A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion

Tian Liang,Xuefang Wang,Sheng Bin

doi:10.1155/2022/5298988

Abstract

This paper adopts the method of multisource big data fusion to conduct an in-depth study and analysis of precision poverty alleviation and uses big data statistical analysis model to explore and analyze it. Combining the characteristics of big data itself and the development of precision poverty alleviation, it focuses on the exploration of big data and introduces the background, development status, and achieved results of poverty alleviation with typical cases, followed by the analysis of the problems in the process of big data precision poverty alleviation and the study of the improvement path of big data technology precision poverty alleviation. Through the comparative analysis of the simulation accuracy of three models, the results show that the random forest model has the lowest error rate, after which the importance degree of indicators is derived using the model. In addition, the empirical analysis of the preprocessed sample data for multidimensional identification of poor households yields the contribution rate of each dimensional indicator that leads to multidimensional poverty of farm households, establishing scientific judging criteria to accurately judge whether farm households are poor on the one hand and selecting accurate identification methods to achieve accurate identification of poor households on the other hand. The tenfold crossover method is used to verify the errors in the test sample set. When the number of classification trees is greater than 100, it will gradually increase. Therefore, it is most appropriate to select the number of trees as 100. The multidimensional accurate identification model of farm household poverty constructed in this paper has an accuracy rate of 90.26% for the identification of poor households. By analyzing the accuracy rate of model identification and the contribution rate of multidimensional indicators leading to the poverty of farm households at the same time, the poverty degree of farm households under each dimensional indicator is derived, to accurately identify the poor households and their poverty status. The results show that the multidimensional accurate identification model of farm household poverty has the accurate identification ability and application value in the identification problem of poor households, and through the implementation of the model algorithm, a good application environment of accurate identification of poverty is created, which provides technical support to help poverty alleviation work and improve the accuracy of identification of poor households.

Highlights

IntroductionModern technology is increasingly linked with government management, and management through modern technology means enables citizens to obtain more convenient government services and carries the innovative initiatives and determination of the state to create modern management means
Big data technology has become an indispensable tool in modernized precision poverty alleviation, which can effectively improve the efficiency of poverty alleviation and enhance the quality of government governance
Results of the Analysis of Precision Poverty Alleviation. e integrated dataset has more comprehensive student information, which includes students’ basic personal information, family information, and family economic situation, but not all data need to be analyzed, such as students’ names, ID card numbers, and home addresses. erefore, it is necessary to filter out the needed data after the data preprocessing is completed and filter out the data not needed for modeling

Summary

Introduction

Modern technology is increasingly linked with government management, and management through modern technology means enables citizens to obtain more convenient government services and carries the innovative initiatives and determination of the state to create modern management means. What the impact of the development of big data technology on the government’s precise poverty alleviation is and how to use big data technology to achieve modern scientific and effective precise poverty alleviation management under this impact are worthy of in-depth consideration and research. E core of intelligent analysis research on precise poverty eradication contains the prediction of the time to get out of poverty and the generalization of the rules of help measures [1]. E purpose of the research on the intelligent analysis of precise poverty eradication is to use the generated rule set for the implementation of help measures to formulate a help plan for poor households, and evaluate and adjust the poverty eradication plan by predicting the time of poverty eradication, to achieve the maximum utilization of resources and the fastest and most stable poverty eradication of poor households [3] The economic strength of povertystricken areas itself is backward, and the proportion of Scientific Programming resources that can be allocated to vocational education is very small. e core of intelligent analysis research on precise poverty eradication contains the prediction of the time to get out of poverty and the generalization of the rules of help measures [1]. e essence of time out of poverty prediction and implementation rules for helping measures is to dig deep into the relationship between poor households, helping measures, and poverty alleviation based on existing poverty alleviation data, the former realizes the mathematical quantification of the inner law between “poor households - helping measures poverty” and explores the mechanism between poor households’ characteristics and helping measures [2]. e latter clarifies the principle of the rules between the basic information of poor households and help measures and further clarifies the correspondence between the characteristics of poor households and help measures. e purpose of the research on the intelligent analysis of precise poverty eradication is to use the generated rule set for the implementation of help measures to formulate a help plan for poor households, and evaluate and adjust the poverty eradication plan by predicting the time of poverty eradication, to achieve the maximum utilization of resources and the fastest and most stable poverty eradication of poor households [3]

Methods

Results

Conclusion