Cultivated land reserve resource potential grading method based on multi-dimensional cultivated land quality evaluation

Abstract

The invention discloses a cultivated land reserve resource potential grading method based on multi-dimensional cultivated land quality evaluation, particularly relates to the technical field of land resource management, and is used for solving the problems of insufficient cultivated land potential grading precision and lack of ecological constraints caused by single dimension and data splitting of an existing evaluation method. Interpreting land types by verifying and fusing cultivated land quality classification codes and remote sensing, complementing constraint fields such as ecological protection areas and pollution levels, and constructing a dynamic model associated with natural quality parameters and ecological limitation indexes; on the basis of land parcel type matching differentiation screening rules, threshold screening and suitability scoring are carried out on unutilized lands, abandoned garden lands and the like in a classified mode; eliminating ecological sensitive or pollution exceeding land parcels through logic verification, calculating a comprehensive potential score in combination with a multi-dimensional parameter weight and a dynamic threshold value, and dividing the comprehensive potential score into high, medium and low grades according to regional resource requirements; and the cultivated land quality multi-dimensional collaborative evaluation and ecological protection two-way decision is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of land resource management. More specifically, the present invention relates to a method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation. Background Art

[0002] In the field of land resource management, the grading of the potential of cultivated land reserve resources is a key technical basis for implementing the policy of balancing cultivated land occupation and compensation and ensuring food security. Existing technologies usually establish an evaluation system based on the natural attributes of cultivated land (such as soil fertility, topographic conditions), and divide the grades of reserve resources through static indicators in a single dimension. For example, traditional methods evaluate the potential by measuring parameters such as soil physical and chemical properties and slope, and combining the grading regulations of agricultural land. Although such methods can reflect the basic quality of cultivated land, their evaluation dimensions are limited to natural conditions.

[0003] Due to the single dimension and data fragmentation of existing evaluation methods, it is difficult to coordinate the ecological protection requirements and multi-dimensional quality indicators when grading the potential of cultivated land reserve resources, resulting in insufficient accuracy of evaluation results. For example, specifically, ecological constraint conditions (such as reserve area restrictions, soil pollution) are not effectively incorporated into the evaluation model, and there is a lack of collaborative analysis between different data systems (such as quality classification codes and grading indicators), making it impossible to accurately quantify the cultivability grade of reserve resources. This directly affects the scientific implementation of the policy of balancing cultivated land occupation and compensation, especially in complex landforms or ecologically fragile areas, where there is a risk of "occupying high-quality land and compensating with low-quality land". Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation, comprising the following steps: S1. Obtain the cultivated land quality classification code and grading evaluation indicators of the target area, compare the consistency between the land type attributes in the cultivated land quality classification code and the remotely sensed interpreted land types, and generate a verified cultivated land quality classification code by fusing remote sensing image data for inconsistent fields; S2. Perform spatial overlay analysis through calling associated geospatial data to complete the missing ecological reserve area or pollution level fields in the verified cultivated land quality classification code; S3. Perform reference mapping on the completed cultivated land quality classification code and grading evaluation indicators to construct an association model including multi-dimensional quality parameters and ecological constraint indicators; S4. Perform a screening operation according to the matching adaptability evaluation rules for the target plot type; S5. Perform a logical check on the multi-dimensional quality parameters and ecological constraint indicators for the screened target plots. If the preset threshold is met, generate a preliminary screening score; otherwise, mark it as an undevelopable plot. S6. Based on the scoring data generated from the multi-dimensional quality parameters output by the association model and the adaptability evaluation rules, calculate the comprehensive score by superimposing and combining it with the preliminary screening score, and output the potential level.

[0006] In a preferred embodiment, in step S1, when obtaining the cultivated land quality classification code and grading evaluation indicators of the target area, call the cultivated land quality classification code of the target area through the land and resources data platform. The grading evaluation indicators include slope grading, soil organic matter content grading, and irrigation condition grading. When comparing the consistency of the land type attributes in the cultivated land quality classification code with the remotely sensed interpreted land types, extract the land type patch boundaries in the remote sensing image through the geographic information system, perform a spatial overlay of the land type patch boundaries with the plot boundaries in the cultivated land quality classification code, and for plots with a boundary coincidence degree lower than the preset threshold, reclassify the land type attributes based on the vegetation cover index of the remote sensing image to generate a verified cultivated land quality classification code.

[0007] In a preferred embodiment, in step S2, when calling the associated geospatial data, obtain the vector boundary data of the ecological protection area from the ecological environment monitoring platform and obtain the pollution monitoring point data from the soil environment database. When using spatial overlay analysis to complete the missing fields, if the ecological protection area field in the cultivated land quality classification code is missing, perform a spatial intersection analysis of the target plot coordinates with the vector boundary of the ecological protection area to determine whether the plot falls within the protection area. If the pollution level field is missing, generate a pollution level raster map through inverse distance weighted interpolation based on the pollution monitoring point data, and extract the pollution level value corresponding to the target plot coordinates to complete the field.

[0008] In a preferred embodiment, determining whether a plot falls within the protection area includes: Load the target plot coordinate point layer and the ecological protection area surface layer into the geographic information system, use the intersection analysis tool to calculate the spatial intersection. If the spatial intersection is greater than zero, determine that the plot falls within the protection area, and complete the ecological protection area field in the cultivated land quality classification code as "yes"; otherwise, complete it as "no".

[0009] In a preferred embodiment, in step S3, the reference mapping establishes a slope grading mapping table by associating the terrain condition level field in the cultivated land quality classification code with the slope grading field in the grading evaluation indicators. Associate the soil physical and chemical property level field in the cultivated land quality classification code with the organic matter content grading field in the gradation evaluation index, establish a soil fertility grading mapping table. In the constructed association model, the multi-dimensional quality parameters include slope grading and soil fertility grading, and the ecological constraint indicators include ecological reserve identification and pollution level.

[0010] In a preferred embodiment, it is characterized in that in step S4, when matching the adaptability evaluation rule according to the target plot type, the plot type is determined based on the land type attribute field in the verified cultivated land quality classification code. When performing restrictive factor threshold screening on unused land, the restrictive factors include slope grading threshold and soil layer thickness grading threshold; When performing double screening on abandoned garden land, combine the slope grading threshold with the suitability score based on soil pH value and field contiguity degree; When performing dynamic adjustment of the reclamation weight for the restored land type, improve the reclamation priority according to the original cultivated land quality grade field; When performing site condition scoring on dry land, generate a score based on the field surface flatness grading and irrigation guarantee rate grading.

[0011] In a preferred embodiment, in step S5, the logical check includes: comparing the slope grading in the multi-dimensional quality parameters with the slope grading threshold. If the slope grading exceeds the slope grading threshold, mark it as undevelopable; Compare the pollution level in the ecological constraint indicators with the pollution level threshold. If the pollution level exceeds the pollution level threshold, mark it as undevelopable; For the plots that pass the check, generate a preliminary screening score based on slope grading, soil fertility grading, and irrigation guarantee rate grading. The preliminary screening score is the weighted sum of each grading index.

[0012] In a preferred embodiment, in step S6, when calculating the comprehensive score by superposition, based on the slope grading weight, soil fertility grading weight, and irrigation guarantee rate weight output by the association model, combine the weight of the preliminary screening score and the scoring weight generated by the adaptability evaluation rule, and calculate the comprehensive score by weighted accumulation. When outputting the potential level, divide it into three levels: high, medium, and low according to the comprehensive score interval.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. Through the systematic integration of multi-dimensional cultivated land quality parameters and ecological constraint indicators, a comprehensive evaluation system covering natural attributes, ecological restrictions, and plot adaptability is constructed. Based on the reference mapping of cultivated land quality classification codes and grading evaluation indicators, the originally fragmented natural parameters such as soil physical and chemical data, topographic conditions, and irrigation capabilities are associated and modeled with constraint conditions such as ecological protection areas and pollution levels, realizing the upgrade of cultivated land potential evaluation from a single dimension to multi-source collaboration. By dynamically matching the adaptability screening rules for plot types (such as unused land threshold screening and double scoring for abandoned gardens and woodlands), the grading accuracy of complex landforms and mixed land classes is significantly improved. At the same time, the logical verification link takes hard constraints such as over-limit slopes and excessive pollution as veto items to ensure development compliance and avoid the risk of "occupying high-quality land and supplementing low-quality land" from the source. 2. Embed the reverse evaluation mechanism into the grading process to automatically generate ecological restoration labels (such as returning farmland to forests and pollution control) for non-compliant plots, promoting the transformation of cultivated land protection from one-way development to "development-restoration" collaborative governance. Through the multi-dimensional parameter weights output by the correlation model and the dynamic threshold adjustment rules, the evaluation results can adapt to the differences in resource endowments in different regions (such as emphasizing fertility in plains and controlling slopes in hills), avoiding misjudgment of soil and water conditions caused by fixed weights in traditional methods. The finally output high, medium, and low potential levels not only reflect the comprehensive cultivable quality of the plots but also clarify the priority of ecological restoration. Description of the Drawings

[0014] Figure 1 It is a flowchart of the method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation of the present invention. Detailed Embodiments

[0015] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0016] Embodiment: Figure 1 A method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation of the present invention is given, including the following steps: S1. Obtain the cultivated land quality classification code and grading evaluation indicators of the target area, compare the consistency between the land type attributes in the cultivated land quality classification code and the remotely sensed interpreted land types, and fuse the remotely sensed image data for the inconsistent fields to generate a verified cultivated land quality classification code. S2. Through calling the associated geospatial data, perform spatial overlay analysis to complete the missing ecological protection area or pollution level fields in the verified cultivated land quality classification code. S3. Perform reference mapping on the completed cultivated land quality classification code and the grading evaluation indicators to construct an association model that includes multi-dimensional quality parameters and ecological constraint indicators; S4. Perform a screening operation according to the adaptability evaluation rules matched with the target plot type; S5. Perform logical verification on the multi-dimensional quality parameters and ecological constraint indicators of the screened target plots. If the preset threshold is met, generate a preliminary screening score; otherwise, mark it as an undevelopable plot; S6. Based on the multi-dimensional quality parameters output by the association model and the scoring data generated by the adaptability evaluation rules, calculate the comprehensive score by overlaying with the preliminary screening score and output the potential level.

[0017] When obtaining the cultivated land quality classification code and grading evaluation indicators of the target area, call the cultivated land quality classification code of the target area through the land and resources data platform. The land and resources data platform is the "National Land Survey Cloud Platform" uniformly managed by the Ministry of Natural Resources. This platform stores national cultivated land resource data. When calling, based on the administrative division code and plot identification code of the target area, batch download the cultivated land quality classification code table that includes land type attributes, terrain condition levels, and soil physical and chemical property levels. The grading evaluation indicators include slope grading, soil organic matter content grading, and irrigation condition grading. Among them, the slope grading is divided into multiple levels according to the "Cultivated Land Quality Grade Standard". For example, a slope ≤ 2° is first-class land, 2° - 6° is second-class land, 6° - 15° is third-class land, 15° - 25° is fourth-class land, 25° - 35° is fifth-class land, and > 35° is sixth-class land; the soil organic matter content grading is divided into multiple levels according to the content range. For example, ≥ 40 g / kg is first-class, 30 - 40 g / kg is second-class, 20 - 30 g / kg is third-class, 10 - 20 g / kg is fourth-class, and < 10 g / kg is fifth-class; the irrigation condition grading is divided into multiple levels according to the water source guarantee rate and the completeness of irrigation facilities. For example, "fully satisfied" is first-class, "basically satisfied" is second-class, "generally satisfied" is third-class, and "not satisfied" is fourth-class.

[0018] When comparing the consistency of the land type attributes in the cultivated land quality classification code with the remotely sensed interpreted land types, the land type patch boundaries in the multispectral satellite images are extracted through a geographic information system. The geographic information system is the ArcGIS software. The specific operation is as follows: Use the "image classification tool" in the ArcGIS platform to perform supervised classification on the multispectral images, select the support vector machine algorithm. The training samples include six land types: cultivated land, forest land, grassland, water area, construction land, and unused land. After generating the land type classification results, use the "raster to polygon tool" to convert the classification results into vector-format land type patch boundaries. Perform spatial overlay on the land type patch boundaries and the plot boundaries in the cultivated land quality classification code. The specific method is: Use the "intersect analysis tool" in ArcGIS to calculate the proportion of the overlapping area of the two sets of boundary data. If the overlap degree is lower than the preset threshold (for example, 85%), it is determined that the land type attributes are inconsistent.

[0019] For plots with a boundary overlap degree lower than the preset threshold, reclassify the land type attributes based on the vegetation cover index of the remote sensing images, which specifically includes: Calculate the normalized difference vegetation index of the multispectral satellite images. The normalized difference vegetation index is obtained by dividing the difference between the reflectance of the near-infrared band and the reflectance of the red band by the sum of the two. Perform segmented processing on the values of the normalized difference vegetation index. For example, set the normalized difference vegetation index ≥ 0.6 as the high vegetation cover area (corresponding to cultivated land or forest land), 0.2 ≤ the normalized difference vegetation index < 0.6 as the medium-low vegetation cover area (corresponding to grassland or unused land), and the normalized difference vegetation index < 0.2 as the non-vegetation cover area (corresponding to construction land or water area). Based on the normalized difference vegetation index interval and spatial distribution characteristics of the land type patches, combined with on-site verification data (such as the cultivated land distribution map of the agricultural department or the field survey records), correct the land type attributes of the inconsistent plots to "cultivated land", "non-cultivated land", or "unused land", and generate a verified cultivated land quality classification code table.

[0020] For example, for a certain area in Chongqing, for a plot marked as "cultivated land" in the original cultivated land quality classification code, through overlay analysis, it is found that its overlap degree with the remotely sensed interpreted forest land boundary is only 70%, and the normalized difference vegetation index value is 0.65 (high vegetation cover). However, on-site verification shows that the plot is actually an orchard (non-cultivated land). Then, the land type attribute of this plot is corrected to "non-cultivated land" and updated to "forest land" in the verified cultivated land quality classification code table. The verified cultivated land quality classification code table will be used as the input data for the subsequent steps, for ecological constraint field completion and associated model construction.

[0021] When calling associated geospatial data, vector boundary data of ecological protection areas is obtained from the ecological environment monitoring platform, which is the "National Ecological Protection Red Line Supervision Platform" uniformly managed by the Ministry of Ecology and Environment. When calling, based on the administrative division code of the target area (such as the code 500101 for Wanzhou District, Chongqing City) and the plot coordinate range, vector boundary data of ecological protection areas including nature reserves, water source protection areas, etc. is downloaded. The data format is Shapefile, which contains the spatial coordinates and attribute information of the vector polygon layer. Pollution monitoring point data is obtained from the soil environment database, which is the "National Soil Pollution Survey Database" managed by the Ministry of Ecology and Environment. When calling, based on the geographical scope of the target area, monitoring point data containing detection values such as heavy metal content (such as cadmium, lead) and organic pollutant concentration is screened. The data format is a table file containing sampling point coordinates, pollutant types, and concentration values.

[0022] When using spatial overlay analysis to complete missing fields, if the ecological protection area field in the cultivated land quality classification code is missing, the spatial intersection analysis is performed between the target plot coordinates and the vector boundary of the ecological protection area through the intersection analysis tool in the geographic information system. The specific operation is as follows: In the ArcGIS software, load the target plot coordinate point layer (in the format of point feature class) and the ecological protection area polygon layer (in the format of polygon feature class), and use the "Intersection Analysis Tool" to calculate the spatial intersection of the target plot coordinate points and the ecological protection area polygon layer. If the intersection area is greater than zero (i.e., the target plot coordinate points are within the boundary of the ecological protection area polygon layer), it is determined that the plot falls within the protection area, and the "Ecological Protection Area" field in the cultivated land quality classification code table is completed as "Yes"; if the intersection area is zero, it is completed as "No". For example, if the coordinates of a plot are 108.5° east longitude and 29.8° north latitude, and through intersection analysis, it is found that it is within a certain nature reserve polygon layer, then mark the "Ecological Protection Area" of this plot as "Yes".

[0023] If the pollution level field is missing, a pollution level raster map is generated based on the pollution monitoring point data through the inverse distance weighted interpolation algorithm. The specific operation is as follows: Load the pollution monitoring point data in the ArcGIS software, select the pollutant type (such as cadmium content) as the interpolation field, use the "Inverse Distance Weighted Interpolation Tool" (IDW), set the search radius to twice the average spacing of the monitoring points (for example, if the average spacing of the monitoring points is 5 km, the search radius is 10 km), perform weighted averaging based on the reciprocal relationship between the pollutant concentration value at the monitoring point and the distance, and generate a raster map of the spatial distribution of the pollutant concentration (resolution: 30 m × 30 m). Subsequently, use the "Extract Values to Points Tool" to extract the raster value (i.e., the pollutant concentration value) corresponding to the target plot coordinates into the cultivated land quality classification coding table, and divide the pollution level field according to the preset pollution level threshold (for example, cadmium content ≤ 0.3 mg / kg is safe, 0.3 - 1.0 mg / kg is slightly polluted, > 1.0 mg / kg is severely polluted). For example, if the raster value corresponding to a plot coordinate is 0.8 mg / kg, mark its pollution level as "slightly polluted".

[0024] The completed cultivated land quality classification coding table will be used as the input data for subsequent steps to construct the association model and perform logical verification.

[0025] Reference mapping establishes a slope grading mapping table by associating the terrain condition level field in the cultivated land quality classification coding with the slope grading field in the grading evaluation indicators. The terrain condition level field includes slope range descriptions (such as flat slope, gentle slope, sloping slope, steep slope, etc.), and the slope grading field in the grading evaluation indicators is divided into multiple grades according to the "Cultivated Land Quality Grade Standard" (for example, slope ≤ 2° is first-class land, 2° - 6° is second-class land, 6° - 15° is third-class land, 15° - 25° is fourth-class land, 25° - 35° is fifth-class land, > 35° is sixth-class land). When associating, match the slope range description of each plot in the cultivated land quality classification coding with the slope grading interval in the grading evaluation indicators. For example, match "gentle slope" with second-class land (2° - 6°) and "steep slope" with fifth-class land (25° - 35°) to generate a slope grading mapping table, which contains the corresponding relationship between the terrain condition level field in the cultivated land quality classification coding and the slope grading field in the grading evaluation indicators.

[0026] Associate the soil physical and chemical property level field in the associated cultivated land quality classification code with the organic matter content grading field in the gradation evaluation index to establish a soil fertility grading mapping table. The soil physical and chemical property level field includes descriptions of the organic matter content range (such as rich, relatively rich, average, relatively barren, barren), and the organic matter content grading field in the gradation evaluation index is divided into multiple grades according to the content range (such as ≥40 g / kg is the first grade, 30 - 40 g / kg is the second grade, 20 - 30 g / kg is the third grade, 10 - 20 g / kg is the fourth grade, <10 g / kg is the fifth grade). When associating, match the description of the organic matter content range of each plot in the cultivated land quality classification code with the organic matter content grading interval in the gradation evaluation index. For example, match "relatively rich" with the second grade (30 - 40 g / kg), and "average" with the third grade (20 - 30 g / kg) to generate a soil fertility grading mapping table. The soil fertility grading mapping table contains the corresponding relationship between the soil physical and chemical property level field in the cultivated land quality classification code and the organic matter content grading field in the gradation evaluation index.

[0027] In the constructed association model, the multi-dimensional quality parameters include slope grading and soil fertility grading. The slope grading comes from the matching grade results in the slope grading mapping table, and the soil fertility grading comes from the matching grade results in the soil fertility grading mapping table. The ecological constraint indicators include ecological reserve identification and pollution level. The ecological reserve identification is assigned with "yes" or "no" fields completed in step S2, and the pollution level is assigned with the pollution level field completed in step S2 (such as safe, slightly polluted, severely polluted). The association model generates a complete parameter table containing slope grading, soil fertility grading, ecological reserve identification, and pollution level for each plot by integrating the multi-dimensional quality parameters and ecological constraint indicators. The complete parameter table will be used as the input data for subsequent steps for adaptive evaluation rule screening and logical verification.

[0028] For example, in the cultivated land quality classification code of a certain plot, the terrain condition level field is "gentle slope", and the soil physical and chemical property level field is "relatively rich". After reference mapping, the slope grading is mapped to the second-class land (2° - 6°), the soil fertility grading is mapped to the second grade (30 - 40 g / kg), and at the same time, the ecological reserve identification is "no" and the pollution level is "safe". Then the multi-dimensional quality parameters of this plot in the association model are slope second grade and soil fertility second grade, and the ecological constraint indicators are unprotected and pollution-free.

[0029] During the construction process of the association model, the weight distribution of slope grading and soil fertility grading is set according to the priority in the gradation evaluation index. For example, the slope grading has a higher weight impact on cultivated land potential, followed by soil fertility grading. The specific weight ratio is determined through expert experience or historical data analysis. For example, the slope grading weight accounts for 60%, and the soil fertility grading weight accounts for 40%. The weight distribution logic is embedded in the association model for calculating the comprehensive score in subsequent steps.

[0030] When matching the adaptability evaluation rules according to the target plot type, the plot type is determined based on the land type attribute field in the verified cultivated land quality classification code. The verified cultivated land quality classification code is the data table containing the corrected land type attributes generated in step S1. The land type attribute fields include types such as unused land, abandoned garden land, restored land type, dry land, etc. When determining the plot type, by traversing the land type attribute fields in the verified cultivated land quality classification code table, the plot is classified into the corresponding type. For example, if the land type attribute field of a certain plot is "unused land", it is determined as the unused land type.

[0031] When performing the restrictive factor threshold screening on unused land, the restrictive factors include the slope grading threshold and the soil layer thickness grading threshold. The slope grading threshold is set according to the slope grading parameters in the association model constructed in step S3. For example, the slope grading of class IV land (15° - 25°) and below is developable, and that of class V land (25° - 35°) and above is not developable; the soil layer thickness grading threshold is set according to the "Cultivated Land Quality Grade Standard". For example, when the soil layer thickness ≥ 30 cm, it is developable, and when < 30 cm, it is not developable. During the screening, traverse the slope grading and soil layer thickness grading fields of the unused land plots. If the slope grading ≤ class IV land and the soil layer thickness ≥ 30 cm, it is marked as a developable plot, otherwise it is marked as a non - developable plot. For example, for an unused land plot with a slope grading of class V land (30°) and a soil layer thickness of 25 cm, it is determined as non - developable.

[0032] When performing double - screening on abandoned garden land, it combines the slope grading threshold and the suitability score based on soil pH value and field block contiguity. The slope grading threshold is the same as the unused land screening rule (for example, slope ≤ class IV land); the soil pH value suitability score is set according to the crop growth suitability. For example, when the pH value is 6.0 - 7.5, it is suitable (score 80 - 100 points), when it is 5.5 - 6.0 or 7.5 - 8.0, it is relatively suitable (score 60 - 80 points), and otherwise it is unsuitable (score < 60 points); the field block contiguity score is set according to the land type consistency of adjacent plots. For example, when the contiguous area ≥ 10 hectares, it is excellent (score 90 - 100 points), when it is 5 - 10 hectares, it is good (score 70 - 90 points), and when < 5 hectares, it is poor (score < 70 points).

[0033] During the screening, for plots with a slope ≤ class IV land, calculate the weighted average of the soil pH value and the field block contiguity score (for example, the pH value weight is 60% and the contiguity weight is 40%). If the comprehensive score ≥ 60 points, it is marked as developable, otherwise it is marked as non - developable. For example, for an abandoned garden land plot with a slope grading of class III land (10°), a soil pH value of 6.8 (score 85 points), and a field block contiguity of 8 hectares (score 80 points), the comprehensive score is 85×0.6 + 80×0.4 = 83 points, and it is determined as developable.

[0034] When dynamically adjusting the reclamation weight for restored land types, the reclamation priority is enhanced based on the original cultivated land quality grade field. The original cultivated land quality grade field refers to the historical cultivated land quality grade of the plot recorded in the verified cultivated land quality classification code (e.g., grades 1 - 10, with grade 1 being the best). The reclamation weight adjustment rule is as follows: If the original cultivated land quality grade is between 7 - 10, the reclamation weight is increased by 20%; if it is between 4 - 6, the weight is increased by 10%; if it is between 1 - 3, the original weight is maintained. For example, if the original cultivated land quality grade of a restored land type plot is 8, the reclamation weight is increased from the base value of 1.0 to 1.2, indicating that its reclamation priority is higher than that of lower - grade plots.

[0035] When conducting site condition scoring for dry land, the score is generated based on the grading of field surface flatness and irrigation assurance rate. The grading of field surface flatness is set according to the elevation difference within the field block. For example, when the elevation difference ≤ 15 cm, it is considered flat (score 90 - 100 points); when the elevation difference is 15 - 30 cm, it is considered relatively flat (score 70 - 90 points); when the elevation difference > 30 cm, it is considered uneven (score < 70 points). The grading of irrigation assurance rate is set according to the stability of water source supply. For example, when the assurance rate ≥ 80%, it is considered sufficient (score 90 - 100 points); when the assurance rate is 50% - 80%, it is considered basic (score 70 - 90 points); when the assurance rate < 50%, it is considered insufficient (score < 70 points). When scoring, a weighted average of the scores for field surface flatness and irrigation assurance rate is taken (e.g., flatness weight 50%, irrigation weight 50%) to generate the comprehensive site condition score. If the score ≥ 60 points, it is marked as developable; otherwise, it is marked as non - developable. For example, if the elevation difference of a dry land's field surface flatness is 10 cm (score 95 points) and the irrigation assurance rate is 75% (score 80 points), then the comprehensive score is 95×0.5 + 80×0.5 = 87.5 points, and it is determined to be developable.

[0036] The screened developable plots will enter the logical verification in step S5, while the non - developable plots are directly marked and excluded. The screening thresholds and scoring weights for various types of plots in the adaptability evaluation rules are set according to the cultivated land protection technical guidelines issued by the agricultural department. For example, the slope grading threshold refers to the "Technical Specification for Sloping Cultivated Land Treatment", and the scoring for field block connectivity refers to the "High - Standard Farmland Construction Standard". The specific values of the thresholds and weights can be adjusted according to the actual needs of local agricultural management agencies. For example, in hilly areas, the slope threshold is relaxed to 20° to adapt to the terrain conditions.

[0037] Step S4 addresses the problem of insufficient accuracy caused by the "single rule applying to all land types" in traditional methods by matching differential evaluation rules according to land parcel types. For unused land, restrictive factor thresholds are used for screening to avoid developing high-risk parcels (such as steep slopes and thin soil layers); for abandoned garden land, slope and suitability scores are combined to take into account both natural constraints and cultivation potential; for restored land types, the reclamation weights are dynamically adjusted to prioritize the restoration of arable land with high historical grades; for dry land, the emphasis is on site condition scoring to ensure cultivation feasibility. Compared with the prior art, the dual screening (slope + soil pH) for abandoned garden land in this step solves the problem of omission of high-quality parcels caused by simply excluding based on slope in the traditional method, and improves the accurate identification rate of developable parcels (for example, parcels with qualified slopes but acidified soil are effectively excluded).

[0038] The logical check includes: comparing the slope classification in the multi-dimensional quality parameters with the slope classification threshold. If the slope classification exceeds the slope classification threshold, it is marked as undevelopable. The slope classification threshold is derived from the slope classification parameters defined in the association model constructed in step S3. For example, the slope classification of grade five land (25° - 35°) and above is the undevelopable threshold. If the slope classification of a land parcel is grade five land or grade six land (such as a slope of 30° or 40°), it is directly marked as an undevelopable land parcel. The slope classification parameters are based on the slope classification mapping table in step S3, which maps the terrain condition level field in the arable land quality classification code to a specific slope interval. For example, "steep slope" corresponds to grade five land (25° - 35°). If the terrain condition level field of a land parcel is "steep slope", the slope classification is grade five land, exceeding the developable threshold of grade four land (15° - 25°), and it is determined to be undevelopable.

[0039] Compare the pollution level in the ecological constraint indicators with the pollution level threshold. If the pollution level exceeds the pollution level threshold, it is marked as undevelopable. The pollution level threshold is set based on the pollution level field supplemented in step S2. For example, the pollution level is divided into three levels: "safe", "lightly polluted", and "severely polluted", and "severely polluted" is set as the undevelopable threshold. If the pollution level field of a land parcel is "severely polluted", it is directly marked as undevelopable. The pollution level field is generated by inverse distance weighted interpolation in step S2. For example, the pollution level raster value corresponding to the coordinates of a land parcel is a cadmium content of 1.5 mg / kg. According to the threshold division rule (cadmium content > 1.0 mg / kg is severely polluted), its pollution level is determined to be "severely polluted".

[0040] For the plots that pass the verification, preliminary screening scores are generated based on slope classification, soil fertility classification, and irrigation guarantee rate classification. The preliminary screening scores are the weighted sum of each classification index. The slope classification and soil fertility classification are derived from the multi-dimensional quality parameters in the association model constructed in step S3. For example, the slope classification is the third-class land (6° - 15°), and the soil fertility classification is the second class (30 - 40 g / kg); the irrigation guarantee rate classification is derived from the irrigation guarantee rate classification field in the dryland site condition scoring in step S4. For example, the irrigation guarantee rate is "basically satisfied" (score 80 points). The weight distribution is set according to the priority of cultivated land quality evaluation. For example, the slope classification weight is 40%, the soil fertility classification weight is 30%, and the irrigation guarantee rate weight is 30%. Then the preliminary screening score is the third-class land (corresponding score 70 points) × 40% + the second-class fertility (corresponding score 80 points) × 30% + the irrigation guarantee rate 80 points × 30% = 70 × 0.4 + 80 × 0.3 + 80 × 0.3 = 76 points. The weight ratio is determined through expert experience or historical data analysis. For example, in hilly areas, due to the greater impact of slope, the slope weight is increased to 50%.

[0041] For example, the slope classification of a certain plot is the fourth-class land (20°), the soil fertility classification is the third class (25 g / kg), the irrigation guarantee rate classification is "generally satisfied" (score 70 points), and the weight distribution is slope 40%, soil fertility 30%, and irrigation guarantee rate 30%. Then the preliminary screening score is the corresponding score 60 points for 20° × 40% + the corresponding score 70 points for 25 g / kg × 30% + 70 points × 30% = 60 × 0.4 + 70 × 0.3 + 70 × 0.3 = 66 points. If the preliminary screening score threshold is set at 70 points, then this plot is marked as undevelopable. The preliminary screening score threshold is dynamically adjusted according to the supply and demand situation of regional cultivated land resources. For example, in areas with scarce cultivated land, the threshold is reduced to 60 points to expand the developable range.

[0042] The plots after logical verification are divided into two categories: the plots that pass the verification enter the comprehensive score calculation in step S6, and the plots that do not pass are marked as undevelopable and an ecological restoration priority identifier is generated. The generation logic of the ecological restoration priority identifier is as follows: if the plot is undevelopable due to exceeding the slope limit, it is marked as "Terrain restriction requires returning farmland to forest"; if it is undevelopable due to exceeding the pollution limit, it is marked as "Soil pollution requires treatment and restoration". For example, a certain plot is marked as undevelopable because its slope classification is the fifth-class land (30°), then an identifier of "Terrain restriction requires returning farmland to forest" is generated to guide subsequent ecological restoration projects.

[0043] Step S5 generates an identification for multi-dimensional parameter verification and reverse ecological restoration to address the decision-making imbalance of "emphasizing development over restoration" in traditional grading methods. Hard thresholds such as slope and pollution level are used as a veto item to prevent misjudging ecologically sensitive or polluted plots as developable. For plots that pass the verification, a preliminary screening score is generated by weighting parameters such as slope and soil fertility to quantify the potential of arable land quality. Compared with the existing technology, it introduces a two-way logic of "verification-restoration". For example, plots with excessive slope are marked with the identification of "returning farmland to forest", which is directly associated with subsequent ecological governance, realizing the synchronous output of development potential grading and ecological restoration needs (such as accurately identifying 30% of degraded plots that need restoration), and promoting the upgrade of cultivated land protection from "passive screening" to "active restoration".

[0044] When calculating the comprehensive score by superposition, based on the slope grading weight, soil fertility grading weight, and irrigation guarantee rate weight output by the correlation model, combined with the score weight generated by the weight of the preliminary screening score and the adaptability evaluation rule, the comprehensive score is calculated by weighted accumulation. The slope grading weight, soil fertility grading weight, and irrigation guarantee rate weight output by the correlation model are derived from the preset weight distribution rules in the correlation model constructed in step S3. For example, the slope grading weight is 40%, the soil fertility grading weight is 30%, and the irrigation guarantee rate weight is 30%. The weight of the preliminary screening score is derived from the priority ratio set in the logical verification of step S5. For example, the weight of the preliminary screening score is 50%. The score weight generated by the adaptability evaluation rule is derived from the classification and screening rules of step S4. For example, the suitability score weight for abandoned garden land is 60%, and the site condition score weight for dry land is 50%. When calculating by weighted accumulation, multiply the weight of each parameter by its corresponding grading score value and accumulate. For example, the slope grading score of a certain plot is 70 points (weight 40%), the soil fertility grading score is 80 points (weight 30%), the irrigation guarantee rate score is 75 points (weight 30%), the preliminary screening score is 76 points (weight 50%), and the adaptability evaluation score is 80 points (weight 50%). Then the comprehensive score is (70×0.4 + 80×0.3 + 75×0.3)×0.5 + 80×0.5 = 73.25 + 40 = 113.25 points (assuming the total score range is 0-200 points).

[0045] When outputting the potential level, it is divided into three levels: high, medium, and low according to the comprehensive score range. The comprehensive score range is dynamically adjusted according to the supply and demand situation of cultivated land resources in the target area. For example, in areas with scarce cultivated land resources, the high-potential level is set as the comprehensive score ≥ 150 points, the medium potential is 100 - 149 points, and the low potential is < 100 points; in areas with abundant cultivated land resources, the high potential is set as ≥ 120 points, the medium potential is 80 - 119 points, and the low potential is < 80 points. For example, if the comprehensive score of a certain plot is 113.25 points, it is classified as a medium-potential level in the scarce area and a high-potential level in the abundant area. The interval adjustment rule is set according to the cultivated land protection policy of the local agricultural management department. For example, to ensure food security, the threshold of the high-potential level is reduced by 10%.

[0046] During the weighted cumulative calculation process, the weight distribution logic is set according to the priority of cultivated land quality evaluation. For example, in plain areas, since soil fertility has a greater impact on cultivated land quality, the weight of soil fertility classification is increased to 40% and the weight of slope classification is reduced to 30%; in hilly areas, due to prominent terrain restrictions, the weight of slope classification is increased to 50%. The weight adjustment rule is determined through expert meetings or historical data analysis. For example, based on the cultivated land development data in a certain area in the past five years, the influence weights of parameters such as slope and fertility on the development success rate are statistically analyzed, and the weight ratio is dynamically optimized.

[0047] For example, a plot in a plain area has a slope classification of third-class land (10°), a soil fertility classification of second-class (35g / kg), an irrigation guarantee rate of "fully satisfied" (scoring 90 points), a preliminary screening score of 85 points, and an adaptability evaluation score of 88 points. When calculating the weighted score, the slope weight is 30%, the soil fertility weight is 40%, the irrigation weight is 30%, the preliminary screening weight is 50%, and the adaptability evaluation weight is 50%. Then the comprehensive score is (70 points corresponding to 10° × 30% + 80 points corresponding to 35g / kg × 40% + 90 points × 30%) × 50% + 88 points × 50% = (21 + 32 + 27) × 0.5 + 44 = 40 + 44 = 84 points. If the threshold of the high-potential level in this area is 80 points, then this plot is classified as a high-potential level.

[0048] The calculation result of the comprehensive score and the potential level division rule are visually output through the cultivated land resource management platform, generating a spatial distribution map and data report containing high, medium, and low potential levels. The output result is directly used for the site selection decision of the cultivated land occupation and compensation balance project. For example, high-potential plots are preferentially developed, and for low-potential plots, a reclamation plan is formulated in combination with ecological restoration identification. For example, in a certain area, the high-potential plots account for 30% of the total area, and 80% of them are located in the plain area with excellent irrigation conditions, which is consistent with the historical cultivated land distribution law, verifying the rationality of the grading of this method.

[0049] Step S6 addresses the problem of insufficient regional adaptability caused by "static weight allocation" and "fixed grading criteria" in traditional methods through multi-source data weight superposition and dynamic grading threshold setting. Compared with the prior art, first, it integrates multi-dimensional weights of correlation model parameters (slope, soil fertility, irrigation weight), preliminary screening scores, and adaptability scoring rules. For example, it increases the soil fertility weight to 40% in plain areas and the slope weight to 50% in hilly areas to achieve adaptive optimization of the grading model. Second, it dynamically adjusts the grading threshold according to the supply and demand of regional cultivated land (such as lowering the high-potential threshold in shortage areas) to avoid resource misallocation caused by "one-size-fits-all". Through the coordination of dynamic weights and thresholds, it improves the regional adaptability of potential grading (such as accurately identifying suitable cultivated areas in plains and restoration areas in hills), and at the same time solves the grading result deviation caused by data fragmentation in traditional methods (such as only using slope or fertility). For example, it corrects a plot with fertile soil but insufficient irrigation from "high potential" to "medium potential".

[0050] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and threshold selections in the calculations are set by those skilled in the art according to the actual situation.

[0051] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0052] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0053] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0054] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0055] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0056] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0057] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes various media that can store program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0058] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0059] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation, characterized in that: The steps include: S1. Obtain the cultivated land quality classification code and grading evaluation index of the target area, compare the consistency of the land attributes in the cultivated land quality classification code with the land class interpreted by remote sensing, and generate the verified cultivated land quality classification code by fusing the remote sensing image data for the inconsistent fields; S2. Perform spatial overlay analysis to complete the missing ecological protection area or pollution level fields in the verified cultivated land quality classification code by calling the associated geographic spatial data; S3. Compare and map the completed cultivated land quality classification codes with the grading evaluation indicators, and construct a correlation model including multi-dimensional quality parameters and ecological constraint indicators; S4, performing screening operations according to the target plot type matching adaptability evaluation rules; S5. Perform logic verification of multi-dimensional quality parameters and ecological constraint indicators on the selected target plots. If the preset threshold is met, a preliminary screening score is generated. Otherwise, it is marked as a non-developable plot. S6. Based on the multidimensional quality parameters output by the association model and the scoring data generated by the adaptive evaluation rules, the initial screening scores are combined to calculate the comprehensive score and output the potential level.

2. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 1 is characterized in that: In step S1, when obtaining the cultivated land quality classification code and grading evaluation index of the target area, the cultivated land quality classification code of the target area is called through the land and resources data platform, and the grading evaluation index includes slope classification, soil organic matter content classification and irrigation condition classification; When comparing the consistency between the land attributes in the cultivated land quality classification code and the land class interpreted by remote sensing, the land class patch boundaries in the remote sensing image are extracted through the geographic information system, and the land class patch boundaries are spatially superimposed with the land plot boundaries in the cultivated land quality classification code. For plots whose boundary overlap is lower than the preset threshold, the land class attributes are reclassified based on the vegetation cover index of the remote sensing image to generate the verified cultivated land quality classification code.

3. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 1 is characterized in that: In step S2, when the associated geographic spatial data is called, the vector boundary data of the ecological protection area is obtained from the ecological environment monitoring platform, and the pollution monitoring point data is obtained from the soil environment database; When the spatial overlay analysis is used to complete missing fields, if the ecological protection zone field is missing in the cultivated land quality classification code, the target plot coordinates are spatially intersected with the vector boundary of the ecological protection zone to determine whether the plot falls within the protection zone. If the pollution level field is missing, a pollution level raster map is generated through inverse distance weighted interpolation based on the pollution monitoring point data, and the pollution level value corresponding to the target plot coordinates is extracted to complete the field.

4. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 3 is characterized in that: Determining whether a plot of land falls within the protected area includes: Load the target plot coordinate point layer and the ecological protection zone surface layer into the geographic information system, and use the intersection analysis tool to calculate the spatial intersection. If the spatial intersection is greater than zero, the plot is determined to fall within the protection zone, and the ecological protection zone field is completed as yes in the cultivated land quality classification code, otherwise it is completed as no.

5. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 1, characterized in that: In step S3, the reference mapping establishes a slope classification mapping table by associating the terrain condition level field in the cultivated land quality classification code with the slope classification field in the grading evaluation index; The soil physical and chemical property level fields in the cultivated land quality classification code are associated with the organic matter content classification fields in the grading evaluation indicators, and a soil fertility classification mapping table is established. In the constructed association model, the multidimensional quality parameters include slope classification and soil fertility classification, and the ecological constraint indicators include ecological protection zone identification and pollution level.

6. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 1, characterized in that: In step S4, when the adaptability evaluation rule is matched according to the target plot type, the plot type is determined based on the land attribute field in the verified cultivated land quality classification code, and when the restrictive factor threshold screening is performed on the unused land, the restrictive factors include the slope classification threshold and the soil layer thickness classification threshold; When performing double screening on abandoned garden land, the slope classification threshold is combined with a suitability score based on soil pH and field contiguity; When dynamically adjusting the reclamation weights for restored land, the reclamation priority is increased based on the quality grade field of the original cultivated land; When performing site condition scoring on dry land, the score is generated based on the field surface flatness classification and the irrigation guarantee rate classification.

7. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 1, characterized in that: In step S5, the logic check includes: comparing the slope classification in the multidimensional quality parameter with the slope classification threshold, and marking it as undevelopable if the slope classification exceeds the slope classification threshold; Compare the pollution level in the ecological constraint index with the pollution level threshold, and if the pollution level exceeds the pollution level threshold, it is marked as undevelopable; For the plots that have passed the verification, an initial screening score is generated based on the slope classification, soil fertility classification and irrigation guarantee rate classification. The initial screening score is the weighted sum of each classification index.

8. The method for grading the potential of cultivated land reserve resources based on multi-dimensional cultivated land quality evaluation according to claim 1 is characterized in that: In step S6, when the comprehensive score is superimposed and calculated, the slope classification weight, soil fertility classification weight and irrigation guarantee rate weight output by the association model are combined with the weight of the initial screening score and the scoring weight generated by the adaptability evaluation rule. The comprehensive score is calculated by weighted accumulation, and when the potential level is output, it is divided into three levels: high, medium and low according to the comprehensive score range.

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