关键词: 知识-业务配置, 知识模块, 支持向量回归, 灰狼算法, 模拟退火算法, 知识服务

Abstract: To overcome the separation problem of business units and knowledge resource allocation during the business process,a business-knowledge allocation strategy based on the Grey Wolf Optimizer for Support Vector Regression (GWO-SVR) and improved Simulated Annealing (SA) algorithm was proposed.In this strategy,knowledge resources were encapsulated as knowledge modules based on the analysis of user requirements and the business scenario.On this basis,through the function of the configurator,the initial configuration between the business units and knowledge modules was realized.A set of synthesis evaluation indicator systems was developed based on the analysis of the evaluation parameters,and the CRITIC—fuzzy comprehensive evaluation method was used to obtain the evaluation scale of business-knowledge configuration.Based on the evaluation scale,a dynamic evaluation model based on GWO-SVR for the composition of business-knowledge configuration was constructed and trained.The functional relationship of this trained model could be imported as the objective function during the improved SA optimization process since GWO-SVR was a regression model.Through the optimization iteration,an optimal composition plan corresponding to the optimal value was found to satisfy business needs.Thus,the optimal allocation of knowledge resources was achieved.Using the processing of the reducer box as an example,the effectiveness of the proposed models and algorithms could be verified.

Key words: knowledge-business configuration, knowledge module, support vector regression, grey wolf optimizer, simulated annealing algorithm, knowledge service