A method for assessing farmland quality and remediation potential based on a reference ecosystem

Abstract

This invention discloses a method for assessing farmland quality and remediation potential based on a reference ecosystem, relating to the field of spatial identification technology. The method first selects basic indicators characterizing farmland ecosystem quality based on regional characteristics, clarifies the calculation methods, and obtains multi-year averages of these indicators. Then, referencing the results of terrestrial ecological zoning, and combining regional natural geography, socio-economic conditions, and other characteristics, site condition zones are delineated. Subsequently, statistical analysis is conducted on the basic indicators of each zone to determine the selection threshold for farmland reference ecosystems, extracting the corresponding regions and using their spatial averages as references for land remediation targets. Finally, the relative farmland quality index is calculated to assess arable land quality, and the land remediation potential of each zone is determined based on farmland quality grading. This invention, by dividing site condition zones and selecting farmland reference ecosystems within these zones, forms zoning target benchmarks adapted to different natural and social conditions, avoiding deviations caused by applying uniform standards and making land remediation target setting more targeted.

Description

Technical Field

[0001] This invention relates to the field of spatial identification technology, and more particularly to a method for assessing farmland quality and remediation potential based on a reference ecosystem. Background Technology

[0002] While existing farmland improvement and quality assessment work can increase the quantity and utilization efficiency of arable land to some extent, the following prominent problems still exist when identifying the potential for improving and improving farmland ecosystem quality: First, the lack of comparable benchmarks leads to a lack of scientific basis for setting rectification targets.

[0003] Existing methods mostly set targets based on empirical thresholds, administrative statistics, or single status indicators. They fail to place farmland in different regions with varying natural geographical conditions, socio-economic conditions, and ecological backgrounds within the same reference framework for comparison. As a result, it is difficult to identify high-quality farmland ecosystems under similar site conditions and to formulate targeted health improvement goals accordingly.

[0004] Second, there is insufficient connection between farmland quality assessment and ecosystem quality, making it difficult to truly reflect the effectiveness of remediation efforts. Existing assessment systems often focus on quantitative or engineering indicators such as arable land area, contiguousness, and completion of engineering projects, while paying insufficient attention to the productive functions, ecological stability, and long-term maintenance capacity of farmland ecosystems. This results in some remediation outcomes only showing improvements in land morphology, without simultaneously improving the quality of farmland ecosystems, thus affecting the sustainability and stability of remediation results.

[0005] Third, the lack of unified and quantifiable grading standards for identifying land consolidation potential makes it difficult to accurately identify key areas. Different studies or regions have inconsistent criteria for judging consolidation potential, often lacking quantitative correlations with farmland quality levels, ecological degradation levels, and reference states. This can easily lead to unclear consolidation priorities, inaccurate identification of key areas, and problems such as over-consolidation, redundant investment, or insufficient consolidation, thereby reducing the efficiency of land consolidation resource allocation.

[0006] Therefore, there is an urgent need for a systematic approach that can identify high-quality farmland reference ecosystems under the same site conditions, and further realize the relative assessment of farmland quality and the hierarchical identification of remediation potential, so as to improve the scientific nature of land remediation target setting, the objectivity of quality evaluation, and the accuracy of remediation decisions. Summary of the Invention

[0007] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0008] In view of the problems existing in the prior art, the present invention is proposed.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A method for assessing farmland quality and remediation potential based on a reference ecosystem includes the following steps: Step 1: Select basic indicators that characterize the quality of farmland ecosystems based on regional characteristics, clarify the calculation methods of basic indicators, and obtain the average values ​​of indicators over multiple years; Step 2: By delineating zoning units with the same natural geographical and socio-economic characteristics, referencing the results of the terrestrial ecological basic zoning, and combining regional characteristics, site condition zoning is delineated. Step 3: Conduct statistical analysis on the basic indicators of each site condition zone, determine the selection threshold for farmland reference ecosystems, extract farmland reference ecosystem areas within each site condition zone according to the thresholds, and statistically analyze the average spatial indicators of farmland reference ecosystem areas as the target reference for land consolidation in that site zone. Step 4: Calculate the relative farmland quality index to assess the quality of cultivated land in each site condition zone, and further combine the farmland quality classification to determine the land consolidation potential in each zone.

[0010] As a preferred embodiment of the farmland quality and remediation potential assessment method based on a reference ecosystem described in this invention, step one includes the following steps: S101: Clearly define the object of representation, prioritize the selection of quantifiable, obtainable, and continuously monitorable indicators, and construct a basic indicator set; S102: Based on the established basic indicators, further determine the indicator calculation methods, collect and acquire basic data, and carry out basic indicator calculations; S103: Calculate basic indicators and compile averages over the past 5-10 years to provide basic data for determining land consolidation health targets and assessing farmland quality.

[0011] As a preferred embodiment of the farmland quality and remediation potential assessment method based on a reference ecosystem described in this invention, step two specifically includes: S201: Determine site condition zoning factors, including: natural ecological condition factors, used to reflect the differences in the natural environment where farmland is located, and socio-economic condition factors, used to reflect the differences in human utilization intensity and management conditions; S202: Combining site condition zoning factors, spatial zoning is used to determine the site condition zoning of the assessment area; The specific method for spatial partitioning is as follows: Factors that play a dominant role in the ecological pattern of farmland are selected, and each factor is processed to be dimensionless to construct a comprehensive feature vector of natural ecology. The feature vector is then classified using clustering or spatial segmentation methods to obtain several initial natural ecological partitions. Based on the initial partitioning, socioeconomic conditions are embedded to modify the partitioning. The modification rule method includes threshold splitting or weighted fusion re-clustering.

[0012] As a preferred embodiment of the farmland quality and remediation potential assessment method based on a reference ecosystem described in this invention, the threshold splitting method includes: selecting at least one socio-economic zoning factor characterizing agricultural use conditions or remediation conditions within the initial zoning. The corresponding grading thresholds are set according to the value range of the socio-economic zoning factors. The evaluation units within the initial partition are compared with the grading threshold. When the partitioning factor value of the evaluation unit meets the preset threshold condition, it is assigned to the corresponding sub-partition. The initial partition is further subdivided to obtain multiple site condition sub-partitions with differentiated socio-economic characteristics.

[0013] As a preferred embodiment of the farmland quality and remediation potential assessment method based on a reference ecosystem described in this invention, the weighted fusion re-clustering method includes: obtaining a first set of partitioning factors representing natural ecological characteristics and a second set of partitioning factors representing socio-economic characteristics, respectively. After standardizing the first partition factor set and the second partition factor set, they are fused according to preset weights to construct comprehensive partition feature data; Based on the comprehensive zoning feature data, spatial classification or cluster analysis methods are used to re-divide the evaluation units, resulting in site condition zoning results that comprehensively consider natural ecological conditions and socio-economic conditions.

[0014] As a preferred embodiment of the farmland quality and remediation potential assessment method based on a reference ecosystem described in this invention, step three specifically includes the following methods: S301: Based on the site condition zoning results obtained in step two, each zoning is processed separately: the basic index values ​​of all evaluation units in the zoning are extracted; the basic indexes are derived from the multi-year average results in step one, forming an index sample set for the zoning. S302: Within each partition, the basic indicators are sorted to obtain multiple candidate subsets; S303: For each candidate subset, the optimal proportion is determined by calculating the dispersion of its indicators; S304: Based on the determined optimal ratio Extract the top-ranked items within the corresponding partition. The evaluation unit is determined, and the reference ecosystem region of the partition is obtained by combining spatial analysis. S305: Calculate the average value of indicators in the reference ecosystem area as the remediation target.

[0015] As a preferred embodiment of the farmland quality and remediation potential assessment method based on a reference ecosystem described in this invention, step four includes: S401: Calculate the ratio of the basic index value of each evaluation unit to the average basic index value of the reference ecosystem within its site condition zone to obtain the relative farmland quality index of the evaluation unit; S402: The relative farmland quality index is graded according to a preset farmland quality grading threshold to obtain the corresponding farmland quality level; S403: Based on the relationship between the farmland quality grade and the preset degradation degree and remediation needs, determine the land remediation and restoration potential of farmland in each site condition zone.

[0016] An apparatus for applying the above-mentioned method for assessing farmland quality and remediation potential based on a reference ecosystem includes: a basic indicator acquisition module, used to select basic indicators characterizing farmland ecosystem quality in combination with regional characteristics, and acquire basic data corresponding to the basic indicators; The basic indicator calculation module is used to calculate the basic indicators based on the basic data, obtain the basic indicator values ​​of each evaluation unit, and calculate the multi-year average of the basic indicators. The site condition zoning module is used to determine site condition zoning factors and spatially zon the evaluation area based on the site condition zoning factors to obtain multiple site condition zonings. The reference ecosystem screening module is used to extract farmland reference ecosystem areas within each site condition zone by combining the statistical analysis results of basic indicators and preset proportion thresholds, and to determine the reference benchmark value for the corresponding site condition zone. The relative farmland quality assessment module is used to calculate the ratio of the basic index value of each evaluation unit to the reference benchmark value in its respective site condition zone, so as to obtain the relative farmland quality index. The quality grading module is used to grade the relative farmland quality index according to a preset farmland quality grading threshold to obtain the farmland quality level. The remediation potential determination module is used to determine the land remediation and restoration potential of farmland within each site condition zone based on the relationship between the farmland quality grade and the preset degradation degree and remediation needs.

[0017] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for assessing farmland quality and remediation potential based on a reference ecosystem.

[0018] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for assessing farmland quality and remediation potential based on a reference ecosystem.

[0019] The beneficial effects of this invention are: 1. This invention divides the site conditions into zones and selects farmland reference ecosystems within each zone to form zone target benchmarks applicable to different natural and social conditions. This avoids the deviation caused by directly applying a unified standard to different regions and makes the setting of land consolidation targets more targeted and operable.

[0020] 2. This invention assesses relative farmland quality based on basic ecological indicators, multi-year averages, and reference ecosystems. It can make a comprehensive judgment based on the productivity, stability, and overall quality of the farmland ecosystem, rather than being limited to the quantity of arable land or the completion status of projects. This helps to more accurately reflect the improvement effect of farmland quality after remediation.

[0021] 3. This invention combines the relative farmland quality index with quality grading, degradation level, and remediation needs to further output remediation potential results, thereby realizing a coherent chain of "quality evaluation—grading—potential identification," making the remediation targets, remediation intensity, and remediation priorities clearer. By classifying and identifying high-potential, medium-potential, and low-potential areas, this invention can help determine key remediation areas and priority implementation areas, reducing over-restoration, redundant investment, and ineffective remediation, and improving the efficiency of land remediation fund utilization and project implementation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating a method for assessing farmland quality and remediation potential based on a reference ecosystem, as proposed in this invention. Figure 2 This is a farmland reference ecosystem spatial pattern map of a preferred embodiment of the present invention; Figure 3 This is a distribution map of farmland ecosystem quality levels according to a preferred embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figure 1 As an embodiment of the present invention, a method for assessing farmland quality and remediation potential based on a reference ecosystem is provided. This method includes the following steps: Step 1: Select basic indicators that characterize the quality of farmland ecosystems based on regional characteristics, clarify the calculation methods of basic indicators, and obtain the average values ​​of indicators over multiple years; Specifically, the core objective of step one is to first establish a basic indicator system that can reflect the quality of farmland ecosystems, then uniformly calculate these indicators, and finally form multi-year average data with time stability, providing basic support for subsequent identification of reference ecosystems, farmland quality assessment, and land consolidation potential analysis.

[0027] Step 2: By delineating zoning units with the same natural geographical and socio-economic characteristics, referencing the results of the terrestrial ecological basic zoning, and combining regional characteristics, site condition zoning is delineated. Specifically, step two involves screening natural ecological and socioeconomic factors that influence differences in farmland quality. Combined with spatial zoning techniques, the assessment area is divided into several site condition zones. This ensures that farmland within the same zone has a relatively consistent site background, while significant differences exist between different zones. This zoning result serves two purposes: firstly, it builds upon the basic indicator calculations from step one; secondly, it provides a spatial basis for step three, which involves statistically analyzing the distribution of basic indicators within each zone, determining reference thresholds, and extracting reference ecosystem regions.

[0028] Step 3: Conduct statistical analysis on the basic indicators of each site condition zone, determine the selection threshold for farmland reference ecosystems, extract farmland reference ecosystem areas within each site condition zone according to the thresholds, and statistically analyze the average spatial indicators of farmland reference ecosystem areas as the target reference for land consolidation in that site zone. Step 3 involves conducting statistical analysis, namely ranking and variance analysis, on the basic indicators within each site condition zone to determine the optimal proportion and extract the reference ecosystem area. Then, the mean of the basic indicators of the reference area is used as the reference value for the land consolidation target of the zone, thereby realizing the transformation from current status evaluation to target guidance.

[0029] Step 4: Calculate the relative farmland quality index to assess the quality of cultivated land in each site condition zone, and further combine the farmland quality classification to determine the land consolidation potential in each zone.

[0030] In summary, step four calculates the relative farmland quality index by comparing the basic index values ​​of each evaluation unit with the average basic index values ​​of the reference ecosystem within its respective site condition zone, and classifies the farmland quality of the region accordingly. Furthermore, by combining the degree of ecological degradation and functional loss corresponding to different quality levels, the potential for farmland remediation and restoration within each zone is determined, thereby achieving the connection between farmland quality assessment and land remediation decision-making.

[0031] In one embodiment, step one includes the following steps: S101: Clearly define the object of representation, prioritize the selection of quantifiable, obtainable, and continuously monitorable indicators, and construct a basic indicator set; Specifically, the first step in implementation is to identify the natural geographical conditions and agricultural use characteristics of the area to be assessed, including but not limited to topography, climate, soil type, irrigation conditions, cropping systems, crop types, and the intensity of human disturbance. Because the dominant limiting factors of farmland ecosystems differ across regions, basic indicators cannot be applied mechanically but should be selected specifically based on regional characteristics. The implementation process is as follows: The selected basic indicators should be able to reflect the quality of farmland ecosystems from different perspectives, such as: productivity indicators, such as net primary productivity (NPP); land use intensity related indicators, such as farmland contiguousness and fragmentation; environmental constraint indicators, such as soil fertility, soil erosion sensitivity, and irrigation guarantee rate; and ecological response indicators, such as vegetation cover and habitat stability.

[0032] In practice, indicators that can be obtained through remote sensing, geographic information data, statistical yearbooks, soil survey data, or agricultural monitoring data should be preferred to ensure their operability. For example, NPP can be obtained through remote sensing inversion or existing ecological products; vegetation cover can be calculated from remote sensing vegetation indices; and basic indicators related to land consolidation can be extracted from spatial databases.

[0033] The set of basic indicators should be constructed. The number of basic indicators is not required to be fixed, but it should at least include key indicators that can represent the production and ecological status of farmland. Taking NPP as an example, it reflects the biomass accumulation capacity per unit area of ​​farmland over a certain period, and can indirectly characterize the productive function and stability of the farmland ecosystem; therefore, it is used as one of the basic indicators.

[0034] By establishing a "regionally adapted" indicator system in the above manner, we can avoid evaluation distortion caused by regional differences and improve the reliability of subsequent quality assessment results from the source.

[0035] S102: Based on the established basic indicators, further determine the indicator calculation methods, collect and acquire basic data, and carry out basic indicator calculations; Specifically, after selecting the indicators, the subsequent steps do not involve direct assignment of values, but rather the establishment of a corresponding calculation path for each indicator. That is, each indicator must correspond to a clear data source, calculation formula, time scale, and spatial scale to ensure that each indicator can be quantified under a unified standard. Different indicators correspond to different calculation methods. For example, for the net primary productivity (NPP) indicator, remote sensing images, meteorological data, and vegetation index data for the corresponding year are obtained. After spatial registration and preprocessing of the data, it is input into a preset NPP inversion model (such as a modified CASA model) to calculate the annual NPP value of the evaluation unit. For the vegetation cover indicator, NDVI or EVI values ​​are extracted from remote sensing images and converted to vegetation cover using a cover conversion model. For soil or soil fertility indicators, soil survey data and cultivated land quality monitoring data are called, and after standardization, a comprehensive soil fertility index is formed. For water conservancy security indicators, the irrigation security index is calculated based on the effective irrigated area, canal system matching rate, and irrigation facility integrity rate. Subsequently, the basic indicators are statistically analyzed annually, and the average value over the past 5 to 10 years is taken as the stable representation value of the basic indicators.

[0036] To ensure accurate calculation results, it is necessary to integrate multi-source basic data, which typically include: remote sensing imagery data; meteorological data; soil survey and arable land quality data; land parcel boundary and administrative division data; crop planting structure data; and farmland irrigation facilities and land use data. Before entering the calculation process, the basic data usually needs to be preprocessed, such as: removing outliers; unifying the coordinate system; standardizing spatial resolution; standardizing the time scale; and repairing or interpolating missing values.

[0037] After data preparation, each item is calculated according to a pre-defined calculation method to generate indicator results for each site unit, plot, or grid unit. For example, for the NPP indicator, an NPP value can be obtained annually for each evaluation unit within the region, thus forming an annual NPP distribution map or indicator library. The core function of this approach is to transform abstract quality characteristics into calculable, comparable, and traceable numerical results, laying the foundation for subsequent threshold selection and reference ecosystem identification.

[0038] S103: Calculate basic indicators and compile averages over the past 5-10 years to provide basic data for determining land consolidation health targets and assessing farmland quality.

[0039] Because the quality of farmland ecosystems is significantly affected by climate fluctuations, crop rotation, extreme weather, and short-term human disturbances, using only single-year data can easily lead to random biases. Therefore, it is necessary to calculate the average over the past 5 to 10 years to reflect the stability level of the regional farmland ecological status. For example, the basic indicator values ​​are first calculated year by year for each evaluation unit, such as: 2016 NPP; 2017 NPP; 2018 NPP; ..., 2025 NPP. This results in a continuous sequence of indicators. Then, the average of the indicator values ​​over the past 5-10 years is calculated to form a stable representative value. For example, the average NPP of a certain evaluation unit over the past 10 years can be expressed as: ; in, To count the number of years, For the first The annual NPP value. Multi-year averages can avoid the excessive influence of extreme years on the results, making the evaluation results closer to the true long-term state of the farmland ecosystem. For example, if a region experiences a sharp drop in NPP due to drought in a particular year, but other years are normal, the multi-year average can still reflect its overall productivity; if a region has consistently low NPP for many years, the average result will stably reflect its weak ecological quality. The statistically obtained multi-year averages are not only used for quality assessment, but can also be further used as: the basis for determining land consolidation health targets; the source of basic thresholds for screening farmland reference ecosystems; and preliminary input data for judging the land consolidation potential of each zone.

[0040] Through the above steps, firstly, basic indicators that can reflect the quality of farmland ecosystems are determined based on regional characteristics to ensure that the selection of indicators is targeted; then, clear calculation methods and data sources are matched for each indicator to make the indicators operable; then, the basic data of the past 5 to 10 years are calculated year by year to avoid the impact of single-year fluctuations; finally, stable basic indicator results are formed through multi-year averages, which serve as the basic input for subsequent reference ecosystem identification and farmland quality assessment.

[0041] In one embodiment, step two specifically includes: S201: Determine site condition zoning factors, including: natural ecological conditions factors, reflecting differences in the natural environment of farmland, such as: topography: elevation, slope, aspect, topographic relief; climate conditions: average annual temperature, annual precipitation, accumulated temperature, drought index; soil conditions: soil type, soil layer thickness, organic matter content, texture; hydrological conditions: groundwater depth, surface water accessibility, irrigation conditions; ecological background: vegetation cover, ecological sensitivity, erosion risk, etc.; socio-economic conditions factors, reflecting differences in human utilization intensity and remediation conditions, such as: farming methods and planting systems; agricultural mechanization level; irrigation facility coverage; accessibility to roads, villages, and markets; land development and utilization intensity; agricultural input level or remediation demand intensity. In practice, not all factors need to be used. Instead, the dominant factors that significantly affect farmland quality differences should be selected based on regional characteristics. For example, in plains areas, soil fertility, irrigation conditions, and field contiguousness may be the primary considerations; while in mountainous areas, slope, topographic relief, and soil and water conservation conditions may be emphasized.

[0042] S202: Combining site condition zoning factors, spatial zoning is used to determine the site condition zones of the assessment area. The basic idea of ​​spatial zoning is that spatial zoning is not arbitrary drawing lines, but rather based on the principles of "strong homogeneity, obvious differences, and reasonable boundaries as much as possible." General requirements: Site conditions within the same zone should be as similar as possible; there should be obvious differences between different zones; the zoning scale should not be too fragmented or too coarse; the zoning boundaries should be as harmonious as possible with objective spatial units such as topography, land parcels, roads, water systems, or administrative boundaries. The specific method for spatial zoning is the natural-social composite zoning method: Factors that play a dominant role in the ecological pattern of farmland are selected and dimensionless processing is performed on each factor to construct a comprehensive feature vector of natural ecology. Clustering or spatial segmentation methods (such as K-means or hierarchical clustering) are used to classify the feature vector to obtain several initial natural ecological partitions. For example, factors that play a dominant role in the ecological pattern of farmland are selected, such as: Topographic factors: elevation H, slope S; climatic factors: annual precipitation P, accumulated temperature T; soil factors: soil type Soil, organic matter content OM; hydrological factors: groundwater depth G, irrigation accessibility W. Since these factors have different dimensions, they need to be dimensionless, for example, by using extreme value standardization. ; By placing each factor within the [0,1] interval, facilitating comprehensive analysis, a feature vector is constructed for each of the above evaluation units (such as grids or plots). The above feature vectors are classified using clustering or spatial segmentation methods (such as K-means or hierarchical clustering) to obtain several natural ecological zones: Each category represents a region with similar natural conditions, such as: plains with high water and heat; hilly areas with moderate water and heat; and mountainous areas with low temperature restrictions.

[0043] While natural zoning reflects the ecological foundation, it does not reflect differences in manageability and utilization intensity. Therefore, further revision is needed. Socioeconomic conditions should be embedded into the initial zoning to revise the zoning. Revision rules and methods include threshold splitting or weighted fusion re-clustering.

[0044] In one embodiment, the threshold splitting method includes: selecting at least one socioeconomic partitioning factor that characterizes agricultural use conditions or management conditions within an initial partition; Set corresponding grading thresholds based on the value range of socioeconomic zoning factors; The evaluation units within the initial partition are compared with the grading threshold. When the partitioning factor value of an evaluation unit meets the preset threshold condition, it is assigned to the corresponding sub-partition. The initial partition was further subdivided to obtain multiple site condition sub-partitions with differentiated socio-economic characteristics.

[0045] Specifically, firstly, after forming initial zones based on natural ecological factors, socioeconomic zoning factors that can characterize differences in farmland utilization conditions are selected within each initial zone. In this embodiment, effective irrigation rate, road accessibility, and land contiguousness are preferably selected as socioeconomic zoning factors. Effective irrigation rate characterizes the level of water conservancy security, road accessibility characterizes the convenience of agricultural production, and land contiguousness reflects the conditions for large-scale operation. Secondly, the socioeconomic zoning factors are processed with unified dimensions, and corresponding grading thresholds are set based on the actual situation of regional agricultural development. For example, the effective irrigation rate is set as a boundary threshold of 0.7; and the distance from the main road to 1 km is set as the accessibility threshold. Set the average area threshold of the land parcel to a preset area value (such as 5 mu or 10 mu).

[0046] Subsequently, within each initial partition, the socioeconomic partitioning factor values ​​of each evaluation unit are compared with the threshold values ​​one by one. When an evaluation unit meets a certain threshold condition, it is assigned to the corresponding sub-partition. For example, when the effective irrigation rate is greater than a preset threshold, it is assigned to a high irrigation guarantee sub-partition; when the road distance is less than a preset threshold, it is assigned to a high accessibility sub-partition. When the contiguousness of a plot exceeds a preset threshold, it is divided into a large-scale advantageous sub-region.

[0047] Furthermore, multiple socioeconomic factors can be combined for judgment, and evaluation units that meet multiple advantages can be divided into comprehensive advantage sub-regions, while evaluation units that do not meet the conditions can be divided into potential enhancement sub-regions.

[0048] Finally, the resulting sub-regions undergo spatial connectivity checks and small patch merging to eliminate sub-regions that are too small or spatially fragmented, forming the final site condition zoning results. Through this method, while maintaining consistency in natural ecological conditions, socio-economic conditions are introduced to refine the initial zoning, ensuring that the resulting zoning reflects both natural differences and the differences in farmland improvement implementation conditions.

[0049] In one embodiment, the weighted fusion re-clustering method includes: obtaining a first set of partitioning factors representing natural ecological characteristics and a second set of partitioning factors representing socio-economic characteristics, respectively. After standardizing the first partition factor set and the second partition factor set, they are fused according to preset weights to construct comprehensive partition feature data; Based on comprehensive zoning feature data, spatial classification or cluster analysis methods are used to re-divide the evaluation units, resulting in site condition zoning results that comprehensively consider natural ecological conditions and socio-economic conditions.

[0050] First, two sets of factors are constructed: a natural ecological zoning factor set and a socio-economic zoning factor set. The natural ecological zoning factor set includes indicators such as elevation, slope, annual precipitation, and soil organic matter content; the socio-economic zoning factor set includes indicators such as effective irrigation rate, agricultural input level, road accessibility, and plot size. Second, the two types of zoning factors are preprocessed, including outlier removal, missing value imputation, and dimensionless processing, to ensure comparability between different indicators. In this embodiment, extreme value standardization can be used to map each indicator to a unified numerical range.

[0051] Subsequently, based on the varying degrees of influence of natural ecological conditions and socioeconomic conditions on the zoning results, corresponding weights were assigned, and the two types of factors were weighted and fused to construct comprehensive zoning feature data. For example, the weight of the natural ecological factor was set to 0.6, and the weight of the socioeconomic factor was set to 0.4, thereby obtaining the comprehensive feature vector of each evaluation unit.

[0052] Furthermore, based on the comprehensive feature vector, cluster analysis is used to classify all evaluation units. In this embodiment, K-means clustering can be used. By setting a reasonable number of clusters, evaluation units with similar features are grouped into the same category, thereby forming several site condition partitions. After completing the preliminary clustering, spatial constraint processing is applied to the partitioning results, that is, small-scale patches that are not spatially discontinuous are merged or redistributed to ensure that the partitions have good spatial connectivity and engineering feasibility.

[0053] Finally, the internal differences and inter-division differences of each zone are examined. When the variance of the basic indicators within a zone is small and the differences between inter-divisions are significant, the final site condition zoning results are determined.

[0054] Through the above methods, the synergistic integration of natural ecological conditions and socio-economic conditions is achieved, enabling the resulting zoning results to simultaneously reflect the regional ecological baseline and the current state of farmland use, thus providing a more reasonable spatial unit basis for subsequent reference ecosystem screening.

[0055] In one embodiment, step three specifically includes the following method: S301: Based on the site condition zoning results obtained in step two, process each zoning separately: extract the basic index values ​​of all evaluation units (plots or grids) within the zoning; The basic indicators are derived from the multi-year average results of step one, forming the indicator sample set for this partition; S302: Within each partition, the basic indicators are sorted (usually positive indicators, with larger values ​​representing higher quality), resulting in multiple candidate subsets. S303: For each candidate subset, determine the optimal proportion by calculating its index dispersion (such as variance); S304: Based on the determined optimal ratio Extract the top-ranked items within the corresponding partition. The evaluation unit is determined, and the reference ecosystem region of the partition is obtained by combining spatial analysis. S305: Calculate the average value of indicators in the reference ecosystem area as the remediation target.

[0056] Specifically, firstly, based on the site condition zones obtained in Step Two, basic indicator data are extracted for each site condition zone. These basic indicator data are the average values ​​of basic indicators obtained in Step One over the past 5 to 10 years. The basic indicators include at least one or more of the following: net primary productivity of vegetation, vegetation cover, comprehensive soil fertility index, and irrigation guarantee index. After statistically summarizing the basic indicator values ​​for each site condition zone, an indicator sample set for that zone is formed and sorted according to the indicator values ​​from highest to lowest, so that evaluation units with higher indicator values ​​are given priority in entering the candidate reference ecosystem area.

[0057] Based on this, the ranked evaluation units are grouped and screened according to a preset proportion range, preferably set as multiple candidate proportions from the top 1% to the top 30%. Specifically, the evaluation units from the top 1%, top 2%, top 3%, ... to the top 30% can be extracted sequentially to form multiple candidate reference regions, and the stability analysis of the basic indicators within each candidate reference region is performed. The stability analysis can be achieved through analysis of variance, coefficient of variation analysis, or a combination of both. When the variance of the basic indicators corresponding to a certain candidate proportion is small and the sample size of the candidate region meets the preset minimum size requirement, it indicates that the region extracted under that proportion has good internal consistency and representativeness.

[0058] Taking analysis of variance as an example: ; in, Represents candidate subsets variance Represents candidate subsets The total number of elements contained. The subset represents the index observation value of the j-th element. The arithmetic mean of all element index values.

[0059] Furthermore, among the analysis results corresponding to different candidate proportions, the proportion that provides high stability of the basic indicators and good spatial connectivity is selected as the optimal proportion. Specifically, the inflection point proportion can be determined based on the variance change trend corresponding to each candidate proportion, or the optimal proportion can be determined based on the comprehensive result between the minimum variance and the area proportion of the region. In this way, while ensuring the high quality of the reference ecosystem, it is possible to avoid insufficient sample representativeness due to an excessively small extraction proportion, or a decline in the quality of the reference area due to an excessively large extraction proportion.

[0060] Once the optimal ratio is determined, the evaluation units corresponding to the optimal ratio are mapped back to spatial locations, and spatial extraction is performed using spatial analysis methods to form candidate reference ecosystem regions. The preferred spatial analysis methods include one or more of the following: adjacency analysis, patch connectivity analysis, area threshold screening, and patch merging. Specifically, spatially adjacent evaluation units with similar indicator characteristics can be merged into continuous regions, and scattered patches with areas below a preset threshold can be removed to ensure the continuity, completeness, and engineering usability of the extraction results.

[0061] After obtaining the reference ecosystem areas within each site condition zone, the basic indicators within these areas are further statistically analyzed, and the mean of each basic indicator is calculated as the reference value for land consolidation targets for that site condition zone. Specifically, for any basic indicator, its value across all evaluation units within the reference ecosystem area is statistically analyzed, and the arithmetic mean is calculated to obtain the corresponding target reference indicator. If there are multiple basic indicators, multiple target reference values ​​are obtained, thereby constructing a set of remediation target indicators for that site condition zone.

[0062] By using the above methods, reference ecosystem areas with high quality and strong stability can be selected within each site condition zone. Then, the average basic indicators of these areas can be converted into reference values ​​for remediation targets, providing a clear benchmark for subsequent assessments of arable land quality and determination of land remediation potential.

[0063] In one embodiment, step four includes: S401: Calculate the ratio of the basic index value of each evaluation unit to the average basic index value of the reference ecosystem within its site condition zone to obtain the relative farmland quality index of the evaluation unit; S402: Based on the preset farmland quality grading threshold, the relative farmland quality index is graded to obtain the corresponding farmland quality level; S403: Based on the correspondence between farmland quality grade and preset degradation degree and remediation needs, determine the land remediation and restoration potential of farmland within each site condition zone.

[0064] Specifically, firstly, based on the reference ecosystem regions for each site condition zone obtained in step three, the average value of basic indicators within the corresponding zone is extracted and used as the reference benchmark value for that zone. For any evaluation unit to be assessed, its basic indicator value is obtained; the basic indicator value is the multi-year average value calculated in step one. Subsequently, the ratio of the basic indicator value of the evaluation unit to the reference benchmark value within its site condition zone is calculated to obtain the relative farmland quality index. By comparing the indicator level of the current evaluation unit with the reference ecosystem under similar site conditions, the bias caused by direct horizontal comparison between farmlands with different natural and socioeconomic backgrounds can be avoided. If there are multiple basic indicators, the relative quality index corresponding to each basic indicator can be calculated separately, or multiple basic indicators can be combined according to preset weights before calculating the comprehensive relative farmland quality index, so that the quality evaluation results are more consistent with the overall condition of the regional farmland ecosystem.

[0065] Next, the calculated relative farmland quality index is graded. Preferably, farmland quality can be divided into five levels: excellent, good, medium, low, and poor. In actual implementation, the system compares the relative farmland quality index of each evaluation unit with the above thresholds level by level, and determines its quality level accordingly. This transforms the continuous relative quality result into a discrete quality classification result, facilitating subsequent remediation decisions and spatial representation.

[0066] Furthermore, based on the correspondence between farmland quality grades and preset degradation levels and remediation needs, the land remediation and restoration potential of farmland within each site condition zone is determined. Specifically, when the farmland quality grade is excellent, it indicates that the farmland ecosystem of the evaluation unit is close to or reaches the state of the reference ecosystem, with relatively complete functions, and is identified as an area with no remediation potential or low remediation needs; when the farmland quality grade is good, it indicates that the evaluation unit has slight degradation, with some functions declining, which can be improved through local optimization and targeted remediation measures, and is identified as an area with low remediation potential; when the farmland quality grade is medium, it indicates that the evaluation unit has experienced moderate degradation, with significant functional damage, requiring systematic remediation and restoration measures, and is identified as an area with medium remediation potential; when the farmland quality grade is low or poor, it indicates that the ecosystem structure and function of the evaluation unit are severely damaged, with a significant gap from the state of the reference ecosystem, requiring comprehensive land remediation and ecological restoration, and is identified as an area with high remediation potential.

[0067] In another implementation, the quality level can be linked and matched with the remediation measures database. Based on different quality levels, corresponding remediation suggestion types can be automatically generated. For example, areas with excellent quality can be identified as protection and maintenance zones, areas with good quality as optimization and improvement zones, areas with medium quality as key remediation zones, and areas with low and poor quality as priority restoration zones. This allows step four to not only complete the farmland quality assessment but also to further generate operationally oriented land remediation decision results. Through this approach, a complete process is achieved—from referencing ecosystem benchmarks—to relativize, classify, and determine the potential of regional farmland quality, thus providing a direct basis for subsequent land remediation plan development.

[0068] refer to Figure 2-3 This preferred embodiment, using Chongqing as the research sample area, applies the method of this invention to identify farmland reference ecosystems and guide the prediction of regional land consolidation health targets and farmland quality assessment. To ensure that the evaluation results accurately reflect the long-term state of the regional farmland ecosystem, this embodiment selects net primary productivity (NPP) as the evaluation index and uses multi-year statistical results as the basis for subsequent reference ecosystem identification and quality classification.

[0069] Step 1: Selection, calculation and multi-year average statistics of basic indicators.

[0070] Based on the regional characteristics of farmland ecosystems in Chongqing, net primary productivity (NPP) was selected as a fundamental indicator to characterize the productive function and ecological quality of farmland ecosystems. In practice, based on remote sensing imagery, meteorological data, and vegetation index data of Chongqing from 2013 to 2023, an improved CASA model was applied to retrieve the NPP of farmland ecosystems across the city year by year. The improved CASA model optimized the regionalization of light energy use efficiency parameters, temperature stress factors, and water stress factors to improve the applicability and accuracy of NPP estimation under complex terrain conditions.

[0071] After completing the year-by-year inversion, the NPP results of farmland ecosystems for each year are spatially overlaid and statistically summarized to extract the multi-year average NPP of farmland ecosystem regions, forming the basic data required for subsequent zonal comparisons and reference ecosystem selection. By using multi-year averages, the impact of climate fluctuations, extreme weather, and short-term human activity disturbances on the evaluation results can be reduced, thus more stably representing the long-term quality level of farmland ecosystems.

[0072] Step 2: Site condition zoning.

[0073] After obtaining the multi-year average values ​​of basic indicators, the assessment area was further divided into site condition zoning based on Chongqing's unique topographic features, natural conditions, administrative unit boundaries, and overall pattern of land space development and protection. Specifically, the site condition zoning scheme was constructed by fully referencing and connecting with the results of my country's terrestrial ecological basic zoning, and comprehensively considering factors such as landform type, slope and aspect, distribution pattern of mountains and plains, agricultural use methods, transportation accessibility, irrigation conditions, and consistency of administrative boundaries.

[0074] In practical implementation, a spatial zoning approach combining natural ecological conditions and socio-economic conditions can be adopted to classify and identify the evaluation area and refine its boundaries. This ensures that the same zoning area has a relatively consistent natural geographical background and socio-economic background, while different zoning areas exhibit significant differences. Based on this, ten site condition zones were established: Western Chongqing Hilly Area, Main Urban Parallel Ridge and Valley Area, Dalou Mountain Low Hilly Area, Dalou Mountain-Wuling Mountain Low and Medium Mountain Area, Eastern Chongqing Parallel Ridge and Valley Area, Wuling Mountain Low and Medium Mountain Area, Wuling Mountain Low Hilly Area, Three Gorges Reservoir Area Core Area, Wushan-Qiyao Mountain Low and Medium Mountain Area, and Daba Mountain High and Medium Mountain Area. This zoning provides a spatial basis for subsequent selection of reference ecosystems under similar or identical site conditions.

[0075] Step 3: Determine the target for ecosystem screening and restoration.

[0076] After completing the site condition zoning, based on the multi-year average NPP data within each zoning, and combined with analysis of variance, the stability of basic indicators within different site condition zoning was statistically analyzed, and farmland reference ecosystems were selected accordingly. Specifically, firstly, the evaluation units within each site condition zoning were sorted from high to low according to their NPP values, and then the regions with NPP in the top i% (e.g., i=1,2,…,30) were statistically analyzed to form multiple candidate reference ecosystem regions.

[0077] In practice, one-way ANOVA was conducted on the NPP sample sets corresponding to different candidate proportions to compare the intra-group dispersion and inter-group differences under each candidate proportion, and the corresponding P-values ​​and F-statistics were calculated. The statistical results showed that the P-values ​​for each region were all 0, indicating that the group differences under each candidate proportion were significant. Further comparison of the F-statistics revealed that the F-statistics for the western Chongqing hilly area, the main urban parallel ridge and valley area, the Dalou Mountain-Wuling Mountain low and medium mountain area, the eastern Chongqing parallel ridge and valley area, the Wuling Mountain low hill and mountain area, and the core area of ​​the Three Gorges Reservoir gradually increased and reached their maximum in the top 30%. Therefore, the reference ecosystems for these areas were initially selected as the top 30%. The Wuling Mountain low and medium mountain area, the Wushan-Qiyao Mountain low and medium mountain area, and the Daba Mountain high and medium mountain area had the largest statistical values ​​in the top 25%, so the reference ecosystems for these areas were initially selected as the top 25%. The Dalou Mountain low hill and mountain area had the largest statistical value in the top 28%, so the reference ecosystems for this area were initially selected as the top 28%.

[0078] After screening candidate areas, spatial connectivity processing is further applied to the extracted high-value areas using spatial analysis. Spatially adjacent and similar evaluation units are aggregated, while small, fragmented patches are removed to form a spatially continuous and structurally stable farmland reference ecosystem region. The statistical results of the reference ecosystem can provide important targets and references for farmland consolidation in each site zone. The average NPP of the farmland reference ecosystem region can be used as the target for land consolidation NPP in that region.

[0079] Further statistical analysis revealed that the mean NPP of the reference ecosystem areas in the ten site zones was much greater than the mean of the entire region, and the standard deviation of the reference ecosystem areas was smaller than the standard deviation of the NPP of the entire region. This indicates that the numerical range of the defined reference ecosystems is more stable and of relatively higher quality. Specifically, the target values ​​are as follows: 583.29 g C / m² for the western hilly area of ​​Chongqing; 649.09 g C / m² for the parallel ridge and valley area of ​​the main urban area; 798.68 g C / m² for the low hilly area of ​​the Dalou Mountains; and 841.03 g C / m² for the low and medium mountainous areas of the Dalou Mountains-Wuling Mountains. For the eastern parallel ridge and valley area of ​​Chongqing, the target values ​​are 690.05 g C / m²; 782.54 g C / m² for the low and medium mountainous areas of the Wuling Mountains; 657.62 g C / m² for the low hilly area of ​​southeastern Chongqing; 646.74 g C / m² for the core area of ​​the Three Gorges Reservoir; 701.91 g C / m² for the low and medium mountainous areas of the Wushan-Qiyao Mountains; and 765.56 g C / m² for the high and medium mountainous areas of the Daba Mountains. These values ​​can serve as a basic reference for setting farmland improvement targets in each sub-region.

[0080] Step 4: Assessment of farmland quality grade and remediation potential.

[0081] After obtaining the reference ecosystem target values ​​within each site condition zone, the quality level and remediation potential of the city's farmland ecosystem are further calculated. Specifically, based on the ratio between the basic index values ​​of each evaluation unit and the average basic index values ​​of the reference ecosystem of its respective site condition zone, a relative farmland quality index is calculated, and the regional farmland quality is evaluated accordingly.

[0082] In a preferred embodiment, the basic index value of each evaluation unit can be recorded as follows: The mean value of the basic index of the reference ecosystem within the corresponding site condition zone is denoted as The relative farmland quality index It can be represented as: The relative farmland quality index is calculated as follows: ; This method can transform farmland quality within zones with different site conditions into standardized indicators of relative reference states, thereby avoiding direct horizontal comparison errors under different natural and social backgrounds.

[0083] Furthermore, the relative farmland quality index is graded according to a preset quality grading standard, typically dividing farmland quality into five levels: excellent, good, medium, low, and poor, with excellent being the lowest. ≥85, Good is 70≤ <85, with a middle value of 50≤ <70, minimum 25≤ <50, the difference is <25. In practice, the relative farmland quality index of each evaluation unit is compared step by step, and its corresponding farmland quality level is determined.

[0084] Further, based on the correlation between farmland quality grades, preset degradation levels, and remediation needs, the land remediation and restoration potential of farmland in each zone was determined. The results showed that the overall farmland ecosystem quality in the city was mainly distributed as excellent, good, and medium, with areas of 8423.28 km², 10311.18 km², and 7755.79 km² respectively, accounting for over 80% of the city's total farmland ecosystem area. These were mainly distributed in western Chongqing, south of the main urban area, and the Three Gorges Reservoir valley region. Ecosystems of poor quality accounted for the smallest proportion, at 4.82%, mainly distributed in areas around the main urban area and other areas significantly affected by human activities. In addition, areas of low quality accounted for 5.44%, scattered in low-to-medium mountain and hilly areas with steep slopes.

[0085] Areas classified as "excellent" in terms of ecological quality require virtually no modification, thus exhibiting low or no restoration potential. Areas classified as "good" show slight degradation and can be appropriately restored locally, with low restoration potential. Areas classified as "medium" exhibit moderate ecological degradation and significant functional impairment, requiring focused restoration, with moderate restoration potential. Areas classified as "low" or "poor" exhibit severe ecological degradation, with severely damaged structure and function, showing a significant gap compared to the reference ecosystem, requiring comprehensive land consolidation and restoration, with high restoration potential. Statistical results indicate that 28.54% of the city's areas have no potential, 34.93% have low potential, 26.27% have medium potential, and 10.26% have high potential. Overall, the city is predominantly characterized by no or low to medium potential, indicating a generally good farmland ecosystem condition. High-potential areas are mainly distributed around the main urban area and in low hills and other areas with steep slopes. These areas are subject to greater human disturbance, more frequent agricultural production activities, or poor baseline conditions. Further measures such as comprehensive land consolidation and ecological restoration can be combined to increase the quantity of high-quality arable land and improve the quality of the farmland ecosystem.

[0086] In one embodiment, a farmland quality and remediation potential assessment device based on a reference ecosystem is also disclosed. The device includes: a basic indicator acquisition module, used to select basic indicators that characterize the quality of farmland ecosystems in combination with regional characteristics and acquire basic data corresponding to the basic indicators; and a basic indicator calculation module, used to calculate the basic indicators based on the basic data, obtain the basic indicator values ​​of each evaluation unit, and calculate the multi-year average of the basic indicators.

[0087] The site condition zoning module is used to determine site condition zoning factors and spatially zon the evaluation area based on the site condition zoning factors to obtain multiple site condition zonings. The reference ecosystem screening module is used to extract farmland reference ecosystem areas within each site condition zone by combining the statistical analysis results of basic indicators and preset proportion thresholds, and to determine the reference benchmark value for the corresponding site condition zone. The relative farmland quality assessment module is used to calculate the ratio of the basic index value of each evaluation unit to the reference benchmark value within its respective site condition zone to obtain the relative farmland quality index; the quality grading module is used to grade the relative farmland quality index according to the preset farmland quality grading threshold to obtain the farmland quality level; the remediation potential determination module is used to determine the land remediation and restoration potential of farmland within each site condition zone based on the correspondence between the farmland quality level and the preset degree of degradation and remediation needs.

[0088] This embodiment also provides a computer device applicable to a method for assessing farmland quality and remediation potential based on a reference ecosystem, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for assessing farmland quality and remediation potential based on a reference ecosystem as proposed in the above embodiment.

[0089] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0090] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for assessing farmland quality and remediation potential based on a reference ecosystem, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing farmland quality and remediation potential based on a reference ecosystem, characterized in that, Includes the following steps: Step 1: Select basic indicators that characterize the quality of farmland ecosystems based on regional characteristics, clarify the calculation methods of basic indicators, and obtain the average values ​​of indicators over multiple years; Step 2: By delineating zoning units with the same natural geographical and socio-economic characteristics, referencing the results of the terrestrial ecological basic zoning, and combining regional characteristics, site condition zoning is delineated. Step 3: Conduct statistical analysis on the basic indicators of each site condition zone, determine the selection threshold for farmland reference ecosystems, extract farmland reference ecosystem areas within each site condition zone according to the thresholds, and statistically analyze the average spatial indicators of farmland reference ecosystem areas as the target reference for land consolidation in that site zone. Step 4: Calculate the relative farmland quality index to assess the quality of cultivated land in each site condition zone, and further combine the farmland quality classification to determine the land consolidation potential in each zone.

2. The method for assessing farmland quality and remediation potential based on a reference ecosystem as described in claim 1, characterized in that: Step one includes the following steps: S101: Clearly define the object of representation, prioritize the selection of quantifiable, obtainable, and continuously monitorable indicators, and construct a basic indicator set; S102: Based on the established basic indicators, further determine the indicator calculation methods, collect and acquire basic data, and carry out basic indicator calculations; S103: Calculate basic indicators and compile averages over the past 5-10 years to provide basic data for determining land consolidation health targets and assessing farmland quality.

3. The method for assessing farmland quality and remediation potential based on a reference ecosystem as described in claim 2, characterized in that: Step two specifically includes: S201: Determine site condition zoning factors, including: natural ecological condition factors, used to reflect the differences in the natural environment where farmland is located; and socio-economic condition factors, used to reflect the differences in human utilization intensity and management conditions. S202: Combining site condition zoning factors, spatial zoning is used to determine the site condition zoning of the assessment area. The specific method for spatial partitioning is as follows: Factors that play a dominant role in the ecological pattern of farmland are selected, and each factor is processed to be dimensionless to construct a comprehensive feature vector of natural ecology. The feature vector is then classified using clustering or spatial segmentation methods to obtain several initial natural ecological partitions. Based on the initial partitioning, socioeconomic conditions are embedded to modify the partitioning. The modification rule method includes threshold splitting or weighted fusion re-clustering.

4. The method for assessing farmland quality and remediation potential based on a reference ecosystem as described in claim 3, characterized in that: The threshold splitting method includes: selecting at least one socio-economic partitioning factor that characterizes agricultural utilization conditions or management conditions within the initial partition; The corresponding grading thresholds are set according to the value range of the socio-economic zoning factors. The evaluation units within the initial partition are compared with the grading threshold. When the partitioning factor value of the evaluation unit meets the preset threshold condition, it is assigned to the corresponding sub-partition. The initial partition is further subdivided to obtain multiple site condition sub-partitions with differentiated socio-economic characteristics.

5. The method for assessing farmland quality and remediation potential based on a reference ecosystem as described in claim 3, characterized in that: The weighted fusion re-clustering method includes: obtaining the first set of factors representing natural ecological characteristics and the second set of factors representing socio-economic characteristics, respectively; After standardizing the first partition factor set and the second partition factor set, they are fused according to preset weights to construct comprehensive partition feature data; Based on the comprehensive zoning feature data, spatial classification or cluster analysis methods are used to re-divide the evaluation units, resulting in site condition zoning results that comprehensively consider natural ecological conditions and socio-economic conditions.

6. The method for assessing farmland quality and remediation potential based on a reference ecosystem as described in claim 5, characterized in that: Step three specifically includes the following methods: S301: Based on the site condition zoning results obtained in step two, process each zoning separately: extract the basic index values ​​of all evaluation units within the zoning; The basic indicators are derived from the multi-year average results of step one, forming an indicator sample set for this partition. S302: Within each partition, the basic indicators are sorted to obtain multiple candidate subsets; S303: For each candidate subset, the optimal proportion is determined by calculating the dispersion of its indicators; S304: Based on the determined optimal ratio Extract the top-ranked items within the corresponding partition. The evaluation unit is determined, and the reference ecosystem region of the partition is obtained by combining spatial analysis. S305: Calculate the average value of indicators in the reference ecosystem area as the remediation target.

7. The method for assessing farmland quality and remediation potential based on a reference ecosystem as described in claim 1, characterized in that: Step four includes: S401: Calculate the ratio of the basic index value of each evaluation unit to the average basic index value of the reference ecosystem within its site condition zone to obtain the relative farmland quality index of the evaluation unit; S402: The relative farmland quality index is graded according to a preset farmland quality grading threshold to obtain the corresponding farmland quality level; S403: Based on the relationship between the farmland quality grade and the preset degradation degree and remediation needs, determine the land remediation and restoration potential of farmland in each site condition zone.

8. A farmland quality and remediation potential assessment device based on a reference ecosystem, applied to the farmland quality and remediation potential assessment method based on a reference ecosystem as described in any one of claims 1-7, characterized in that: The device includes: The basic indicator acquisition module is used to select basic indicators that characterize the quality of farmland ecosystems based on regional characteristics, and to acquire the basic data corresponding to the basic indicators. The basic indicator calculation module is used to calculate the basic indicators based on the basic data, obtain the basic indicator values ​​of each evaluation unit, and calculate the multi-year average of the basic indicators. The site condition zoning module is used to determine site condition zoning factors and spatially zon the evaluation area based on the site condition zoning factors to obtain multiple site condition zonings. The reference ecosystem screening module is used to extract farmland reference ecosystem areas within each site condition zone by combining the statistical analysis results of basic indicators and preset proportion thresholds, and to determine the reference benchmark value for the corresponding site condition zone. The relative farmland quality assessment module is used to calculate the ratio of the basic index value of each evaluation unit to the reference benchmark value in its respective site condition zone, so as to obtain the relative farmland quality index. The quality grading module is used to grade the relative farmland quality index according to a preset farmland quality grading threshold to obtain the farmland quality level. The remediation potential determination module is used to determine the land remediation and restoration potential of farmland within each site condition zone based on the relationship between the farmland quality grade and the preset degradation degree and remediation needs.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for assessing farmland quality and remediation potential based on a reference ecosystem as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for assessing farmland quality and remediation potential based on a reference ecosystem as described in any one of claims 1 to 7.

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