CN110781249A - Knowledge graph-based multi-source data fusion method and device for thermal power plant - Google Patents
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Abstract
The invention belongs to the field of power plant operation data processing, and particularly relates to a knowledge graph-based multi-source data fusion method and device for a thermal power plant. The method is used for solving the difficult problems that the existing data fusion technology is difficult to effectively integrate the distributed industry knowledge systems from different sources and update the knowledge base when the data fusion technology is oriented to the whole industry application field. The application provides a construction method and a system of a knowledge graph in the thermal power field, and meanwhile, multi-source data are obtained and fused based on the constructed knowledge graph.
Description
Technical Field
The invention belongs to the field of power plant operation data processing, and particularly relates to a knowledge graph-based multi-source data fusion method and device for a thermal power plant.
Background
With the rapid development of the information construction of the thermal power plant, a data fusion platform is established, and management information including equipment characteristic data, fault data, maintenance data, fault reasons, processing information, long-value logs and the like, and data including an equipment operation state, process parameters, equipment performance and the like are integrated, so that an equipment life-cycle database is established, and support is provided for unit operation and maintenance decisions. However, the thermal power plant data has the difficulties of multiple types, strong heterogeneity, fast data growth speed and the like, and the fusion of the data faces a great challenge.
A knowledge graph is a vast knowledge network used to describe concepts and their relationships in the real world. In general, nodes of a knowledge graph represent entities or concepts, and edges are composed of attributes or relationships. Knowledge maps have been used to refer broadly to a variety of large-scale knowledge bases. In the composition of the knowledge graph, an entity generally refers to something that is distinguishable and independent, such as a certain sensing device or the like. Attributes refer to different features intrinsic or extrinsic to an entity, for example, different points of a sensor may be considered attributes. Relationships are different associations between an entity and other entities, such as dependencies between different measure points.
The existing data fusion technology is mainly realized by depending on main technical means such as entity extraction, semantic similarity calculation, entity fusion and the like. However, for one industry, there are wide knowledge sources and strong isomerism; the knowledge quality is uneven, and the knowledge from different sources has the phenomena of redundancy, repetition and even mutual contradiction; in addition, the development speed of the industry is very high, and the knowledge base also faces the problems that the hierarchical structure is outdated, needs to be updated in time and the like.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a knowledge graph-based multisource data fusion method and device for a thermal power plant, which are used for solving the difficult problems that the existing data fusion technology is difficult to effectively integrate distributed industry knowledge systems from different sources when being oriented to the whole industry application field, the knowledge base is difficult to update and the like.
The invention is realized in this way, and the heat-engine plant multi-source data fusion method based on the knowledge graph is characterized in that: the method comprises the following steps:
step S101, establishing a basic framework of a knowledge graph according to a standardization system of equipment codes of a thermal power plant and a universal data standard, wherein the knowledge graph is logically divided into a mode layer and a data layer, the data layer mainly comprises a series of facts, and the knowledge is stored by taking the facts as a unit;
step S102, uniformly standardizing the relation among all entities in the basic framework to obtain a standardized dictionary table with standard specifications; an entity is a real-world concept. Either thermal power plant equipment or a component of the equipment. But also device-independent concepts such as people, weather, etc.
Step S103, acquiring structured data related to the content in the knowledge graph: extracting related structured data from data information including characteristic data, fault data, maintenance data and the like of thermal power plant equipment, management information such as fault reasons, processing methods, value long logs and the like, equipment running state, process parameters and equipment performance data according to contents defined in a knowledge graph;
step S104, extracting entity information of the key entity from the structured data; on the basis of the thermal power plant data acquired in step S103, key entities related to production and operation are extracted from the structured data, and mapped with concepts and attributes in the standard dictionary table in the knowledge graph, so as to achieve entity alignment. Entity alignment (entity alignment) is the process of determining whether 2 entities in the same or different datasets point to the same object in the real world. Entity alignment can help solve the problems of semantic isomerism, semantic conflict and the like of multi-source data in the fusion process. And meanwhile, establishing entity attributes and the association relationship between the entities according to the data association described in the knowledge graph.
Step S105, fusing entities and attribute information contained in different source data according to a standard dictionary table in the knowledge map to finally form consistent and normative structured data;
and step S106, generating corresponding triple data pairs based on the structured data, and storing the triple data pairs as a knowledge graph. The triple data is stored to Neo4j graph database. The triple data pair is composed of nodes, relations and attributes, and the condition of any entity can be completely described through the triple. Meanwhile, attributes can be given to the relations, and the network model of the knowledge graph can be flexibly expanded. The pair of triple data pairs is a tuple in English.
In the step S102, the unified specification specifically refers to converting the entity attribute of the entity into triple data RDF, and performing unified specification on the relationship type and the naming rule between the entity attribute and the entity according to the triple data RDF.
The step S105 is implemented by the following steps:
step S105-1, judging whether the entity, the attribute and the relation contained in the data are consistent with those defined in the standard specification: if the entity, the attribute and the relationship are obviously inconsistent, executing the step S105-2, and carrying out data fusion on the entity information by relying on the standard dictionary table to form structured data;
s105-2, mapping the entity name, the entity attribute and the entity relationship according to the specification definition of the knowledge graph, and fusing on the basis of mapping to form final standardized structured data; specifically, the method comprises the following steps: mapping the entity name with the content in the standard dictionary table to obtain a synonymous entity name, and finally determining a synonymous entity name; performing data fusion on the synonymous entities, names, attributes and relations to form structured data;
and S105-3, for other entities, attributes and relations inconsistent with the standard, adopting a method of submitting to manual work for auditing and editing, eliminating the conditions of isomerism and inconsistency through professional knowledge of people, and finally realizing consistency between different data sources and the knowledge graph.
A multi-source data fusion device of a thermal power plant based on a knowledge graph is characterized by comprising
A knowledge graph infrastructure establishing module 10, configured to establish a knowledge graph infrastructure according to a universal data standard;
the standard dictionary table generating module 20 is configured to perform unified specification on relationships between entities in the infrastructure to obtain a standard dictionary table with standard specifications;
a multi-source data acquisition module 30 for acquiring structured data related to content in the knowledge-graph;
a data entity extraction module 40, which is based on the definition of the knowledge graph and is used for extracting entity information of key entities from the structured data;
the multi-source data fusion module 50 is used for performing data fusion on the entity names, attributes and relationships according to the knowledge graph and the standard dictionary table to form structured data;
a triplet data storage module 60 for storing triplet data pairs into the Neo4J database.
The standard dictionary table generating module 20 includes:
a triple data structure conversion unit 21, configured to convert the attribute and the relationship of the entity into triple data RDF;
and the ternary group data unified definition unit 22 is used for carrying out unified specification on the relationship type and the naming rule of the entity attribute and the entity according to the RDF.
The source data fusion module 50 comprises
A judging unit 51, configured to judge whether the entity information meets a standard specification;
and the data fusion unit 52 is configured to perform data fusion on the entity information according to the standard dictionary table to form a triple data pair when the entity information conforms to the standard specification.
The invention has the advantages and positive effects that:
the problems that the existing data fusion technology is difficult to effectively integrate distributed industry knowledge systems from different sources and update a knowledge base when the data fusion technology is oriented to the whole industry application field are solved. The application provides a construction method and a system of a knowledge graph in the thermal power field, and meanwhile, multi-source data are obtained and fused based on the constructed knowledge graph.
Drawings
FIG. 1 is an example of structured data collected in real time from thermal power plant equipment sites according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for constructing a knowledge graph according to an embodiment of the present invention;
FIG. 3 is an architecture diagram of a knowledge graph building system provided by an embodiment of the invention.
Detailed Description
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1:
as shown in fig. 1-2, fig. 2 is a flowchart of a method for constructing a knowledge graph according to an embodiment of the present invention. For the understanding of the embodiment, the method for constructing a knowledge graph disclosed in the embodiment of the present invention will be described in detail first.
(1) And S101, establishing a basic framework of the knowledge graph according to a standardization system of the thermal power plant equipment codes and a universal data standard. The knowledge graph can be logically divided into a mode layer and a data layer, wherein the data layer mainly comprises a series of facts, and the knowledge is stored by taking the facts as units.
(2) Step S102, the relationship among all the entities in the basic framework is unified and normalized, and a standardized dictionary table with standard specifications is obtained. Specifically, the method comprises the following steps: converting the entity attribute of the entity into triple data RDF (resource description Framework); and uniformly standardizing the entity attributes and the relationship types and naming rules of the entities according to RDF. The fact is expressed by a triple of (entity 1, relationship, entity 2), (entity, attribute value), and a graph database can be selected as a storage medium. For example, embodiments of the present invention may convert attributes of coal combustion into the following triples: the coal quality combustion indexes comprise: main steam temperature, reheated steam temperature, desuperheating water amount, hearth pressure, hearth temperature, wall temperature out-of-limit and the like. Wherein, the wall temperature transfinites and is an entity, includes again: the wall temperature of the final superheater tube, the wall temperature of the high-temperature reheater tube, the wall temperature of the superheater separating screen, the wall temperature of the superheater rear screen and the like. There is also an inclusion relationship between the coal quality combustion index and the wall temperature violation.
(3) Step S103, acquiring structured data related to the content in the knowledge-graph. The thermal power plant comprises data information such as equipment characteristic data, fault data and maintenance data, management information such as fault reasons, processing methods and value logs, and diversified structured data such as equipment running state, process parameters and equipment performance. Relevant structured data will be extracted from these data sets based on the relevant content defined in the knowledge-graph. The data is structured as shown in figure 1.
(4) Step S104, extracting entity information of the key entity from the structured data; on the basis of the thermal power plant data acquired in step S103, key entities (such as coal quality, wall temperature, furnace, etc.) related to production and operation are extracted from the structured data, and mapped with concepts and attributes in a standard dictionary table in a previously established knowledge graph, so that the problems of semantic isomerism, semantic conflict, etc. of multi-source data in the fusion process are solved, and entity alignment is achieved. And meanwhile, establishing entity attributes and the association relationship between the entities according to the data association described in the knowledge graph.
(5) And step S105, fusing entities and attribute information contained in different source data according to a standard dictionary table in the knowledge map, and finally forming consistent and normative structured data. Step S105 can be implemented by taking the following steps:
step S105-1, judging whether the entity, the attribute and the relation contained in the data are consistent with those defined in the standard specification. If the entity, the attribute and the relationship are obviously inconsistent, executing the step S105-2, and carrying out data fusion on the entity information by relying on the standard dictionary table to form structured data;
and S105-2, mapping the entity name, the entity attribute and the entity relationship according to the standard definition of the knowledge graph, and fusing on the basis of mapping to form final standard structured data. Specifically, the method comprises the following steps: mapping the entity name with the content in the standard dictionary table to obtain a synonymous entity name, and finally determining a synonymous entity name; and carrying out data fusion on the synonymous entities, names, attributes and relations to form structured data.
And step S105-3, for other entities, attributes and relationships inconsistent with the standard, adopting a method of submitting to manual work for auditing and editing. The heterogeneous and inconsistent situations are eliminated by the professional knowledge of people. Finally, consistency between different data sources and the knowledge graph is achieved. Meanwhile, on the basis, the final fusion of data from different sources is realized.
(6) And step S106, generating corresponding triple data pairs based on the structured data, and storing the triple data pairs as a knowledge graph. The triple data is stored to Neo4j graph database. The triple data pair is composed of nodes, relations and attributes, and the condition of any entity can be completely described through the triple. Meanwhile, attributes can be given to the relations, and the network model of the knowledge graph can be flexibly expanded.
Example 2:
FIG. 3 is a schematic diagram of a knowledge graph building system according to an embodiment of the invention. Referring to fig. 3, the knowledge-graph building system includes:
(1) a knowledge graph infrastructure establishing module 10, configured to establish a knowledge graph infrastructure according to a universal data standard;
(2) and a standard dictionary table generating module 20, configured to perform unified specification on relationships between entities in the infrastructure to obtain a standard dictionary table with standard specifications. The unified specification module 20 includes:
a triple data structure conversion unit 21, configured to convert the attribute and the relationship of the entity into triple data RDF;
and the ternary group data unified definition unit 22 is used for carrying out unified specification on the relationship type and the naming rule of the entity attribute and the entity according to the RDF.
(3) A multi-source data acquisition module 30 for acquiring structured data related to content in the knowledge-graph;
(4) a data entity extraction module 40, which is based on the definition of the knowledge graph and is used for extracting entity information of key entities from the structured data;
(5) the multi-source data fusion module 50 is used for performing data fusion on the entity names, attributes and relationships according to the knowledge graph and the standard dictionary table to form structured data;
a judging unit 51, configured to judge whether the entity information meets a standard specification;
and the data fusion unit 52 is configured to perform data fusion on the entity information according to the standard dictionary table to form a triple data pair when the entity information conforms to the standard specification.
(6) A triplet data storage module 60 for storing triplet data pairs into the Neo4J database.
The knowledge graph construction system provided by the embodiment of the invention has the same technical characteristics as the knowledge graph construction method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
Claims (6)
1. A thermal power plant multi-source data fusion method based on knowledge graph is characterized in that: the method comprises the following steps:
step S101, establishing a basic framework of a knowledge graph according to a standardization system of equipment codes of a thermal power plant and a universal data standard, wherein the knowledge graph is logically divided into a mode layer and a data layer, the data layer mainly comprises a series of facts, and the knowledge is stored by taking the facts as a unit;
step S102, uniformly standardizing the relation among all entities in the basic framework to obtain a standardized dictionary table with standard specifications;
step S103, acquiring structured data related to the content in the knowledge graph: extracting related structured data from data information including characteristic data, fault data, maintenance data and the like of thermal power plant equipment, management information such as fault reasons, processing methods, value long logs and the like, equipment running state, process parameters and equipment performance data according to contents defined in a knowledge graph;
step S104, extracting entity information of the key entity from the structured data; on the basis of the thermal power plant data acquired in step S103, extracting key entities related to production and operation from the structured data, and mapping the key entities with concepts and attributes in a standard dictionary table in a knowledge graph to achieve entity alignment;
step S105, fusing entities and attribute information contained in different source data according to a standard dictionary table in the knowledge map to finally form consistent and normative structured data;
and step S106, generating corresponding triple data pairs based on the structured data, and storing the triple data pairs as a knowledge graph. The triple data is stored to Neo4j graph database. The triple data pair is composed of nodes, relations and attributes, and the condition of any entity can be completely described through the triple. Meanwhile, attributes can be given to the relations, and the network model of the knowledge graph can be flexibly expanded.
2. The knowledge-graph-based multi-source data fusion method for the thermal power plant as claimed in claim 1, wherein in the step S102, the uniform specification specifically means that entity attributes of the entities are converted into triple data RDF, and the relationship types and naming rules of the entity attributes and the entities are uniformly specified according to the triple data RDF.
3. The knowledge-graph-based thermal power plant multi-source data fusion method as claimed in claim 1, wherein the step S105 is implemented by adopting the following steps:
step S105-1, judging whether the entity, the attribute and the relation contained in the data are consistent with those defined in the standard specification: if the entity, the attribute and the relationship are obviously inconsistent, executing the step S105-2, and carrying out data fusion on the entity information by relying on the standard dictionary table to form structured data;
s105-2, mapping the entity name, the entity attribute and the entity relationship according to the specification definition of the knowledge graph, and fusing on the basis of mapping to form final standardized structured data; specifically, the method comprises the following steps: mapping the entity name with the content in the standard dictionary table to obtain a synonymous entity name, and finally determining a synonymous entity name; performing data fusion on the synonymous entities, names, attributes and relations to form structured data;
and S105-3, for other entities, attributes and relations inconsistent with the standard, adopting a method of submitting to manual work for auditing and editing, eliminating the conditions of isomerism and inconsistency through professional knowledge of people, and finally realizing consistency between different data sources and the knowledge graph.
4. The knowledge-graph-based thermal power plant multi-source data fusion device of claim 1, comprising
A knowledge graph infrastructure establishing module 10, configured to establish a knowledge graph infrastructure according to a universal data standard;
the standard dictionary table generating module 20 is configured to perform unified specification on relationships between entities in the infrastructure to obtain a standard dictionary table with standard specifications;
a multi-source data acquisition module 30 for acquiring structured data related to content in the knowledge-graph;
a data entity extraction module 40, which is based on the definition of the knowledge graph and is used for extracting entity information of key entities from the structured data;
the multi-source data fusion module 50 is used for performing data fusion on the entity names, attributes and relationships according to the knowledge graph and the standard dictionary table to form structured data;
a triplet data storage module 60 for storing triplet data pairs into the Neo4J database.
5. The knowledge-graph-based thermal power plant multi-source data fusion device according to claim 4, wherein the standard dictionary table generating module 20 comprises:
a triple data structure conversion unit 21, configured to convert the attribute and the relationship of the entity into triple data RDF;
and the ternary group data unified definition unit 22 is used for carrying out unified specification on the relationship type and the naming rule of the entity attribute and the entity according to the RDF.
6. The knowledge-graph-based thermal power plant multi-source data fusion apparatus of claim 4, wherein the source data fusion module 50 comprises
A judging unit 51, configured to judge whether the entity information meets a standard specification;
and the data fusion unit 52 is configured to perform data fusion on the entity information according to the standard dictionary table to form a triple data pair when the entity information conforms to the standard specification.
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