Design and Application of Power Data Asset Pricing Model Based on Reinforcement Learning

Aiming at the problems of inconsistent data value evaluation standards and lack of dynamic adjustment mechanism in the current power data pricing process, a power data pricing model based on reinforcement learning is proposed. Firstly, the random forest algorithm is used to construct a model to calculate the importance of features, and the degree of each feature affecting the pricing strategy is obtained to mine the data value. Secondly, the power data pricing model of Q learning algorithm is constructed to determine the data value evaluation standard. Finally, the dynamic adjustment price based on Q learning algorithm is completed, and the dynamic pricing of power data is realized. At the same time, the experimental results show that the scheme satisfies the dynamic adjustment mechanism of the real market.