Multi-level collaborative planning system and resource allocation method for EOD project

Through the multi-level collaborative planning system and blockchain technology, the resource allocation of EOD projects has solved the problems of data heterogeneity and static weight distribution, achieved scientific and stable resource allocation, and improved the implementation efficiency and governance effect of EOD projects.

CN120655233APending Publication Date: 2025-09-16CHINESE ACAD OF ENVIRONMENTAL PLANNING +1
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Patent Information

Application Number
CN202510769432.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-10
Publication Date
2025-09-16

AI Technical Summary

Technical Problem

Existing EOD projects have problems in collaborative planning and resource allocation, such as static weight distribution, conflict between data heterogeneity and real-time performance, resource mismatch, and low decision-making credibility. These problems make it difficult to dynamically capture changes in ecological restoration efficiency and industrial returns, resulting in inflexible and inaccurate resource allocation strategies.

Method used

A multi-level collaborative planning system is adopted, and blockchain technology is used to align and encrypt heterogeneous data from governments, enterprises, and communities. An asymmetric game model is constructed, and the Monte Carlo method is combined for resource scheduling. A dynamic heterogeneous graph and resource allocation strategy matrix are generated to dynamically adjust the resource input ratio.

Benefits of technology

It achieves robustness and flexibility in resource allocation under uncertain conditions, improves the implementation efficiency and governance effect of EOD projects, and ensures the scientific nature and stability of resource allocation.

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Abstract

The invention discloses an EOD project-oriented multi-level collaborative planning system and a resource allocation method, and relates to the technical field of project management and resource scheduling optimization. The method comprises the following steps: carrying out credible alignment and encrypted aggregation on heterogeneous data of a government chain, an enterprise chain and a community chain through a block chain, and generating a dynamic heterogeneous graph containing government task nodes and industrial development nodes; a government, enterprise and community three-party asymmetric game model is constructed, contribution degrees of all parties are calculated, and priority weights are determined. Through a gating time sequence attention mechanism, a node state is predicted, a coupling influence value of a governance efficiency attenuation factor and an industrial income fluctuation factor is calculated, a near-end strategy optimization algorithm is combined, and the investment proportion of funds, equipment and human resources is dynamically adjusted by taking comprehensive income maximization as a target. A disturbance sample is simulated by using a Monte Carlo method, the robustness of a priority weight is evaluated, a resource configuration strategy is formed, and the node state of the heterogeneous graph is iteratively updated in real time, so that resource configuration is optimized and the system cooperation efficiency is improved.
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