Enterprise digital operation management method and system

By constructing a metadata knowledge graph and introducing a collaborative mechanism, the problems of data silos and collaboration in enterprise digital operation and management are solved, achieving high cohesion and low coupling within the intelligent body, improving the system's scalability and robustness, and ensuring optimal global decision-making.

CN122367082AInactive Publication Date: 2026-07-10JIANGSU PROSPECT INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202610825850.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-09
Publication Date
2026-07-10
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Enterprises face data silos and a lack of efficient collaboration mechanisms in their digital operations management, making it difficult to achieve global optimization.

Method used

By constructing a metadata knowledge graph, key business activities are identified, probabilistic prediction models are used for individual decision-making, and a collaborative mechanism is introduced when there is coupling of shared resources. Shadow prices are used to correct independent decisions to achieve global optimality.

Benefits of technology

It achieves high cohesion and low coupling within the intelligent body, reduces communication and dependency overhead, improves the scalability and robustness of the system, and ensures globally optimal decision-making.

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Abstract

The invention relates to the technical field of enterprise digital management, in particular to an enterprise digital operation management method and system, and the method comprises the steps: obtaining business activity data, extracting the interaction strength between business activities, and clustering a plurality of business activities to a plurality of agents; extracting technical metadata from a data dictionary and an interface document of the target system, constructing a metadata knowledge graph by utilizing the technical metadata, and providing a data interface for the intelligent agent by utilizing the data knowledge graph; on the basis of the data interface, each agent generates probability distribution of future external variables by using a probability prediction model, and an independent decision is made on the basis of the probability distribution; and judging whether the independent decisions of the two agents have shared resource coupling or not, if so, introducing a collaboration mechanism, and correcting the respective independent decisions through shadow prices, so that the independent decisions are globally optimal.
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