基于弱信号谱系的未来产业情报前瞻体系构建研究OACHSSCD

[Purpose/Significance]Future industries represent an important direction of the new round of scientific and technological revolution and industrial transformation.Their development is characterized by uncertain technological trajectories,dispersed innovation knowledge,dynamically changing industrial boundaries,and relatively long cultivation cycles.Conventional intelligence service models based on mature industrial structures and explicit indicators are inade-quate for the timely identification of early changes and for effective support of strategic decision making.To address this problem,this study introduces the concept of weak signal genealogy and constructed an intelligence foresight system for future industry development,with the aim of supporting early opportunity identification,strategic planning,and innovation resource allocation under conditions of uncertainty.[Method/Process]Based on the development characteristics of future industries and their intelligence foresight requirements,the study first analyzed differentiated intelligence needs across three dimensions,including intelligence users,intelligence objects,and intelligence tasks.On this basis,an intelligence foresight system centered on weak signal genealogy was constructed.The system comprised four interrelated stages,inclu-ding weak signal sensing for future industries driven by multisource information fusion,weak signal genealogy construction and relational analysis,identification of evolutionary trends and foresight assessment for future industries,and foresight intelligence transformation and decision empowerment for future industries.Meanwhile,the quantum information industry was selected as a typical scenario to illustrate the application process of the proposed system.Operational support mecha-nisms were also designed from the perspectives of strategic demand orientation,information resource assurance,multi-actor collaborative innovation,and feedback optimization.[Result/Conclusion]The results show that intelligence support for future industries needs to move beyond the traditional information analysis paradigm oriented toward existing industries and shift toward a cognitive paradigm of foresight under uncertainty.Through weak signal sensing,genealogy construction,trend assessment,and decision empowerment,the proposed system enables a transition from fragmented information acqui-sition to systematic cognitive support,thereby supporting direction identification,technology deployment,and resource allocation for future industries.The present study remains primarily theoretical,and the quantum information industry is used mainly to illustrate a typical application scenario rather than to provide systematic empirical validation.Future research can conduct case studies based on real-world data in representative industries and integrate large language mo-dels,knowledge graphs,and related technologies to further improve weak signal identification,knowledge association rea-soning,and trend forecasting capabilities.