CN111309828B - Knowledge graph construction method and device for large-scale equipment - Google Patents
Knowledge graph construction method and device for large-scale equipment Download PDFInfo
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- CN111309828B CN111309828B CN202010234402.3A CN202010234402A CN111309828B CN 111309828 B CN111309828 B CN 111309828B CN 202010234402 A CN202010234402 A CN 202010234402A CN 111309828 B CN111309828 B CN 111309828B
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- 238000004590 computer program Methods 0.000 claims description 17
- 238000013461 design Methods 0.000 claims description 12
- 238000000354 decomposition reaction Methods 0.000 claims description 10
- 238000012423 maintenance Methods 0.000 claims description 8
- 238000004519 manufacturing process Methods 0.000 claims description 8
- 239000007787 solid Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
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- 230000005540 biological transmission Effects 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a knowledge graph construction method and device of large-scale equipment, terminal equipment and a readable storage medium, wherein the method comprises the following steps: creating a knowledge representation guidance model for large-scale engineering equipment; acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model; based on the knowledge representation guiding model, constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data; extracting nodes for constructing a knowledge graph according to the knowledge representation model; based on the nodes and the relation among the nodes, a knowledge graph of the target large-scale equipment is constructed by utilizing a preset graph database. By implementing the invention, the data, information and knowledge of large-scale engineering equipment can be systematically acquired, represented and stored, so that the knowledge graph can be better applied to the industrial field, the time for engineers to search the data and the technical scheme is greatly reduced, and the working efficiency is improved.
Description
Technical Field
The present invention relates to the technical field of knowledge graphs, and in particular, to a method and apparatus for constructing a knowledge graph of a large-scale device, a terminal device, and a readable storage medium.
Background
At present, the knowledge graph is mainly applied to the industries of finance, medical treatment and social contact, and the functions of semantic retrieval, intelligent question-answering, intelligent recommendation and the like are realized by recording the interrelation among the knowledge. But these existing application hierarchies are also relatively coarse and shallow. Because the research on the application of the knowledge graph in industry is less, and meanwhile, the difficulty of the construction and application of the knowledge graph is increased by the complex and multi-source heterogeneous data in the industry field, the knowledge graph is not applied in large scale and deep in the industry field.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a knowledge graph construction method, a device, a terminal device and a readable storage medium of large-scale equipment, which can systematically acquire, represent and store data, information and knowledge of the large-scale equipment, thereby effectively solving the problem of limitation of application of the knowledge graph in the industrial field.
In order to solve the above technical problems, an embodiment of the present invention provides a knowledge graph construction method for a large-scale device, including:
creating a knowledge representation guidance model for large-scale engineering equipment; wherein the knowledge representation guidance model comprises an entity guidance model and an event guidance model;
acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model;
constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data based on the knowledge representation guidance model;
extracting nodes for constructing a knowledge graph according to the knowledge representation model;
and constructing a knowledge graph of the target large-scale equipment by using a preset graph database based on the nodes and the relation among the nodes.
Further, the creating of the knowledge representation guidance model for large-scale engineering equipment is specifically as follows:
analyzing the acquired knowledge of the large-scale engineering equipment, extracting the characteristic information of the large-scale engineering equipment, and creating the knowledge representation guidance model for the large-scale engineering equipment according to the characteristic information.
Further, the entity guidance model comprises a concept representation unit, a function representation unit and a structure representation unit; the event guidance model comprises a design event representing unit, a manufacturing event representing unit and an operation and maintenance event representing unit.
Further, the knowledge representation guiding model is constructed according to the entity information data and the event information data, and the knowledge representation model of the target large-scale equipment specifically comprises the following steps:
based on the entity guidance model, constructing an entity model of the target large-scale equipment according to the entity information data;
based on the entity model of the target large-scale equipment, constructing an event model of the target large-scale equipment according to the event information data;
and integrating the entity model of the target large-scale equipment and the event model of the target large-scale equipment to construct a knowledge representation model of the target large-scale equipment.
Further, the building the entity model of the target large-scale equipment according to the entity information data based on the entity guidance model specifically comprises the following steps:
performing functional system division on the target large-scale equipment according to the entity information data, and adjusting the functional representation unit according to the result of the functional system division;
performing physical structure decomposition on the target large-scale equipment according to the entity information data, and adjusting the structure representation unit according to the physical structure decomposition result;
and constructing and obtaining the entity model of the target large-scale equipment according to the entity information data based on the adjusted entity guidance model.
Further, the graphic database is a Neo4j graphic database.
In order to solve the same technical problems, the invention also provides a knowledge graph construction device of the large-scale equipment, which comprises:
the instruction model creation module is used for creating a knowledge representation instruction model for large-scale engineering equipment; wherein the knowledge representation guidance model comprises an entity guidance model and an event guidance model;
the data acquisition module is used for acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model;
the knowledge model construction module is used for constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data based on the knowledge representation guidance model;
the map information extraction module is used for extracting nodes for constructing a knowledge map and the relation among the nodes according to the knowledge representation model;
and the knowledge graph construction module is used for constructing the knowledge graph of the target large-scale equipment by utilizing a preset graph database based on the nodes and the relation among the nodes.
Further, the guidance model creation module is specifically configured to analyze the acquired knowledge of the large-scale engineering equipment, extract feature information of the large-scale engineering equipment, and create the knowledge representation guidance model for the large-scale engineering equipment according to the feature information.
In order to solve the same technical problems, the invention also provides a knowledge graph construction terminal device of a large device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled with the processor, and the knowledge graph construction method of any large device is realized when the processor executes the computer program.
In order to solve the same technical problems, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program controls the equipment where the computer readable storage medium is located to execute the knowledge graph construction method of any one of the large-scale equipment when running.
Compared with the prior art, the invention has the following beneficial effects:
the embodiment of the invention provides a knowledge graph construction method and device of large-scale equipment, terminal equipment and a readable storage medium, wherein the method comprises the following steps: creating a knowledge representation guidance model for large-scale engineering equipment; acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model; constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data based on the knowledge representation guidance model; extracting nodes for constructing a knowledge graph according to the knowledge representation model; and constructing a knowledge graph of the target large-scale equipment by using a preset graph database based on the nodes and the relation among the nodes. By implementing the invention, the data, information and knowledge of large-scale engineering equipment can be systematically acquired, represented and stored, so that the knowledge graph can be better applied to the industrial field, the time for engineers to search the data and the technical scheme is greatly reduced, and the working efficiency is improved.
Drawings
Fig. 1 is a flow chart of a knowledge graph construction method of a large-scale device according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a knowledge representation model, provided by an embodiment of the invention;
FIG. 3 is a conceptual representation unit schematic diagram provided by an embodiment of the present invention;
FIG. 4 is a functional exploded view of an embodiment of the present invention;
FIG. 5 is an exploded view of the sub-functions provided by an embodiment of the present invention;
FIG. 6 is an exploded view of one embodiment of the present invention;
FIG. 7 is a schematic diagram of a semantic relationship design according to one embodiment of the present invention;
fig. 8 is a schematic structural diagram of a knowledge graph construction apparatus for a large-scale device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a knowledge graph construction method for a large-scale device, including the steps of:
s1, creating a knowledge representation guidance model for large-scale engineering equipment; wherein the knowledge representation guidance model comprises an entity guidance model and an event guidance model. Further, the entity guidance model comprises a concept representation unit, a function representation unit and a structure representation unit; the event guidance model comprises a design event representing unit, a manufacturing event representing unit and an operation and maintenance event representing unit.
In the embodiment of the present invention, further, step S1 specifically includes:
analyzing the acquired knowledge of the large-scale engineering equipment, extracting the characteristic information of the large-scale engineering equipment, and creating the knowledge representation guidance model for the large-scale engineering equipment according to the characteristic information.
Referring to fig. 2, in the embodiment of the present invention, step S1 is to create a knowledge representation model for large engineering equipment knowledge acquisition and representation, including a entity guidance model (entity model) and an event guidance model (event model). The entity model is used for acquiring data, information and knowledge related to the physical entity of the large-scale engineering equipment; further, the solid model includes three parts, namely concepts, functions and structures. The concept section collects basic ideas/concepts, indexes, related disciplines, literature data, laws and regulations, related institutions, etc. related to large-scale engineering equipment on a concept level. The functional part is to analyze and decompose the functions of the equipment into a plurality of functional systems, including a power system, a transmission system, an execution system, a control system, an electronic circuit system, a sealing lubrication system, an alarm system and the like, and the sub-functions of the systems. The structural part is used for representing the function realized by each system and decomposing the physical structure into each mechanism and each part. The event model is used for systematically capturing and storing data, information and knowledge used in the design, manufacturing and operation and maintenance processes of large-scale engineering equipment; event models are frameworks for acquiring and representing knowledge, and are approaches for applying knowledge, and based on the entity models, events of design, manufacturing and operation and maintenance processes, and information and knowledge included in the events are recorded.
It should be noted that the scheme of the invention is applied to the industrial field and is different from the knowledge graph in the medical, financial and educational fields. The essential difference is the knowledge representation model used. The knowledge graph captures, stores and reuses knowledge formed by large-scale engineering equipment in the processes of design, manufacture and operation and maintenance in terms of functions, structures and behaviors.
S2, acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model.
In the embodiment of the present invention, it may be understood that, when a specific knowledge graph for a large-scale device is constructed, relevant data of the large-scale device, including entity information data and event information data, needs to be acquired first.
And S3, constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data based on the knowledge representation guidance model.
In the embodiment of the present invention, further, step S3 specifically includes:
s31, constructing a solid model of the target large-scale equipment according to the solid information data based on the solid guidance model;
s32, constructing an event model of the target large-scale equipment according to the event information data based on the entity model of the target large-scale equipment;
s33, integrating the entity model of the target large-scale equipment and the event model of the target large-scale equipment to construct a knowledge representation model of the target large-scale equipment.
In the embodiment of the present invention, further, step S31 specifically includes:
s311, performing functional system division on the target large-scale equipment according to the entity information data, and adjusting the functional representation unit according to the functional system division result;
s312, performing physical structure decomposition on the target large-scale equipment according to the entity information data, and adjusting the structure representation unit according to the physical structure decomposition result;
s313, constructing and obtaining the entity model of the target large-scale equipment according to the entity information data based on the adjusted entity guidance model.
Referring to fig. 3 to 6, in an embodiment of the present invention, step S3 is to construct a knowledge representation model of the target large-scale device according to the obtained entity information data and the event information data. Taking a shield machine as an example, firstly constructing a physical model of the shield machine according to a knowledge representation model, wherein the acquired physical information data comprise related concepts, functions and structures. The concepts comprise basic concepts and concepts related to the shield machine, such as definition, basic principle, energy requirement, operation range, operation object and the like; the indexes to be realized include performance indexes, comfort indexes, environmental protection standards and the like; organization institutions upstream and downstream thereof, such as manufacturers, accessory manufacturers, sales companies, finance companies, insurance companies, army associations, and the like; related discipline knowledge, literature, legal regulations, and the like. The shield machine is functionally divided into a plurality of functional systems, including a power system, a transmission system, an execution system, a control system, an electronic circuit system, a sealing lubrication system, an alarm system and the like. Each functional system is further subdivided into individual sub-functions. And (3) the structural representation unit is combined with a functional system of the shield machine and sub-functions to be realized, and is subjected to structural decomposition. For example, the structure of the shield machine is first decomposed into a front shield, a middle shield and a rear shield according to the main functions to be realized by the shield machine, namely tunneling, construction and transportation. The front shield is used for excavating soil, the middle shield is used for splicing and constructing tunnel segments, and the rear shield is used for assisting and transporting the soil. The front shield has a main mechanical structure comprising a cutter head and a cutter, and can be divided into a plane round angle cutter head, a plane oblique angle cutter head and a plane right angle cutter head according to different functions. Other structural decomposition is similar.
And S4, extracting the nodes for constructing the knowledge graph according to the knowledge representation model.
Referring to fig. 7, step S4 is to extract nodes for constructing a knowledge graph and relationships between the nodes according to the knowledge representation model. It can be understood that the knowledge representation model forms the semantic type of the knowledge graph, wherein the entities and the events are used as nodes of the graph database, and the semantic relationship extracted according to the preset semantic relationship design rule is used as the connecting line between the nodes.
S5, constructing a knowledge graph of the target large-scale equipment by using a preset graph database based on the nodes and the relation among the nodes. Preferably, the graphic database is Neo4j graphic database.
After the nodes used for constructing the knowledge graph and the relation among the nodes are obtained, the knowledge graph of the large-scale engineering equipment can be constructed by using the open source graph database Neo4 j.
Compared with the prior art, the scheme of the invention can systematically acquire, express and store the data, information and knowledge of large-scale engineering equipment, further realize the rapid semantic retrieval of multi-source data in the forms of intelligent question-answering and intelligent recommendation, greatly reduce the time of engineers for searching the data and technical scheme, and improve the working efficiency.
It should be noted that, for simplicity of description, the above method or flow embodiments are all described as a series of combinations of acts, but it should be understood by those skilled in the art that the embodiments of the present invention are not limited by the order of acts described, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are all alternative embodiments and that the actions involved are not necessarily required for the embodiments of the present invention.
Referring to fig. 8, in order to solve the same technical problem, the present invention further provides a knowledge graph construction device for a large-scale device, including:
the instruction model creation module 1 is used for creating a knowledge representation instruction model for large-scale engineering equipment; wherein the knowledge representation guidance model comprises an entity guidance model and an event guidance model;
the data acquisition module 2 is used for acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model;
a knowledge model construction module 3, configured to construct a knowledge representation model of the target large-scale device according to the entity information data and the event information data based on the knowledge representation guidance model;
the map information extraction module 4 is used for extracting nodes for constructing a knowledge map and relations among the nodes according to the knowledge representation model;
and the knowledge graph construction module 5 is used for constructing the knowledge graph of the target large-scale equipment by utilizing a preset graph database based on the nodes and the relation among the nodes.
Further, the guiding model creating module 1 is specifically configured to analyze the acquired knowledge of the large-scale engineering equipment, extract feature information of the large-scale engineering equipment, and create the knowledge representation guiding model for the large-scale engineering equipment according to the feature information.
It can be understood that the embodiment of the device is corresponding to the embodiment of the method of the present invention, and the knowledge graph construction device for the large-scale device provided by the embodiment of the present invention can implement the knowledge graph construction method for the large-scale device provided by any one of the embodiments of the method of the present invention.
In order to solve the same technical problems, the invention also provides a knowledge graph construction terminal device of a large device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled with the processor, and the knowledge graph construction method of any large device is realized when the processor executes the computer program.
The knowledge graph construction terminal equipment of the large-sized equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general processor may be a microprocessor or the processor may be any conventional processor, etc., where the processor is a control center of the large-scale device knowledge graph construction terminal device, and connects various parts of the entire large-scale device knowledge graph construction terminal device by using various interfaces and lines.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
In order to solve the same technical problems, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program controls the equipment where the computer readable storage medium is located to execute the knowledge graph construction method of any one of the large-scale equipment when running.
The computer program may be stored in a computer readable storage medium, which computer program, when being executed by a processor, may carry out the steps of the various method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (9)
1. The knowledge graph construction method of the large-scale equipment is characterized by comprising the following steps of:
creating a knowledge representation guidance model for large-scale engineering equipment; wherein the knowledge representation guidance model comprises an entity guidance model and an event guidance model;
according to the described
The knowledge representation guides the model to acquire entity information data and event information data of the target large-scale equipment;
constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data based on the knowledge representation guidance model;
extracting nodes for constructing a knowledge graph according to the knowledge representation model;
the knowledge representation model forms a semantic type of a knowledge graph, wherein entities and events are used as nodes of a graph database, and semantic relations extracted according to a preset semantic relation design rule are used as connecting lines between the nodes;
based on the nodes and the relation among the nodes, constructing a knowledge graph of the target large-scale equipment by using a preset graph database;
the knowledge representation guiding model is constructed according to the entity information data and the event information data, and the knowledge representation model of the target large-scale equipment specifically comprises the following steps:
based on the entity guidance model, constructing an entity model of the target large-scale equipment according to the entity information data;
the solid model comprises: a conceptual portion, a functional portion, and a structural portion; the concept part collects basic concepts, indexes, relevant disciplines, literature data, laws and regulations and relevant institutions related to large engineering equipment on a concept level;
the function part is used for carrying out function analysis and decomposition on the equipment into a plurality of function systems;
the structure part is used for representing the physical structure of the system to be decomposed into various mechanisms and parts, and the functions are realized corresponding to various systems;
based on the entity model of the target large-scale equipment, constructing an event model of the target large-scale equipment according to the event information data;
the event model is used for systematically capturing and storing data, information and knowledge used in the design, manufacturing and operation and maintenance processes of large engineering equipment;
and integrating the entity model of the target large-scale equipment and the event model of the target large-scale equipment to construct a knowledge representation model of the target large-scale equipment.
2. The knowledge graph construction method of large-scale equipment according to claim 1, wherein the creating of the knowledge representation guidance model for large-scale equipment is specifically:
analyzing the acquired knowledge of the large-scale engineering equipment, extracting the characteristic information of the large-scale engineering equipment, and creating the knowledge representation guidance model for the large-scale engineering equipment according to the characteristic information.
3. The knowledge graph construction method of a large-scale apparatus according to claim 2, wherein the entity guidance model includes a concept representation unit, a function representation unit, and a structure representation unit; the event guidance model comprises a design event representing unit, a manufacturing event representing unit and an operation and maintenance event representing unit.
4. The knowledge graph construction method of the large-scale equipment according to claim 3, wherein the constructing the entity model of the target large-scale equipment according to the entity information data based on the entity guidance model specifically comprises:
performing functional system division on the target large-scale equipment according to the entity information data, and adjusting the functional representation unit according to the result of the functional system division;
performing physical structure decomposition on the target large-scale equipment according to the entity information data, and adjusting the structure representation unit according to the physical structure decomposition result;
and constructing and obtaining the entity model of the target large-scale equipment according to the entity information data based on the adjusted entity guidance model.
5. The knowledge graph construction method of a large-scale apparatus according to any one of claims 1 to 4, wherein the graph database is Neo4j graph database.
6. The knowledge graph construction device of the large-scale equipment is characterized by comprising the following components:
the instruction model creation module is used for creating a knowledge representation instruction model for large-scale engineering equipment; wherein the knowledge representation guidance model comprises an entity guidance model and an event guidance model;
the data acquisition module is used for acquiring entity information data and event information data of the target large-scale equipment according to the knowledge representation guidance model;
the knowledge model construction module is used for constructing a knowledge representation model of the target large-scale equipment according to the entity information data and the event information data based on the knowledge representation guidance model;
the map information extraction module is used for extracting nodes for constructing a knowledge map and the relation among the nodes according to the knowledge representation model;
the knowledge representation model forms a semantic type of a knowledge graph, wherein entities and events are used as nodes of a graph database, and semantic relations extracted according to a preset semantic relation design rule are used as connecting lines between the nodes;
the knowledge graph construction module is used for constructing the knowledge graph of the target large-scale equipment by utilizing a preset graph database based on the nodes and the relation among the nodes
The knowledge representation guiding model is constructed according to the entity information data and the event information data, and the knowledge representation model of the target large-scale equipment specifically comprises the following steps:
based on the entity guidance model, constructing an entity model of the target large-scale equipment according to the entity information data;
the solid model comprises: a conceptual portion, a functional portion, and a structural portion; the concept part is used for collecting basic concepts, indexes, related disciplines, literature data, laws and regulations and related institutions related to large engineering equipment on a concept level;
the function part is used for carrying out function analysis and decomposition on the equipment into a plurality of function systems;
the structure part is used for representing the physical structure of the system to be decomposed into various mechanisms and parts, and the functions are realized corresponding to various systems;
based on the entity model of the target large-scale equipment, constructing an event model of the target large-scale equipment according to the event information data;
the event model is used for systematically capturing and storing data, information and knowledge used in the design, manufacturing and operation and maintenance processes of large engineering equipment;
and integrating the entity model of the target large-scale equipment and the event model of the target large-scale equipment to construct a knowledge representation model of the target large-scale equipment.
7. The knowledge graph construction device of large-scale equipment according to claim 6, wherein the guidance model creation module is specifically configured to analyze acquired knowledge of the large-scale engineering equipment, extract feature information of the large-scale engineering equipment, and create the knowledge representation guidance model for the large-scale engineering equipment according to the feature information.
8. A knowledge graph construction terminal device of a large device, characterized by comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the memory being coupled to the processor, and the processor implementing the knowledge graph construction method of a large device according to any of claims 1 to 5 when the computer program is executed by the processor.
9. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein the computer program, when executed, controls a device in which the computer-readable storage medium is located to execute the knowledge graph construction method of a large device according to any one of claims 1 to 5.
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