Abstract:The power industry is undergoing a transformation led by big data and smart technologies. This paper delves into the application of big data analytics in real-time monitoring and control of power grid dispatch, with the aim of improving the efficiency and security of power grid operation through intelligent methods. Based on data mining and machine learning algorithms, the study implements real-time analyses of grid operation data to achieve more accurate load forecasting and fault diagnosis. The analysis results reveal the key role of big data analytics in optimising grid scheduling strategies, especially in improving scheduling efficiency and responsiveness. This study constructs an integrated data-driven grid scheduling system that reinforces the central role of artificial intelligence and machine learning in processing complex data from the grid. Meanwhile, the convergent application of IoT, cloud computing and edge computing provides innovative technological solutions for grid monitoring. In response to the technical and managerial challenges faced by grid scheduling, this paper proposes strategies aimed at driving grid scheduling towards efficiency, intelligence and security. The innovation of the study is to propose an integrated data collection, processing, analysis and decision support system, and to prospectively analyse the application of emerging technologies in grid scheduling.