Construction of distributed photovoltaic operation and maintenance knowledge base based on time series knowledge map

By constructing a distributed photovoltaic operation and maintenance knowledge base based on a time series knowledge graph, the problems of lack of specific knowledge base support and low efficiency in formulating operation and maintenance plans have been solved. The entity recognition method using multiple neural networks and attention mechanisms successfully recognized entities in text, and generated an operations knowledge graph through an entity relationship extraction model based on the JSA model. At the same time, by using incremental updates and redundancy processing methods, time series information is combined with the operation and maintenance knowledge graph to form an efficient and non redundant operation and maintenance knowledge base. The experimental results show that the similarity of the time series knowledge graph is low, always below 0.02, and the redundancy of operation and maintenance knowledge is small. The maximum power generation time of the operation and maintenance scheme is significantly shortened, thereby improving the power generation efficiency of the distributed photovoltaic operation and maintenance scheme.