Method and system for constructing and applying spacecraft integrated test knowledge graph

Abstract

The application provides a spacecraft comprehensive test knowledge graph construction and application method and system, comprising the following steps: determining a spacecraft comprehensive test knowledge type; determining a spacecraft comprehensive test knowledge data source; performing knowledge extraction on the spacecraft comprehensive test knowledge data source to obtain knowledge triples; performing knowledge fusion on the extracted knowledge triples to reduce redundant information and error information; performing database representation and storage on the knowledge triples, and performing knowledge visualization to construct a spacecraft comprehensive test field knowledge graph and complete knowledge representation; the application provides a new idea for spacecraft comprehensive test knowledge application, has the abilities of knowledge query, abnormal diagnosis, fault disposal and the like, and improves the utilization rate of spacecraft comprehensive test knowledge.

Description

Technical Field

[0001] This invention belongs to the field of satellite integrated testing, specifically, it relates to a method and system for constructing and applying a knowledge graph for spacecraft integrated testing. Background Technology

[0002] Knowledge graphs (KG) originated from semantic networks. As a revolutionary form of expression, they present knowledge in a more visual way. Because of their ability to simplify and visualize complex knowledge, while also possessing certain knowledge reasoning capabilities, knowledge graph technology has gradually become a hot research topic in recent years, demonstrating strong application potential in the aerospace field.

[0003] For example, patent document CN115796278A discloses a knowledge graph-based method for analyzing the supportability conditions of aerospace launch and test. This method includes: constructing a knowledge graph in the aerospace launch and test domain, representing launch and test knowledge in the form of entities and relationships, enabling semantic search of knowledge through the knowledge graph; establishing a supportability analysis workflow, and based on the aerospace launch and test domain knowledge graph, pushing knowledge to each step of the workflow to achieve supportability condition analysis of the launch and test process from a multi-disciplinary collaborative perspective; and utilizing the relationships between entity nodes in the knowledge graph to push relevant auxiliary reference knowledge to professional personnel during the supportability condition analysis. This invention improves the efficiency and accuracy of supportability condition analysis by constructing a knowledge graph in the aerospace launch and test domain and gradually pushing relevant auxiliary reference knowledge to professional personnel according to the workflow.

[0004] For example, patent document CN117687824A discloses a satellite fault diagnosis system based on a knowledge graph of quality problems. It includes two parts: the construction of a satellite quality problem knowledge graph and a satellite fault diagnosis method based on the knowledge graph of quality problems. First, it establishes multi-dimensional relationships between historical satellite fault case data by constructing a satellite quality problem knowledge graph. Second, it adopts a multi-dimensional retrieval and interaction paradigm based on the knowledge graph, including input processing for identifying key fault description elements based on text convolutional neural networks, and recommending possible causes of faults and maintenance measures based on similarity calculation methods combining keywords, representation learning, and meta-paths. This assists engineers in making satellite health management decisions and realizes intelligent fault diagnosis at the satellite system level and the single-unit level.

[0005] For example, patent document CN120235231B proposes a knowledge graph generation method, device and storage medium based on SAR satellites, which constructs the corresponding knowledge graph of the target area, improves the understandability, computability and applicability of SAR images, and facilitates the technical effect of operators in analyzing and utilizing SAR images. However, it does not mention the knowledge of spacecraft integrated testing and cannot be applied to the field of spacecraft integrated testing.

[0006] For example, patent document CN117573881A proposes a method for constructing and applying an on-orbit fault knowledge graph for a spacecraft control and propulsion system. This method provides an effective approach for data management and mining of individual units in the spacecraft control and propulsion subsystem, demonstrating the feasibility and superiority of knowledge graph technology in the fields of satellite fault location, processing, and tracing. However, it is limited to the spacecraft control and propulsion subsystem and its scope of application cannot cover the field of comprehensive spacecraft testing. For example, patent document CN120523942A proposes a fault diagnosis method for satellite power systems based on knowledge graphs. It constructs a knowledge graph of the satellite power subsystem and can quickly locate the cause of the fault and shorten the diagnosis time through a structured and intelligent approach. However, it is limited to satellite power systems and its scope of application cannot cover the field of spacecraft integrated testing.

[0007] In summary, to improve the utilization rate of spacecraft integrated testing knowledge and address the challenges of understanding, representing, associating, reasoning, and applying multi-source, heterogeneous spacecraft integrated testing knowledge, it is urgent to design a knowledge graph construction and application method for the field of spacecraft integrated testing. This method should possess capabilities such as knowledge retrieval, anomaly diagnosis, and fault handling, providing a new approach for the application of spacecraft integrated testing knowledge and offering technical reserves for future satellite, star cluster, and constellation integrated testing technologies. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the purpose of this invention is to provide a system for constructing and applying a spacecraft integrated testing knowledge graph, comprising: Module M1: Determine the type of knowledge required for integrated spacecraft testing; Module M2: Determine the data source for integrated spacecraft testing knowledge; Module M3: Extracts knowledge from the spacecraft integrated test knowledge data source to obtain knowledge triples; Module M4: Performs knowledge fusion on the extracted knowledge triples to reduce redundant and erroneous information; Module M5: Represents and stores knowledge triples in a database, visualizes the knowledge, constructs a knowledge graph for the spacecraft integrated testing domain, and completes knowledge representation; Module M6: Applying knowledge using a knowledge graph ontology from the field of spacecraft integrated testing; Module M7: It uses the application results of the domain knowledge graph as new spacecraft integrated testing knowledge, and completes the knowledge update through knowledge extraction, knowledge fusion, knowledge representation and knowledge completion, improves the existing spacecraft integrated testing domain knowledge graph, and continuously iterates and optimizes it.

[0009] Preferably, the types of spacecraft integrated testing knowledge in module M1 include: structured spacecraft integrated testing knowledge, semi-structured spacecraft integrated testing knowledge, and unstructured spacecraft integrated testing knowledge; further, the spacecraft integrated testing knowledge includes: spacecraft overall design knowledge, spacecraft subsystem design knowledge, spacecraft space-to-ground testing interface knowledge, spacecraft ground testing equipment knowledge, spacecraft testing methods and procedures knowledge, spacecraft testing anomaly knowledge, and spacecraft testing risk knowledge.

[0010] Preferably, the spacecraft integrated testing knowledge data source in module M2 includes the sources of spacecraft integrated testing knowledge; the spacecraft integrated testing knowledge data source includes: spacecraft overall design documents, spacecraft subsystem design documents, spacecraft electrical test outline documents, spacecraft ground test equipment development documents, spacecraft test specifications, spacecraft test standards, spacecraft test anomaly documents, spacecraft test risk documents, and spacecraft zeroing reports; in module M3, knowledge extraction specifically refers to extracting knowledge from the spacecraft integrated testing knowledge data source; knowledge extraction includes: entity extraction, relation extraction, and attribute extraction; a knowledge triple is a knowledge representation method in a knowledge graph, which is the atomic unit for constructing a knowledge graph. A knowledge triple uses the structure <entity, relation, entity> or <entity, attribute, value> to explicitly state declarative facts.

[0011] Preferably, knowledge fusion in module M4 refers to integrating the knowledge obtained from knowledge extraction into knowledge triples through coreference resolution, entity disambiguation, and knowledge merging methods to reduce redundant and erroneous information; the database in module M5 refers to describing and representing knowledge triples in the form of a resource description framework; the storage of the database in module M5 refers to storing knowledge triples in the Neo4j graph database; and the knowledge visualization in module M5 refers to presenting the abstract knowledge in the Neo4j graph database in the form of graphical elements through visual layout and visual encoding.

[0012] Preferably, the knowledge application in module M6 includes: knowledge query, anomaly diagnosis, and fault assistance; the knowledge update in module M7 refers to completing the knowledge update through the aforementioned knowledge extraction, knowledge fusion, and knowledge representation methods, improving the existing knowledge graph in the field of spacecraft integrated testing, and continuously iterating and optimizing it.

[0013] This invention proposes a method for constructing and applying a spacecraft integrated testing knowledge graph, and employs the aforementioned system for constructing and applying a spacecraft integrated testing knowledge graph, including: Step S1: Determine the type of knowledge required for integrated spacecraft testing; Step S2: Determine the data source for the spacecraft's integrated test knowledge; Step S3: Extract knowledge from the spacecraft integrated test knowledge data source to obtain knowledge triples; Step S4: Perform knowledge fusion on the extracted knowledge triples to reduce redundant and erroneous information; Step S5: Represent and store the knowledge triples in a database, visualize the knowledge, construct a knowledge graph for the spacecraft integrated testing domain, and complete the knowledge representation; Step S6: Apply knowledge using the knowledge graph ontology in the field of spacecraft integrated testing; Step S7: Use the application results of the domain knowledge graph as new spacecraft integrated testing knowledge, and complete the knowledge update through knowledge extraction, knowledge fusion, knowledge representation and knowledge completion methods to improve the existing spacecraft integrated testing domain knowledge graph and continuously iterate and optimize it.

[0014] Preferably, the types of spacecraft integrated testing knowledge in step S1 include: structured spacecraft integrated testing knowledge, semi-structured spacecraft integrated testing knowledge, and unstructured spacecraft integrated testing knowledge; further, spacecraft integrated testing knowledge includes: spacecraft overall design knowledge, spacecraft subsystem design knowledge, spacecraft space-to-ground testing interface knowledge, spacecraft ground testing equipment knowledge, spacecraft testing methods and procedures knowledge, spacecraft testing anomaly knowledge, and spacecraft testing risk knowledge.

[0015] Preferably, the spacecraft integrated test knowledge data source in step S2 includes the sources of spacecraft integrated test knowledge; the spacecraft integrated test knowledge data source includes: spacecraft overall design documents, spacecraft subsystem design documents, spacecraft electrical test outline documents, spacecraft ground test equipment development documents, spacecraft test specifications, spacecraft test standards, spacecraft test anomaly documents, spacecraft test risk documents, and spacecraft zeroing reports; in step S3, knowledge extraction specifically refers to extracting knowledge from the spacecraft integrated test knowledge data source; knowledge extraction includes: entity extraction, relation extraction, and attribute extraction; a knowledge triple is a knowledge representation method in a knowledge graph, which is the atomic unit for constructing a knowledge graph. A knowledge triple uses the structure <entity, relation, entity> or <entity, attribute, value> to explicitly state declarative facts.

[0016] Preferably, the knowledge fusion in step S4 refers to integrating the knowledge obtained from knowledge extraction into the knowledge triples through coreference resolution, entity disambiguation, and knowledge merging methods to reduce redundant and erroneous information; the database in step S5 refers to describing and representing the knowledge triples in the form of a resource description framework; the storage of the database in step S5 refers to storing the knowledge triples in the Neo4j graph database; and the knowledge visualization in step S5 refers to presenting the abstract knowledge in the Neo4j graph database in the form of graphical elements through visual layout and visual encoding.

[0017] Preferably, the knowledge application in step S6 includes: knowledge query, anomaly diagnosis, and fault assistance; the knowledge update in step S7 refers to completing the knowledge update through the aforementioned knowledge extraction, knowledge fusion, and knowledge representation methods, improving the existing knowledge graph in the field of spacecraft integrated testing, and continuously iterating and optimizing it.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a knowledge graph in the field of spacecraft integrated testing and applies this knowledge to knowledge retrieval, anomaly diagnosis, and fault handling. It opens up new avenues for the application of spacecraft integrated testing knowledge and effectively improves its utilization rate.

[0019] 2. This invention classifies and formats various spacecraft knowledge source files, including spacecraft zeroing reports, extracts entities, attributes, and relationships, and performs knowledge fusion to construct a knowledge graph for the comprehensive testing of spacecraft with strong knowledge correlation. This not only improves the usability and utilization of spacecraft knowledge but also enhances the correlation between knowledge, facilitating big data mining and subsequent knowledge applications.

[0020] 3. This invention possesses powerful massive data processing capabilities, knowledge reasoning capabilities, and knowledge fusion capabilities, enabling it to continuously improve and optimize the knowledge graph in the field of spacecraft integrated testing, thereby meeting the needs of future spacecraft knowledge applications. Attached Figure Description

[0021] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the method for constructing and applying a comprehensive spacecraft testing knowledge graph proposed in this invention. Figure 2 This is a schematic diagram of the knowledge graph node links for the method of constructing and applying a spacecraft integrated testing knowledge graph proposed in this invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0023] This invention proposes a system for constructing and applying a knowledge graph for integrated spacecraft testing, comprising: Module M1: Determine the type of knowledge required for integrated spacecraft testing; The types of spacecraft integrated testing knowledge in module M1 include: structured spacecraft integrated testing knowledge, semi-structured spacecraft integrated testing knowledge, and unstructured spacecraft integrated testing knowledge. Furthermore, comprehensive spacecraft testing knowledge includes: spacecraft overall design knowledge, spacecraft subsystem design knowledge, spacecraft space-to-ground testing interface knowledge, spacecraft ground testing equipment knowledge, spacecraft testing methods and procedures knowledge, spacecraft testing anomaly knowledge, and spacecraft testing risk knowledge. Module M2: Determine the data source for integrated spacecraft testing knowledge; The spacecraft integrated test knowledge data source in module M2 includes the source of spacecraft integrated test knowledge in module M1; the spacecraft integrated test knowledge data source includes: spacecraft overall design documents, spacecraft subsystem design documents, spacecraft electrical test outline documents, spacecraft ground test equipment development documents, spacecraft test specifications, spacecraft test standards, spacecraft test anomaly documents, spacecraft test risk documents, and spacecraft zeroing reports; Module M3: Extracts knowledge from the spacecraft integrated test knowledge data source to obtain knowledge triples; In module M3, knowledge extraction specifically refers to extracting knowledge from the spacecraft integrated test knowledge data source. Knowledge extraction includes entity extraction, relation extraction, and attribute extraction. A knowledge triple is a knowledge representation method in a knowledge graph, which is the atomic unit for constructing a knowledge graph. A knowledge triple uses the structure <entity, relation, entity> or <entity, attribute, value> to explicitly state declarative facts. As shown in Tables 1 and 2, the knowledge triple for a satellite belonging to a spacecraft is <satellite, belongs to, spacecraft>.

[0024] Table 1 Examples of Triple Relationships

[0025] Table 2. Node Link Relationship Table of Spacecraft Integrated Testing Knowledge Graph Module M4: Performs knowledge fusion on the extracted knowledge triples to reduce redundant and erroneous information; the knowledge fusion in module M4 refers to integrating the knowledge obtained from knowledge extraction into the knowledge triples through coreference resolution, entity disambiguation, and knowledge merging methods to reduce redundant and erroneous information. Module M5: Represents and stores knowledge triples in a database, visualizes the knowledge, constructs a knowledge graph for the spacecraft integrated testing domain, and completes knowledge representation. The database in Module M5 refers to the description and representation of knowledge triples in the form of a Resource Description Framework (RDF). The storage of the database in Module M5 refers to storing the knowledge triples in a Neo4j graph database. The knowledge visualization in Module M5 refers to presenting the abstract knowledge in the Neo4j graph database as graphical elements through visual layout and visual encoding. Module M6: Utilizes a knowledge graph ontology in the field of spacecraft integrated testing for knowledge application; the knowledge applications in Module M6 include: knowledge query, anomaly diagnosis, and fault assistance. Module M7: The application results of the domain knowledge graph are used as new spacecraft integrated testing knowledge. Through knowledge extraction, knowledge fusion, knowledge representation and knowledge completion, knowledge updates are completed, the existing spacecraft integrated testing domain knowledge graph is improved and continuously iterated and optimized. The knowledge update in Module M7 refers to the completion of knowledge updates through the aforementioned knowledge extraction, knowledge fusion and knowledge representation methods, the improvement of the existing spacecraft integrated testing domain knowledge graph and continuous iterative optimization.

[0026] This invention proposes a method for constructing and applying a spacecraft integrated testing knowledge graph, and employs the aforementioned system for constructing and applying a spacecraft integrated testing knowledge graph, as shown in the attached figure. Figure 1 As shown, it includes the following steps: Step S1: Determine the type of spacecraft integrated testing knowledge; the type of spacecraft integrated testing knowledge in step S1 includes: structured spacecraft integrated testing knowledge, semi-structured spacecraft integrated testing knowledge, and unstructured spacecraft integrated testing knowledge. Furthermore, comprehensive spacecraft testing knowledge includes: spacecraft overall design knowledge, spacecraft subsystem design knowledge, spacecraft space-to-ground testing interface knowledge, spacecraft ground testing equipment knowledge, spacecraft testing methods and procedures knowledge, spacecraft testing anomaly knowledge, and spacecraft testing risk knowledge. Step S2: Determine the spacecraft integrated test knowledge data source; the spacecraft integrated test knowledge data source in Step S2 includes the source of spacecraft integrated test knowledge in Step S1; the spacecraft integrated test knowledge data source includes: spacecraft overall design documents, spacecraft subsystem design documents, spacecraft electrical test outline documents, spacecraft ground test equipment development documents, spacecraft test specifications, spacecraft test standards, spacecraft test anomaly documents, spacecraft test risk documents, and spacecraft zeroing reports; Step S3: Extract knowledge from the spacecraft integrated testing knowledge data source to obtain knowledge triples; in step S3, knowledge extraction specifically refers to extracting knowledge from the spacecraft integrated testing knowledge data source; knowledge extraction includes: entity extraction, relation extraction, and attribute extraction; a knowledge triple is a knowledge representation method in a knowledge graph, and is the atomic unit for constructing the knowledge graph, as shown in the appendix. Figure 2 As shown, knowledge triples use the structure <entity, relation, entity> or <entity, attribute, value> to explicitly state declarative facts. For example, the knowledge triple for a satellite belonging to a spacecraft is <satellite, belongs to, spacecraft>. Step S4: Perform knowledge fusion on the extracted knowledge triples to reduce redundant and erroneous information; the knowledge fusion in step S4 refers to integrating the knowledge obtained from knowledge extraction into the knowledge triples through coreference resolution, entity disambiguation, and knowledge merging methods to reduce redundant and erroneous information. Step S5: Represent and store the knowledge triples in a database, visualize the knowledge, construct a knowledge graph for the spacecraft integrated testing domain, and complete the knowledge representation; The database in step S5 refers to the description and representation of knowledge triples in the form of a Resource Description Framework (RDF); the storage of the database in step S5 refers to storing the knowledge triples in the Neo4j graph database; the knowledge visualization in step S5 refers to presenting the abstract knowledge in the Neo4j graph database in the form of graphical elements through visual layout and visual encoding. Step S6: Apply knowledge using a knowledge graph ontology in the field of spacecraft integrated testing; the knowledge application in step S6 includes: knowledge query, anomaly diagnosis, and fault assistance. Step S7: The application results of the domain knowledge graph are used as new spacecraft integrated testing knowledge. Knowledge is updated through knowledge extraction, knowledge fusion, knowledge representation, and knowledge completion methods to improve the existing spacecraft integrated testing domain knowledge graph and continuously iterate and optimize it. The knowledge update in step S7 refers to updating the knowledge through the aforementioned knowledge extraction, knowledge fusion, and knowledge representation methods to improve the existing spacecraft integrated testing domain knowledge graph and continuously iterate and optimize it.

[0027] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0028] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A system for constructing and applying a knowledge graph for integrated spacecraft testing, characterized in that, include: Module M1: Determine the type of knowledge required for integrated spacecraft testing; Module M2: Determine the data source for integrated spacecraft testing knowledge; Module M3: Extracts knowledge from the spacecraft integrated test knowledge data source to obtain knowledge triples; Module M4: Performs knowledge fusion on the extracted knowledge triples to reduce redundant and erroneous information; Module M5: Represents and stores knowledge triples in a database, visualizes the knowledge, constructs a knowledge graph for the spacecraft integrated testing domain, and completes knowledge representation; Module M6: Applying knowledge using a knowledge graph ontology from the field of spacecraft integrated testing; Module M7: It uses the application results of the domain knowledge graph as new spacecraft integrated testing knowledge, and completes the knowledge update through knowledge extraction, knowledge fusion, knowledge representation and knowledge completion, improves the existing spacecraft integrated testing domain knowledge graph, and continuously iterates and optimizes it.

2. The system for constructing and applying a spacecraft integrated testing knowledge graph according to claim 1, characterized in that, The types of spacecraft integrated testing knowledge in module M1 include: structured spacecraft integrated testing knowledge, semi-structured spacecraft integrated testing knowledge, and unstructured spacecraft integrated testing knowledge; further, spacecraft integrated testing knowledge includes: spacecraft overall design knowledge, spacecraft subsystem design knowledge, spacecraft space-to-ground testing interface knowledge, spacecraft ground testing equipment knowledge, spacecraft testing methods and procedures knowledge, spacecraft testing anomaly knowledge, and spacecraft testing risk knowledge.

3. The system for constructing and applying a spacecraft integrated testing knowledge graph according to claim 1, characterized in that, The spacecraft integrated test knowledge data source in module M2 includes the sources of spacecraft integrated test knowledge; The spacecraft integrated test knowledge data source includes: spacecraft overall design documents, spacecraft subsystem design documents, spacecraft electrical test outline documents, spacecraft ground test equipment development documents, spacecraft test specifications, spacecraft test standards, spacecraft test anomaly documents, spacecraft test risk documents, and spacecraft zeroing reports. In module M3, knowledge extraction specifically refers to extracting knowledge from the spacecraft integrated test knowledge data source. Knowledge extraction includes: entity extraction, relation extraction, and attribute extraction. A knowledge triple is a knowledge representation method in a knowledge graph, serving as the atomic unit for constructing the knowledge graph. Knowledge triples use the structure <entity, relation, entity> or <entity, attribute, value> to explicitly state declarative facts.

4. The system for constructing and applying a spacecraft integrated testing knowledge graph according to claim 1, characterized in that, The knowledge fusion in module M4 refers to integrating the knowledge obtained from knowledge extraction into knowledge triples through coreference resolution, entity disambiguation, and knowledge merging methods to reduce redundant and erroneous information. The database in module M5 refers to describing and representing knowledge triples in the form of a resource description framework. The storage of the database in module M5 refers to storing knowledge triples in the Neo4j graph database. The knowledge visualization in module M5 refers to presenting the abstract knowledge in the Neo4j graph database in the form of graphical elements through visual layout and visual encoding.

5. The system for constructing and applying a spacecraft integrated testing knowledge graph according to claim 1, characterized in that, The knowledge application in module M6 includes: knowledge query, anomaly diagnosis, and fault assistance; the knowledge update in module M7 refers to completing the knowledge update through the aforementioned knowledge extraction, knowledge fusion, and knowledge representation methods, improving the existing knowledge graph in the field of spacecraft integrated testing, and continuously iterating and optimizing it.

6. A method for constructing and applying a spacecraft integrated testing knowledge graph, characterized in that, The system employing the spacecraft integrated test knowledge graph construction and application described in claim 1 includes: Step S1: Determine the type of knowledge required for integrated spacecraft testing; Step S2: Determine the data source for the spacecraft's integrated test knowledge; Step S3: Extract knowledge from the spacecraft integrated test knowledge data source to obtain knowledge triples; Step S4: Perform knowledge fusion on the extracted knowledge triples to reduce redundant and erroneous information; Step S5: Represent and store the knowledge triples in a database, visualize the knowledge, construct a knowledge graph for the spacecraft integrated testing domain, and complete the knowledge representation; Step S6: Apply knowledge using the knowledge graph ontology in the field of spacecraft integrated testing; Step S7: Use the application results of the domain knowledge graph as new spacecraft integrated testing knowledge, and complete the knowledge update through knowledge extraction, knowledge fusion, knowledge representation and knowledge completion methods to improve the existing spacecraft integrated testing domain knowledge graph and continuously iterate and optimize it.

7. The method for constructing and applying a spacecraft integrated testing knowledge graph according to claim 6, characterized in that, The types of spacecraft integrated testing knowledge in step S1 include: structured spacecraft integrated testing knowledge, semi-structured spacecraft integrated testing knowledge, and unstructured spacecraft integrated testing knowledge; further, spacecraft integrated testing knowledge includes: spacecraft overall design knowledge, spacecraft subsystem design knowledge, spacecraft space-to-ground testing interface knowledge, spacecraft ground testing equipment knowledge, spacecraft testing methods and procedures knowledge, spacecraft testing anomaly knowledge, and spacecraft testing risk knowledge.

8. The method for constructing and applying a spacecraft integrated testing knowledge graph according to claim 6, characterized in that, The spacecraft integrated test knowledge data source in step S2 includes the source of spacecraft integrated test knowledge; The spacecraft integrated test knowledge data source includes: spacecraft overall design documents, spacecraft subsystem design documents, spacecraft electrical test outline documents, spacecraft ground test equipment development documents, spacecraft test specifications, spacecraft test standards, spacecraft test anomaly documents, spacecraft test risk documents, and spacecraft zeroing reports. In step S3, knowledge extraction specifically refers to extracting knowledge from the spacecraft integrated test knowledge data source. Knowledge extraction includes: entity extraction, relation extraction, and attribute extraction. A knowledge triple is a knowledge representation method in a knowledge graph, which is the atomic unit for constructing a knowledge graph. A knowledge triple uses the structure <entity, relation, entity> or <entity, attribute, value> to explicitly state declarative facts.

9. The method for constructing and applying a spacecraft integrated testing knowledge graph according to claim 6, characterized in that, The knowledge fusion in step S4 refers to integrating the knowledge obtained from knowledge extraction into the knowledge triples through coreference resolution, entity disambiguation, and knowledge merging methods to reduce redundant and erroneous information. The database in step S5 refers to describing and representing the knowledge triples in the form of a resource description framework. The storage of the database in step S5 refers to storing the knowledge triples in the Neo4j graph database. The knowledge visualization in step S5 refers to presenting the abstract knowledge in the Neo4j graph database in the form of graphical elements through visual layout and visual encoding.

10. The method for constructing and applying a spacecraft integrated testing knowledge graph according to claim 6, characterized in that, The knowledge application in step S6 includes: knowledge query, anomaly diagnosis, and fault assistance; the knowledge update in step S7 refers to completing the knowledge update through the aforementioned knowledge extraction, knowledge fusion, and knowledge representation methods, improving the existing knowledge graph in the field of spacecraft integrated testing, and continuously iterating and optimizing it.

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