Risk Prediction Model Using Encoder-Discriminator-Classifier Architecture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting public emergencies such as epidemic spreads or meteorological disasters lack efficiency in predicting risks in regions without prior outbreaks, relying on infectious disease models that require accurate understanding and professional knowledge, and are insufficient for early detection and prevention.
Innovation Solution
A risk prediction model is established using a training process with an encoder, discriminator, and classifier, learning region features like POI, demographic, and user travel data to predict risk grades in regions with unknown conditions, enabling early identification of high-risk areas without relying on prior outbreaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infectious disease models are used for predicting public emergencies, then prediction accuracy in regions with prior outbreaks is improved, but prediction capability in regions without prior outbreaks deteriorates
Solution Approach 1:
The patent introduces a pre-trained language model as an intermediary that processes regional data and generates risk predictions without requiring region-specific outbreak data. The language model acts as a mediator that translates various regional features (POI, demographic, travel data) into risk assessments, enabling predictions in regions without prior outbreaks while maintaining accuracy in regions with outbreak data.
Solution Approach 2:
The patent transforms the prediction approach by changing from disease-specific parameters to general regional feature parameters. Instead of relying on infectious disease model parameters that require outbreak data, the system uses language models to process and analyze regional characteristics (points of interest, demographic information, user travel patterns) to generate risk predictions applicable to any region regardless of outbreak history.
2Ease of operation
If traditional prediction methods are used, then professional knowledge requirements are reduced, but prediction timeliness and early detection capability deteriorate
Solution Approach 1:
The patent replaces traditional mechanical infectious disease models with an intelligent language model system. This substitution eliminates the need for professional epidemiological knowledge while enabling timely predictions through automated processing of regional data. The language model automatically analyzes regional features and generates risk assessments without requiring manual expert intervention.
Solution Approach 2:
The system enables self-service prediction capability where the language model automatically processes regional data and generates risk predictions without requiring professional knowledge input. The model independently analyzes POI data, demographic information, and user travel patterns to produce timely risk assessments, eliminating dependency on expert systems while maintaining prediction timeliness.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
The present disclosure discloses a method and apparatus for establishing a risk prediction model as well as a regional risk prediction method and apparatus, and relates to a big data technology in the field of artificial intelligence technologies. The technical solution includes: acquiring training data including annotation results of a risk grade of each sample region and a risk grade of a district to which each sample region belongs; and training an initial model including an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier after the training process; where the encoder performs a coding operation using region features of the sample regions to obtain a feature representation of each sample region; the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region; the training process has targets of minimizing a difference of identification of the sample regions belonging to the districts with different risk grades by the discriminator, and minimizing a difference between the identification result of the sample region by the classifier and the annotation result.