The large and complex electromechanical equipment is more and more widely used in aerospace, remote sensing and intelligent manufacturing industries. A real-time scheduling method for complex measurement task based on improved sparrow search algorithm is proposed to address the real-time measurement problem of status information of large and complex electromechanical equipment during storage and transportation, especially the complex scheduling issue for measurement processes. Firstly, the initial population of sparrows is initialized using a combination of tent chaos mapping and reverse learning to enhance the quality of initial solutions. Subsequently, the information exchange mechanism of the grey wolf optimization algorithm is introduced to improve the explorer search strategy and enhance algorithm global search capability. Finally, the sine-cosine mechanism is combined with the follower position update process and the variable neighborhood search is carried out to improve the convergence speed of the scheduling algorithm and prevent the algorithm from falling into the local optimal. In order to verify the comprehensive performance of the scheduling method, a large number of comparative experiments are conducted. The experimental results indicate that the proposed method reduces the system computation time by 14.3% and optimizes the maximum completion time by 46.6% compared with the traditional method, which validates its effectiveness and stability in the scheduling of complex measurement tasks.