基于机器学习的新能源汽车核心技术识别及布局研究

Research on Identification and Layout of Core Technologies of New Energy Vehicles Based on Machine Learning

摘要:

核心技术早期识别可以帮助国家和企业更好地实施专利布局战略,抓住市场机会,从而对价值链攀升产生积极影响。基于机器学习和经验模态分解(Empirical Mode Decomposition,EMD)方法,构建了一种从海量数据中核心技术识别方法,运用该方法识别出新能源汽车领域20项核心技术与21项潜力技术。之后,运用专利相对优势指标法(RPA)分析了我国新能源汽车领域关键技术的布局现状,并与其他国家的技术布局进行对比。结果表明我国在电池冷却或保持低温技术和车辆电池应用技术领域具有较高的相对技术优势。通过对新能源汽车核心技术的识别与分析,可以全面了解该领域的研究现状及未来发展趋势,为我国新能源汽车技术研发与战略布局提供一定的借鉴和参考。

Abstract:

Early identification of core technologies can help countries and enterprises to better implement patent layout strategies and seize market opportunities, thus positively influencing the value chain climbing. Based on the machine learning and Empirical Mode Decomposition (EMD) method, a core technology identification method is constructed from massive data, and 20 core technologies and 21 potential technologies in the new energy vehicle field are identified using this method. After that, the RPA method was applied to analyze the current status of the layout of key technologies in China"s new energy vehicle field and compare it with the technology layout of other countries. The results show that China has a high relative technological advantage in the fields of battery cooling or keeping low temperature technology and vehicle battery application technology. Through the identification and analysis of the core technologies of new energy vehicles, we can fully understand the research status quo and future development trend in this field, and provide reference and reference for China"s new energy vehicle technology research and development and strategic layout.