Data model construction method, device and equipment and storage medium

Abstract

The invention discloses a data model construction method, device and equipment and a storage medium. The method comprises the steps that model metadata is acquired, and the model metadata comprises aprofessional model, a logic model, a physical model and an incidence relation among the professional model, the logic model and the physical model; according to the technical scheme, the model metadata is stored in the meta model, the meta model is of a library structure, and through the technical scheme, the unified data standard can be achieved, comprehensive supporting of the CNOOC (China National Offshore Oil Corporation) exploration and development service is achieved, multi-disciplinary collaborative work is achieved, the exploration and development level and the operation and managementlevel are improved, and the production efficiency is improved.

Description

technical field [0001] Embodiments of the present invention relate to computer technology, and in particular, to a method, device, device, and storage medium for constructing a data model. Background technique [0002] Due to historical and management system reasons, various professional databases and application systems were built at different times, belonged to different management units, and adopted different technical methods. Therefore, these systems did not adopt a unified and complete data model standard, which could no longer meet the information sharing at the current stage. In summary, there are mainly problems in the following aspects: [0003] ①Inconsistency and irregularity of the data model [0004] "Name" conflicts: there are synonymous and synonymous names in different databases, and different interfaces need to be designed for data conversion between different systems to solve application integration; "value domain" conflicts: the value of data in the same ...

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Problems solved by technology

[0003] ①Inconsistency and irregularity of the data model

[0004] "Name" conflicts: there are synonymous and synonymous names in different databases, and different interfaces need to be designed for data conversion between different systems to solve application integration; "value domain" conflicts: the value of data in the same database The scope and units of measurement are not uniform, which affects the quality and efficiency of database operation; the classification is not uniform: different disciplines and applications have different classifications for the same type of information due to different starting points; a large number of non-standard derived data: due to statistical analysis or According to the needs of the work plan, various statistics are made on the same object, and a large number of new data item names are derived from this, and corresponding value ranges are defined respectively, which intensifies the conflict of data attributes; the descriptive data is not standardized: should Make these non-standard descriptive data quantitative through code; the model design is unreasonable, and the application-oriented database structure design leads to poor stability and scalability of the table; due to the lack of standard design methods and workflows for database design , resulting in disorderly modification of the standard during the use of the standard, etc.

The consistency and standardization of these data models directly lead to difficulties in data application and sharing

[0005] ②The data model is incomplete

[0006] Some databases do not consider all the data in the professional field, and cannot fully cover professional applications, making it impossible to develop some professional applications; the logical relationship between some data items is not clear and accurate, which brings inconvenience to comprehensive applications; the relationship between data sources and professional databases The relationship between professional databases and professional databases, between professional databases and comprehensive databases, and between comprehensive databases and project databases has not been fully clarified, which affects cross-disciplinary data access, data integration, and sharing

[0007] ③Data model coverage is not comprehensive enough

[0012] The PPDM data model and the POSC Epicentre data model are models with relatively wide business coverage, but there are still some problems: the PPDM data model is a commercial data model product, and currently it can only support some exploration and development application fields, which cannot satisfy Integrated application requirements for all services; the POSC Epicentre data model is an object-oriented model that requires an object-oriented interface to operate. In addition, its access efficiency is relatively low, and there are no commercial products and no successful practical cases.

[0015] The EPDM data model is built according to PetroChina's exploration and development business. Currently, it does not fully support the offshore exploration and development business. At the same time, it is only a data model. The relationship with the business is not realized through the model-driven architecture, and it cannot achieve rapid response to business changes. , not a complete model system

[0016] Sinopec SPDM cannot implement model version control and hierarchical management, and it is difficult to achieve rapid response to business changes

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