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| Weak Signal Perception Model of Technical Demand: Interdisciplinary Insights from Brachistochrone Curve |
| Yu Hui1,2, Wu Yunjing1,2,3, Xia Wenlei4,5 |
1.School of Economics and Management, Hubei University of Technology, Wuhan 430068
2.Hubei Innovation Research Center of Rural Social Management, Wuhan 430068
3.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
4.School of Entrepreneurship, Wuhan University of Technology, Wuhan 430070
5.Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan 430070 |
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Abstract The perception of weak signals in technological demand functions as a critical sensing and advisory mechanism within the field of science and technology intelligence. It plays a pivotal role in understanding and forecasting emerging technological development trends, as well as informing timely technological interventions. This study proposes a weak signal perception model grounded in the physical principle of the brachistochrone and evaluates its effectiveness and robustness. First, the study examines the brachistochrone trajectory of technological development within a two-dimensional“quantity-innovation”technology space, thereby elucidating the characteristics of technological progress. Second, a multi-layered temporal technological space is constructed based on technological distance, revealing both the developmental states and spatial distribution patterns of technologies. Third, the brachistochrone curve is fitted within this space to establish a fastest-spindle field, designed to identify weak signals of technological demand. Finally, the proposed model is validated through comparative analysis with alternative curve-fitting approaches. Empirical results based on real-world data on technological development demonstrate the superior effectiveness and robustness of the proposed model relative to benchmark models, enabling more accurate and efficient detection of weak signals in technological demand. Moreover, owing to the inherently latent nature of weak signals, the long-term effectiveness of the proposed model requires further time-based validation. Overall, the proposed model demonstrates strong performance in perceiving weak signals and exhibits preliminary decision-making potential for identifying technological development trends within the domain of frontier science and technology intelligence.
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Received: 25 July 2025
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