Chemical process simulation data management system and method based on mathematical model

By constructing a chemical process simulation data management system, the differentiated needs of multi-scale model data management were addressed, intelligent matching and optimization of cross-scale data were achieved, the accuracy and applicability of simulation results were improved, and the system was adapted to the complex environment of chemical production.

CN121996971AInactive Publication Date: 2026-05-08QINGDAO SANRUI ENERGY SAVING & ENVIRONMENTAL PROTECTION TECH CO LTD
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Patent Information

Application Number
CN202610127047.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-29
Publication Date
2026-05-08
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In existing technologies, data management related to chemical simulation has failed to effectively address the differentiated data requirements of multi-scale mathematical models, leading to implicit mismatches in data retrieval, reducing the reliability and accuracy of simulation operations, and making it difficult to support precise optimization and scientific decision-making in chemical production.

Method used

We construct a quantitative feature system with granularity, accuracy, and spatiotemporal dimensions, establish dynamic mapping rules, identify model scale through feature vectors, perform multi-dimensional attribute verification and targeted push, and realize data matching and optimization of cross-scale models.

Benefits of technology

It significantly improves the accuracy and reliability of cross-scale collaborative simulation results, ensures data quality, adapts to fluctuations in production conditions, and enhances the applicability and robustness of the simulation system in complex operating environments.

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Abstract

The invention discloses a mathematical model-based chemical process simulation data management system and method, and relates to the technical field of data management. The system comprises a data feature system construction module, a dynamic mapping rule generation module, a cross-scale collaborative data processing module and a matching relation adaptive optimization module. The method comprises the following steps: constructing a multi-scale data quantization feature system and generating feature vectors; establishing a dynamic mapping mechanism and an association matching rule set, wherein the association matching rule set comprises a cross-scale collaborative weight and a working condition dynamic threshold factor; checking data in multiple dimensions, complementing missing attributes and pushing the data as required; the feature vectors and mapping rules may be iteratively optimized through deviation analysis. The problem of data and model hidden mismatching is solved, working condition fluctuation is adapted, cross-scale simulation accuracy and robustness are improved, and safe and efficient circulation of data is achieved.
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