A charging pile optimization method and system based on big data

Abstract

The present application relates to the technical field of data processing, in particular to a charging pile optimization method and system based on big data, comprising the following steps: obtaining an operation log to construct a transition probability matrix, collecting equipment real-time signals to generate a distribution vector, multiplying the matrix and the vector to obtain equipment expected values, calculating the difference between the expected values and the number of charging equipment to obtain an expected power increment, calculating the physical remaining capacity of the transformer minus the expected power increment to obtain a corrected capacity, and setting the corrected capacity as the upper limit of power distribution.In the present application, the transition probability matrix is constructed by analyzing the operation log, the distribution vector is generated in combination with real-time signals, and the future equipment state and power increment are predicted by using matrix operation to realize automatic power allocation, the distribution upper limit is set according to the difference between the transformer load and the predicted increment to ensure that the demand is met and a safety margin is reserved, the hidden danger of manual adjustment lag is solved, the capacity utilization efficiency is improved, and the safe and efficient operation of the charging station is ensured.

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