数据驱动的大群体应急决策公众专家动态协同方法

doi:10.12305/j.issn.1001-506X.2023.12.17

Abstract

Abstract:

Aiming at the complex and changeable environment and the low public participation in large-group emergency decision-making, a data-driven dynamic collaborative method of public and experts in large-group emergency decision-making is proposed. Firstly, the sentiment analysis is carried out on text data in social media platforms to evaluate the quality of large-group emergency decision-making from attribute level and comprehensive level respectively. Secondly, according to the decision-making quality of attribute level, the dynamic update rules of attribute weight are proposed. Then, under the social network environment, the trust relationship between different experts is dynamically updated based on the comprehensive quality of decision-making and the distance between experts'preference and groups preference, and the expert weight is calculated by the PageRank algorithm. Finally, the effectiveness and superiority of the proposed method are verified through the case application and comparative analysis of an aircraft accident.

Key words: social media data, sentiment analysis, large-group emergency decision-making, dynamic collaboration, aircraft accident

CLC Number:

N945

Xuanhua XU, Yucheng ZHU. Data-driven dynamic collaborative method of public and experts in large-group emergency decision-making[J]. Systems Engineering and Electronics, 2023, 45(12): 3875-3886.

Figures/Tables 19

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References 43

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变量符号	变量含义
E={e_i\|i=1, 2, …, M}	专家集合
X={x_l\|l=1, 2, …, P}	备选方案集合
C={c_j\|j=1, 2, …, N}	属性集合
t=0, 1, …, T	决策阶段, 其中T代表阶段总数
Ω^t=(ω_i^t, i=1, 2, …, M)	专家权重向量, 其中ω_i^t代表专家e_i在阶段t时的权重, 满足 $ \sum_{i=1}^{M} \omega_{i}^{t}=1$
W^t=(w_j^t, j=1, 2, …, N)	属性权重向量, 其中w_j^t代表属性c_j在阶段t时的权重, 满足 $ \sum_{j=1}^{N} w_{j}^{t}=1$
A^ti=(L_lj^ti(p))_P×N	专家e_i的评价矩阵, 其中L_lj^ti(P)为概率语言术语集, 代表专家e_i在阶段t时对方案x_l关于属性c_j的评价值
G^t(E, B)	阶段t时专家的社会网络

变量符号	变量含义
P^t={p_h^t\|h=1, 2, …, H_t}	社交媒体中突发事件相关的帖子集合
V^j={v_k^j\|k=1, 2, …, K_j}	属性c_j相关的关键词集合
U^th={u_s^th\|s=1, 2, …, S_th}	帖子p_h^t的分词列表
P^tj={p_h^tj\|h=1, 2, …, H_tj}	P^t中有关属性c_j的帖子集合

情感分类	情感得分
s₀	[0, 0.2)
s₁	[0.2, 0.4)
s₂	[0.4, 0.6)
s₃	[0.6, 0.8)
s₄	[0.8, 1]

ω₁⁰	ω₂⁰	…	ω₁₉⁰	ω₂₀⁰
0.042 4	0.026 9	…	0.053 1	0.063 0

属性	关键词	关键词数量
人员搜救	搜救、救援、生命、勘察、遇难、人员、…、消防员、旅客、幸存者、安全、找到	31
安置与善后	家属、亲人、噩耗、赔偿、接待、安置、…、心理、保险、遗物、妥善、家庭	26
舆情管理	传播、宣传、散播、转发、谣言、公布、…、发布会、新闻、微博、消息、报道	28
事故原因及追责	原因、调查、责任、黑匣子、分析、检验、…、无线电、真相、追责、译码、技术	30
时效性	突发、紧急、应急、响应、争分夺秒、第一时间、…、迅速、火速、启动、应急、响应、指挥	24

Data-driven dynamic collaborative method of public and experts in large-group emergency decision-making

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备选方案	加权贴近度	方案排序
x₁	0.711 1	1
x₂	0.552 4	3
x₃	0.560 9	2
x₄	0.254 7	4

帖子集合	帖子数量	内容示例
人员搜救	69 585	“向救援队伍致敬, 绝不能放弃找到幸存者的希望！”
安置善后	37 371	“不要过度打扰遇难乘客家属, 以免造成二次伤害！”
舆情管理	78 560	“原来是发布会引发的谣言, 以官方消息为准吧。”
事故原因及追责	53 506	“希望尽快找到黑匣子, 尽快还原事件的真相。”
时效性	35 022	“一定要把握黄金48小时, 争分夺秒进行搜救。”

决策属性	决策质量
人员搜救	{s₀(0.07), s₁(0.15), s₂(0.31), s₃(0.34), s₄(0.13)}
安置善后	{s₀(0.18), s₁(0.11), s₂(0.46), s₃(0.19), s₄(0.06)}
舆情管理	{s₀(0.16), s₁(0.27), s₂(0.29), s₃(0.12), s₄(0.15)}
事故原因及追责	{s₀(0.10), s₁(0.35), s₂(0.28), s₃(0.18), s₄(0.09)}
时效性	{s₀(0.06), s₁(0.22), s₂(0.33), s₃(0.25), s₄(0.14)}

ω₁¹	ω₂¹	…	ω₁₉¹	ω₂₀¹
0.042 4	0.027 1	…	0.053 0	0.063 1

ω₁²	ω₂²	…	ω₁₉²	ω₂₀²
0.042 3	0.027 5	…	0.052 8	0.063 4

方法	原理简述
本文方法	对社交媒体数据使用情感分析计算属性层的决策质量, 通过上一阶段的属性权重及决策质量确定当前阶段的属性权重
文献[42]方法	基于当前决策阶段内的公众大数据计算各属性下关键词的TF-IDF (term frequency-inverse document frequency)值, 将其累加得到属性权重
文献[43]方法	根据当前决策阶段内各属性下专家偏好的熵值确定属性权重, 熵值越大, 则属性权重越大

方法	属性权重W⁰	属性权重W¹
本文方法	(0.32, 0.18, 0.16, 0.14, 0.20)	(0.28, 0.21, 0.18, 0.14, 0.19)
文献[42]方法	(0.23, 0.21, 0.25, 0.12, 0.19)	(0.27, 0.12, 0.16, 0.21, 0.24)
文献[43]方法	(0.43, 0.14, 0.27, 0.11, 0.05)	(0.41, 0.10, 0.23, 0.19, 0.07)

方法	决策质量评价方法	专家权重更新方法
本文方法	对与决策相关的社交媒体数据进行情感分析, 将其转化为概率语言术语集后集结得到综合决策质量评价值	根据综合决策质量和专家个体偏好与群体偏好的距离计算专家间的信任关系, 基于此使用改进的PageRank算法计算专家权重
文献[18]方法	对决策相关的社交媒体数据进行情感分析, 将其转化为直觉模糊数并与基准值比较, 从而判断决策质量的高低	根据决策质量和专家个体偏好与群体偏好的距离将专家分为“决策正确”和“决策错误”两组, 并基于专家历史决策正确率计算专家权重

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w₁¹	w₂¹	w₃¹	w₄¹	w₅¹
0.28	0.21	0.18	0.14	0.19

w₁²	w₂²	w₃²	w₄²	w₅²
0.26	0.24	0.17	0.20	0.13