The invention discloses a multi-network
cascading failure propagation prediction method based on a heterogeneous graph neural network under a disaster. The method comprises the following steps: acquiring multi-
source data of disaster,
electric power, communication and traffic networks; constructing a four-layer heterogeneous
graph model, and defining multi-type interlayer edges; establishing a mapping relation between disaster physical quantities and physical node
health states, and predicting an initial fault state at a disaster
impact moment; defining a multi-scale and multi-mechanism propagation rule based on interlayer edges, and simulating a
cascading failure process; a space-time heterogeneous graph neural
network model is constructed, a heterogeneous graph neural
network module and a gating circulation unit module are stacked in the model, and a double-end prediction layer is adopted to predict the
continuous operation state and the discrete function state of nodes in an autoregression mode; and post-
processing a state
time sequence output by the model, and reconstructing a cross-domain
cascading failure propagation path through a causal attribution
algorithm. According to the method, the whole evolution process from disaster occurrence to multi-network cascading failure can be accurately simulated, and multi-scale failure prediction and
causal analysis are realized.