A cross-regional sewage data collaborative modeling method and system

Abstract

The present application relates to the technical field of groundwater environment monitoring and pollution prevention and control, and discloses a cross-regional sewage data collaborative modeling method and system. A local water quality sequence is obtained, and a graph convolution network is used to encode the node space topological adjacency relationship in the water system basin into a water system topological space feature matrix. Based on the local water quality sequence and the feature matrix, a time series prediction network with a causal intervention module is used for forward propagation, a counterfactual regularization term is introduced through backward propagation, the coupling influence of temperature, industrial and domestic sewage proportion environmental mixed variables is stripped, and a causal invariant feature vector is output. A center server calculates the Wasserstein distance of the environmental covariant distribution of each node, allocates a dynamic aggregation weight according to the distance, updates the global bottom layer shared parameter and issues it. The present application decouples environmental mixed factors, eliminates the cross-regional collaborative modeling negative transfer phenomenon, and ensures the independent update of specific process parameters in each region.

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