Black land quality comprehensive evaluation system and method

Abstract

The invention discloses a black land quality comprehensive evaluation system, which relates to the technical field of agricultural information and comprises a data acquisition module, a data processing module, an evaluation system construction module, a weight setting module and a comprehensive evaluation module. The data acquisition module is used for collecting multi-aspect data of black land, wherein the multi-aspect data comprises soil physical property data, soil chemical property data and land utilization and management data. The invention further discloses a black land quality comprehensive evaluation method. According to the comprehensive evaluation system and method for the black land quality, comprehensive evaluation of the black land quality is achieved by comprehensively collecting information in multiple aspects of soil physical properties, chemical properties, land utilization and management data and meteorological data, and the comprehensive evaluation mode is more accurate and comprehensive compared with traditional single index evaluation; and the actual condition of the black land can be reflected more truly, so that the evaluation is more in line with the actual agricultural production condition.

Description

Technical Field

[0001] The present invention relates to the field of agricultural information technology, and in particular to a black soil quality comprehensive evaluation system and method. Background Art

[0002] Black soil quality assessment is a crucial topic in the field of agricultural information technology, directly linked to the rational utilization of land resources and the sustainable development of agricultural production. Existing technologies for black soil quality assessment rely primarily on traditional soil testing methods, which typically involve soil sample collection, laboratory analysis, and data processing. The assessment primarily examines the physical and chemical properties of the soil, such as texture, bulk density, moisture content, pH, and organic matter and nutrient content. Land use and management data, such as tillage practices and crop planting history, are also considered. These assessment methods can, to a certain extent, reflect the quality of black soil and provide guidance for agricultural production.

[0003] However, existing technologies still have many problems and shortcomings when comprehensively evaluating black soil quality. On the one hand, traditional methods often focus on the detection of single soil physical and chemical indicators, ignoring the integrity and complexity of the soil ecosystem, making it difficult to fully and accurately reflect the quality status of black soil. On the other hand, the existing evaluation system lacks scientific and systematic approach in terms of weight setting and comprehensive evaluation, resulting in high subjectivity and uncertainty in the evaluation results. In addition, with climate change and changes in agricultural production methods, black soil quality faces new challenges, such as soil degradation and nutrient imbalance, which are becoming increasingly prominent. Existing evaluation methods seem to be unable to cope with these new problems. To this end, we propose a comprehensive black soil quality evaluation system and method. Summary of the Invention

[0004] In order to solve the above technical problems, a black soil quality comprehensive evaluation system and method is provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A black soil quality comprehensive evaluation system includes: a data acquisition module, a data processing module, an evaluation system construction module, a weight setting module and a comprehensive evaluation module;

[0007] The data acquisition module is used to collect various data on black soil, including soil physical properties data, soil chemical properties data, and land use and management data;

[0008] The data processing module is used to filter and clean the collected data, and process and normalize outliers;

[0009] The evaluation system construction module is used to construct an evaluation index system based on the ecological function and production potential characteristics of black soil. The evaluation index system includes: soil productivity, soil environmental quality, and soil biological health;

[0010] The weight setting module is used to determine the weight of each evaluation indicator through an algorithm;

[0011] The comprehensive evaluation module is used to combine the processed data with the weights to obtain a comprehensive evaluation value of the black soil quality and to classify the black soil quality grades according to pre-set grade standards.

[0012] Preferably, the data acquisition module collects various data on black soil, specifically including:

[0013] The soil physical property data collected by the data acquisition module include: soil texture, soil bulk density, soil porosity and soil moisture content:

[0014] The soil texture is determined by sieving and hydrometer methods, which separate soil particles of different sizes and calculate the proportion of each size fraction to determine the soil texture type.

[0015] The soil bulk density is measured by the ring knife method, which uses a ring knife to take a certain volume of original soil, dry it, and weigh it to calculate the mass of dry soil per unit volume.

[0016] The soil porosity is calculated based on the soil bulk density and soil particle density using the following formula:

[0017] Soil p =(1-Soil bd / Soil pd )×100

[0018] Where, Soil p Indicates soil porosity, Soil bd Indicates soil bulk density, Soil pd represents the density of soil particles;

[0019] The soil moisture content was determined by the drying method, where the ratio of the difference in mass before and after drying to the mass before drying was used to determine the soil moisture content.

[0020] Soil chemical properties data include: soil pH, soil organic matter content, soil nitrogen, phosphorus and potassium nutrient content:

[0021] Among them, soil pH was measured using a pH meter;

[0022] Among them, soil organic matter content was determined by potassium dichromate volumetric method-external heating method;

[0023] Among them, soil nitrogen content was determined by Kjeldahl method, phosphorus content was determined by molybdenum antimony colorimetry, and potassium content was determined by flame photometry;

[0024] Land use and management data covers: land use type, cropping system, fertilizer application and irrigation amount:

[0025] Among them, land use types are determined through satellite remote sensing image interpretation combined with field surveys;

[0026] Among them, land planting system was obtained by reviewing local agricultural production records and field visits;

[0027] Among them, the amount of fertilizer and irrigation is obtained by collecting statistics on farmers' fertilizer and irrigation records.

[0028] Preferably, the data processing module screens and cleans the collected data by:

[0029] For the soil physical properties data of soil texture, soil bulk density, soil porosity and soil moisture content, the set value ranges are:

[0030] Soil bulk density is 1.0-1.5g / cm 3 If it exceeds this range, it is considered an abnormal value;

[0031] Soil porosity is between 30% and 60%. If it exceeds this range, it is considered an abnormal value;

[0032] For soil chemical property data, the set value ranges are:

[0033] The normal range of soil pH is between 5.5 and 8.5;

[0034] The soil organic matter content was set within a reasonable range based on the previous data of the region, and data outside the range were considered as outliers;

[0035] In the land use and management data, the amount of fertilizer and irrigation is set within a reasonable range based on the region's past data, and data exceeding the threshold is marked as abnormal;

[0036] The outlier processing adopts the mean substitution method, and the outlier value of a certain indicator is replaced by the average value of all normal data of the indicator;

[0037] Normalization processing adopts linear normalization method. For a certain indicator data x, the normalization formula is:

[0038]

[0039] In the formula, x' represents the normalized value of the indicator data, min represents the minimum value of the indicator data, and max represents the maximum value of the indicator data. This method maps all data to the range of 0-1.

[0040] Preferably, when constructing the evaluation index system in the evaluation system construction module, soil productivity is further subdivided into crop yield potential, soil nutrient supply capacity, and soil water and fertilizer retention capacity;

[0041] Crop yield potential is assessed by building a regression model based on multi-year average crop yield data combined with local climate, soil conditions and other factors. The formula is:

[0042] Y=α+b1X1+b2X2+…+b n X n

[0043] Where Y represents the crop yield potential, α represents the constant term, b1-b n represents the regression coefficient, X1-X n Indicates factors that affect crop yields, including but not limited to: soil nutrient content, soil moisture content;

[0044] The soil nutrient supply capacity is comprehensively evaluated based on the effective content of nitrogen, phosphorus, and potassium nutrients in the soil and the soil cation exchange capacity. The calculation formula is:

[0045] Soil nsc =k1×N eff +k2×P eff +k3×K eff +k4×CEC

[0046] Where, Soil nsc Indicates soil nutrient supply capacity, N eff Indicates the effective content of nitrogen nutrients in the soil, P eff Indicates the effective content of phosphorus nutrients in the soil, K eff represents the effective content of potassium nutrients in the soil, CEC represents the soil cation exchange capacity, k1-k4 represent the effective content of nitrogen, phosphorus and potassium nutrients in the soil and the weight coefficient of soil cation exchange capacity respectively;

[0047] The soil water and fertilizer retention capacity is calculated comprehensively through soil texture, soil porosity and soil organic matter content, and the formula is:

[0048] Soil wafrc =m1×Soil ti +m2×Soil por +m3×Soil omc

[0049] Where, Soil wafrc Indicates the soil's ability to retain water and fertilizer. ti Indicates soil texture index, Soil por Indicates soil porosity, Soil omc represents soil organic matter content, m1-m3 represent the weight coefficients of soil texture index, soil porosity and soil organic matter content respectively;

[0050] Soil environmental quality indicators include soil heavy metal content, soil pesticide residues, and soil pH stability. Soil heavy metal content is determined by atomic absorption spectrometry, soil pesticide residues are detected by gas chromatography-mass spectrometry, and soil pH stability is assessed by evaluating past soil pH change trends.

[0051] Soil biological health indicators include soil microbial count, soil enzyme activity and soil animal diversity. The soil microbial count is determined by the plate counting method, the soil enzyme activity is detected by the enzyme activity determination method, and the soil animal diversity is evaluated by investigating the types and numbers of different groups of soil animals through sampling methods.

[0052] Preferably, the weight setting module determines the weight of each evaluation indicator by:

[0053] A hierarchical model was constructed, with the comprehensive evaluation target of black soil quality as the target layer, soil productivity, soil environmental quality, and soil biological health as the criterion layer, and the specific evaluation indicators under each criterion layer as the solution layer.

[0054] Construct a judgment matrix. For the elements in the criterion layer, compare the importance of each element to the target layer through expert scoring to construct the judgment matrix:

[0055] A=(a ij )n×n

[0056] Where a ij Indicates the ratio of the relative importance of the i-th element to the j-th element, which must satisfy n represents the order of the judgment matrix, that is, the number of evaluation indicators;

[0057] Calculate the eigenvector and maximum eigenroot of the judgment matrix. The formula is:

[0058] A×W=λ max ×W

[0059] Where W represents the eigenvector, λ max represents the maximum characteristic root;

[0060] By consistency test formula:

[0061]

[0062] In the formula, CI represents the consistency index, CR = CI / RI, where CR represents the consistency ratio and RI represents the random consistency index. When CR < 0.1, the judgment matrix passes the consistency test;

[0063] For the solution layer, the judgment matrix is ​​constructed and the weights are calculated for each criterion layer according to the above method;

[0064] Finally, the combined weight of each evaluation index relative to the target layer is obtained. The combined weight is obtained by synthesizing the hierarchical single ranking weights. The formula is:

[0065] ω total =ω cl ×ω al

[0066] Where, ω total Represents the combined weight of each evaluation index, ω cl represents the weight of the criterion layer relative to the target layer, ω al Indicates the weight of the solution layer relative to the criterion layer.

[0067] Preferably, the comprehensive assessment module combines the processed data with the weights to derive a comprehensive assessment value of black soil quality as follows:

[0068] Assume that the weight of soil productivity index is ω1, and the weights of its sub-indexes are ω 11 、ω 12 、ω 13 The sub-index data after processing are x, which correspond to the crop yield potential, soil nutrient supply capacity, and soil water and fertilizer retention capacity. 11 、x 12 、x 13 ;

[0069] The weight of soil environmental quality index is ω2, and the weight of each sub-index is ω 21 、ω 22 、ω 23 Corresponding to soil heavy metal content, soil pesticide residues, and soil pH stability, the sub-index data after processing is x 21 、x 22 、x 23 ;

[0070] The weight of the soil biological health indicator is ω3, and the weight of each sub-indicator is ω 31 、ω 32 、ω 33 Corresponding to the number of soil microorganisms, soil enzyme activity, and soil animal diversity, the sub-index data after processing is x 31 、x 32 、x33 ;

[0071] The comprehensive score of soil productivity S1 is:

[0072] S1=ω 11 ×x 11 +ω 12 ×x 12 +ω 13 ×x 13

[0073] The comprehensive score of soil environmental quality S2 is:

[0074] S2=ω 21 ×x 21 +ω 22 ×x 22 +ω 23 ×x 23

[0075] The comprehensive score of soil biological health S3 is:

[0076] S3=ω 31 ×x 31 +ω 32 ×x 32 +ω 33 ×x 33

[0077] Comprehensive assessment value of black soil quality Z to for:

[0078] Z to =ω1×S1+ω2×S2+ω3×S3

[0079] The black soil quality grades are divided according to the pre-set grade standards:

[0080] When Z to When ≥0.8, the black soil quality grade is excellent; when 0.6≤Z to <0.8, the grade is good; when 0.4≤Z to <0.6, the grade is medium; when Z to When <0.4, the grade is poor.

[0081] Preferably, the data acquisition module also collects meteorological data, including annual average temperature, annual precipitation, and sunshine hours;

[0082] The annual average temperature is calculated by averaging the observation data of the local weather station over many years. The annual precipitation is calculated by averaging the total precipitation of each month over many years. The sunshine hours are also calculated based on the average sunshine hours recorded by the weather station over many years.

[0083] In the evaluation system construction module, meteorological data is incorporated into the factors affecting soil productivity to further improve the assessment model of crop yield potential and modify it to:

[0084] Y=α+b1X1+b2X2+…+b n X n +c1T+c2P+c3S

[0085] Where c1, c2, and c3 are the regression coefficients of meteorological factors, T is the annual average temperature, P is the annual precipitation, and S is the number of hours of sunshine.

[0086] Preferably, the data acquisition module records the fallow rotation cycle information of the land when collecting the land use and management data;

[0087] Information on fallow rotation cycles was obtained by reviewing planning documents from local agricultural departments and interviewing farmers;

[0088] In the evaluation system construction module, the fallow rotation cycle is included in the assessment factors of soil productivity. For land with a reasonable fallow rotation cycle, the corresponding indicator weight coefficient is increased by 0.05-0.1 when calculating the soil nutrient supply capacity and soil water and fertilizer retention capacity to reflect its positive impact on land quality.

[0089] Preferably, after normalization, the data processing module uses nonlinear transformation processing for the soil pH in the soil chemical property data:

[0090] If the soil pH is between 5.5-7.0, the conversion formula is:

[0091] y sn =(x sn -5.5) / 1.5

[0092] If the soil pH is between 7.0-8.5, the conversion formula is:

[0093] y sn =1-(8.5-x sn ) / 1.5

[0094] Where x sn represents the normalized soil pH value, y sn It represents the soil pH value after nonlinear transformation. The value after this processing is used for the calculation of the subsequent comprehensive evaluation module, so that the influence of soil pH on the comprehensive evaluation value of black soil quality is more in line with the actual soil ecological laws.

[0095] A black soil quality comprehensive evaluation method, used to implement a black soil quality comprehensive evaluation system as described above, comprising:

[0096] Collect various data on black soil, including soil physical properties, soil chemical properties, land use and management data, and meteorological data, and record information on land fallow and rotation cycles;

[0097] The collected data is screened and cleaned, outliers are identified based on the set value range and processed using the mean substitution method, and then the data is normalized using the linear normalization method. Nonlinear transformation processing is also required for soil pH;

[0098] An evaluation index system was established based on the ecological functions and production potential characteristics of black soil, covering soil productivity, soil environmental quality, and soil biological health. Meteorological data and fallow rotation cycles were incorporated into soil productivity assessment factors to improve relevant models.

[0099] Construct a hierarchical model, construct a judgment matrix through expert scoring, calculate the eigenvector and maximum eigenroot, and perform consistency test to determine the weights of the criterion layer and the scheme layer, and then obtain the combined weights of each evaluation indicator;

[0100] The processed data are combined with the weights to calculate the comprehensive scores of soil productivity, soil environmental quality, and soil biological health according to the corresponding formulas, and then the comprehensive assessment value of black soil quality is obtained by summarizing them.

[0101] The black soil quality grades are divided according to pre-set grade standards.

[0102] Compared with the prior art, the present invention has the following beneficial effects:

[0103] The black soil quality comprehensive evaluation system and method proposed in the present invention realizes the comprehensive evaluation of black soil quality by comprehensively collecting various information such as soil physical properties, chemical properties, land use and management data, and meteorological data. This comprehensive evaluation method is more accurate and comprehensive than the traditional single indicator evaluation, and can more truly reflect the actual situation of black soil. The introduction of fallow rotation cycle information further improves the soil productivity evaluation model, making the evaluation more in line with actual agricultural production conditions. In terms of data processing, linear normalization and nonlinear transformation methods are used to effectively improve the accuracy and comparability of data, laying a solid foundation for subsequent comprehensive evaluation. The weight of each evaluation indicator is determined through a hierarchical model and expert scoring method, ensuring the objectivity and scientificity of the evaluation, and obtaining a comprehensive evaluation value of black soil quality. The quality level is divided according to preset grade standards, providing an important reference for the rational use of land resources and the sustainable development of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 It is a system module framework diagram of the present invention;

[0105] Figure 2The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0106] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may conceive of other obvious variations.

[0107] Reference Figure 1 As shown, a black soil quality comprehensive evaluation system includes: a data acquisition module, a data processing module, an evaluation system construction module, a weight setting module and a comprehensive evaluation module;

[0108] The Data Acquisition Module is responsible for collecting a wide range of black soil data, including data on soil physical and chemical properties, as well as land use and management data. This module comprehensively collects key information, providing rich material for subsequent analysis and ensuring the integrity of the evaluation basis. This module facilitates a comprehensive understanding of the black soil situation and lays the foundation for accurate assessment.

[0109] The data processing module screens and cleans the collected data, processing outliers and performing normalization. This process removes interfering data and improves data quality. This process makes the data more comparable and usable, providing reliable data support for subsequent evaluations and ensuring the accuracy of evaluation results.

[0110] The evaluation system builds a carefully constructed index system based on the ecological functions and production potential of black soil, encompassing dimensions such as soil productivity, soil environmental quality, and soil biological health. This system closely aligns with the characteristics of black soil, comprehensively reflecting its quality, making the evaluation more targeted and scientific, and providing a rational framework for black soil quality assessment.

[0111] The weighting module uses an algorithm to determine the weights of each evaluation indicator. This scientific algorithm objectively measures the importance of each indicator, avoiding subjectivity and ensuring the fairness and objectivity of the evaluation results, making them more reflective of the actual quality level of black soil.

[0112] The comprehensive assessment module combines processed data with weights to produce a comprehensive assessment of black soil quality, which is then graded according to pre-defined standards. This module integrates data and weights from various sources to produce intuitive and clear assessment results, providing a clear basis for the rational use and protection of black soil, and assisting in the development of targeted strategies to promote its sustainable development.

[0113] The data acquisition module collects various data on black soil, including:

[0114] The soil physical property data collected by the data acquisition module include: soil texture, soil bulk density, soil porosity and soil moisture content:

[0115] The soil texture is determined by sieving and hydrometer methods, which separate soil particles of different sizes and calculate the proportion of each size fraction to determine the soil texture type.

[0116] The soil bulk density is measured by the ring knife method, which uses a ring knife to take a certain volume of original soil, dry it, and weigh it to calculate the mass of dry soil per unit volume.

[0117] The soil porosity is calculated based on the soil bulk density and soil particle density using the following formula:

[0118] Soil p =(1-Soil bd / Soil pd )×100

[0119] Where, Soil p Indicates soil porosity, Soil bd Indicates soil bulk density, Soil pd represents the density of soil particles;

[0120] The soil moisture content was determined by the drying method, where the ratio of the difference in mass before and after drying to the mass before drying was used to determine the soil moisture content.

[0121] Soil chemical properties data include: soil pH, soil organic matter content, soil nitrogen, phosphorus and potassium nutrient content:

[0122] Among them, soil pH was measured using a pH meter;

[0123] Among them, soil organic matter content was determined by potassium dichromate volumetric method-external heating method;

[0124] Among them, soil nitrogen content was determined by Kjeldahl method, phosphorus content was determined by molybdenum antimony colorimetry, and potassium content was determined by flame photometry;

[0125] Land use and management data covers: land use type, cropping system, fertilizer application and irrigation amount:

[0126] Among them, land use types are determined through satellite remote sensing image interpretation combined with field surveys;

[0127] Among them, land planting system was obtained by reviewing local agricultural production records and field visits;

[0128] Among them, the amount of fertilizer and irrigation is obtained by collecting statistics on farmers' fertilizer and irrigation records.

[0129] Collecting data on soil physical properties can accurately determine soil structure and moisture conditions. For example, screening methods can determine soil texture, providing a basis for crop selection and soil improvement. The ring knife method and other methods can measure soil bulk density, porosity, and moisture content, facilitating appropriate irrigation and deep tillage. Collecting data on soil chemical properties can clarify soil fertility and acid-base conditions. Potassium dichromate volumetric methods can measure organic matter and nutrients, guiding precise fertilization. Collecting data on land use and management, using satellite remote sensing to determine land use types and reviewing land records to understand cropping systems and fertilization and irrigation amounts, can help optimize land planning and resource allocation, ensuring the sustainable use of black soil and the efficient development of agriculture.

[0130] The data processing module screens and cleans the collected data in the following ways:

[0131] For the soil physical properties data of soil texture, soil bulk density, soil porosity and soil moisture content, the set value ranges are:

[0132] Soil bulk density is 1.0-1.5g / cm 3 If it exceeds this range, it is considered an abnormal value;

[0133] Soil porosity is between 30% and 60%. If it exceeds this range, it is considered an abnormal value;

[0134] For soil chemical property data, the set value ranges are:

[0135] The normal range of soil pH is between 5.5 and 8.5;

[0136] The soil organic matter content was set within a reasonable range based on the previous data of the region, and data outside the range were considered as outliers;

[0137] In the land use and management data, the amount of fertilizer and irrigation is set within a reasonable range based on the region's past data, and data exceeding the threshold is marked as abnormal;

[0138] The outlier processing adopts the mean substitution method, and the outlier value of a certain indicator is replaced by the average value of all normal data of the indicator;

[0139] Normalization processing adopts linear normalization method. For a certain indicator data x, the normalization formula is:

[0140]

[0141] In the formula, x' represents the normalized value of the indicator data, min represents the minimum value within the indicator data, and max represents the maximum value within the indicator data. This method maps all data to the range of 0-1. The value ranges for soil bulk density and porosity are set based on normal soil structure and the requirements for functions such as aeration and water retention. Soil chemical property data: The pH range corresponds to the environment suitable for most plant growth, and the organic matter content is set based on historical regional data to reflect the normal state of local soil fertility. Land use and management data: Fertilization and irrigation amounts are referenced to historical regional data and determined within reasonable ranges based on local crop needs and resource conditions. The data processing module eliminates unreasonable data interference by setting value ranges, processing outliers, and normalizing data, ensuring data authenticity and reliability. This makes data from different indicators comparable, provides high-quality data support for subsequent evaluations, improves the accuracy and scientific nature of black soil quality assessments, and facilitates scientific decision-making and rational management.

[0142] Evaluation system construction module When constructing the evaluation index system, the soil productivity aspect is further subdivided into crop yield potential, soil nutrient supply capacity, and soil water and fertilizer retention capacity;

[0143] Crop yield potential is assessed by building a regression model based on multi-year average crop yield data combined with local climate, soil conditions and other factors. The formula is:

[0144] Y=α+b1X1+b2X2+…+b n X n

[0145] Where Y represents the crop yield potential, α represents the constant term, b1-b n represents the regression coefficient, X1-X n Indicates factors that affect crop yields, including but not limited to: soil nutrient content, soil moisture content;

[0146] The soil nutrient supply capacity is comprehensively evaluated based on the effective content of nitrogen, phosphorus, and potassium nutrients in the soil and the soil cation exchange capacity. The calculation formula is:

[0147] Soil nsc =k1×N eff +k2×P eff +k3×K eff +k4×CEC

[0148] Where, Soil nsc Indicates soil nutrient supply capacity, N eff Indicates the effective content of nitrogen nutrients in the soil, P eff Indicates the effective content of phosphorus nutrients in the soil, K effrepresents the effective content of potassium nutrients in the soil, CEC represents the soil cation exchange capacity, k1-k4 represent the effective content of nitrogen, phosphorus and potassium nutrients in the soil and the weight coefficient of soil cation exchange capacity respectively;

[0149] The soil water and fertilizer retention capacity is calculated comprehensively through soil texture, soil porosity and soil organic matter content, and the formula is:

[0150] Soil wafrc =m1×Soil ti +m2×Soil por +m3×Soil omc

[0151] Where, Soil wafrc Indicates the soil's ability to retain water and fertilizer. ti Indicates soil texture index, Soil por Indicates soil porosity, Soil omc represents soil organic matter content, m1-m3 represent the weight coefficients of soil texture index, soil porosity and soil organic matter content respectively;

[0152] Soil environmental quality indicators include soil heavy metal content, soil pesticide residues, and soil pH stability. Soil heavy metal content is determined by atomic absorption spectrometry, soil pesticide residues are detected by gas chromatography-mass spectrometry, and soil pH stability is assessed by evaluating past soil pH change trends.

[0153] Soil biological health indicators include soil microbial count, soil enzyme activity and soil animal diversity. The soil microbial count is determined by the plate counting method, the soil enzyme activity is detected by the enzyme activity determination method, and the soil animal diversity is evaluated by investigating the types and numbers of different groups of soil animals through sampling methods.

[0154] In terms of soil productivity, a multivariate regression model is used to assess crop yield potential, enabling accurate yield predictions and providing a basis for planting planning. A comprehensive assessment of soil nutrient supply and water and fertilizer retention capacity comprehensively reflects soil fertility maintenance. Soil environmental quality indicators cover heavy metals, pesticide residues, and pH stability, monitoring soil pollution and environmental stability to ensure soil ecological safety. Soil biological health indicators assess microbial, enzyme, and animal diversity to understand soil biological activity and help maintain soil ecological balance, providing a scientific and comprehensive framework for black soil quality assessment.

[0155] The method for the weight setting module to determine the weight of each evaluation indicator is:

[0156] A hierarchical model was constructed, with the comprehensive evaluation target of black soil quality as the target layer, soil productivity, soil environmental quality, and soil biological health as the criterion layer, and the specific evaluation indicators under each criterion layer as the solution layer.

[0157] Construct a judgment matrix. For the elements in the criterion layer, compare the importance of each element to the target layer through expert scoring to construct the judgment matrix:

[0158] A=(a ij )n×n

[0159] Where a ij Indicates the ratio of the relative importance of the i-th element to the j-th element, which must satisfy n represents the order of the judgment matrix, that is, the number of evaluation indicators;

[0160] Calculate the eigenvector and maximum eigenroot of the judgment matrix. The formula is:

[0161] A×W=λ max ×W

[0162] Where W represents the eigenvector, λ max represents the maximum characteristic root;

[0163] By consistency test formula:

[0164]

[0165] In the formula, CI represents the consistency index, CR = CI / RI, where CR represents the consistency ratio and RI represents the random consistency index. When CR < 0.1, the judgment matrix passes the consistency test;

[0166] For the solution layer, the judgment matrix is ​​constructed and the weights are calculated for each criterion layer according to the above method;

[0167] Finally, the combined weight of each evaluation index relative to the target layer is obtained. The combined weight is obtained by synthesizing the hierarchical single ranking weights. The formula is:

[0168] ω total =ω cl ×ω al

[0169] Where, ω total Represents the combined weight of each evaluation index, ω cl represents the weight of the criterion layer relative to the target layer, ω al Indicates the weight of the solution layer relative to the criterion layer.

[0170] A hierarchical model was constructed, clearly dividing the objectives, criteria, and scenarios into distinct layers. This ensured a logically coherent evaluation system, making it easier to understand and operate. A judgment matrix was constructed and scored by experts, integrating professional opinions and reflecting the relative importance of each indicator. Calculating eigenvectors, maximum eigenroots, and performing consistency checks ensured the rationality of the judgment matrix and the accuracy of weights, avoiding subjective arbitrariness. This same process was repeated at the scenario level, resulting in the synthesis of combined weights. This comprehensive and systematic determination of the weights for each indicator provides a scientific and quantitative basis for the comprehensive evaluation of black soil quality, enhancing the credibility and authority of the evaluation results.

[0171] The comprehensive assessment module combines the processed data with the weights to derive the comprehensive assessment value of black soil quality as follows:

[0172] Assume that the weight of soil productivity index is ω1, and the weights of its sub-indexes are ω 11 、ω 12 、ω 13 The sub-index data after processing are x, which correspond to the crop yield potential, soil nutrient supply capacity, and soil water and fertilizer retention capacity. 11 、x 12 、x 13 ;

[0173] The weight of soil environmental quality index is ω2, and the weight of each sub-index is ω 21 、ω 22 、ω 23 Corresponding to soil heavy metal content, soil pesticide residues, and soil pH stability, the sub-index data after processing is x 21 、x 22 、x 23 ;

[0174] The weight of the soil biological health indicator is ω3, and the weight of each sub-indicator is ω 31 、ω 32 、ω 33 Corresponding to the number of soil microorganisms, soil enzyme activity, and soil animal diversity, the sub-index data after processing is x 31 、x 32 、x 33 ;

[0175] The comprehensive score of soil productivity S1 is:

[0176] S1=ω 11 ×x 11 +ω 12 ×x 12 +ω 13 ×x 13

[0177] The comprehensive score of soil environmental quality S2 is:

[0178] S2=ω21 ×x 21 +ω 22 ×x 22 +ω 23 ×x 23

[0179] The comprehensive score of soil biological health S3 is:

[0180] S3=ω 31 ×x 31 +ω 32 ×x 32 +ω 33 ×x 33

[0181] Comprehensive assessment value of black soil quality Z to for:

[0182] Z to =ω1×S1+ω2×S2+ω3×S3

[0183] The black soil quality grades are divided according to the pre-set grade standards:

[0184] When Z to When ≥0.8, the black soil quality grade is excellent; when 0.6≤Z to <0.8, the grade is good; when 0.4≤Z to <0.6, the grade is medium; when Z to When <0.4, the grade is poor.

[0185] By clarifying the weights of each indicator and combining them with processed data for a tiered, weighted calculation, the assessment comprehensively and meticulously considers multiple dimensions of black soil quality, making the results more scientific and accurate. By categorizing quality levels according to varying scopes, the complex assessment values ​​are converted into intuitive and easily understood grades, enabling managers, researchers, and related practitioners to quickly understand the quality of black soil. This provides a clear basis for developing targeted land protection, improvement, and utilization strategies, and supports the rational development and sustainable development of black soil resources.

[0186] The data acquisition module also collects meteorological data, including annual average temperature, annual precipitation, and sunshine hours;

[0187] The annual average temperature is calculated by averaging the observation data of the local weather station over many years. The annual precipitation is calculated by averaging the total precipitation of each month over many years. The sunshine hours are also calculated based on the average sunshine hours recorded by the weather station over many years.

[0188] In the evaluation system construction module, meteorological data is incorporated into the factors affecting soil productivity to further improve the assessment model of crop yield potential and modify it to:

[0189] Y=α+b1X1+b2X2+…+b n X n +c1T+c2P+c3S

[0190] Where c1, c2, and c3 are the regression coefficients of meteorological factors, T is the annual average temperature, P is the annual precipitation, and S is the number of hours of sunshine.

[0191] Meteorological factors have a significant impact on crop growth. Incorporating annual average temperature, annual precipitation and sunshine hours into the improved crop yield potential assessment model can more comprehensively and accurately reflect the impact of the environment on soil productivity, provide a more scientific basis for black soil planting planning and yield forecasting, and help improve black soil utilization efficiency and agricultural production benefits.

[0192] The data acquisition module records the fallow rotation cycle information of the land when collecting land use and management data;

[0193] Information on fallow rotation cycles was obtained by reviewing planning documents from local agricultural departments and interviewing farmers;

[0194] In the evaluation system construction module, the fallow rotation cycle is included in the assessment factors of soil productivity. For land with a reasonable fallow rotation cycle, the corresponding indicator weight coefficient is increased by 0.05-0.1 when calculating the soil nutrient supply capacity and soil water and fertilizer retention capacity to reflect its positive impact on land quality.

[0195] Fallow rotation can restore soil fertility and improve soil structure. Recording this information and incorporating it into the evaluation system, along with appropriate weighting, can scientifically reflect its positive impact on soil productivity, guide rational land use, promote the sustainable development of black soil, and ensure long-term, stable agricultural production.

[0196] After normalization, the data processing module uses nonlinear transformation to process the soil pH in the soil chemical property data:

[0197] If the soil pH is between 5.5-7.0, the conversion formula is:

[0198] y sn =(x sn -5.5) / 1.5

[0199] If the soil pH is between 7.0-8.5, the conversion formula is:

[0200] y sn =1-(8.5-x sn ) / 1.5

[0201] Where x sn represents the normalized soil pH value, y snThis represents the soil pH value after a nonlinear transformation. This processed value is used in the subsequent calculations of the comprehensive assessment module, ensuring that the impact of soil pH on the comprehensive assessment of black soil quality is more consistent with actual soil ecological patterns. Using specific transformation formulas based on different soil pH ranges can more accurately reflect their impact on black soil quality. This process aligns with actual soil ecological patterns, avoids the limitations of simple linear processing, and makes the calculation results of the comprehensive assessment module more scientific and reasonable, providing a more accurate basis for black soil quality evaluation.

[0202] Reference Figure 2 As shown, a black soil quality comprehensive evaluation method is used to implement a black soil quality comprehensive evaluation system as described above, including:

[0203] During the data collection phase, comprehensive coverage of the relevant characteristics of black soil is necessary. Specifically, soil physical property data includes indicators such as soil texture, porosity, and bulk density; soil chemical property data includes parameters such as organic matter content, concentrations of nutrients such as nitrogen, phosphorus, and potassium, and pH; land use and management data should record information such as land use, fertilization, and irrigation frequency; and meteorological data should include long-term monitoring data such as precipitation, temperature, light intensity, and wind speed. Furthermore, special attention should be paid to information on land fallow and rotation cycles, such as the number of years of fallow, the types of rotation crops, and their impact on the soil.

[0204] During data preprocessing, the collected raw data must first be screened and cleaned. This process includes removing duplicate data, filling in missing values, and detecting and handling outliers. For outliers, mean substitution can be used, replacing outliers outside the set range with the mean value of the variable. Subsequently, all data are normalized to eliminate dimensional differences. While linear normalization is suitable for most indicators, indicators with nonlinear distribution characteristics, such as soil pH, require nonlinear transformations to better meet the needs of subsequent analysis.

[0205] When constructing an evaluation index system, it is necessary to fully consider the ecological function and production potential of black soil. The evaluation index system can be designed from the following three dimensions:

[0206] Soil productivity: reflects the ability of soil to support crop growth, including indicators such as soil nutrient content, organic matter level, and pH suitability. It also takes into account meteorological data and fallow rotation cycles to assess their impact on soil productivity.

[0207] Soil environmental quality: measures the cleanliness and pollution status of the soil, focusing on potential pollutant indicators such as heavy metal content and pesticide residues.

[0208] Soil biological health: reflects the level of biological activity in the soil ecosystem, including indicators such as the number of soil microorganisms and enzyme activity.

[0209] Next, in applying the AHP model, a judgment matrix must be constructed through expert consultation. This matrix quantifies the relative importance of evaluation indicators. By calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, the weight coefficients for each indicator can be determined. A consistency check is also required to ensure the rationality of the judgment matrix. Ultimately, the AHP model yields the combined weights for the criterion and solution levels.

[0210] During the comprehensive assessment phase, preprocessed data is combined with the weights assigned to each indicator to calculate comprehensive scores for soil productivity, environmental quality, and biological health. For example, scores for each dimension can be calculated using a weighted summation method, ultimately yielding a comprehensive assessment of black soil quality. Finally, this comprehensive assessment is categorized into different quality levels based on pre-defined quality standards.

[0211] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive evaluation system for black soil quality, characterized by: include: Data acquisition module, data processing module, evaluation system construction module, weight setting module and comprehensive evaluation module; The data acquisition module is used to collect various data on black soil, including soil physical properties data, soil chemical properties data, and land use and management data; The data processing module is used to filter and clean the collected data, and process and normalize outliers; The evaluation system construction module is used to construct an evaluation index system based on the ecological function and production potential characteristics of black soil. The evaluation index system includes: soil productivity, soil environmental quality, and soil biological health; The weight setting module is used to determine the weight of each evaluation indicator through an algorithm; The comprehensive evaluation module is used to combine the processed data with the weights to obtain a comprehensive evaluation value of the black soil quality and to classify the black soil quality grades according to pre-set grade standards.

2. A black soil quality comprehensive evaluation system according to claim 1, characterized in that: The data acquisition module collects various data on black soil, including: The soil physical property data collected by the data acquisition module include: soil texture, soil bulk density, soil porosity and soil moisture content: The soil texture is determined by sieving and hydrometer methods, which separate soil particles of different sizes and calculate the proportion of each size fraction to determine the soil texture type. The soil bulk density is measured by the ring knife method, which uses a ring knife to take a certain volume of original soil, dry it, and weigh it to calculate the mass of dry soil per unit volume. The soil porosity is calculated based on the soil bulk density and soil particle density using the following formula: Soil p =(1-Soil bd / Soil pd )×100 Where, Soil p Indicates soil porosity, Soil bd Indicates soil bulk density, Soil pd represents the density of soil particles; The soil moisture content was determined by the drying method, where the ratio of the difference in mass before and after drying to the mass before drying was used to determine the soil moisture content. Soil chemical properties data include: soil pH, soil organic matter content, soil nitrogen, phosphorus and potassium nutrient content: Among them, soil pH was measured using a pH meter; Among them, soil organic matter content was determined by potassium dichromate volumetric method-external heating method; Among them, soil nitrogen content was determined by Kjeldahl method, phosphorus content was determined by molybdenum antimony colorimetry, and potassium content was determined by flame photometry; Land use and management data covers: land use type, cropping system, fertilizer application and irrigation amount: Among them, land use types are determined through satellite remote sensing image interpretation combined with field surveys; Among them, land planting system was obtained by reviewing local agricultural production records and field visits; Among them, the amount of fertilizer and irrigation is obtained by collecting statistics on farmers' fertilizer and irrigation records.

3. A black soil quality comprehensive evaluation system according to claim 1, characterized in that: The data processing module screens and cleans the collected data in the following ways: For the soil physical properties data of soil texture, soil bulk density, soil porosity and soil moisture content, the set value ranges are: Soil bulk density is 1.0-1.5g / cm 3 If it exceeds this range, it is considered an abnormal value; Soil porosity is between 30% and 60%. If it exceeds this range, it is considered an abnormal value; For soil chemical property data, the set value ranges are: The normal range of soil pH is between 5.5 and 8.5; The soil organic matter content was set within a reasonable range based on the previous data of the region, and data outside the range were considered as outliers; In the land use and management data, the amount of fertilizer and irrigation is set within a reasonable range based on the region's past data, and data exceeding the threshold is marked as abnormal; The outlier processing adopts the mean substitution method, and the outlier value of a certain indicator is replaced by the average value of all normal data of the indicator; Normalization processing adopts linear normalization method. For a certain indicator data x, the normalization formula is: In the formula, x' represents the normalized value of the indicator data, min represents the minimum value of the indicator data, and max represents the maximum value of the indicator data. This method maps all data to the range of 0-1.

4. A black soil quality comprehensive evaluation system according to claim 1, characterized in that: Evaluation system construction module When constructing the evaluation index system, the soil productivity aspect is further subdivided into crop yield potential, soil nutrient supply capacity, and soil water and fertilizer retention capacity; Crop yield potential is assessed by building a regression model based on multi-year average crop yield data combined with local climate, soil conditions and other factors. The formula is: Y=α+b1X1+b2X2+…+b n X n Where Y represents the crop yield potential, α represents the constant term, b1-b n represents the regression coefficient, X1-X n Indicates factors that affect crop yields, including but not limited to: soil nutrient content, soil moisture content; The soil nutrient supply capacity is comprehensively evaluated based on the effective content of nitrogen, phosphorus, and potassium nutrients in the soil and the soil cation exchange capacity. The calculation formula is: Soil nsc =k1×N eff +k2×P eff +k3×K eff +k4×CEC Where, Soil nsc Indicates soil nutrient supply capacity, N eff Indicates the effective content of nitrogen nutrients in the soil, P eff Indicates the effective content of phosphorus nutrients in the soil, K eff represents the effective content of potassium nutrients in the soil, CEC represents the soil cation exchange capacity, k1-k4 represent the effective content of nitrogen, phosphorus and potassium nutrients in the soil and the weight coefficient of soil cation exchange capacity respectively; The soil water and fertilizer retention capacity is calculated comprehensively through soil texture, soil porosity and soil organic matter content, and the formula is: Soil wafrc =m1×Soil ti +m2×Soil por +m3×Soil omc Where, Soil wafrc Indicates the soil's ability to retain water and fertilizer. ti Indicates soil texture index, Soil por Indicates soil porosity, Soil omc represents soil organic matter content, m1-m3 represent the weight coefficients of soil texture index, soil porosity and soil organic matter content respectively; Soil environmental quality indicators include soil heavy metal content, soil pesticide residues, and soil pH stability. Soil heavy metal content is determined by atomic absorption spectrometry, soil pesticide residues are detected by gas chromatography-mass spectrometry, and soil pH stability is assessed by evaluating past soil pH change trends. Soil biological health indicators include soil microbial count, soil enzyme activity and soil animal diversity. The soil microbial count is determined by the plate counting method, the soil enzyme activity is detected by the enzyme activity determination method, and the soil animal diversity is evaluated by investigating the types and numbers of different groups of soil animals through sampling methods.

5. The black soil quality comprehensive evaluation system according to claim 1 is characterized in that: The method for the weight setting module to determine the weight of each evaluation indicator is: A hierarchical model was constructed, with the comprehensive evaluation target of black soil quality as the target layer, soil productivity, soil environmental quality, and soil biological health as the criterion layer, and the specific evaluation indicators under each criterion layer as the solution layer. Construct a judgment matrix. For the elements in the criterion layer, compare the importance of each element to the target layer through expert scoring to construct the judgment matrix: A=(a ij )n×n Where a ij Indicates the ratio of the relative importance of the i-th element to the j-th element, which must satisfy a ii =1, n represents the order of the judgment matrix, that is, the number of evaluation indicators; Calculate the eigenvector and maximum eigenroot of the judgment matrix. The formula is: A×W=λ max ×W Where W represents the eigenvector, λ max represents the maximum characteristic root; By consistency test formula: In the formula, CI represents the consistency index, CR = CI / RI, where CR represents the consistency ratio and RI represents the random consistency index. When CR < 0.1, the judgment matrix passes the consistency test; For the solution layer, the judgment matrix is ​​constructed and the weights are calculated for each criterion layer according to the above method; Finally, the combined weight of each evaluation index relative to the target layer is obtained. The combined weight is obtained by synthesizing the hierarchical single ranking weights. The formula is: oh total =ω cl ×ω al Where, ω total Represents the combined weight of each evaluation index, ω cl represents the weight of the criterion layer relative to the target layer, ω al Indicates the weight of the solution layer relative to the criterion layer.

6. A black soil quality comprehensive evaluation system according to claim 1, characterized in that: The comprehensive assessment module combines the processed data with the weights to derive the comprehensive assessment value of black soil quality as follows: Assume that the weight of soil productivity index is ω1, and the weights of its sub-indexes are ω 11 、ω 12 、ω 13 Corresponding to crop yield potential, soil nutrient supply capacity, and soil water and fertilizer retention capacity, the processed sub-indicator data are x 11 、x 12 、x 13 ; The weight of soil environmental quality index is ω2, and the weight of each sub-index is ω 21 、ω 22 、ω 23 Corresponding to soil heavy metal content, soil pesticide residues, and soil pH stability, the sub-index data after processing is x 21 、x 22 、x 23 ; The weight of the soil biological health indicator is ω3, and the weight of each sub-indicator is ω 31 、ω 32 、ω 33 Corresponding to the number of soil microorganisms, soil enzyme activity, and soil animal diversity, the sub-index data after processing is x 31 、x 32 、x 33 ; The comprehensive score of soil productivity S1 is: S1=ω 11 ×x 11 +oh 12 ×x 12 +oh 13 ×x 13 The comprehensive score of soil environmental quality S2 is: S2=ω 21 ×x 21 +oh 22 ×x 22 +oh 23 ×x 23 The comprehensive score of soil biological health S3 is: S3=ω 31 ×x 31 +oh 32 ×x 32 +oh 33 ×x 33 Comprehensive assessment value of black soil quality Z to for: WITH to =ω1×S1+ω2×S2+ω3×S3 The black soil quality grades are divided according to the pre-set grade standards: When Z to When ≥0.8, the black soil quality grade is excellent; when 0.6≤Z to When <0.8, the grade is good; When 0.4≤Z to <0.6, the grade is medium; when Z to When <0.4, the grade is poor.

7. The black soil quality comprehensive evaluation system according to claim 1 is characterized in that: The data acquisition module also collects meteorological data, including annual average temperature, annual precipitation, and sunshine hours; The annual average temperature is calculated by averaging the observation data of the local weather station over many years. The annual precipitation is calculated by averaging the total precipitation of each month over many years. The sunshine hours are also calculated based on the average sunshine hours recorded by the weather station over many years. In the evaluation system construction module, meteorological data is incorporated into the factors affecting soil productivity to further improve the assessment model of crop yield potential and modify it to: Y=α+b1X1+b2X2+…+b n X n +c1T+c2P+c3S Where c1, c2, and c3 are the regression coefficients of meteorological factors, T is the annual average temperature, P is the annual precipitation, and S is the number of hours of sunshine.

8. The black soil quality comprehensive evaluation system according to claim 1 is characterized in that: The data acquisition module records the fallow rotation cycle information of the land when collecting land use and management data; Information on fallow rotation cycles was obtained by reviewing planning documents from local agricultural departments and interviewing farmers; In the evaluation system construction module, the fallow rotation cycle is included in the assessment factors of soil productivity. For land with a reasonable fallow rotation cycle, the corresponding indicator weight coefficient is increased by 0.05-0.1 when calculating the soil nutrient supply capacity and soil water and fertilizer retention capacity to reflect its positive impact on land quality.

9. The black soil quality comprehensive evaluation system according to claim 1, characterized in that: After normalization, the data processing module uses nonlinear transformation to process the soil pH in the soil chemical property data: If the soil pH is between 5.5-7.0, the conversion formula is: and sn =(x sn -5.5) / 1.5 If the soil pH is between 7.0-8.5, the conversion formula is: and sn =1-(8.5-x sn ) / 1.5 Where x sn represents the normalized soil pH value, y sn It represents the soil pH value after nonlinear transformation. The value after this processing is used for the calculation of the subsequent comprehensive evaluation module, so that the influence of soil pH on the comprehensive evaluation value of black soil quality is more in line with the actual soil ecological laws.

10. A comprehensive evaluation method for black soil quality, characterized in that: A system for implementing a comprehensive black soil quality evaluation system as claimed in any one of claims 1 to 9, comprising: Collect various data on black soil, including soil physical properties, soil chemical properties, land use and management data, and meteorological data, and record information on land fallow and rotation cycles; The collected data is screened and cleaned, outliers are identified based on the set value range and processed using the mean substitution method, and then the data is normalized using the linear normalization method. Nonlinear transformation processing is also required for soil pH; An evaluation index system was established based on the ecological functions and production potential characteristics of black soil, covering soil productivity, soil environmental quality, and soil biological health. Meteorological data and fallow rotation cycles were incorporated into soil productivity assessment factors to improve relevant models. Construct a hierarchical model, construct a judgment matrix through expert scoring, calculate the eigenvector and maximum eigenroot, and perform consistency test to determine the weights of the criterion layer and the scheme layer, and then obtain the combined weights of each evaluation indicator; The processed data are combined with the weights to calculate the comprehensive scores of soil productivity, soil environmental quality, and soil biological health according to the corresponding formulas, and then the comprehensive assessment value of black soil quality is obtained by summarizing them. The black soil quality grades are divided according to pre-set grade standards.

Images