Research on Adaptive Signal Optimization Algorithm of Intelligent Decision Support Enterprise Based on Deep Learning

In order to solve the problems such as low efficiency of cloud manufacturing resource allocation and unreasonable selection of manufacturing services, this project intends to study the resource-oriented cloud manufacturing resource allocation strategy based on user demand. By introducing the methods of data warehouse, online analysis and processing, and data mining, the functional model of ERP data analysis is established. The dual-track mechanism of case base and model base is applied to the process design of model intelligent decision support system (IDSS) and the corresponding algorithm is proposed. A multi-index optimization modeling method for cloud manufacturing based on time-expense-quality-of-service and customer satisfaction was constructed. In this project, Chebyshev filter is combined with second-order vibration to solve the problem of early convergence of particle swarm. The variation factor is added to the subjective weight to reflect the contrast strength of the target data. The numerical simulation shows that the new algorithm has a wide range of solutions and good convergence performance, and also proves the correctness of the proposed theory and decision method.