Enterprise logistics data on-demand fusion method based on mixing rule

Abstract

The invention belongs to the field of enterprise data management, and provides an enterprise logistics data on-demand fusion method based on a mixing rule; the method comprises the following steps: collecting a structured data source in an enterprise; defining a business concept atlas according to enterprise business content; defining a mixing rule for instantiation for each concept; creating, by a user, a data fusion task as required, and defining a target concept set and a fused data range; constructing a rule queue according to the data fusion task, and instantiating by adopting a corresponding rule engine; and accessing, by the user, the fused service instance atlas. According to the method, the mixed rule of mapping, reasoning and calculation is adopted, the intermediate state or indirect information which is not stored in the business process is obtained, certain flexibility is achieved, and meanwhile, the business process of formulating fusion tasks according to needs and reflecting a specific target scene is supported.

Description

technical field [0001] The invention belongs to the field of enterprise data management, and relates to an on-demand fusion method for enterprise assembly line production data based on mixing rules. Background technique [0002] With the continuous expansion of the production scale of enterprises and factories, the types and quantities of raw materials, waste, semi-finished products and products in the production and operation process are increasing, and the importance of enterprise logistics management has become increasingly prominent. key factor for efficiency. On the other hand, with the rapid development of intelligent manufacturing technology, traditional manual transportation is gradually being replaced by automated equipment, and the logistics data entered manually has also been transformed into automatic generation by machines. Compared with traditional manual data entry, these logistics data automatically generated by machines have the characteristics of large sca...

AI Technical Summary

Problems solved by technology

At present, data fusion methods at the logical level generally match and fuse data through fixed fusion processes and matching modes. They lack the necessary reasoning and computing capabilities, and it is difficult to reflect the intermediate state and indirect information in the logistics process. When the form of data storage changes, the existing method lacks flexibility and it is difficult to quickly realize new fusion requirements

At the same time, the data matching mode of the existing method is opaque, does not support the description of the specified logistics scene, and often requires global integration before filtering, resulting in waste of resources

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