The rapid pace of technological innovation and paradigm shifts has elevated accurate technological forecasting from an academic pursuit to a strategic necessity in the global competition for technological leadership. Emerging technologies, with their strategic importance, are pivotal for innovation strategy, especially in the face of oligopolistic and monopolistic pressures in key technological sectors. Accurately predicting these technologies is vital for China's tech advancement and innovation-driven growth, despite their inherent uncertainty and complexity posing significant challenges.
This paper proposes an innovative technological forecasting model that integrates patent analysis methods with TRIZ (Theory of Inventive Problem Solving) to accurately identify and predict the development stages and future trends of emerging technologies. The core innovation of the study lies in breaking through the limitations of traditional technology forecasting methods. Commonly used technology forecasting methods include qualitative methods (such as expert surveys, systems analysis) and quantitative methods (such as trend analysis, econometric models). Qualitative forecasting methods often rely on experts' judgment, and when facing rapidly developing emerging technologies, cognitive biases and other subjective limitations are magnified. Quantitative forecasting models, although scientific to a certain extent, are usually based on historical data and struggle to capture the nonlinear leaps of emerging technologies. Moreover, existing research mainly focuses on patent numbers, lacking sufficient understanding and interpretation regarding the essence of technology. TRIZ theory, as a systematic innovation methodology, can predict the possible future evolution paths of technology by studying the objective laws and development trends of technological systems. This theory considers the internal contradictions and problems of technological systems, allowing for a more comprehensive assessment of the maturity of existing technologies, and its predictive results are more forward-looking and scientifically valuable. This paper creatively combines patent analysis with TRIZ theory to form a forecasting model that complements qualitative and quantitative methods. Patent analysis provides objective data support for the model, while TRIZ theory injects a systematic innovative thinking framework, allowing for a deeper exploration of the internal contradictions and development laws of technological systems.
The methodology of the model is based on a systematic patent data analysis strategy. First, high-quality patent information sets are established through precise collection and strict preprocessing from multiple patent databases. Second, time series analysis methods are used to deeply mine the temporal trends of patent applications and publications. By constructing multi-dimensional indicators such as the number of patent applications and citation frequency, technology evolution maps are drawn, forming a dynamic and panoramic understanding of emerging technology fields. Third, technology development directions are accurately identified through patent text clustering and multi-dimensional analysis of International Patent Classification (IPC) numbers. Fourth, the S-curve method and patent calculation methods are combined to judge the level of technology development by analyzing the technology life cycle. Fifth, a multi-dimensional assessment model for technology development potential is established through literature text clustering and patent portfolio analysis techniques.
The paper takes the technology of new energy vehicle power battery as a specific research object to empirically test the proposed forecasting model. The study selects patent data from 2000 to 2023, and the model's prediction accuracy reaches 86.67%, fully verifying the scientific reliability of the model. The study finds that this emerging technology field has shown a continuous high-speed growth trend in recent years and exhibits significant dynamic technological evolution characteristics. In various subfields such as energy management technology, system optimization technology, electrical measurement and state monitoring, vehicle air conditioning, mechanical balance testing, and vehicle maintenance, there is significant technological development potential and broad innovation space. Specifically, innovation in energy management technology has become a key driver for breakthroughs in new energy vehicle battery technology. The rapid development of battery recycling and cascade utilization technology not only reflects the systematic progress of technology but also reflects China's strategic layout in the field of sustainable development technology. This research fills the theoretical void in existing technology forecasting methods and provides significant methodological support for national strategies for scientific and technological innovation.