Research on Evaluation of Scientific and Technological Innovation Capability Based on Multi-level Indicators Cleaning and Aggregation
In the emerging trend of integrating multi-source and heterogeneous data in scientific and technological evaluation and management,China lacks a universal framework for comprehensive evaluation methods.At the same time,the current methods for evaluating the scientific and technological innovation capabilities of key players in innovation activities cannot analyze the complex relationships among indicators.Thus,it is essential to establish a multi-level comprehensive evaluation framework that enhances data reliability,supports transferability,and adapts to various algorithms.To this end,this paper proposes an integrated evaluation framework based on multi-level indicator cleaning and aggregation.The framework comprises three algorithmic layers:data processing,indicator aggregation,and comprehensive evaluation.The dual-flow indicator cleaning algorithm identifies and corrects outliers and extreme values in the data by analyzing correlations and distance relationships between indicators,thereby providing highly reliable data.Additionally,the grey relational analysis(GRA)combined with the technique for order of preference by similarity to ideal solution(TOPSIS),constructs an adaptive evaluation algorithm that enables intelligent indicator aggregation according to the characteristics of the application scenario,overcoming limitations of existing methods in various contexts.Supported by the project from the Science and Technology Innovation Committee of Shenzhen-Platform and Carrier(International Science and Technology Information Center),research data is collected through official government channels,interviews,and secondary data sources.This dataset consists of 214 scientific public institutions in the Pearl River Delta from 2016 to 2021.The comprehensive quantitative evaluation of their scientific and technological innovation capabilities is based on four primary indicators:the foundational environment for scientific and technological innovation,innovation output capacity,innovation investment,and technology project execution capacity,along with their secondary indicators.The results show that the innovation capabilities of these 214 institutions have steadily increased over the past five years,reaching a peak in 2021,with substantial differences across institutions.There is a significant rise in innovation investment,innovation output capacity,and project execution capacity,but the overall foundational environment for scientific and technological innovation still requires improvement.Furthermore,the hierarchy among leading innovation institutions is relatively stable,with differing growth trajectories for emerging leaders.These differences suggest that strengthening advantages and addressing weaknesses according to specific characteristics would be beneficial.The outcomes derived from the proposed comprehensive evaluation framework offer high comparability,accuracy,and robustness,effectively revealing various innovation entities'main strengths,development trends,innovation potential,and weaknesses in the Pearl River Delta region.
evaluation of scientific and technological innovation capacitymulti-level indicatorsdata cleaningindicator aggregationdual-flow indicator cleaning algorithmgrey relational analysisquantitative evaluationscientific public institutions
万方数据
维普
