Filling missing data is one of the key steps in building panoramic data of the dispatching and control system to ensure the completeness and accuracy of information. According to the features of the historical data in the power dispatching and control system

a missing data filling algorithm for the uniform data model in the panoramic dispatching and control system is proposed. In view of the incomplete data appearing in the process of building panoramic data

the best parameters corresponding to the mean and covariance of the incomplete data are estimated by the improved chaos genetic optimization algorithm. Then

based on the known data

the missing data are estimated by the improved Markov Chain Monte Carlo(MCMC)method. The results show that the algorithm is able to get the best parameters of incomplete data with less iterations

while improving the estimated value accuracy of missing data to ensure the completeness and accuracy of the data.