A structured agricultural public data set pricing method, device and equipment

Abstract

This invention relates to the field of data processing, and provides a method, apparatus, and equipment for pricing structured agricultural public datasets. The method includes: acquiring a structured agricultural government dataset to be priced, and determining the dataset's level based on a preset data classification system; selecting an appropriate pricing model to calculate the price based on the dataset's level, and outputting the pricing result; wherein the data classification system includes at least three levels: public welfare level, government collaboration level, and value-added service level; the pricing models include: a zero-price and fiscal compensation model for public welfare level data, a cost-plus pricing model for government collaboration level data, and a scenario-based value-sharing pricing model for value-added service level data. This invention solves the problem in existing technologies where pricing models are disconnected from data levels, making differentiated and precise pricing impossible, and achieves the goal of automatically matching the appropriate pricing model based on the data level.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method, apparatus, and equipment for pricing structured agricultural public datasets. Background Technology

[0002] With the deepening development of the digital economy, data has become a key factor of production. In the agricultural and rural sector, a large amount of structured government data involving production, circulation, and governance, such as farmland quality, meteorological monitoring, and production and sales early warning, contains enormous value. To promote the legal and efficient circulation and value release of data elements, it is necessary to establish a reasonable pricing mechanism for this data.

[0003] Currently, there are some common methods or models in the industry for data pricing, such as cost-based pricing and market supply and demand-based pricing. However, these existing technologies have significant limitations when applied to structured agricultural public datasets with distinct industry characteristics.

[0004] Existing technologies typically employ a "one-size-fits-all" pricing model, failing to fully consider the inherent regionality, timeliness, and deep integration with the agricultural production cycle of agricultural data. This makes it difficult for existing pricing models to accurately adapt to various specific application scenarios in agriculture and rural areas, and to flexibly and reasonably price data based on its actual use value, application environment, and dynamic changes. Consequently, the pricing results are severely disconnected from the economic value and social benefits of the data itself. Summary of the Invention

[0005] This invention provides a structured pricing method, apparatus, and equipment for agricultural public datasets, which solves the problem in the prior art where the pricing model is disconnected from the data level, making it impossible to achieve differentiated and accurate pricing, and realizes the goal of automatically matching the corresponding pricing model based on the data level.

[0006] This invention provides a pricing method for structured agricultural public datasets, comprising the following steps: Obtain a structured agricultural government data set to be priced, and determine the level of the data set according to a preset data classification system; Based on the level to which the dataset belongs, select the appropriate pricing model to perform pricing calculations and output the pricing results; The data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level. The pricing models include: a zero-price and fiscal compensation model for public welfare and inclusive data, a cost-plus pricing model for government collaboration data, and a scenario-based value-sharing pricing model for value-added service data.

[0007] According to the present invention, a structured pricing method for agricultural public datasets is provided, wherein the scenario-based value-sharing pricing model is calculated based on the following four dimensions: cost dimension, used to determine the baseline cost of the dataset; value dimension, used to quantify the contribution value of the dataset to agricultural production activities through a machine learning model; scenario dimension, used to adjust the pricing coefficient according to the differences in application scenarios; and dynamic dimension, used to dynamically adjust the pricing coefficient based on real-time acquired external fluctuation factors; the final price is obtained by combining the base of the cost dimension and the coefficients of the other three dimensions.

[0008] According to the present invention, a pricing method for structured agricultural public datasets includes cost dimensions such as data collection cost, cleaning and labeling cost, rights confirmation and compliance cost, storage and maintenance cost, and agricultural-specific costs throughout the entire lifecycle of maintenance and updates, plus a reasonable profit margin.

[0009] According to the present invention, a pricing method for structured agricultural public datasets includes the following steps: extracting features that reflect the dataset's performance in improving agricultural production efficiency, circulation efficiency, risk control and mitigation, and decision support; scoring the features using a trained machine learning model to obtain scores for each value dimension; and weighting and summing the scores according to preset weights to obtain the value coefficient of the dataset.

[0010] According to the pricing method for structured agricultural public datasets provided by the present invention, the adjustment coefficient of the scenario dimension is determined based on at least one of the following factors: application industry type, data coverage area, data timeliness, and data authorization method.

[0011] According to the pricing method for structured agricultural public datasets provided by the present invention, the adjustment coefficient of the dynamic dimension is calculated based on at least one of the following fluctuation factors obtained in real time through a standardized data interface: market supply and demand, changes in agricultural policies, occurrence of natural disasters, and agricultural seasonality factors.

[0012] According to the present invention, a pricing method for structured agricultural public datasets is provided, wherein the zero-pricing and fiscal compensation model and the cost-plus pricing model are calculated based on agricultural-specific costs covering the entire lifecycle of the data.

[0013] This invention also provides a pricing device for structured agricultural public datasets, comprising the following modules: The data acquisition module is used to acquire structured agricultural government data sets to be priced, and to determine the level of the data sets according to a preset data classification system. The pricing module is used to select the appropriate pricing model based on the level to which the dataset belongs, perform pricing calculations, and output the pricing results. The data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level. The pricing models include: a zero-price and fiscal compensation model for public welfare and inclusive data, a cost-plus pricing model for government collaboration data, and a scenario-based value-sharing pricing model for value-added service data.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the structured agricultural public dataset pricing method as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the structured agricultural public dataset pricing method as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the structured agricultural public dataset pricing method as described above.

[0017] This invention provides a structured pricing method, apparatus, and equipment for agricultural public datasets, which offers the following advantages: By using a pre-defined data classification system, the dataset to be priced is objectively divided into levels with different public welfare and economic attributes, thus overcoming the drawbacks of existing technologies that apply a single standard to pricing data with different value attributes. Then, based on the different levels, a pricing model matching the core characteristics of that level is adaptively invoked for calculation. For public welfare and inclusive data, a zero-pricing and fiscal compensation model is used to ensure the public good attributes and accessibility of basic data; for government collaboration data, a cost-plus pricing model is used to reflect its semi-public welfare attributes and support for government functions; for value-added service data, a scenario-based value-sharing pricing model is used to fully explore its commercial value and promote market circulation. This "classification-adaptation" technical approach enables the final pricing result to more accurately reflect the intrinsic value differences and core application logic of different types of data, thereby significantly improving the overall rationality, relevance, and effectiveness of pricing. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the structured agricultural public dataset pricing method provided by the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of the pricing device for the structured agricultural public dataset provided by the present invention.

[0021] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] The following is combined Figures 1-3 The embodiments of the present invention are described in detail.

[0024] The structured agricultural public dataset pricing method provided in this embodiment of the invention is executed by a structured agricultural public dataset pricing device, which can be configured in a computer. The computer can be a local computer or a cloud computer. The local computer can be a computer, tablet, etc., and no specific limitation is made here.

[0025] Figure 1 This is a flowchart illustrating the structured agricultural public dataset pricing method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps: S110. Obtain the structured agricultural government data set to be priced, and determine the level of the data set according to the preset data classification system.

[0026] S120. Based on the level to which the dataset belongs, select the appropriate pricing model to perform pricing calculations and output the pricing results.

[0027] The data classification system includes at least three levels: public welfare and inclusive data, government collaboration data, and value-added service data. The pricing models include: a zero-price and fiscal compensation model for public welfare and inclusive data, a cost-plus pricing model for government collaboration data, and a scenario-based value-sharing pricing model for value-added service data.

[0028] Specifically, a structured agricultural government data set to be priced is acquired, such as "Dynamic Monitoring Data of Cultivated Land" from a certain province. The dataset is then assessed based on a pre-defined data classification system comprising at least three levels, constructed according to the data's public interest attributes, sensitivity, and primary application scenarios. In this embodiment, the "Dynamic Monitoring Data of Cultivated Land" is formed after deep cleaning, fusion, and analysis of multi-source data, including satellite remote sensing and IoT sensors. It dynamically reflects crop growth, acreage, and pest and disease risks. Its core application scenario is serving the precision planting management and market decision-making of agricultural enterprises, possessing direct economic value; therefore, it is classified as a "value-added service level." After determining the level, the system automatically selects the corresponding "scenario-based value-sharing pricing model" for pricing calculation. This model's calculation does not simply rely on cost but introduces a composite calculation framework.

[0029] Specifically, pricing first considers a safety net cost price, which covers the entire lifecycle processing costs from field sensor deployment, data collection, professional cleaning and labeling, compliance and rights confirmation to storage, maintenance, and periodic updates, ensuring the sustainability of the pricing. Then, a trained machine learning model automatically quantifies the value of the dataset. This model extracts features of the data related to increasing yield per acre, reducing losses, providing early warning of risks, and supporting decision-making, and outputs a comprehensive value coefficient. Next, the system determines a scenario premium coefficient based on factors such as the specific application of the dataset (e.g., facility agriculture), region (e.g., major grain-producing areas), timeliness (e.g., critical growth stages of crops), and licensing method (e.g., commercial exclusive licensing). Furthermore, the system uses standardized data interfaces to access real-time external dynamic information reflecting market supply and demand, agricultural policies, natural disasters, and seasonal fluctuations, and calculates a dynamic adjustment coefficient accordingly. Finally, the pricing result is derived from a combination of the safety net cost price, the value coefficient, the scenario premium coefficient, and the dynamic adjustment coefficient, or, for example, their product. This pricing result is then output as the basis for data transactions or service agreements.

[0030] According to the present invention, a structured pricing method for agricultural public datasets is provided. The scenario-based value-sharing pricing model is calculated based on the following four dimensions: cost dimension, used to determine the bottom-line cost of the dataset; value dimension, used to quantify the contribution value of the dataset to agricultural production activities through a machine learning model; scenario dimension, used to adjust the pricing coefficient according to the differences in application scenarios; dynamic dimension, used to dynamically adjust the pricing coefficient based on real-time acquired external fluctuation factors; the final price is obtained by combining the base of the cost dimension and the coefficients of the other three dimensions.

[0031] According to the pricing method for structured agricultural public datasets provided by the present invention, the cost dimensions include data collection costs, cleaning and labeling costs, rights confirmation and compliance costs, storage and maintenance costs, and agricultural-specific costs throughout the entire lifecycle of operation, maintenance and updates, plus a reasonable profit margin.

[0032] According to the present invention, a pricing method for structured agricultural public datasets includes the following steps for quantifying the value dimensions: extracting features that reflect the dataset's effectiveness in improving agricultural production efficiency, circulation efficiency, risk control and mitigation, and decision support; using a trained machine learning model to score the features and obtain scores for each value dimension; and weighting and summing the scores according to preset weights to obtain the value coefficient of the dataset.

[0033] According to the pricing method for structured agricultural public datasets provided by the present invention, the adjustment coefficient of the scenario dimension is determined based on at least one of the following factors: application industry type, data coverage area, data timeliness, and data authorization method.

[0034] According to the pricing method for structured agricultural public datasets provided by the present invention, the adjustment coefficient of the dynamic dimension is calculated based on at least one of the following fluctuation factors obtained in real time through a standardized data interface: market supply and demand, changes in agricultural policies, occurrence of natural disasters, and agricultural seasonality factors.

[0035] Specifically, when pricing a structured agricultural dataset classified as "value-added service level" (e.g., "provincial agricultural product production and sales early warning data") is required, the system initiates a composite calculation model based on four dimensions: cost, value, scenario, and dynamics. In terms of cost, the system calculates the entire lifecycle processing cost of the dataset, including the cost of collecting and transmitting field IoT monitoring data, the cost of cleaning and agricultural-specific labeling of multi-source heterogeneous data, the cost of data anonymization and compliance auditing for rights confirmation and compliance, the storage and maintenance costs of structured storage and link maintenance, and the maintenance and update costs of quarterly synchronous updates. The sum of these costs constitutes the baseline cost of the dataset, denoted as C.

[0036] In the value dimension, the system invokes a pre-trained machine learning model (e.g., built based on a gradient boosting tree algorithm) to automatically quantify the value contribution of the dataset. This machine learning model receives historical or predicted indicators reflecting the application effect of the dataset as input features, which are categorized into four value categories: production efficiency improvement, circulation efficiency improvement, risk control and mitigation, and decision support. For example, input features might include the percentage increase in average production-sales matching rate brought about by the early warning data in historical applications, the accuracy rate of price fluctuation early warnings, etc. After processing and analysis, the machine learning model outputs scores for these four value dimensions. Subsequently, the system performs a weighted sum of the four scores according to a preset weight configuration (e.g., production efficiency has the highest weight, and decision support has a relatively lower weight), ultimately calculating a value coefficient V greater than or equal to 1.

[0037] At the scenario level, the system determines a set of scenario factors and calculates the scenario premium coefficient S based on the specific circumstances of this data transaction or application. This includes: a basic industry coefficient S1 based on the agricultural sector primarily served by the data (e.g., agricultural product distribution); a regional coefficient S2 based on the geographical area covered by the data (e.g., major grain-producing areas); a timeliness coefficient S3 based on the data's timeliness requirements (e.g., requiring real-time alerts for the next week); and an authorization coefficient S4 based on the agreed-upon scope and method of data usage (e.g., granting an agricultural enterprise a one-year non-exclusive commercial usage right). The scenario premium coefficient S is the product of these factors.

[0038] In the dynamic dimension, the system accesses external data sources in real time through standardized application programming interfaces (such as REST APIs) to obtain dynamic fluctuation information affecting the value of the data. This includes the market supply and demand of agricultural products involved in the early warning data, the latest agricultural support policies, whether large-scale natural disasters have occurred, and the current agricultural season. Based on preset rules, the system quantifies this information into specific supply and demand factors, policy factors, disaster factors, and seasonal factors, and then calculates the dynamic adjustment coefficient D through weighted averages.

[0039] Ultimately, the pricing P of this "provincial-level agricultural product production and sales early warning data" is derived by multiplying the base cost C (calculated from the cost dimension) by the value coefficient V, the scenario premium coefficient S, and the dynamic adjustment coefficient D, respectively, i.e., P = C × V × S × D. This price will serve as the core basis for value sharing or service fees with data requesters.

[0040] This embodiment ensures that pricing covers basic inputs through cost dimensions, guaranteeing the financial sustainability of data supply. Utilizing intelligent machine learning models to quantify value dimensions allows pricing to be closely linked to the actual objective benefits generated by data in agricultural production, distribution, and risk control, significantly enhancing the scientific rigor and persuasiveness of pricing. The introduction of a scenario dimension enables pricing to precisely respond to value differences arising from different business types, regional characteristics, timeliness requirements, and authorization models, enhancing pricing flexibility and market adaptability. A real-time calibration mechanism for dynamic dimensions allows pricing to sensitively reflect immediate changes in external factors such as market supply and demand, policy environment, and natural conditions, ensuring that pricing results are always timely and realistically reasonable. This embodiment organically integrates the cost floor, the value of intelligent assessment, the premium for specific scenarios, and the dynamic fluctuations of the external environment into a single computational framework, working together to generate a more accurate and reasonable final price that reflects intrinsic value while adapting to external changes.

[0041] According to the present invention, a pricing method for structured agricultural public datasets is provided, in which zero pricing and fiscal compensation models and cost-plus pricing models are calculated based on agricultural-specific costs covering the entire lifecycle of the data.

[0042] Specifically, taking the first-level "public welfare and inclusive" data (such as the "crop variety catalog data" of a certain region) as an example, it adopts a zero-pricing and fiscal compensation model. The pricing process first accurately calculates the full life-cycle agriculture-related costs of the dataset, including the data collection costs of collecting variety information compiled by the Ministry of Agriculture and Rural Affairs and local governments and conducting on-site spot checks and verifications, the cleaning and labeling costs of standardizing and classifying the variety information, the rights confirmation and compliance costs of ensuring data copyright and compliant publication, the storage and maintenance costs of structured storage and daily maintenance on the agricultural data platform, and the maintenance and update costs of regular updates based on annual or new variety approval status.

[0043] The total cost of the dataset is obtained by summing the five costs mentioned above. Due to the zero-pricing model, the data product is priced at zero yuan for end users, meaning it is provided free of charge. However, to ensure the sustainability of data production and services, this total cost serves as the basis for fiscal compensation, and is fully or partially compensated by the relevant fiscal budget. For secondary "government collaboration level" data (such as "farmland quality grade data" for a certain county), a cost-plus pricing model is adopted. Its cost accounting scope is exactly the same as that of the primary data mentioned above, also covering the five agriculture-specific costs throughout the entire lifecycle: data collection, cleaning and labeling, rights confirmation and compliance, storage and maintenance, and maintenance and updates, which are summed to form the cost base. Based on this, the operating organization (such as a government-designated data operating entity) can, according to relevant regulations, add a reasonable profit margin constrained by government guidance prices (e.g., setting an upper limit based on market benchmarks) to the cost base. The final calculated price is the pricing of the data for specific users such as government departments, for example, in the form of an annual subscription fee.

[0044] This embodiment, for public welfare and inclusive data, provides a clear, transparent, and reasonable financial compensation basis for the zero-pricing strategy by accurately calculating its entire lifecycle costs specific to agriculture. This ensures that the policy goal of free access and sharing is built on sustainable financial security, avoiding the problem of declining data supply quality or service interruption due to insufficient cost coverage. For government collaboration-level data, the same comprehensive cost accounting is used as a basis, superimposed with a reasonable, regulated profit margin. This ensures that pricing truly reflects the actual investment in data production, while providing appropriate incentives for operating entities, guaranteeing the continuous, stable, and high-quality provision of data products and services for government collaboration scenarios. Both models together ensure that the pricing or compensation benchmark for agricultural public data, whether free or paid for sharing, is based on comprehensive, objective, and auditable cost accounting. This enhances the credibility, operability, and sustainability of the pricing system, effectively supporting the implementation of hierarchical and classified management of public data and differentiated pricing strategies.

[0045] The present invention will be described more fully below through examples.

[0046] This invention proposes a four-dimensional pricing model combining "cost protection + core value + scenario premium + dynamic adjustment," integrating the XGBoost intelligent algorithm with standardized dynamic data interfaces to construct a complete technical system based on "hierarchical classification, intelligent quantification as the core, scenario adaptation as support, and real-time calibration as a guarantee," as detailed below: 1. Data classification and grading system (pricing basis) Based on the public interest attributes, sensitivity, and application scenarios of agricultural government data, a hierarchical classification framework of "3 levels and 3 categories" is constructed, clarifying pricing authority and applicable scenarios as shown in Table 1: Table 1

[0047] 2. Four-Dimensional Pricing Core Model General formula: P = C × V × S × D Wherein, P is the final price; C is the guaranteed cost price (covering agriculture-specific costs); V is the value coefficient (quantifying the value of production efficiency); S is the scenario premium coefficient (adapting to specific agriculture-related scenarios); and D is the dynamic adjustment coefficient (responding to agricultural volatility).

[0048] (1) Bottom-line cost price (C): Locking in the bottom line of pricing Covering the entire lifecycle of structured agricultural data with dedicated costs, ensuring pricing is not lower than the cost floor, the formula is as follows: C=( )×(1+ ); (Data collection cost): Includes agriculture-specific costs such as field sensor deployment / rental, manual measurement, government data authorization, and germplasm sampling and testing, calculated based on actual expenditures; (Cleaning and labeling cost): Includes the cost of field standardization, outlier removal, and agricultural labeling (type of pests and diseases, grade of cultivated land), calculated as "workload × unit price"; (Cost of confirming ownership and compliance): Includes costs for ownership confirmation, data anonymization, and compliance audit and certification, calculated with reference to third-party service quotes; (Storage operation and maintenance costs): Includes structured data storage, data acquisition link maintenance, and quality monitoring costs, calculated based on storage volume and operation and maintenance hours; (Operation and maintenance update costs): including costs for updating according to the agricultural cycle and data iteration, calculated based on update frequency and workload; (Reasonable Profit Margin): A reasonable profit margin shall not exceed the yield of 10-year Treasury bonds of the same period plus 2 percentage points (approximately 4.15% in 2025). (For public welfare organizations) =0.

[0049] (2) Value Coefficient (V): Basis for Core Premium Based on the quantification of the actual contribution of data to agricultural production, distribution, risk control, and decision-making, an automated calculation is achieved by introducing "feature engineering + XGBoost algorithm", as shown in the following formula: in, Assigning weights to each value dimension. The scores for each dimension (1-2 points) are shown in Table 2, along with the corresponding weights, quantitative indicators, and scoring rules for each value dimension. Table 2

[0050] XGBoost algorithm implementation process: Feature engineering: Extract four types of original features, including production efficiency improvement and circulation efficiency improvement, and preprocess them by Min-Max normalization, 3σ outlier removal, and KNN (K=5) missing value filling; Model Training: A training set was built based on 1000+ agricultural data application cases. Grid search was used to optimize hyperparameters (learning rate 0.08, tree depth 4, regularization coefficient λ=0.2). Model validation metrics were MSE≤0.01 and R0.01. 2 ≥0.95; Scoring Output: The model automatically outputs scores for each dimension, and the scores are weighted to obtain the value coefficient V (V≥1).

[0051] (3) Scenario premium coefficient (S): Precisely adapt to demand Based on the differential calculation involving agricultural status, region, timeliness, and authorization authority, the formula is as follows: S= (Basic business coefficients): Field grain planting 1.0 (benchmark), facility agriculture 1.2-1.4, livestock and poultry breeding 1.3-1.5, aquaculture 1.2-1.4, agricultural product circulation 1.1-1.3, agricultural government affairs 1.4-1.6; (Regional coefficient): Major grain-producing areas / cash crop advantageous areas 1.1-1.3, non-advantageous areas 1.0, remote and special planting and breeding areas 1.2; (Timeliness coefficient): Busy farming season / critical growth period / high disaster incidence period 1.2-1.5, off-season 0.9-1.0, real-time data 1.3-1.6, historical archived data 0.8-0.9; (Authorization Permission Coefficient): Non-exclusive authorization 1.0, Exclusive authorization 2.0-3.0, Data not leaving the domain authorization 1.5-2.0, Commercial authorization 1.2-1.4, Scientific research authorization 0.8-1.0.

[0052] (4) Dynamic adjustment coefficient (D): adapting to agricultural fluctuations Calibrated based on fluctuations in supply and demand, policies, disasters, and seasons, the data is acquired in real time through a standardized interface, as shown in the following formula: (Supply and demand factors): Supply shortage 1.3-1.8, supply and demand balance 1.0, supply surplus 0.8-0.9; (Policy Factors): New agricultural policies introduced 1.1-1.3, no change 1.0; (Disaster Factors): Drought / Flood / Pest / Disease Outbreak 1.2-1.5, No Disaster 1.0; (Seasonal factors): Seasonal crop market period 1.1-1.2, off-season 1.0.

[0053] Dynamic factor interface design: RESTful API (for connecting to government / meteorological data) and WebSocket (for connecting to transaction platform data) are adopted, with an update frequency of 15-60 minutes / time to ensure that the value of D is accurate and real-time, and the dynamic response delay is ≤30 minutes.

[0054] 3. Specific pricing methods for different levels of data (1) Level 1 data (public welfare and inclusive level): zero pricing + fiscal compensation method Pricing formula: = =( )×(1+ ) (Data collection cost): Includes agriculture-specific costs such as field sensor deployment / rental, manual measurement, government data authorization, and germplasm sampling and testing, calculated based on actual expenditures; (Cleaning and labeling cost): Includes the cost of field standardization, outlier removal, and agricultural labeling (type of pests and diseases, grade of cultivated land), calculated as "workload × unit price"; (Cost of confirming ownership and compliance): Includes costs for ownership confirmation, data anonymization, and compliance audit and certification, calculated with reference to third-party service quotes; (Storage operation and maintenance costs): Includes structured data storage, data acquisition link maintenance, and quality monitoring costs, calculated based on storage volume and operation and maintenance hours; (Operation and maintenance update costs): including costs for updating according to the agricultural cycle and data iteration, calculated based on update frequency and workload; public welfare organizations =0.

[0055] Implementation method: Provide free download, API call, and visual query services through the provincial agricultural data open platform, and ensure the cost by including it in a special budget.

[0056] (2) Secondary data (government collaboration level): cost-plus pricing method Pricing formula: =( )×(1+ ) R is reasonable: a reasonable profit margin, not exceeding the yield of 10-year treasury bonds plus 2 percentage points (approximately 4.15% in 2025).

[0057] Implementation method: Shared only with government departments and public welfare organizations on a paid basis, with annual subscription fees.

[0058] (3) Level 3 data (value-added service level): Scenario-based value-sharing pricing method Pricing formula: P = C × V × S × D Wherein, P is the final price; C is the guaranteed cost price (covering agriculture-specific costs); V is the value coefficient (quantifying the value of production efficiency); S is the scenario premium coefficient (adapting to specific agriculture-related scenarios); and D is the dynamic adjustment coefficient (responding to agricultural volatility).

[0059] Implementation method: Provide customized services to enterprises and other market entities, and sign service agreements to clarify revenue sharing terms.

[0060] This invention is the first to construct a four-dimensional combined model of "cost guarantee + core value + scenario premium + dynamic adjustment," comprehensively covering the unique characteristics of agricultural data such as regionality and agricultural activity linkage, solving the problem of "incompatibility" of general models; it introduces "feature engineering + XGBoost algorithm" to establish a quantitative system directly linked to agricultural production indicators, realizing automated calculation of value coefficients, with a model-to-actual value deviation of ≤3%; through a "3-level, 3-category" hierarchical classification framework, it adopts three modes: zero pricing, cost-plus pricing, and value sharing, ensuring both the accessibility of basic production data to all citizens and the release of value-added data; by establishing a trigger-based dynamic adjustment mechanism based on production cycles, policy changes, and data iteration, it ensures that pricing always adapts to dynamically changing application scenarios.

[0061] The pricing device for structured agricultural public datasets provided by this invention will be described below. The pricing device for structured agricultural public datasets described below can be referred to in correspondence with the pricing method for structured agricultural public datasets described above.

[0062] like Figure 2 The image shows a pricing device for a structured agricultural public dataset provided by the present invention, comprising: The data acquisition module 210 is used to acquire the structured agricultural government data set to be priced, and to determine the level of the data set according to the preset data classification system. The pricing module 220 is used to select the appropriate pricing model based on the level to which the dataset belongs, perform pricing calculations, and output the pricing results. The data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level. The pricing models include: a zero-price and fiscal compensation model for public welfare and inclusive data, a cost-plus pricing model for government collaboration data, and a scenario-based value-sharing pricing model for value-added service data.

[0063] Specifically, the functions of each module in the user account management system provided in this embodiment of the invention correspond one-to-one with the operation flow of each step in the above method-like embodiments, and the achieved effects are also the same. For details, please refer to the above embodiments, and this will not be repeated in this embodiment of the invention.

[0064] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a structured agricultural public dataset pricing method. This method includes: acquiring the structured agricultural government dataset to be priced, and determining the dataset's level according to a preset data classification system; selecting the appropriate pricing model based on the dataset's level to perform pricing calculations, and outputting the pricing result; wherein the data classification system includes at least three levels: public welfare level, government collaboration level, and value-added service level; the pricing models include: a zero-price and fiscal compensation model for public welfare level data, a cost-plus pricing model for government collaboration level data, and a scenario-based value-sharing pricing model for value-added service level data.

[0065] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the structured agricultural public dataset pricing method provided by the above methods. The method includes: acquiring the structured agricultural government data set to be priced, and determining the level of the dataset according to a preset data classification system; selecting the appropriate pricing model to perform pricing calculation according to the level of the dataset, and outputting the pricing result; wherein the data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level; the pricing model includes: a zero-price and fiscal compensation model for public welfare and inclusive level data, a cost-plus pricing model for government collaboration level data, and a scenario-based value-sharing pricing model for value-added service level data.

[0067] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a pricing method for structured agricultural public datasets provided by the methods described above. The method includes: acquiring a structured agricultural government data set to be priced, and determining the level to which the dataset belongs based on a preset data classification system; selecting a corresponding pricing model to calculate the pricing based on the level to which the dataset belongs, and outputting the pricing result; wherein the data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level; the pricing model includes: a zero-price and fiscal compensation model for public welfare and inclusive level data, a cost-plus pricing model for government collaboration level data, and a scenario-based value-sharing pricing model for value-added service level data.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0069] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pricing method for structured agricultural public datasets, characterized in that, include: Obtain a structured agricultural government data set to be priced, and determine the level of the data set according to a preset data classification system; Based on the level to which the dataset belongs, select the appropriate pricing model to perform pricing calculations and output the pricing results; The data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level. The pricing models include: a zero-price and fiscal compensation model for public welfare and inclusive data, a cost-plus pricing model for government collaboration data, and a scenario-based value-sharing pricing model for value-added service data.

2. The pricing method for structured agricultural public datasets according to claim 1, characterized in that, The scenario-based value-sharing pricing model is calculated based on the following four dimensions: The cost dimension is used to determine the fallback cost of the dataset; The value dimension is used to quantify the contribution value of a dataset to agricultural production activities through machine learning models. The scenario dimension is used to adjust the pricing coefficient according to the differences in application scenarios; The dynamic dimension is used to dynamically adjust the pricing coefficient based on real-time acquired external fluctuation factors; The final price is calculated by combining the base of the cost dimension and the coefficients of the other three dimensions.

3. The pricing method for structured agricultural public datasets according to claim 2, characterized in that, The cost dimension includes agriculture-specific costs throughout the entire lifecycle, which include data collection costs, cleaning and labeling costs, rights confirmation and compliance costs, storage and maintenance costs, and maintenance and update costs, plus a reasonable profit margin.

4. The pricing method for structured agricultural public datasets according to claim 2, characterized in that, The quantification process of the value dimension includes: Extract features that reflect the dataset's effectiveness in improving agricultural production efficiency, enhancing circulation efficiency, controlling risks and mitigating risks, and supporting decision-making. The trained machine learning model is used to score the features to obtain scores for each value dimension; The scores are weighted and summed according to preset weights to obtain the value coefficient of the dataset.

5. The pricing method for structured agricultural public datasets according to claim 2, characterized in that, The adjustment coefficient for the scenario dimension is determined based on at least one of the following factors: application type, data coverage area, data timeliness, and data authorization method.

6. The pricing method for structured agricultural public datasets according to claim 2, characterized in that, The adjustment coefficient of the dynamic dimension is calculated based on at least one of the following fluctuation factors obtained in real time through a standardized data interface: market supply and demand, changes in agricultural policies, occurrence of natural disasters, and agricultural seasonality.

7. The pricing method for structured agricultural public datasets according to claim 1, characterized in that, The zero-pricing and fiscal compensation model and the cost-plus pricing model are calculated based on agriculture-specific costs covering the entire lifecycle of data.

8. A pricing device for structured agricultural public datasets, characterized in that, include: The data acquisition module is used to acquire structured agricultural government data sets to be priced, and to determine the level of the data sets according to a preset data classification system. The pricing module is used to select the appropriate pricing model based on the level to which the dataset belongs, perform pricing calculations, and output the pricing results. The data classification system includes at least three levels: public welfare and inclusive level, government collaboration level, and value-added service level. The pricing models include: a zero-price and fiscal compensation model for public welfare and inclusive data, a cost-plus pricing model for government collaboration data, and a scenario-based value-sharing pricing model for value-added service data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the structured agricultural public dataset pricing method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the structured agricultural public dataset pricing method as described in any one of claims 1 to 7.

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