| 张 越 曹 悦 白 晨.企业技术风险阈值激活模型构建研究——以智能网联汽车产业链为例[J].中国科技资源导刊,2024,(3):1~9 |
| 企业技术风险阈值激活模型构建研究——以智能网联汽车产业链为例 |
| Research on Enterprise Technical Risk Threshold Activation Model Construction——Take Intelligent Connected Vehicle (ICV) Industrial Chain as an Example |
| 投稿时间：2023-11-14 |
| DOI： |
| 中文关键词: 企业技术风险；阈值激活；分类预测；数据挖掘；智能网联汽车 |
| 英文关键词: enterprise technology risk, threshold activation, classification prediction, data mining, intelligent connected vehicle(ICV) |
| 基金项目:中国科学技术信息研究所创新研究基金青年项目“产业风险阈值激活模型构建研究”（QN2023-03） |
| 作者 | 单位 | | 张 越 曹 悦 白 晨 | （中国科学技术信息研究所，北京 100038） |
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| 中文摘要: |
| 响应强化产业风险监测能力的实际管理需求，提升管理部门和智库研究人员对特定产业中企业技术风险问题的分析能力，为技术风险激活或抑制的规律性认识提供机器学习模型支撑。首先，对企业风险的定义和多维度类型进行划分，采用基于机器学习的方法构建企业技术风险阈值激活模型，深入挖掘企业风险特征。其次，运用随机森林、XGBoost等8种机器学习算法训练参数变量，以学习企业技术风险的属性，并对模型有效性进行评价。再次，以智能网联汽车产业链为例利用自动化方法揭示企业风险激活的规律性特征，3种梯度提升综合模型的分类预测准确率达到82.59%，实现从大量相关数据变量中识别出具有潜在技术风险的企业。最后，提出未来的研究将是进一步提高模型的预测精度及稳定性，并加强其在其他领域企业技术风险评估中的应用。 |
| 英文摘要: |
| In response to the practical management needs for strengthening industrial risk monitoring capabilities, this approach enhances the analytical abilities of management departments and think tank researchers in analyzing enterprise technology risk issues within specific industries. It also provides support through machine learning models for a systematic understanding of technology risk activation or suppression.By defining enterprise risk and categorizing it into multiple dimensions, a machine learning-based method is utilized to construct an Enterprise Technology Risk Threshold Activation Mode, which aims to deeply explore the characteristics of enterprise risk. It employs eight machine learning algorithms, including Random Forest,XGBoost, etc., to train parameter variables for learning the attributes of enterprise technology risk, as well as for evaluating the model’s effectiveness. It reveals the regularity characteristics of corporate risk activation using automated methods in the case of the Intelligent Connected Vehicle (ICV) industrial chain, with the classification prediction accuracy of three gradient boosting synthesis models reaching 82.59%. This enables the identification of enterprises with potential technical risks from a large dataset of relevant variables. Future work will focus on further improving the prediction accuracy and stability of these models, as well as expanding their application in enterprise technology risk assessment across various fields. |
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