Design of Intelligent Customer Service Questioning and Answering a System for Power Business Scenario Based on AI Technology

In order to solve the problems of large demand for power business and small number of customer service, an intelligent customer service questioning and answering a system for power business scenario based on AI technology is designed. The approach first uses the particle swarm optimization algorithm to automatically classify the question attributes and then uses the fuzzy c-means clustering algorithm to match the answers with the highest similarity to the questions and return to the customers. The system collects the questions raised by customers through the acquisition module, uploads the question work order to the knowledge base through the information assistance module, and stores the preprocessed questions to the knowledge base. After completing the problem attribute classification through the particle swarm clustering algorithm classification model in the batch analysis and calculation module, the question answers are matched through fuzzy c-means clustering. At the same time, the similarity of different keywords is calculated to find a series of related questions. After the obtained data are analyzed in real time through the self-service customer service module, the answer is extracted and fed back to the customer, and the question answer is presented to the customer in the system interface. The experimental results show that the designed system has low worst-case time complexity, which is up to 0.35 only. The reason is that the system in this paper can use priority information to deal with the problems raised by customers, which is different from the past work where dealing with customers request via priority information is not used. The system can give the corresponding answers according to the customer’s options. It has convenient operation, high integrated control ability, and good information management performance. Compared with the traditional approach which could waste a lot of resource and data, the proposed approach can reduce the differences between problem data, eliminate invalid data, and simplify the data classification process. The application of the system can effectively accelerate the information transmission efficiency of the power company and can be used for power exchange platform automation in the future.