Intelligent management platform architecture based on spatiotemporal graph networks and multi-modal anomaly identification under low-carbon constraints
In response to the problems such as high coupling degree of multi-source heterogeneous data, complex spatiotemporal correlations, difficulty in depicting the evolution mechanism of abnormal emissions, and the discreteness of control response links in the ecological governance system under the low-carbon goal, an intelligent control platform architecture for ecological governance modernization is proposed. The platform adopts edge-cloud collaboration technology, builds a monitoring unit graph model, a unified encoding mechanism for multi-source data, and a low-carbon constraint mapping method, to achieve standardized access, spatiotemporal alignment, and fusion processing of sensing monitoring data, emission data, meteorological data, and equipment operation data. At the model layer, an ecological state propagation and estimation model is established based on the spatiotemporal graph network, multi-modal feature fusion and abnormal scoring functions are used to identify abnormal emissions, and multi-objective optimization algorithms are used to complete the scheduling of governance tasks under low-carbon constraints. The platform further combines the stream-batch integrated processing link and the model service orchestration mechanism to form a closed-loop technical process of state perception, risk warning, scheduling solution, and feedback update, which can provide support for the intelligent, low-carbon, and engineering implementation of ecological governance.