Big data-based cadmium accumulation character analysis method for multiflower kidney beans

Abstract

The invention discloses a big-data-based cadmium accumulation character analysis method for multiflower kidney beans, and relates to the technical field of accumulation character analysis, and the method comprises the steps: collecting soil properties, environment information, multi-year grain cadmium and management records by taking a land parcel as a unit, constructing a land parcel safety index and an environment behavior feature vector, and dividing safety environment types; establishing a cadmium transmission meta-model in each safety environment type, and extracting a cadmium transmission fingerprint of the variety to obtain an inherent hazard baseline of the variety and a management controllable margin; calculating a safety margin, a safety yield, a safety profit and a multi-metal index based on the above results, setting a risk budget, and determining a safety domain and a minimum management adjustment amount; the safety characters are compared with agricultural land soil pollution risk management and control standards and food, green, organic and low-cadmium-accumulation variety guide rules, multi-year planting plans of land parcels, farmers and areas are formed, and safe utilization and income coordination of the multiflower kidney beans are achieved.

Description

Technical Field

[0001] This invention relates to the field of accumulation trait analysis technology, specifically to a method for analyzing the accumulation trait of cadmium in *Gnaphalium affine* based on big data. Background Technology

[0002] Existing work mainly focuses on the study of cadmium accumulation characteristics and optimization of remediation measures in typical plots and a few major varieties through soil remediation material experiments, comparisons of a small number of varieties, and the formulation of safe planting technical regulations. This has provided some guidance for the safe planting of common bean in polluted farmland. However, most of these studies are based on single-point or a few experimental sites, and are mostly based on experimental data from one to two years. They focus on comparing the effects of different amendments and management models on the cadmium content and yield of grains in the current season. They are difficult to systematically reflect the dynamic evolution of soil cadmium availability in complex mountain farmland under multi-year crop rotation, and they are also difficult to capture in a timely manner the differences in the inherent cadmium transport characteristics of different common bean varieties under different safe environmental conditions. Furthermore, it is difficult to quantify the safety improvement space and yield change trends of different management combinations based on the inherent hazards of the varieties.

[0003] In this context, when developing safe planting plans for wild bean for specific plots, it is often necessary to rely on scattered monitoring data and empirical judgments, and to carry out production according to uniform passivation dosages or rough variety recommendations. There is a lack of quantitative analysis tools that can simultaneously integrate soil pollution levels, environmental types, inherent varietal hazards, management control margins, and multi-metal safety requirements. Especially in the case of continuous planting over many years, soil conditions will gradually evolve with the accumulation of remediation materials, changes in fertilization habits, and adjustments in crop rotation structures. There is a clear correlation between soil cadmium availability and varietal risk levels between different years. If planting and management plans only consider the test results of a single year and ignore this evolutionary process, on the one hand, some plots may meet the standards in the short term, but in the long term, concentrated exceedances may occur due to risk accumulation, triggering brand sampling incidents and a market trust crisis in the entire region. On the other hand, it may also lead to a significant decrease in farmers' income and a reduction in land resource utilization efficiency due to overly conservative uniform increases in passivation intensity or blindly stopping the planting of wild bean, making it difficult to balance safe utilization and economic benefits.

[0004] Therefore, the practical technical problem lies in the lack of a quantitative analysis and decision-making tool in the production of common bean in cadmium-contaminated farmland. This tool can comprehensively consider the degree of soil pollution, the type of safe environment, the inherent cadmium transport characteristics of common bean varieties, and the long-term impact of different planting and management measures on soil conditions at both the plot scale and multi-year time scale. This tool would provide a basis for selecting common bean varieties and formulating planting and management combinations that balance food safety standards with planting profitability for specific plots. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a big data-based method for analyzing the cadmium accumulation traits of multi-flowered beans. By establishing cadmium transport meta-models within various safety environment types, extracting cadmium transport fingerprints for varieties, and obtaining the inherent hazard baseline and management controllability margin for each variety, the method calculates safety margin, safe yield, safe return, and polymetallic index based on the above results. It then sets a risk budget and determines the safety domain and minimum management adjustment. By comparing the safety traits with agricultural land soil pollution risk control standards and guidelines for food, green, organic, and low-cadmium-accumulation varieties, the method formulates multi-year planting plans for plots, farmers, and regions, achieving a balance between safe utilization and returns for multi-flowered beans and solving the technical problems described in the background art.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A big data-based method for analyzing the cadmium accumulation traits of soybean (Gynostemma pentaphyllum) includes: reading soil heavy metal and physicochemical properties, multi-year grain cadmium monitoring, variety information, and management records from a database by plot; calculating behavioral safety indicators for each plot; and combining these indicators with soil and environmental data to form an environmental behavior feature vector. The environmental behavior feature vector is then clustered to obtain multiple safe environment types. Within each safe environment type, a unified cadmium transport meta-model is invoked, and based on multi-organ cadmium data from representative plots, the cadmium transport fingerprint and the inherent hazard baseline of the corresponding variety are determined. Within the safe environment type, a management controllable margin function is established based on management records. For each plot, variety, and management combination, the inherent hazard baseline of the variety and the management controllable margin function are called to calculate the safety margin and a set of safety trait indicators. Based on the risk budget, the safety domain and the minimum management adjustment amount are identified. The safety margin, safety trait indicators, safety domain, and minimum management adjustment amount are compared with standard thresholds to generate planting plan data. The planting plan is then calculated and output on a rolling basis based on the soil condition update rules.

[0007] Furthermore, when calculating the site behavior safety index, the monitoring results of cadmium in the seeds of beans and flowers of the same site over many years are read in chronological order. A single value is obtained by weighting and summing the differences between the monitoring values ​​of each year and the corresponding limit values. When constructing the environmental behavior feature vector, the site behavior safety index is connected with soil heavy metal content, pH, organic matter, cation exchange capacity and site topography and climate index in a fixed order.

[0008] Furthermore, when clustering the environmental behavior feature vectors, dimensionless standardization is first performed on the data of each dimension, and then the weighted distance between the environmental behavior feature vectors of any two plots is calculated according to the preset weight coefficients. The clustering algorithm is used to classify plots with a weighted distance less than the threshold into the same safe environment type, and to ensure that each safe environment type contains multiple plots with similar soil conditions and safety performance.

[0009] Furthermore, the unified cadmium transport meta-model represents available cadmium in the soil, roots, aboveground vegetative organs, pods, and grains as multiple sequentially connected compartments. By collecting cadmium content data for each compartment on representative plots, the transport parameters between compartments are derived using parameter estimation methods. The cadmium transport fingerprints and inherent hazard baselines of the corresponding varieties are stored in each safe environment type for subsequent retrieval.

[0010] Furthermore, the management controllability margin function uses the amount of lime applied, biochar applied, organic fertilizer applied, fertilizer ratio, and irrigation and drainage methods in the management records as independent variables, and the change in safety status obtained under the unified cadmium transport element model as the dependent variable. A fitting method is used to establish the mapping relationship between the independent and dependent variables, which is used to calculate the corresponding management controllability margin under different management combinations.

[0011] Furthermore, the safety indicators for each plot, variety, and management combination include at least safe output, safe revenue, and a multi-metal safety index. Safe output is determined based on the estimated output when the safety margin is not lower than the set lower limit. Safe revenue is calculated based on safe output, product unit price, and input costs. The multi-metal safety index is synthesized based on the predicted content and limit values ​​of multiple heavy metals such as cadmium, lead, and zinc according to preset weights.

[0012] Furthermore, the risk budget includes three levels: plot level, farmer level, and regional level. The plot level sets a lower limit for safety margin and an upper limit for polymetallic safety index for each plot. The farmer level limits the number or area of ​​plots under the same farmer whose safety margin is close to the lower limit to a preset proportion. The regional level limits the number of unqualified batches and the area of ​​unqualified samples within the statistical period to a preset limit. The three levels of budget are used together as constraints when identifying safety domains.

[0013] Furthermore, the planting plan data is stored using a multi-dimensional index structure that includes plot identifiers, variety identifiers, management combination identifiers, and planting years. Each record is associated with the corresponding plot's safety margin, safety status indicators, safety domain type, and minimum management adjustment amount in the target year. When generating the planting plan data, the records are aggregated by farmer and region for different levels of users to query and access.

[0014] Furthermore, the soil condition update rules extrapolate the available cadmium and related physicochemical indicators for the next year based on the soil heavy metal content, pH, organic matter, and the amount of lime, biochar, and organic fertilizer applied corresponding to the selected management combination in the previous year. During the rolling calculation process, the soil condition update rules and planting scheme data are called up year by year to update the plot behavior safety indicators and environmental behavior characteristic vectors.

[0015] (III) Beneficial Effects This invention provides a method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data, which has the following beneficial effects: By constructing safety indicators and environmental behavior characteristic vectors for each plot and classifying safety environment types, safety assessments are based on long-term planting performance rather than single-year testing. This facilitates a clear distinction between robust, compliant plots and marginally sensitive or high-risk plots, providing a reliable hierarchical foundation for subsequent modeling. Within each safety environment type, a unified meta-model of cadmium transport from soil to roots, aboveground vegetative organs, pods, and grains is established. Cadmium transport fingerprints for each variety are extracted, forming a baseline of inherent hazard for each variety. Simultaneously, a management controllability margin function is used to characterize the impact of combinations of lime, biochar, organic fertilizer, and irrigation / drainage on safety performance, thereby separating inherent varietal differences from acquired management modifications within the same structure.

[0016] Based on the baseline of inherent hazards of varieties and the management controllable margin function, the safety margin, safe yield, safe return, and polymetallic safety index are jointly calculated. Risk budgets are set at three levels: plot, farmer, and region. By solving for the safety domain and minimum management adjustment, the output of the mechanistic model is transformed into specific adjustments to fertilization, passivation materials, and cultivation systems, ensuring that adjustments to individual plots simultaneously meet the constraints of farmers and the overall region. The safety margin, polymetallic safety index, safety domain, and minimum management adjustment are compared with standards for agricultural land soil pollution risk control, food safety standards for legumes, vegetables, and wild beans, green food standards, organic product standards, and technical guidelines for screening low-cadmium-accumulating crop varieties. Land use categories, product grades, and multi-year planting plans are generated from the perspectives of plot, farmer, and region, transforming abstract indicators into executable planting layouts and variety arrangements. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process for analyzing the cadmium accumulation traits of *Gnaphalium affine* based on big data, as per the present invention. Detailed Implementation

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

[0019] Please see Figure 1 This invention provides a method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data, including: Step 1: Using land parcels as the basic unit, and adhering to the principles of comprehensive, accurate, and useful data collection, complete the following steps in sequence: multi-source data collection and timeline organization, construction of annual safety behavior indicators, generation of environmental behavior feature vectors, and classification of safety environment types.

[0020] Only by recording soil heavy metals, soil physicochemical properties, information on multi-flowered bean varieties, and management measures under a unified spatiotemporal coordinate system can we trace the environmental and operational conditions behind each grain cadmium test in subsequent steps, avoiding situations where we only know that cadmium exceeds the standard but do not know why.

[0021] Therefore, the first step is to clearly define the plot number, monitoring year, and sampling level, dividing the plot into basic units with unique identifiers, and then systematically collecting and archiving data around these units.

[0022] Each plot of land corresponds to a number of consecutive monitoring years in each of the multiple flowering bean planting years. For each year, the soil cadmium and other heavy metal content, pH, organic matter, cation exchange capacity, texture, slope, variety name, and specific planting and management records for that year are recorded simultaneously.

[0023] Sampling in the field involves technicians taking soil samples from the surface layer of the plots at fixed depths before sowing and before and after harvest. These samples are numbered with the plot number, year, and soil layer marker. Simultaneously, during harvest, pod and seed samples of cauliflower are collected evenly in rows and columns. The corresponding variety, sowing date, fertilizer formula and application time, amount of lime or biochar applied, and information about the previous crop are recorded on-site. All these records are entered into a time-series structure using a standardized data form, ensuring a one-to-one correspondence between plot numbers and soil conditions and management practices for each year.

[0024] First, the spatial boundaries of each plot are fixed, and a mapping relationship is established between soil and crop samples from previous years, based on the plot number and geographical coordinates. Second, during the data entry stage, the cadmium content of seeds measured in common bean each year is paired with the amount of fertilizer and passivation material used in that year, forming observation entries indexed by year.

[0025] Taking a downstream town of a mine as an example, before spring plowing, technicians divide the land into villages and groups, number each plot of land planned to be planted with wild beans, take soil samples from the four corners and the center of the plot and mix them, establish the correspondence between soil samples and plot numbers, and conduct laboratory tests on the wild bean seeds of the plot after the growing season. The test results are archived together with the fertilizer ledger, lime and biochar storage and requisition records for that year, thus forming a complete annual record.

[0026] This ensures that each plot of land forms a consistent and complete record unit for each monitoring year, including soil conditions, crop varieties, and management information. Subsequently, when constructing annual safety behavior indicators and environmental behavior feature vectors, searches and summaries can be performed directly by plot number + year, eliminating the need for tedious manual comparisons. Furthermore, to accommodate different regional implementation scenarios, the sampling frequency can be adjusted from once a year to twice a year, or even increased in certain years, while maintaining the unique mapping between plot number and year index, thus still meeting the requirements.

[0027] Furthermore, the focus of safe production for wild bean is not on whether standards are met in a single year, but rather on the overall safety performance and fluctuation characteristics of a plot of land over several consecutive years. Using only a single or simple average cadmium value for grains is insufficient to distinguish between plots that consistently meet standards and those that sometimes meet standards and sometimes exceed them, nor can it reflect the priority of monitoring results from recent years in the assessment. Therefore, it is necessary to extract indicators reflecting safe behavior from the annual monitoring records of the plots and summarize them into a representative plot behavior safety index using a time-weighted method.

[0028] First, in each monitoring year, an annual safety margin is constructed based on food safety limits and measured cadmium content in grains. Then, a time decay function is used to weight and aggregate the annual safety margins, assigning higher weight to the performance of the most recent year, thereby forming a land parcel behavior safety index. Therefore, an annual safety margin is introduced. And construct a land parcel behavior safety index. : Among them, the land plot behavior safety index Plot number is The plot of land in the continuous observation years Weighted safety performance within the range; Annual safety margin Plot number is In the year number At that time, the difference between the cadmium test result of grains and the corresponding food safety limit, the annual safety margin. A value greater than zero indicates that a safety margin has been set for the year; the annual safety margin. A value less than zero indicates a risk of exceeding the standard in that year; Number of years observed The number of consecutive years in which *Viola multiflora* was planted on this plot of land, and the number of years observed. It is a positive integer; the time decay coefficient : A positive real number that controls the rate of weight decay in different years; the time decay coefficient. The larger the value, the more it tends to highlight the safety performance of the most recent year, and the more it reflects the time decay coefficient. The value range is set within a reasonable range based on regulatory requirements and data stability; Annual safety margin during implementation The land plot behavior safety index can be obtained directly from annual inspection results and limit values ​​without complex calculations. The calculation can then be completed in one go using the above formula in data processing software or general-purpose computing tools. For newly developed plots with a shorter monitoring period, a smaller monitoring period can be temporarily used. And adjust the time decay coefficient accordingly. The weighting of years with fewer historical years is increased to obtain a land behavior safety index that is of reference value in the early stages.

[0029] Condensing years of monitoring results into a single indicator This approach preserves interannual fluctuation information while highlighting the safety status of the most recent year, providing a unified measurement basis for subsequent classification of safety environment types and assessment of land stability.

[0030] Furthermore, while a single site behavior safety index can reflect safety performance over many years, it cannot distinguish between sites with low soil cadmium levels and sites that rely on intensive management measures to maintain compliance, nor can it express the long-term impact of soil physicochemical properties and topographic conditions on safety behavior.

[0031] Therefore, it is necessary to maintain the safety index of land use behavior. As a key component, soil environmental characteristics, topographic features, and safety behavior characteristics are combined into an environmental behavior feature vector, so that each plot has stable coordinates in a multi-dimensional space.

[0032] First, the original soil physicochemical indicators of each plot were normalized to form a soil environmental characteristic vector. The morphological information, such as slope, aspect, and elevation, is processed to form a terrain feature vector. Then, the annual safety margin, the proportion of years exceeding the standard, and the land plot behavior safety index will be included. Safety behavior indicators are combined to form a safety performance feature vector. .

[0033] Subsequently, the three sub-vectors are scaled using grouping weight coefficients to obtain the environmental behavior feature vector. : Among them, environmental behavior feature vector Plot number is Coordinates in the environmental behavior feature space; Soil environmental feature vector Plot number is The soil composition, including cadmium and other heavy metal content, pH, organic matter, and cation exchange capacity, after being standardized at different scales; topographic feature vectors. Plot number is Spatial characteristics such as slope, aspect, and elevation; safety performance characteristic vector. Plot number is Annual safety margin sequence, proportion of years exceeding standards, and land plot behavior safety index The combination; Grouping weight coefficient , , Positive real numbers, used to adjust the soil environmental feature vector. Terrain feature vector Safety performance feature vector Environmental behavior feature vector The relative degree of influence.

[0034] Grouping weight coefficient , , This can be tailored to regulatory priorities and empirical rules, such as improving the safety performance characteristic vector in downstream irrigation areas of mining areas. In environmental behavior feature vector The weights in the classification are determined to make the impact of long-term safety behaviors on environmental classification more significant. Alternatively, group weight coefficients can be determined through expert scoring or a multi-objective compromise, provided that consistency in group weight coefficients is maintained within the same batch of land parcels.

[0035] When used, complex, multi-source information is compressed into a single environmental behavior feature vector. This allows subsequent clustering and classification operations to be performed in a unified space, while preserving the structural differences in soil environment, topography, and safety behavior.

[0036] Furthermore, only by classifying numerous plots into several stable safe environment types based on safe behaviors and environmental conditions can subsequent steps establish a model of the inherent hazards and controllable margins of the multi-flowered bean variety within each type.

[0037] Grouping land parcels solely based on a single soil index or administrative division can easily lead to plots within the same group exhibiting completely different safety performances, reducing the model's specificity and interpretability. Therefore, a model based on environmental behavior feature vectors is necessary. Construct a reasonable dissimilarity metric and perform clustering based on it to obtain safety environment type labels.

[0038] Firstly, based on environmental behavior feature vectors Environmental dissimilarity between structural blocks The distance is expressed in the form of a weighted matrix: Among them, environmental dissimilarity Plot number is With plot number The degree of difference in the environmental behavior feature space is a non-negative real number; the environmental behavior feature vector and environmental behavior feature vector The plots are numbered as follows: and the land parcel number is Environmental behavior feature vector; Weighted matrix It is a symmetric positive definite real matrix used to set importance weights and correlation structures among different feature dimensions; it is a weighted matrix. The specific values ​​can be determined through expert experience, historical classification effect evaluation, or data-driven methods. As long as symmetry and positive definiteness are maintained, environmental dissimilarity can be guaranteed. This is the effective distance.

[0039] After calculating the environmental dissimilarity Subsequently, agglomerative hierarchical clustering, partitioning clustering, or graph-based community partitioning methods can be used to analyze the environmental behavior feature vectors of all plots. Clustering is performed to group plots with high similarity and similar long-term security behavior into the same security environment type.

[0040] After clustering, assign a safe environment type label to each plot. Safety Environment Type Label The land parcel is categorized as either a long-term stable compliance type, a borderline sensitive type, or a high-risk type, with each category corresponding to a set of environmental behavior feature vectors. Typical distribution range and land parcel behavior safety index The range.

[0041] When used, plots with similar soil environment, topographic conditions and long-term safe behavior are grouped into a compact safe environment type, which provides a clear environmental stratification for the subsequent construction of the inherent hazard baseline and management control margin model of multi-flowered bean varieties within the type, and reduces heterogeneity within the type.

[0042] Step 2: Using the safety environment type label obtained in Step 1 Based on the premise of distinguishing between inherent differences in varieties and controllable differences in management measures, the behavior of multi-flowered bean varieties within each safety environment type is decomposed into two parts: the baseline of inherent harm of the variety and the controllable margin of management.

[0043] In actual field settings, under the same safe environmental conditions, the absorption and transfer of available cadmium in the soil by root systems, stems, leaves, pods, and seeds of common bean plants is not a simple proportional relationship, but rather an accumulation through a continuous migration process between multiple organs. If only the cadmium content in the seeds is used as a single point to regress the soil cadmium level, it is difficult to distinguish differences in root retention capacity, stem and leaf storage capacity, and translocation capacity to pods and seeds among varieties, and it is also impossible to clarify how changes in soil conditions due to management measures lead to cadmium transfer between organs.

[0044] Therefore, within each type of safe environment, the organs of *Vigna arvense* are considered as interconnected compartments to construct a unified cadmium transport meta-model structure, and the observation results of each plot and each variety on this structure are expressed by organ-level concentration vectors.

[0045] For labels belonging to the same safety environment type Using the soil available cadmium levels and corresponding multi-organ cadmium measurement data already prepared in step one, several plots were numbered. - Variety Number - The measured values ​​from the observation year are reconstructed into an organ concentration vector to characterize the complete migration trajectory of the species in this environmental type.

[0046] Among them, organ cadmium concentration vectors are introduced. and cadmium transmission fingerprint matrix of varieties The following formula is used to describe the land parcel. and varieties The relationship between them: Among them, organ cadmium concentration vector This is a column vector representing the parcel number. The planting variety number is The cadmium concentration arrangement results of the four compartments of the root system, above-ground vegetative organs, pods and seeds of *Gynostemma pentaphyllum* were obtained with a fixed dimension of four. Variety Cadmium Transmission Fingerprint Matrix for A column matrix of dimension, representing the variety number. Within this safe environment type, the unit of available cadmium in the soil will be... The proportional structure transmitted to the four compartments, and the cadmium-transmitted fingerprint matrix of the species. Each element is a non-negative real number; Soil available cadmium levels The scalar represents the parcel number. The available cadmium intensity in soil, calculated through soil chemical extraction or empirical modeling under baseline management conditions; the level of available cadmium in soil. It is a non-negative real number.

[0047] In practice, technicians selected several representative plots and the main variety of common bean in a specific safety environment. Using uniform fertilization and irrigation measures during the same season, they collected cadmium concentration vectors from each plant before harvest, separating the roots, stems, leaves, pods, and seeds for individual organ testing. .

[0048] Simultaneously, soil extraction methods or existing soil-available state models were used to estimate the soil available cadmium level for each plot. Then, by solving the relationships in the formula, the cadmium transmission fingerprint matrix of the variety was obtained. A preliminary estimate. When using this method, the previously scattered multi-organ cadmium measurements are uniformly entered into the organ cadmium concentration vector. In, and through the cadmium transfer fingerprint matrix of varieties With soil available cadmium levels Establishing clear connections allows each variety to have a structured migration fingerprint in each safety environment type, laying the foundation for the subsequent introduction of cross-crop priors and management influences.

[0049] Furthermore, even within the same safe environment type, the cadmium transport patterns of various varieties of common bean still share common structural characteristics in different organs. For example, the overall transport ratio from roots to aboveground parts and the aggregation ratio from aboveground parts to pods are somewhat similar to those of other leguminous or gramineous crops.

[0050] If we rely entirely on limited multi-organ data from common bean to directly fit a cultivar cadmium transmission fingerprint matrix... This approach is prone to instability when the sample size is small or when there is strong correlation between organs. Therefore, it is necessary to introduce a priori cadmium transport template matrix across crops, and then scale and fine-tune each variety of common bean based on this matrix, so as to inherit the common structure while preserving the differences between varieties.

[0051] Among them, a priori cadmium transport template matrix is ​​introduced. Variety scaling diagonal matrix The cadmium transfer fingerprint matrix of the variety is obtained through matrix relationships. Represented as a scaled version of the prior structure: Among them, the prior cadmium transfer template This can be obtained by performing linear regression on cadmium data from multiple organs of rice, wheat, or other crops in the same region, and then arranging them in organ order; Variety scaling matrix. :for Diagonal matrix, main diagonal elements Characterizes the magnification or reduction factor of roots, stems, leaves, pods, and seeds relative to prior structures.

[0052] Prior Cadmium Transfer Template Matrix for The Villie matrix is ​​derived from the average transport structure obtained by summarizing and analyzing cadmium data from multiple organs of rice, wheat, or other legumes under similar security environments, and is a priori cadmium transport template matrix. Each element is a non-negative real number; Variety scaling diagonal matrix for A 3D diagonal matrix, where the four elements on the main diagonal are the variety numbers. Scaling factors for roots, above-ground vegetative organs, pods, and grain chambers; variety scaling diagonal matrix. The main diagonal elements are positive real numbers, and the remaining positions are zero.

[0053] Combined with the obtained organ cadmium concentration vector With soil available cadmium levels It can and Combined, form a diagonal matrix of variety scaling. The system of equations was then used to determine the results of matrix solutions within a safe environment type, utilizing observations from multiple sites and over multiple years. .

[0054] Furthermore, a regularized matrix equation solver can be used, which restricts the scaling of the diagonal matrix by variety. The magnitude of the deviation from the identity matrix ensures the cadmium transmission fingerprint matrix of the variety. Inheriting the prior cadmium transmission template matrix While maintaining the overall shape, appropriate adjustments were made based on actual measurement data of multi-flowered green beans.

[0055] For example, in a certain edge-sensitive security environment, technicians selected three main varieties of broad bean and simultaneously monitored cadmium content in multiple organs across several representative plots, using existing rice data to construct a priori cadmium transport template matrix. Then, the variety scaling diagonal matrix of the three varieties is obtained through the matrix solver. Thus, the corresponding cadmium transmission fingerprint matrix of the variety is obtained. .

[0056] In use, a priori cadmium transmission template matrix is ​​used. Variety scaling diagonal matrix The combination of these factors ensures that the estimation of cadmium migration parameters in multi-flowered bean varieties remains stable and biologically reasonable even with limited samples, while preserving the differences in cadmium migration capacity in various organs among different varieties, thus providing reliable parameters for the subsequent construction of an inherent hazard baseline.

[0057] Different planting and management practices affect the level of available cadmium in the soil by altering key factors such as soil pH and organic matter content, which is reflected in the input of the cadmium transport meta-model. Simply using empirical coefficients to directly map management practices to grain cadmium content makes it difficult to achieve a consistent expression across different safety environments and to correlate it with the cadmium transport fingerprint matrix of different varieties. Therefore, it is necessary to abstract the impact of management measures on soil conditions into an impact fingerprint, and to express the effect of a combination of management measures on the level of available cadmium in the soil in an explicit functional form. Its function.

[0058] Number each combination of management measures. Based on changes in soil pH and changes in soil organic matter content As the main control variable, the soil available cadmium adjustment coefficient is introduced. and The level of available cadmium in the soil after management. Compared with the baseline soil available cadmium level The exponential relationship between them: Among them, the level of available cadmium in the soil after management. The scalar represents the parcel number. The combination of management measures is numbered as follows: Subsequent soil available cadmium intensity; baseline soil available cadmium level As a scalar, it represents the available cadmium intensity of the same plot under baseline management conditions; Changes in soil pH The scalar indicates that the management measure combination number is... The change in soil pH relative to baseline management conditions can be positive or negative; the change in soil organic matter content... The scalar indicates that the management measure combination number is... The magnitude of increase or decrease in soil organic matter content; Soil available cadmium adjustment coefficient and Let be scalars, representing the changes in soil pH, respectively. and changes in soil organic matter content Levels of available cadmium in soil The adjustment intensity, soil available cadmium adjustment coefficient and It is a non-negative real number, determined by fitting historical restoration or pot experiment data.

[0059] In field implementation, representative plots within a specific safe environment type can be selected as the target. Multi-flowered bean plots can be established under three management combinations: baseline management, increased lime application, increased biochar application, and combined lime and biochar application. By comparing changes in soil pH, organic matter content, and available cadmium levels before and after the application, the soil available cadmium adjustment coefficient can be estimated using the exponential relationship given by the formula. and .

[0060] When using this method, the impact of a combination of management measures on the level of available cadmium in the soil is compared with the level of available cadmium in the soil after the management measures. This unified quantitative expression allows the input of the cadmium transport meta-model to directly call the result, thereby achieving a continuous expression of baseline soil state - management change - new soil state within the same safety environment type. This facilitates the conversion of management adjustments into quantitative effects on safety characteristics in subsequent steps.

[0061] The cadmium accumulation performance of multiflorum bean varieties in the same safe environment type depends on both the cadmium transport fingerprint matrix of the variety itself and the cadmium transport fingerprint matrix of the variety. and environment type It also depends on the combination of management measures actually taken. Levels of available cadmium in soil The adjustment of these two parts is crucial. Without clearly separating these differences, it's difficult to answer the practical question of to what level safety performance can be improved through management adjustments without changing the variety.

[0062] Therefore, it is necessary to construct a form within the environmental type that combines the inherent hazard baseline of the variety with the manageable margin in an additive manner, so that the safety statistic of each plot-variety-management combination has a decomposable structure.

[0063] Introducing an inherent hazard baseline Management controllable margin The land parcel was numbered as Variety number is Management measures combination number is Corresponding safety status indicators Represented as the sum of two parts: Among them, the inherent hazard baseline : Can be used in safe environment types A baseline management combination (e.g., conventional fertilization, no remediation materials) was selected. Grain cadmium levels were predicted using a cadmium transport meta-model across multiple plots, converted to a safety margin, and then averaged. The resulting management controllable margin was then calculated. In the same environment type Internally, compare management portfolio The difference in security performance between the baseline management combination and the cadmium transport meta-model, i.e.

[0064] Safety indicators The physical meaning of can be clearly defined as "expected safety margin" or "safety margin under a certain confidence level", which can be obtained through Monte Carlo or quantile estimation.

[0065] Safety indicators As a scalar, it indicates the type of safe environment label. Under the corresponding environmental conditions, the plot number is Variety numbering is adopted And implement management measures combination numbered as The predicted safety performance at that time can be selected as the expected safety margin or a similar safety status with dimensions; The inherent hazard baseline is a scalar, representing the baseline value in the safe environment type label. Under internal and benchmark management conditions, the variety number is The average security performance is determined by the cadmium transport fingerprint matrix of the cadmium transport meta-model. The results were obtained by combining the available cadmium levels in representative soils of this environmental type and by statistical estimation across multiple sites. Management controllability margin As a scalar, it indicates the type of safe environment label. Internal, implementation management measures combination number is The gain or loss in safety performance relative to baseline management conditions is given by the formula for the level of available cadmium in the soil after management. Baseline of inherent hazards The propagation results in the cadmium transport meta-model were compared.

[0066] In practice, for example, within a certain edge-sensitive environmental type, for a frequently planted variety of bean aralia... The inherent hazard baseline can be estimated first on several plots of land that maintain baseline management, based on multi-year multi-organ data and cadmium transport meta-models. Then, in the same environmental type, control plots were established using lime, biochar, and combined application methods. The levels of available cadmium in the soil after management were then assessed. Driven by the cadmium transport meta-model, calculate the corresponding safety status indices. By comparing with the inherent hazard baseline The difference determines the manageable margin. .

[0067] When using it, the complex relationship between variety, environmental type, and management is addressed through safety indicators. Inherent hazard baseline Management controllable margin The method is broken down into two parts: one part varies only with the variety and environmental type, and the other part varies only with the combination of management measures. This provides a clear calculation path for subsequent steps to solve for the minimum management adjustment for specific plots and to compare the potential safety space of different varieties under the same environmental type.

[0068] Step 3, using the safety status indicators established in Step 2 Using this as the main thread, the data is coupled step by step with the physical yield, cost, and price information of wild soybeans to form safe yield and safe profit indicators. Then, combined with the predicted content of heavy metals such as cadmium, lead, and zinc, a multi-metal safety index is constructed. Based on this, risk budgets are set up at three levels: plots, farmers, and regions.

[0069] Subsequently, using these safety status and risk budget parameters, a safety domain is defined, all candidate management measure combinations are evaluated, and a management adjustment cost function is applied. Find the least costly management combination within the security domain, thereby providing the minimum management adjustment amount for each plot.

[0070] In actual bean production management, decision-makers are concerned not only with maintaining safety within cadmium limits, but also with the actual yield and net profit that can be achieved under these safety constraints. Focusing solely on safety indicators... While safety indicators can reflect the magnitude of the safety margin, they are insufficient to directly guide variety selection and management measures. Therefore, it is necessary to consider safety status indicators. Based on this, physical output and input costs are introduced, and... Transform into safe production and security benefits These two indicators allow security risks and economic benefits to be quantified within the same framework.

[0071] Firstly, the cadmium transport meta-model and the inherent hazard baseline were used. and management controllability margin In a given plot number Variety number Management measure combination number Under these conditions, safety status indicators are obtained. This can be understood as the expected safety margin under this combination.

[0072] Subsequently, land parcel numbers were introduced. Variety number Management measure combination number Corresponding physical yield forecast This physical yield forecast It can be obtained by combining historical sample data and agronomic models. To incorporate safety indicators... This is converted into a deduction ratio for physical output, and a safety reduction function is introduced. This function is used in safety status indicators When the value is high, it is close to In terms of safety indicators It gradually decreases as it approaches the safety lower limit, and is used to characterize the proportion of outputs that can be considered safe after taking safety risks into account. Therefore, a safe output is constructed. as follows: Among them, safe production The scalar represents the parcel number. Using variety number Management measures combination number is When considering safety risk reduction, the safe output can be determined; Physical yield forecast As a scalar, it represents the yield predicted based on agronomic models and historical data without considering safety constraints; safety status index It is a scalar, with the same meaning as defined in step two; Safety reduction function This is a mapping function from safety status indicators to yield reduction ratios, and its value range is limited to... arrive Between, with safety status indicators The function can be monotonically increasing, and its shape can be either slowly increasing or piecewise smooth to meet actual management preferences.

[0073] Among them: safety status indicators This indicates that the land parcel number is Variety number is The management portfolio number is Under the given conditions, the safety margin calculated from the cadmium transport meta-model and the limit value can be understood as the grain cadmium limit value for the corresponding combination minus the predicted grain cadmium level; the upper safety margin. A positive real number is used to provide a reference upper limit for the safety margin. When the safety margin reaches or exceeds this value, the combination is considered to no longer require additional production reductions in terms of safety. The safety margin upper limit is... The safety reduction function can be selected based on the higher quantile of the safety margin distribution in historical monitoring data, or it can be directly set by the management department based on experience; The value ranges from zero to one, when the safety status index When the value is less than or equal to zero, it is taken as zero; when the safety status index... Greater than or equal to the safety margin limit When the value is 1, it increases linearly with the safety status index between zero and the upper safety margin.

[0074] To achieve safe production Then, a unit price parameter related to the product grade of multi-flowered green beans was introduced. and plot number Variety number Management measure combination number Total cost Total cost This covers the costs of seeds, fertilizers, passivation materials, labor, and potential certification and testing. A safety net is then constructed based on this. : Among them, safety benefits As a scalar, it represents the expected net income of the plot-variety-management combination over a planting cycle, under the premise of meeting safety constraints; grade unit price. The scalar value indicates that the variety number is The unit product price at which the proposed quality level is achieved; this price can be determined based on the local market and certification system; total cost. As a scalar, it represents the value at parcel number 0. The above uses variety number as And implement management measures combination numbered as All costs and expenditures.

[0075] In one specific implementation, in a township downstream of the mine, a multi-flowered bean planting technician entered all the plot numbers and historical yields under a farmer's name into the system, and selected the variety number that the farmer planned to continue using. Combined with current management measures number The system first uses the safety status index obtained in step two. and physical yield forecast Calculate safe production Then, the unit price is determined according to the quality grade corresponding to that variety. and registered costs Automatically calculate security benefits .

[0076] Technicians can view the safe output and safe revenue for each plot of land in the interface, thereby understanding the net revenue level that each plot can contribute under safe conditions while maintaining the existing management plan. When using it, the abstract security status index Specifically translated into safe output and security benefits This allows subsequent management adjustments to no longer be based solely on whether standards are exceeded, but rather to provide a direct comparison of the economic performance of different combinations under safety constraints, thus providing a quantitative basis for selecting management solutions.

[0077] Furthermore, the safety management of multi-metallic beans must not only consider the safety performance of cadmium alone, but also take into account the synergistic risks of heavy metals such as lead and zinc, and based on this, provide risk budget parameters at three levels: plot, farmer, and region. If only cadmium is assessed as a single factor, it is prone to bias in areas with high background levels of lead and zinc; if the risk tolerance of different management levels is not differentiated, it is difficult to ensure regional brand safety while providing farmers with sufficient profit margins. Therefore, it is necessary to construct a multi-metal safety index. And combine it with hierarchical risk budget parameters.

[0078] Therefore, by using the cadmium transport meta-model and predictions of other heavy metal contents in the soil, the plot number can be obtained as follows: Variety number is Management measures combination number is The predicted content of each heavy metal element in the grain is denoted as . Heavy metal index These correspond to elements of concern, such as cadmium, lead, and zinc.

[0079] To unify these contents into a comprehensive index, a heavy metal weighting parameter is introduced. Risk transformation function for heavy metals Constructing a multi-metal safety index as follows:] Among them, the multi-metal safety index As a scalar value, it represents the overall safety level of the land plot-product-management combination when considering the combined effects of multiple heavy metals; the higher the value, the higher the overall risk. Heavy metal weighting parameters It is a non-negative scalar, used to reflect the first... The relative importance of heavy metals in terms of regulatory limits, toxicity, and consumer concern; heavy metal weighting parameters. It can be determined through policy documents and expert assessment; Heavy metal index maximum value This represents the total number of heavy metals considered.

[0080] Heavy metal risk transformation function For from the first The mapping function from the predicted content of a certain heavy metal to its risk contribution takes non-negative real values. The function approaches zero when the predicted content is close to zero and rises rapidly when the predicted content approaches or exceeds the limit. It can be presented as a continuous, monotonic function with an upper bound for easier interpretation. The [missing information - likely a typo, should be "the first"]. The mass limit for each heavy metal is denoted as ,definition: In the formula: : No. The predicted grain content of heavy metals was obtained from the cadmium transport element model or the corresponding metal model. : No. The regulatory limits for these heavy metals are derived from current standards; The slope parameter controls the rate of risk growth near the limit. It is set by the management based on the principle that risk increases significantly when approaching the limit. Interval.

[0081] Based on this, risk budget parameters are set at three levels: land parcel, farmer, and region. A land parcel safety threshold parameter is set at the land parcel level. The safety indicators of the land-variety-management combination are required. Not lower than a certain threshold, and with a multi-metal safety index Not exceeding the land parcel safety threshold parameter .

[0082] The farmer level is each farmer household Define farmer risk budget parameters This parameter is used to constrain the acceptable number of edge combinations and overall multimetallic risk for all plots under the farmer's name throughout the entire planting cycle; regional risk budget parameters are set at the regional level. This is used to limit the percentage of high-risk combinations that can occur within a specific township or brand base in a given year.

[0083] In practical implementation, during the project initiation phase, representatives from agricultural technology extension departments, local regulatory authorities, and cooperatives can be organized to determine the multi-metal safety index through discussion. Weight parameters and risk transformation function The approximate shape of the site was determined, and initial site safety threshold parameters were set based on local regulatory practices. Farmers' risk budget parameters and regional risk budget parameters Subsequently, several farmers were selected in a pilot village, and the safety indicators obtained in step two were used to... and polymetallic content prediction Import the data into the system, which then calculates the multi-metal safety index. Based on risk budget parameters, it is determined which land parcel-variety-management combinations are in the safe zone, which are in the marginal zone, and which have exceeded the region's risk tolerance.

[0084] When using it, the multi-metal safety index is used. The construction of hierarchical risk budget parameters enables the security status to no longer be limited to a single element and a single plot, but to provide a clear quantitative basis for subsequent security domain division from the perspective of multiple elements and multiple levels.

[0085] Furthermore, once the safety profile and risk budget parameters have been constructed, an operational approach is still needed to clearly identify which management combinations simultaneously meet the safety requirements at the plot, farmer, and regional levels within the soil condition-variety fingerprint-management space, and to define these combinations as points within the safety domain. Simply checking whether each indicator exceeds the limit without considering the logical relationships between risk budgets at different levels may result in situations where the plot level appears safe, but risks overlap at the farmer or regional level.

[0086] Therefore, it is necessary to include safety status indicators. Safe production Safety and benefits and the multi-metal safety index The risk budget parameters are integrated into the judgment process, a set definition of security domains is constructed, and judgment steps are given.

[0087] In terms of processing method, each plot of land is numbered. In its respective safety environment type label Internally enumerate all feasible variety numbers Combined with management measures number The results are used to calculate the corresponding safety status index. Safe production Safety and benefits and polymetallic safety index .

[0088] Subsequently, the combination is first checked at the parcel level to see if it meets the parcel safety threshold parameters. Requirements, namely safety status indicators Does it exceed the minimum safety performance threshold for the land parcel, and is the multi-metal safety index... Is it below the maximum allowable limit for the land parcel? Secondly, at the farmer level, candidate combinations of all plots under the same farmer's name are summarized, and the proportion of marginal combinations and the overall level of the corresponding multi-metal safety index under the proposed combination scheme are calculated, and compared with the farmer's risk budget parameters. If the comparison exceeds the acceptable level at the farmer level, it is marked as infeasible; Finally, at the regional level, statistics were compiled on all farmer plans within the same township or brand base area to determine the number and distribution of plots with high-risk combinations, in relation to regional risk budget parameters. If the comparison is made and the budget is not exceeded, then the above three constraints (plot number) will be satisfied. Variety number Management measure combination number The triple is assigned to the security domain.

[0089] In one specific embodiment, after collecting the proposed varieties of the cooperative's farmers, the cooperative's technicians combine these preferences with candidate management measures in the system to generate a candidate combination list. The system automatically calculates the safety profile and compares it with the risk budget parameters one by one according to the above process.

[0090] On the screen, technicians can clearly see which combinations for each plot are marked as safe among all candidate combinations, which combinations are eliminated because they do not meet the plot-level indicators, and which combinations are excluded even though they are safe at the plot level but cause budget overruns at the farmer or regional level.

[0091] In practice, a safety domain determination process is provided that integrates multidimensional safety profiles with a three-tiered risk budget. This transforms the safety domain from a set of single-indicator thresholds into a set of available combinations that meet certain conditions, comprehensively considering plot performance, overall farmer risk, and regional brand safety. This provides a clear constraint range for selecting the minimum management adjustments within the safety domain. In an alternative implementation, if a region has not yet implemented farmer or regional level budget management, a primary safety domain can be constructed at the plot level, with higher-level budget constraints gradually added in the future.

[0092] Once the security domain has been determined, the land parcels are numbered. Variety number In this regard, there are often multiple management measures combined and numbered. At the same time, within the safe domain, these combinations have safe output. and security benefits While the aspects may be similar, there are differences in management complexity, additional costs, and the degree of deviation from existing management measures. Without introducing a clear management adjustment cost function, relying solely on experience to select from these combinations can easily lead to recommendations that are either too aggressive (too large an adjustment range) or too conservative (insufficient safety margin to cope with potential fluctuations).

[0093] Therefore, it is necessary to construct a management adjustment cost function that takes into account both safety performance and management adjustment costs. And find the management adjustment cost function within the security domain. The combination of management measures with the smallest value is taken as the minimum management adjustment plan for this plot of land.

[0094] Therefore, a management adjustment cost function is defined on the set of management measures combinations within the security domain. And introduce a safety performance lower limit parameter. Safety performance penalty coefficient Management adjustment of cost weights and management adjustment cost indicators The management adjustment cost function can be expressed as: Among them, the management adjustment cost function As a scalar, it represents the value at parcel number 0. The above uses variety number as And select the management measure combination number as The overall cost over time; lower limit parameters of safety performance As a scalar, it represents the target security performance level expected to be achieved across all combinations of security domains, typically exceeding the site security threshold parameter. To allow for a buffer period; Safety performance penalty coefficient As a scalar, it represents the safety status index of the portfolio. The target safety performance level was not achieved. At the same time, the intensity of the penalty imposed on such deficiencies; management adjustment cost weighting. As a scalar, it represents the cost indicator adjusted by management under the premise that safety performance meets the requirements. Relative weight in the overall cost; Management adjusts cost indicators This is a scalar quantity used to quantify the combination of management measures. Compared to the combined costs of additional materials, additional labor, and operational complexity of the current management measures, the cost can be calculated by assigning a fixed cost coefficient to each type of management action and then summing them up: , among which The difference in application rate relative to the baseline management, each The cost per unit of materials or labor. For additional working days.

[0095] In the specific implementation process, for a plot of land within a certain edge-sensitive security environment type, agricultural technicians first combine and number the current management measures. Based on this, calculate the safety profile index of the combination. and security benefits If safety indicators Below the lower limit of safety performance parameters The system automatically enumerates all candidate management measure combinations within the security domain. Adjust cost indicators for each combination of calculations and management. (For example, increasing the amount of lime applied and biochar applied each corresponds to a cost increment), and this is substituted into the management adjustment cost function. .

[0096] Subsequently, the system employs a comparison-by-comparison approach or a solver suitable for discrete combinations to search for the management adjustment cost function within the security region. The minimum value corresponds to the combination of management measures. This combination, along with the difference from the baseline combination, is presented to the technicians in the form of minimum management adjustments. For example, it may show the need to increase lime application from one level to another and to add an organic fertilizer application in a specific year.

[0097] When using it, the cost function is adjusted through management. By taking safety performance and management adjustment costs into account, the recommended management plan can meet the multi-level risk constraints within the safety domain, and also be as close as possible to current practices in terms of the scope and cost of management adjustments, thereby increasing the likelihood that the plan will be accepted and implemented by farmers.

[0098] Step four, firstly, based on the safety status indicators obtained in step three... Safe production Safety and benefits and the multi-metal safety index By combining the limit boundaries of various standards and guidelines, and through a unified standard mapping logic, each plot-variety-management combination is assigned a land use category, product quality grade, and recommended level for the variety in that safe environment type.

[0099] Subsequently, based on the land parcels, the above results are presented to farmers and technicians in graphical and tabular form, and executable management adjustment instructions are given according to the minimum management adjustment amount. On this basis, the land parcel-level decision-making is sorted out across years to form aggregated indicators from the perspectives of farmers and regions, construct multi-year benefit and risk assessments, and form multi-year benefit indicators through discounted aggregation, providing a quantitative basis for three- to five-year planting plans.

[0100] Safety status indicators obtained in step three Multimetal safety index and safe production These indicators are continuous quantities and cannot be directly used to determine whether a particular combination should be categorized into discrete categories such as maintaining existing grain and legume cultivation, allowing the commercial sale of multi-flowered beans, allowing but restricting their use, or not suitable for multi-flowered bean cultivation. Regulatory and market systems typically operate around clearly defined standard limits; therefore, it is necessary to establish a mapping logic that connects safety status with the standard system, so that each combination can be assigned a clear land use category and product quality grade label.

[0101] Among them, standard level scores are introduced. By using safety status indicators and polymetallic safety index Map it to the same metric, and then use a set threshold to divide it into different levels.

[0102] Therefore, a security contribution function is introduced. and risk contribution function These two factors are used to convert safety margin and overall risk into a scoring format, respectively, and then combined with weighting coefficients. and Construct standard level scores: Among them, the standard level score The scalar represents the parcel number. Variety numbering is adopted And implement management measures combination numbered as The overall score within the standard evaluation space; Security contribution function As a scalar, it is an indicator of safety status. A monotonically increasing function whose range of values ​​can be set to . arrive Between, safety status indicators The larger the value, the greater the safety contribution function. The closer to ; To maintain consistency in the function system, it is preferable to define the safety contribution function in the same form as the safety reduction function, i.e.: The safety reduction function Using the aforementioned piecewise linear form:

[0103] Furthermore: safety status indicators This indicates the safety margin for the specific plot, variety, and management combination under cadmium limits, and its definition and calculation method remain consistent with those in step three; the upper limit of the safety margin. This is a positive real number used to define the upper limit of the safety margin. When the safety margin reaches or exceeds this value, it is considered that a full score can be awarded in the standard grade score calculation. (Safety margin upper limit) It is pre-set by the management based on historical monitoring results or security policies.

[0104] Risk contribution function As a scalar, it is a measure of the safety index of multimetals. A monotonically decreasing function, the range of its values ​​can also be limited to . arrive Between, the safety index of multimetals The smaller the value, the lower the risk contribution function. The closer to Weighting coefficient and It is a nonnegative scalar, used to balance safety status indicators in comprehensive evaluation. With the safety index of multimetals The degree of influence usually meets the following criteria. The specific value of the weighting coefficient can be determined through discussion based on the local regulatory authorities' emphasis on margin and multi-metal superposition risk.

[0105] In a preferred embodiment, the risk contribution function is a bounded function that monotonically decreases with the multi-metal safety index, reflecting that the greater the risk of the multi-metal, the lower the risk contribution. It can be defined as: Among them: polymetallic safety index This indicates the comprehensive risk level of the site, the crop variety, and the management combination when considering the impact of multiple heavy metals such as cadmium, lead, and zinc. Its structural configuration has been defined in step three; risk adjustment coefficient. The variable is a non-negative real number used to control the strength of the influence of the multi-metal safety index on the risk contribution function. When the risk adjustment coefficient... When the value is large, changes in the multi-metal safety index will have a more significant impact on the risk contribution function, and the risk adjustment coefficient... The risk contribution function is set by the management based on their sensitivity to multi-metal risks. The value of is between zero and one. When the multi-metal safety index approaches zero, the risk contribution function approaches one. When the multi-metal safety index increases, the risk contribution function gradually decreases.

[0106] In practical applications, standard level scores The system establishes a connection between various standard thresholds and preset interval correspondences. For example, it selects several score intervals and maps them to labels such as "candidate for green food," "can be marketed as ordinary commodities but not suitable for green food application," and "suitable only for feed or green manure." These labels are then correlated with provisions of the agricultural land soil pollution risk control standards and the relevant guidelines for *Gynostemma pentaphyllum*. After loading the results from step three into the software, the program calculates the standard level score according to the above formula. It also automatically assigns land use category and product quality grade labels to the plots.

[0107] When in use, continuous safety status is converted into a level label that corresponds completely to the current standards, enabling subsequent land parcel decisions and regional planning to be directly converted between legal and technical language.

[0108] Although scored according to standard levels While land use and product grade labels can be provided for each plot-variety-management combination, farmers who actually implement planting management are more concerned with whether a particular plot needs adjustment under the current variety and management measures, the extent of the adjustment, and the impact on yield after the adjustment. If only static grade labels are provided, farmers cannot glean specific operational suggestions from them.

[0109] Therefore, it is necessary to focus on the land parcel number. Integrated Environment Type Tags The safety status, safety domain affiliation, and minimum management adjustment amount are output in the form of a single farmland decision card.

[0110] Specifically, a decision summary is generated for each land parcel, including the parcel number. and its safety environment type label ; in the current variety number Combined with current management measures number Safety indicators Safe production Safety and benefits and the multi-metal safety index The land use category and product grade label corresponding to the combination under the standard mapping; and whether the combination is within the security domain defined in step three.

[0111] If the combination is already in the safe zone and has a standard grade score If the level is higher than the preset target level, the decision card should clearly indicate that the current variety and management measures are recommended to be maintained, and a concise explanation should be given of the balance between safety and profitability of the combination. If the combination is on the edge of the safety domain or slightly below the target level, the minimum management adjustment amount calculated in step three should be used to number the recommended management measure combination. With the current combination number Perform differential analysis, specifically describing visible actions such as adding a certain passivating material once or adjusting a certain fertilizer formula on the existing basis, and simultaneously listing the corresponding safety indicators after the adjustment. and security benefits This allows farmers to intuitively understand the necessity of the adjustment and the changes in their income.

[0112] In one embodiment, township agricultural technicians open the system interface in the cooperative's meeting room and view the land decision cards one by one according to village group order. For the numbered The interface displays that the plot of land belongs to the edge-sensitive security environment type. Current variety number Combined with management measures number Safety indicators Slightly higher than the land parcel safety threshold parameter Multimetal safety index Below the standard requirement, but with a standard grade score. It is slightly below the threshold for green food certification.

[0113] The system then provides a minimal management adjustment plan. The decision card includes a written suggestion to add one more application of lime in the first season and switch to a compound fertilizer with higher organic matter content in the second season, along with the safety benefits under this plan. Agricultural technicians print out this decision-making card and give it to farmers, who can then directly follow the management actions on the card.

[0114] When in use, the complex model output is transformed into clear instructions for individual plots, enabling farmers and grassroots technicians to make reasonable adjustments to varieties and management even without understanding the complex formulas.

[0115] Furthermore, while plot-level decision cards can guide the operation of individual farmlands, cooperatives, farmers, and local governments need more aggregated information for farmers and regions when formulating contracted planting quantities, regional layouts, and branding strategies. This includes information such as the total safe planting area of ​​multi-flowered beans, the safety compliance rate, and the proportion of products at each quality grade in a given year. Relying solely on manual aggregation of information from plot-by-plot decision cards is not only inefficient but also makes it difficult to identify structural problems in the layout in a timely manner. Therefore, based on the plot-variety-management combinations determined in step three, it is necessary to aggregate these combinations annually to provide annual aggregated indicators.

[0116] First, we introduce the planting year index. The planning period is divided into several years, so that the land parcel number can be recorded in each year. Corresponding decision combination And whether the combination is within the security domain constructed in step three.

[0117] Introducing land area and annual required safety performance threshold and annual regional polymetallic safety threshold Establish an annual safe planting area The calculation formula: Among them, the annual safe planting area As a scalar, it represents the index in the planting year. The total planting area of ​​all plots that simultaneously meet the safety performance threshold and the regional polymetallic safety threshold under the selected variety and management combination within the corresponding year; land parcel collection A set of numbers for all land parcels included in the plan; land parcel area. The scalar represents the parcel number. Actual planting area; annual required safety performance threshold As a scalar, it represents the index in the planting year. For the corresponding year, policymakers' assessment of safety indicators Minimum requirements; annual regional polymetallic safety threshold As a scalar, it is a measure of the safety index of multimetals. Regional-level upper limit constraints for that year; Indicator Function This is a mapping from logical conditions to numerical values; the value is taken as true when the condition in parentheses is true. When the condition is not met, the value is taken as... Therefore, when summing, only the areas of plots that meet the conditions are added.

[0118] In practical engineering applications, after determining the multi-flowered bean planting plan for a specific year, the cooperative or county-level agricultural department will assign a number to the variety selected for each plot of land in that year. Management measure combination number Input into the system to calculate the annual safe planting area. It also provides the total area of ​​wild bean plantations for that year, the area that reached higher quality levels, and the labels for different safety environment types. The corresponding area distribution.

[0119] Technicians can see color-coded zones for different villages on the map interface. For example, a certain river valley is marked as a high-risk type in the safe environment category, indicating that the suitable safe planting area in this area is relatively small. In contrast, the upstream terrace area is marked as a long-term stable and compliant type, suitable for undertaking more multi-flower bean planting tasks.

[0120] When used, the layout information at the farmer and regional levels can be accurately extracted from the plot-level results through simple summation and conditional filtering, thereby providing a quantitative basis for contract area allocation, planting restrictions in high-risk areas, and regional brand promotion.

[0121] Furthermore, while the aforementioned annual summary indicators can reflect the area and grade composition of a particular year, decision-makers, when planning the planting layout of wild bean in three to five years, are more concerned with the cumulative revenue level and its temporal distribution over the entire planning period. Without a unified measurement of cross-year revenue, it is difficult to compare the two strategies: appropriately reducing the area of ​​high-risk regions in the first year to obtain higher returns later, versus maintaining the current layout to ensure stable returns.

[0122] Therefore, annual safety benefits are required. Based on this, an annual index and discount rate parameter are introduced to construct a multi-year income discount aggregation index, providing a reference for the optimal selection of planting planning schemes.

[0123] Among them, the number of years of the planning period is introduced. and annual discount rate And expand security benefits to a form with an annual index. , indicating the index in the planting year Within the corresponding year, the land parcel number is In the selected variety number Management measure combination number The safe return is determined based on this. The net present value (NPV) for the planning period is then defined. : Among them, the net present value during the planning period For scalar purposes, it represents the sum of discounted safe returns for all plots under the selected crop type and management combination arrangement during the planning period; the number of years in the planning period. It is a positive integer, typically ranging from 3 to 5; annual discount rate It is a non-negative scalar, representing the degree of discounting of future annual income in present value calculations, and the annual discount rate. The annual safe return is set by decision-makers based on their preference for funding costs and return timing. As a scalar, it indicates the index in the year. Within the corresponding year, the land parcel number is The meaning of the safety return under the selected combination is the same as the safety return in step three. It's consistent, except for the addition of the annual dimension.

[0124] In one application scenario, when county-level agricultural departments were formulating a three-year plan for the construction of bean and cauliflower bases, they used the system to construct two different plans: Plan 1 focused on rapidly expanding the safe planting area in the first year, while Plan 2 conservatively controlled the area in the first year, focusing on the management and adjustment of high-risk areas, with the expectation of gradually releasing more safe planting area in the second and third years.

[0125] Technicians combined the land parcels from each of the two plans for each year. Import the data into the system, and the system will calculate the net present value for the corresponding planning period. The document also lists the annual safe planting area for each year. And quality grading structure. Decision-makers can see the differences between various options in terms of benefits, risk mitigation progress, and planting area structure in the report, thus selecting a multi-year planning option that better suits local conditions.

[0126] When used, it is determined by the net present value over the planning period. This discounted aggregate indicator unifies the safety returns across multiple years onto a single evaluation scale, enabling multi-year planting plans to move beyond single-point annual comparisons and take into account the time-series characteristics of investment followed by benefits, supporting layout decisions over a three- to five-year timeframe. In an equivalent implementation, if regional brand risk needs to be explicitly reflected in the net present value calculation, it can be expressed in the annual safety returns. Deductions and Excesses in Regional Risk Budget Parameters The corresponding projected costs, as long as the net present value during the planning period is maintained. The discounted aggregation form can remain unchanged.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for analyzing the cadmium accumulation traits of *Gynostemma pentaphyllum* based on big data, characterized by: include, Soil heavy metal and physicochemical properties, multi-year grain cadmium monitoring, variety information and management records are read from the database by plot. Behavioral safety indicators for each plot are calculated and combined with soil and environmental data to form environmental behavior feature vectors. The environmental behavior feature vectors are clustered to obtain multiple safe environment types. Within each safe environment type, a unified cadmium transport meta-model is called. Based on multi-organ cadmium data of representative plots, the cadmium transport fingerprint of the corresponding variety and the baseline of the inherent hazard of the variety are determined. Within the safe environment type, a management controllability margin function is established based on management records. For each plot, variety, and management combination, the inherent hazard baseline of the variety and the management controllability margin function are called to calculate the safety margin and a set of safety status indicators. Based on the risk budget, the safety domain and minimum management adjustment amount are identified. The planting scheme data is generated by comparing the safety margin, safety status index, safety domain, and minimum management adjustment amount with the standard threshold, and the planting scheme is calculated and output based on the soil condition update rules.

2. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 1, characterized in that: When calculating the safety index of land plot behavior, the monitoring results of cadmium in the seeds of beans and flowers of the same plot over many years are read in chronological order. A single value is obtained by weighting and summing the differences between the monitoring values ​​of each year and the corresponding limit values. When constructing the environmental behavior feature vector, the safety index of land plot behavior is connected with soil heavy metal content, pH, organic matter, cation exchange capacity and land plot topography and climate indicators in a fixed order.

3. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 2, characterized in that: When clustering environmental behavior feature vectors, dimensionless standardization is first performed on each dimension of data. Then, the weighted distance between any two plots of environmental behavior feature vectors is calculated according to the preset weight coefficients. The clustering algorithm is used to classify plots with a weighted distance less than the threshold into the same safe environment type, and to ensure that each safe environment type contains multiple plots with similar soil conditions and safety performance.

4. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 3, characterized in that: The unified cadmium transport meta-model represents available cadmium in the soil, roots, aboveground vegetative organs, pods, and grains as multiple sequentially connected compartments. By collecting cadmium content data for each compartment on representative plots, the transport parameters between compartments are derived using parameter estimation methods. Furthermore, the cadmium transport fingerprints and inherent hazard baselines of the corresponding varieties are stored in each safety environment type for subsequent retrieval.

5. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 4, characterized in that: The management controllability margin function uses the amount of lime applied, biochar applied, organic fertilizer applied, fertilizer ratio, and irrigation and drainage methods in the management records as independent variables, and the change in safety status obtained under a unified cadmium transport element model as the dependent variable. A fitting method is used to establish the mapping relationship between the independent and dependent variables, which is used to calculate the corresponding management controllability margin under different management combinations.

6. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 5, characterized in that: The safety indicators for each plot, variety, and management combination include at least safe output, safe revenue, and a multi-metal safety index. Safe output is determined based on the estimated output when the safety margin is not lower than the set lower limit. Safe revenue is calculated based on safe output, product unit price, and input costs. The multi-metal safety index is synthesized based on the predicted content and limit values ​​of multiple heavy metals such as cadmium, lead, and zinc according to preset weights.

7. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 6, characterized in that: The risk budget consists of three levels: land parcel level, farmer level, and regional level. The land parcel level sets a lower limit for safety margin and an upper limit for the multi-metal safety index for each land parcel. The farmer level limits the number or area of ​​land parcels under the same farmer whose safety margin is close to the lower limit to a preset proportion. The regional level limits the number of unqualified batches and the area of ​​unqualified products sampled within the statistical period to a preset limit. All three levels of budget are used together as constraints when identifying safety domains.

8. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 7, characterized in that: The planting plan data is stored using a multi-dimensional index structure that includes plot identifiers, variety identifiers, management combination identifiers, and planting years. Each record is associated with the corresponding plot's safety margin, safety status indicators, safety domain type, and minimum management adjustment amount in the target year. When generating the planting plan data, the records are aggregated by farmer and region for different levels of users to query and access.

9. The method for analyzing the cadmium accumulation trait of *Gnaphalium affine* based on big data according to claim 8, characterized in that: The soil condition update rule calculates the available cadmium and related physicochemical indicators for the next year based on the soil heavy metal content, pH, organic matter, and the amount of lime, biochar, and organic fertilizer applied corresponding to the selected management combination in the previous year. During the rolling calculation process, the soil condition update rule and planting scheme data are called up year by year to update the plot behavior safety indicators and environmental behavior characteristic vectors.

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