As enterprise operation management faces increasingly complex multi-source heterogeneous data, dynamic resource scheduling and decision support problems, traditional optimization methods and decision-making systems can no longer meet the needs of efficient and intelligent management. In order to improve the efficiency of enterprise operation management and the performance of the decision support system, a hybrid intelligent algorithm based on FA-BP and AGA is proposed. Innovative technologies such as deep reinforcement learning, quantum evolution strategy, and microservice architecture are combined to build an enterprise management system with efficient decision support and powerful computing capabilities. The proposed system has been validated in a manufacturing enterprise, demonstrating a 25% increase in production efficiency and 18% reduction in raw material waste. Its microservice architecture enables seamless integration with existing enterprise resource planning (ERP) systems, facilitating real-time data-driven decisions. Furthermore, through multi-modal data fusion and spatiotemporal feature embedding algorithms, efficient data integration is achieved under the federated learning framework of enterprise multi-source heterogeneous data, providing comprehensive and accurate information support for enterprise decision-making. Different performances of enterprise production efficiency, raw material utilization rate, labor cost, and team task completion rate. In 87 business cycles, the highest production efficiency reached 98.76%, and the lowest was 34.56%. This shows that the enterprise has large efficiency fluctuations in some cycles, which may be affected by factors such as the external environment, production process, or equipment maintenance. Businesses need to conduct an in-depth analysis of inefficiency cycles to identify the causes and improve them. The average level of raw material utilization is 67.89%, but it can reach 83.25% at the highest.
Zhou Jiang (Mon,) studied this question.