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Multi-modal electric power knowledge graph construction method and device, equipment and storage medium

A knowledge map and construction method technology, applied in the field of data processing big data, can solve problems such as complex structure and inability to break multi-source heterogeneous knowledge

Pending Publication Date: 2022-02-18
国家电网有限公司大数据中心
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For multi-modal data in the electric power field, there are many types of knowledge and complex structures. In addition to relational data, there are many other forms of knowledge and data, such as time series data and multimedia data, which cannot break the barriers of multi-source heterogeneous knowledge.

Method used

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  • Multi-modal electric power knowledge graph construction method and device, equipment and storage medium
  • Multi-modal electric power knowledge graph construction method and device, equipment and storage medium
  • Multi-modal electric power knowledge graph construction method and device, equipment and storage medium

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Embodiment 1

[0029] figure 1 This is a schematic flowchart of a method for constructing a multimodal power knowledge graph provided in Embodiment 1 of the present invention. This embodiment is applicable to the construction of a multimodal power knowledge graph, and the method can be constructed by a multimodal power knowledge graph constructing device. To perform, the apparatus may be implemented by hardware / software and is generally integrated in computer equipment. The method specifically includes the following steps:

[0030] S110. Based on the multi-modal data in the electric power field, determine the entity feature vector of each entity involved in the electric power field in the same dimensional space.

[0031] Among them, the data in the electric power field may include knowledge data in various forms, which may be embodied in the form of text, images, and the like. The data in the electric power field can be obtained from multiple databases, and there is no specific limitation ...

Embodiment 2

[0079] Figure 5 A schematic structural diagram of a multi-modal power knowledge graph construction device provided in Embodiment 2 of the present invention, the device includes: an entity feature vector determination module 21, a layered module 22, a comprehensive feature vector determination module 23 and an electric power knowledge graph acquisition module twenty four. in:

[0080] The entity feature vector determination module 21 is used to determine the entity feature vector of each entity involved in the power field under the same dimension space based on the multi-modal data in the power field;

[0081] The layering module 22 is used to determine the neighbor entities of each entity through a preset entity triplet, and to layer each neighbor entity according to the set rule;

[0082] The comprehensive feature vector determination module 23 is used to determine the comprehensive feature vector representing the association relationship between the entities based on the ...

Embodiment 3

[0107] Image 6 A schematic structural diagram of a computer device provided in Embodiment 3 of the present invention, such as Image 6 As shown, the computer device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the device can be one or more, Image 6 Take a processor 31 as an example in the above; the processor 31, the memory 32, the input device 33 and the output device 34 in the device can be connected by a bus or other means, Image 6 Take the connection through the bus as an example.

[0108] As a computer-readable storage medium, the memory 32 can be used to store software programs, computer-executable programs, and modules, such as modules corresponding to the method for constructing a multimodal power knowledge graph in the embodiment of the present invention (for example, an entity feature vector determination module). 21. Hierarchical module 22, comprehensive feature vector determination module 23 a...

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Abstract

The embodiment of the invention discloses a multi-modal electric power knowledge graph construction method and device, equipment and a storage medium, and the method comprises the steps: determining the entity feature vector of each entity involved in the electric power field in the same dimension space based on the multi-modal data of the electric power field; determining neighbor entities of each entity through a preset entity triple, and layering each neighbor entity according to a set rule; on the basis of an entity relationship model formed after layering, combined with each entity feature vector, determining a comprehensive feature vector representing an association relationship between the entities; and based on the comprehensive feature vector, obtaining an electric power knowledge graph representing an association relationship between the entities. According to the technical scheme provided by the embodiment of the invention, the multi-modal data in the electric power field is represented by the unified comprehensive feature vector, and data support is provided for related intelligent application and big data analysis in the electric power field.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of data processing big data, and in particular, to a method, apparatus, device, and storage medium for constructing a multimodal power knowledge graph. Background technique [0002] As the knowledge pillar in the field of artificial intelligence, knowledge graph has attracted extensive attention from academia and industry for its powerful knowledge representation and reasoning capabilities. In recent years, knowledge graphs have been widely used in semantic search, question answering, knowledge management and other fields. [0003] The representation learning of multi-modal knowledge graphs is divided into intra-modal representation and inter-modal representation. The intra-modal representation is not much different from the representation learning of traditional knowledge graphs. The representation entities in the modality are in the same embedding space, which can be directly to lear...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F40/295G06N3/04G06V10/764G06V10/774G06V10/82G06K9/62
CPCG06F16/367G06F40/295G06N3/044G06N3/045G06F18/24G06F18/214
Inventor 纪鑫武同心王宏刚杨成月何禹德杨智伟褚娟张海峰李建芳董林啸
Owner 国家电网有限公司大数据中心
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