Park energy big data management method and system based on machine learning

The invention discloses a park energy big data management method and system based on machine learning, and the method comprises the steps: employing a Monte Carlo simulation method, and randomly selecting a preset number of arbitrary values in a preset interval for simulation based on a time vector sequence of park energy; training a data set for the simulated sample by using a Bayesian loop network, generating a target network, judging posterior distribution of the weight of the target network, and carrying out network updating iteration; and controlling switching of energy types based on an output layer classification result of the Bayesian cycle network. The method does not need to explicitly specify a probability model or fit probability distribution characteristics, and due to the characteristics of the data model, it is determined that the method has the convenience of not needing sampling steps and manually marking data, and the accuracy is ensured. In addition, a Bayesian formula probabilistic reasoni