Intelligent early warning method and system for highway road risk based on spatiotemporal coupling model

Abstract

The present invention discloses an intelligent early warning method and system for highway road-domain risks based on a spatiotemporal coupling model. The method comprises: first, acquiring road-domain monitoring data, extracting and fusing its temporal and spatial dimension features to obtain a first road-domain spatiotemporal coupling feature; second, acquiring risk warning rules and extracting their first rule embedding features, and performing heterogeneous feature coupling processing on the two; then, based on a first learnable parameter determined by training after fixing the risk warning model parameters, optimizing the coupling result to obtain a target rule embedding feature; and finally, calling the risk warning model to predict risks based on the target rule embedding feature and outputting a warning result. This method enhances the multi-dimensional feature expression capability of road-domain risks through spatiotemporal feature fusion and heterogeneous rule feature coupling. Combined with learnable parameter optimization, it enhances the model's adaptability to dynamic scenarios and effectively improves the accuracy of risk warnings.

Claims

1. An intelligent early warning method for highway road risk based on a spatiotemporal coupling model, characterized by: include: Acquiring road domain monitoring data, extracting time dimension features and space dimension features of the road domain monitoring data, and fusing the time dimension features and the space dimension features to obtain a first road domain spatiotemporal coupling feature of the road domain monitoring data; Acquire a risk warning rule, extract a first rule embedding feature of the risk warning rule, and perform heterogeneous feature coupling processing on the first rule embedding feature and the first road domain spatiotemporal coupling feature, wherein the risk warning rule is used in a risk warning model to perform intelligent risk warning on the road domain monitoring data; Optimizing a heterogeneous feature coupling processing result based on the first learnable parameter, and superimposing the optimized heterogeneous feature coupling processing result and the first rule embedding feature to obtain a second rule embedding feature; performing feature conversion on the second rule embedding feature, and optimizing the feature conversion result based on a second learnable parameter to obtain a target rule embedding feature, wherein the first learnable parameter is determined by training after fixing the parameters of the risk warning model, and the second learnable parameter is determined by training after fixing the parameters of the risk warning model; Calling the risk warning model to perform risk prediction based on the target rule embedding feature to obtain a risk warning result of the road area monitoring data; The risk warning model is provided with a plurality of feature extraction components operating in series. The risk warning model is called to perform risk prediction based on the target rule embedded features to obtain the risk warning result of the road monitoring data, including: Extracting features from the target rule embedding features based on the plurality of feature extraction components, performing risk prediction based on the extracted feature representation, and obtaining a risk warning result of the road area monitoring data; Wherein, the target rule embedding feature is the input of the first feature extraction component; For any remaining feature extraction component except the feature extraction component at the last position, heterogeneous feature coupling processing is performed on the feature extraction result of the current feature extraction component and the first road domain spatiotemporal coupling feature, the heterogeneous feature coupling processing result is optimized based on the first learnable parameter, feature superposition is performed on the optimized heterogeneous feature coupling processing result and the feature extraction result of the current feature extraction component, feature conversion is performed on the features obtained after feature superposition, and after optimizing the feature conversion result based on the second learnable parameter, it is loaded into the subsequent feature extraction component for feature extraction.

2. The method according to claim 1, characterized in that Each of the feature extraction components is respectively configured with a corresponding first learnable parameter and a corresponding second learnable parameter. Before optimizing the heterogeneous feature coupling processing result based on the first learnable parameter, the method further includes: Obtaining a road monitoring data instance, fixing the parameters of the risk warning model, and initially assigning zero values ​​to each of the first learnable parameters and each of the second learnable parameters; Each of the first learnable parameters and each of the second learnable parameters are trained based on the road domain monitoring data instance.

3. The method according to claim 1, characterized in that The acquiring of the risk warning rule, extracting the first rule embedding feature of the risk warning rule, and performing heterogeneous feature coupling processing on the first rule embedding feature and the first road domain spatiotemporal coupling feature include: Extracting a second road-domain spatiotemporal coupling feature similar to the first road-domain spatiotemporal coupling feature, and a baseline road-domain risk state associated with the second road-domain spatiotemporal coupling feature, wherein the baseline road-domain risk state is a risk state of baseline road-domain monitoring data, the second road-domain spatiotemporal coupling feature is obtained by fusing time dimension features and spatial dimension features of the baseline road-domain monitoring data, and the spatiotemporal dimensions of the second road-domain spatiotemporal coupling feature are the same as the spatiotemporal dimensions of the first road-domain spatiotemporal coupling feature; Adding a start symbol and a stop symbol at the beginning and end of the reference road risk state to obtain a rule element text corresponding to the reference road risk state; Adding the start symbol after the last rule element text to obtain a risk warning rule, and extracting the first rule embedding feature of the risk warning rule; The second road-domain spatiotemporal coupling feature is superimposed on the first road-domain spatiotemporal coupling feature to obtain an integrated road-domain spatiotemporal coupling feature, and heterogeneous feature coupling processing is performed on the first feature component of the rule element text in the first rule embedding feature and the second road-domain spatiotemporal coupling feature corresponding to the first feature component in the integrated road-domain spatiotemporal coupling feature; Heterogeneous feature coupling processing is performed on the second feature component of the start symbol located at the last position in the first rule embedding feature and the first road-domain spatiotemporal coupling feature in the integrated road-domain spatiotemporal coupling feature.

4. The method according to claim 3, characterized in that The performing heterogeneous feature coupling processing on the first feature component of the rule element text in the first rule embedding feature and the second road domain spatiotemporal coupling feature corresponding to the first feature component in the integrated road domain spatiotemporal coupling feature includes: Determine a rule feature mapping vector based on the first rule embedding feature, determine a spatiotemporal feature mapping vector based on the integrated road domain spatiotemporal coupling feature, and obtain an association strength vector based on a multiplication result of the rule feature mapping vector and the spatiotemporal feature mapping vector after feature space transformation; Performing feature masking on the association strength vector based on a feature masking vector, and normalizing the masked association strength vector to obtain a feature coupling weight vector, wherein the feature masking vector is used to dynamically suppress remaining vector feature values ​​in the association strength vector except for a target feature value, and the target feature value is determined based on a product operation result of a first feature component of the rule element text in the first rule embedding feature and a second road-domain spatiotemporal coupling feature corresponding to the first feature component in the integrated road-domain spatiotemporal coupling feature; A spatiotemporal feature representation vector is determined according to the second road domain spatiotemporal coupling feature, and a heterogeneous feature coupling processing result is obtained according to a multiplication result of the feature coupling weight vector and the spatiotemporal feature representation vector.

5. The method according to claim 3, characterized in that The extracting of a second road domain spatiotemporal coupling feature similar to the first road domain spatiotemporal coupling feature, and a reference road domain risk state associated with the second road domain spatiotemporal coupling feature, includes: Acquire a plurality of the reference road domain monitoring data and a reference road domain risk status of each of the reference road domain monitoring data, fuse the time dimension feature and the space dimension feature of each of the reference road domain monitoring data, and obtain the second road domain spatiotemporal coupling feature of each of the reference road domain monitoring data; Each second road domain spatiotemporal coupling feature is associated with the corresponding baseline road domain risk state and stored in a risk feature library, and the second road domain spatiotemporal coupling feature similar to the first road domain spatiotemporal coupling feature and the baseline road domain risk state associated with the second road domain spatiotemporal coupling feature are extracted from the risk feature library.

6. The method according to claim 5, characterized in that The reference road monitoring data is a reference road monitoring image sequence, and the associating each of the second road spatiotemporal coupling features with the corresponding reference road risk state and storing them in a risk feature library includes: For each of the reference road monitoring image sequences, performing mean aggregation on the monitoring image features of the reference road monitoring image sequence in the spatial dimension to obtain a risk value sequence feature of the same dimension as the time series data feature of the reference road monitoring image sequence in the temporal dimension; Performing mean aggregation on the risk value sequence features and the time series data features of the reference road monitoring image sequence in the time dimension to obtain an index feature of each reference road monitoring image sequence; Each of the index features, the second road domain spatiotemporal coupling features and the corresponding reference road domain risk state are associated and stored in a risk feature library.

7. The method according to claim 1, characterized in that The road monitoring data is a road monitoring image sequence to be evaluated, and extracting the time dimension features and space dimension features of the road monitoring data includes: Performing feature extraction on each road area monitoring image in the road area monitoring image sequence to be evaluated to obtain monitoring image features of the road area monitoring image sequence to be evaluated in a spatial dimension; Extracting time feature information from the road area monitoring image sequence to be evaluated, performing feature extraction on the time feature information, and obtaining time series data features of the road area monitoring image sequence to be evaluated in a time dimension.

8. A server system, characterized in that: The method comprises a server, wherein the server is configured to execute the method according to any one of claims 1 to 7.

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