Value Modeling And Value Optimization Analysis Method For The Internet Of Services

As new technologies are widely applied in the service industry,more new service models and industrial forms are emerging.The modern service industry is developing rapidly,the needs of users are becoming more and more personalized,and the business is becoming more and more complex.The Internet of Services(Io S)can be seen as a networked,integrated,and complex service ecosystem.Io S can provide various services in the form of integration of multiple fields,networks,and worlds and provide service value to all service participants to the maximum extent,promoting the development of a new economy.Therefore,to finally realize the creation and addition of service value in the Io S,how to portray the generation,conversion,and transfer of value in the service process becomes an important research issue of the Io S.This paper proposes a value model system,a semi-automated value network modeling method,and an analysis method of value network in time,space,and domain,describes the following four aspects in detail.1.To portray value comprehensively and reflect the semantic characteristics of the service value network,this paper first proposes a value model system for the service Internet.First,the concept of service value is extended and refined,and then this paper presents a complete value system model for a comprehensive representation of service value.Starting from the user’s demand carving,all the way to the combination of valuequality-capability,the value dimension model is formed: Goal Model,Value Proposition Model,Io S-oriented Value Network Model,and Value-Quality-Capability Multi-layer Indicator System Model.2.To describe the cross-domain,cross-organization,and cross-value chain characteristics of the Io S from a value perspective,this paper proposes a semi-automatic modeling method for Io S-oriented Value Network Model(IVN)based on external public data.We first propose an intelligent domain entity recognition algorithm based on multidimensional web data to help value network designers realize efficient recognition of service participants.This method extracts and classifies service participant nodes by extracting features from five-dimensional data in web pages and combines BERT+Light GBM model to assist value network designers in achieving efficient identification of service participants.And based on generating service participant nodes,external news data is introduced,and a deep learning-based intelligent extraction method of value exchange relationships is proposed to assist in identifying value exchange relationships,thus initially forming an Io S-oriented value network model.3.To extend the value network constructed semi-automatically with cross-border semantics,this paper proposes a pattern-based domain-specific value chain(DVC)extraction method to complete the extraction of DVC.It includes offering a typical value chain pattern library generated based on domain a priori knowledge.On this basis,combined with pruning methods,the DVC is extracted from the complete value network model.For the space-time semantics of the IVN,space-time analysis is proposed to calculate the space-time matching degree of the whole value network by the space-time attributes of the corresponding instance nodes of the service participant roles.It is also combined with a case study to explain the rationality and effectiveness of the algorithm.4.To verify the theoretical research results,this paper designs and implements a value system modeling tool and a semi-automatic generation tool for IVN and carves out the value clearly in a project by modeling the value model system proposed in this paper.Then the effect is verified by using the semi-automatic modeling tool for IVN to assist the modeler in using our semi-automatic modeling IVN.