基于HMM的多维数据下扶贫对象状态预测

doi:10.16182/j.issn1004731x.joss.20-1006

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (5): 1118-1126.doi: 10.16182/j.issn1004731x.joss.20-1006

基于HMM的多维数据下扶贫对象状态预测

何俊^1,³(), 洪孙焱^1,³(), 周义方², 申时凯¹, 邹目权^1,³

^1.昆明学院　信息工程学院，云南　昆明　650214
^2.昆明学院　信息中心，云南　昆明　650214
^3.云南省高校数据治理与智能决策重点实验室，云南　昆明　650214

收稿日期:2020-12-15 修回日期:2021-02-24 出版日期:2022-05-18 发布日期:2022-05-25
通讯作者: 洪孙焱 E-mail:369885901@qq.com;hongsunyan@126.com
作者简介:何俊（1977-），男，博士，教授，研究方向为数据分析、智能语音。E-mail：369885901@qq.com
基金资助:
国家自然科学基金(62066023);国家级新工科研究与实践项目(E-JSJRJ20201342);云南省地方本科高校基础研究联合专项(2017FH001-058);云南省教育厅科学研究基金(2018JS391)

State Prediction of Poverty Alleviation Objects Based on HMM and Multidimensional Data

Jun He^1,³(), Sunyan Hong^1,³(), Yifang Zhou², Shikai Shen¹, Muquan Zou^1,³

^1.College of Information Engineering, Kunming University, Kunming 650214, China
^2.Information Center, Kunming University, Kunming 650214, China
^3.Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan, Kunming 650214, China

Received:2020-12-15 Revised:2021-02-24 Online:2022-05-18 Published:2022-05-25
Contact: Sunyan Hong E-mail:369885901@qq.com;hongsunyan@126.com

摘要/Abstract

摘要：

针对扶贫领域中贫困、脱贫和返贫状态预测不准确，影响状态变迁的关键因素难以识别的问题，从扶贫基础数据和多个行业数据中提取8个关键特征和22个观测状态，构建观察状态和隐含状态关联关系，建立扶贫对象状态预测隐马尔可夫模型（hidden markov model，HMM）。以某深度贫困县连续3年的数据为样本，进行参数训练、测试实验和结果验证，结果表明该方法对返贫、贫困和脱贫状态有较强的预测能力，误差率较低，且能准确识别出影响返贫的关键要素。该方法对指导精准扶贫工作具有非常重要的实际意义。

关键词: 隐马尔可夫模型, 精准扶贫, 数据分析, 预测方法, 返贫

Abstract:

In order to solve the problems of inaccurate prediction of poverty, poverty reduction and poverty returen, and the difficulty in identifying the key factors affecting the state transition, 8 key features and 22 observed states are extracted from the poverty reduction basic data and multi-industry data. The relationship between observed state and implied state is constructed, and the hidden markov model (HMM) of poverty alleviation is established. Data of a deep poverty county for three consecutive years are used as samples for parameter training, test experiment and result verification. The results show that the method has a strong prediction ability for back poverty, poverty and poverty alleviation with low error rate, and can accurately identify the key elements affecting poverty return. The method is of great practical significance for guiding the precise poverty alleviation work.

Key words: hidden markov model(HMM), precise poverty alleviation, data analysis, prediction method, return to poverty

中图分类号:

何俊, 洪孙焱, 周义方, 申时凯, 邹目权. 基于HMM的多维数据下扶贫对象状态预测[J]. 系统仿真学报, 2022, 34(5): 1118-1126.

Jun He, Sunyan Hong, Yifang Zhou, Shikai Shen, Muquan Zou. State Prediction of Poverty Alleviation Objects Based on HMM and Multidimensional Data[J]. Journal of System Simulation, 2022, 34(5): 1118-1126.

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序号	关键特征(观察状态)
1	家庭特征(6人及以下)
2	家庭特征(6人以上)
3	收入特征(2500元以下)
4	收入特征(2 500~5 000元)
5	收入特征(5 000元以上)
6	教育特征(文化程度中学及以下)
7	教育特征(文化程度中学以上)
8	教育特征(在校生3人及以下)
9	教育特征(在校生3人以上)
10	生产特征(占家庭人口50%及以下)
11	生产特征(占家庭人口50%以上)
12	主观特征(平均务工时间6个月及以下)
13	主观特征(平均务工时间6个月以上)
14	主观特征(务工月收入3 000元及以下)
15	主观特征(务工月收入3 000元以上)
16	客观特征(因负担致贫5种)
17	客观特征(因发展条件致贫5种)
18	政策特征(享受民政兜底)
19	政策特征(危房改造或异地搬迁)
20	政策特征(享受生态补偿)
21	政策特征(有护林员收入)
22	其它特征(家庭有残疾人)

多维数据集	2016年	2017年	2018年
基础数据集	23 556	25 030	22 584
林草数据集	8 613	9 061	6 365
残联数据集	1 136	1 520	1 722
人社数据集	220	346	376
教体数据集	5 165	5 182	4 937
住建数据集	1 072	1 246	938
民政数据集	7 415	6 822	5 194
合计	47 177	49 207	42 116

基于HMM的多维数据下扶贫对象状态预测

State Prediction of Poverty Alleviation Objects Based on HMM and Multidimensional Data

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