A cloud-edge collaboration-based cascade hydropower dispatching data, model and management collaboration method

Abstract

A collaborative method for cascade hydropower scheduling data, models, and management based on cloud-edge collaboration is proposed. This method employs an architecture where a cloud platform and an edge-side intelligent scheduling platform operate collaboratively. First, the edge platform dynamically parses and accesses multi-source hydropower data via protocols, and uses the Laplace feature mapping algorithm to project the high-dimensional raw data onto a low-dimensional manifold space, generating a semantic index package that preserves the topological structure, achieving data preprocessing that balances privacy protection and semantic integrity. The cloud platform decouples the global model based on hydrological conditions, generating a lightweight scene model for distribution. During online incremental learning, the edge platform incorporates the global workspace model distributed from the cloud as a knowledge anchor point for constraint, preventing parameter drift. The cloud platform uses a reconstructed dictionary based on the mutual information maximization criterion to restore the physical probability distribution and employs content-addressed directed acyclic graph technology to ensure the atomic alignment of the cloud-edge model state and perception logic. Finally, through an edge-side autonomous fusion with a cloud-hosted model, a closed-loop governance system covering data, models, and services is constructed. This method effectively solves technical problems such as non-standardized multi-source heterogeneous data interaction, insufficient adaptability due to generalized model deployment, and decentralized cloud-edge management in the context of large-scale cross-basin operations.

Images