Power grid dispatching knowledge graph data optimization method and system

A technology of knowledge map and power grid dispatching, which is applied in the field of power grid dispatching knowledge map data optimization, can solve the problems of insufficient intelligence level of the dispatching system, and achieve high accuracy and dynamic update reduction effect

Pending Publication Date: 2022-02-22
NARI TECH CO LTD +3
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to realize the full life cycle management of power grid dispatching knowledge map: knowledge extraction-knowledge fusion-knowledge map storage update-expired knowledge elimination, and solve the technical problem of insufficient intelligent level of dispatching system in the prior art

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  • Power grid dispatching knowledge graph data optimization method and system
  • Power grid dispatching knowledge graph data optimization method and system
  • Power grid dispatching knowledge graph data optimization method and system

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Experimental program
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Embodiment 1

[0028] This embodiment provides a power grid dispatching knowledge graph data optimization method including the following steps:

[0029] step one. Establish a graph database-based power grid dispatching knowledge graph model, including power grid historical text subgraphs and power grid equipment subgraphs, such as figure 1 As shown in Fig. 1, the records of the historical text subgraphs of each power grid are connected through the relevant power stations, lines, equipment and grid equipment subgraphs to construct a power grid dispatching knowledge graph model.

[0030] In addition to the connection relationship and knowledge graph structure in the production site above, this kind of data also often shows a relationship in geographical location. Many data in the system contain geographic location information, such as company stations, substations, distribution stations, power stations, etc.

[0031] my country's administrative system is generally divided into four levels, ...

Embodiment 2

[0061] On the basis of step one to step five of embodiment 1, further comprising:

[0062] Step six. Carry out incremental data update and knowledge fusion, including the following steps:

[0063] 61) In step 5, on the basis of the trained entity recognition model and relationship recognition model, the training sets of the entity recognition model and the relationship recognition model are constructed with the power grid equipment information and power grid dispatching knowledge, and the training sets are respectively the entity recognition model and a core set of relation recognition models;

[0064] 62) For the newly added scheduling plan data that is constantly changing over time, use the existing entity recognition model and relationship recognition model to automatically complete entity extraction and relationship extraction, build a new data training set, and build On the core set, the entity recognition model and the relationship recognition model incrementally learn...

Embodiment 3

[0081] On the basis of step one to step five of embodiment 1 or embodiment 2, further comprising:

[0082] Step seven. Dynamic update, storage and recovery of knowledge based on time section, specifically including the following steps:

[0083] Use an open source graph database such as Neo4j for graph storage. First, mark the entities, relationships, and entity and relationship attributes in the graph database of the power grid dispatching knowledge graph with timestamp (time stamp) marks. The timestamp contains two timestamps, one start time Stamp, an end timestamp, the start timestamp is the time when it is added to the knowledge graph, and the end timestamp is the time when it is deleted from the knowledge graph;

[0084] When an entity or relationship is stamped with an end timestamp, it indicates the end of the entire life cycle of related knowledge;

[0085] The deletion of batch entities and relationships in the power grid dispatching knowledge map is backed up to fac...

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Abstract

The invention discloses a power grid dispatching knowledge graph data optimization method and system. The method comprises the following steps: firstly carrying out automatic mining on high-quality phrases of a field through a deep learning method, and completing automatic recognition and equivalent disambiguation of a dispatched entity; then, completing dispatched entity global relationship extraction according to deep learning technology, so as to complete entity relationship recognition and verification, and achieve the purpose of establishing an initial power grid dispatching knowledge graph; on the basis that the two steps are completed, using a natural language learning knowledge fusion technology, and conducting incremental training on newly-added scheduling plan data based on timestamps; meanwhile, introducing life cycle management of knowledge graph knowledge content in the completion process of the steps; finally, completing a continuous learning dynamic knowledge graph under the cooperation of the steps. According to the invention, the high precision of a power grid dispatching optimization decision knowledge graph is ensured, and the consumption of computing resources and time during updating training is reduced while dynamic updating of incremental knowledge is ensured.

Description

technical field [0001] The invention belongs to the technical field of power grid dispatching, and in particular relates to a method for optimizing power grid dispatching knowledge map data. Background technique [0002] With the continuous expansion of the scale of the power system and the increase in the proportion of new energy sources, the difficulty of active power dispatching is increasing. As one of the most important system control methods of the power system, accurate and safe dispatch not only involves the development of the national economy, but also guarantees the safe and efficient operation of the entire power system. [0003] At the current stage, due to the rapid growth of the number of power grid equipment and the total amount of power system knowledge, traditional knowledge organization and management methods have long been unable to meet the needs. Compared with the basic database, knowledge bases including rules, intelligent decision-making systems and t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06Q50/06
CPCG06F16/367G06Q50/06Y02D10/00Y04S10/50
Inventor 唐宁恺陆继翔旷文腾谢峰严晴李红
Owner NARI TECH CO LTD
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