新兴技术弱信号识别:理论模型与测度方法

Identification of weak signals of emerging technologies:Theoretical model and measurement method

习近平总书记曾指出“要以小见大、见微知著,尽早解读、科学预见形势发展走势和隐藏在信号背后的风险挑战。”这正对当前愈加重要的科技自立自强和技术预见工作提出了更高要求,新兴技术弱信号的识别研究价值凸显。研究中,以范式转移理论、知识层次理论和意义建构理论为基础,梳理了新兴技术弱信号识别的理论基础,构建出新兴技术弱信号识别的理论模型;从新颖性、关注度和成长性三个维度设计出6项二级指标及其测度方法,创建了基于三元计量特征的新兴技术弱信号识别方法。最后,以量子信息技术领域为实证对象,2011-2016年数据进行方法检验,2017-2022年数据进行识别预测,评估判断出量子中继、量子网络、量子纠缠浓缩、量子密钥分发和量子点医学共5项当前量子领域新兴技术弱信号,证实了本研究方法的有效性,可以辅助研发人员和管理决策者做未来技术的研发判断。

President Xi Jinping stressed"We should observe the small to understand the big,perceive the subtle to grasp the significant,and promptly interpret the trends and risks hidden behind the signals,with a scientific approach to foreseeing the development of the situation."This statement places higher demands on the increasingly critical research into technological self-reliance and foresight,emphasizing the importance of identifying and studying emerging technologies earlier and more accurately.In contemporary scientific research,there is an urgent need for robust evaluation methods to tackle the challenges of predicting emerging technologies.Therefore,research on weak signal identification,which incorporates the distinct characteristics of weak signals,has gained prominence.In our research,we systematically reviewed the theoretical framework for recognizing weak signals in emerging technologies.We grounded this review in paradigm shift theory,knowledge hierarchy theory,and meaning construction theory.By integrating the concepts of technological breakthroughs from paradigm shift theory,noise filtering from knowledge hierarchy theory,and intelligent generation from meaning construction theory,we developed a theoretical model for identifying weak signals in emerging technologies.Next,we referred to existing research to identify three fundamental characteristics of weak signals:high novelty,low attention,and high growth.Using bibliometrics,text mining,topic clustering,similarity calculation,and social network analysis,we designed quantitative methods for six secondary indicators:attention width,attention intensity,individual growth,group growth,time novelty,and content novelty.This culminated in a method for recognizing weak signals in emerging technologies based on ternary econometric features.We then applied this method to the field of quantum information technology.By analyzing journal papers and patent data from 2011 to 2016,we identified five emerging weak signals:quantum cryptography research,quantum algorithm research,quantum communication research,quantum computer research,and superconducting quantum research.The comparison of these results with actual developments validated the effectiveness of our proposed theory and method.Furthermore,we utilized literature data from 2017 to 2022 to predict weak signals in the field of quantum information.We identified five current weak signals:quantum relay research,quantum network research,quantum entanglement concentration research,quantum key distribution research,and quantum dot medical research.Our entire research has demonstrated the effectiveness of the theories and methods we have designed.Firstly,in terms of theoretical models,our proposed model for recognizing weak signals in emerging technologies effectively applies the concepts of paradigm shift,knowledge hierarchy,and meaning construction in the field of technology foresight.This expands the application scenarios of these theories and enriches the connotation and theoretical dimensions of weak signal recognition in emerging technologies.Secondly,our proposed measurement method clarifies the characteristic dimensions of weak signals in emerging technologies,enhancing the analytical validity of weak signal recognition.This is a beneficial exploration for research related to technology foresight.Additionally,our overall research emphasizes the timeliness of recognizing and predicting weak signals in emerging technologies,addressing the forward-looking and foresight needs of scientific and technological intelligence information.This can assist technology researchers and decision-makers in making informed judgments about future technological directions.However,our research also has certain limitations.Future researchers can further optimize it by exploring a greater diversity of data types and broadening the scope of recognition fields.