社会网络行为数据驱动的大群体应急决策共识模型

doi:10.12305/j.issn.1001-506X.2023.07.19

摘要/Abstract

摘要：

针对大群体应急决策中决策参考信息不足和共识达成困难的问题, 构建了一种由社会网络行为数据驱动的大群体应急决策共识模型。首先, 针对决策参考信息不足的问题, 提出基于公众行为大数据分析的属性挖掘方法。该方法将公众关注度作为数据聚类标准, 挖掘社交媒体数据中的公众关注主题和权重，将其作为决策参考信息, 应用到大群体应急决策过程。其次, 针对大群体共识达成困难的问题, 建立了基于信任-相似分析的决策共识模型。该模型在专家社会网络聚类的基础上, 引入自信系数和保留系数, 设计共识反馈规则以调整专家偏好, 提高了群体共识水平。最后, 通过公共安全事件案例验证了所提模型的可行性和合理性。

关键词: 社会网络, 行为数据, 大群体, 公共安全, 应急决策

Abstract:

Aiming at the problems of insufficient decision-making reference information and difficulty in reaching consensus in large-group emergency decision-making, a large-group emergency decision-making consensus model driven by social network behavior data is constructed. Firstly, in view of the lack of decision-making reference information, an attribute mining method based on public behavior big data analysis is proposed. The proposed method takes public attention as the data clustering standard, and mines the topics and weights of public concern topic in social media data as decision reference information to participate in the large group emergency decision-making process. Secondly, considering the difficulty in reaching consensus in large group, a decision consensus model based on trust-similarity analysis is established. Based on expert social network clustering, the proposed model introduces confidence coefficient and retention coefficient, and designs consensus feedback rules to adjust experts'preferences and improves the level of group consensus. As a result, the proposed model is applied to a public security incident case to verify its feasibility and rationality.

Key words: social network, behavior data, large group, public security, emergency decision-making

中图分类号:

N945

徐选华, 肖婷. 社会网络行为数据驱动的大群体应急决策共识模型[J]. 系统工程与电子技术, 2023, 45(7): 2086-2097.

Xuanhua XU, Ting XIAO. Consensus model for large group emergency decision-making driven by social network behavior data[J]. Systems Engineering and Electronics, 2023, 45(7): 2086-2097.

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社会关系矩阵	网络关系图	代数式表示
$ \begin{array}{c} \boldsymbol{T}=\left[t_{i-j}\right]=\\ \left[\begin{array}{cccc} 0 & 0.4 & 0 & 1 \\ 0 & 0 & 0 & 0 \\ 0.7 & 0.2 & 0 & 0 \\ 0 & 0 & 0.3 & 0 \end{array}\right] \end{array}$		e₁Re₂, e₁Re₄, e₃Re₁, e₃Re₂, e₄Re₃

信息	点赞	评论	转发
数量指标权重	290 036	94 987	54 378
数量指标权重	0.107	0.325	0.568

数据集	DS_i(LN)	DS_i(CN)	DS_i(FN)	关注系数
DS₁	220	60	47	0.690
DS₂	76	25	12	0.232
DS₃	12	5	4	0.078

属性	关键词	ω(DS₁)	ω(DS₂)	ω(DS₃)	ω
实施保障	医护人员、奔赴前线、援助等	0.135	0.428	0.470	0.229
感染风险	干咳、发烧、呼吸困难等	0.329	0.130	0.183	0.271
监测控制	指挥、防控、专题会议等	0.242	0.218	0.080	0.224
生活影响	复工、封城、返岗等	0.295	0.224	0.267	0.276

聚集	人数	聚集权重	专家组成
C¹	4	0.21	{e₃, e₄, e₇, e₁₄}
C²	6	0.29	{e₂, e₅, e₈, e₁₁, e₁₈, e₂₀}
C³	4	0.20	{e₆, e₁₀, e₁₃, e₁₅}
C⁴	6	0.30	{e₁, e₉, e₁₂, e₁₆, e₁₇, e₁₉}