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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (8): 31-40    DOI: 10.11925/infotech.2096-3467.2021.1042
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Discovering Technology Opportunities with Causal Knowledge: Case Study of EV Charging Stations
Liu Linlin1,2,Gong Daqing3(),Zhang Yujie4,Bai Rujiang4
1Institute of Artificial Intelligence and Big Data, Zibo Vocational Institute, Zibo 255314, China
2The Global Development Institute, The University of Manchester, Manchester M139PL, UK
3School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
4Institute of Information Management, Shandong University of Technology, Zibo 255049, China
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Abstract  

[Objective] This paper proposes a new method to identify technology opportunities from documents with the help of causal knowledge. [Methods] The proposed method includes three steps of automatic extraction of causal pairs, construction of causal network and discovery of matching tech-opportunities. Firstly, we used the rule matching method to automatically extract the causal pairs from multi-source data based on causal trigger words and rule templates. We also represented these pairs by triple structure. Then, we constructed the causal network including technical elements and found the demand factors in the process of use. Finally, we completed the potential causal correlation with the link prediction of causal network, which was matched with user demand factors and helped us discover tech-opportunities. [Results] We examined the proposed model with charging stations data of the EVs. We found the battery performance and charging costs are the key factors to improve technical performance and user experience. The GraphSAGE algorithm can more accurately predict the edge connection than Node2Vec, which effectively identify the potential technical opportunities. [Limitations] The accuracy of the proposed method needs to be improved. [Conclusions] The proposed method could effectively discover sci-tech innovation opportunities, as well as potential uncertain issues, which provides reference for further technology optimization and industry upgrading.

Key wordsTechnology Opportunity Discovery      Electric Vehicle Charging Stations      Causal AI     
Received: 16 September 2021      Published: 23 September 2022
ZTFLH:  G35  
Fund:National Social Science Fund of China(21BTQ071);Natural Science Foundation of Beijing(9222025)
Corresponding Authors: Gong Daqing,ORCID:0000-0001-9421-6379     E-mail: dqgong@bjtu.edu.cn

Cite this article:

Liu Linlin, Gong Daqing, Zhang Yujie, Bai Rujiang. Discovering Technology Opportunities with Causal Knowledge: Case Study of EV Charging Stations. Data Analysis and Knowledge Discovery, 2022, 6(8): 31-40.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1042     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I8/31

The Framework of Causal Knowledge Guided Technology Opportunity Discovery
Example of Causal Extraction Based on Pattern Template
Example of Technology Opportunity Discovery
类型 数量
论文 4 132
专利 5 503
新闻报道 1 327
用户评论 738
Data Type Distribution of Electric Vehicle Charging Pile
年份 数量 年份 数量
2021 1 097 2011 336
2020 1 213 2010 546
2019 1 482 2009 12
2018 1 640 2008 7
2017 1 941 2006 2
2016 1 589 2005 2
2015 811 2004 1
2014 464 1998 2
2013 267 1992 1
2012 287
Time Distribution of Electric Vehicle Charging Pile Data
S A O
模块化结构 使得 容量拓展性好
电缆高温 引起 火灾问题
插卡处 导致 读卡失灵问题
大量谐波电流 造成 负面影响
块底座结构 使得 外壳安装紧固
桩故障 导致 收费问题
漏电情况 造成 人员受伤
系统损耗 引起 节点电压波动
组团式城市规划 促进 新能源汽车进入快车道
环境污染 促进 电动汽车产业发展
... ... ...
Part of Causal Triplet of the Charging Pile Technology Elements
Causal Network of Technical Elements of Charging Pile
节点 PR值
电线 0.013 962 08
谐波 0.012 630 43
环境温度 0.009 804 63
压力 0.007 569 61
配电网 0.005 521 61
充电机 0.005 440 81
环境污染 0.004 022 72
成本增加 0.002 777 03
声光效应 0.002 756 23
安全事故 0.001 911 90
环境污染问题 0.001 489 74
产业政策 0.001 489 74
PR Value of Some Nodes of Charging Point Network
指标 Node2Vec GraphSAGE
训练集 测试集 训练集 测试集
损失 0.625 0.666 1.042 1.487
准确率 0.729 0.740 0.793 0.765
Node2Vec and GraphSAGE Algorithm Training Results
正向关键词 频次 负向关键词 频次
便利 99 停车费 48
环境 66 油车 40
流畅 54 32
专人 33 故障 28
设施 30 等待 27
停车费 24 24
19 不稳定 9
还不错 5 错误 4
方便 5 不准 3
很不错 3 不方便 3
Keywords and Frequency under Different Emotions
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