Simulation of agricultural digital economy development and policy support system based on resource sensitivity index

The development of science and technology has also led to the progress of rural digital economy. In China, the construction of new countryside has always been an important part of economic development. The application of big data technology in rural areas can not only promote the development of rural economy, but also improve the results of environmental protection. Using the matching coefficient method of agricultural water and soil environmental characteristics, it match the rural water and soil environmental characteristics and the impact of the water environment in the autonomous region. An indispensable driving force in the process, agricultural digitalization will update the rural economic development model, weaken the barriers to the dual structure of urban and rural areas, stabilize agricultural production, promote sustainable rural development, and promote rural economic development. The current government policy support, the urgent need for new development momentum for rural economic development, and the advancement of changes in the lifestyle of urban and rural residents are promoting high-quality development of the rural economy. At the same time, we are facing challenges such as the lack of local network coverage, the lack of logistics, sales systems, and the lack of talents with digital skills. Expanding rural network coverage, optimizing rural logistics and distribution, promoting the development of agricultural big data, and strengthening the development of digital human resources are important ways for agricultural digitalization to promote rural economic development. The research focus of this paper is the parallel development of rural digital economy and environmental protection under the big data economy, and its application in the current agricultural development can achieve a win–win situation between man and nature.


Introduction
The characteristics of the agricultural water and soil environment, that is, the water and soil resources used for agricultural production, are the basic resources for regional economic development and the main limiting factor for food production [1].The world's agricultural area is only about 0.7 hectares, accounting for 37.9% of the world's per capita land area; of which irrigated arable land only accounts for 20% of the total arable land, and its productivity accounts for 40% of the global agricultural productivity.When analyzing the characteristics of the agricultural water and soil environment in each region of the autonomous region, it was found that the agricultural level here is high or low and the distribution of agricultural water and soil resources in the autonomous region is very uneven [2].The reconciliation of the agricultural water and soil environmental characteristics has a great impact on agricultural production, and the characteristics of the rural water and soil environment are obvious.It presents a serious "congenital de ciency", with scarce rainfall, large evaporation, and complex terrain [3].Proposing a reasonable improvement plan and drawing the conclusion that optimizing the characteristics of the rural water and soil environment can promote economic and social changes [4][5].For example, how to protect the fragile ecological environment of the region while promoting economic development, and how to maximize the harmonious development between man and nature by using limited resources are the key points to promote the transformation of regional development from high speed to high quality [6].In December 2019, the Chief Engineer for Industry and Commerce focused on promoting high-quality development, vigorously developing the digital economy, and clarifying the fundamental direction of the transformation of business promotion methods.Based on the latest network and big data, an important part of production is another economic form where the digitalization of agriculture and the real economy continue to merge [7].Agricultural digitalization has a powerful leading role.It is continuously integrated with agricultural technology and information, and is continuously improved in the process to make it more complete, which can make rural economy produce certain change.This shows that big data can promote the upgrading of rural industries [8].In the context of increasing emphasis on digital-driven rural economic development, it is necessary to clarify the role mechanism of digital-driven agriculture in rural economic development, clarify its proportion in rural economic development, and fully clarify that digitaldriven agriculture opportunities and challenges in the development of rural economy, understanding the economic development there, and discovering and supporting the realization path of rural economic quality development, has high theoretical value and practical signi cance [9].

The matching coe cient method of agricultural water and soil resources
The offset angle and the center of gravity distance re ect the development process and spatial differences of the research variables in a certain area.The distance between the two attributes of the center of mass model and the offset angle represent the spatial imbalance between the two attributes.
The barycentric coordinates of the factor at time t ( Xt, Yt) can be expressed as: 1 2 Among them, v ti refers to the attribute value of subunit i at time t.
The following formula can be used to calculate the moving direction of the center of gravity: In the formula,  represents the offset angle of time t2 relative to the center of gravity of t1, respectively.k = 0, 1, 2, θ(-180°, 180°), the counterclockwise direction in this article is set to the positive direction, and the positive east direction is set to 0°. 4 5 6 Among them, SMIWL_Fi represents the spatial mismatch index between the agricultural irrigation water consumption and the cultivated land area in the i district, and SMIWL_Ei represents the spatial mismatch index between the agricultural irrigation water consumption and the effective irrigated area in the i district.Wi, Fi, Ei respectively represent the agricultural irrigation water consumption, arable land area and effective irrigation area of the i-th county.Represents the sum of the spatial mismatch index of agricultural irrigation water consumption and cultivated land (effective irrigation area) in the autonomous region.The higher the spatial mismatch index, the better the agricultural irrigation effect per unit area, that is, the more water consumption, that is, the more water and the less land; the smaller the spatial mismatch index, the less the irrigation per unit area of arable land, which means more land and Less water [10].In this paper, the natural breakpoint classi cation method was chosen and sensitivity index was introduced to explore the potential relationship between water and soil resources [11][12].

Application framework of agricultural digital resources
With the advent of the era of big data, people living on the Internet can no longer do without various network resources.As the amount of information increases, users need to be able to obtain necessary knowledge and information from massive amounts of data in a timely manner.The knowledge acquisition database analyzes the relationship between the collected data in detail to identify the types of user needs.More importantly, in the knowledge service process, users not only need the data itself, but also need to pay attention to the high value of data resources [13].At this stage, library knowledge services need to combine mature machine learning algorithms to deepen the value of resources and form a push-type knowledge service.To solve the current problem of inactive library knowledge services.
Taking into account the problems faced by the National Agricultural Library and the deep mining and resource analysis in the big data environment, this paper designs an architecture system based on Hbase + Spark, and loads the data to be processed at a high speed [14].The data platform combines the highspeed storage and calculation of Spark The ability is combined with related data mining model algorithms for in-depth data analysis, as shown in Fig. 1: The agricultural digital resource storage framework system based on big data technology designed by this laboratory is based on the storage framework and imported into the Spark big data platform through e cient data collection at the data storage layer.The ultimate goal of using big data solutions is to improve the use and e ciency of digital resources [15].After saving the resources, design, analyze and mine functional components as needed.The business processing layer combines Spark's proprietary machine learning algorithm library or encapsulates custom data mining algorithms and calculations on the Spark platform according to user needs.After processing and storage, it supports related functions such as service level visualization, predictive analysis, and knowledge push.It can dig deeper into the value of the resources in the user's eld of interest, as well as their knowledge search and knowledge service skills in the digital library.

Evaluation indicators for the development of rural digital economy
The development indicators of rural digital economy constructed in this paper are shown in Table One is the application of information technology such as the Internet of Things in agriculture.This indicator is used to measure the degree of development of the Internet of Things in rural areas.The second is the number of agricultural and rural digital innovation centers to build agricultural and rural digital innovation bases to serve the development of agricultural and rural digital economy.This indicator measures the environmental advantages and disadvantages of the development of digital industries in rural areas of the region.The third is digital product consumption and information services, which are used to measure the consumption of digital products by local residents.The fourth is to measure the number and scale of local online payments for third-party rural party payments and comprehensive nancing.

Results
According to statistical data, the average arable land area, effective irrigation area and irrigation water rate of the autonomous region from 2010 to 2020 are calculated, and the distribution characteristics of water and soil resources in the autonomous region are analyzed.As shown in Fig. 2, except for individual counties in the north, due to the area under the jurisdiction of this city Or the area of zoning, the area of arable land and the effective irrigation area are small.The large area of arable land and effective irrigated area in the county are mainly concentrated in the central and northern regions.In contrast, most of the eastern and southern counties have larger areas, but the proportion of arable land in effective irrigated area is low (< 5%).Counties with a higher proportion of irrigation water are divided into northern, central, and western regions.The Yellow River irrigation is the main area.
As shown in Table 2, the change of regional water and soil resources can be discussed by summarizing the offset Angle and distance: (2) The structural impact of water use has an adverse effect on the growth of agricultural output value in the autonomous region.The cumulative effect is 1.362 billion yuan.The proportion of agricultural water in the total water consumption of the autonomous region has dropped from 0.92 in 2010 to 0.85 in 2020.2017 The national agricultural water consumption occupies 62.3% of the total water consumption.The agricultural water consumption of the autonomous region is much higher than the national average.The utilization e ciency of agricultural water resources is low, and the proportion has declined.Therefore, reducing the proportion of agricultural water consumption is still the direction of water structure adjustment in some autonomous regions.
(3) Cumulative impact of water stress shows that from 2010 to 2020, the growth rate of agricultural production in the autonomous region showed a volatile downward trend.In 2011, the cumulative impact of water stress showed a downward trend.The cumulative value changed in 2014.When it reaches a positive value, it quickly drops to a negative value.The water resource pressure index is affected by the total amount of water used and the total amount of water resources.The in uence of pressure also shows a uctuating trend.The cumulative value of the pressure effect of water resources during the entire study period is 2.816 billion yuan, which has a negative effect on the growth of agricultural output value.
As shown in Fig. 3: As shown in Table 3, the coordination degree of water and soil resources, agriculture and rural economyecological environment system can be divided into 10 levels: According to the above research methods, the autonomous region's agricultural water and soil resource system, agricultural and rural economic system, and ecological environment The environmental system has carried out a comprehensive index calculation.The analysis result is shown in Fig. 4: From 2010 to 2020, the comprehensive evaluation index of each subsystem has shown a signi cant upward trend.The agricultural/rural economic evaluation index has the largest increase, from 0.062 in 2010 to 0.865 in 2020.The 2010 agricultural/rural economic system evaluation index is signi cantly lower than that of agriculture the evaluation index of water and soil resource system and ecological environment system, the development of agriculture/rural economy is slightly lagging behind.With the rapid development of the agricultural and rural economy, the gap between the comprehensive index and the other two subsystems has gradually narrowed.It achieved rapid growth in 2015 and has steadily expanded since then.The Ecosystem Comprehensive Score Index has risen from 0.337 to 0.716 over the past 11 years, the smallest increase.The comprehensive rating index of the ecological environment system was the highest before 2013, but since 2015, the comprehensive rating index has been lagging behind the agricultural water and soil resource system and the agricultural and rural economy.
Table 4 shows the coupling calculation results of system water and soil resources-agriculture-ecologyenvironment from 2010 to 2020:

Good coupling and coordination type
During the assessment period, the comprehensive evaluation index of agricultural water and soil resources, agricultural and rural economy and ecological environment system has been continuously increased from 0.2199 in 2010 to 0.7898 in 2020.The agricultural water and soil resources system, agricultural and rural economic system and ecological environment system have been at a relatively high level.In 2010, the subsystems were in a running-in period, but the coupling degree reached 0.8025.Since 2011, the degree of coordination and coupling between the subsystems has increased, and the degree of coupling has been further improved.In the large-scale agricultural system, the interaction between the various subsystems is constantly strengthened, and the connection is constantly improved.From the perspective of coupling and coordination, the large-scale system is in the transitional adaptation stage from 2010 to 2020, and the type of coupling adaptation is on the verge of imbalance and decline.In 2014, it entered the stage of adaptation and development, and the level of clutch adaptation continued to improve.During the 11-year study period, the autonomous region's agricultural and rural economyecological environment large-scale agricultural water and soil resource system gradually changed from "high coupling-low adjustment" to "high coupling-high adjustment", and system development became more coordinated and orderly.
The paired coupling and regulation characteristics of agricultural water and soil resources-agricultureecological environment of the system can further prove the interaction between different subsystems.
The speci c results are shown in Table 5.

Correlation analysis of the development of agricultural digital economy
The inevitable trend of the development of agricultural and rural informatization in the new era of digital village construction is the inevitable trend of rural revitalization and the inherent need of strategy.The national digital village strategic plan emphasizes all areas of production, operation, management and services in the overall idea of building basic data resources, Internet infrastructure and other digital environments.The close relationship between global agricultural development boundaries and concerns can become the long-term development of agriculture and rural areas in the future.Figure 6 shows the overall framework of the current digital village construction, responding to the main mission of China to promote the construction of digital villages.
On the one hand, most of the rst-level indicators in the above-mentioned foreign digital economy indicator system are at several levels such as allowable framework conditions, technology applications, economic performance, and service inputs.On the other hand, domestic research from the perspective of new economy and new industries also uses the input-output framework to evaluate the development level of smart cities and smart industries, to understand the role of input-output in indicator design, and to design rural areas from the perspective of input-output Digital economy indicators integrate the content of digital industrialization and industrial digitization into the digital economy, service value and dynamics, fully embody the power of agricultural and rural digital industrialization in the development of agricultural and rural areas, digital empowerment logic, and integrate basic environmental support functions.

Research on Big Data Processing Technology of Agricultural Economy
Agricultural information big data contains massive amounts of data, leading to diversi cation of data sources and diversi cation of data structures.The use of big data to reduce rapid changes, redundancy and noise reduction, data storage and technology integration characteristics, analysis of agricultural planting and breeding and other classi cation information processing intentions, development An intelligent analysis system for the collection, transformation and classi cation of agricultural information big data, according to speci c strategies to remove usage or misinformation, classi es the remaining information data, forms different types of databases, and automatically generates source data, data collection methods and environments And other background information.
Develop a decision-making ontology system based on the domain object relationship model, and build a data fusion model to integrate small agricultural experts with large data sets to form a huge multivariate data set, which can accurately determine the relationship between data sets based on the decision model.
In this way, the data system structure of different agricultural type decision ontology constructed.In such large-scale agricultural decision ontology, data can be entered and extracted at any time as needed, and the semantics can be continuously and accurately derived based on the chain relationship, as shown in Fig. 7.
The main focus of the R&D task is: (1) Create an ontology for agricultural data collection through advanced data integration, data association, and representation of coverage.Due to the large geographic differences and complexity of agriculture, relevant information and data also show geographic areas.
This makes the collection of industry information data sets very extensive, and it is more complicated, cumbersome and di cult to collect.Therefore, one of the key technologies to better complete the intelligent big data decision-making ontology is to build a rich, diverse and e cient relational ontology technology for agricultural data sets.(2) Integration of agricultural information intelligent decisionmaking models in the context of big data: Many agricultural information decision-making models already exist in today's agriculture, most of which are more practical but with more uni ed intelligence.Therefore, intelligent decision-making models related to agricultural informatization big data the integration is to analyze these decision-making models, and nd the relationship between them and many situations such as human-computer interaction through model parameter analysis.The system has intelligent features such as real-time response, pre-action response, interactive description, and e cient processing.. (3) Extraction and analysis of intelligent decision-making results based on agricultural knowledge combined with big data: From the perspective of agricultural information data, through the development and progress of advanced data processing technology and re ned data mining technology, agricultural big data processing and analysis needs, agricultural knowledge base, The transformation and representation of knowledge information, data exchange and the realization of interoperability in related elds must be combined.The rst step in agricultural information analysis and decision-making is to perform intelligent preliminary operations on relevant information, store the captured results in the knowledge base, use knowledge base technology to capture the preliminary results, and perform correlation adjustment and analysis.

Opportunities and Challenges for the Development of Agricultural Digital Economy
local e-commerce.Local e-commerce is based on the latest information technology such as computers, centralizes management and operation, optimizes the products and services, and commerce of local information service business, forms a consortium with various regions and industries, and uses non-cash online payment to sell products or purchases the development of products and e-commerce has greatly improved the e ciency of the ow of information, funds, products and businesses between urban and rural areas.In the past agricultural product trade market, the scale of agricultural product trade was small and relatively scattered, and transaction information was greatly affected by region and time, and it was di cult to meet the needs of various consumers.At the same time, the new agricultural product circulation system is based on the existing agricultural product circulation system, providing accurate and comprehensive agricultural product information and agricultural product distribution channel information for rural economic development, providing agricultural product sales and consumption positioning information, shortening the circulation of agricultural products, and reducing sales costs.
(1) The rural network coverage rate is not high.According to the 46th statistical report of the Internet Information Center in September 2020, the rural Internet penetration rate in the rst half of 2020 was 52.3%, 24.1 percentage points lower than the urban Internet penetration rate.The sparse construction of local network base stations is an important reason for poor local network coverage.
(2) The logistics distribution system is not perfect.Rural logistics is the general term for a series of connections between production and farmers' lives, such as transportation, loading and unloading, packaging, processing and storage.At present, the local logistics distribution system is restricted and affected by many factors, and its development is relatively slow: First, the rural railway, highway, aviation and other transportation facilities are relatively weak.Although the attention of rural road facilities has increased in recent years, the number of rural roads is small.Problems such as deteriorating road conditions and insu cient after-care protection still exist.The second is that the local express logistics distribution network is sparse and the network distribution points are not fully covered.Farmers cannot use the convenient and fast express services like urban residents.The goods that farmers buy online rarely reach them directly.Most of the products are in the city.The farmer picks it up.The rural area is vast and sparsely populated, the geographical area is large, and the villages are not centralized.Rural logistics has the characteristics of "long logistics routes and low consumption".The logistics costs are high and it is di cult to directly deliver to farmers.The substantial increase in costs has led to a decline in the pro tability of local e-commerce, hindering the development of local e-commerce, and adversely affecting the development of local economic quality.
(3) The training system is not perfect.At present, most of the e-commerce courses held in rural areas are theoretical, and there are few e-commerce courses that combine lectures, eld trips and simulated operations.Most training courses are mainly based on lectures, and the effect is not good.Although the number of people with digital skills in rural areas has increased signi cantly compared to a few years ago, they are still far from expectations and are closely related to the pro t orientation of capital.If digital talents choose to work in well-equipped developed cities or return to their hometowns or rural areas, opportunity costs will be incurred.If digital talents decide to stay in the countryside to work, it is worth losing their wages to work in the city.Human reason unconsciously drives talents to work in places with low opportunity costs and high wages.When choosing between urban and rural areas, most people with digital literacy will be attracted by more job opportunities, fewer opportunity costs, and higher wages in cities.Therefore, rural areas lack human resources with digital capabilities.They focus on agriculture and have few data personnel.In addition, from the perspective of farmers, farmers' digital awareness is low, digital information training is not perfect, and professional agricultural digital information skills are lacking, making agricultural digital information actually productive and di cult to transform and use.This makes it di cult to apply to agricultural production.

Strategies for high-quality development of agricultural digital economy
Network coverage: Network operators need to actively look for partners from all parties and attract investment through various methods.The government also needs to adopt a policy of prioritizing the construction of local networks, subsidizing network expenses for farmers in poor areas, reducing farmers' online costs, and building network base stations The location selection problem at the time will also affect the local network coverage.Due to the high and remote location of local network base stations, the construction of network base stations requires speci c analysis of speci c conditions.In scienti c and reasonable site selection, it is necessary to increase network coverage while ensuring network stability and smoothness.

5.
In this article, we studied an autonomous prefecture, analyzed the physical geography of the region, and created an application framework for the large agricultural digital resource database.Through in-depth analysis of regional resources, environment and economic development, it is found that the uneven development of arable land hydraulics and agricultural water-saving development has led to different development trends in the matching degree between the irrigation water consumption and the effective irrigation area of each district and county.The overall effect of agricultural water use e ciency is a positive driving force for the growth of agricultural output value, and the impact of water use structure will have an adverse effect on the overall growth of agricultural production in the autonomous region.
Digital agriculture research under the background of big data has seen the development and change trend of the agricultural big data service market in the future.,The process of constructing and developing agricultural digital in a big data environment is long and arduous.According to previous development models and plans, it is no longer possible to develop old concepts in the current environment.However, in the process of practice, it can be found that there are still some shortcomings in the level of science and technology in rural areas, and the application of big data in the development of digital economy will be subject to certain limitations.Therefore, relevant staff should combine the actual situation of rural areas and the actual needs of farmers to carry out their work, so that digital technology can be truly applied to people's lives and environmental protection.

Declarations
Compliance with Ethical Standards

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Figure 1 The
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Figure 4 Comprehensive
Figure 4

Table 2
Moving direction and distance of the gravity center of agricultural water and soil resources in an autonomous regionCounties with lower water consumption for agricultural irrigation are also mainly distributed in the east and south.With the exception of individual counties, the effective irrigation area of most counties has increased to varying degrees, and irrigation water consumption has decreased.In general, the development of arable land irrigation and agricultural water source protection is unbalanced, resulting in different degrees of correspondence between irrigation water consumption and effective irrigated area in In the 11 years from 2010 to 2020, with the exception of 2017 and 2020, the overall effect of agricultural water use e ciency has a positive effect on the growth of agricultural output value.With the development of the economy and society and the improvement of the level of science and technology, the autonomous region's agricultural e ciency and water-saving work has been continuously improved, and the agricultural unit water output has continued to increase.In 2010, the unit output of agricultural water in the autonomous region was 1.69 yuan/cubic meter, and by 2020, the agricultural water production will increase to 3.05 yuan/cubic meter, an average annual increase of 8.
03%.The positive driving effect of agricultural water-saving effects is the largest, with a cumulative effect value of 8.917 billion yuan, which is the most important factor in increasing the value of agricultural output.Limited by the extent of the bypass of the Yellow River, the autonomous region has always attached importance to water resources protection and improved agricultural water use e ciency, re ecting the development of agricultural watersaving technologies and the improvement of management levels.Continue to promote the use of highe ciency water-saving irrigation projects such as sprinkler irrigation and drip irrigation, continue to use rice irrigation and other technologies combined with water price adjustments and water-saving advertisements and other economic control measures to further improve e ciency.The agricultural water consumption of the autonomous region is the future arable land of the autonomous region and is watersaving.Development leads the way.

Table 5
Pairwise coupling degree and coupling coordination degree of agricultural water and soil resourcesagricultural rural economy-ecological environment system Judging from the overall trend chart of the agricultural digital economy development index from 2016 to 2020, the agricultural digital economic development in the agricultural digital economic zone and the rural digital economic zone continue to develop steadily.The South Coast Economic Zone is gradually catching up, and the Southwest Special Economic Zone and the North Coast Special Economic Zone are gradually catching up.It is close to the three major special economic zones of the East Coast Special Economic Zone, and the lowest level of development is the Northeast Special Economic Zone and the Great Northwest Comprehensive Economic Zone.As shown in Fig.5.