RESEARCH ON AUXILIARY DIAGNOSIS OF POWER COMMUNICATION FIELD OPERATION AND MAINTENANCE BASED ON CBR

摘要

Aiming at managing on-site operation and maintenance of power communication, based on the case-based reasoning (CBR) technology, an optimization method using GRNN neural network is proposed to improve CBR retrieval efficiency, which realize the self-learning and self-growth of an on-site problem diagnosis, and the problems of the traditional CBR algorithm such as low matching degree and slow convergence speed are effectively avoided. The simulation results show that the CBR retrieval algorithm based on the GRNN neural network has a high recall rate and precision rate, which can provide more credible auxiliary diagnosis cases for on-site operation and maintenance personnel and has strong practicability.