Aiming at the problem of decreased photovoltaic power generation efficiency caused by inaccurate start-up timing determination of photovoltaic cleaning robots， and based on a comprehensive consideration of power generation fluctuations induced by complex meteorological conditions and solar panel aging， an intelligent decision-making method for solar panel cleaning timing based on Multi-Sensor Fusion （MSF） was proposed to enhance the cleaning efficiency and the photovoltaic power generation efficiency of solar panels. Firstly， multi-source sensor data were collected in real-time by a microcontroller， such as solar panel output power and ambient temperature. Then， a predicted photovoltaic power generation value under the influence of multiple factors was calculated after the optimization of a Proportion Integration Differentiation （PID） algorithm. Finally， by comparing the predicted photovoltaic power generation value with the real-time power generation capacity of the solar panels， intelligent determination of the cleaning timing and automatic start-up control of the photovoltaic cleaning robot were implemented through the employment of data-level and decision-level fusion techniques. Experimental results indicate that the MSF-based method performs excellently across different test scenarios. The scenario of sunny weather with soiled solar panels， which best reflects the value of cleaning is taken as an example and a determination accuracy of 96% is achieved by the method. Compared with the decision-making method using the ResNet50-CA model， the MSF-based method achieves a relative accuracy improvement of 4.35% in the scenario of sunny weather with soiled panels. Furthermore， in the same scenario， compared to the decision-making methods based on the K-Nearest Neighbors （KNN） algorithm， Random Forest （RF） model， and Kalman Filter （KF）， the MSF-based method has the advantages more significant， with accuracy improvements of 35.21%， 45.45%， and 10.34%， respectively. It can be seen that the proposed method can enhance the timeliness and accuracy of cleaning operations effectively， providing a reliable technical solution for maintaining high efficiency of photovoltaic power generation systems under complex meteorological conditions and equipment aging states.
