In order to realize the intelligent operation and maintenance of electrochemical energy storage power station and make the working process of the power station battery more efficient, stable and safe, this paper establishes a safety monitoring system of electrochemical energy storage power station through multimodal fusion sensing technology. The multi-sensor fusion technology and multi-sensor calibration process are proposed, and the Kalman joint filter fusion algorithm is obtained based on the traditional Kalman filter extension, which fuses the collected multi-modal sensing data to realize the real-time detection of the state information of each battery of the energy storage power station. Simulation experiments are carried out to verify the reliability of the Kalman joint filter fusion algorithm, and the deviation value of this algorithm in the filter fusion processing is only 0.1426, which is lower than that of the comparative sliding average filtering algorithm. The RMSE values of X-axis and Y-axis in the motion target tracking experiments are less than those of the comparative mean drift algorithm 0.189 and 0.1412, and in the speed, they are less than those of 0.0062 and 0.0073, which are better in terms of accuracy performance. And in the application practice of battery safety monitoring system for electrochemical energy storage power station, the error between SOC estimation and actual value is less than 5% in either DST condition or UDDS condition, and the internal resistance 0R change curve is similar to the actual value of the internal resistance, and the estimation error is less than 4%.