Intelligent evaluation and transformation management system for scientific and technological achievements

The intelligent evaluation and transformation management system for scientific and technological achievements, which combines DNN, RNN, and XGBoost models, solves the problem that existing systems cannot fully capture multi-dimensional features and time-series information, and achieves efficient evaluation and transformation management of scientific and technological achievements, improving evaluation accuracy and commercialization speed.

CN120197826BActive Publication Date: 2026-06-26SHENZHEN FUTURE IND CENTER CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHENZHEN FUTURE IND CENTER CO LTD
Filing Date
2025-03-11
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technology achievement evaluation and transformation systems cannot fully capture multi-dimensional characteristics and time-series information, and lack a complete and efficient data management system, thus failing to meet the needs of modern technology achievement evaluation and transformation.

Method used

A smart evaluation and transformation management system for scientific and technological achievements is constructed by combining deep neural networks (DNN), recurrent neural networks (RNN), and gradient boosting-based decision tree (XGBoost) models. The system includes a basic database module, a data acquisition and processing module, a data storage and backup module, a service layer module, an application layer module, and a user interface layer module to achieve intelligent evaluation and achievement transformation.

Benefits of technology

It has improved the accuracy and efficiency of scientific and technological achievement evaluation, promoted the connection between scientific and technological achievements and market demand, accelerated the commercialization process, ensured the security and accessibility of data, and provided scientific decision-making reference.

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Abstract

The application discloses a scientific and technological achievement intelligent evaluation and transformation management system and belongs to the technical field.The combination of DNN and RNN provides the system with powerful feature learning and representation capabilities.DNN can extract high-level features from complex data of scientific and technological achievements, and RNN can capture long-term dependence in time series data.Meanwhile, the XGBoost model algorithm can use the features extracted by DNN and RNN and the original features, build multiple decision trees and perform gradient boosting to improve the accuracy and reliability of prediction, effectively process the nonlinear relationship between features, reduce model bias, and make the evaluation result of scientific and technological achievements more accurate.The combination of the three algorithms makes the advantages of each model complementary, the deep feature extraction of DNN, the time series processing capability of RNN and the integrated learning mechanism of XGBoost jointly act, and the overfitting risk can be effectively reduced.The diversity makes the model have better generalization capability when facing unknown data.
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