Large-scale regional precision agricultural business financial data fusion system and method

Abstract

The invention relates to a large-scale regional precision agriculture business financial data fusion system and method, and discloses a large-scale multi-region precision agriculture business financial data fusion system and method. One-field-one-code operation management data is taken as a core, and a business and financial code is taken as a grasp for a business and financial fusion approval process. Business data is gradually converted into financial data after being subjected to module functions such as approval, reimbursement, exchange management or farming accounting in the system, a to-be-processed voucher is automatically generated by the system, general ledger information is finally generated after financial staff processes the to-be-processed voucher, and meanwhile the system supports report query of data of all branch modules. According to the invention, accurate operation can be realized, resource allocation can be optimized, and economic benefits can be improved. And agricultural operators can better understand the operation risk and make adjustments in time. The method is helpful for establishing an agricultural cooperative and a complete agricultural value chain, is helpful for improving the resource utilization efficiency, and realizes scientific management and sustainable development of agricultural production.

Description

Technical Field

[0001] The present invention relates to a system and method for integrating agricultural business and financial data, and in particular to a system and method for integrating precision agricultural business and financial data in a large-scale area. Background Art

[0002] In recent years, with the rapid development of science and technology and the deep adjustment of economic structure, agriculture, as the cornerstone of the national economy, has also shown an unprecedented speed in its modernization process. Under this background, agricultural mechanization and intensification have become the two core driving forces for agricultural development. The widespread application of agricultural mechanization has greatly improved agricultural production efficiency and reduced the labor intensity of farmers; and the implementation of agricultural intensification has achieved high quality and high efficiency of agricultural production by optimizing resource allocation and strengthening scientific and technological support. However, with the continuous advancement of agricultural modernization, a new challenge has also come along - how to achieve the deep integration of agricultural business and finance. As the two core elements of agricultural production, the integration of business and finance is not only related to the efficiency and benefits of agricultural production, but also to the long-term development and market competitiveness of agricultural enterprises. Therefore, applying business and financial integration to the agricultural field has become an important direction for current agricultural development. Specifically, it is necessary to organically integrate agricultural production, sales, management and other links with financial management through the application of information technology to achieve data sharing, process optimization and decision support. In this way, agricultural enterprises can more accurately grasp market trends, optimize resource allocation, and improve production efficiency, thereby achieving sustainable development of agriculture. In the article "Research on Financial Transformation in the Context of Digital Economy" published in the journal "Friends of Accounting" in 2021, Zhang Yingming proposed that with the continuous development of fields such as information technology and big data, they have gradually been widely used in the financial field, which makes it possible for enterprises to implement the financial management model of business-finance integration reform. At the same time, it also indicates that the business management model of the enterprise is more modernized and can better adapt to its future development needs. Liang Heng's "Research on the Construction of Financial Shared Center with Business-Financial Integration" published in the journal "Accounting Communications" in 2020 shows that the information system with finance as the core orientation has gradually failed to adapt to the development needs of enterprises. In the future, enterprises need to build more integrated platforms for the integration of business and finance, realize the mutual integration of business and finance, and implement a rapid response mechanism for changes in external and internal related factors, so as to reduce operating costs to a large extent and improve efficiency.

[0003] In the article "Construction of Business-Finance Integration Model Based on Joint Value Creation" published in the journal "Accounting Monthly" in 2020, Li Youhua, Zhu Yongjuan and others believe that although the financial sharing model has improved the degree of informatization of the enterprise and improved the efficiency of information transmission, it is still finance-oriented in essence and has certain limitations. Business-finance integration should be business-oriented, integrating business and finance, and connecting them to each other to maximize the value of the enterprise.

[0004] In the article "Application of the "Three-in-one" Full-process Business-Finance Integration Model in Jiangxi Communications Vocational and Technical College", published in the journal "Finance and Accounting" in 2019, He Meilan and Wang Ying proposed that the integration of business, finance and corporate value can comprehensively improve the progress, efficiency and effectiveness of the company's business management. In the article "A Brief Analysis of the Positive Effect of Building a Compound Talent Team through Talent Integration on Management Accounting Work" published in the journal "Financial World" in 2020, Huang Wei pointed out that the main difficulties for enterprises to implement business-finance integration are imperfect systems, fragmented information systems and lack of corresponding talents, and put forward corresponding suggestions. In the article "A Brief Analysis of Problems and Implementation Methods in the Business-Finance Integration Transformation" published in the journal "Finance and Accounting" in 2020, Yu Jing believes that factors such as professional talents, technological development, process streamlining and information construction of business-finance integration have a great impact on the effect of enterprise construction and implementation of business-finance integration. Summary of the invention

[0005] The problem to be solved by the present invention is to provide a system and method for integrating financial data of precision agriculture business in a large area.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] Technical solution 1:

[0008] A precision agriculture business financial data fusion system for large-scale areas, including a business unit, a financial unit and a statistical analysis unit;

[0009] Among them, the business units include one field one code module, farming activity module, and farming expense module;

[0010] The financial unit includes the business and financial code module and the expense approval module;

[0011] The statistical analysis unit includes a data statistics module and a financial early warning module;

[0012] The business unit obtains the main information data of each cultivated land area through the one-field-one-code module, and obtains agricultural-related production materials, budget, and time information and inputs them into the agricultural activity module to match the one-field-one-code information with the agricultural information, and approve agricultural activities; the expense approval module inputs the agricultural approval information into the agricultural expense module for processing based on the agricultural historical expense information and agricultural expense application information, and realizes the expense approval of agricultural applications; the statistical analysis unit collects and organizes various financial data through the data statistics module, and issues financial warnings to managers and users through the financial warning module.

[0013] Technical solution 2:

[0014] A fusion method applied to the data fusion system described in technical solution 1, comprising the following steps:

[0015] Step A1: Obtain the main information of the cultivated land area and construct a one-field-one-code system;

[0016] Step A2: Obtain agricultural application information, input the one-field-one-code information and the agricultural application information into the agricultural activity module for matching processing, and obtain agricultural approval information;

[0017] Step A3: Obtain historical agricultural expense information, input it into the agricultural expense module together with the agricultural approval information and agricultural expense application information, and perform agricultural expense approval operation. If it meets the approval requirements, it will be approved and go to step A4, otherwise it will be rejected.

[0018] Step A4: Obtain and organize quasi-financial data for analysis and early warning.

[0019] Further, the step A1 consists of the following specific steps:

[0020] Step A101: Acquire regional cultivated land information data and agricultural data. The regional cultivated land information data includes geographical location coordinates, land quality, and the person in charge of the plot; the agricultural data includes region, planting plan, planting time, seed variety, required sowing amount per mu, total seed usage, harvesting type, estimated yield per mu, and estimated agricultural cost;

[0021] Step A102: Generate an electronic map for each field using GIA technology;

[0022] Step A103: Associating the electronic map, agricultural data and regional farmland information data to generate a one-field-one-code for regional farmland agricultural activities.

[0023] Further, the step A3 consists of the following specific steps:

[0024] Step A301: Collect historical agricultural cost data, sort and classify them, and establish a multivariate linear model;

[0025] Step A302: Obtain the agricultural activity cost value according to the multivariate linear model, and determine whether the agricultural application has passed the preliminary approval; if it has passed the preliminary approval, send the approval to the financial unit through the internal interface and wait for the second approval of the management personnel; otherwise, the application is rejected;

[0026] Step A303: Manual secondary review and approval, detailed review and comparison of the expense application; consists of the following specific steps:

[0027] Step B1: Construct business and financial code to generate a unique business and financial code for agricultural business activities;

[0028] Step B2: Collect expense application, budget, and related contract financial information, associate this information with the business and financial code generated in B1, write the relevant information into the business and financial code, and then input it into the expense approval module for processing;

[0029] Step B3: Obtain preliminary approval information and business and financial code information for agricultural applications, analyze and compare expense applications based on detailed financial rules and approval standards; if approved, obtain electronic contract, electronic invoice, and expense report information, and update the business and financial code data; otherwise, reject the application.

[0030] Further, the step A4 consists of the following specific steps:

[0031] Step C1: Get the business unit field code information and the financial unit business code information, and input them into the data statistics module. Clean, classify and organize the data. Generate monthly, quarterly and annual data tables of contract revenue and expenditure, actual revenue and expenditure, and budget information for each field. Various forms can be generated through conditional screening to provide users and managers with clear and accurate data statistics results.

[0032] Step C2: Obtain historical farming business financial information and market standards. According to historical farming business financial information and market standards, formulate the average value, regional average value and threshold value of financial data.

[0033] Step C3: Get the statistical results of the data in C1 and the average value, regional average value, and threshold value of the financial data in C2. Judge the data to detect whether the current financial data exceeds the average value, regional average value, and threshold value. If it exceeds the set threshold, average value, or is significantly different from the regional average value, the early warning mechanism is triggered, and the early warning information is sent to users and managers through the system for early warning.

[0034] The beneficial effects of the technical solution of the present invention are:

[0035] 1. By integrating business and financial data, agricultural practitioners can more accurately understand the costs, revenues, and profits of each production link, which helps to achieve precise management, optimize resource allocation, and improve economic efficiency.

[0036] 2. Through comprehensive analysis of financial data, agricultural operators can better understand business risks and make timely adjustments. For example, through financial data analysis, factors that may affect agricultural production, such as market fluctuations, can be predicted, so that corresponding risk management measures can be taken.

[0037] 3. By sharing financial information, the integration of business and finance helps to establish agricultural cooperatives and a complete agricultural value chain. The cooperatives and various links in the agricultural chain can work together better to optimize resources and maximize benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0039] The following examples serve to illustrate the invention.

[0040] Example 1

[0041] A precision agriculture business and financial integration solution based on large-scale multi-region, including business unit, financial unit and statistical analysis unit;

[0042] The business units include one field one code module, farming activity module, and farming expense module;

[0043] The financial unit includes the business and financial code module and the expense approval module;

[0044] The statistical analysis unit includes a data statistics module and a financial early warning module.

[0045] The business unit obtains the main information data of each cultivated land area through the one-field-one-code module, and obtains the relevant production materials, budget, and time information of farming and inputs them into the farming activity module to match the one-field-one-code information with the farming information and approve the farming activities. Finally, based on the farming historical expense information and farming expense application information, the farming approval information is input into the farming expense module for processing to realize the expense approval of the farming application. Specifically:

[0046] Step A1: Build a one-field-one-code system to obtain the main information of the cultivated land area;

[0047] Step A2: Obtain agricultural application information, input the one-field-one-code information and agricultural application information obtained in A1 into the agricultural activity module for processing, and obtain agricultural approval information;

[0048] Step A3: Obtain historical agricultural expense information and agricultural expense application information, input the agricultural approval information, historical agricultural expense information and agricultural expense application information obtained in A2 into the agricultural expense module for processing, and conduct preliminary expense approval on the agricultural application;

[0049] Step A4: Obtain and organize quasi-financial data for analysis and early warning.

[0050] Step A1 is specifically as follows:

[0051] Step A101: Acquire regional cultivated land information data and agricultural data. The regional cultivated land information data includes geographical location coordinates, land quality, and land plot person in charge; the agricultural data includes region, planting plan, planting time, seed variety, required sowing amount per mu, total seed usage, harvesting type, estimated yield per mu, and estimated agricultural cost;

[0052] Step A102: Generate an electronic map for each field using geographic information system technology software;

[0053] Step A103: Associating the electronic map, agricultural data and regional farmland information data to generate a one-field-one-code for regional farmland agricultural activities.

[0054] Step A3 is specifically as follows:

[0055] Step A301: Collect historical agricultural cost data, sort and classify them, and establish a multivariate linear model;

[0056] Step A302: Obtain the farming activity cost value based on the multivariate linear model and determine whether the farming application has passed the preliminary approval.

[0057] Step A303: If the application passes the preliminary approval, it will be sent to the financial unit through the internal interface and wait for the second approval by the management personnel. Otherwise, the application will be rejected.

[0058] The finance unit generates a unique financial identification code for each business activity through the business code module, and collects relevant expense application, budget, and contract information and inputs it into the expense approval module. In the expense approval module, manual secondary approval is performed to conduct detailed review and comparison of expense applications. Specifically:

[0059] Step B1: Construct business and financial code to generate a unique business and financial code for agricultural business activities.

[0060] Step B2: Collect expense application, budget, and related contract financial information, and associate this information with the business and financial code generated in step B1, write the relevant information into the business and financial code, and then input it into the expense approval module for processing.

[0061] Step B3: Obtain the preliminary approval information of the agricultural application and the business and financial code information in step B2. The management personnel will conduct in-depth analysis and comparison of the expense application based on the detailed financial rules and approval standards to make reasonable expense approval decisions.

[0062] Step B4: If approved, obtain the electronic contract, electronic invoice, and expense report information, and update the business and financial code.

[0063] The statistical analysis unit collects and organizes various financial data through the data statistics module, and issues financial warnings to managers and users through the financial warning module. Specifically:

[0064] Step C1: Get the business unit field code information and the financial unit business code information, and input them into the data statistics module. Clean, classify and organize the data. Generate monthly, quarterly and annual data tables of contract revenue and expenditure, actual revenue and expenditure, and budget information for each field. Various forms can be generated through conditional screening to provide users and managers with clear and accurate data statistics results.

[0065] Step C2: Obtain historical farming business financial information and market standards. According to historical farming business financial information and market standards, formulate the average value, regional average value and threshold value of financial data.

[0066] Step C3: Obtain the statistical results of the data in step C1 and the average value, regional average value, and threshold value of the financial data in step C2. Judge the data to detect whether the current financial data exceeds the average value, regional average value, and threshold value. If it exceeds the set threshold, average value, or is significantly different from the regional average value, the early warning mechanism is triggered, and the early warning information is sent to users and managers through the system for early warning.

[0067] At present, the technical solution of the present invention has been piloted, and a user survey has been conducted in a small range. The survey results show that user satisfaction is high, and preparations for the transformation and promotion of the results have begun. At the same time, a survey on intellectual property risk warning has been carried out.

Claims

1. A precision agriculture business financial data fusion system for large-scale areas, characterized by: It includes business unit, financial unit and statistical analysis unit; The business units include one field one code module, farming activity module, and farming expense module; The financial unit includes the business and financial code module and the expense approval module; The statistical analysis unit includes a data statistics module and a financial early warning module; The one-field-one-code module is used to respond to QR code scanning operations and provide the main information data of the corresponding cultivated land area. The main information data includes agricultural-related production materials, budget, and agricultural time information; the agricultural activity module matches the received main information data with the agricultural information data and approves agricultural activities; the agricultural expense module is used to approve agricultural historical expense information and agricultural expense application information, and input the agricultural approval information into the agricultural expense module for processing to realize the expense approval of agricultural applications; the statistical analysis unit collects and organizes financial data including electronic contracts, electronic invoices, and reimbursement form information through the data statistics module, and issues financial warnings to managers and users through the financial warning module.

2. A fusion method applied to the data fusion system according to claim 1, characterized in that: The following steps are involved: Step A1: Obtain the main information of each cultivated land area, and construct a corresponding one-field-one-code based on the main information of the cultivated land area; Step A2: Obtain agricultural application information, input the one-field-one-code information and the agricultural application information into the agricultural activity module for matching, and obtain agricultural activity approval information; Step A3: Obtain historical farming expense information, input it into the farming expense module together with the farming activity approval information and farming expense application information, and conduct farming expense approval operation. If it meets the preset approval requirements, it will be approved and go to step A4; otherwise, it will be rejected; Step A4: Obtain and organize quasi-financial data for analysis and early warning.

3. The data fusion method according to claim 2, characterized in that: The step A1 consists of the following steps: Step A101: Acquire regional cultivated land information data and agricultural data. The regional cultivated land information data includes geographical location coordinates, land quality, and land plot person in charge; the agricultural data includes region, planting plan, planting time, seed variety, required sowing amount per mu, total seed usage, harvesting type, estimated yield per mu, and estimated agricultural cost; Step A102: Generate an electronic map for each field using ArcGIS software based on Geographic Information System (GIS) technology; Step A103: Associating the electronic map, agricultural data and regional farmland information data to generate a one-field-one-code for regional farmland agricultural activities.

4. The data fusion method according to claim 2, characterized in that: The step A3 consists of the following steps: Step A301: Collect historical agricultural cost data, clean and pre-process the data, and establish a linear regression model for agricultural cost; Step A302: Obtain the agricultural activity cost value according to the linear regression model, and determine whether the agricultural application has passed the preliminary approval; if it has passed the preliminary approval, send the approval to the financial unit through the internal interface and wait for the second approval of the management personnel; otherwise, the application is rejected; Step A303: Manual secondary review and approval, detailed review and comparison of the expense application; consists of the following steps: Step B1: Construct business and financial code to generate a unique business and financial code for agricultural business activities; Step B2: Collect expense application, budget, and related contract financial information, associate this information with the business and financial code generated in B1, write the relevant information into the business and financial code, and then input it into the expense approval module for processing; Step B3: Obtain preliminary approval information and business and financial code information for agricultural applications, analyze and compare expense applications based on detailed financial rules and approval standards; if approved, obtain electronic contract, electronic invoice, and expense report information, and update the business and financial code data; otherwise, reject the application.

5. The data fusion method according to claim 2, characterized in that: The step A4 consists of the following steps: Step C1: Obtain the business unit field-code information and the financial unit business code information, and input them into the data statistics module; clean, classify and organize the data; generate monthly, quarterly and annual data tables of contract-based income and expenditure, actual income and expenditure, and budget information for each field. Various forms can be generated through conditional screening to provide users and managers with clear and accurate data statistics results; Step C2: Obtain historical farming business financial information and market standards. According to historical farming business financial information and market standards, formulate the average value, regional average value and threshold value of financial data; Step C3: Obtain the statistical results of the data in C1 and the average, regional average, and threshold of the financial data in C2; judge the data to detect whether the current financial data exceeds the average, regional average, and threshold; if it exceeds the set threshold, average, or is significantly different from the regional average, trigger the early warning mechanism and send the early warning information to users and managers through the system for early warning.

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