Intelligent master model management system based on data driving

Abstract

The invention provides an intelligent master model management system based on data driving. The intelligent master model management system comprises a collaborative data area, a master model, a pedigree relationship, a professional data packet and a data item. The collaborative data area comprises basic information of model projects and project teams, the collaborative data area corresponds to one model project, and one collaborative data area comprises a plurality of main models of different design stages; the project team comprises project personnel, project roles and project permissions; the main model comprises basic information of a model scheme, a pedigree relationship and a professional role; and one main model corresponds to one model scheme, and one main model comprises a plurality of professional data packets. The data exchange cost between the professions is reduced, the collaboration efficiency is improved, the pedigree relationship between the professions is clearer, and the consistency of technical states in the iteration process can be ensured.

Description

technical field [0001] The invention relates to the field of intelligent master model management, in particular to a data-driven intelligent master model management system. Background technique [0002] With the development and application of computer technology, network technology and communication technology, enterprise information management has become an important guarantee for improving enterprise management level and core competitiveness. However, the current R&D project management mode of many enterprises still stays in the traditional offline management. However, the traditional R&D management system often has the following problems: [0003] 1. Lack of effective management of process data [0004] At present, many PDM systems are implemented, which only solve the electronic centralized storage and management of the phased result data of product development. The comprehensiveness and integrity of data management are not enough, and there is still a lack of unified ...

AI Technical Summary

Problems solved by technology

The comprehensiveness and integrity of data management are not enough, and there is still a lack of unified management of R&D process data (requirements, design, simulation, experiment), and the integration and data interaction between various application systems are low, and the effective sharing of process data cannot be realized. Efficient exchange, the repeatability and traceability of the R&D process are not high

[0005] 2. Demand information and technical indicators are difficult to trace

However, the current demand information mainly relies on document management. The demand information lacks structure and cannot establish a close relationship with the product development data. It is impossible to quickly and accurately analyze and evaluate the impact of partial demand changes on the overall R&D, and it is difficult to effectively track demand compliance

Less responsive, maintainable, and traceable to information and metrics changes

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