Database Server for 3D Structure Model Retrieval
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Solution Overview
Problem
The reverse Monte Carlo method used to determine the three-dimensional structure of rubber materials from scattering data has high computational complexity, requiring significant computation time.
Innovation Solution
A database and server system that stores sample data and associated three-dimensional structure models, allowing for immediate retrieval of a three-dimensional structure model for a target sample by searching for similar sample data using a similarity measure such as cosine similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the reverse Monte Carlo method is used to determine the three-dimensional structure of a target sample from scattering data, then the accuracy of the three-dimensional structure model is improved, but the computation time increases significantly
Solution Approach 1:
The patent pre-calculates and stores three-dimensional structure models for multiple samples with known structures in a database before actual analysis. When analyzing a target sample, the system retrieves pre-computed models that match the scattering data pattern, avoiding the need to perform computationally intensive reverse Monte Carlo calculations in real-time. This preliminary preparation resolves the contradiction by trading initial computational effort for rapid subsequent analysis.
Solution Approach 2:
The patent creates a database of pre-computed three-dimensional structure models that serve as templates or copies. Instead of performing complex calculations on the target sample itself, the system finds and retrieves matching pre-computed models from the database. This copying approach maintains high accuracy while dramatically reducing computation time during actual analysis.
2Reliability
If the reverse Monte Carlo method is used to obtain the three-dimensional structure of particles constituting a sample, then the reliability of the structure determination is improved, but the productivity of the analysis process decreases
Solution Approach 1:
The system performs reliability-ensuring reverse Monte Carlo calculations in advance for reference samples and stores the results. During actual analysis, it retrieves these pre-validated models, maintaining reliability while improving productivity by eliminating repeated complex calculations.
Solution Approach 2:
The patent pre-calculates and stores three-dimensional structure models for multiple samples with known structures in a database before actual analysis. When analyzing a target sample, the system retrieves pre-computed models that match the scattering data pattern, avoiding the need to perform computationally intensive reverse Monte Carlo calculations in real-time. This preliminary preparation resolves the contradiction by trading initial computational effort for rapid subsequent analysis.
Solution Approach 3:
The patent creates a database of pre-computed three-dimensional structure models that serve as templates or copies. Instead of performing complex calculations on the target sample itself, the system finds and retrieves matching pre-computed models from the database. This copying approach maintains high accuracy while dramatically reducing computation time during actual_analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables immediate and efficient retrieval of a three-dimensional structure model for a target sample, reducing the need for high-computation-load methods like the reverse Monte Carlo method.
Implementation Method 1
sample data representing diffraction or scattering when electromagnetic waves or particles are incident on the sample
Implementation Method 2
sample data representing diffraction or scattering when electromagnetic waves or particles are incident on the sample
Data Source
AI summary
A server receives target sample data sent from the user equipment, and searches sample data in a database using the target sample data as a search query to identify the sample data whose similarity measure to the target sample data is equal to or greater than a threshold. The server identifies a three-dimensional structure model associated with the identified sample data, and outputs the identified three-dimensional structure model as a three-dimensional structure model corresponding to the target sample data.


