The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

Guo, Yan; Hu, Xiaonan; Ze-peng, Wang; Tang, Wei; De-yu, Liu; Yun-zhong, Luo; Xu, Huaizhe

doi:10.17221/128/2021-agricecon

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…For example, the "book smart government" tells some innovative ideas of big data in the field of government governance, which believes that big data is not only a kind of massive data state and processing technology, but also a way of thinking; proposes to introduce the means and methods of big data into the management field, which can realize the modernization of management; and advocates that the government can tap the huge value from the huge amount of data and become the leader in the era of big data [9]. e difficulties of implementing a big data governance approach in government are analyzed, and the significance and impact of big data in terms of improving the governance capacity of our government are analyzed, effectively giving guidance on government work [10]. A data management platform for precise poverty alleviation is designed and implemented, and a sound and complete file of poor households is established.…”

Section: Related Workmentioning

confidence: 99%

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

Liang

Wang

2022

Scientific Programming

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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.

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Section: Related Workmentioning

confidence: 99%

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

Liang

Wang

2022

Scientific Programming

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“…That is, a feature can only fuse the information contained in the two pieces of data before and after it, but not the information of the data separated from it directly. In this paper, we combined ERNIE-Gram and TextGCN for modeling [ 22 , 23 , 24 ]. Feature extraction was performed using ERNIE-Gram for Chinese, taking into full consideration the relationship between coarse-grained and fine-grained data.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Yang

Duan

Xiao

et al. 2022

IJERPH

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Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.

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“…For the development of agricultural products regional brands, the development of agricultural products regional brands is currently a very important way to develop agriculture. The integration of agricultural products and regional brands can accelerate the development of supply measurement reform on the one hand and promote rural revitalization and agricultural industrialization on the other [3][4][5]. For agriculture or agricultural products, the traditional smallholder model has been unable to rapidly promote agricultural development.…”

Section: Introductionmentioning

confidence: 99%

“…Meisi Xu. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] …”

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Exploration of Strategies for Improving Brand Competitiveness of Agricultural Products in the Context of Digitization

2023

Applied Mathematics and Nonlinear Sciences

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Brand competitiveness of agricultural products has become an important source of agricultural production development. This paper proposes the source and generation mechanism of brand competitiveness through the analysis of brand equity differences, combined with enterprise and consumer levels. Based on the composition of brand competitiveness, brand cognition, quality perception, association and loyalty are selected as the influencing dimensions, and the initial hypothesis of competitiveness is initially constituted by applying relevant factors. Structural equation modeling was used to complete the measurement of the research data so that the model was revised, empirical research was conducted, and strategies for competitiveness enhancement were proposed through the discussion of the influence path coefficients. Brand awareness and brand quality perception have a direct positive effect on the competitiveness of regional brands of agricultural products, and the direct influence path coefficients of brand competitiveness are 0.294 and 0.195, respectively. The path coefficients of the influence of the regional natural environment on brand awareness reach 1.169, with an S.E. value of 0.725 and a C.R. value of 11.063, which are significant. The exploration of digitization shows a clear path for improving the competitiveness of agricultural product brands.

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The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

Cited by 6 publications

References 35 publications

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

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

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Exploration of Strategies for Improving Brand Competitiveness of Agricultural Products in the Context of Digitization

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