情报科学 ›› 2024, Vol. 42 ›› Issue (1): 143-152.

• 业务研究 • 上一篇    下一篇

基于隐马尔可夫模型的技术融合趋势识别研究

  

  • 出版日期:2024-01-05 发布日期:2024-06-03

  • Online:2024-01-05 Published:2024-06-03

摘要:

【目的/意义】识别技术融合状态并把握技术融合的发展趋势有助于更准确地进行创新发展,抓住新兴技术
机会。【方法/过程】本研究采用隐马尔可夫模型(Hidden Markov Model,HMM),基于 PATSTAT 专利数据,依靠技
术融合的相似性特征与互补性特征对35个技术领域的技术融合状态进行识别。【结果/结论】分析发现,技术融合状
态主要分为封闭、低跨度、高跨度、开放,四种不同的类型。随着时间的推移,不同的技术领域展现出不同状态间切
换的特征。从低跨度状态转移到高跨度或开放状态最为常见,体现了技术的多元融合趋势。【创新/局限】本研究首
次采用HMM模型从动态视角刻画技术融合状态并追踪其长期趋势,但模型的实际应用需要根据场景进一步构建,
后续研究可进一步进行相关探索。

Abstract:

【Purpose/significance】 Identifying the state of technology convergence (TC) and grasping the development trend of technol⁃
ogy fusion is helpful in conducting innovation activities and benefiting from emerging technology.
Method/process】 This study ana⁃
lyzed the PATSTAT patent data to identify the technology convergence in 35 technological fields. Specifically, Hidden Markov Model
(HMM) was adopted to estimate the hidden states of technology convergence based on the observations of complementary technology
convergence and substitutability technology convergence.【Result/conclusion】 The analysis results show that the hidden status of the
technology convergence state has four types, namely closed, low-span, high-span and open. Over time, technological fields exhibit the
characteristics of switching between different states. The transition from low-span state to high-span state or open state is the most
common, reflecting the trend of integrating multiple technologies.【Innovation/limitation】The study innovatively adopts the HMM
model to depict the state of technology convergence from a dynamic perspective and track its long-term trend. However, the actual ap⁃
plication of the model needs to be further constructed according to the scene, and relevant exploration can be further conducted in sub⁃
sequent studies.