A machine learning-based model for evaluating and optimizing efficiency in the green circular economy
TL;DR
A closed-loop energy efficiency assessment and global optimization architecture for green circular economy based on spatiotemporal graph attention mechanism and constrained multi-objective deep reinforcement learning and an adaptive feature correction operator is constructed to achieve lossless decoupling of transient resource-consuming nodes.
Abstract
This paper proposes a closed-loop energy efficiency assessment and global optimization architecture for green circular economy based on spatiotemporal graph attention mechanism and constrained multi-objective deep reinforcement learning. Addressing the inherent nonlinear high-dimensional sparsity characteristics and the Pareto conflict between environmental and economic factors in cross-regional industrial networks, an adaptive feature correction operator is constructed to achieve lossless decoupling of transient resource-consuming nodes. A deep deterministic policy gradient mechanism is used to approximate the extreme thermodynamic resource conversion boundary. Experimental results show that the model achieves a high lock-in of 91.2% on the resource recycling rate (VAR), a carbon reduction potential index (CRP) of 85.6%, and a global Pareto hypervolume score that leaps 19.9% compared to traditional benchmark networks. When deployed on edge physical terminals using INT8 quantization compilation, ultra-fast, low-latency hard real-time control with a single response time of only 14.5 ms is achieved. This provides a highly versatile core algorithm template and engineering implementation framework for the autonomous adaptive collaborative control of next-generation deep low-carbon heavy industrial cluster systems.