This study proposes a multi-source heterogeneous data fusion method based on joint Kalman filtering to solve the problems of large data fusion errors and low efficiency in traditional distribution network data processing, thereby improving the accuracy and fusion effect of energy data in power regulation systems. Firstly, by constructing a parameter estimation optimization model and utilizing improved chaos algorithm and Markov Monte Carlo algorithm to fill the missing data in the power dispatch system, the accuracy of parameter processing has been improved. Subsequently, a comparative analysis was conducted on three fusion algorithms: joint Kalman filter, time-domain Gaussian process model, and neural network, and fusion tests were conducted on key parameters. The results indicate that the joint Kalman filter performs the best in terms of fusion error, especially in key indicators such as root mean square error. Specifically, in the process of power data fusion, the joint Kalman filter algorithm has almost no fluctuation in weight during iteration, and with a relative error of 2% as the standard, the probability of the three-phase total active power and reactive power satisfying the error is 100% and 92.45%, and the probability of the two-phase total active power and reactive power satisfying the error is 100% and 98.11%, respectively, within 53 test cycles. Therefore, this data fusion method has significant advantages in improving the accuracy and efficiency of data processing in distribution networks, providing strong support for the intelligence and reliability of distribution networks.