This study investigates the research on nickel-cobalt-copper productive collaboration and intelligent decision-making technology for symbiotic coupling enterprises in the Gansu Province of China. The aim is to address the problems of low resource utilization efficiency, weak production collaboration, and an insufficient intelligent decision-making level in the nonferrous metallurgy industry. First, the present situation of nickel-cobalt-copper industry chain-level collaboration in the agglomeration area is analyzed extensively, and the corresponding problems are proposed. Second, the functional framework of productive collaboration and intelligent decision-making is presented from the industrial chain and industrial agent levels. In addition, the design methods of various balance strategies in the production collaboration within the industrial agent are provided. These can realise the daily balance of material, metal, and energy data in an individual industrial agent. Finally, with regard to intelligent decision-making at the industrial chain level, six key measures surrounding different themes are provided to support the implementation of productive collaboration and intelligent decision-making in the nonferrous metallurgy agglomeration area.

In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. First, the feature sets of 11 kinds of monitoring data are optimized and selected based on MIC for self-powered power supply system. By eliminating redundant variables and insensitive variables, feature variable sets with great influence on abnormal diagnosis are selected. Second, by upgrading the selection method of control parameter σ from linear to nonlinear, an improved GWO-SVM algorithm that can take into account both global and local search capabilities is proposed. Furthermore, the optimal feature set which has great influence on abnormal diagnosis is selected as the input of the proposed algorithm, and then the abnormal diagnosis method combining the improved GWO-SVM with MIC is constructed for self-powered power supply system. The specific algorithm flow and step are given. Finally, compared with other algorithm, the simulation experiments show that the GWO-SVM method has a higher accuracy and a higher recall rate for the abnormal diagnosis in the self-powered power supply system.