data graph.

[0107] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0108] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a map in the railway field, characterized in that, include: A railway construction energy consumption map is constructed based on an ontology approach. A knowledge graph ontology model is built, and the graph ontology model is mapped to entities to generate graph data, including: S1. Obtain energy consumption data for railway construction, analyze the data resources, divide them into structured data and heterogeneous data, and construct a knowledge graph ontology model; S2, based on the correspondence between entities in the ontology model and the relational database, maps the attributes of the ontology model to the fields in the database to obtain the data mapping relationship; S3, based on data mapping relationships, extracts data into the graph database, generates graph data, and obtains a data graph; A knowledge graph for energy conservation in railway construction is constructed based on natural language algorithms. A dataset is selected, a list of control factors for energy consumption in railway construction is created, and a knowledge graph for energy conservation in construction is constructed based on the list of control factors. The process of acquiring railway construction energy consumption data, analyzing data resources, dividing them into structured data and text data, and constructing a knowledge graph ontology model includes: Identify structured data in the railway construction energy consumption data that conforms to the constructed ontology data pattern, and determine the first ontology based on the constructed ontology data pattern and the structured data; Based on the heterogeneous data in the railway construction energy consumption data that does not conform to the constructed ontology data pattern, an ontology data pattern is extracted, and a second ontology is determined according to the extracted ontology data pattern and the heterogeneous data. Merge the first ontology and the second ontology to obtain the knowledge graph ontology model; The merging of the first and second ontologies yields a knowledge graph ontology model, including: Based on the structure, concepts, and relationships of the first and second ontologies, the similarities and differences between them are analyzed to determine the parts that need to be merged and generate analysis results. Mapping rules are established based on the analysis results, and matching is performed. During the mapping process, naming conflicts and semantic inconsistencies are resolved, and the merged ontology is ensured to be semantically and logically consistent. Based on the mapping rules, the concepts, attributes, and relationships in the two ontology are integrated to form a comprehensive ontology model. During the integration process, duplicate concepts and relationships are filtered out and normalized and standardized to obtain the knowledge graph ontology model. The merged ontology model is validated to ensure that the merged knowledge graph ontology model meets the expected logical and semantic requirements.

2. The railway field map construction method according to claim 1, characterized in that, The mapping relationship between entities in the ontology model and the relational database is established by mapping ontology model attributes to database fields to obtain data mapping relationships, including: Analyze the concepts, attributes, and relationships in the ontology model to determine the attributes that need to be mapped to the relational database, and understand their data types and constraints; Review the structure and table design of the target relational database to ensure that the database has appropriate entities and relationships to store the information in the ontology model; Establish corresponding relationships based on the attributes of the ontology model and the fields of the relational database; Perform data type matching, constraint matching, multi-value attribute processing, and relationship establishment on relational databases. Organize the data mapping relationships into documents and record the correspondence between ontology model attributes and relational database fields.

3. The railway field map construction method according to claim 1, characterized in that, The process of extracting data into a graph database based on data mapping relationships, generating graph data, and obtaining a data graph includes: Based on the data mapping relationship, extract the corresponding data from the relational database and convert the format of the extracted data; Based on the data mapping relationship, create nodes and edges in the graph database; The transformed data is imported into a graph database, and indexes are created for the nodes and edges in the graph database.

4. The railway field map construction method according to claim 1, characterized in that, The method of constructing a knowledge graph based on natural language algorithms involves selecting a dataset, creating a list of energy consumption control factors for railway construction, and constructing a construction energy conservation knowledge graph based on the list of control factors, including: Based on historical railway construction and machinery configuration experience, data related to railway machinery energy consumption is extracted as the basic data for constructing a construction energy-saving knowledge graph, and a dataset is generated. Summarize and organize the historical factors that affect railway construction efficiency and machinery configuration and their corresponding countermeasures to form a list of factors for controlling railway construction energy consumption. The control factors and their corresponding measures in the control factor list are quantified. The dataset is parsed using natural language processing techniques to extract entities, attributes, and relationships related to the control factor list. The extracted information is organized into a structured knowledge graph of energy conservation in construction. Users can query the construction energy-saving knowledge graph based on the list of railway construction energy consumption control factors to obtain guidance and suggestions related to railway construction and machinery configuration.

5. The railway field map construction method according to claim 4, characterized in that, The process of organizing the extracted information into a structured construction energy conservation knowledge graph includes: Code the control factors on the list of energy consumption control factors in railway construction. The quantitative values ​​of the control measures are made dimensionless, and a knowledge template of the control factors and their corresponding control measures is constructed. The control factors, treatment measures, and quantitative values ​​are classified into levels, and entities are extracted from them. Relationships between entities are established based on the classified levels, and a knowledge ontology for the control factor treatment domain is constructed based on knowledge templates. All control factors and control measures in the domain knowledge ontology are assigned unique search terms, and a search term sequence list is constructed.

6. A railway mapping system, characterized in that, include: Ontology construction module: Constructs a railway construction energy consumption map based on ontology, builds a knowledge graph ontology model, and generates graph data by mapping the graph ontology model with entities; Natural Language Construction Module: Constructs a graph based on natural language algorithms, selects a dataset, creates a list of energy consumption control factors for railway construction, and constructs a construction energy-saving knowledge graph based on the list of control factors; The ontology construction module includes: Construction Unit: Acquire energy consumption data for railway construction, analyze data resources, and construct a knowledge graph ontology model; Mapping Unit: Based on the correspondence between entities in the ontology model and the relational database, the ontology model attributes and database fields are mapped to obtain the data mapping relationship; Extraction Unit: Based on the data mapping relationship, the data is extracted into the graph database to generate graph data and obtain the data graph; The process of acquiring railway construction energy consumption data, analyzing data resources, and constructing a knowledge graph ontology model includes: Identify structured data in the railway construction energy consumption data that conforms to the constructed ontology data pattern, and determine the first ontology based on the constructed ontology data pattern and the structured data; Based on the heterogeneous data in the railway construction energy consumption data that does not conform to the constructed ontology data pattern, an ontology data pattern is extracted, and a second ontology is determined according to the extracted ontology data pattern and the heterogeneous data. Merge the first ontology and the second ontology to obtain the knowledge graph ontology model; The merging of the first and second ontologies yields a knowledge graph ontology model, including: Based on the structure, concepts, and relationships of the first and second ontologies, the similarities and differences between them are analyzed to determine the parts that need to be merged and generate analysis results. Mapping rules are established based on the analysis results, and matching is performed. During the mapping process, naming conflicts and semantic inconsistencies are resolved, and the merged ontology is ensured to be semantically and logically consistent. Based on the mapping rules, the concepts, attributes, and relationships in the two ontology are integrated to form a comprehensive ontology model. During the integration process, duplicate concepts and relationships are filtered out and normalized and standardized to obtain the knowledge graph ontology model. The merged ontology model is validated to ensure that the merged knowledge graph ontology model meets the expected logical and semantic requirements.

Images