大数据背景下贵州农村精准扶贫识别机制研究
发布时间：2019-06-13 06:49
【摘要】：精准识别是进行精准扶贫的首要环节,精准识别的精准度直接关系着致贫分析、帮扶措施、资金安排等一系列问题,最终影响精准扶贫的效果,关系到贫困户是否能走出贫困。精准扶贫的提出是由于中国扶贫已经走到攻坚阶段,是啃硬骨头、打硬仗的时期,以往的扶贫已经很难再取得进展性的突破,贫困户的贫困问题也是老大难问题,在外部因素和内部因素的共同作用之下,扶贫也就必须走创新之路,找出新办法,解决老问题。精准扶贫的开展第一步就是精准识别,对精准识别的研究可以探讨识别机制中存在哪些优势,存在的问题,以及如何解决问题,帮助更好的精准识别出合适的贫困户。在大数据时代发展的今天,数据量大、分散而且隐蔽,但是信息价值确是不可估量的,这可以从许多案例中找到答案。大数据背景下的精准识别也就更具有意义。首先大数据的收集、处理、管理让精准识别更加科学合理,这种机械式的识别能够避免参杂太多主观因素判断,而导致的恶意排斥和主观排斥,这是大数据能够帮助解决的;其次就是大数据共享,信息及时高效,这样不管是政府帮扶单位还是企事业帮扶或是非政府组织的帮扶,都可以根据数据库里面的实时动态信息及时了解贫困户情况,有利于制定或是更改帮扶措施,达到最优化高效的扶贫;最后就是大数据下的退出机制,这样可以根据贫困户自身发展走势,挖掘出他们的内生力,并且把已经脱贫的农户及时退出数据平台,然后重新识别符合条件的贫困户进行帮扶,依次就形成一个自循环的帮扶系统。本文是利用文献收集法和经验分析法对贵州省大数据背景下的精准识别进行定性研究,以此来探讨大数据下的精准识别存在的优势和劣势、存在的问题,以及提出相关建议。本文是以贵州省为研究对象,查找了一些地方的精准识别工作机制,并对一个个案进行分析,深入探讨贵州大数据下的精准识别。一是大数据对精准扶贫的影响,这是说明大数据的加入带给精准扶贫的变化;二是对贵州个案在大数据背景下的精准识别进行详细介绍,从而了解精准识别机制的运作过程及其中存在的优点和缺点;三是阐述大数据下,贵州精准识别存在的不足:家庭收入难确定、贫困户指标受限、干部对扶贫认识不足、临界贫困户难甄别等,并对相应的问题进行原因分析;四是提出解决方法,如完善扶贫政策、调动识别积极性、完善识别指标等;五是对全文进行总结。
[Abstract]:Precision identification is the first link of precision poverty alleviation. The accuracy of precision identification is directly related to poverty-causing analysis, support measures, fund arrangement and a series of problems, which ultimately affect the effect of precision poverty alleviation and whether poor households can get out of poverty. The accurate poverty alleviation is due to the fact that poverty alleviation in China has reached the stage of tackling key problems, which is a period of gnawing hard bones and fighting hard battles. It is difficult to make further breakthroughs in poverty alleviation in the past, and the poverty problem of poor households is also an old and difficult problem. Under the joint action of external and internal factors, poverty alleviation must take the road of innovation, find out new ways to solve the old problems. The first step in the development of precision poverty alleviation is precision identification. The study of precision recognition can explore the advantages and problems in the identification mechanism, as well as how to solve the problems, so as to help better and accurately identify the suitable poor households. With the development of big data era, the amount of data is large, scattered and hidden, but the value of information is inestimable, which can be found in many cases. The precision recognition under the background of big data is more meaningful. First of all, big data's collection, processing and management makes accurate identification more scientific and reasonable. This kind of mechanical identification can avoid too many subjective factors to judge, and the malicious exclusion and subjective exclusion, which big data can help to solve. Secondly, big data shares, the information is timely and efficient, so that whether it is the government support units or enterprises or institutions or non-governmental organizations to help, can timely understand the situation of poor households according to the real-time dynamic information in the database, which is conducive to the formulation or change of support measures to achieve the optimal and efficient poverty alleviation; Finally, the exit mechanism under big data, according to the development trend of the poor households themselves, can excavate their endogeny, and withdraw the farmers who have been lifted out of poverty in time from the data platform, and then re-identify the eligible poor households to help, and then form a self-circulation support system in turn. This paper makes a qualitative study on the precision recognition under the background of big data in Guizhou Province by using the method of literature collection and empirical analysis, in order to explore the advantages and disadvantages of precision recognition under big data, the existing problems, and put forward some relevant suggestions. This paper takes Guizhou Province as the research object, finds out the working mechanism of precision recognition in some places, analyzes a case, and probes into the precision recognition under big data in Guizhou Province. One is big data's influence on precision poverty alleviation, which explains the changes brought about by big data's entry into precision poverty alleviation, and the other is to introduce in detail the precision identification of Guizhou cases under the background of big data, so as to understand the operation process of precision identification mechanism and its advantages and disadvantages. The third is to explain the shortcomings of accurate identification in Guizhou under big data: difficult to determine family income, limited indicators of poor households, insufficient understanding of poverty alleviation by cadres, difficult to identify critical poor households, and so on, and to analyze the causes of the corresponding problems; fourth, to put forward solutions, such as perfecting poverty alleviation policies, mobilizing enthusiasm for identification, perfecting identification indicators, etc.; fifth, summing up the full text.
【学位授予单位】：贵州民族大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F323.8
本文编号：2498327
[Abstract]:Precision identification is the first link of precision poverty alleviation. The accuracy of precision identification is directly related to poverty-causing analysis, support measures, fund arrangement and a series of problems, which ultimately affect the effect of precision poverty alleviation and whether poor households can get out of poverty. The accurate poverty alleviation is due to the fact that poverty alleviation in China has reached the stage of tackling key problems, which is a period of gnawing hard bones and fighting hard battles. It is difficult to make further breakthroughs in poverty alleviation in the past, and the poverty problem of poor households is also an old and difficult problem. Under the joint action of external and internal factors, poverty alleviation must take the road of innovation, find out new ways to solve the old problems. The first step in the development of precision poverty alleviation is precision identification. The study of precision recognition can explore the advantages and problems in the identification mechanism, as well as how to solve the problems, so as to help better and accurately identify the suitable poor households. With the development of big data era, the amount of data is large, scattered and hidden, but the value of information is inestimable, which can be found in many cases. The precision recognition under the background of big data is more meaningful. First of all, big data's collection, processing and management makes accurate identification more scientific and reasonable. This kind of mechanical identification can avoid too many subjective factors to judge, and the malicious exclusion and subjective exclusion, which big data can help to solve. Secondly, big data shares, the information is timely and efficient, so that whether it is the government support units or enterprises or institutions or non-governmental organizations to help, can timely understand the situation of poor households according to the real-time dynamic information in the database, which is conducive to the formulation or change of support measures to achieve the optimal and efficient poverty alleviation; Finally, the exit mechanism under big data, according to the development trend of the poor households themselves, can excavate their endogeny, and withdraw the farmers who have been lifted out of poverty in time from the data platform, and then re-identify the eligible poor households to help, and then form a self-circulation support system in turn. This paper makes a qualitative study on the precision recognition under the background of big data in Guizhou Province by using the method of literature collection and empirical analysis, in order to explore the advantages and disadvantages of precision recognition under big data, the existing problems, and put forward some relevant suggestions. This paper takes Guizhou Province as the research object, finds out the working mechanism of precision recognition in some places, analyzes a case, and probes into the precision recognition under big data in Guizhou Province. One is big data's influence on precision poverty alleviation, which explains the changes brought about by big data's entry into precision poverty alleviation, and the other is to introduce in detail the precision identification of Guizhou cases under the background of big data, so as to understand the operation process of precision identification mechanism and its advantages and disadvantages. The third is to explain the shortcomings of accurate identification in Guizhou under big data: difficult to determine family income, limited indicators of poor households, insufficient understanding of poverty alleviation by cadres, difficult to identify critical poor households, and so on, and to analyze the causes of the corresponding problems; fourth, to put forward solutions, such as perfecting poverty alleviation policies, mobilizing enthusiasm for identification, perfecting identification indicators, etc.; fifth, summing up the full text.
【学位授予单位】：贵州民族大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F323.8
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