ANALYSIS OF DIGITAL INFORMATION
MANAGEMEN
T OF PRODUCT MARKET
COMPETITION UNDER THE ENVIRONMENT
OF AGRICULTURAL PRODUCT E-COMMERCE
Zhibiao Zhu
College of Accounting, Shaoyang University, Shaoyang, 422000, (China)
E-mail: zhibiao.zhu@hotmail.com
Aimin Zheng
College of Accounting, Shaoyang University, Shaoyang, 422000, (China)
Reception: 02/11/2022 Acceptance: 17/11/2022 Publication: 29/12/2022
Suggested citation:
Zhibiao Zhu and Amin Zheng (2022). Analysis of digital information Management of product market
competition under the environment of agricultural product e-commerce. 3C Empresa. Investigación y
pensamiento crítico, 11(2), 198-212. https://doi.org/10.17993/3cemp.2022.110250.198-212
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ABSTRACT
With the rapid development of China's regional agriculture, its consumption of energy, the squeezing
and encroachment of the environment and the pollution of the ecological environment have been put
on the agenda, which stems from the unreasonable management and regulation of the rapidly
developing agricultural infrastructure. Aiming at the evaluation index cluster of agricultural
management in Northeast China, the main structure of the digital information management platform
of green ecological agriculture we built is divided into variable layer, middle layer and evaluation
index layer, which is a superimposed and progressive layer structure design.The results show that
compared with 2018, the use index of electronic agricultural products, per capita greening index, soil
organic matter content index and per capita water content index increased by 30.34%, 6.14%, 25.34%
and 30.26% respectively in 2019.The index of per capita desert land area decreased by 10.97%.The
Sustainability Index experienced an unusual decrease in 2017-2018, with a drop of 0.08. Compared
with 2018, the green ecological index from 2019 to 2021 increased by 5.94%, 8.58% and 12.87%
respectively. This provides guidance for the structure and design of China's future agricultural
development.
KEYWORDS
A
gricultural ecology, green environmental protection, sustainable development, information
management, data platform g
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Introduction
The development of green ecological agriculture can protect and improve the ecological
environment, prevent pollution, maintain ecological balance [1, 2], improve the safety of
agricultural products [3], take the road of sustainable ecological development [4], and closely
integrate environmental construction with economic development. Combined [5, 6], it can
improve the income of agricultural workers while developing agriculture. Green ecological
agriculture, in simple terms, is to use the principles of ecology, ecological economics and
systematic scientific methods to organically combine the achievements of modern science and
technology with the essence of traditional agricultural technology [7, 8], and integrate
agricultural production, rural economic development and ecological. It is a new
comprehensive agricultural system with ecological rationality and a virtuous cycle of
functions that integrates environmental governance and protection, resource cultivation and
efficient utilization [9, 10]. There are three main models of green ecological agriculture,
mainly including space-time structure type, food chain type and space-time food chain
comprehensive type, as shown in Table 1. Ecological agriculture is an agro-ecological
economic complex system. According to the principle of "whole, coordinated circulation and
regeneration", the agricultural ecological system and the agricultural economic system are
comprehensively integrated to realize the multi-level utilization of natural resources energy,
so as to achieve the maximum ecological economy overall benefit [11, 12].At the same time,
it can integrate agriculture, forestry, animal husbandry, sideline and fishery industries[13, 14]
to form a comprehensive development model of large-scale agricultural production,
processing and sales, adapting to the development of the socialist market economy[15].
However, with the rapid rise of China's e-commerce and the rapid development of
informatization, the application of various digital high-tech information technology and the
analysis and management of modern green ecological agriculture are the inevitable trends in
the development of agricultural modernization.
Table 1 Main modes of green ecological agriculture
Green ecological agriculture is an inevitable way to realize modern agriculture, and
efficient and reasonable organization and management are the foundation and guarantee for
the development of ecological agriculture. Scientific management concepts, tools and
methods are the basic means to achieve green agriculture [16, 17]. Green ecological
management reflects the choice of ecological agriculture development model and the
innovation of green technology management. Select and manage agricultural production
models from the perspective of agricultural product production and ecological economics, and
research can best reflect ecological benefits and economy. Nowadays, in the field of analysis
agricultural model Features
space-time structure According to the biological, ecological characteristics and a
rationally formed ecosystem of mutually beneficial symbiotic
relationships between organisms
food chain A virtuous cycle agro-ecosystem designed according to the
energy flow and material cycle laws of the agro-ecosystem
Integrated spatiotemporal
food chain
The organic combination of space-time structure type and food
chain type is a mode type with moderate input, high output, less
waste, no pollution and high efficiency
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and management of green ecological agriculture, many experts and scholars have made a lot
of discussions. For example, for agriculture and ecological management under uncertain
conditions, Chen, J [18] proposed a reliability-based interval multi-objective crop area
planning model. The integration, developed considering the economic and ecological benefits
of the research system [19, 20], was developed to deal with interval and ambiguous
uncertainties. It focuses on crop area optimization, and the interval objective function is to
maximize system benefits, maximize watershed area, and maximize system benefits per unit
area. Rural agro-ecosystems have an important impact on the development of China's
economy, society and ecological environment at any time. Chen, F [21] took big data as the
research background and based on the complex system theory to construct an indicator system
for the ecological management system of rural agro-ecosystems. The fertilizer was used in the
experiment, and the consumption, water pollution degree, pest and disease degree, carbon and
nitrogen absorption and agricultural economic benefit of the rural agricultural ecosystem in a
certain area were taken as the systematic indicators of the ecological management system.
Using data mining technology in big data [22, 23], collecting and processing relevant data in
the network, analyzing and understanding agricultural ecosystems through complex systems,
and finally calculating and analyzing data of various indicators, their research shows that
agro-ecological management Institutions have a positive effect on rural agro-ecosystems. In
the development of green ecological agriculture, the optimal water distribution model is an
effective tool to provide a reasonable water distribution scheme [24], Pan, Q. [25] proposed
an interval multi-objective fuzzy interval credible constraint nonlinear programming model,
combined with the estimation of ecological vegetation space water demand , to solve the
problem of agricultural and ecological water allocation in irrigated areas under uncertain
conditions. Excessive fertilization can cause land pollution [26, 27], which is not conducive to
the development of ecological agriculture. Li, X [28] established a linear regression equation
to predict the runoff in the study area, and then determine the pollution in the area. Zhu, Z
[29] built a 5G IoT-based agricultural product circulation information system to realize real-
time positioning, information sharing and security assurance of supply chain circulation. Liu,
X [30]'s research shows that in agricultural product e-commerce, product quality, brand
image, e-commerce platform and logistics distribution have a significant positive impact on
customer satisfaction, and have an important impact on the sales of agricultural products.
Based on the research of many scholars, we found that in e-commerce, the quality and safety
of agricultural products are decisive factors for the sales of products. Through digital
information management, we can achieve coordinated development and the environment, and
form two virtuous circles in ecology and economy, the unity of the three major benefits of
economy, ecology and society. By studying the data in the development of green ecological
agriculture, and constructing a green ecological management system model by analyzing
these data, data mining, etc., the economic benefits of ecological agriculture can be improved.
The research on green ecological agriculture management is of great significance to the
development of ecological agriculture and the solution of various drawbacks and crises
brought by modern agriculture. However, in the current e-commerce sales, the safety and
quality of agricultural products cannot be presented to customers. Based on this, in our
research, we build an information-based digital management platform, which includes
developed languages, frameworks and database. In the digital information management
platform, we track and monitor the agricultural product information of green ecological
agriculture in Northeast China throughout the whole process, so as to ensure the safety and
quality of the agricultural products during the sale of the agricultural products on the e-
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commerce platform. In addition, we also discussed the economic benefits of this digital
information platform for green ecological agriculture.
Construction of information digital management platform
In order to better understand the situation of green ecological agriculture in Northeast
China, this chapter mainly introduces the development languages, development frameworks
and tools used in the electronic platform of agricultural products, and gives a brief
introduction to them according to the situation of green ecological agriculture in Northeast
China. The advantages and reasons for selection are analyzed one by one. These theories or
tools include: languages, frameworks, and databases.
Java languages introduction
(1) There are not many Java language features, and there is no need to consider issues
such as multiple inheritance, operator overloading, automatic forced conversion, etc.;
(2) Abstract the real green ecological agriculture by Java in Northeast China through
classes, represent agricultural products through objects, and extract common attributes and
behaviors between things through inheritance;
(3) Java can detect type errors in time in the process of compiling the electronic platform
for agricultural products, and can automatically recycle garbage so that the memory of the
electronic platform for agricultural products does not occupy much space;
(4) Java can perform language conversion in the virtual machines corresponding to
different agricultural product electronic platforms, and parse and run on different agricultural
product electronic platforms;
(5) Java can support the multi-threaded agricultural product electronic platform model to
ensure the synchronization between the agricultural product electronic platform threads.
This system uses Java7 to develop the electronic platform of green ecological
agricultural agricultural products in Northeast China. As of Java7, the information features of
the electronic platform for agricultural products have been reflected, annotated, generic,
and concurrency.
Framework construction principle
The
framework in the agricultural product electronic platform adopts a layered
structure, which consists of five well-designed electronic framework sub-modules, as
shown in Figure 1.
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Figure 1: The Framework of the Agricultural Products Electronic Platform
Any module in the agricultural product electronic platform can be used independently,
and can also be used in parallel with some modules of other agricultural product electronic
platforms. The five electron spring framework submodules are as follows:
(1) Bean container. The Bean container is the basis for the electronic Spring framework
to realize the IOC layered structure. By reading the XML file or by parsing the language of
the agricultural product electronic platform, it generates the agricultural product electronic
platform information of green ecological agriculture in Northeast China defined by Bean, and
fills it into the core in a container;
(2) Spring AOP module.This module extracts the agricultural product information from
the aspects in the business process of the agricultural product electronic platform, and
encapsulates those behaviors that are not related to the business logic of the agricultural
product electronic platform, but are required to be called by many functional modules in the
agricultural product electronic platform. Duplicate codes in the platform reduce the coupling
degree of the electronic platform of agricultural products;
(3) Spring DAO module.This module is not related to the specific situation of green
ecological agriculture in Northeast China.Through this module system, abnormal semantics in
the platform system can be identified;
(4) Spring Web module. Common development basic functions such as file uploading
and downloading in the agricultural product electronic platform, binding request parameters
to objects, etc. are included in this module.
(5) Spring MVC framework. This module is used to configure view parsing related to
green ecological agricultural products in Northeast China, and to define the priority of
processing.
database
Cord cage
AOP Instrument
Context
Beans
Web severData access
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The database supports the storage engine settings of various electronic platforms, and
there are different data types of storage methods within the electronic platforms, so that the
access speeds to the electronic platforms are different. In addition, the creation of monitoring
electronic platform big data will only be used for query, and will not be added, deleted or
modified. Since the addition of the database supports setting the storage engine at the table
level of the electronic platform, combined with the characteristics of the green ecological
agriculture in Northeast China, different storage engines can be selected for different
electronic platforms in a more targeted manner to optimize their performance.
Model Validation
With the popularization of various smart mobile devices, the promotion of agricultural
information and the promotion and sale of agricultural products can solve the problems of
difficulty in obtaining rural information, low commercialization, and unsalable commodities.
The functional test of this system is mainly based on black boxes. Testing is the main means.
Therefore, iterative training is very necessary in the underlying data of the agricultural
product electronic platform, and the model operation accuracy can be tested through iterative
training. The details are as follows:
(1) Accuracy. Precision is the proportion of positive classes that resolve to samples
identified as positive classes. The specific calculation process is as follows:
（1）
Among them, TP is a true example, and FP is a false positive example.
(2) Recall rate. Used to solve for the proportion of all positive class samples that are
correctly identified as positive classes. The specific calculation process is as follows:
（2）
Among them, FN is a false negative example. In the field of agricultural product sales,
Internet information technology should be fully utilized to help small farmers become the
main body of agricultural product e-commerce business, break the restrictions on trading
venues, reduce transaction costs, and reduce the number of agricultural products in
circulation.
(3) Accuracy is a metric used to evaluate classification models. Simply put, it is the
proportion of the total number of correct predictions by the model. The calculation process is
as follows:
（3）
Among them, TN is a true negative example. Apply Internet information technology to
market analysis, variety selection, intensive farming, and pest and disease analysis to create
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precision agricultural production, achieve fine management of the industrial chain, and
transform traditional agriculture into smart agriculture.
We compared the different accuracy comparison models, took into account the
background of the agricultural product electronic platform and other backgrounds, and
adopted appropriate algorithms for evaluation, and finally considered the accuracy rate. In
Figure 2, as the number of iterations increases, the training accuracy in the input agricultural
product information is also increasing. When the number of iterations is 50, the accuracy of
97.33% is reached, and then the accuracy tends to stabilize; the number of iterations is 25.
The second time, the test set accuracy reached 95.34%. As the number of iterations increases
to 50, the accuracy rises to 97.52%, which shows that our agricultural product electronic
platform has high prediction accuracy for the underlying data. The description of the data set
parameters is shown in Table 1.
Table 1：The relationship between iteration accuracy and number of interactions
Figure 2: Iterative Accuracy Graph
Number of interactions Iteration accuracy
12.5 90.55
25 95.34
37.5 96.59
50 97.33
62.5 97.35
75 97.41
87.5 97.48
100 97.52
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Results and Analysis
For the development of green ecological agriculture in China, the rural revitalization
strategy has greatly improved the level of regional agriculture. This improvement is related to
economic benefits and the structure of agricultural allocation. However, the substantial
development of agriculture is still a double-edged sword. With the rapid development of
China's regional agriculture, its consumption of energy, the squeezing and encroachment of
the environment and the pollution of the ecological environment have been put on the agenda.
This problem largely stems from the unreasonable management and regulation of the rapidly
developing agricultural infrastructure. Therefore, the demand and management of green
ecological agriculture in China is a top priority.
First of all, the conventional evaluation indicators of regional sustainable utilization of
agricultural resources can be used as the main evaluation indicators and guiding principles for
the demand and management of green ecological agriculture in China, which provides a solid
foundation for us to establish an effective management system. For the evaluation index
cluster of agricultural management in Northeast China, we can regard the index cluster as a
series of variables that are correlated and complementary, and have strong responsiveness to
the sustainable utilization of agricultural natural resources and agricultural socio-economic
resources of. The number of elements in the variable population is large, but they are all
basically continuous distribution, so they can form an indicator vector or indicator matrix. A
series of indicators formed by various digital information outputted by the spatial database.
We have built an evaluation system for the demand and management of China's green
ecological agriculture before, and introduced the construction principles and methods in the
digital system. Among them, several index systems stored in the index library include the
content of sustainable utilization of agricultural resources. In specific applications, they can
be called directly through the user interface of scientific engineering, and then input into the
evaluation model.
Specifically, the main structure of the digital information management platform of green
ecological agriculture we built is divided into variable layer, middle layer and evaluation
index layer, which is a superimposed and progressive layer structure design. The variable
layer includes the utilization rate of electronic agricultural products, per capita green area, per
capita desert land area, soil organic matter content and per capita water resources content. The
hidden environmental variables in the middle layer are determined as natural population
growth rate, desertification development rate, soil organic matter loss rate, water resource
decay rate, and vegetation index. For the final evaluation index layer variables, we chose the
sustainable development index and the green ecological index as the final comprehensive
evaluation index.
Influence of variable layer parameters of digital information management
platform
According to the collection of a large amount of relevant data in 2017, we have
continuously revised and learned the forecasting module in the digital information
management platform of green ecological agriculture, and used the digital information
management platform to analyze various data of the variable layer during 2017-2021. Data
collection and mining were carried out. This data collection and mining comes from multiple
sources of information such as provincial agricultural bureaus, environmental bureaus and
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local regional monitoring points in the Northeast region. After processing the data, the
platform retains data points that are useful for future evaluation metrics. The annual average
data collected from 2017 to 2021 were normalized after screening to facilitate subsequent
analysis and to build multiple regression curves. The results of the analysis are shown in
Figure 3. It is observed that the use index of electronic agricultural products, the per capita
greening index, the soil organic matter content index and the per capita water content index
all show an upward trend with the years, while the per capita desert land area index shows a
decreasing index. Among the related variables, one variable is regarded as the dependent
variable, and one or more other variables are regarded as independent variables, and a
statistical analysis method is used to establish a linear or nonlinear mathematical model
quantitative relationship between multiple variables and use sample data for analysis. The
overall trend of each variable parameter has a large change range from 2018 to 2019. It is
observed that compared with 2018, the use index of electronic agricultural products, per
capita greening index, soil organic matter content index and per capita water content index in
2019 are observed. Up 30.34%, 6.14%, 25.34% and 30.26% respectively. The index of per
capita desert land area decreased by 10.97%. This shows that during the period of 2018-2019,
the management and control of green ecological agriculture in Northeast China has achieved a
more significant effect. In the following 2019-2021 years, the changes of per capita greening
index, soil organic matter content index, per capita water content index and per capita desert
land area index tended to be stable, which indicates that the management of green ecological
agriculture in this region is in the realization of the underlying structure. After the
transformation, the government began to carry out stable development, which is conducive to
further evaluating the advantages and disadvantages brought about by the structural
transformation and providing guidance for subsequent development.
Figure 3 Changes of each index in the variable layer with years
Sustainable development assessment of green ecological agriculture
Then, we forecast the variable layer data for the period 2017-2021 after using the data
collected in 2017 to revise the learning of the forecasting module within the digital
information management platform of green ecological agriculture. Effective analysis data has
been obtained. In this section, we use the prediction module in the digital information
management platform of green ecological agriculture to analyze the output layer variables we
2017 2018 2019 2020 2021
0.2
0.4
0.6
0.8
Evaluation index
Year of collection
Index of e-Agricultural use
Index of green area per capita
Index of desert area per capita
Index of soil organic content
Index of water resources per capita
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care about. Among them, the change trend of the sustainable development index over the
years is shown in Figure 4. An unusual decrease in the Sustainability Index was observed
during 2017-2018, with a decrease of 0.08. This is inconsistent with the trend change results
of the variable layer in Figure 3.
Therefore, we judged and analyzed the results according to the data changes in the
middle layer. We found that the excessively large development area of farmland makes the
corresponding soil and water resources environment polluted to a certain extent, which
eventually leads to the reduction of the sustainable development index. And with the
improved measures, in 2019, the observed sustainability index increased by 0.013, compared
to the growth rate of 4.28% in 2018. This shows that the implementation of the adjustment
measures of control is feasible. From 2019 to 2021, the growth of the sustainable
development index also stabilized, at 0.317, 0.319 and 0.322, respectively.
Figure 4 Changes in the sustainable development index over the years
Green ecological assessment of green ecological agriculture
Finally, we use the prediction module in the digital information management platform of
green ecological agriculture to analyze the changes of the green ecological index with the
development year. The results are shown in Figure 5. A smaller increase in the green
ecological index was observed during 2017-2018, at only 2.36%. As can be seen from Figures
3 and 4, this period is a critical stage for structural and policy regulation. During the period
from 2018 to 2019, the green ecological index has been significantly improved, which is due
to the comprehensive results of the agricultural environment, the abundance of soil nutrients
and water resources in Figure 3, which are conducive to green and sustainable development. It
was observed that compared with 2018, the green ecological index from 2019 to 2021
increased by 5.94%, 8.58% and 12.87% respectively.
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Figure 5 Changes of Green Ecological Index with Years
Discussion
With the rapid development of China's regional agriculture, its consumption of energy,
the squeezing and encroachment of the environment and the pollution of the ecological
environment have been put on the agenda. This problem largely stems from the unreasonable
management and regulation of the rapidly developing agricultural infrastructure. Therefore,
the demand and management of green ecological agriculture in China is a top priority. This
provides a solid foundation for us to establish an effective management system. Aiming at the
evaluation index cluster of agricultural management in Northeast China, the main structure of
the digital information management platform of green ecological agriculture we built is
divided into variable layer, middle layer and evaluation index layer, which is a superimposed
and progressive layer structure design. We focus on the analysis of the variable layer and the
evaluation index layer. The conclusions are as follows:
(1) The overall trend of each variable parameter has a large change range from 2018 to
2019. Compared with 2018, the use index of electronic agricultural products, per capita
greening index, soil organic matter content index and per capita water content index increased
respectively in 2019 30.34%, 6.14%, 25.34% and 30.26%. The index of per capita desert land
area decreased by 10.97%. This shows that during the period of 2018-2019, the management
and control of green ecological agriculture in Northeast China has achieved a more significant
effect. In the following 2019-2021 years, the per capita greening index, soil organic matter
content index, per capita water resource content index and per capita desert land area index
tended to stabilize;
(2) The sustainable development index dropped abnormally during 2017-2018, with a
drop of 0.08. This is inconsistent with the trend change results at the variable level. This is
due to the excessive development of cultivated land, which pollutes the corresponding soil
and water resources to a certain extent, which ultimately leads to a decrease in the sustainable
development index. And with the improved measures, in 2019, the Sustainability Index rose
by 0.013, compared to 4.28% in 2018. This shows that the implementation of the adjustment
measures of control is feasible. From 2019 to 2021, the growth of the sustainable
development index also stabilized, at 0.317, 0.319 and 0.322 respectively;
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(3) During the period of 2017-2018, the growth rate of the green ecological index was
small, only 2.36%, because this period was a key stage of structural and policy regulation.
During the period from 2018 to 2019, the green ecological index has been significantly
improved, which is a comprehensive result of the improvement of the agricultural
environment, soil nutrients and water resources, which is conducive to green and sustainable
development. Compared with 2018, the green ecological index from 2019 to 2021 increased
by 5.94%, 8.58% and 12.87% respectively.
In the process of ecological compensation, the government should coordinate and
integrate ecological compensation funds, give unified leadership to ecological compensation
activities, coordinate management and operation, and establish a supervision mechanism to
make the process of ecological compensation open and transparent. Barriers, it is necessary to
transform the ecological compensation mechanism of a single element into a comprehensive
compensation mechanism centered on the entire region, and make overall planning and
coordinated promotion. In the selection of compensation objects, this paper has not yet
achieved accurate compensation, and only makes reasonable choices based on the local
conditions of the compensation area. Strengthen mass participation, pay attention to the will
of the masses before compensation, incorporate mass participation into the evaluation system
of ecological compensation implementation effect, increase mass participation, and better
play the positive role of public participation.
In the digital information management platform, we track and monitor the information of
green and ecological agricultural agricultural products in Northeast of China throughout the
process to ensure the safety and quality of agricultural products in the online sales process.
business platform. In addition, we also discuss the economic benefits of this digital
information platform for green ecological agriculture.
Data availability statement
The original contributions presented in the study are included in the article/
supplementary material, further in quiries can be directed to the corresponding author.
Funding
Research on the mechanism and value effect of product market competition on
technological innovation of enterprises. Natural Science Foundation of Hunan Province,
project number S2019JJQNJJ154
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Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.
https://doi.org/10.17993/3cemp.2022.110250.198-212
3C Empresa. Investigación y pensamiento crítico. ISSN: 2254-3376
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