Permanent basic farmland management method, device, equipment, medium and product

Abstract

The application discloses a permanent basic farmland management method, device, equipment, medium and product, and relates to the technical field of agriculture. The method comprises the following steps: acquiring multi-source data of a region to be managed; determining a priority classification list of the permanent basic farmland to be removed from the region to be managed according to the multi-source data and a preset multi-factor identification model; determining a score list of the permanent basic farmland to be added from a reserve area of the permanent basic farmland in the region to be managed according to the multi-source data and a preset multi-dimensional scoring model; and determining an optimized management scheme according to the priority classification list of the permanent basic farmland to be removed and the score list of the permanent basic farmland to be added. The application solves the technical problems that, in the prior art, it is difficult to scientifically identify the removed plot and evaluate the reserve area plot to be added when the permanent basic farmland is managed.

Description

Technical Field

[0001] This application relates to the field of agricultural technology, and in particular to a method, apparatus, equipment, medium, and product for the management of permanent basic farmland. Background Technology

[0002] Permanent basic farmland is the core carrier for ensuring regional grain production capacity and the pattern of arable land protection, and it occupies a key position in the work of land space management and routine management of arable land. At present, there are still obvious technical shortcomings in the dynamic management of permanent basic farmland at the grassroots level: on the one hand, for farmland plots that are scattered, fragmented in form, and have limited cultivation conditions, there is a lack of unified quantitative identification standards and automated screening methods, and they rely heavily on manual experience for judgment, which is highly subjective and lacks accuracy; on the other hand, the existing arable land baseline, topography and geology, ecological management, water conservancy zoning and other multi-source spatial data are fragmented and have not formed an integrated analysis capability, making it difficult to accurately identify plots with poor site conditions and unsuitable for long-term cultivation; at the same time, there is a lack of a systematic access evaluation and ranking system for high-quality reserve arable land.

[0003] In summary, existing technologies for managing permanent basic farmland suffer from technical problems, including the difficulty in scientifically identifying the plots that need to be retrieved from the map and the difficulty in evaluating the plots to be transferred to the reserve area. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment, medium and product for the management of permanent basic farmland, which can solve the technical problems of existing technologies in the management of permanent basic farmland, such as the difficulty in scientifically identifying the plots in the map and the difficulty in evaluating the plots in the reserve area to be transferred.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for managing permanent basic farmland, including: Acquire multi-source data for the area to be managed; Based on multi-source data and a pre-set multi-factor identification model, a priority grading list is determined from the permanent basic farmland in the area to be managed. The factors in the pre-set multi-factor identification model include plot fragmentation factor, unsuitability for cultivation restriction factor, and constraint grading factor. Based on multi-source data and a pre-set multi-dimensional scoring model, a list of permanent basic farmland parcels to be transferred in is determined from the permanent basic farmland reserve area in the area to be managed. The dimensions of the pre-set multi-dimensional scoring model include source type, quality conditions, and site conditions. Based on the priority list for land transfer-out and the scoring list for land parcels to be transferred in, an optimized management plan is determined. This optimized management plan is used to guide the transfer of permanent basic farmland in and out.

[0006] Optionally, based on multi-source data and a preset multi-factor identification model, a priority grading list is determined from the permanent basic farmland in the area to be managed. Specifically, this includes: for each map patch in the permanent basic farmland, determining the plot fragmentation factor based on the plot perimeter and area, where the plot perimeter and area are included in the multi-source data; for each map patch in the permanent basic farmland, determining the fragmentation factor based on the permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone areas, and strictly controlled soil environmental quality arable land. The unsuitability for cultivation restrictive factors are determined, including permanent basic farmland layers, slope maps, ecological protection red lines, river and lake management areas, geological disaster-prone areas, and farmland under strict soil environmental quality control, all of which are included in multi-source data. For each map patch in permanent basic farmland, constraint grading factors are determined based on plot fragmentation factors and unsuitability for cultivation restrictive factors. The plot fragmentation factors, unsuitability for cultivation restrictive factors, and constraint grading factors corresponding to each map patch in permanent basic farmland are input into a preset multi-factor recognition model to obtain a priority grading list.

[0007] Optionally, based on multi-source data and a preset multi-dimensional scoring model, a list of map parcels to be transferred from the permanent basic farmland reserve area in the area to be managed is determined. Specifically, this includes: for each map parcel in the permanent basic farmland reserve area, determining a source type score based on the source type, where the source type is included in the multi-source data; for each map parcel in the permanent basic farmland reserve area, determining a quality condition score based on the arable land quality grade, where the arable land quality grade is derived from the multi-source data; for each map parcel in the permanent basic farmland reserve area, determining a site condition score based on the degree of concentration and contiguousness, slope, and water resource security conditions, where the degree of concentration and contiguousness, slope, and water resource security conditions are included in the multi-source data; and inputting the source type score, quality condition score, and site condition score corresponding to each plot in the permanent basic farmland reserve area into the preset multi-dimensional scoring model to determine the list of map parcels to be transferred.

[0008] Optionally, an optimized management plan is determined based on the priority list for removal and the score list for map features to be transferred in. Specifically, this includes: determining multiple sub-management plans based on the priority list for removal and the score list for map features to be transferred in; evaluating the multiple sub-management plans; and determining the optimized management plan.

[0009] Optionally, multiple sub-management schemes are evaluated to determine an optimized management scheme. Specifically, this includes: for each sub-management scheme, determining the average quality level of each sub-management scheme according to a preset quality level calculation formula; for each sub-management scheme, determining the average area change rate of each sub-management scheme according to a preset area change rate calculation formula; and determining the optimized management scheme based on the average quality level and average area change rate corresponding to each sub-management scheme.

[0010] Optionally, after determining the optimization management plan based on the priority list for removal and the scoring list for map features to be transferred in, the method further includes: generating standardized ledger data based on the optimization management plan, wherein the standardized ledger data includes ledger data for map features to be removed, ledger data for map features to be transferred in, and ledger data for map features in the reserve area.

[0011] Secondly, this application provides a permanent basic farmland management device, comprising: The system comprises the following modules: an acquisition module for acquiring multi-source data of the area to be managed; a first determination module for determining a priority list of land parcels to be transferred out from the permanent basic farmland in the area to be managed, based on the multi-source data and a preset multi-factor identification model, wherein the factors in the preset multi-factor identification model include land fragmentation factors, unsuitability for cultivation restrictive factors, and constraint classification factors; a second determination module for determining a score list of permanent basic farmland parcels to be transferred in from the permanent basic farmland reserve area in the area to be managed, based on the multi-source data and a preset multi-dimensional scoring model, wherein the dimensions of the preset multi-dimensional scoring model include source type, quality conditions, and site conditions; and an optimization module for determining an optimized management plan based on the priority list of land parcels to be transferred out and the score list of parcels to be transferred in, wherein the optimized management plan guides the transfer in and out of permanent basic farmland.

[0012] Thirdly, this application provides a computer 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 steps of the permanent basic farmland management method described in any one of the above.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the permanent basic farmland management method described above.

[0014] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the permanent basic farmland management method described above.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, apparatus, equipment, medium, and product for the management of permanent basic farmland. The method collects multi-source data from the area to be managed and, relying on a multi-factor identification model that includes three categories of factors—plot fragmentation, unsuitability for cultivation, and constraint grading—achieves quantitative and automatic screening and scientific priority classification of permanent basic farmland plots to be removed from the management area. This eliminates the need for traditional manual subjective judgment and effectively solves the problems of inconsistent identification standards, inaccurate judgment, and lack of hierarchical order for plots to be removed. Simultaneously, it utilizes a multi-dimensional scoring model covering source type, quality conditions, and site conditions to quantitatively score reserve area plots in multiple dimensions. By establishing a standardized and implementable map patch access evaluation system through orderly sorting, the shortcomings of lacking systematic evaluation basis and being unable to select the best map patches for transfer to the reserve area are addressed. Furthermore, by combining the priority grading list for transfer-out with the scoring list of map patches to be transferred in, an optimized management plan can be formed through overall matching and deduction. This enables the scientific matching and overall optimization of the layout of permanent basic farmland transfer-out and transfer-in, thereby improving the quantitative, automated, and scientific level of dynamic management of permanent basic farmland. In turn, it solves the technical problems of existing technologies in the management of permanent basic farmland, which make it difficult to scientifically identify map patches to be transferred out and difficult to evaluate map patches to be transferred to the reserve area. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an application environment diagram of a permanent basic farmland management method in one embodiment of this application; Figure 2 A flowchart illustrating a permanent basic farmland management method provided in an embodiment of this application; Figure 3 A flowchart illustrating a method for managing permanent basic farmland, provided as an embodiment of this application; Figure 4 A structural block diagram of a permanent basic farmland management device provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a land parcel identification module provided in one embodiment of this application; Figure 6 A schematic diagram of the structure of a reserve area suitability evaluation module provided in an embodiment of this application; Figure 7 A schematic diagram illustrating a multi-dimensional evaluation of an entry / exit balance simulation module provided in one embodiment of this application; Figure 8This is a schematic diagram of the structure of a permanent basic farmland management device provided in one embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The permanent basic farmland management method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send multi-source data of the area to be managed to server 104. After receiving the multi-source data, server 104 determines a priority list for removal from permanent basic farmland in the area to be managed, based on the multi-source data and a preset multi-factor identification model. The factors in the preset multi-factor identification model include land fragmentation factors, unsuitability for cultivation restrictive factors, and constraint classification factors. Based on the multi-source data and a preset multi-dimensional scoring model, server 104 determines a scoring list of permanent basic farmland to be transferred in from the permanent basic farmland reserve area in the area to be managed. The dimensions of the preset multi-dimensional scoring model include source type, quality conditions, and site conditions. Based on the priority list for removal and the scoring list of land parcels to be transferred in, server 104 determines an optimized management plan, which guides the transfer in and out of permanent basic farmland. Server 104 can feed back the optimized management plan to terminal 102. In addition, in some embodiments, the permanent basic farmland management method can also be implemented by server 104 or terminal 102 alone. For example, terminal 102 can directly process the multi-source data of the area to be managed, or server 104 can obtain the multi-source data of the area to be managed from the data storage system and process it.

[0021] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, and IoT devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0022] In one exemplary embodiment, such as Figure 2 As shown, a method for managing permanent basic farmland is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204. Wherein: Step S201: Obtain multi-source data for the area to be managed.

[0023] In this embodiment, step S201 is the data base preparation stage for the entire permanent basic farmland management, comprehensively collecting various types of spatial and operational data related to land, arable land, ecology, and engineering in the area to be managed. For example, this includes land change survey data, permanent basic farmland delineation results, arable land quality grade results, ecological protection red line data, river and lake management scope data, geological disaster-prone area data, high-standard farmland mapping and database entry data, and land consolidation project data.

[0024] Optionally, coordinate system unification, format conversion, and data cleaning preprocessing can be performed on the collected multi-source data to solve the problems of inconsistent standards and inability to overlay and analyze multi-source data, and to provide unified, standardized, and usable basic data support for subsequent land parcel identification, suitability evaluation, and scheme simulation.

[0025] Step S202: Based on multi-source data and a preset multi-factor identification model, determine the priority grading list from the permanent basic farmland in the area to be managed. The factors in the preset multi-factor identification model include land fragmentation factor, unsuitable for cultivation restriction factor, and constraint grading factor.

[0026] In this embodiment of the application, step S202 is the precise screening and grading process for permanent basic farmland plots to be removed from the map.

[0027] Specifically, based on the preprocessed multi-source data, a preset multi-factor identification model is invoked to conduct quantitative analysis from three dimensions: The land fragmentation factor identifies scattered, fragmented, irregularly shaped, and inconveniently cultivated permanent basic farmland patches by calculating the land area and land shape index, combined with regional statistical thresholds.

[0028] Unsuitable cultivation limiting factors were identified using GIS spatial overlay analysis to screen out map patches that are unsuitable for cultivation at the natural or regulatory level, such as slopes greater than 15 degrees, areas within ecological protection red lines, river and lake management areas, areas prone to geological disasters, and areas with strict soil environmental control.

[0029] The constraint grading factor automatically divides the identified land parcels into three priority categories: high, medium, and low, according to the strength of control. Farmland within the ecological red line, within the river and lake management area, or within the category of strictly controlled soil environmental quality is classified as high priority; farmland on steep slopes or within the scope of potential geological hazards is classified as medium priority; and only scattered and fragmented land parcels are classified as low priority.

[0030] The final output is a structured priority list of land parcels to be removed, which transforms the subjective judgment of the land parcels to automatic identification and hierarchical sorting by model quantification, thus solving the technical problem of the difficulty in scientifically identifying land parcels to be removed.

[0031] Step S203: Based on multi-source data and a preset multi-dimensional scoring model, determine the list of permanent basic farmland parcels to be transferred from the permanent basic farmland reserve area parcels in the area to be managed. The dimensions of the preset multi-dimensional scoring model include source type, quality conditions, and site conditions.

[0032] In this embodiment of the application, step S203 is the evaluation of the best plots in the permanent basic farmland reserve area.

[0033] Specifically, supported by multi-source data, a pre-set multi-dimensional scoring model is used to quantitatively score the corresponding land parcels in the reserve area from three major evaluation dimensions: In terms of source type, different scores are assigned based on the source of different plots, such as high-standard farmland, newly added cultivated land from comprehensive land consolidation, and cultivated land restored from orchards and forests.

[0034] In terms of quality conditions, the farmland is graded and scored according to its quality level, with higher grades receiving higher scores.

[0035] In terms of site conditions, a comprehensive score is assigned based on the degree of concentration and contiguousness of the plot with existing permanent basic farmland, the slope of the plot, and the conditions for ensuring irrigation resources.

[0036] By determining the weights of the three dimensions through the analytic hierarchy process, the comprehensive scores of the land parcels are calculated using weighted averages and sorted from high to low to form a list of land parcels to be transferred in. This constructs a unified and quantifiable evaluation and admission system for land parcels in the reserve area, solving the problems of traditional reserve area land parcels lacking standards, difficulty in selecting the best, and lack of quantitative evaluation basis.

[0037] Step S204: Based on the priority list for removal and the scoring list of plots to be transferred in, determine the optimization management plan, which is used to guide the transfer in and out of permanent basic farmland.

[0038] In this embodiment, step S204 is the optimization decision-making process. Using a priority-based list of land parcels to be removed as a candidate pool and a scoring list of land parcels to be added as a candidate pool for replenishing reserve areas, multiple adjustment schemes—conservative, balanced, and optimized—are generated based on the principles of balanced area inflow and outflow, no reduction in quality, and optimized layout contiguousness. A comprehensive evaluation is then conducted from three dimensions: quantity balance, changes in arable land quality, and improvement in land parcel contiguousness, to select the optimal management scheme. This scheme clearly defines the specific land parcels to be removed, the corresponding land parcels to be added, the adjustment sequence, and the matching relationship. It is directly used to guide the standardized removal of permanent basic farmland at the grassroots level and the selective addition of land parcels to reserve areas, achieving scientific overall planning, layout optimization, and closed-loop management of inflow and outflow balance throughout the entire process.

[0039] Based on the description of the above embodiments, the method provided by the present invention collects multi-source data of the area to be managed and relies on a multi-factor identification model that includes three types of factors: land fragmentation, unsuitability for cultivation, and constraint classification. This enables the quantitative automatic screening and scientific priority classification of permanent basic farmland parcels to be removed from the land reserve, eliminating the need for traditional manual subjective judgment and effectively solving the problems of inconsistent identification standards, inaccurate judgment, and lack of hierarchical order for parcels to be removed. Simultaneously, by utilizing a multi-dimensional scoring model covering source type, quality conditions, and site conditions, the method performs multi-dimensional quantitative scoring and orderly sorting of parcels in the reserve area, establishing... Establishing a standardized and implementable map patch access evaluation system addresses the shortcomings of lacking systematic evaluation criteria and making it impossible to select the best map patches for transfer to the reserve area. By combining the priority grading list for transfer-out with the scoring list of map patches to be transferred in, an optimized management plan can be formed through overall matching and deduction. This enables the scientific matching and overall optimization of the layout of permanent basic farmland transfer-out and transfer-in, and improves the quantitative, automated, and scientific level of dynamic management of permanent basic farmland. In turn, it solves the technical problems of existing technologies in the management of permanent basic farmland, which make it difficult to scientifically identify map patches to be transferred out and difficult to evaluate map patches to be transferred to the reserve area.

[0040] Optionally, in step S202, based on multi-source data and a preset multi-factor identification model, a priority grading list is determined from the permanent basic farmland in the area to be managed, specifically including the following steps: Step S2021: For each patch in the permanent basic farmland, determine the patch fragmentation factor based on the patch perimeter and patch area, where the patch perimeter and patch area are included in the multi-source data.

[0041] In this embodiment of the application, step S2021 extracts two basic spatial attributes, namely the perimeter and area of ​​each permanent basic farmland patch in the area to be managed, from multi-source data; calculates the regularity of the plot using a fixed shape index formula; and then, in combination with the area threshold and shape index threshold set by regional statistics, quantifies the degree of fragmentation of the plot, thereby forming a plot fragmentation factor.

[0042] Step S2021: From the perspectives of plot scale and form regularity, identify plots that are too small in area and tortuous in shape to be conducive to large-scale farming, which are taken as the first basic indicator for subsequent removal judgment.

[0043] As an example, set a plot area threshold Ta and a shape index threshold Ts, and mark plots with an area < Ta and a shape index > Ts as scattered and fragmented; The method for determining the plot area threshold Ta is: based on the statistical analysis of the areas of permanent basic farmland plots in the area to be managed, take the plot area corresponding to a cumulative area percentage of 20% as the initial value of Ta, and make corrections in combination with the actual situation of the area to be managed.

[0044] The method for determining the plot shape index threshold Ts is: calculate the shape indices of all permanent basic farmland plots in the area to be managed, sort the indices in ascending order of value, and take the shape index value corresponding to the 80th percentile as the threshold Ts, that is, plots with a shape index greater than this value are regarded as relatively fragmented plots. In practical applications, the percentile needs to be appropriately adjusted (such as 75% or 85%) in combination with the topographic features, farming habits and field survey conditions of the area to be managed. The calculation formula of the shape index is as follows: Where: S represents the plot shape index, which is dimensionless and ≥ 1; P represents the perimeter of the plot (in meters), and A represents the area of the plot (in square meters). The more irregular the plot shape and the more tortuous the boundary, the larger S will be.

[0045] Step S2022: For each patch in the permanent basic farmland, determine the non-arable limiting factor according to the permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone area, and strictly controlled cultivated land for soil environmental quality, wherein the permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone area, and strictly controlled cultivated land for soil environmental quality are included in the multi-source data.

[0046] In the embodiment of the present application, step S2022 relies on six types of spatial layers contained in the multi-source data: the permanent basic farmland layer, the slope map, the ecological protection red line, the river and lake management scope, the geological disaster-prone area, and the strictly controlled cultivated land for soil environmental quality, and uses GIS spatial overlay and intersection analysis means to perform spatial matching judgment on each permanent basic farmland patch one by one: whether the slope exceeds the standard, or falls into any non-arable management scope such as the ecological red line, river and lake scope, geological disaster-prone area, and strict soil control area. As long as any spatial condition unsuitable for farming is met, the plot is marked as a non-arable plot, forming a non-arable limiting factor.

[0047] Step S2022 screens plots of land that are unsuitable for continued preservation as permanent basic farmland from multiple hard control dimensions, including topographic conditions, ecological management, water conservancy boundaries, geological safety, and soil environment, either naturally or in terms of policy.

[0048] For example, using GIS intersection or identification tools, the permanent basic farmland layer is spatially overlaid with slope maps (extracting areas with slope ≥15°), ecological protection red lines, river and lake management areas, geological disaster-prone areas, and arable land under strict soil environmental quality control, etc., to extract the patches in the permanent basic farmland that overlap with any unsuitable arable layer. The identification results of each layer are then merged to obtain the unsuitable permanent basic farmland patches as unsuitable arable limiting factors.

[0049] Step S2023: For each patch in the permanent basic farmland, determine the constraint classification factor based on the plot fragmentation factor and the unsuitability for cultivation restriction factor.

[0050] In this embodiment of the application, step S2023, based on the already obtained land fragmentation factor and unsuitable for cultivation restriction factor, classifies each plot according to the degree of control enforcement and policy strength rules: plots that meet any of the conditions such as ecological red line, river and lake range, and strict soil control are given priority to be removed; plots with a slope ≥15° or high risk of geological disasters are recommended to be removed first; and plots that are simply scattered and fragmented can be optimized to be removed. The constraint strength is divided into constraint grading factors.

[0051] Step S2023 transforms the objective judgment results of the previous fragmentation and unsuitability for cultivation into high, medium and low priority levels, providing grading rules to support the subsequent grading list.

[0052] For example, based on the strength of constraints, map patches with strong constraints, such as those located within ecological protection red lines, river and lake management areas, or cultivated land areas under strict soil environmental quality control, are marked as high priority; map patches with relatively strong constraints, such as those with a slope of ≥15° or located in areas prone to geological disasters, are marked as medium priority; and map patches with relatively weak constraints, such as those with scattered and fragmented cultivation, are marked as low priority.

[0053] Step S2024: Input the land fragmentation factor, unsuitability for cultivation restriction factor and constraint classification factor corresponding to each map patch in the permanent basic farmland into the preset multi-factor identification model to obtain the priority classification list.

[0054] In this embodiment of the application, step S2024 inputs the three indicators corresponding to each permanent basic farmland patch—plot fragmentation factor, unsuitable for cultivation restriction factor, and constraint classification factor—into a preset multi-factor identification model. The model automatically performs comprehensive analysis, classification, and sorting of all patches according to built-in quantification rules, overlay logic, and classification standards, and finally outputs a complete priority classification list for the removal of permanent basic farmland.

[0055] Step S2024 achieves multi-factor fusion and comprehensive judgment, replacing manual subjective judgment, and clearly distinguishes the list of map patches with different priorities of high, medium and low removal, providing an accurate list of map patches to be removed for subsequent optimization of permanent basic farmland layout, supplementation of reserve areas, and preparation of entry and exit balance plans.

[0056] For example, if the area to be managed contains three permanent basic farmland plots: Plot A, Plot B, and Plot C, and the fragmentation factor, unsuitability for cultivation, and constraint classification factor for each plot have been pre-determined, after inputting them into a preset multi-factor identification model, the model automatically determines the level and generates a priority list for retrieval. The specific process is as follows: Plot A: Fragmentation factor: Area < Ta threshold, shape index > Ts threshold, sporadic fragmentation.

[0057] Unsuitable for cultivation: falling within the ecological protection red line and / or river and lake management area.

[0058] Constraint grading factor: belongs to the type of strong constraint priority removal.

[0059] Plot B: Fragmentation factor: Large area, regular shape, and no fragmentation.

[0060] Restrictive factors for unsuitable cultivation: slope ≥15°, located in an area prone to geological disasters.

[0061] Constraint grading factor: This belongs to the category of strong constraints and suggests removing it from the list.

[0062] Plot C: Fragmentation factor: small area, tortuous shape, severe sporadic fragmentation.

[0063] Unsuitable for cultivation: No ecological red line, no steep slope, no geological disasters, and no soil control restrictions.

[0064] Constraint grading factor: belongs to the type of weak constraint optimization.

[0065] Next, all three factors of the map patch are entered into the model. The model automatically compares them according to the built-in rules, prioritizing the identification of map patches with ecological red lines, rivers and lakes, or strict soil control factors that are unsuitable for cultivation, and directly designating them as high-priority patches for removal. Secondly, it identifies map patches with steep slopes or areas prone to geological disasters that do not have overlapping red lines and designates them as medium-priority patches for removal. Finally, it identifies map patches that only exceed the fragmentation standard and have no strict control restrictions and designates them as low-priority patches for removal.

[0066] Model output results (priority tier list): Patch A - high priority (adjusted first); Patch B - medium priority (adjusted next); Patch C - low priority (adjusted later for layout optimization).

[0067] Optionally, in step S203, based on multi-source data and a preset multi-dimensional scoring model, a list of permanent basic farmland parcels to be transferred in is determined from the permanent basic farmland reserve area in the area to be managed. This specifically includes the following steps: Step S2031: For each plot in the permanent basic farmland reserve area, determine the source type score according to the source type, wherein the plot source type is included in the multi-source data.

[0068] In this embodiment of the application, step S2031 extracts the source type of each map patch in the reserve area from multi-source data, including high-standard farmland, newly added cultivated land from comprehensive land consolidation, cultivated land restored from orchards and forests, and cultivated land from other sources. According to the preset fixed scoring rules, different source types correspond to different basic scores: high-standard farmland has the highest score, followed by newly added cultivated land from comprehensive land consolidation, then cultivated land restored from orchards and forests, and other sources have the lowest score. A single score for the source type is generated for each map patch, representing the inherent construction qualification and compliance level of the map patch.

[0069] For example, source type scoring (R) is performed on map patches from different sources, such as high-standard farmland, newly added arable land from comprehensive land consolidation, and arable land restored from orchards and woodlands. s High-standard farmland plots receive 100 points; newly added arable land plots from comprehensive land consolidation receive 90 points; plots of arable land restored from orchards and woodlands receive 80 points; and arable land plots from other sources receive 70 points. Step S2032: For each map patch in the permanent basic farmland reserve area, determine the quality condition score based on the arable land quality grade, wherein the arable land quality grade is derived from multi-source data.

[0070] In this embodiment of the application, step S2032 relies on the farmland quality grade results in the multi-source data to retrieve the farmland quality grade corresponding to each candidate map patch; according to the grading and scoring rule that the higher the grade, the lower the score, the first, second and third grades receive high scores, the fourth, fifth and sixth grades receive medium scores, and the seventh, eighth, ninth and tenth grades receive low scores; and directly generates a quality condition single score for each map patch based on the official farmland quality grade to characterize the grain production capacity and soil background quality of the farmland map patch.

[0071] For example, quality condition scoring R based on arable land quality grade. q Grades one, two, and three receive 100 points; grades four, five, and six receive 80 points; and grades seven, eight, nine, and ten receive 60 points. The lower the grade, the better the quality and the higher the score.

[0072] Step S2033: For each map patch in the permanent basic farmland reserve area, determine the site condition score based on the degree of concentration and contiguousness, slope and water resource security conditions of the map patch. The degree of concentration and contiguousness, slope and water resource security conditions are included in the multi-source data.

[0073] In this embodiment, for each candidate plot in the reserve area, a comprehensive site condition score is determined and combined based on three indicators: **concentration and contiguousness:** The spatial distance between the candidate plot and existing permanent basic farmland is calculated; the closer the distance and the better the contiguousness, the higher the score. **plot slope:** A gentler slope is more conducive to cultivation; flat land scores high, gentle slopes score medium, and slopes close to 15° score low. **Water resource security:** Located within the coverage area of ​​irrigation projects scores high, while the absence of a stable irrigation water source scores low. The three indicators are then combined using an arithmetic mean to obtain a comprehensive site condition score for each plot, representing the location, topography, and irrigation / cultivation support of the farmland. The concentration and contiguousness, slope, and water resource security conditions of each candidate plot in the reserve area are included in multi-source data.

[0074] For example, site conditions are scored based on factors such as the degree of contiguousness, slope, and water resource security, and R is calculated using the arithmetic mean. l The degree of contiguousness is scored by calculating the distance *d* between the candidate plots and existing permanent basic farmland. The smaller *d*, the higher the score: *d* < 50 meters = 100 points, *d* 50-150 meters = 90 points, *d* 150-300 meters = 80 points, *d* 300-500 meters = 70 points, and *d* ≥ 500 meters = 60 points. Slope < 2° = 100 points, 2-6° = 80 points, 6-15° = 60 points, and ≥ 15° = 30 points. Water resource security conditions are determined based on whether the plots are within the coverage area of ​​farmland irrigation projects: 100 points for those within the coverage area, and 70 points for others. The score for water resource security conditions is based on whether the plots are within the coverage area of ​​farmland irrigation projects; higher scores are given for those within the coverage area, and lower scores are given for others.

[0075] Step S2034: Input the source type score, quality condition score and site condition score corresponding to each map patch in the permanent basic farmland reserve area into the preset multidimensional scoring model to determine the list of map patch scores to be imported.

[0076] In this embodiment, the calculated scores for source type, quality conditions, and site conditions for each reserve area patch are input into a preset multidimensional scoring model. The model uses fixed weights determined by the analytic hierarchy process to perform a weighted summation of the three scores, calculating the overall score for each patch. All candidate patches are then sorted from highest to lowest overall score, automatically generating a list of patches to be added. The list clearly shows the order of priority for reserve area patches, with higher scores receiving priority for inclusion in the permanent basic farmland rezoning, providing a quantitative basis for selecting the best patches in the subsequent balance of entry and exit schemes.

[0077] For example, the weights of the three sub-factors (source type, quality conditions, and site conditions) are determined using the analytic hierarchy process combined with expert scoring. The specific method is as follows: First, a hierarchical model is established, with the comprehensive score of map features as the target layer and the source type (R) as the layer. s ), quality conditions (R) q ), site conditions (R l The criteria layer consists of a 1-9 scale used by experts to compare the relative importance of each criterion pairwise, constructing a judgment matrix as follows: The weights of each criterion layer are obtained by calculating the matrix using pre-set software. CI = 0.0194, the order of the judgment matrix is ​​3, RI = 0.58, CR = CI / RI = 0.0334 < 0.1, which meets the consistency test conditions.

[0078] Then, the scores of each factor and their corresponding weights are summed to calculate the comprehensive score R of the land parcel. total The calculation formula is as follows: Where R total It is the comprehensive score of the land parcel. The higher the comprehensive score of the land parcel, the better the overall performance of the source type, quality conditions and site conditions of the cultivated land. These are the weights of source type, quality condition, and site condition, determined by the analytic hierarchy process combined with expert scoring, and... All candidate map features are sorted from highest to lowest based on their comprehensive scores to form a list of map features to be added, which serves as the candidate pool for the balance simulation.

[0079] Optionally, in step S204, an optimization management plan is determined based on the priority grading list and the list of map features to be transferred in, specifically including the following steps: Step S2041: Based on the priority grading list and the map feature scoring list to be transferred in, determine multiple sub-management schemes.

[0080] In this embodiment of the application, the priority list for removal is used as the candidate pool for removal plots, and the range of plots that can be removed is divided into high, medium and low removal priorities; the list of plots to be transferred in is used as the candidate pool for transfer-in plots, and high-quality reserve resources for transfer-in are formed by sorting them according to their comprehensive scores.

[0081] Based on the combination and matching logic of the two lists, and following a control approach that progresses from conservative to comprehensive, multiple differentiated sub-management plans are generated: The conservative sub-scheme selects only high-priority map patches to be removed and only matches the highest-scoring high-standard farmland map patches for equal-value transfer; the balanced sub-scheme selects all high and medium-priority map patches to be removed and matches the high-scoring high-quality reserve area map patches for equal-value transfer; the optimized sub-scheme allows all high, medium, and low-priority map patches to be removed and matches the high-scoring reserve area map patches with the best contiguousness with existing permanent basic farmland to be transferred across the entire region.

[0082] It should be noted that each sub-plan follows the principle of balancing the total area transferred out and the total area transferred in, but the size of the area transferred out and the selection strategy for the transferred-in map patches are different, forming three sub-management plans that can be compared and selected.

[0083] Step S2042: Evaluate multiple sub-management plans and determine the optimal management plan.

[0084] In this application embodiment, a quantitative evaluation is conducted on the generated conservative, balanced, and optimized sub-management schemes from three core dimensions: In terms of quantity balance, the total area transferred in each plan is verified to be strictly equal to the total area transferred out, meeting the requirements for balance between input and output. In terms of quality change, the average quality level of the map patches is calculated using the area weighting method to ensure that the overall quality of the transferred map patches is not lower than that of the transferred map patches, achieving no quality degradation. In terms of contiguousness improvement, the average change rate of permanent basic farmland area before and after the adjustment is calculated to evaluate the effect of layout regularization and concentrated contiguous improvement.

[0085] By comparing and scoring the three indicators of each sub-plan, and comprehensively weighing the stability of control, the improvement of cultivated land quality, and the effect of layout optimization, the plan with the best comprehensive benefits and the best meeting the goals of maintaining the quantity, quality, and layout optimization of permanent basic farmland was selected as the final optimized management plan and served as the formal basis for the actual transfer of permanent basic farmland and layout adjustment.

[0086] Optionally, in step S2042, multiple sub-management schemes are evaluated to determine the optimal management scheme, specifically including the following steps: Step S2042a: For each sub-management scheme, determine the average quality level of each sub-management scheme according to the preset quality level calculation formula.

[0087] In this embodiment of the application, for each sub-management scheme, the area of ​​all plots and the corresponding cultivated land quality grade values ​​of the plots to be transferred out and the plots to be transferred in are counted respectively. The average quality grade is determined using a pre-defined area-weighted average quality grade calculation formula: The level value i This refers to the numerical value of the i-th arable land quality grade (1 for grade 1, 2 for grade 2, and so on), and the area. i This refers to the cultivated land area corresponding to the i-th cultivated land grade.

[0088] Step S2042a uses a unified quantitative formula to objectively measure the overall quality of the farmland transferred in and out of each scheme, and rigorously verifies that the average quality level of the transferred-in plots is not lower than that of the transferred-out plots, so as to ensure that the permanent basic farmland is only better and not worse, and that the quality does not decline.

[0089] Step S2042b: For each sub-management scheme, determine the average area change rate of each sub-management scheme according to the preset area change rate calculation formula.

[0090] In this embodiment of the application, step S2042b, for each sub-management scheme, calculates the average area of ​​permanent basic farmland in the entire area before and after the adjustment, by plot; and calculates the improvement in contiguousness of each scheme using the preset area change rate calculation formula. Where A after To determine the adjusted average plot area, A before This represents the average plot area before adjustment.

[0091] Step S2042b uses the change rate value to intuitively reflect the effect of each scheme on the integration of scattered and fragmented plots and the optimization of concentrated and contiguous layout. The higher the change rate, the more obvious the improvement in farmland regularity and large-scale farming conditions.

[0092] Step S2042c: Determine the optimized management plan based on the average quality level and average area change rate corresponding to each sub-management plan.

[0093] In the implementation of this application, step S2042c uses the average quality level and average area change rate calculated for each sub-management scheme as two major evaluation indicators for comprehensive horizontal comparison: First, the basic requirements of balancing the inflow and outflow of land and ensuring that the quality of land transferred in is greater than or equal to the quality of land transferred out are met. Then, priority is given to selecting the scheme with a better average quality grade, a greater average area change rate of the plots, and a more significant improvement effect of contiguous land. Under the premise of taking into account the stability of control, the quality of cultivated land, and the optimization effect of contiguous layout, the scheme with the best overall performance is selected from multiple sub-schemes and determined as the optimization management scheme to be officially implemented.

[0094] Optionally, after determining the optimization management plan based on the priority list for removal and the scoring list for map features to be transferred in, the method further includes: generating standardized ledger data based on the optimization management plan, wherein the standardized ledger data includes ledger data for map features to be removed, ledger data for map features to be transferred in, and ledger data for map features in the reserve area.

[0095] In this embodiment of the application, after comparing multiple options by retrieving the priority grading list and the scoring list of the land parcels to be transferred in, and finally finalizing the optimized management plan for the transfer of permanent basic farmland, the system enters the full-cycle closed-loop traceability management stage. Based on the determined optimized management plan, it automatically organizes and generates standardized ledger data. The ledger is divided into three categories: the ledger of land parcels to be transferred out, the ledger of land parcels to be transferred in, and the ledger of land parcels in the reserve area, so as to realize the full-process traceability, queryability, reporting capability, and traceability of the business.

[0096] Specifically, based solely on the final optimized management plan, the proposed Yongnong map patches to be transferred out, the proposed reserve area map patches to be transferred in, and the remaining candidate map patches remaining in the reserve area that have not been transferred in are all collected in batches according to the preset unified field specifications. Information such as attributes, space, approval, time, and correlation matching relationships are then organized into three sets of structured and standardized ledgers to solve the previous problems of messy data formats, incomplete information, lack of traceability, and fragmented updates.

[0097] For each permanent basic farmland parcel identified for removal in the optimized management plan, a dedicated ledger is established to fully record core management information, including parcel number, original permanent basic farmland batch number, reason for removal, area removed, farmland quality grade, approval document number, removal date, and corresponding replacement parcel number. The ledger clearly records the identity, reason, scale, quality, approval process, and corresponding replacement relationship of each removed parcel, ensuring full traceability, auditability, and reporting capability throughout the removal process.

[0098] For each land parcel to be transferred from the reserve area to permanent basic farmland as determined in the optimized management plan, a ledger should be established. This ledger should include fields such as parcel number, land source type, source project name, acceptance date, farmland quality grade, land slope, whether it is contiguous with existing permanent basic farmland, effective date of transfer, and corresponding parcel number to be transferred out. The ledger should fully record the source qualifications, construction background, natural conditions, contiguousness, and one-to-one matching relationship between the transferred-in parcel and the transferred-out parcel, solidifying the input-output balance correspondence for easy future verification and database updates.

[0099] For all candidate land parcels within the reserve area, including those already transferred in and those still in the reserve, a unified and routine ledger will be established. This ledger will include the land parcel number, the date of entry into the reserve area, the source type, the quality grade, the slope, the contiguousness of the land, irrigation support conditions, and the current usage status (in the reserve, transferred out or transferred in, expired, etc.). The entire reserve area's reserve farmland resources will be subject to routine entry into the reserve, dynamic status updates, and full life-cycle management, thus pre-emptively reserving high-quality reserve resources for the next year's permanent basic farmland assessment and optimization.

[0100] Understandably, by generating three standardized ledgers for transfer-out, transfer-in, and reserve areas at once, the business data of the entire process of permanent basic farmland inspection, reserve, and optimization is standardized, structured, and correlated. This not only meets the business requirements of hierarchical reporting and database updates, but also ensures that the source of map patch adjustments is traceable, the destination is trackable, the rights and responsibilities are traceable, and the status is controllable, thus supporting the dynamic and refined management of permanent basic farmland throughout its entire life cycle.

[0101] Optionally, refer to Figure 3 In some embodiments of the present invention, the permanent basic farmland management method is implemented as follows: S1, Data Acquisition and Preprocessing, is the starting point of the methodology and forms the data foundation for the entire process. It involves collecting multi-source spatial data, such as land use change surveys, farmland quality data, ecological red lines, and slope data, and performing coordinate unification, format conversion, and data cleaning to create a standardized, usable foundational data pool. This directly supports the model analysis in subsequent S2 and S3.

[0102] S2, the identification of land parcels to be removed, is the screening step in the methodology and the first branch decision point. Based on preprocessed data, a multi-factor identification model is used to screen out candidate land parcels with high fragmentation, poor site conditions, and unsuitability for long-term cultivation from existing permanent basic farmland.

[0103] Branch decision: No (no candidate to be removed): The process jumps directly to S3 without adjusting the existing stock; only the reserve area is evaluated.

[0104] Yes (candidates for removal exist): Proceed to the next step to generate a priority list for removal.

[0105] Generating a priority list for land parcels to be removed is a direct output of S2. Based on three factors—land parcel fragmentation, unsuitability for cultivation, and constraint classification—candidate parcels to be removed are categorized into high, medium, and low priorities, forming a structured list. This list serves as the data foundation for the removal end in the subsequent S4 inbound-outbound balance simulation.

[0106] S3, Suitability Evaluation of Reserve Areas, is the reserve selection step in the methodology. Based on multi-source data, a multi-dimensional scoring model is used to quantitatively score and rank the map patches of permanent basic farmland reserve areas from three dimensions: source type, quality conditions, and site conditions, forming a score list of map patches to be transferred in. This list serves as the data foundation for the subsequent S4 inflow-outflow balance simulation.

[0107] S4, the simulation and deduction of the balance between inbound and outbound operations, is the scheme optimization stage of the methodology and process, and also the second branch decision point. Combining the priority list for outbound operations and the scoring list of map patches to be transferred in, multiple sub-management schemes are generated, such as conservative, balanced, and optimized schemes, and evaluated and compared from three dimensions: quantity balance, no reduction in quality, and improvement in contiguousness.

[0108] Branch decision: No (the solution does not meet the control objectives): Return to S4, adjust the matching rules, regenerate the solution, and form an iterative optimization mechanism.

[0109] Yes (the solution meets the control objectives): Determine the final optimized management solution and proceed to S5.

[0110] S5, the dynamic management ledger generation, is the endpoint of the process and a key to business closure. Based on the final optimized management solution, three types of standardized ledger data are generated: the ledger for retrieved map features, the ledger for imported map features, and the ledger for reserve area map features, achieving full-process traceability and manageability.

[0111] Optionally, refer to Figure 4 This application also provides a permanent basic farmland management device, comprising: The data acquisition and preprocessing module is used to acquire and integrate multi-source data, including land change survey data, permanent basic farmland delineation results, cultivated land quality grade results, ecological protection red line data, river and lake management scope data, geological disaster-prone area data, high-standard farmland mapping and database data, and land consolidation project data.

[0112] For example, taking a district in Beijing as the area to be managed, the data obtained includes: 2025 land use change survey data, permanent basic farmland delineation results (2024), annual update results of cultivated land quality grades (2025), ecological protection red line data, river and lake management scope data, geological disaster-prone area data, cultivated land soil environmental quality classification data, high-standard farmland mapping and database entry data from the National Farmland Construction Comprehensive Monitoring and Supervision Platform, and land consolidation project filing data. The above data undergoes coordinate system one (unified to CGCS2000 coordinate system), format conversion (unified to Shapefile format), and data cleaning (removing redundant fields and handling null values).

[0113] The land parcel identification module is connected to the data acquisition and preprocessing module. It is used to automatically filter permanent basic farmland parcels that meet the removal conditions based on a multi-factor comprehensive identification model and generate a removal priority classification list.

[0114] The reserve area suitability evaluation module is connected to the data acquisition and preprocessing module. It is used to perform multi-dimensional evaluation and comprehensive scoring and ranking of permanent basic farmland reserve area patches, and generate a list of patch scores to be imported.

[0115] The inflow-outflow balance simulation and deduction module is connected to the outflow land parcel identification module and the reserve area suitability evaluation module, respectively. It is used to generate a variety of layout optimization and adjustment schemes and conduct multi-dimensional evaluation based on the list of candidate outflow land parcels and the list of land parcels to be transferred in.

[0116] The dynamic management ledger module is connected to the land parcel identification module, the reserve area suitability evaluation module, and the inflow-outflow balance simulation module, respectively. It is used to generate and manage standardized ledgers covering the entire process of land transfer-reservation-transfer-out, and supports data reporting and traceability.

[0117] For example, refer to Figure 5 The land parcel identification module is connected to the data acquisition and preprocessing module. It is used to automatically screen permanent basic farmland parcels that meet the removal criteria based on a multi-factor comprehensive identification model and generate a priority grading list for removal. This module includes a fragmentation evaluation unit, an unsuitability for cultivation evaluation unit, and a multi-factor comprehensive grading unit.

[0118] The fragmentation evaluation unit calculates the fragmentation index of permanent basic farmland plots. In this embodiment, the plot area threshold Ta is set to 5 mu (based on the statistical analysis of the permanent basic farmland plot area in the study area, the plot area corresponding to a cumulative area percentage of 20% is 4.8 mu, rounded to 5 mu). The plot shape index is calculated using the formula S=P / (2×√(π×A)), where P is the plot perimeter, A is the plot area, and the shape index threshold Ts is set to 2.0. Plots with an area < 5 mu and a shape index > 2.0 are marked as scattered fragmentation candidates.

[0119] The unsuitable-for-cultivation assessment unit identifies permanent basic farmland patches with slopes ≥15°, overlapping with ecological protection red lines, located within river and lake management areas, located in geologically hazardous areas, or located in areas under strict soil pollution control. In this embodiment, the permanent basic farmland layer is spatially overlaid with the slope map (generated based on DEM), the ecological protection red line layer, the river and lake management area layer, the geologically hazardous area layer, and the soil pollution strictly control area layer, and the overlapping results of each layer are merged.

[0120] The multi-factor comprehensive grading unit prioritizes map patches based on constraints: patches located within ecological protection red lines, river and lake management areas, or farmland under strict soil environmental quality control are marked as "high priority"; patches with a slope ≥15° or located in geologically hazardous areas are marked as "medium priority"; and scattered, fragmented patches are marked as "low priority". This embodiment identified 23 high-priority patches (125 mu), 67 medium-priority patches (342 mu), and 156 low-priority patches (687 mu).

[0121] For example, refer to Figure 6 The suitability evaluation module for reserve areas, connected to the data acquisition and preprocessing module, is used to perform multi-dimensional evaluation and comprehensive scoring and ranking of permanent basic farmland reserve area patches, generating dynamic update suggestions for the reserve areas. This module includes source type evaluation unit, quality condition evaluation unit, site condition evaluation unit, and comprehensive scoring and ranking unit.

[0122] In this embodiment, the candidate map patches in the reserve area include: newly constructed high-standard farmland map patches in 2025, newly added cultivated map patches from comprehensive land consolidation in the past three years, and cultivated map patches from restored orchards and forests (based on map patches extracted from changes in the 2024-2025 land change survey). A total of 289 candidate map patches (total area of ​​2345 mu) were obtained.

[0123] The source type evaluation unit scores the source type as follows: high-standard farmland scores 100 points, newly added cultivated land from comprehensive land consolidation scores 90 points, cultivated land restored from orchards and forests scores 80 points, and cultivated land from other sources scores 70 points.

[0124] The quality condition evaluation unit scores the farmland based on its quality grade: Grades I, II and III receive 100 points, Grades IV, V and VI receive 80 points, and Grades VII, VIII, IX and X receive 60 points.

[0125] The site condition evaluation unit is scored based on the degree of contiguousness, slope, and water resource security conditions, and R is calculated using the arithmetic mean. l The degree of contiguousness is scored by calculating the distance *d* between the candidate plots and existing permanent basic farmland: 100 points for *d* < 50 meters, 90 points for 50-150 meters, 80 points for 150-300 meters, 70 points for 300-500 meters, and 60 points for ≥ 500 meters. Slope < 2°: 100 points, 80 points for 2°-6°, 60 points for 6°-15°, and 30 points for ≥ 15°. Water resource security conditions are determined based on whether the plots are within the coverage area of ​​farmland irrigation projects: 100 points for those within the coverage area, and 70 points otherwise.

[0126] The comprehensive scoring and ranking unit uses weighted summation based on weights determined by the analytic hierarchy process (AHP). This embodiment obtains weighted scores from five experts (in the fields of farmland protection, farmland construction, and water resources) to ultimately determine the source type weight w. s =0.261, weight of quality condition w q =0.633, Site Condition Weight w l =0.106. Using formula R total =0.261×R s +0.633×R q +0.106×R l Calculate the overall score and sort them from highest to lowest.

[0127] For example, the inflow / outflow balance simulation module is connected to the outflow parcel identification module and the reserve area suitability evaluation module, respectively, and is used to generate multiple layout optimization and adjustment schemes and perform multi-dimensional evaluations based on the outflow grading list and the inflow scoring list. This module includes a scheme generation unit and a multi-dimensional evaluation unit.

[0128] In this embodiment, the total potential for transfer is 1154 mu (125 mu for high priority + 342 mu for medium priority + 687 mu for low priority). The potential of the reserve area is 2345 mu, of which the top 1154 mu in terms of comprehensive score are considered as potential for transfer.

[0129] The scheme generation unit generates three schemes: Conservative Option A: Transfer out 125 mu of high-priority land and transfer in 125 mu of high-standard farmland from the reserve area based on the comprehensive score; Balanced Option B: 467 mu of high and medium priority land parcels will be transferred out, and 467 mu of high-standard farmland and high-quality land parcels with the highest comprehensive scores will be transferred in from the reserve area; Optimized Option C: Remove a total of 1,154 mu of high, medium and low priority land parcels, and prioritize the transfer of reserve land parcels that can form a contiguous area with the existing permanent basic farmland (the top 1,154 mu in terms of comprehensive score).

[0130] The multi-dimensional evaluation unit assessed each scheme from three dimensions: quantity balance, quality change, and improvement in contiguousness. The evaluation results are as follows: Figure 7 As shown: Option A: Quantity balance (125 mu transferred in = 125 mu transferred out), the average quality grade of the transferred-in plots is 6.2, which is higher than the average quality grade of the transferred-out plots of 8.5 (quality improvement of 27.06%), and the average area of ​​permanent basic farmland plots after the adjustment increases from 11.3 mu to 11.8 mu (contiguousness improvement of 4.4%). Option B: Balance the quantity (467 mu transferred in = 467 mu transferred out), the average quality grade of the transferred-in plots is 6.5, which is higher than the average quality grade of the transferred-out plots of 8.1 (quality improvement of 19.75%), and the average area of ​​permanent basic farmland plots after the adjustment increases from 11.3 mu to 12.4 mu (contiguousness improvement of 9.7%). Option C: Quantity balance (1154 mu transferred in = 1154 mu transferred out), the average quality grade of the transferred-in plots is 6.8, which is higher than the average quality grade of the transferred-out plots of 7.8 (quality improvement of 12.82%), and the average area of ​​permanent basic farmland plots after the adjustment increases from 11.3 mu to 13.6 mu (contiguousness improvement of 20.4%).

[0131] For example, the dynamic management ledger module is connected to the land parcel identification module, the reserve area suitability evaluation module, and the inflow-outflow balance simulation module, respectively, to generate and manage standardized ledgers covering the entire process of inflow-reserve-outflow.

[0132] Retrieve map feature ledger (partial examples):

[0133] Importing map feature ledgers (partial examples):

[0134] Reserve area map plot ledger (partial example):

[0135] Based on the same inventive concept, this application also provides a permanent basic farmland management device for implementing the aforementioned permanent basic farmland management method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the permanent basic farmland management device provided below can be found in the limitations of the permanent basic farmland management method described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 8As shown, a permanent basic farmland management device 80 includes: an acquisition module 801 for acquiring multi-source data of the area to be managed; a first determination module 802 for determining a priority grading list of permanent basic farmland to be transferred out from the permanent basic farmland in the area to be managed based on the multi-source data and a preset multi-factor identification model, wherein the factors of the preset multi-factor identification model include land fragmentation factors, unsuitability for cultivation restrictive factors, and constraint grading factors; a second determination module 803 for determining a scoring list of permanent basic farmland to be transferred in from the permanent basic farmland reserve area in the area to be managed based on the multi-source data and a preset multi-dimensional scoring model, wherein the dimensions of the preset multi-dimensional scoring model include source type, quality conditions, and site conditions; and an optimization module 804 for determining an optimized management plan based on the priority grading list of landland to be transferred out and the scoring list of landland to be transferred in, wherein the optimized management plan is used to guide the transfer in and out of permanent basic farmland.

[0137] Optionally, the first determining module 802 is further configured to: for each patch in the permanent basic farmland, determine a plot fragmentation factor based on the plot perimeter and plot area, wherein the plot perimeter and plot area are included in multi-source data; for each patch in the permanent basic farmland, determine an unsuitable-for-cultivation restrictive factor based on the permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone areas, and strictly controlled soil environmental quality cultivated land, wherein the permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone areas, and strictly controlled soil environmental quality cultivated land are included in multi-source data; for each patch in the permanent basic farmland, determine a constraint grading factor based on the plot fragmentation factor and the unsuitable-for-cultivation restrictive factor; and input the plot fragmentation factor, unsuitable-for-cultivation restrictive factor, and constraint grading factor corresponding to each plot in the permanent basic farmland into a preset multi-factor recognition model to obtain a priority grading list.

[0138] Optionally, the second determining module 803 is further configured to: for each map patch in the permanent basic farmland reserve area, determine a source type score based on the source type, wherein the map patch source type is included in multi-source data; for each map patch in the permanent basic farmland reserve area, determine a quality condition score based on the arable land quality grade, wherein the arable land quality grade is derived from multi-source data; for each map patch in the permanent basic farmland reserve area, determine a site condition score based on the concentration and contiguousness of the map patch, slope, and water resource security conditions, wherein the concentration and contiguousness of the map patch, slope, and water resource security conditions are included in multi-source data; input the source type score, quality condition score, and site condition score corresponding to each map patch in the permanent basic farmland reserve area into a preset multi-dimensional scoring model to determine a list of map patch scores to be imported.

[0139] Optionally, the optimization module 804 is also used to: determine multiple sub-management schemes based on the priority grading list and the score list of the map features to be transferred; evaluate the multiple sub-management schemes and determine the optimized management scheme.

[0140] Optionally, the optimization module 804 is further configured to: determine the average quality level of each sub-management scheme according to a preset quality level calculation formula; determine the average area change rate of each sub-management scheme according to a preset area change rate calculation formula; and determine the optimized management scheme based on the average quality level and average area change rate corresponding to each sub-management scheme.

[0141] Optionally, the optimization module is also used to: generate standardized ledger data according to the optimization management plan, wherein the standardized ledger data includes ledger data of map features that have been transferred out, ledger data of map features that have been transferred in, and ledger data of map features in the reserve area.

[0142] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores multi-source data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a permanent basic farmland management method.

[0143] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0144] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0145] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0146] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0148] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0149] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0150] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for managing permanent basic farmland, characterized in that, The methods for managing permanent basic farmland include: Acquire multi-source data for the area to be managed; Based on the multi-source data and the preset multi-factor identification model, a priority grading list is determined from the permanent basic farmland in the area to be managed. The factors of the preset multi-factor identification model include land fragmentation factor, unsuitability for cultivation restriction factor, and constraint grading factor. Based on the multi-source data and the preset multi-dimensional scoring model, a list of permanent basic farmland parcels to be transferred in is determined from the permanent basic farmland reserve area in the area to be managed. The dimensions of the preset multi-dimensional scoring model include source type, quality conditions and site conditions. Based on the priority list for removal and the scoring list of plots to be transferred in, an optimized management plan is determined, wherein the optimized management plan is used to guide the transfer in and out of the permanent basic farmland.

2. The method for managing permanent basic farmland according to claim 1, characterized in that, The step of determining a priority list for removal from the permanent basic farmland in the area to be managed, based on the multi-source data and a preset multi-factor identification model, specifically includes: For each patch in the permanent basic farmland, a fragmentation factor is determined based on the patch perimeter and the patch area, wherein the patch perimeter and the patch area are included in the multi-source data; For each patch in the permanent basic farmland, the unsuitable cultivation restriction factor is determined based on the permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone areas, and strictly controlled soil environmental quality cultivated land. The permanent basic farmland layer, slope map, ecological protection red line, river and lake management scope, geological disaster-prone areas, and strictly controlled soil environmental quality cultivated land are included in the multi-source data. For each patch in the permanent basic farmland, the constraint classification factor is determined based on the plot fragmentation factor and the unsuitability for cultivation restriction factor; The fragmentation factor, unsuitability for cultivation restriction factor, and constraint classification factor corresponding to each patch in the permanent basic farmland are input into the preset multi-factor identification model to obtain the priority classification list.

3. The method for managing permanent basic farmland according to claim 1, characterized in that, The step of determining the list of permanent basic farmland parcels to be transferred from the permanent basic farmland reserve area in the area to be managed, based on the multi-source data and the preset multi-dimensional scoring model, specifically includes: For each map patch in the permanent basic farmland reserve area, a source type score is determined based on the source type, wherein the source type is included in the multi-source data; For each plot in the permanent basic farmland reserve area, a quality condition score is determined based on the arable land quality grade, wherein the arable land quality grade is derived from the multi-source data; For each patch in the permanent basic farmland reserve area, a site condition score is determined based on the patch's degree of concentration and contiguousness, slope, and water resource security conditions, wherein the degree of concentration and contiguousness, slope, and water resource security conditions are included in the multi-source data; The source type score, quality condition score, and site condition score corresponding to each map patch in the permanent basic farmland reserve area are input into the preset multidimensional scoring model to determine the list of map patch scores to be imported.

4. The method for managing permanent basic farmland according to claim 1, characterized in that, The step of determining an optimization management plan based on the priority list for removal and the scoring list of map features to be transferred in specifically includes: Based on the priority list for removal and the scoring list of map features to be transferred in, multiple sub-management schemes are determined; The multiple sub-management schemes are evaluated to determine the optimized management scheme.

5. The method for managing permanent basic farmland according to claim 4, characterized in that, The evaluation of the multiple sub-management schemes to determine the optimized management scheme specifically includes: For each sub-management plan, the average quality level of each sub-management plan is determined according to the preset quality level calculation formula; For each sub-management plan, the average area change rate of each sub-management plan is determined according to the preset area change rate calculation formula; The optimized management scheme is determined based on the average quality level and the average area change rate corresponding to each sub-management scheme.

6. The method for managing permanent basic farmland according to claim 1, characterized in that, After determining the optimization management plan based on the priority grading list and the map feature scoring list to be transferred in, the method further includes: According to the optimized management plan, standardized ledger data is generated, which includes ledger data of map features that have been transferred out, ledger data of map features that have been transferred in, and ledger data of map features in the reserve area.

7. A permanent basic farmland management device, characterized in that, The permanent basic farmland management device includes: The acquisition module is used to acquire multi-source data for the area to be managed. The first determining module is used to determine a priority grading list from the permanent basic farmland in the area to be managed based on the multi-source data and the preset multi-factor identification model, wherein the factors of the preset multi-factor identification model include land fragmentation factor, unsuitable for cultivation restriction factor and constraint grading factor. The second determining module is used to determine the list of permanent basic farmland parcels to be transferred from the permanent basic farmland reserve area in the area to be managed, based on the multi-source data and the preset multi-dimensional scoring model. The dimensions of the preset multi-dimensional scoring model include source type, quality conditions and site conditions. The optimization module is used to determine an optimization management plan based on the priority list for removal and the scoring list of plots to be transferred in, wherein the optimization management plan is used to guide the transfer in and out of the permanent basic farmland.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the permanent basic farmland management method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the permanent basic farmland management method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the permanent basic farmland management method as described in any one of claims 1-6.

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