Simulation System with Learnable Parameter Optimization
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Solution Overview
Problem
Traditional simulators face limitations in complex scenarios due to a lack of domain knowledge for rules and parameters, restricting their applicability and accuracy, especially in scenarios like supply chain and epidemic deduction where parameters such as infection numbers are difficult to estimate accurately.
Innovation Solution
A data-driven simulation system that includes a user interface module, a control module, a simulation module, and an optimization module to receive external inputs, control behaviors, run simulation logic programs, and optimize learnable parameters based on output data, reducing dependence on domain knowledge and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulators use domain knowledge to define rules and parameters, then simulation accuracy is improved, but applicability is reduced due to lack of domain knowledge in complex scenarios
Solution Approach 1:
The simulation system automatically learns rules and parameters from historical data without requiring manual domain knowledge input. The system performs self-service by using machine learning models to extract simulation parameters from data, enabling it to handle complex scenarios like supply chain and epidemic deduction where domain knowledge is insufficient.
2Productivity
If traditional simulators rely on manually determined parameters, then simulation speed is improved, but measurement precision deteriorates due to inaccurate parameter estimation
Solution Approach 1:
The patent replaces the manual mechanical process of parameter determination with an automated data-driven system. Machine learning models automatically learn simulation parameters from historical data, substituting the manual expert judgment process and providing both speed and accuracy through automated parameter learning.
3Ease of operation
If traditional simulators operate at high abstraction levels, then ease of operation is improved, but measurement precision deteriorates due to inability to utilize detailed real-world data
Solution Approach 1:
The patent adds a data-driven dimension to traditional simulation by combining high-level abstraction with detailed real-world data. The system operates at multiple levels: maintaining ease of use through high-level simulation logic while simultaneously leveraging detailed historical data through machine learning to improve accuracy, effectively adding a new dimension of data utilization to the simulation process.
Data Source
AI summary
Provided are a simulation system and a simulation method, an epidemic deduction simulation system and an epidemic deduction simulation method. The simulation system includes: a user interface module configured to receive an external input; a control module configured to control a behavior described in a preset simulation logic program based on the external input, wherein the preset simulation logic program describes a state in a simulated item and a behavior that drives a change in the state in code, wherein when the external input comprises a learnable parameter, an optimized value of the learnable parameter is obtained, and the step of controlling the behavior described in the preset simulation logic program based on the external input is performed by using the optimized value as a value of the learnable parameter; a simulation module configured to run the preset simulation logic program in response to the controlling; an optimization module configured to optimize the value of the learnable parameter based on output simulation result data.