Design And Implementation Of Dynamic Knowledge Graph System About Preparing For Decision

With the arrival of the era of big data,there are new opportunities and challenges in decision-making.Data is an important support for determining whether a system can make reasonable decisions.The complexity of the decision-making environment and the high cost of obtaining professional knowledge restrict the development of decision-making systems.At the same time,the vast amount of data also promotes the rapid development of knowledge graph.Knowledge graph has been widely used in many fields with its powerful representation,retrieval and reasoning ability.The characteristics of graph fit well for assistant decision-making,but the graph itself lacks the ability of cognitive reasoning.Based on the above problems,a new assistant decision system based on knowledge graph.By combining the powerful and flexible representation and reasoning capabilities of knowledge graphs and creatively adding cognitive nodes with logical computing capabilities,the algorithm is integrated into the knowledge graph.This not only enriches the representation and cognitive ability of the knowledge graph,but also enables the knowledge graph to explore the internal relationship of domain knowledge.It also draws on the relevant research and ideas of neural network and brain science,activates the cognitive nodes by setting trigger conditions,dynamically copes with the changes in the knowledge graph,and diffuses the influence through the cognitive chain,ultimately achieving the purpose of assisting decision-making..The system uses a front-end and back-end separation mode.The front-end uses the Vue framework.The back-end uses the Spring Boot framework,And the overall architecture of micro-services is used to improve the availability and maintainability of the system.The system has completed a comprehensive test after implementation,and has been applied in the field of vehicle failure and maintenance to provide users with information to assist decision-making when solving maintenance problems.In the application process,the system runs stably,operates simply and performs well,which helps users to easily manage and use knowledge graph to describe and represent data in problem areas,flexibly set up cognitive nodes to cope with complex and variable decision-making environment,and rely on the knowledge graph themselves to make cognitive inference and preparing for decisions.