Wind power equipment data coding method and system based on knowledge graph

Abstract

The invention relates to a wind power equipment data coding method and system based on a knowledge graph, and the method comprises the steps: obtaining multi-source wind power equipment data, extracting the entities, relationships and attributes in the wind power equipment data, and obtaining the data of the multi-source wind power equipment; performing standardized naming, co-reference resolution and entity ambiguity elimination on the entities to obtain optimized entities; constructing a knowledge graph based on the optimized entities, relationships and attributes, and generating a coding dictionary of a tree structure according to the knowledge graph; measuring point information is obtained, corresponding nodes are determined in the coding dictionary of the tree structure according to the measuring point information, and codes of measuring points are determined based on the nodes.

Description

Technical Field

[0001] The present application relates to the technical field of wind power equipment monitoring and data management, and in particular to a wind power equipment data encoding method and system based on a knowledge graph. Background Art

[0002] Wind power equipment from different manufacturers uses a variety of equipment components and measurement point naming methods, which leads to significant differences in the naming of similar measurement points between different equipment. This non-standard naming problem greatly hinders the integrated processing, in-depth analysis and practical application efficiency of wind power equipment data.

[0003] At present, in the field of wind power, the meanings of terms in the coding dictionary are too broad and lack sufficient precision, making it difficult to accurately distinguish entities that are essentially the same but have different names. As a result, even if different measurement points are essentially the same, they may be mistakenly assigned different codes simply because of the difference in naming. This situation not only seriously weakens the consistency level of data, greatly reduces the relevance and comparability of data, but also greatly increases the complexity and difficulty of data management.

[0004] Currently, there are problems in the wind power field such as low data consistency and high data management complexity. Summary of the invention

[0005] The embodiments of the present application provide a wind power equipment data encoding method and system based on a knowledge graph, so as to at least solve the problems of low data consistency level and high data management complexity in related technologies.

[0006] In a first aspect, an embodiment of the present application provides a wind power equipment data encoding method based on a knowledge graph, comprising: Acquire multi-source wind power equipment data, extract entities, relationships and attributes in the wind power equipment data, and perform standardized naming, coreference resolution and entity disambiguation on the entities to obtain optimized entities; Building a knowledge graph based on the optimized entities, relationships and attributes, and generating a tree-structured coding dictionary based on the knowledge graph; The measuring point information is acquired, a corresponding node is determined in the coding dictionary of the tree structure according to the measuring point information, and a coding of the measuring point is determined based on the node.

[0007] In one embodiment, the constructing a knowledge graph based on the optimized entities, relationships, and attributes includes: Building an ontology library of wind power equipment based on the wind power equipment data; Design the logical model of the knowledge graph, and represent knowledge with the triple structure of entity-relationship-entity and entity-attribute-attribute value; A knowledge graph is constructed based on the ontology library, the logical model and the optimized entities, relationships and attributes, and the knowledge graph is stored in a graph database.

[0008] In one embodiment, the step of normalizing the naming of the entity includes: Based on the relationship between the entities and the attributes of the entities, the similarity and relevance between the entities are calculated, and the entities whose similarity is greater than a first threshold and whose relevance is greater than a second threshold are unified as the same entity.

[0009] In one embodiment, the step of generating a tree-structured coding dictionary according to the knowledge graph includes: Building a tree-structured standard dictionary based on the knowledge graph, wherein the levels of the tree-structured standard dictionary are, from top to bottom, device type, component, data type, and data code; A coding dictionary is obtained by assigning a code to each node in the standard dictionary of the tree structure.

[0010] In one embodiment, determining a corresponding node in the coding dictionary according to the measuring point information, and determining the coding of the measuring point based on the node, includes: Nodes matching the measuring point information are searched in the tree-structured coding dictionary, codes of the nodes are acquired in sequence, and the codes are concatenated in the acquisition order to obtain codes of the measuring points.

[0011] In one embodiment, the method further comprises: The knowledge graph is updated based on the acquired new type of device, and the encoding dictionary is updated according to the updated knowledge graph.

[0012] In one embodiment, after acquiring the multi-source wind power equipment data, the method further includes: Filtering the wind power equipment data to remove duplicate, erroneous and irrelevant data; The filtered data are standardized to ensure data consistency.

[0013] In a second aspect, an embodiment of the present application provides a wind power equipment data coding system based on a knowledge graph, including: Acquisition module: used to acquire multi-source wind power equipment data, extract entities, relationships and attributes in the wind power equipment data, and perform standardized naming, coreference resolution and entity disambiguation on the entities to obtain optimized entities; Coding dictionary module: used to construct a knowledge graph based on the optimized entities, relationships and attributes, and generate a tree-structured coding dictionary according to the knowledge graph; Coding module: used for obtaining measuring point information, determining corresponding nodes in the coding dictionary of the tree structure according to the measuring point information, and determining the coding of the measuring point based on the nodes.

[0014] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the wind power equipment data encoding method based on the knowledge graph as described in the first aspect above is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wind power equipment data encoding method based on the knowledge graph as described in the first aspect above.

[0016] The embodiments of the present application provide a wind power equipment data encoding method and system based on a knowledge graph, which have at least the following technical effects.

[0017] This application ensures the comprehensiveness and accuracy of wind power equipment, components and measuring point information through multi-source data integration. Through coreference resolution, entity disambiguation and standardized naming, the problem of inconsistent naming is solved and the consistency and accuracy of data are improved. The hierarchical relationship is clearly represented by a tree-structured coding dictionary, and the coding is automatically matched and generated in the tree structure based on the acquired measuring point information. The coding efficiency and accuracy are improved, and the traceability of measuring point information is achieved. Through unified data standards and coding rules, data sharing and intercommunication between different manufacturers and different equipment are promoted, the consistency level of data is improved, and the complexity of data management is reduced.

[0018] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a flow chart of a wind power equipment data encoding method based on a knowledge graph according to an embodiment of the present application; Figure 2 is a schematic diagram of a structure of a standard dictionary with a tree structure according to an exemplary embodiment; Figure 3 is a schematic diagram showing automatic update of a knowledge graph and a coding dictionary according to an exemplary embodiment; Figure 4It is a structural block diagram of a wind power equipment data coding system based on a knowledge graph provided according to an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0021] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0022] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0023] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantity limitation, and may indicate the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0024] In a first aspect, the present application provides a method for encoding wind power equipment data based on a knowledge graph. Figure 1 is a flow chart of a wind power equipment data encoding method based on a knowledge graph according to an embodiment of the present application, such as Figure 1 As shown, the method includes: Step S101, acquiring multi-source wind power equipment data, extracting entities, relationships and attributes in the wind power equipment data, and performing standardized naming, coreference resolution and entity disambiguation on the entities to obtain optimized entities.

[0025] Optionally, the naming information, attribute description, relationship description and other information of wind power equipment, components and measuring points are collected from the official websites, technical documents, product manuals, equipment systems and other channels of multiple wind power equipment manufacturers. The relationship description information includes the relationship information between equipment and equipment, and between equipment and measuring points, such as the component system included in the equipment, the measuring points included in the component system, etc. The attribute description includes the detailed technical parameters of each device, such as the measurement range and accuracy of the measuring point. In this way, through the integration of multi-source data, the comprehensiveness and accuracy of wind power equipment, components and measuring points are ensured.

[0026] Optionally, entities, relationships and attributes in wind power equipment data are extracted. Entities represent wind power equipment, components and measuring points. Attributes represent the properties of entities, such as the name, model, function and location of components; the name, type, unit and range of measuring points. Relationships represent the relationships between entities, such as the assembly relationship between components, the association relationship between measuring points and components, and the mapping relationship between different names. Named entity recognition (NER) technology is used to automatically identify named entities, such as equipment names, measuring point names, etc., from the acquired multi-source text data. Relationship extraction technology is used to extract relationships between entities from text data, such as the inclusion relationship between equipment and measuring points. In this way, by automatically extracting entities, attributes and relationships, manual intervention is reduced and the efficiency and accuracy of knowledge graph construction are improved.

[0027] Optionally, coreference resolution is used to identify whether different phrases (such as pronouns, noun phrases, etc.) in the text refer to the same entity. Entity disambiguation is used to match fuzzy entities appearing in the text with unique entities in the knowledge base, where fuzzy entities include entities with multiple meanings. Through natural language processing or semantic web technology, semantic analysis is performed on the new name to find the name that best matches the existing entity in the knowledge graph. Based on the result of semantic matching, the new name is automatically associated with the best matching entity name. In the case of naming conflicts, conflict resolution is performed based on relationships and attributes to determine the correct and unique identifier of entities with multiple meanings to ensure the accuracy and consistency of the naming. Comprehensively consider multiple factors such as equipment structure, operating principle, and location and purpose of measurement points to accurately associate measurement point data with corresponding components. (1) Clarify the location and purpose of the measurement point so that it can be associated with the corresponding component. For example, three-phase voltage and three-phase current measurement points are usually set at the output end of the generator to monitor the electrical performance of the generator. (2) According to the functions and characteristics of each component, obtain the measurement points that may be involved. For example, as an important transmission component, the oil level and pressure measurement points of the gearbox are crucial for monitoring the status of its lubrication and cooling systems. (3) Combined with the equipment operation status, the analysis is carried out to accurately determine the component status reflected by the measurement point data. In this way, the component name and the measurement point name are resolved by co-reference. The problem of inconsistent naming is solved by semantic matching technology, co-reference resolution and entity disambiguation technology, which further enhances the semantic expression ability of the knowledge graph and provides support for the knowledge management of complex equipment systems.

[0028] In one example, step S101 includes: calculating similarities and associations between entities based on relationships between entities and attributes of entities, and unifying entities whose similarities are greater than a first threshold and whose associations are greater than a second threshold as the same entity.

[0029] Optionally, by calculating the similarity and correlation between the measuring points, the measuring points that are essentially the same but have different names are unified under the same entity. For each component and measuring point, a mapping relationship between the names of different manufacturers is established to clarify the names that essentially point to the same component or measuring point. Among them, the first threshold and the second threshold can be set according to actual application requirements. In this way, the measuring points that are essentially the same but have different names are unified under the same entity, which solves the problem of inconsistent naming and improves the consistency and accuracy of the data.

[0030] In one example, after obtaining multi-source wind power equipment data, the method further includes: filtering the wind power equipment data to remove duplicate, erroneous and irrelevant data; and standardizing the filtered data to ensure data consistency. In this way, data cleaning and standardization improve the quality of the data, laying a solid foundation for subsequent knowledge graph construction and coding.

[0031] Step S102, constructing a knowledge graph based on the optimized entities, relationships and attributes, and generating a tree-structured encoding dictionary based on the knowledge graph.

[0032] Optionally, a tree structure including device type, component, data type and data code is constructed, and a unique code is assigned to each node in the tree structure to form a coding dictionary. The coding method based on the tree structure can clearly represent the hierarchical relationship between devices, components, data types and data codes.

[0033] In one example, step S102 includes: Step S1021, constructing an ontology library of wind power equipment based on wind power equipment data. Optionally, based on the acquired professional knowledge in the field of wind power equipment (ie, wind power equipment data), construct an ontology library of equipment, including classification, attributes, relationships, etc. of equipment.

[0034] Step S1022, design the logical model of the knowledge graph, and represent knowledge with the triple structure of entity-relationship-entity and entity-attribute-attribute value. Optionally, design the logical model of the knowledge graph, including the representation of entities, the representation of relationships, etc. In this application, the triple structure of "entity-relationship-entity" and "entity-attribute-attribute value" is used to represent knowledge.

[0035] Step S1023, construct a knowledge graph based on the ontology library, logical model and optimized entities, relationships and attributes, and store the knowledge graph in a graph database. Optionally, a knowledge graph is constructed by a knowledge graph construction tool based on the preprocessed data and ontology library. The constructed knowledge graph is stored in the graph database for subsequent knowledge retrieval and application.

[0036] In this way, the knowledge graph construction technology is innovatively applied in the field of wind power equipment. Through automated entity extraction, attribute extraction, relationship extraction and other steps, a unified representation of the wind power equipment field knowledge graph is constructed to clearly display the hierarchical relationship and attribute information between equipment, components and measurement points.

[0037] Furthermore, the knowledge graph constructed by this application can optimize the relationships in the knowledge graph based on feedback and needs in actual applications. And with the continuous development of the wind power equipment field and the emergence of new equipment, the knowledge graph can be continuously updated to ensure the timeliness and accuracy of the knowledge.

[0038] In one example, step S102 includes: constructing a tree-structured standard dictionary based on the knowledge graph, wherein the levels of the tree-structured standard dictionary are device type, component, data type, and data code from top to bottom, and assigning a code to each node in the tree-structured standard dictionary to obtain a code dictionary.

[0039] Optionally, a standard dictionary with a tree structure including device types, components, data types and data codes is constructed. Figure 2 is a schematic diagram of a tree-structured standard dictionary according to an exemplary embodiment. Figure 2 As shown in the figure, the device type node is the root node, each device type node is connected to multiple component nodes, each component node is connected to multiple data type nodes, and each data type node is connected to multiple data code nodes. Each node in the tree-structured standard dictionary is encoded to obtain a coding dictionary. The coding dictionary stores the coding of each node and its corresponding name, hierarchical relationship and other information. The hierarchical relationship between devices, components, data types and data codes is clearly represented by the tree-structured coding dictionary. The coding dictionary can adapt to the changes in the needs of different devices and measurement points, and has high scalability and flexibility.

[0040] Continue to refer to Figure 1 , after step S102, step S103 is performed, as follows.

[0041] Step S103 , obtaining measurement point information, determining corresponding nodes in a tree-structured coding dictionary according to the measurement point information, and determining coding of the measurement points based on the nodes.

[0042] Optionally, analyze the measuring point to obtain data such as the equipment, component system, data type, etc. to which the measuring point belongs. According to the measuring point information, match and search for the corresponding node in the tree-structured coding dictionary to obtain the coding of the measuring point. The measuring point information includes the equipment type, component, data type, and data code. Assign codes to each entity according to different levels (equipment, component, data class, data code) to build a standardized tree-structured coding dictionary.

[0043] In this way, by normalizing the entity and hierarchical relationship, the equipment measurement points are organized according to the unified coding rules, and the unique code is automatically matched and searched in the tree structure according to the acquired measurement point information, which improves the coding efficiency and accuracy, reduces the cost and risk of manual intervention, and realizes the unique representation and traceability of measurement point information. This enables the measurement points to have efficient representation and query capabilities in equipment management, data acquisition and monitoring systems.

[0044] In one example, step S103 includes: searching for nodes matching the measuring point information from a tree-structured coding dictionary, acquiring the codes of the nodes in sequence, and concatenating the codes in the acquisition order to obtain the codes of the measuring points.

[0045] Optionally, the device code, component code, data type code and data code are obtained in sequence, and the obtained codes are spliced ​​in the order of acquisition to form a unique code for the measuring point. In this way, according to the obtained measuring point information, the unique code is automatically matched and searched in the tree structure and generated, which ensures the uniqueness and traceability of the measuring point information and facilitates the operation and maintenance management of the equipment. It is convenient to accurately locate the location of the actual data collection in practical applications.

[0046] In one example, the method further includes: updating the knowledge graph based on the acquired new type of device, and updating the encoding dictionary according to the updated knowledge graph.

[0047] Optionally, when encountering a new type of equipment, the update process is automatically triggered, including steps such as data collection, knowledge graph construction, naming standardization, and coding dictionary construction. The automatically triggered update mechanism ensures the timeliness and accuracy of the knowledge graph and coding dictionary, thereby better adapting to the development and changes in the field of wind power equipment. Figure 3 is a schematic diagram showing automatic update of a knowledge graph and a coding dictionary according to an exemplary embodiment. Figure 3 As shown in the figure, when a new type of equipment cannot be coded, the data collection steps are automatically repeated to extract the entities of the new type of equipment, perform name normalization on the entities, update the knowledge graph based on the normalized entities and the extracted relationships and attribute information, and update the coding dictionary based on the updated knowledge graph. According to the updated knowledge graph and coding dictionary, the measurement point coding is performed to obtain the updated coding. This ensures the timeliness and accuracy of the knowledge graph and coding dictionary, so that the system can continue to meet the changing needs of wind power equipment management and operation and maintenance.

[0048] In summary, this application ensures the comprehensiveness and accuracy of wind power equipment, components and measuring point information through multi-source data integration. The quality of data is improved by cleaning and standardizing multi-source data. The problem of inconsistent naming is solved through semantic matching technology, coreference resolution, entity disambiguation and standardized naming, and the consistency and accuracy of data are improved. The hierarchical relationship between equipment, components, data types and data codes is clearly represented by a tree-structured coding dictionary, and the unique code is automatically matched and generated in the tree structure according to the acquired measuring point information. The coding efficiency and accuracy are improved, and the unique representation and traceability of measuring point information are realized. Through unified data standards and coding rules, data sharing and intercommunication between different manufacturers and different equipment are promoted. The measuring points have efficient representation and query capabilities in equipment management, data acquisition and monitoring systems. The timeliness and accuracy of the knowledge graph and coding dictionary are ensured through an automatically triggered update mechanism, so as to better adapt to the development and changes in the field of wind power equipment.

[0049] In a second aspect, the present application embodiment provides a wind power equipment data coding system based on a knowledge graph. Figure 4 is a structural block diagram of a wind power equipment data encoding system based on a knowledge graph provided in an embodiment of the present application, such as Figure 4 As shown, the system includes: Acquisition module 100: used to acquire multi-source wind power equipment data, extract entities, relationships and attributes in the wind power equipment data, perform standardized naming, coreference resolution and entity disambiguation on entities to obtain optimized entities, and construct a knowledge graph based on the optimized entities, relationships and attributes.

[0050] Coding dictionary module 200: used to construct a knowledge graph based on optimized entities, relationships and attributes, and generate a tree-structured coding dictionary based on the knowledge graph.

[0051] The coding module 300 is used to obtain the measuring point information, determine the corresponding node in the coding dictionary of the tree structure according to the measuring point information, and determine the coding of the measuring point based on the node.

[0052] In one example, the encoding dictionary module 200 includes: Used to build an ontology library of wind power equipment based on wind power equipment data.

[0053] A logical model used to design a knowledge graph that represents knowledge in the form of entity-relationship-entity triples and entity-attribute-attribute value triples.

[0054] Used to build knowledge graphs based on ontology libraries, logical models, and optimized entities, relationships, and attributes, and store the knowledge graphs in graph databases.

[0055] In one example, the acquisition module 100 includes: calculating the similarity and correlation between entities based on the relationship between entities and the attributes of the entities in the knowledge graph, and unifying entities whose similarity is greater than a first threshold and whose correlation is greater than a second threshold as the same entity.

[0056] In one example, the encoding dictionary module 200 includes: A tree-structured standard dictionary is constructed based on the knowledge graph. The levels of the tree-structured standard dictionary are device type, component, data type and data code from top to bottom.

[0057] A coding dictionary is obtained by assigning a code to each node in the tree-structured standard dictionary.

[0058] In one example, the encoding module 300 includes: searching for nodes matching the measurement point information from a tree-structured encoding dictionary, sequentially acquiring the codes of the nodes, and concatenating the codes in the acquisition order to obtain the codes of the measurement points.

[0059] In one example, the system further includes: updating the knowledge graph based on the acquired new type of device, and updating the encoding dictionary according to the updated knowledge graph.

[0060] In one example, the system further includes: filtering the wind power equipment data to remove duplicate, erroneous and irrelevant data, and performing standardization on the filtered data to ensure data consistency.

[0061] In summary, this application ensures the comprehensiveness and accuracy of wind power equipment, components and measuring point information through multi-source data integration. The quality of data is improved by cleaning and standardizing multi-source data. The problem of inconsistent naming is solved through semantic matching technology, coreference resolution, entity disambiguation and standardized naming, and the consistency and accuracy of data are improved. The hierarchical relationship between equipment, components, data types and data codes is clearly represented by a tree-structured coding dictionary, and the unique code is automatically matched and searched in the tree structure according to the acquired measuring point information. The coding efficiency and accuracy are improved, and the unique representation and traceability of measuring point information are realized. Through unified data standards and coding rules, data sharing and intercommunication between different manufacturers and different equipment are promoted. The measuring points have efficient representation and query capabilities in equipment management, data acquisition and monitoring systems. The timeliness and accuracy of the knowledge graph and coding dictionary are ensured through an automatically triggered update mechanism, so as to better adapt to the development and changes in the field of wind power equipment.

[0062] In a third aspect, an embodiment of the present application provides an electronic device, Figure 5A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the wind power equipment data encoding method based on the knowledge graph provided in the first aspect is implemented. Figure 5 The electronic device 60 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0063] The electronic device 60 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 60 may include but are not limited to: at least one processor 61, at least one memory 62, and a bus 63 connecting different system components (including the memory 62 and the processor 61).

[0064] The bus 63 includes a data bus, an address bus, and a control bus.

[0065] The memory 62 may include a volatile memory, such as a random access memory (RAM) 621 and / or a cache memory 622 , and may further include a read-only memory (ROM) 623 .

[0066] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624, such program modules 624 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0067] The processor 61 executes various functional applications and data processing by running the computer program stored in the memory 62, such as the wind power equipment data encoding method based on the knowledge graph provided in the first aspect of the present application.

[0068] The electronic device 60 may also communicate with one or more external devices 64 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 65. Furthermore, the model-generated device 60 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 66. As shown, the network adapter 66 communicates with other modules of the model-generated device 60 via a bus 63. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the model-generated device 60, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0069] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0070] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the wind power equipment data encoding method based on the knowledge graph provided in the first aspect is implemented.

[0071] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0072] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of wind power equipment data encoding based on the knowledge graph provided in the first aspect.

[0073] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0074] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A wind power equipment data encoding method based on knowledge graph, characterized in that: include: Acquire multi-source wind power equipment data, extract entities, relationships and attributes in the wind power equipment data, and perform standardized naming, coreference resolution and entity disambiguation on the entities to obtain optimized entities; Building a knowledge graph based on the optimized entities, relationships and attributes, and generating a tree-structured coding dictionary based on the knowledge graph; The measuring point information is acquired, a corresponding node is determined in the coding dictionary of the tree structure according to the measuring point information, and a coding of the measuring point is determined based on the node.

2. The wind power equipment data encoding method based on knowledge graph according to claim 1 is characterized in that: The constructing of a knowledge graph based on the optimized entities, relationships and attributes includes: Building an ontology library of wind power equipment based on the wind power equipment data; Design the logical model of the knowledge graph, and represent knowledge with the triple structure of entity-relationship-entity and entity-attribute-attribute value; A knowledge graph is constructed based on the ontology library, the logical model and the optimized entities, relationships and attributes, and the knowledge graph is stored in a graph database.

3. The wind power equipment data encoding method based on knowledge graph according to claim 1 is characterized in that: The standardized naming of the entity includes: Based on the relationship between the entities and the attributes of the entities, the similarity and relevance between the entities are calculated, and the entities whose similarity is greater than a first threshold and whose relevance is greater than a second threshold are unified as the same entity.

4. The wind power equipment data encoding method based on knowledge graph according to claim 1 is characterized in that: The step of generating a tree-structured coding dictionary according to the knowledge graph includes: Building a tree-structured standard dictionary based on the knowledge graph, wherein the levels of the tree-structured standard dictionary are, from top to bottom, device type, component, data type, and data code; A coding dictionary is obtained by assigning a code to each node in the standard dictionary of the tree structure.

5. The wind power equipment data encoding method based on knowledge graph according to claim 4 is characterized in that: The step of determining a corresponding node in the coding dictionary according to the measuring point information, and determining the coding of the measuring point based on the node, includes: Nodes matching the measuring point information are searched in the tree-structured coding dictionary, codes of the nodes are acquired in sequence, and the codes are concatenated in the acquisition order to obtain codes of the measuring points.

6. The wind power equipment data encoding method based on knowledge graph according to claim 1 is characterized in that: The method further comprises: The knowledge graph is updated based on the acquired new type of device, and the encoding dictionary is updated according to the updated knowledge graph.

7. The wind power equipment data encoding method based on knowledge graph according to claim 1 is characterized in that: After acquiring the multi-source wind power equipment data, the method further includes: Filtering the wind power equipment data to remove duplicate, erroneous and irrelevant data; The filtered data are standardized to ensure data consistency.

8. A wind power equipment data coding system based on knowledge graph, characterized in that: include: Acquisition module: used to acquire multi-source wind power equipment data, extract entities, relationships and attributes in the wind power equipment data, and perform standardized naming, coreference resolution and entity disambiguation on the entities to obtain optimized entities; Coding dictionary module: used to construct a knowledge graph based on the optimized entities, relationships and attributes, and generate a tree-structured coding dictionary according to the knowledge graph; Coding module: used for obtaining measuring point information, determining corresponding nodes in the coding dictionary of the tree structure according to the measuring point information, and determining the coding of the measuring point based on the nodes.

9. An electronic device, characterized in that: include Memory, processor, and A computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the wind power equipment data encoding method based on the knowledge graph as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the wind power equipment data encoding method based on the knowledge graph as described in any one of claims 1 to 7 is implemented.

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