Construction Of Scientific Effects Knowledge Database System Based On TRIZ

Product innovation is the core driving force for enterprises to survive and develop in a fiercely competitive market,and conceptual design is a key step in product innovation.TRIZ is a systematic methodology that can guide people to innovate and solve engineering problems.As an important innovation resource in TRIZ,the knowledge base of scientific effects plays an important role in the conceptual design of products.The research on the knowledge base of scientific effects mainly focuses on the knowledge expression of scientific effects,and there is relatively little research on the knowledge map of scientific effects.This article explores the construction process of the knowledge map of scientific effects,and constructs the scientific effect by means of knowledge maps.The knowledge base provides new resources and channels for product conceptual design,and it is of practical significance to study how to construct a knowledge base of scientific effects.Mainly completed the following work.First,combine the current literature research on scientific effects,redefine the scientific effects,and classify the effects in combination with the definitions.The web crawler technology is used to crawl the effect knowledge,which is the data source constructed by the ontology after translation,modification and other operations.According to the existing ontology construction method,choose to use Protégésoftware to construct the interrelated ontology knowledge from the concept category,entity,attribute and relationship on the effect,function,and practical application,and add constraints on the attribute and relationship,and finally use RDF/XML ontology file is stored in the form of OWL.Second,analyze and compare the triple storage mode of the ontology with the Neo4j graph database storage mode.According to the pattern data,the classes,entities,attributes,and relationships in the ontology correspond to the labels,nodes,attributes,and relationships in the Neo4j graph database.The OWL ontology file is parsed into RDF format data,which is stored in the Neo4j graph database through the semantic import module in the Neo4j graph database to form a knowledge graph.Check and modify the data of the knowledge graph as required,and finally establish 2477 related nodes and 8212 triple data.And established a knowledge map of scientific effects.Finally,through Python development tools combined with Neo4j graph database,according to demand analysis and architecture design,the front end adopts the Echarts framework for data visualization,the back end adopts the Flask framework,and the use of knowledge graphs to realize the visual display,query,and addition of scientific effects and knowledge.Which allows users to find the knowledge relationship between effects,functions,and practical applications through entity query,so as to complete the construction of the scientific effect knowledge base system.