Knowledge graph construction method and system for verification of electric energy metering device

Abstract

The invention belongs to the technical field of knowledge maps, and provides a knowledge map construction method and system for verification of an electric energy metering device, and the method comprises the steps: obtaining a verification knowledge base of the electric energy metering device; based on the obtained verification knowledge base, constructing an ontology layer model of the electric energy metering device detection practical training knowledge graph; according to the constructed ontology layer model, obtaining multi-level entities calibrated by the electric energy metering device and a relationship between the entities; performing knowledge fusion on the obtained multi-level entities and the relationship between the entities to obtain fusion data; and storing the obtained fusion data in the graph database to complete construction of the knowledge graph for verification of the electric energy metering device based on the graph database. According to the method, the semantic relationship among the entities of the electric energy metering device is accurately represented by constructing the complete knowledge graph, and the query efficiency of the knowledge points of the electric energy metering device is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of knowledge graph technology, and specifically relates to a knowledge graph construction method and system for calibration of electric energy metering devices. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] A knowledge graph is a structured semantic knowledge base that describes entities and their relationships in the form of a graph. It formally describes core elements such as "entities," "attributes," and "relationships" in the form of triples, effectively integrating large-scale network information and visually organizing, managing, and storing the many entities, relationships, and attributes of the internet. A knowledge graph is a directed graph that connects and presents knowledge using a network topology. This allows for the integration and extraction of diverse and heterogeneous data, establishing richer relationships between entities, and providing personalized services to users by inferring implicit information. It is widely used in many fields, including intelligent search and text classification.

[0004] As the terminal equipment of the power grid, the accuracy of the measurement of electric energy metering devices is directly related to the legitimate rights and interests of electricity users and power supply companies. Therefore, the calibration of electric energy metering devices is extremely important. The calibration of electric energy metering devices includes on-site inspection of metering transformers and calibration of electric energy meters. It involves the basis, equipment, steps, and test items for on-site inspection of metering transformers, and the basis, calibration equipment, and calibration items for electric energy meters. Due to the large number of calibration equipment and calibration steps for electric energy metering devices, it is difficult to understand the concept of metering devices and implement the calibration steps. Therefore, clearly and accurately extracting the relationships between related entities can effectively improve the efficiency of querying knowledge points about electric energy metering device calibration and help metering device calibration workers build a knowledge framework.

[0005] Currently, knowledge graphs have been widely used in many fields. However, there are relatively few knowledge graphs related to the calibration of electric energy metering devices, and there is no knowledge graph for the calibration of electric energy metering devices with high completeness. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a knowledge graph construction method and system for the calibration of electric energy metering devices. By constructing a complete knowledge graph to accurately represent the semantic relationship between the various entities of the electric energy metering device, the query efficiency of the knowledge points of the electric energy metering device is effectively improved.

[0007] According to some embodiments, a first solution of the present invention provides a method for constructing a knowledge graph for electric energy metering device verification, which adopts the following technical solutions:

[0008] A knowledge graph construction method for electric energy metering device verification, comprising:

[0009] Obtain the verification knowledge base of electric energy metering devices;

[0010] Based on the acquired verification knowledge base, an ontology layer model of the knowledge graph for practical training of electric energy metering device detection is constructed;

[0011] According to the constructed ontology layer model, the multi-level entities and the relationships between the entities for the verification of the electric energy metering device are obtained;

[0012] Perform knowledge fusion on the obtained multi-level entities and the relationships between entities to obtain fused data;

[0013] The obtained fused data is stored in the graph database, completing the construction of the knowledge graph for the calibration of electric energy metering devices based on the graph database.

[0014] As a further technical limitation, the obtained fusion data is stored in the import folder of the neo4j software in the form of a CSV database, and the nodes and relationships are imported into the graph database respectively through the Cypher language to complete the construction of the knowledge graph for the calibration of electric energy metering devices based on the graph database.

[0015] As a further technical limitation, the calibration knowledge base of the electric energy metering device obtained includes at least the on-site calibration knowledge base of the transformer and the indoor calibration knowledge base of the electric energy meter; the on-site calibration knowledge base of the transformer includes the electric energy metering device, the power transformer, inspection items, inspection equipment and inspection steps, and the indoor calibration knowledge base of the electric energy meter includes the electric energy meter, the electric energy meter calibration device and the electric energy meter calibration items.

[0016] As a further technical limitation, based on the obtained calibration knowledge base and combined with the knowledge framework of the electric energy metering device, the logical relationship of "entity-relationship-entity" is established, and the ontology layer model of the electric energy metering device detection training knowledge graph is obtained.

[0017] As a further technical limitation, the typical cases, question bank and difficult points of practical training for the calibration of electric energy metering devices are defined as "entity-attribute-attribute value", and the constructed ontology layer model is modeled through the CSV database to obtain the first-level entities and second-level entities of the calibration of electric energy metering devices. The first-level entity is the course.csv file for on-site inspection of power transformers and indoor inspection of electric energy meters, and the second-level entity is the task.csv file for electric energy metering devices, power transformers, inspection items, inspection equipment and inspection steps.

[0018] As a further technical limitation, in the process of knowledge fusion, entity alignment, entity disambiguation and data classification are performed on the obtained multi-level entities and the relationships between entities; the entity alignment is to uniquely identify the entity name as a non-empty string for data information from different verification knowledge bases, that is, to obtain the entity number; the entity disambiguation is performed through manual verification to unify different references to the same entity and obtain unambiguous entity information; the data classification is to classify the entities in the electric energy metering device verification knowledge base to obtain different types of hierarchical entity data.

[0019] According to some embodiments, a second solution of the present invention provides a knowledge graph construction system for electric energy metering device verification, which adopts the following technical solutions:

[0020] A knowledge graph construction system for electric energy metering device verification, comprising:

[0021] An acquisition module is configured to acquire a verification knowledge base of an electric energy metering device; based on the acquired verification knowledge base, an ontology layer model of a knowledge graph for practical training of electric energy metering device detection is constructed;

[0022] A fusion module is configured to obtain multi-level entities and relationships between entities for verification of the electric energy metering device based on the constructed ontology layer model; perform knowledge fusion on the obtained multi-level entities and relationships between entities to obtain fused data;

[0023] A construction module is configured to store the obtained fusion data in a graph database, and complete the construction of a knowledge graph for the calibration of electric energy metering devices based on the graph database.

[0024] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0025] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the knowledge graph construction method for calibrating an electric energy metering device as described in the first embodiment of the present invention.

[0026] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0027] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the knowledge graph construction method for calibrating an electric energy metering device as described in the first embodiment of the present invention.

[0028] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0029] A computer program product includes software code, wherein the program in the software code executes the steps in the knowledge graph construction method for calibrating an electric energy metering device as described in the first embodiment of the present invention.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention constructs a complete knowledge graph to accurately represent the semantic relationship between the various entities of the electric energy metering device, thereby effectively improving the query efficiency of the knowledge points of the electric energy metering device. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0033] Figure 1 This is a flow chart of a method for constructing a knowledge graph for verifying an electric energy metering device in the first embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the knowledge framework structure of the electric energy metering device verification knowledge graph in the first embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the knowledge graph module branches in Example 1 of the present invention;

[0036] Figure 4 A component diagram of the electric energy metering device verification knowledge graph in the first embodiment of the present invention;

[0037] Figure 5 This is a node attribute diagram of the installed electric energy meter in the first embodiment of the present invention;

[0038] Figure 6 This is the standard electric energy meter node attribute diagram in the first embodiment of the present invention;

[0039] Figure 7 This is a structural block diagram of a knowledge graph construction system for calibrating an electric energy metering device in Example 2 of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0043] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention, and should not be understood as limiting the present invention.

[0044] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0045] Example 1

[0046] Embodiment 1 of the present invention introduces a knowledge graph construction method for calibration of electric energy metering devices.

[0047] like Figure 1 The method for constructing a knowledge graph for electric energy metering device verification shown includes:

[0048] Obtain the verification knowledge base of electric energy metering devices;

[0049] Based on the acquired verification knowledge base, an ontology layer model of the knowledge graph for practical training of electric energy metering device detection is constructed;

[0050] According to the constructed ontology layer model, the multi-level entities and the relationships between the entities for the verification of the electric energy metering device are obtained;

[0051] Perform knowledge fusion on the obtained multi-level entities and the relationships between entities to obtain fused data;

[0052] The obtained fused data is stored in the graph database, completing the construction of the knowledge graph for the calibration of electric energy metering devices based on the graph database.

[0053] This embodiment uses Neo4j, a commonly used NoSQL graph database with high performance, strong practicality, and lightweight architecture. Data is stored on a network as nodes and relationships rather than in tables. In a graph database, nodes represent entities, and relationships represent the association between two entities, enabling complex connections and efficient queries between nodes. This embodiment extracts effective knowledge units for metrological device verification, establishes topological relationships between knowledge modules, and constructs a knowledge framework for metrological device verification, thereby improving the effectiveness of metrological device verification.

[0054] As one or more implementation methods, the course is divided into two major areas: on-site transformer inspection and indoor energy meter calibration, based on the course training plan, syllabus, key knowledge points, etc. Based on the characteristics of each course module, on-site transformer inspection is divided into energy metering devices, power transformers, inspection items, inspection equipment, and inspection steps. Indoor energy meter calibration is divided into energy meters, energy meter calibration equipment, and energy meter calibration items. Based on this foundation, power transformers can be divided into their functions, classifications, current transformers, and voltage transformers. Based on the course knowledge framework, "entity-relationship-entity" and related "concepts" are defined, and an ontology-level model of the knowledge graph for practical training in metering device calibration is established from top to bottom.

[0055] As one or more implementation methods, typical cases, question banks, and key and difficult points in metering device calibration training are defined as "entity-attribute-attribute value." The constructed ontology layer model of the complete metering device calibration training is modeled using a CSV database. First-level entities, such as on-site inspection of power transformers and indoor calibration of electric energy meters, are defined as the course.csv file, which consists of two columns: course_id and title. Second-level entities, such as electric energy metering devices, power transformers, inspection items, inspection equipment, and inspection steps, are defined as the task.csv file, which consists of two columns: task_id and name. The relationship between these two columns is defined as too.csv, consisting of two columns: course_id and task_id. Entities at each level are defined as files called task2, task3, and so on.

[0056] The fused data obtained in this embodiment can be divided into transformer on-site inspection and electricity meter calibration. Among them, the transformer on-site inspection is preliminarily divided from the basis, equipment, steps, test items, etc. of the on-site inspection of the metering transformer. At the third level, it is divided from the key points of the on-site inspection, transformer classification, characteristics, functions, on-site inspection steps, items, etc., and each key point is set as a "node". Based on this, knowledge decomposition is further carried out, and important knowledge points are selected from the electricity metering question bank and set as "attributes" and "attribute values".

[0057] The obtained fused data is preliminarily divided into the basis for calibration of electric energy meters, calibration devices, calibration items, etc., and is divided at the third level into the classification of electric energy meters, nameplate parameters, composition of calibration devices, calibration items, calibration basis, etc., and further decomposed into knowledge at the next level based on this "node", embedding important contents of the electric energy meter calibration knowledge base into the model. For example, in the next level of accuracy level in the classification, the accuracy levels of standard meters and installed meters, as well as the characteristics of s, the latest standard specifications based on which the accuracy level is based, and the requirements for the accuracy level of standard meters and installed meters are set as "attributes" and "attribute values", integrating important knowledge of electric energy metering into the knowledge.

[0058] This embodiment stores entities, relationships, attributes, etc. in a Neo4j graph database. By using the query and visualization tools of the graph database, a knowledge graph for metrological device calibration training can be formed, which can realize autonomous learning and knowledge exploration functions.

[0059] This example screens out important information about transformer field inspection items, equipment, and steps, as well as electricity meter verification items, devices, and concepts, from the metering device verification knowledge base. It then organizes these information and establishes topological relationships between these items. Entities include entity names and entity-containing attributes, where the attributes of these entities include attribute describe values and description values. This defines and explains the entity triples corresponding to the metering device verification knowledge graph. Based on entities, relationships, and attributes, the content corresponding to each knowledge module in metering device verification can be constructed.

[0060] This embodiment uses Neo4j graph database to build a graph, stores information in the graph based on the relationship between nodes and nodes, and effectively handles the relationship modeling between knowledge units. The measurement device calibration knowledge graph model built based on Neo4j is as follows: Figure 2 As shown in the figure, nodes and relationships form a directed graph, and attributes are assigned to relationships, forming a Neo4j-based knowledge graph for metrology device verification. Nodes represent entities, representing knowledge units such as the name of a metrology device, a verification procedure, or an item. Relationships represent the connections between knowledge units, with options such as include and divide. Attributes generally represent the properties of each node, such as important knowledge points like function and definition, and can also include corresponding test questions.

[0061] This embodiment stores the obtained fusion data in the form of a CSV database in the import folder of the neo4j software, and imports the nodes and relationships into the graph database using the Cypher language, completing the construction of a knowledge graph for the calibration of electric energy metering devices based on the graph database.

[0062] In this embodiment, the calibration of electric energy meters is taken as an example. The accuracy level knowledge unit is selected and the key and difficult test points are integrated into the knowledge graph. The constructed electric energy meter accuracy level knowledge graph and node attributes are as follows: Figure 3 、 Figure 4 、 Figure 5 and Figure 6 As shown, Figure 3 The knowledge graph module branch for the accuracy level, Figure 4 This is the construction diagram of the knowledge graph for the verification of electric energy metering devices. Figure 5 It is the node property of the installed electric energy meter. Figure 6 It is a standard electricity meter node attribute. From the attribute value, it can be derived that the key and difficult knowledge units related to the accuracy level of electricity metering have been included.

[0063] In this embodiment, a total of 77 entities and 75 relationships are constructed in the complete measurement device verification knowledge graph. Some entity-attribute contents are shown in Table 1:

[0064] Table 1 Entity-attribute content

[0065]

[0066]

[0067] This embodiment effectively improves the query efficiency of knowledge points of electric energy metering devices by constructing a complete knowledge graph to accurately represent the semantic relationships between the various entities of the electric energy metering device; based on the entities and relationships, the semantic relationships between the various entities of the metering device calibration are accurately represented, which can be applied to the query of knowledge points of metering device calibration, covering all knowledge points in the metering device calibration outline, which can effectively improve the efficiency of query of knowledge points of metering device calibration and help metering device calibration workers build a knowledge framework.

[0068] Example 2

[0069] The second embodiment of the present invention introduces a knowledge graph construction system for electric energy metering device calibration.

[0070] like Figure 7 A knowledge graph construction system for electric energy metering device verification is shown, comprising:

[0071] An acquisition module is configured to acquire a verification knowledge base of an electric energy metering device; based on the acquired verification knowledge base, an ontology layer model of a knowledge graph for practical training of electric energy metering device detection is constructed;

[0072] A fusion module is configured to obtain multi-level entities and relationships between entities for verification of the electric energy metering device based on the constructed ontology layer model; perform knowledge fusion on the obtained multi-level entities and relationships between entities to obtain fused data;

[0073] A construction module is configured to store the obtained fusion data in a graph database, and complete the construction of a knowledge graph for the calibration of electric energy metering devices based on the graph database.

[0074] The detailed steps are the same as the knowledge graph construction method for electric energy metering device calibration provided in Example 1, and will not be repeated here.

[0075] Example 3

[0076] A third embodiment of the present invention provides a computer-readable storage medium.

[0077] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of a knowledge graph construction method for calibrating an electric energy metering device as described in the first embodiment of the present invention.

[0078] The detailed steps are the same as the knowledge graph construction method for electric energy metering device calibration provided in Example 1, and will not be repeated here.

[0079] Example 4

[0080] A fourth embodiment of the present invention provides an electronic device.

[0081] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the knowledge graph construction method for calibrating an electric energy metering device as described in Example 1 of the present invention.

[0082] The detailed steps are the same as the knowledge graph construction method for electric energy metering device calibration provided in Example 1, and will not be repeated here.

[0083] Example 5

[0084] A fifth embodiment of the present invention provides a computer program product.

[0085] A computer program product includes software code, wherein the program in the software code executes the steps in the knowledge graph construction method for calibrating an electric energy metering device as described in the first embodiment of the present invention.

[0086] The detailed steps are the same as the knowledge graph construction method for electric energy metering device calibration provided in Example 1, and will not be repeated here.

[0087] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0092] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0093] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A knowledge graph construction method for electric energy metering device verification, characterized in that: include: Obtain the verification knowledge base of electric energy metering devices; Based on the acquired verification knowledge base, an ontology layer model of the knowledge graph for practical training of electric energy metering device detection is constructed; According to the constructed ontology layer model, the multi-level entities and the relationships between the entities for the verification of the electric energy metering device are obtained; Perform knowledge fusion on the obtained multi-level entities and the relationships between entities to obtain fused data; The obtained fused data is stored in the graph database, completing the construction of the knowledge graph for the calibration of electric energy metering devices based on the graph database.

2. A knowledge graph construction method for electric energy metering device verification as claimed in claim 1, characterized in that: The obtained fused data is stored in the import folder of the neo4j software in the form of a CSV database. The nodes and relationships are imported into the graph database respectively through the Cypher language to complete the construction of the knowledge graph for the calibration of electric energy metering devices based on the graph database.

3. The method for constructing a knowledge graph for electric energy metering device verification as claimed in claim 1, characterized in that: The obtained calibration knowledge base of the electric energy metering device includes at least the on-site calibration knowledge base of the transformer and the indoor calibration knowledge base of the electric energy meter; the on-site calibration knowledge base of the transformer includes the electric energy metering device, the power transformer, the inspection items, the inspection equipment and the inspection steps, and the indoor calibration knowledge base of the electric energy meter includes the electric energy meter, the electric energy meter calibration device and the electric energy meter calibration items.

4. A knowledge graph construction method for electric energy metering device verification as claimed in claim 1, characterized in that: Based on the obtained verification knowledge base and combined with the knowledge framework of electric energy metering devices, the "entity-relationship-entity" logical relationship is established, and the ontology layer model of the electric energy metering device detection training knowledge graph is obtained.

5. The method for constructing a knowledge graph for electric energy metering device verification as claimed in claim 1, characterized in that: The typical training cases, question banks, and key and difficult points for the verification of electric energy metering devices are defined as "entity-attribute-attribute value". The constructed ontology layer model is modeled using a CSV database to obtain the first-level entities and second-level entities of the verification of electric energy metering devices. The first-level entity is the course.csv file for on-site inspection of power transformers and indoor inspection of electric energy meters. The second-level entity is the task.csv file for electric energy metering devices, power transformers, inspection items, inspection equipment, and inspection steps.

6. A knowledge graph construction method for electric energy metering device verification as claimed in claim 1, characterized in that: In the process of knowledge fusion, entity alignment, entity disambiguation and data classification are performed on the obtained multi-level entities and the relationships between entities; the entity alignment is to uniquely identify the entity name with a non-empty string for data information from different verification knowledge bases, that is, to obtain the entity number; the entity disambiguation is performed through manual verification, and different references to the same entity are unified to obtain unambiguous entity information; the data classification is to classify the entities in the electric energy metering device verification knowledge base to obtain different types of hierarchical entity data.

7. A knowledge graph construction system for electric energy metering device verification, characterized in that: include: an acquisition module configured to acquire a verification knowledge base of the electric energy metering device; Based on the acquired verification knowledge base, an ontology layer model of the knowledge graph for practical training of electric energy metering device detection is constructed; A fusion module is configured to obtain multi-level entities and relationships between entities for verification of the electric energy metering device based on the constructed ontology layer model; Perform knowledge fusion on the obtained multi-level entities and the relationships between entities to obtain fused data; A construction module is configured to store the obtained fusion data in a graph database, and complete the construction of a knowledge graph for the calibration of electric energy metering devices based on the graph database.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a method for constructing a knowledge graph for calibration of an electric energy metering device as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of a knowledge graph construction method for electric energy metering device calibration as described in any one of claims 1-6 are implemented.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of a knowledge graph construction method for electric energy metering device calibration as described in any one of claims 1-6.

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