The current rapid development of artificial intelligence technology represented by deep learning has attracted much attention in all walks of life. The real-time regulation and operation of large-scale power grids is a typical combination of knowledge experience-based and online analysis. In particular, the fault disposal of power grids mainly depends on the pre-compiled texts of failure scenarios, which is actually the induction of prior knowledge and summary of dispatchers. Based on the above scenarios, this paper proposes a deep learning based dispatching fault disposal robot technology. Firstly, the natural language processing technology is used to learn, understand and extract the key information of the fault preplan text, and then a fault disposal knowledge map is built on this basis. Through the reasoning and analysis of knowledge, automatic/semi-automatic disposal of faults can be achieved.

Research on Deep Learning Based Dispatching Fault Disposal Robot Technology

With the increasing environmental awareness of the country and the people and the implementation of strong environmental protection measures, China’s atmospheric quality was improved obviously, but local atmospheric pollution events still occurred frequently In order to make up for the missing detection of local atmospheric pollution events caused by sparse fixed State-controlled monitoring stations, the paper proposed a spatialization method for atmospheric quality public opinion information based on natural language processing Using Chinese word segmentation, part of speech tagging and other methods, the paper extracted addresses from public atmospheric pollution complaints data Through an effective combination of those addresses, the paper realized address matching of those complaint points, and spatialized those key complaint areas in Shandong Province in the form of heat map Through comparison and analyzing with the atmospheric quality monitoring data of national control stations, it showed that the key areas of public complaints were highly consistent with the key pollution areas which were monitored by national control stations The research result showed that the public could perceive the atmospheric quality directly, and reflect the local atmospheric pollution at a smaller space-time scale effectively, which was a robust supplementation to the monitoring data of the national control station

Spatialization Method of Atmospheric Quality Public Opinion Based on Natural Language Processing

Semantic role labeling(SRL)is an important way to get semantic information.Many existing systems forSRL make use of full syntactic parses.But due to the low performance of the existing Chinese parser,the performance of labeling based on the full syntactic parses is still not satisfactory.This paper realizes SRL methods based on shallow parsing.In shallow parsing stage,this paper makes use of word formation to get fake head morpheme information,which alleviates the problem of data sparseness,and imporves the performance of the parser with the F-score up to 0.93.In the stage of semantic role labeling,this paper applies word formation to get morpheme information of the target verb,which describes the structure of word in fine granualrity,and provides more information for semantic role labeling.In addition,this paper also proposes a coarse frame feature as an approximation of the sub-categorization information existing full syntactic parsing.F-score of this semantic role labeling system has reached 0.74,a significant improvements over the best reported SRL performance(0.71) in the literature.

Sui Zhifang

Papers

Research on Chinese Semantic Role Labeling Based on Shallow Parsing