|本期目录/Table of Contents|

[1]何建佳,刘文虹.产业互联下面向城市突发公共卫生事件应急场景的数据智能推荐*[J].中国安全生产科学技术,2024,20(9):13-19.[doi:10.11731/j.issn.1673-193x.2024.09.002]
　HE Jianjia,LIU Wenhong.Data intelligent recommendation for emergency scenarios of urban public health emergencies under industry interconnection[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(9):13-19.[doi:10.11731/j.issn.1673-193x.2024.09.002]
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产业互联下面向城市突发公共卫生事件应急场景的数据智能推荐*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年9期
页码:
13-19
栏目:
学术论著
出版日期:
2024-09-30

文章信息/Info

Title:
Data intelligent recommendation for emergency scenarios of urban public health emergencies under industry interconnection
文章编号:
1673-193X(2024)-09-0013-07
作者:
何建佳; 刘文虹
（1.上海理工大学 管理学院，上海 200093；
2.上海理工大学 超网络研究中心，上海 200093）
Author(s):
HE Jianjia;  LIU Wenhong
(1.Business School,University of Shanghai for Science and Technology,Shanghai 200093,China;
2.Super Network Research Center,University of Shanghai for Science and Technology,Shanghai 200093,China)
关键词:
产业互联; 城市应急场景; 突发公共卫生事件场景; 智能推荐; 混合协同过滤
Keywords:
industry interconnection;  urban emergency scenario;  public health emergency scenario;  intelligent recommendation;  hybrid collaborative filtering
分类号:
X913
DOI:
10.11731/j.issn.1673-193x.2024.09.002
文献标志码:
A
摘要:
为实现突发公共卫生事件场景下应急产业互联主体对场景数据的响应需求，通过场景要素分析，构建产业互联下城市突发公共卫生事件场景知识图谱，明晰应急产业互联主体与疫情场景数据之间的关联关系。然后，在上述基础上提出联合语义关联度和个性化网页排名的改进算法对场景数据进行应急企业偏好推荐，并以武汉新型冠状病毒感染疫情为例验证该改进算法的可行性。研究结果表明：联合语义关联优化后节点的个性化网页排名相比初始个性化网页排名推荐度更高，形成的数据推荐集更符合疫情场景下的数据推荐服务。研究结果可为突发公共卫生事件场景下基于数据智能推荐的应急响应和决策提供参考。
Abstract:
In order to realize the response demand of emergency industry interconnection subjects to scenario data under the scenario of public health emergencies,the knowledge graph of urban public health emergency scenarios under the industry interconnection was constructed through the analysis of scenario elements,and the correlation relationship between emergency industry interconnection subjects and the epidemic scenario data was clarified.On this basis,the improved algorithm of joint semantic relevance and personalized PageRank was proposed to provide the emergency enterprise preference recommendation of scenario data,and finally the feasibility of the improved algorithm was verified with the example of COVID-19 pandemic in Wuhan.The results show that the joint semantic association optimized node PPR′ is more recommendable compared to the initial PPR,and the formed data recommendation set is more in line with the data recommendation service in epidemic scenarios.The research results can provide a reference for the emergency response and decision-making based on data intelligent recommendation under the scenarios of public health emergencies.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期： 2024-03-13
* 基金项目： 国家自然科学基金项目(71871144) ；教育部人文社会科学规划项目（23YJA630031）
作者简介： 何建佳，博士，教授，主要研究方向为应急产业互联、企业供需网。
通信作者： 刘文虹，硕士研究生，主要研究方向为应急产业互联。
更新日期/Last Update: 2024-10-08