A soil quality evaluation method and soil information collection device

Abstract

The present invention provides a soil quality evaluation method and a soil information collection device, which relate to the technical field of quality evaluation. The present invention comprehensively considers physical, chemical, environmental and biological factors, uses expert judgment method and principal component analysis to refine soil multi-dimensional indicators, scientifically evaluates soil quality, and provides accurate and detailed improvement suggestions; introduces a deviation fine-tuning model and a hierarchical analysis method to enhance the accuracy and flexibility of the evaluation results, ensuring that the actual quality of specific soil types such as black soil is reflected; by calculating the soil quality classification index S and performing quality grade classification and deviation fine-tuning, quantitative expression of the results and high-precision evaluation are achieved, providing agricultural managers with a basis for targeted improvement measures, promoting continuous improvement of soil quality, and optimizing agricultural production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of soil quality evaluation, and in particular to a soil quality evaluation method and a soil information collection device. Background Art

[0002] With the growing demand for global food security and sustainable agricultural development, accurate evaluation and management of soil quality has become particularly important. In this context, soil quality evaluation technology has experienced a leapfrog development from simple physical and chemical analysis in the early stage to large-scale soil monitoring using remote sensing technology and geographic information system (GIS); in particular, the application of global navigation satellite system (GNSS) in soil quality evaluation has greatly improved the accuracy and efficiency of soil sample positioning; at the same time, by integrating soil physical, chemical, environmental and biological indicators, researchers and engineers have been able to more comprehensively evaluate soil health; however, despite continuous technological advances, current soil quality evaluation methods still face many challenges;

[0003] Existing deficiencies: Existing soil quality evaluation technologies often focus on the measurement of specific types of indicators, lack a comprehensive evaluation framework, and cannot fully reflect the actual condition of the soil; in addition, traditional methods are usually oversimplified in data processing and evaluation models, ignoring the complexity and dynamic changes of soil quality evaluation; for example, few methods can combine historical data for dynamic analysis, or adjust the evaluation model according to specific soil types and regional characteristics, which leads to the evaluation results often cannot accurately guide the implementation of soil management and improvement measures;

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a soil quality evaluation method and a soil information collection device to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A soil quality evaluation method, the specific steps comprising:

[0008] Step S1, obtaining a GNSS signal from a GNSS receiver to determine the location of a sampling point, and obtaining corresponding historical soil quality evaluation data based on the location of the sampling point;

[0009] Step S2, collecting soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators of the sampling point, and each indicator includes a number of collection parameters, and screening the several collection parameters included in each indicator through an expert judgment method to generate a first preset indicator;

[0010] Step S3, obtaining the first preset index, standardizing the original data of the first preset index, and performing principal component analysis on the processed data to generate the second preset index;

[0011] Step S4, obtaining the historical second preset index contained in the historical soil quality evaluation data, and generating a deviation fine-tuning model after comparing and analyzing the historical second preset index with the second preset index of step S3; and expressing the deviation fine-tuning model as a deviation value D;

[0012] Step S5, after obtaining the second preset index, perform a hierarchical analysis method, the hierarchical analysis method includes a target layer, a criterion layer and an index layer, and measure the index value of the index layer to generate a third preset index;

[0013] Step S6, after obtaining the third preset index, analyze and process it to generate a soil quality classification index S, and classify the soil quality grades based on the soil quality classification index S, and the formula of the soil quality classification index S is as follows;

[0014]

[0015] Among them, S is the soil quality classification index, i represents the i-th index in the third preset index, and f i Score the soil sample for the i-th indicator in the third preset indicator, W i It represents the relative weight of the ith indicator in the third preset indicator;

[0016] Step S7, after obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into level intervals, each level interval represents the corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range.

[0017] A soil information collection device, the device is used to execute the soil quality evaluation method, the collection device includes a soil sampling robot, the soil sampling robot is composed of a soil sampling and storage module, a solar power supply module, a walking mechanism based on GNSS positioning, a sensor detection module and a wireless network transmission module, and the soil sampling robot also includes the following black soil quality evaluation system:

[0018] Data acquisition module: used to obtain the GNSS signal of the GNSS receiver to determine the location of the sampling point, and obtain the corresponding historical soil quality evaluation data based on the sampling point location;

[0019] The first indicator generation module is used to collect soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators of the sampling point, and each indicator includes a number of collection parameters. The several collection parameters included in each indicator are screened by expert judgment method to generate the first preset indicators;

[0020] The second indicator generation module is used to obtain the first preset indicator, standardize the original data of the first preset indicator, and perform principal component analysis on the processed data to generate the second preset indicator;

[0021] Model building module: used to obtain the historical second preset index contained in the historical soil quality evaluation data, compare and analyze the historical second preset index with the second preset index of step S3, and generate a deviation fine-tuning model; the deviation fine-tuning model is represented as a deviation value D;

[0022] The third indicator generation module: after obtaining the second preset indicator, perform a hierarchical analysis method, the hierarchical analysis method includes a target layer, a criterion layer and an indicator layer, and measure the indicator value of the indicator layer to generate the third preset indicator;

[0023] Index generation module: used to obtain the third preset index and then analyze and process it, generate the soil quality classification index S, and classify the soil quality grades based on the soil quality classification index S. The formula of the soil quality classification index S is as follows;

[0024]

[0025] Among them, S is the soil quality classification index, i represents the i-th index in the third preset index, and f i Score the soil sample for the i-th indicator in the third preset indicator, W i It represents the relative weight of the ith indicator in the third preset indicator;

[0026] Fine-tuning construction module: After obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into grade intervals. Each grade interval represents the corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. By collecting multi-dimensional indicators of soil and conducting a comprehensive evaluation, including using expert judgment to screen important indicators and applying principal component analysis and other methods to refine data, this step makes soil quality assessment more scientific and comprehensive;

[0029] 2. By comprehensively considering physical, chemical, environmental and biological factors, it can analyze soil quality in an all-round way, thus providing more accurate and detailed soil improvement suggestions. It introduces expert judgment method and principal component analysis to optimize indicator selection and data processing flow, aiming to improve the accuracy and reliability of evaluation. In particular, the introduced deviation fine-tuning model and hierarchical analysis method not only enhance the accuracy of evaluation results, but also allow flexible adjustment according to the specific conditions of the soil, thus ensuring that the evaluation results can better reflect the actual quality of specific soil types such as black soil.

[0030] 3. The soil quality classification index S is calculated based on the comprehensive analysis results, and the quality grades are divided and the deviations are fine-tuned based on the index. This step not only achieves the quantitative expression of the soil quality assessment results, but also ensures the high accuracy and practicality of the assessment results through the fine-tuning mechanism. In addition, the classification of quality grades makes it easier for agricultural managers to quickly understand the soil conditions and provides a basis for targeted soil improvement measures, ultimately achieving the beneficial effect of promoting the continuous improvement of soil quality and optimizing agricultural production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0032] Figure 2 Schematic diagram of the weight of each indicator;

[0033] Figure 3 It is a schematic diagram of the evaluation layer, criterion layer and indicator layer for the third layered processing of preset indicators. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0035] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0036] See also Figure 1 , Figure 2 and Figure 3 , the present invention provides a technical solution:

[0037] Embodiment 1:

[0038] A soil quality evaluation method, the specific steps comprising:

[0039] Step S1, obtaining a GNSS signal from a GNSS receiver to determine the location of a sampling point, and obtaining corresponding historical soil quality evaluation data based on the location of the sampling point;

[0040] The specific steps to determine the location of the sampling point are as follows:

[0041] GNSS receiver configuration and initialization: Before you begin, make sure the GNSS receiver has been properly configured and initialized. This includes setting the correct time, date, and geolocation parameters of the receiver.

[0042] Make sure the receiver has a clear view of the antenna so it can pick up signals from multiple satellites;

[0043] Signal acquisition and positioning: GNSS receivers capture signals transmitted from global satellite systems such as GPS, GLONASS, and BeiDou;

[0044] The receiver uses the information contained in these signals to calculate its precise position on Earth. Each satellite transmits information about its position and the time it was transmitted. The receiver uses this data to calculate the distance to each satellite.

[0045] With data from at least three satellites, the receiver can accurately calculate the longitude and latitude of its geographic location. If more satellites are added, the accuracy of positioning will be further improved;

[0046] Ensure positioning accuracy: Use differential GNSS (DGNSS) technology to further improve the accuracy of the position. This technology corrects errors by comparing the data of two receivers, one for a fixed-position base station and the other for a mobile station.

[0047] Recording and marking of sampling points: Once the receiver determines its exact location, this location is recorded as the sampling point for the soil sample;

[0048] Record and mark these locations in a geographic information system (GIS) or other mapping tool to facilitate subsequent soil collection and analysis;

[0049] Step S2, collecting soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators of the sampling point, and each indicator includes a number of collection parameters, and screening the several collection parameters included in each indicator through an expert judgment method to generate a first preset indicator;

[0050] Step S3, obtaining the first preset index, standardizing the original data of the first preset index, and performing principal component analysis on the processed data to generate the second preset index;

[0051] Step S4, obtaining the historical second preset index contained in the historical soil quality evaluation data, and generating a deviation fine-tuning model after comparing and analyzing the historical second preset index with the second preset index of step S3; and expressing the deviation fine-tuning model as a deviation value D;

[0052] Step S5, after obtaining the second preset index, perform a hierarchical analysis method, the hierarchical analysis method includes a target layer, a criterion layer and an index layer, and measure the index value of the index layer to generate a third preset index;

[0053] Step S6, after obtaining the third preset index, analyze and process it to generate a soil quality classification index S, and classify the soil quality grades based on the soil quality classification index S, and the formula of the soil quality classification index S is as follows;

[0054]

[0055] Among them, S is the soil quality classification index, i represents the i-th index in the third preset index, and f i Score the soil sample for the i-th indicator in the third preset indicator, W i It represents the relative weight of the ith indicator in the third preset indicator;

[0056] Step S7, after obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into level intervals, each level interval represents the corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range.

[0057] Embodiment 2:

[0058] The historical soil quality evaluation data is represented by the evaluation data of the sampling point location N times before, and the sampling point locations corresponding to the N evaluation data are all limited to the same field. The season, month and field number of the current sampling point and the historical sampling point on the same field are the same, so as to ensure that the fluctuation range of soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators is within the limited threshold, thereby reducing the impact on the evaluation results;

[0059] The expert judgment method is used to screen several acquisition parameters contained in each indicator to generate the first preset indicator, which specifically includes the following contents:

[0060] The steps of the expert evaluation method are: diversity expert formation, index preliminary selection, preliminary evaluation, expert scoring, summary and screening, and final screening;

[0061] The parameters include the following 43 indicator parameters:

[0062] Organic matter, total nitrogen, available phosphorus, available potassium, water content, cation exchange capacity, humus content, iron and manganese, black soil layer thickness, soil layer thickness, gully erosion area, pH value, vegetation coverage, heavy metal concentration, land degradation (salinity), microbial content and enzyme activity, and the following other parameters;

[0063] Soil density: affects the aeration and water retention capacity of the soil;

[0064] Capillary porosity: reflects the water conductivity of the soil;

[0065] Non-capillary porosity: affects the circulation of soil air;

[0066] Total porosity: affects soil structure and root growth;

[0067] Soil permeability: determines the rate at which water and nutrients move through the soil;

[0068] Soil particle composition, such as the proportion of sand, silt, and clay: affects the texture and other physical properties of the soil;

[0069] Soil structure type: affects the soil's air permeability and water retention capacity;

[0070] Soil saturation: reflects the maximum water retention capacity of the soil;

[0071] Soil temperature: affects plant growth and microbial activity;

[0072] Soil evaporation: reflects the rate of soil moisture loss;

[0073] Soil aggregation: affects the structural stability and anti-erosion ability of the soil;

[0074] Soil wind erosion rate: reflects the soil's ability to resist wind erosion;

[0075] Soil organic carbon: an important indicator of soil organic matter, affecting soil fertility;

[0076] Carbon-nitrogen ratio: reflects the characteristics of soil organic matter decomposition and nutrient cycling;

[0077] Exchangeable Potassium: A measure of the amount of potassium in the soil that is available for plant uptake;

[0078] Exchangeable Calcium: affects soil chemistry and structure;

[0079] Exchangeable magnesium: an important nutrient that affects plant growth;

[0080] Soil conductivity: reflects the salinity of the soil;

[0081] Sulfur content: one of the important nutrients in the soil;

[0082] Silicon content: important for the growth of certain crops, especially grasses;

[0083] Soil respiration: reflects the intensity of soil microbial activity;

[0084] Rhizosphere microbial diversity: reflects soil biological activity and health status;

[0085] Pathogen quantity: affects crop growth and yield;

[0086] Biomass carbon: reflects the ability of soil microorganisms to transform organic matter;

[0087] Soil faunal diversity, such as nematodes and soil insects: influence soil structure and organic matter decomposition;

[0088] All the parameters collected above are normalized to make them quantifiable;

[0089] Expert formation:

[0090] Forming a team of experts: This step involves selecting a group of domain experts with extensive soil science knowledge and experience; these experts come from different fields such as soil physics, chemistry, biology, and environmental science to ensure the comprehensiveness of the assessment;

[0091] Diversity of experts: Selecting experts with different backgrounds and expertise ensures that different perspectives and expertise are taken into account during the evaluation process;

[0092] Initial selection of indicators:

[0093] Provide a list of indicators: First, a list of 43 soil indicators covering physical, chemical, environmental and biological aspects is required;

[0094] Preliminary assessment: The expert team will conduct a preliminary assessment of these indicators to examine the importance, applicability and relevance of each indicator to the assessment of black soil quality;

[0095] Expert Rating:

[0096] Scoring method: Experts score each indicator using a quantitative method of 1-10 points;

[0097] Scoring criteria: Scoring focuses on the contribution and sensitivity of the indicator in evaluating the quality of black soil; this requires experts to comprehensively consider the scientific basis, practical applicability and evaluation effect of the indicator;

[0098] Aggregation and Filtering:

[0099] Aggregate score: Aggregate the scores of all experts and calculate the average score for each indicator for easy comparison and analysis;

[0100] Discussion of low-scoring indicators: In-depth discussion of low-scoring indicators, where experts can provide detailed explanations and opinions from their own professional perspectives;

[0101] Final Screening:

[0102] Determine the screening results: Based on the experts’ scoring and discussion, determine which indicators are most important and applicable for the quality evaluation of the current black soil sampling point locations;

[0103] Through this process, the most representative and practical soil indicators can be effectively screened out, thereby accurately and comprehensively evaluating the quality of black soil.

[0104] Embodiment three:

[0105] Further explanation based on Example 2:

[0106] The second step of obtaining the preset index includes:

[0107] Determine the original index of the first preset index, where the original index is the index parameter type selected by the first preset index among the 43 soil indexes described in Example 2;

[0108] Data standardization is performed to set the determined original indicators to have an average value of 0 and a standard deviation of 1. The specific formula is:

[0109]

[0110] Among them, X is the original data, μ is the mean, σ is the standard deviation, and Z is the standardized data;

[0111] The principal component analysis (PCA) method is used on the standardized data set to calculate the eigenvalue and eigenvector of each indicator; the eigenvalue reflects the contribution of each principal component to the total variation of the data; the eigenvector indicates the direction of each principal component;

[0112] Set up conditions to select the principal components:

[0113] Eigenvalue is greater than the first design value: The eigenvalue represents the amount of data variance explained by the principal component. Only when the eigenvalue of the principal component is greater than a preset threshold, the principal component is considered important. The first design value represented by this threshold needs to be pre-set according to the specific analysis purpose and the characteristics of the data set;

[0114] The cumulative contribution rate of the main factor is greater than the first design value: the selected principal component should be able to explain most of the total variation of the data. Usually a threshold of the cumulative contribution rate is set, such as 90%, which means that the selected principal component should at least explain this proportion of the total variation;

[0115] Quantization of mathematical expressions:

[0116] Suppose there are n original indicators and m samples, and the eigenvalue of each principal component j obtained by principal component analysis is λ j ;

[0117] Eigenvalue condition: λ j >M, where M represents the first design value. When the eigenvalue λ of the principal component j Only when it is greater than M, it can be used as the pre-selected indicator parameter for the second preset indicator;

[0118] The cumulative contribution rate condition formula is:

[0119]

[0120] Where k is the number of principal components selected, M l is the cumulative contribution rate of the first design value, and when the pre-selected index parameters meet the cumulative contribution rate condition formula, the selected index parameters are used as the second preset index;

[0121] By applying the above steps and conditions, the most representative principal components can be effectively screened out from a large number of indicators, simplifying the subsequent analysis process while retaining the main information in the data set.

[0122] Embodiment 4:

[0123] Further explanation is given on the basis of Example 3.

[0124] The generation of the deviation fine-tuning model specifically includes the following contents:

[0125] The deviation fine-tuning model is represented as the deviation value D, and the deviation value D is calculated as follows:

[0126]

[0127] Among them, w j is the importance weight of the second preset indicator based on the PCA result, through the eigenvalue λ j Normalized, the formula is as follows:

[0128]

[0129] m represents the sample size;

[0130] It is the kth evaluation data of the second historical preset indicator for the jth principal component;

[0131] y j is the evaluation data of the current second preset index for the jth principal component;

[0132] N is the number of historical evaluations;

[0133] By calculating the difference between historical data and current data and weighting them according to the importance of each principal component, a total deviation value D is finally obtained to fine-tune the current soil quality evaluation; this design ensures that changes in each parameter will affect the deviation value D, which is reflected in the fine-tuning of the soil quality evaluation;

[0134] Range explanation:

[0135] The range of the formula depends on the variation range of the actual soil quality evaluation data, but in theory the range of D should be limited. The range of the deviation value D is set to [-R, R], where R is the maximum positive deviation value based on the variation range of historical data, and R1 is set to a positive value within the range [-R, R], where R1 is the fine-tuning start threshold close to a positive value.

[0136] When D ≥ R1, it means that the current soil quality assessment is too low and needs to be adjusted upward by one level unit;

[0137] When D≤-R1, it means that the soil quality assessment is too high and needs to be adjusted down by one grade unit;

[0138] When R1>D>-R1, it means that the soil quality assessment results meet the requirements and no adjustment is required.

[0139] Embodiment five:

[0140] Further explanation is given based on the fourth embodiment. Figure 3 ;

[0141] The acquisition of the third preset indicators specifically includes the following contents:

[0142] The target layer is set as black soil quality evaluation.

[0143] The criterion layer is black soil nutrients, black soil environment, black soil layer thickness and black soil ecological indicators, which correspond to the soil chemical indicators, soil environmental indicators, soil physical indicators and soil biological indicators in step S2 in sequence. The criterion layer is a subset of various indicators collected in step S2;

[0144] The indicator layer is organic matter, total nitrogen, available phosphorus, available potassium, water content, cation exchange capacity, humus, iron, manganese, black soil layer thickness, soil layer thickness, gully erosion area, pH value, vegetation coverage, heavy metal concentration, land salinization, microbial content and enzyme activity index, which constitute the third preset index; the above data is an example after screening, and does not limit the fixed indicator parameter type, and is adjusted and replaced according to the actual screening;

[0145] Land salinization refers to the salt content of land degradation, i.e. land degradation (salt content);

[0146] First, the criterion layer is judged, and the priority order of the criterion layer is arranged to obtain black soil nutrients>black soil layer thickness>black soil soil environment>black soil ecological index; the relative weight of each item is calculated, and a 4×4 judgment matrix T1 is constructed, and then the importance comparison is constructed using the pairwise comparison method;

[0147] The judgment matrix scale is defined as follows:

[0148] Scale meaning 1 Both factors are equally important 2 Take the middle of 1 and 3 3 Compared with the two factors, the former is more important than the latter. 4 Take the middle of 3 and 5 5 Compared with the two factors, the former is more important than the latter. 6 Take the middle of 5 and 7 7 Compared with the two factors, the former is more important than the latter. 8 Take the middle of 7 and 9 9 Compared with the two factors, the former is more important than the latter. reciprocal When comparing two factors, the latter is the reciprocal of the former's

[0149] According to the scale-matching criterion layer, the judgment matrix T1 is constructed, and its relative weight is calculated using the square root method. The construction data of the judgment matrix T1 is as follows:

[0150]

[0151] So the ratio of black soil nutrient A1 to black soil nutrient A1 is 1.

[0152] The ratio of black soil layer thickness A2 to black soil nutrient A1 is 1 / 2.

[0153] The ratio of black soil environment A3 to black soil nutrients A1 is 1 / 3.

[0154] The ratio of black soil ecological index A4 to black soil nutrient A1 is 1 / 4.

[0155] The ratio of black soil nutrients A1 to black soil layer thickness A2 is 2.

[0156] The ratio of the thickness of the black soil layer A2 to the thickness of the black soil layer A2 is 1.

[0157] The ratio of black soil environment A3 to black soil layer thickness A2 is 1 / 2.

[0158] The ratio of black soil ecological index A4 to black soil layer thickness A2 is 1 / 3.

[0159] The ratio of black soil nutrient A1 to black soil environment A3 is 3.

[0160] The ratio of the thickness of the black soil layer A2 to the black soil environment A3 is 2.

[0161] The ratio of black soil environment A3 to black soil environment A3 is 1,

[0162] The ratio of black soil ecological index A4 to black soil environment A3 is 1 / 2.

[0163] The ratio of black soil nutrient A1 to black soil ecological index A4 is 4.

[0164] The ratio of the thickness of the black soil layer A2 to the black soil ecological index A4 is 3.

[0165] The ratio of black soil environment A3 to black soil ecological index A4 is 2.

[0166] The ratio of black soil ecological index A4 to black soil ecological index A4 is 1;

[0167] The judgment matrix T1 is processed as follows;

[0168]

[0169] Multiply the elements of the judgment matrix T1 row by row to get a new vector, and raise each component of the new vector to the nth power to get the following content, where n is 4:

[0170]

[0171] And normalize M1, M2, M3 and M4 so that the sum of all elements is equal to 1. The following is the weight vector set W1:

[0172]

[0173]

[0174] In the Analytic Hierarchy Process (AHP), CI stands for Consistency Index. The consistency index is an indicator used to test the consistency of the judgments made by decision makers when comparing different factors or alternatives. In AHP, the calculation of the consistency index CI involves the use of the random consistency index (RI). RI is a set of random consistency indicators determined empirically under the given dimension of the judgment matrix, that is, the order of the judgment matrix. According to the RI value, the consistency ratio (CR) can be calculated to evaluate whether the decision maker's judgment is within an acceptable range. If the CR value is less than the preset threshold of 0.1, the consistency of the judgment matrix is ​​considered acceptable, otherwise the judgment matrix needs to be adjusted to improve consistency.

[0175] Perform consistency check and define the test formula as follows:

[0176]

[0177] The average random consistency index is as follows:

[0178]

[0179] The consistency ratio is calculated as follows:

[0180]

[0181] where λ max is the maximum eigenvalue of the judgment matrix T1; W i is the sum of the weights of the elements in the i-th row of the judgment matrix, W 1i Indicates the weight corresponding to the i-th criterion, and the prefix 1 in 1i is the index number. The following contents are explained in the same way and will not be repeated here. The larger the CI value, the greater the deviation of the judgment matrix from complete consistency, and the smaller the CI value, the better the consistency of the judgment matrix.

[0182] By looking up the table to find the corresponding average random consistency index RI, RI is the average value of the consistency index of the same order random judgment matrix. The introduction can overcome the disadvantage that the consistency judgment index increases significantly with the increase of n to a certain extent;

[0183] When CR < 0.1, the judgment matrix is ​​considered to have satisfactory consistency. When CR ≥ 0.1, the judgment matrix is ​​considered inconsistent and the matrix is ​​adjusted to meet CR < 0.1.

[0184] Therefore, in the criterion layer, the weight of black soil nutrients is 0.47, the weight of black soil layer thickness is 0.27, the weight of black soil environment is 0.16, and the black soil ecological index is 0.10;

[0185] In calculating the weight of the index layer, the black soil layer thickness and the soil layer weight in the black soil layer thickness are calculated first, and the importance of the black soil layer thickness is greater than the soil layer thickness;

[0186] The construction data of judgment matrix T2 is as follows;

[0187]

[0188] The ratio of the thickness of the black soil layer B1 to the thickness of the black soil layer B1 is 1, the ratio of the thickness of the soil layer B2 to the thickness of the black soil layer B1 is 1 / 5, the ratio of the thickness of the black soil layer B1 to the thickness of the soil layer B2 is 5, and the ratio of the thickness of the soil layer B2 to the thickness of the soil layer B2 is 1, and the following 2×2 judgment matrix T2 is established;

[0189]

[0190] Multiply the elements of the judgment matrix T2 row by row to obtain a new vector, and raise each component of the new vector to the nth power;

[0191]

[0192] And normalize M5 and M6 so that the sum of all elements is equal to 1. The following is the weight vector set W2;

[0193]

[0194] To perform a consistency check:

[0195]

[0196]

[0197] The average random consistency index is shown below;

[0198]

[0199] Because RI = 0 when n = 2, there is no need for consistency test. In the black soil layer thickness, the weight of the black soil layer thickness indicator is 0.83, and the weight of the soil layer thickness is 0.17. Similarly, in the black soil ecological index, because the scale ratio of microbial content to enzyme activity is equal to the scale ratio of black soil layer thickness to upper soil layer thickness in the black soil layer thickness, the importance of microbial content is greater than the importance of enzyme activity. Therefore, in the black soil ecological index, the weight of the indicator microbial content is 0.83, and the weight of enzyme activity is 0.17;

[0200] Calculate the indicators of gully erosion area, pH, vegetation coverage, heavy metal concentration, and land degradation (salt content) weight in the black soil environment, and the importance of vegetation coverage> the importance of heavy metal concentration> the importance of pH> the importance of land salinization> the importance of gully erosion area;

[0201] The construction data of judgment matrix T3 is as follows:

[0202]

[0203] The vegetation coverage ratio C1 is 1 compared to the previous vegetation coverage ratio C1.

[0204] The heavy metal concentration C2 is 1 / 2 of the vegetation coverage C1.

[0205] The pHC3 is 1 / 3 of the vegetation coverage C1.

[0206] The land degradation C4 is 1 / 4 of the vegetation coverage C1.

[0207] The gully erosion area C5 is 1 / 5 of the vegetation coverage.

[0208] The vegetation coverage ratio C1 is 2 to the heavy metal concentration C2.

[0209] The ratio of heavy metal concentration C2 to the upper heavy metal concentration C2 is 1.

[0210] pHC3 is 1 / 2 of the heavy metal concentration C2.

[0211] The land degradation C4 is 1 / 3 of the heavy metal concentration C2.

[0212] The erosion area C5 is 1 / 4 of the heavy metal concentration C2.

[0213] The vegetation coverage ratio C1 is 3 compared to pHC3.

[0214] The ratio of heavy metal concentration C2 to pH C3 is 2.

[0215] pHC3 is 1 compared to the previous pHC3.

[0216] Land degradation is 1 / 2 of pHC3.

[0217] The groove erosion area is 1 / 3 of pHC3.

[0218] The vegetation coverage ratio C1 is 4 compared to the land degradation ratio C4.

[0219] The ratio of heavy metal concentration C2 to land degradation C4 is 3,

[0220] pHC3 is 2 compared to land degradation C4.

[0221] Land degradation C4 is 1 compared to the previous land degradation C4.

[0222] The gully erosion area C5 is 1 / 2 of the land degradation C4.

[0223] The vegetation coverage ratio C1 is 5 times the gully erosion area C5.

[0224] The ratio of heavy metal concentration C2 to the upper groove erosion area C5 is 4.

[0225] pHC3 is 3 compared to the upper groove erosion area C5.

[0226] The land degradation ratio of the upper gully erosion area C5 is 2,

[0227] The ratio of the groove erosion area C5 to the upper groove erosion area C5 is 1; and the following judgment matrix T3 is established:

[0228]

[0229] Multiply the elements of the judgment matrix T3 row by row to obtain a new vector, and raise each component of the new vector to the nth power;

[0230]

[0231]

[0232] And normalize E1, E2, E3, E4 and E5 so that the sum of all elements is equal to 1. The following is the weight vector set W3;

[0233]

[0234] To perform a consistency check:

[0235]

[0236] Average random consistency index

[0237]

[0238] The consistency ratio is calculated as follows:

[0239]

[0240] In the black soil environment, the weight of the index vegetation coverage is 0.42, the weight of heavy metal concentration is 0.26, the weight of pH is 0.16, the weight of land degradation (salinity) is 0.1, and the weight of gully erosion area is 0.06;

[0241] Calculate the weights of black soil nutrients including organic matter, total nitrogen, available phosphorus, available potassium, water content, cation exchange capacity, humus layer, iron, and manganese;

[0242] The construction data of judgment matrix T4 is as follows:

[0243]

[0244] And establish the following judgment matrix T4:

[0245]

[0246] Multiply the elements of the judgment matrix T4 row by row to get a new vector, and raise each component of the new vector to the nth power.

[0247]

[0248]

[0249] And normalize F1, F2, F3, F4, F5, F6, F7, F8, and F9 so that the sum of all elements is equal to 1. The following is the weight vector set W4;

[0250]

[0251] To perform a consistency check:

[0252]

[0253]

[0254] Average random consistency index:

[0255]

[0256] Calculate the consistency ratio:

[0257]

[0258] In the black soil nutrients, the weight of organic matter is 0.254, the weight of total nitrogen is 0.171, the weight of available phosphorus is 0.18, the weight of available potassium is 0.148, the weight of water content is 0.1, the weight of cation exchange capacity is 0.062, the weight of humus layer is 0.019, the weight of iron is 0.03, and the weight of manganese is 0.036;

[0259] Score the indicator value of the third preset indicator: According to the indicator value of the third preset indicator, the corresponding membership degree of the indicator, the membership degree × 100 is the score of the indicator. The following is the relationship between the indicator value and the membership degree;

[0260] Membership degree of pH value:

[0261]

[0262] For the black soil nutrients, organic matter, total nitrogen, available phosphorus, quick-acting potassium, cation exchange capacity, humus layer, black soil layer thickness, vegetation coverage in the black soil environment, black soil ecological indicators, indicator microbial content, enzyme activity are brought into the f(x) formula:

[0263]

[0264] Among them, X1, X2 are the preset critical values ​​of each indicator, which are applicable to the characteristics of f(x) membership function: the score will increase with the increase of indicator content. For black soil nutrients, indicators are iron and manganese, and in black soil environment, indicators are heavy metal concentration, land degradation (salt content), and gully erosion area;

[0265]

[0266] X1, X2 are the preset critical values ​​for each indicator. The characteristics of the membership function f2(x) are: for some indicators, it is beneficial within a certain range, but it will cause harm if it exceeds a certain range;

[0267] X1, X2 critical value table:

[0268] index Critical value X1 Critical value X2 Organic matter 2g / kg 35g / kg Total nitrogen 0.5g / kg 2g / kg Available Phosphorus 3g / kg 40g / kg Fast-Acting Potassium 30mg / kg 40mg / kg iron 0.1mg / kg 18mg / kg manganese 1.0mg / kg 35 mg / kg Cation exchange capacity 6.2cmol / kg 20cmol / kg Water content 15％ 20％ Vegetation coverage 25％ 100％ Thickness of black soil layer 10cm 100cm Soil thickness 30cm 100cm Heavy metal concentration 5mg / kg 10mg / kg Salt content 0.7％ 1.2％ Microbial Content 3.0×105 / g 6.5×105 / g

[0269] Black soil quality scoring process: First, for the indicator layer, sampling is carried out, and then the corresponding membership degree is calculated according to the indicator value of the indicator. The membership degree × 100 is the evaluation score of the indicator. The score of each criterion layer corresponding to each indicator is added up to obtain the comprehensive score of each item in the criterion layer. Then, the weight of each item in the criterion layer is multiplied by the comprehensive score of each item to finally obtain the comprehensive score of the black soil quality.

[0270] The criteria layer indicators are divided into different levels;

[0271]

[0272]

[0273] The black soil score levels are divided as follows:

[0274] grade Fraction Level 1 100-85 Level 2 85-70 Level 3 70-60 Level 4 ＜60

[0275] Embodiment six:

[0276] Further explanation based on the fifth embodiment, after obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into level intervals, each level interval of the division represents the corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range, specifically including the following contents;

[0277] Divide the deviation value D value range [-R, R] into five intervals, each interval represents a specific adjustment range;

[0278] Furthermore, R2 is set as a fine-tuning sensitivity parameter to adjust the degree of detail of the fine-tuning amplitude;

[0279] Based on the most recent time period, assuming that the time period is one year, R1 is dynamically calculated according to the volatility of soil quality evaluation data within one year to ensure that the fine-tuning model can adapt to the variability of data in different time periods; if the data in the most recent year changes greatly, R1 is reduced accordingly to improve the sensitivity of fine-tuning;

[0280] For the fine-tuning sensitivity R2, an evaluation based on soil quality importance indicators is introduced. For soils with high contribution to ecosystem services, the R2 value is increased, thereby increasing its variation range during fine-tuning to reflect the importance of soil quality to the ecosystem;

[0281] The fine-tuned soil quality partition index is defined as S', and the fine-tuned soil quality partition index S' is expressed by the following expression:

[0282] S′=S+ΔS

[0283] Among them, ΔS is the adjustment value determined according to the interval of the value of the deviation fine-tuning model D;

[0284] When D≥R1, ΔS=min(D×P1,T max -S);

[0285] When D≤R1, ΔS=max(D×P2,T min -S);

[0286] Among them, P1 and P2 are adjustment factors, which are selected according to the current grade and deviation of the soil; T max and T min are the upper and lower limit scores of the current level; this design ensures that the fine-tuned score is still within a reasonable range and prevents over-adjustment;

[0287] When D ≥ R1, a nonlinear adjustment strategy is adopted to increase the percentage of S, that is, ΔS, which is not only based on the interval of D, but also considers the value of R2; the percentage of S increase depends not only on the specific value of D, but also on R2, to achieve more precise adjustment;

[0288] When D≤-R1, the nonlinear adjustment strategy is also adopted to reduce the percentage of S, considering the interval of D and the value of R2;

[0289] When R1>D>-R1, considering that small fluctuations in the data do not represent substantial changes in soil quality, S is not adjusted in this interval;

[0290] First-level soil quality adjustment range: when (85≤S<100),

[0291] If D ≥ R1, the score is underestimated, ΔS = min(D × P1, T max -S);

[0292] If D≤-R1, the score is overestimated, ΔS=max(D×P2,T min -S);

[0293] Specifically, when D ≥ R1, that is, when the score is underestimated, the calculation method of the adjustment value ΔS is to ensure that the adjustment can reflect the actual situation of the score and will not exceed the upper limit of the grade; the calculation expression here is ΔS = min (D × P1, T max -S) means that the adjustment amplitude ΔS is determined by the smaller value of the two parts:

[0294] (D×P1) represents the product of the deviation D and a pre-set proportional factor P1; P1 represents the adjustment ratio corresponding to each unit deviation D, that is, for each unit of underestimated score, how many points should be added to adjust S; this product directly reflects the amount of positive adjustment required due to underestimated score;

[0295] (T max -S) indicates that the current score is S to the upper limit of its level T max The gap between the two levels is the maximum amount by which the score S can increase without any adjustment to ensure that the score remains within the current level after adjustment;

[0296] By taking the smaller of these two values ​​as ΔS, two important principles are ensured:

[0297] The precision of the adjustment indicates that the adjustment amount calculated by (D×P1) ensures that the magnitude of the score adjustment matches the actual situation of the underestimated score, thus ensuring the rationality of the adjustment and the accuracy of the target;

[0298] The adjustment limit is expressed by (T max -S) compares and takes the smaller value, limiting the adjustment range so that the score does not exceed the upper limit of its current level, thus avoiding over-adjustment;

[0299] The specific ΔS is determined by the following steps:

[0300] First, the preliminary adjustment range is calculated based on the degree of underestimation D of the actual score and the proportional factor P1;

[0301] Secondly, determine the gap between the current score and the level cap, which represents the maximum adjustable space without exceeding the level cap;

[0302] Compare and select the smaller value: Finally, compare the above two values ​​and take the smaller one as the final adjustment amplitude ΔS;

[0303] In this way, the determination of ΔS not only takes into account the need for score adjustment, but also avoids adjusting the score to an unreasonable range, thus ensuring the rationality and feasibility of the adjustment;

[0304] For ease of expression, assuming that R = 10, the five intervals are [-10, -6], (-6, -2], (-2, 2), [2, 6] and (6, 10], and the adjustment range of the soil quality classification index S for each interval is as follows:

[0305] When the interval D is in the range [-10, -6], S decreases by (1% + 0.1% × R2);

[0306] When the interval D is in the range (-6,-2], S decreases by (2%+0.2%×R2);

[0307] In these expressions, [1% + 0.1% × R2] means that for each unit deviation D, S will decrease by 1% plus one-tenth of R2, and [2% + 0.2% × R2] similarly means that for each unit deviation D, S will decrease by 2% plus one-fifth of R2;

[0308] When the interval D takes the value interval (-2,2), S remains unchanged;

[0309] When the interval D takes the value range [2,6], S increases by (3% + 0.3% × R2);

[0310] When the interval D is (6,10], S increases by (5%+0.5%×R2);

[0311] Secondary soil quality adjustment range:

[0312] The scoring range for secondary soil is (70≤S<85):

[0313] If D ≥ R1, the score is underestimated, ΔS = min(D × P1, T max -S); where T max =85;

[0314] If D≤-R1, the score is overestimated, ΔS=max(D×P2,T min -S); where T min =70;

[0315] Adjustment range of three-level soil quality:

[0316] The scoring range of the third-level soil is (60≤S<70):

[0317] If D ≥ R1, the score is underestimated, ΔS = min(D × P1, T max -S); where T max =70;

[0318] If D≤-R1, the score is overestimated, ΔS=max(D×P2,T min -S); where T min =60;

[0319] Adjustment range of four levels of soil quality:

[0320] The scoring range of the fourth grade soil is (S<60):

[0321] If D ≥ R1, the score is underestimated, ΔS = min(D × P1, T max -S); where T max =55;

[0322] If D≤-R1, the score is overestimated, ΔS=max(D×P2,T min -S); where T min =40;

[0323] In the above adjustment strategy, P1 and P2 are adjustment proportional factors for different soil grades, which can be set according to actual conditions;

[0324] The determination of P1 and P2 is specifically referred to the following method:

[0325] First, historical soil quality evaluation data and related experimental results were collected;

[0326] These data were analyzed with a particular focus on the deviation D between the soil quality score S and the actual soil quality;

[0327] Based on the analysis results, the adjustment scale factors, P1 and P2, are determined according to the size of the deviation value and the current soil grade to achieve a reasonable adjustment of the score;

[0328] The adjustment scaling factor P1 is used to increase S when the score is underestimated, while P2 is used to reduce S when the score is overestimated;

[0329] Verify the determined P1 and P2, and confirm the rationality and effectiveness of these factors through simulation experiments of historical data or adjustments based on expert opinions.

[0330] Embodiment seven:

[0331] It is further explained on the basis of Example 6 that before designing this example, the purpose of the experiment is first determined: to verify that the fine-tuning strategy refinement can effectively adjust the soil quality score to more accurately reflect the actual situation of the soil. The experimental design revolves around the fine-tuning strategy refinement, and by comparing the score changes of different soil quality grades before and after the application of the new strategy, the effectiveness and innovation of the strategy are proved;

[0332] The experiment is divided into three stages: preparation, execution, and analysis. In the preparation stage, four representative soil samples were selected, belonging to the first, second, third, and fourth soil quality levels respectively; the preliminary score of each sample was obtained based on the traditional evaluation method, and no fine-tuning strategy was applied at this time; at the same time, R = 10, P1 = 0.8, P2 = 0.6 were set as the adjustment scale factors, respectively, and these parameters will be used in the subsequent adjustment process;

[0333] In the execution phase, the D value of each sample, i.e., the difference between the pre-score and the actual score, is recorded in detail. Based on the positive and negative value of the D value and its size, the adjustment range ΔS is calculated according to the rules refined by the fine-tuning strategy. This process requires precise adjustment of the score of each sample to ensure that each adjustment is within an acceptable error range and ultimately make the score closer to the actual soil quality.

[0334] In order to prove the effectiveness of the fine-tuning strategy, the changes in soil quality scores before and after the implementation of the fine-tuning strategy were compared. The initial score, adjusted score, D value and adjustment range ΔS of each sample were recorded in detail and used for subsequent data analysis.

[0335] Excel table data:

[0336] The following is the experimental data, organized into Excel table format:

[0337]

[0338] Tabular data analysis:

[0339] From the above table, we can see that after applying the fine-tuning strategy, the score of each soil sample is closer to its actual quality status; the score of the first-level soil sample increased by 4 points after adjustment, which shows that the strategy can effectively identify and correct the underestimated soil quality score; similarly, the scores of the second-level and fourth-level soil samples decreased after adjustment, accurately reflecting the overestimation of the scores; and the score adjustment of the third-level soil sample further verified the applicability of this strategy in medium-quality soil, and the adjustment range was in line with expectations, making the score more accurate;

[0340] Through in-depth analysis of experimental data, the preliminary score, adjustment range, and adjusted score of each soil sample are described in detail;

[0341] First, consider a first-level soil sample, whose preliminary score is 87 points, which is within the scoring range of first-level soil quality 85≤S<100; the D value of this sample is 5, indicating that the preliminary score is lower than the actual situation; after applying the fine-tuning strategy, its adjustment range ΔS is 4 points, which is calculated based on the D value and the adjustment scale factor P1=0.8; the adjusted score is 91 points, which is closer to the actual high-quality state of the soil sample; this adjustment result proves the effectiveness of the fine-tuning strategy for underestimated high-quality soil samples, and can appropriately improve the score to reflect its true soil quality;

[0342] For the secondary soil sample, the preliminary score was 73 points, which is within the scoring range of secondary soil quality 70≤S<85; its D value was -4, indicating that the preliminary score was higher than the actual situation; according to the fine-tuning strategy, the adjustment range ΔS was -2.4 points. Considering P2=0.6, the adjusted score was reduced to 70.6 points, which more accurately expressed the actual soil quality of the sample; this result shows the correction effect of the fine-tuning strategy on the overestimated score, which accurately matches the soil quality grade by reducing the score;

[0343] The situation of the third-grade soil sample is also very illustrative; its initial score was 65 points, which, according to its D value of 3, indicated that the score was slightly lower than the actual score; after adjustment ΔS = 2.4, the final score was 67.4 points, which more accurately reflected the medium quality of the soil; this process demonstrated the applicability of the fine-tuning strategy in the scoring of medium-quality soils, correcting the slightly underestimated situation by appropriately increasing the score;

[0344] Finally, the initial score of the fourth-level soil sample was 50 points, and the D value was -5, suggesting that the score was overestimated; after adjustment ΔS = -3, the score dropped to 47 points, which more accurately represented the actual situation of the soil sample, that is, lower soil quality; this result further confirmed the effectiveness of the fine-tuning strategy in soil quality scoring at all levels, especially in adjusting the score downward to reflect the actual lower quality soil samples;

[0345] In summary, through the introduction of fine-tuning strategy refinement, not only can the soil quality score be effectively adjusted to more accurately reflect the actual soil conditions, but this process also shows good adaptability and accuracy in different soil quality grades; each adjustment is based on the actual score deviation, ensuring that the score adjustment is both reasonable and targeted, and ultimately achieving a closer consistency between the score and the actual soil quality.

[0346] Embodiment eight:

[0347] A soil information collection device, the device is used to execute the soil quality evaluation method, the collection device comprises a soil sampling robot, the soil sampling robot comprises a soil sampling and sample storage module, a solar power supply module, a walking mechanism based on GNSS positioning, a sensor detection module and a wireless network transmission module, and comprises a control module for control, wherein the sampling module is used to break the soil of black soil in different seasons, the sensor detection device is used to acquire data of the black soil after breaking the soil, the GNSS and path planning algorithm are used to grid the sampling plot and plan the path between the sampling points, the machine vision and the wireless transmission network device are used to divide the black soil into evaluation units and upload the soil detection results of the corresponding sampling points to the cloud;

[0348] The control module controls the process of the traveling mechanism. The control device receives the GNSS signal through the GNSS receiver to determine the location of the device, and performs grid segmentation according to the remote sensing map of the field where the device is located to determine the location of the sampling point; and plans the travel route according to the algorithm and controls the forward, turning, backward and other travel modes of the traveling device based on the location of the device; the control module controls the sampling device based on whether the device reaches the sampling point;

[0349] The soil sampling robot also includes the following black soil quality evaluation system:

[0350] Data acquisition module: used to obtain the GNSS signal of the GNSS receiver to determine the location of the sampling point, and obtain the corresponding historical soil quality evaluation data based on the sampling point location;

[0351] The first indicator generation module is used to collect soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators of the sampling point, and each indicator includes a number of collection parameters. The several collection parameters included in each indicator are screened by expert judgment method to generate the first preset indicators;

[0352] The second indicator generation module is used to obtain the first preset indicator, standardize the original data of the first preset indicator, and perform principal component analysis on the processed data to generate the second preset indicator;

[0353] Model building module: used to obtain the historical second preset index contained in the historical soil quality evaluation data, compare and analyze the historical second preset index with the second preset index of step S3, and generate a deviation fine-tuning model; the deviation fine-tuning model is represented as a deviation value D;

[0354] The third indicator generation module: after obtaining the second preset indicator, perform a hierarchical analysis method, the hierarchical analysis method includes a target layer, a criterion layer and an indicator layer, and measure the indicator value of the indicator layer to generate the third preset indicator;

[0355] Index generation module: used to obtain the third preset index and then analyze and process it, generate the soil quality classification index S, and classify the soil quality grades based on the soil quality classification index S. The formula of the soil quality classification index S is as follows;

[0356]

[0357] Among them, S is the soil quality classification index, i represents the i-th index in the third preset index, and f i Score the soil sample for the i-th indicator in the third preset indicator, W i It represents the relative weight of the ith indicator in the third preset indicator;

[0358] Fine-tuning construction module: After obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into grade intervals. Each grade interval represents the corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range.

[0359] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0360] The specific values ​​of δ, ε, ∈, σ, etc. in the formula are generally determined by those skilled in the art according to actual conditions. The essence of the formula of the present application is to perform comprehensive analysis by weighted summation. Those skilled in the art collect multiple groups of sample data and set corresponding preset proportional coefficients for each group of sample data; substitute the preset proportional coefficients and the collected sample data into the formula, any four formulas constitute a set of four-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of δ, ε, ∈, σ, etc.;

[0361] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by email, or by a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0362] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0363] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A soil quality evaluation method, characterized in that: The specific steps include: Step S1, obtaining a GNSS signal from a GNSS receiver to determine the location of a sampling point, and obtaining corresponding historical soil quality evaluation data based on the location of the sampling point; Step S2, collecting soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators of the sampling point, and each indicator includes a number of collection parameters, and screening the several collection parameters included in each indicator through an expert judgment method to generate a first preset indicator; Step S3, obtaining the first preset index, standardizing the original data of the first preset index, and performing principal component analysis on the processed data to generate the second preset index; Step S4, obtaining the historical second preset index contained in the historical soil quality evaluation data, comparing and analyzing the historical second preset index with the second preset index of step S3, and generating a deviation fine-tuning model; The deviation fine-tuning model is represented as the deviation value D, and the deviation value D is calculated as follows: Among them, w j It is the importance weight of the second preset index based on the PCA result of the principal component analysis method, through the eigenvalue λ of each principal component j j Normalized, the formula is as follows: m represents the sample size; It is the kth evaluation data of the second historical preset indicator for the jth principal component; y j is the evaluation data of the current second preset indicator for the jth principal component; N is the number of historical evaluations; By calculating the difference between historical data and current data and weighting them according to the importance of each principal component, a total deviation value D is finally obtained to fine-tune the current soil quality evaluation; the range of the deviation value D is set to [-R, R], where R is the maximum positive deviation value based on the change amplitude of historical data, R1 is set to a positive value within the range [-R, R], and R1 is the fine-tuning start threshold close to a positive value; When D ≥ R1, it means that the current soil quality assessment is too low and needs to be adjusted upward; When D≤-R1, it means that the soil quality assessment is too high and needs to be adjusted downward; When R1>D>-R1, it means that the soil quality assessment results meet the conditions and no adjustment is required; Step S5, after obtaining the second preset index, perform a hierarchical analysis method, the hierarchical analysis method includes a target layer, a criterion layer and an index layer, and measure the index value of the index layer to generate a third preset index; Step S6, after obtaining the third preset index, analyze and process it to generate a soil quality classification index S, and classify the soil quality grades based on the soil quality classification index S, and the formula of the soil quality classification index S is as follows; Among them, S is the soil quality classification index, i represents the i-th index in the third preset index, and f i Score the soil sample for the i-th indicator in the third preset indicator, W i It represents the relative weight of the ith indicator in the third preset indicator; Step S7, after obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into level intervals, each level interval represents a corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range; Divide the deviation value D value range [-R, R] into five intervals, each interval represents a specific adjustment range; Set R2 as the fine-tuning sensitivity parameter to adjust the degree of fine-tuning; Based on the most recent time period, assuming that the time period is one year, R1 is dynamically calculated according to the volatility of soil quality evaluation data within one year to ensure that the fine-tuning model can adapt to the variability of data in different time periods; if the data in the most recent year changes greatly, R1 is reduced accordingly to improve the sensitivity of fine-tuning; For the fine-tuning sensitivity R2, an evaluation based on soil quality importance indicators is introduced. For soils with high contribution to ecosystem services, the R2 value is increased, thereby increasing its variation range during fine-tuning to reflect the importance of soil quality to the ecosystem; The fine-tuned soil quality partition index is defined as S', and the fine-tuned soil quality partition index S' is expressed by the following expression: S′=S+ΔS Among them, ΔS is the adjustment value determined according to the interval of the value of the deviation fine-tuning model D; When D≥R1, ΔS=min(D×P1,T max -S); When D≤R1, ΔS=max(D×P2,T min -S); Among them, P1 and P2 are adjustment factors, which are selected according to the current grade and deviation of the soil; T max and T min are the upper and lower scores of the current level.

2. A soil quality evaluation method according to claim 1, characterized in that: The historical soil quality evaluation data is represented by the evaluation data of the sampling point location N times before, and the sampling point locations corresponding to the N times of evaluation data are all limited to the same field; The steps of the expert evaluation method are: forming a diverse expert group, preliminary selection of indicators, preliminary evaluation, expert scoring, summary and screening, and final screening.

3. A soil quality evaluation method according to claim 2, characterized in that: The second step of obtaining the preset index includes: Determine the original indicators of the first preset indicators, and perform data standardization on the original indicators. Set the determined original indicators to have an average value of 0 and a standard deviation of 1. The specific formula is: Among them, X is the original data, μ is the mean, σ is the standard deviation, and Z is the standardized data set; The principal component analysis (PCA) method is used on the standardized data set to calculate the eigenvalue and eigenvector of each indicator; Suppose there are n original indicators and m samples, and the eigenvalue of each principal component j obtained by principal component analysis is λ j ; Setting λ j >M, where M represents the first design value, when the eigenvalue λ of the principal component j Only when it is greater than M, it can be used as the pre-selected indicator parameter for the second preset indicator; The conditional formula for defining the cumulative contribution rate is: Among them, k is the number of principal components selected, Ml is the cumulative contribution rate of the first design value, and when the pre-selected indicator parameters meet the cumulative contribution rate condition formula, the screened indicator parameters are used as the second preset indicators.

4. A soil quality evaluation method according to claim 3, characterized in that: The acquisition of the third preset indicators specifically includes the following contents: The analytic hierarchy process is used for the second preset index; the target layer is set as the black soil quality evaluation, The criterion layer is black soil nutrients, black soil environment, black soil layer thickness and black soil ecological indicators, which correspond to the soil chemical indicators, soil environmental indicators, soil physical indicators and soil biological indicators in step S2 in sequence. The criterion layer is a subset of various indicators collected in step S2; The indicator layers are organic matter, total nitrogen, available phosphorus, available potassium, water content, cation exchange capacity, humus content, iron, manganese, black soil layer thickness, soil layer thickness, gully erosion area, pH value, vegetation coverage, heavy metal concentration, land salinization, microbial content and enzyme activity indicators, which constitute the third preset indicators.

5. A soil information collection device, characterized in that: The device is used to execute the soil quality evaluation method according to any one of claims 1 to 4. The collection device includes a soil sampling robot, which is composed of a soil sampling and storage module, a solar power supply module, a walking mechanism based on GNSS positioning, a sensor detection module, and a wireless network transmission module. The soil sampling robot also includes the following black soil quality evaluation system: Data acquisition module: used to obtain the GNSS signal of the GNSS receiver to determine the location of the sampling point, and obtain the corresponding historical soil quality evaluation data based on the sampling point location; The first indicator generation module is used to collect soil physical indicators, soil chemical indicators, soil environmental indicators and soil biological indicators of the sampling point, and each indicator includes a number of collection parameters. The several collection parameters included in each indicator are screened by expert judgment method to generate the first preset indicators; The second indicator generation module is used to obtain the first preset indicator, standardize the original data of the first preset indicator, and perform principal component analysis on the processed data to generate the second preset indicator; Model building module: used to obtain the historical second preset index contained in the historical soil quality evaluation data, compare and analyze the historical second preset index with the second preset index of step S3, and generate a deviation fine-tuning model; the deviation fine-tuning model is represented as a deviation value D; The third indicator generation module: after obtaining the second preset indicator, perform a hierarchical analysis method, the hierarchical analysis method includes a target layer, a criterion layer and an indicator layer, and measure the indicator value of the indicator layer to generate the third preset indicator; Index generation module: used to obtain the third preset index and then analyze and process it, generate the soil quality classification index S, and classify the soil quality grades based on the soil quality classification index S. The formula of the soil quality classification index S is as follows; Among them, S is the soil quality classification index, i represents the i-th index in the third preset index, and f i Score the soil sample for the i-th indicator in the third preset indicator, W i It represents the relative weight of the ith indicator in the third preset indicator; Fine-tuning construction module: After obtaining the deviation value D corresponding to the deviation fine-tuning model, the value range of the deviation value D is divided into grade intervals. Each grade interval represents the corresponding adjustment range, and the output result of the soil quality classification index S is fine-tuned according to the adjustment range.

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