An organic fertilizer evaluation method, system and storage medium

Abstract

This invention relates to the field of agricultural organic fertilizer evaluation technology, and particularly to an organic fertilizer evaluation method, system, and storage medium. The method includes the following steps: extracting pore and moisture characteristics from organic fertilizer samples to obtain an organic fertilizer structure spectrum; performing humidity gradient adsorption-desorption measurements on organic fertilizer samples to obtain a set of humidity response curves; performing dynamic water exchange measurements on organic fertilizer samples based on the humidity response curve set to obtain a dynamic water exchange graph; performing hydraulic parameter response analysis based on the humidity response curve set and the dynamic water exchange graph to obtain a hydraulic response index; acquiring and extracting soil improvement demand spectra from typical soil samples of different types; and evaluating the structural complementarity of the soil improvement demand spectra and the organic fertilizer structure spectrum to obtain a structural complementarity scoring table. This invention significantly improves the targeting and resource utilization efficiency of soil improvement by analyzing the precise matching of organic fertilizer with different soil types and quantitatively predicting the improvement effect.

Description

Technical Field

[0001] This invention relates to the field of agricultural organic fertilizer evaluation technology, and in particular to an organic fertilizer evaluation method, system and storage medium. Background Technology

[0002] Existing organic fertilizer evaluation systems primarily rely on macroscopic physicochemical indicators and field trial results, neglecting the key microscopic mechanisms by which organic fertilizers affect soil structure and moisture dynamics, thus failing to reveal the essential process by which organic fertilizers improve soil. Furthermore, due to the lack of systematic analysis of the microstructure and hydraulic properties of organic fertilizers, existing methods struggle to accurately predict their specific effects in different soil matrices (such as sandy soil, clay, and loam), often resulting in unstable or lower-than-expected improvement effects in practical applications. In addition, insufficient predictive capabilities prevent farmers and soil remediation projects from precisely selecting the most suitable organic fertilizer products based on specific soil problems, forcing them to rely on experience or simple experiments, reducing the targetedness and efficiency of soil improvement, and increasing resource waste and economic costs.

[0003] In summary, existing technologies suffer from problems such as a lack of microscopic mechanism analysis, inaccurate prediction of improvement effects, and imprecise guidance on soil-fertilizer matching, which urgently need to be addressed. Summary of the Invention

[0004] Therefore, it is necessary to provide an organic fertilizer evaluation method, system, and storage medium to solve at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, an organic fertilizer evaluation method includes the following steps:

[0006] Step S1: Extract pore and moisture characteristics from the organic fertilizer sample to obtain the organic fertilizer structure spectrum;

[0007] Step S2: Perform humidity gradient adsorption-desorption measurement on the organic fertilizer sample to obtain a set of humidity response curves; perform dynamic water exchange measurement on the organic fertilizer sample based on the set of humidity response curves to obtain a dynamic water exchange graph; perform hydraulic parameter response analysis based on the set of humidity response curves and the dynamic water exchange graph to obtain the hydraulic response index.

[0008] Step S3: Obtain and extract soil improvement demand spectrum from typical soil samples of different types; evaluate the structural complementarity of soil improvement demand spectrum and organic fertilizer structure spectrum to obtain a structural complementarity scoring table; calculate hydraulic synergy based on soil improvement demand spectrum, structural complementarity scoring table and hydraulic response index to obtain a hydraulic synergy index map; construct a soil-fertilizer adaptation matrix based on hydraulic synergy index map and structural complementarity scoring table.

[0009] Step S4: Analyze the impact of environmental and fertilization rates based on the soil-fertilizer adaptation matrix to obtain the fertilization rate effect curve and environmental response adjustment factor; calculate the improvement index and predict the effect based on the environmental response adjustment factor and fertilization rate effect curve to obtain the expected soil improvement report.

[0010] This invention utilizes low-field nuclear magnetic resonance (NMR) and microcomputed tomography (MCT) techniques to quantitatively obtain the pore structure parameters (pore size distribution, porosity, connectivity) of organic fertilizers and their interaction with water (the proportion of water in different bound states) at the microscopic level. This systematic microscopic characterization breaks through the limitations of traditional physicochemical indicators, constructing a unique "structural hydraulic fingerprint" for organic fertilizers, providing fundamental data for a deeper understanding of their physical mechanisms of soil improvement. Obtaining the structural spectrum of organic fertilizers allows for an assessment of their inherent properties beyond the surface level, enabling insights into their intrinsic potential to influence soil moisture and aeration. Through dynamic vapor adsorption and simulated rainfall / drying experiments, the water absorption rate, water retention capacity, water release regulation characteristics, and stability of organic fertilizers under non-equilibrium conditions are quantified. These dynamic hydraulic characteristic data directly reflect the actual hydraulic behavior of organic fertilizers in simulated field environments, compensating for the deficiencies of traditional static physicochemical indicators (such as total moisture content). By analyzing hydraulic parameter responses, complex dynamic behaviors are extracted into quantified hydraulic response indices, providing key, dynamic performance indicators for subsequent matching analysis with soil properties. This makes the evaluation of the hydraulic function of organic fertilizer more accurate and comprehensive. The structure and hydraulic characteristics of organic fertilizer are systematically matched with the structural defects and water requirements of different soil types. Through pore size difference analysis and connectivity matching degree calculation, the complementary improvement potential of organic fertilizer on soil structure is quantified, generating a structural complementarity scoring table. Through synergistic analysis of infiltration, water retention, and water release, the synergistic regulatory capacity of organic fertilizer and soil moisture characteristics is quantified, generating a hydraulic synergy index map, and considering the enhancing effect of structural improvement on hydraulic synergy. The final constructed soil-fertilizer fit matrix intuitively expresses the degree of fit of the organic fertilizer to various soil types with quantitative scores, overcoming the drawbacks of the "one-size-fits-all" approach of traditional evaluation methods. This achieves precise matching guidance between organic fertilizer and target soil, significantly improving the pertinence and effectiveness of soil improvement programs. The soil-fertilizer fit is transformed into predictions of specific improvement effects under different fertilization rates and environmental conditions. Through gradient design of fertilization rates and analysis of environmental factors, a quantitative relationship model (regression equation system) was established between fertilization rate, environmental factors, and soil improvement effects. This model can predict the specific improvement of soil moisture retention, aeration, and drought resistance by organic fertilizer under different application scenarios (different fertilization rates, different regional climates). The generated soil improvement forecast report not only provides quantitative prediction results and optimal application rate recommendations, but also includes economic benefit analysis and long-term maintenance plans. This provides farmers and soil remediation projects with a scientific and reliable basis for decision-making, avoiding blind fertilization and resource waste, and improving the efficiency and economic benefits of agricultural production and soil remediation.

[0011] Therefore, this invention provides an organic fertilizer evaluation method. By establishing a systematic evaluation process of "microstructure characterization - hydraulic property assessment - soil compatibility analysis - improvement benefit prediction", it utilizes technologies such as low-field nuclear magnetic resonance and micro-CT scanning to obtain the microstructural characteristics and hydraulic dynamics of organic fertilizer, constructing a "hydraulic fingerprint" of organic fertilizer. Through the principles of structural complementarity and hydraulic synergy, it achieves precise matching of organic fertilizer with different soil types and quantitative prediction of improvement effects, providing a scientific basis for farmers and soil remediation projects, and significantly improving the targeting and resource utilization efficiency of soil improvement.

[0012] Preferably, the present invention also provides an organic fertilizer evaluation system for performing the organic fertilizer evaluation method described above, the organic fertilizer evaluation system comprising:

[0013] The microstructure characterization module is used to extract pore and moisture characteristics from organic fertilizer samples to obtain organic fertilizer structure maps.

[0014] The hydraulic characteristic assessment module is used to measure the humidity gradient adsorption and desorption of organic fertilizer samples to obtain a set of humidity response curves; to measure the dynamic water exchange of organic fertilizer samples based on the set of humidity response curves to obtain a dynamic water exchange graph; and to analyze the hydraulic parameter response based on the set of humidity response curves and the dynamic water exchange graph to obtain the hydraulic response index.

[0015] The soil compatibility analysis module is used to acquire and extract soil improvement demand spectra from typical soil samples of different types; to evaluate the structural complementarity of the soil improvement demand spectra and organic fertilizer structure spectra, and to obtain a structural complementarity scoring table; to calculate hydraulic synergy based on the soil improvement demand spectra, the structural complementarity scoring table and the hydraulic response index, and to obtain a hydraulic synergy index map; and to construct a soil-fertilizer compatibility matrix based on the hydraulic synergy index map and the structural complementarity scoring table.

[0016] The soil improvement benefit prediction module is used to analyze the impact of environmental and fertilizer application rates based on the soil-fertilizer adaptation matrix, and obtain the fertilizer application rate effect curve and environmental response adjustment factor. Based on the environmental response adjustment factor and fertilizer application rate effect curve, the module calculates improvement indicators and predicts effects, and obtains a soil improvement expectation report.

[0017] This organic fertilizer evaluation system integrates and automates core evaluation processes such as microstructural characterization, hydraulic property assessment, soil compatibility analysis, and soil improvement benefit prediction. It achieves comprehensive scientific quantification of the entire process, from organic fertilizer characteristics to soil improvement effect prediction, overcoming the limitations of traditional methods that rely on experience and macroscopic indicators, and significantly improving evaluation efficiency and accuracy. The system provides quantitative results such as organic fertilizer structure maps, hydraulic response indices, and soil-fertilizer compatibility matrices, offering a scientific basis for accurately identifying the advantages and disadvantages of organic fertilizers. In particular, the soil compatibility analysis module assesses structural complementarity and hydraulic synergy based on organic fertilizer characteristics and different soil requirements, achieving precise matching between organic fertilizer and target soils and avoiding indiscriminate application. The soil improvement benefit prediction module further combines fertilizer application rate and environmental factors to predict specific improvement effects, providing optimized application suggestions and economic benefit analysis. This provides reliable decision support for farmers and soil remediation projects, ultimately helping to improve the targeting and efficiency of soil improvement and reduce resource waste and economic costs.

[0018] Preferably, a computer-readable storage medium stores a computer program that, when executed, implements the organic fertilizer evaluation method as described above. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of an organic fertilizer evaluation method.

[0020] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] In this embodiment of the invention, reference Figure 1 The diagram shown is a flowchart illustrating the steps of the organic fertilizer evaluation method of the present invention. In this example, the organic fertilizer evaluation method includes the following steps:

[0025] Step S1: Extract pore and moisture characteristics from the organic fertilizer sample to obtain the organic fertilizer structure spectrum;

[0026] In this embodiment of the invention, the relaxation time distribution of water molecules in different binding states in an organic fertilizer sample is measured using a low-field nuclear magnetic resonance spectrometer to distinguish and quantify the proportions of strongly bound water, weakly bound water, and free water, generating a water binding spectrum. Subsequently, a micro-computed tomography scanner is used to perform high-resolution three-dimensional scanning imaging of the organic fertilizer sample, obtaining a set of original grayscale images of its internal pore structure. Adaptive threshold binarization is performed on the grayscale images to distinguish between the solid matrix and pore space. Then, morphological operations are performed to remove noise and optimize pore boundaries. Three-dimensional connected component analysis is performed on the binary pore images to identify each independent pore region and calculate its volume, surface area, shape factor, and other geometric parameters. The number and volume distribution of pores of different sizes are statistically analyzed, and the connectivity of the pore network is analyzed to obtain a pore parameter table. Finally, a correlation matrix between water-bound states and pore characteristics is established. A weighted fusion algorithm is used to integrate the data from the water-bound spectrum and the pore parameter table, and a three-dimensional coordinate system (pore size distribution, bound water ratio, spatial connectivity) is constructed. The fused data is mapped to a set of feature points, and a set of feature curves is generated by polynomial fitting. The statistical parameters of the feature point set and the curves are calculated, and finally, an organic fertilizer structure spectrum that quantitatively describes the microstructure and water-bound characteristics of organic fertilizer is formed.

[0027] Step S2: Perform humidity gradient adsorption-desorption measurement on the organic fertilizer sample to obtain a set of humidity response curves; perform dynamic water exchange measurement on the organic fertilizer sample based on the set of humidity response curves to obtain a dynamic water exchange graph; perform hydraulic parameter response analysis based on the set of humidity response curves and the dynamic water exchange graph to obtain the hydraulic response index.

[0028] In this embodiment of the invention, firstly, the completely dried organic fertilizer sample is placed in a dynamic vapor adsorption instrument. Under a relative humidity gradient of 10%-95%, the equilibrium moisture content of the sample at each humidity point and the time required to reach equilibrium are measured. Hygroscopic-dehumidification isotherms and hysteresis loops are plotted, generating a set of humidity response curves. Next, the air-dried organic fertilizer sample is placed on a microgravity sensor to simulate rainfall processes of different intensities (2, 5, 10 mm / h). The sample mass changes are recorded in real time, and the water absorption dynamics are monitored. After the rainfall stops, the high-moisture-content sample is transferred to environments with different temperatures (15, 25, 35℃). Mass changes are continuously monitored, water release dynamics are measured, and a dry-wet cycle stability test is conducted. The changes in key hydraulic parameters with the number of cycles are evaluated. The effectiveness of the released water is assessed using the humidity response curve set, and finally, a dynamic water exchange graph containing information on water absorption, water release, and cycle stability is constructed. Finally, based on the humidity response curve, humidity sensitivity is calculated, water absorption kinetic parameters (such as initial water absorption rate, saturated water absorption capacity, and rate constant) are extracted from the water exchange dynamic diagram, and water absorption efficiency (such as the time required to reach 75% saturation) is evaluated. Water retention capacity (such as moisture content after drainage and residual moisture content after standard drying) is measured, water buffering capacity (such as hysteresis loop area and release slope in critical intervals) is calculated, and structural adaptability (such as the cyclic change rate of key parameters) is evaluated. These quantitative indicators are integrated into a set of hydraulic characteristic parameters, and standardized weighted calculations are performed according to preset weights to construct a comprehensive hydraulic response index composed of three quantitative parameters: water absorption rate coefficient, water retention capacity value, and release control factor.

[0029] Step S3: Obtain and extract soil improvement demand spectrum from typical soil samples of different types; evaluate the structural complementarity of soil improvement demand spectrum and organic fertilizer structure spectrum to obtain a structural complementarity scoring table; calculate hydraulic synergy based on soil improvement demand spectrum, structural complementarity scoring table and hydraulic response index to obtain a hydraulic synergy index map; construct a soil-fertilizer adaptation matrix based on hydraulic synergy index map and structural complementarity scoring table.

[0030] In this embodiment of the invention, firstly, data such as pore size distribution, pore connectivity, and water-holding curves for various soil types are extracted from a standardized database containing microstructure and hydraulic properties of typical soil samples, including sandy soil, clay, and loam. Based on agronomic requirements, key indicators requiring improvement and their weights for each soil type are determined, forming a soil improvement demand spectrum. Next, the pore size distribution and connectivity data from the organic fertilizer structure spectrum are compared with the soil structure characteristics in the soil improvement demand spectrum. The complementary contributions of organic fertilizer within different pore size ranges and the degree of matching between the pore network and the soil network are analyzed. Changes in pore size distribution and specific surface area after mixing are predicted. Based on these structural complementarities and improvement potentials, a structural complementarity score is calculated, resulting in a structural complementarity scoring table. Then, using the water absorption rate, water retention capacity, and water release regulation factor in the hydraulic response index, combined with the soil hydraulic characteristics and water requirements in the soil improvement demand spectrum, the synergistic effects of organic fertilizer on infiltration, water retention, and water release with different soil types were analyzed. Infiltration coordination, water retention gain, and water release uniformity were quantified, and a comprehensive hydraulic synergy index was calculated. Simultaneously, the enhancing effect of structural complementarity on hydraulic synergy was considered, and the hydraulic synergy performance under different environments was evaluated, ultimately constructing a hydraulic synergy index map. Finally, the quantitative scores from the structural complementarity scoring table and the hydraulic synergy index map were weighted and integrated to construct a two-dimensional fit evaluation system, generating a soil-fertilizer fit matrix. This matrix uses a quantitative score of 0-100 to intuitively represent the comprehensive fit of the organic fertilizer on various typical soil types in terms of structural improvement, water regulation, and aeration enhancement, and provides preliminary suggestions for the optimal application ratio.

[0031] Step S4: Analyze the impact of environmental and fertilization rates based on the soil-fertilizer adaptation matrix to obtain the fertilization rate effect curve and environmental response adjustment factor; calculate the improvement index and predict the effect based on the environmental response adjustment factor and fertilization rate effect curve to obtain the expected soil improvement report.

[0032] In this embodiment of the invention, firstly, representative soil types are selected based on the compatibility score in the soil-fertilizer compatibility matrix, and different gradients of organic fertilizer application rates (calculated by weight ratio or tons / hectare) are designed. Combining soil bulk density and tillage depth, the increase in soil organic matter and changes in theoretical pore structure under different fertilization rates are predicted. The preliminary improvement effect is estimated based on the hydraulic characteristics of organic fertilizer, and a fertilization rate-effect curve is plotted to illustrate the relationship between fertilization rate and improvement effect. The economic benefits under different fertilization rates are then analyzed. Next, environmental data such as rainfall, temperature, and evaporation in typical areas are extracted from a meteorological database to establish a dynamic model of soil moisture-air-temperature. This model simulates changes in soil moisture and gas under different environmental conditions with different fertilization rates, quantifying the influence of environmental factors on the improvement effect and obtaining an environmental response moderating factor. Then, using the fertilization rate-effect curve (baseline effect) and the environmental response moderating factor, a multivariate regression equation system is established to calculate the predicted values ​​of soil moisture retention improvement rate, aeration improvement degree, and drought resistance enhancement index under different combinations of fertilization rates and environmental conditions. These values ​​are then verified and corrected using small-scale experimental data to obtain a calibrated set of improvement effect indicators. Finally, based on the set of improvement effect indicators, a visual chart showing the relationship between improvement effect and fertilizer application and environmental factors is drawn, an economic benefit analysis is conducted, the duration of improvement effect is estimated and a reapplication cycle is proposed, customized application suggestions are provided for specific soil problems and crop needs, and all analysis results are integrated into a graphic and textual expected report on soil improvement, providing scientific decision-making guidance for practical applications.

[0033] Preferably, step S1 specifically includes:

[0034] Step S11: Analyze the water-bound state of the organic fertilizer sample using a low-field nuclear magnetic resonance spectrometer to obtain the water-bound spectrum;

[0035] Step S12: Construct three-dimensional pores of the organic fertilizer sample based on the moisture binding spectrum to obtain a set of original pore images;

[0036] Step S13: Extract structural features from the original pore image set to obtain a pore parameter table;

[0037] Step S14: Combine the pore parameter table and the moisture binding spectrum to construct a spectral diagram, and obtain the organic fertilizer structure spectrum.

[0038] In this embodiment of the invention, approximately 1 gram of dried organic fertilizer sample was carefully placed into a 10 mm diameter glass sample tube specifically designed for nuclear magnetic resonance (NMR). The sample was gently compacted using a pressure rod to ensure uniform and appropriate packing height within the tube. The sample tube containing the sample was then placed into the probe of a 20 MHz low-field nuclear magnetic resonance (LF-NMR) spectrometer. T was set... The pulse sequence parameters required for relaxation time measurement, for example, setting the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence, echo time (τ) The parameters are set to 2 milliseconds, the echo number (NECH) to 1024, and the repetition time (TR) to 4 seconds to allow for full recovery of the proton longitudinal magnetization. Start the instrument and execute T... Relaxation measurements were performed to collect data on the decay of water proton signal intensity over time in the sample. The collected signal decay curves reflected the differences in the relaxation rate of water protons under different environments. The inverse Laplace transform (ILT) algorithm was used to analyze the collected T... The relaxation decay data is processed to decompose the complex decay curve into distribution spectra of different relaxation time components. Based on the tightness of binding between water molecules and the solid matrix in the organic fertilizer, the relaxation time distribution spectrum is divided into different regions. For example, signals with relaxation times less than 10 milliseconds are classified as strongly bound water, signals with relaxation times between 10 and 100 milliseconds are classified as weakly bound water, and signals with relaxation times greater than 100 milliseconds are classified as free water. The integral area of ​​the signal intensity within each relaxation time region is calculated to obtain the relative content or percentage of each bound water molecule. This data is then organized and visualized to generate a "water binding spectrum," which typically consists of one or more peaks. The position of the peak indicates the dominant relaxation time, and the area of ​​the peak indicates the relative abundance of the corresponding bound water molecules.

[0039] The organic fertilizer sample is prepared into a standard cylinder, for example, by compressing the sample into a cylindrical block with a diameter of 5 mm and a height of 5 mm using a mold. The prepared sample is fixed on the sample stage of a micro-computed tomography (Micro-CT) scanner. Scanning parameters are set, for example, the X-ray tube voltage is set to 80 kV, the current is set to 100 μA, and the spatial resolution is set to 5 μm to ensure that a detailed image of the pore structure is obtained. The scanning angle range is set to 0° to 360°, with a step angle of less than 1°, to acquire X-ray projection images of the sample at different angles. The Micro-CT instrument is started to scan, acquiring hundreds or thousands of two-dimensional projection images at different angles. Filtered back projection (FBP) or other 3D reconstruction algorithms, such as iterative reconstruction algorithms, are used to process the acquired two-dimensional projection images to reconstruct the three-dimensional grayscale image volume data of the organic fertilizer sample. In this grayscale image volume data, different gray values ​​represent density differences at different locations of the sample, thereby distinguishing the solid matrix and pore space. Based on the water binding spectrum data obtained in step S11, the pore size range for different bound water states is inferred (e.g., assuming micropores mainly contain strongly bound water, mesopores contain weakly bound water, and macropores contain free water). In the reconstructed 3D grayscale image volume data, preliminary image analysis techniques (such as setting grayscale threshold ranges) are used to attempt to initially label or distinguish pore regions corresponding to different bound water states, although this labeling is based on inference rather than direct measurement. The reconstructed 3D grayscale image volume data and the preliminary pore region labeling information are stored as a "raw pore image set," which serves as the basis for subsequent structural feature extraction.

[0040] Load the original pore image set (3D grayscale image volume data) obtained in step S12. An adaptive threshold segmentation algorithm is used to process the grayscale image. This algorithm automatically determines the threshold based on the grayscale distribution of local regions, converting the grayscale image into a binary image. One grayscale value (e.g., white) represents the pore space, and the other grayscale value (e.g., black) represents the solid matrix. A series of morphological operations are applied to the binary image, such as opening operations (erosion followed by dilation) to remove isolated noise points (e.g., scanning artifacts or tiny solid particles misidentified as pores), and closing operations (dilation followed by erosion) to fill tiny voids within the pores and smooth the pore boundaries. A connected component analysis algorithm is used to analyze the pore pixels or voxels in the binary image, identifying and labeling all interconnected pore regions, treating each independent pore or pore network as a connected component. For each identified connected component, its volume (by multiplying the number of voxels by the volume per unit voxel), surface area (by calculating the number of voxels at the boundary of the connected component or using a surface meshing method), and shape factor (such as sphericity and elongation, reflecting the geometry of the pores) are calculated. The size distribution of all identified pores is statistically analyzed. For example, pores are divided into different size ranges based on diameter or equivalent sphere diameter (e.g., micropores <0.1 μm, mesopores 0.1-10 μm, macropores >10 μm). The number of pores and the proportion of total pore volume to the overall pore volume in each size range are calculated to obtain pore size distribution data. The proportion of total pore volume to the total sample volume is calculated to obtain the total porosity. A skeleton extraction algorithm (such as a thinning algorithm) is applied to process the pore network, simplifying it into a one-dimensional skeleton line or graph structure. The connectivity of the skeleton (such as the number of nodes, the number of branches, and the Euler number) and the tortuosity of the channels (such as the ratio of the skeleton path length to the straight-line distance between the two endpoints) are analyzed to quantify the connectivity and complexity of the pore network. The quantitative parameters obtained from the above calculations, such as pore size distribution, total porosity, specific surface area, pore connectivity (e.g., connectivity index), and channel tortuosity, are compiled into a "pore parameter table".

[0041] A matrix is ​​established to correspond to water-bound states (from the water-bound spectrum) and pore characteristics (from the pore parameter table). This matrix, based on physical principles or empirical models, quantifies the relationship between different pore size ranges and connectivity characteristics and the capacity to accommodate water in different bound states. For example, an element in the matrix represents the contribution weight of a specific pore size range to strongly bound water. A weighted fusion algorithm is used to integrate the proportions of various types of bound water (strongly bound water, weakly bound water, and free water) in the water-bound spectrum with key indicators in the pore parameter table (such as the pore volume ratio and pore connectivity index for a specific pore size range) according to preset weighting coefficients. For example, a comprehensive index can be calculated to reflect the potential impact of large pores (affecting free water) and high connectivity on overall hydraulic behavior. A three-dimensional coordinate system is constructed, defining the X-axis as a quantitative indicator of the pore size distribution characteristics of the organic fertilizer (e.g., the proportion of pore volume within a specific pore size range or the weighted average pore size), the Y-axis as a quantitative indicator of the bound water ratio of the organic fertilizer (e.g., the total ratio of strongly bound water to weakly bound water), and the Z-axis as a quantitative indicator of the spatial connectivity of the organic fertilizer (e.g., the connectivity index of the pore network). The integrated data is mapped onto this three-dimensional coordinate system to form a feature point cloud representing the combination of the microstructure and moisture characteristics of the organic fertilizer. Using polynomial fitting or other curve fitting methods, the feature point cloud is fitted onto various two-dimensional projection planes or in three-dimensional space to generate feature curves or surfaces reflecting the relationship between pore size distribution, bound water ratio, and spatial connectivity. The area under these feature curves or the morphological parameters of the curves (e.g., slope, curvature) are calculated; these parameters further quantify the structural characteristics of the organic fertilizer. The final "organic fertilizer structure map" is a visual representation of quantitative data and their interrelationships that includes three key dimensions: pore size distribution, bound water ratio, and spatial connectivity. It comprehensively reflects the microstructural characteristics of organic fertilizer and its potential for water regulation.

[0042] Of particular importance is that the structural feature extraction in step S13 specifically involves:

[0043] The original pore image set was binarized to obtain binary pore images;

[0044] Morphological optimization is performed on the binary pore image to obtain the optimized pore image;

[0045] Connected component labeling is performed on the optimized pore image to obtain a labeled connected image;

[0046] Geometric parameters are calculated on the marked connected image to obtain the pore geometry table;

[0047] A pore size distribution table is obtained by statistically analyzing the pore geometry table.

[0048] Connectivity analysis was performed on the pore size distribution table and the marked connectivity image to obtain the pore parameter table;

[0049] In this embodiment of the invention, the three-dimensional grayscale image volume data of the organic fertilizer sample obtained in step S12 is loaded. This data consists of a series of voxels with different grayscale values, which reflect the density at different locations within the sample. Typically, the grayscale values ​​of solid skeleton voxels are higher, while the grayscale values ​​of pore space voxels (containing air or low-density filler) are lower. To distinguish between the solid skeleton and pore space, the grayscale image needs to be converted into a binary image. An adaptive thresholding segmentation algorithm is used for processing. This algorithm does not use a single global threshold, but instead determines the optimal segmentation threshold for each region based on the grayscale statistics of local image regions (such as local average grayscale, local standard deviation, or local histogram). For example, the three-dimensional image volume data can be divided into several small local regions (such as 32×32×32 voxel blocks), and a local threshold can be calculated within each region (e.g., using a local Otsu algorithm or an algorithm based on the local mean minus a constant). Then, the gray value of each voxel is compared with the threshold of its local region: voxels with gray values ​​below the threshold are marked as pores (e.g., assigned a value of 1), and voxels with gray values ​​above or equal to the threshold are marked as solids (e.g., assigned a value of 0). After processing, a three-dimensional binary image volume data is obtained, in which the voxel values ​​are either 0 or 1, clearly representing the distribution of pore space and solid skeleton. This data is the "binary pore image".

[0050] Three-dimensional morphological operations are applied to binary pore images to remove minor noise generated during scanning (such as isolated misclassified voxels) and smooth pore boundaries, making the pore morphology closer to reality. First, a three-dimensional opening operation (erosion followed by dilation) is performed. The erosion operation uses a three-dimensional structuring element (e.g., a 3×3×3 voxel cube) to slide across the binary image. If the structuring element is completely contained within the pore region, the central voxel remains a pore; otherwise, it becomes solid. This shrinks the pore region and eliminates isolated pore noise points smaller than the structuring element. A dilation operation is then performed using the same structuring element. If the structuring element overlaps with the pore region in any way, the central voxel becomes a pore. The dilation operation expands the pore region, restoring the pore size shrank after erosion, but does not restore noise points completely eliminated by erosion. Next, a three-dimensional closing operation (dilation followed by erosion) is performed to fill small voids inside the pores or connect broken pore channels, smoothing the pore boundaries. The optimized binary image volume data, with a more continuous and smooth pore region and significantly reduced noise, is the "optimized pore image."

[0051] A 3D connected component analysis algorithm is performed on the optimized pore image to identify and distinguish all interconnected pore regions in the image. Connectivity is defined based on the adjacency relationships between voxels, such as 26-connectivity (considering all neighboring voxels that share faces, edges, or vertices with the central voxel). The algorithm traverses all pore voxels in the image. When an unlabeled pore voxel is encountered, it is marked as a new connected component. Starting from this voxel, a breadth-first search or depth-first search is used to find all unlabeled pore voxels that are 26-connected to it and mark them as the same connected component. This process is repeated until all pore voxels are assigned a connected component label. Each identified independent connected pore region is assigned a unique integer label (e.g., starting from 1 and incrementing). The final result is a 3D image volume data, where solid voxels have a value of 0, and voxels belonging to different connected pore regions are assigned their corresponding unique label values. This data is the "labeled connected image".

[0052] Traverse the labeled connected graph and, for each unique porosity connected component label, calculate its corresponding geometric parameters. For the connected component labeled i: 1. Volume (V ): Statistics on this tag Number of voxels N Multiply by the unit voxel volume set for Micro-CT scanning. =N ×v_voxel. 2. Surface area (S) ): Estimate the surface area of ​​the connected component. One method is to count the number of components belonging to that label. In a set of voxels, how many voxels are adjacent to solid voxels (voxels labeled 0)? Multiply the number of adjacent voxel faces by the area of ​​a unit voxel face, a_face. ≈ Number of adjacent solid voxel faces × a_face. A more accurate method is to generate a surface mesh using the voxel data of this connected region (e.g., using the Marching Cubes algorithm), and then calculate the surface area of ​​the mesh. 3. Shape factor (F Shape factor (SEM) is an index that calculates the degree to which the shape of a pore deviates from a sphere. Commonly used shape factors include sphericity. The sphericity value is between 0 and 1, indicating a sphere. =1, the more irregular or flat and elongated the shape, the smaller the sphericity. Calculate the volume, surface area, and sphericity of each connected component and store this data in a table. Each row corresponds to a connected component label, and the columns contain its volume, surface area, sphericity, and other values. This table is called the "pore geometry table".

[0053] Based on the volume V in the pore geometry table Calculate the equivalent sphere diameter (ESD) for each pore connected domain. ), ESD =(6V / π) Define a series of pore size ranges (e.g., micropores: ESD < 1 μm; mesopores: 1 μm ≤ ESD < 10 μm; macropores: ESD ≥ 10 μm). Traverse the pore geometry table, based on the ESD of each pore. They are categorized into the corresponding pore size ranges. The number of pores within each pore size range is counted, along with the total volume of these pores (V = the volume of all pores in that range). (Sum). Calculate the percentage of the total pore volume within each pore size range to the total pore volume. Organize these statistical results, including the number of pores, total volume, and volume percentage for each size range, into a table or histogram. This data is the "pore size distribution table".

[0054] By combining pore size distribution tables and labeled connected images, parameters describing the overall connectivity of the pore network are calculated. First, the largest (or most voxel-rich) connected region is identified from the labeled connected image; this typically represents the most prevalent, interconnected pore network in the sample. The percentage of this largest connected region's volume relative to the total volume of all pores is calculated, serving as the pore network connectivity index. A higher index indicates a more continuous pore network and smoother transport of moisture and air. Furthermore, a 3D skeleton extraction algorithm can be applied to the optimized pore image, simplifying the pore network to a one-dimensional skeleton line. The topological structure of the skeleton can be analyzed, such as calculating the total length of the skeleton, the number of branch points, the number of loops, and the average tortuosity of the channels (the average ratio of the skeleton path length to the straight-line distance to its endpoints). The contents of the pore size distribution table (volume percentage of each size range), total porosity (the percentage of the total volume of all pores to the total volume of the sample, calculated by statistically analyzing the proportion of pore voxels to total voxels in the binary pore image), total specific surface area (the sum of the surface areas of all pores divided by the sample volume or mass), pore network connectivity index, and average tortuosity are integrated into a single table, which is the final "pore parameter table," comprehensively summarizing the microscopic pore structure characteristics of the organic fertilizer sample.

[0055] Of particular importance is the specific method for spectral synthesis construction in step S14:

[0056] A correlation matrix was constructed from the pore parameter table and the water binding spectrum to obtain the water-pore correlation matrix.

[0057] The parameters of the water hole correlation matrix are fused to obtain the parameter fusion table;

[0058] A coordinate system is established based on the parameter fusion table to obtain a three-dimensional coordinate frame.

[0059] Based on the three-dimensional coordinate frame, the parameter fusion table is mapped with data points to obtain the feature point set;

[0060] The feature point set is fitted with curves to generate a set of feature curves;

[0061] The spectral parameters of the feature point set are calculated to obtain the organic fertilizer structure spectrum;

[0062] In this embodiment of the invention, a correlation matrix is ​​constructed based on pore size distribution data (e.g., pore volume percentage for different pore size ranges such as <1μm, 1-10μm, and >10μm) from a pore parameter table and bound water ratio data (proportion of strongly bound water, weakly bound water, and free water) from a water binding spectrum. This matrix aims to quantify the potential physical correlation strength between different pore size ranges and different water binding states. A matrix A of dimension m×n is constructed, where m represents the number of pore size ranges (e.g., m=3) and n represents the number of water binding states (e.g., n=3). Matrix element A Indicates the first The pore size range (e.g., <1μm pores) is related to the first... The correlation weights or contribution levels of water binding states (e.g., strongly bound water). For example, based on capillary condensation and adsorption theories, micropores (<1 μm) are highly correlated with strongly bound water and some weakly bound water, mesopores (1-10 μm) are correlated with weakly bound water and some free water, and macropores (>10 μm) are mainly correlated with free water. These correlation weights (A... The value can be determined based on known physical models, empirical data, or through regression analysis on a series of standard samples. For example, A can be set... High value (micropores - strongly bound water), A High value (macropore-free water), A The median value is (mesopores - weakly bound water). This matrix A is the "water-pore correlation matrix," which establishes a quantitative relationship between pore structure and water binding characteristics.

[0063] Using the water-pore correlation matrix A, pore size distribution data from the pore parameter table, and bound water proportion data from the water binding spectrum, parameter fusion calculations are performed. The goal is to generate a quantitative index that better represents the overall hydraulic structure of organic fertilizer. For example, a weighted pore size distribution index X_struct is calculated, which comprehensively considers the contribution of different pore sizes to the overall structure: X_struct = ∑ (aperture range) Volume percentage × weight w ), weight w This reflects the importance of the orifice size to the overall hydraulic function. A weighted bound water proportion index, Y_water, is calculated, which comprehensively reflects the relative importance of water in different bound states: Y_water = ∑ (the proportion of water state j × weight v) ), weight v This reflects the degree to which the bound water state affects soil moisture retention or release. Furthermore, the spatial connectivity index Z_conn from the pore parameter table is directly used as the third key parameter. The calculated quantitative indicators such as X_struct, Y_water, and Z_conn, along with more detailed fusion indicators based on the water-pore correlation matrix (e.g., calculating "micropore volume percentage associated with strong bound water" = pore size <1μm volume percentage × A), are combined. +Volume percentage of pore size 1-10μm × A The data (including the combined parameters) are organized into a table, with each row representing the data for one organic fertilizer sample and the columns containing the combined parameter values. This table is called the "Parameter Fusion Table." These fused parameters represent a comprehensive quantitative description of the organic fertilizer's structure and moisture properties.

[0064] Based on the key fusion parameters in the parameter fusion table (e.g., X_struct represents the fusion index related to pore size distribution, Y_water represents the fusion index related to bound water ratio, and Z_conn represents the spatial connectivity index), a three-dimensional Cartesian coordinate system is established. The X-axis represents the "pore size distribution fusion index," the Y-axis represents the "bound water ratio fusion index," and the Z-axis represents the "spatial connectivity index." The numerical range of each axis is determined; for example, the original fusion parameter values ​​in the parameter fusion table can be standardized and scaled to the range of 0 to 100 for easier comparison and visualization. The standardization method can use linear scaling: parameter_value_standardized = (original value - minimum value) / (maximum value - minimum value) × 100. This three-dimensional coordinate system, along with its axis definitions and numerical ranges, constitutes a "three-dimensional coordinate framework," providing a spatial foundation for subsequent data visualization and spectral construction.

[0065] Each row of data in the parameter fusion table (corresponding to an organic fertilizer sample or its specific fusion feature) is mapped to an established three-dimensional coordinate frame. The three key fusion parameter values ​​(normalized X_struct, Y_water, Z_conn) of each row in the parameter fusion table constitute the (x, y, z) coordinates of a point in space. For example, if the normalized fusion parameters of an organic fertilizer sample are (85, 72, 91), then a point is plotted in the three-dimensional coordinate system with X-axis coordinates of 85, Y-axis coordinates of 72, and Z-axis coordinates of 91. The fusion parameter values ​​of all samples or features are converted into corresponding three-dimensional spatial points, which together form a "feature point set" describing the combined characteristics of the organic fertilizer's structure and moisture properties.

[0066] The feature point set is analyzed to reveal the relationship trends between the three-dimensional parameters. The three-dimensional point set can be projected onto three two-dimensional planes (XY plane, XZ plane, and YZ plane). Curve fitting is performed on the projected point set in each two-dimensional plane. For example, in the XY plane, a polynomial fitting is performed on the point set consisting of (X_struct, Y_water) (e.g., fitting a quadratic polynomial Y_water = aX_struct² + bX_struct + c using the least squares method), resulting in a feature curve reflecting the relationship between the pore size distribution fusion index and the bound water proportion fusion index. Similarly, a curve reflecting the relationship between X_struct and Z_conn is fitted in the XZ plane, and a curve reflecting the relationship between Y_water and Z_conn is fitted in the YZ plane. These three two-dimensional fitted curves, or their mathematical expressions (e.g., polynomial coefficients), together constitute a "feature curve set," which expresses the intrinsic connections between the different dimensional characteristics of the organic fertilizer sample in a simplified form.

[0067] A set of representative quantitative parameters is extracted from the feature point set and the feature curve set. These parameters together constitute the "organic fertilizer structure spectrum". These parameters may include: 1. Statistical parameters of the feature point set: for example, the centroid coordinates of the feature point set ( ), representing the center position of the organic fertilizer sample in the three-dimensional space of the structural spectrum; the standard deviation (σ) of the feature point set along each coordinate axis. ,σ ,σ 1. **Structure Index:** This reflects the variability or distribution range of the sample's structure and moisture properties. 2. **Characteristic Curve Parameters:** For example, the area under the XY plane fitting curve reflects the overall correlation strength between pore size distribution and bound water ratio; the average slope of the XZ plane fitting curve reflects the degree of influence of pore size structure changes on connectivity; the morphological characteristic parameters (such as peak and valley values) of the YZ plane fitting curve reflect the nonlinear relationship between bound water ratio and connectivity. 3. **Specific Spectral Indices:** For example, define a comprehensive structure index combining X_struct and Z_conn; define a comprehensive moisture index combining Y_water and water availability data (although this data comes from S2, it can be introduced during spectrum construction). The calculated centroid coordinates, standard deviation, area under the curve, average slope, and other quantitative parameters are combined to form a structured data table or vector. This data set is the "organic fertilizer structure spectrum" of the organic fertilizer sample. It is a high-dimensional quantitative representation that captures key information about the microstructure and water-binding characteristics of organic fertilizer, and can be used for subsequent soil matching and improvement effect prediction.

[0068] Preferably, the dynamic water exchange measurement in step S2 specifically includes:

[0069] Rainfall simulation and weight recording were performed on organic fertilizer samples to obtain rainfall and weight parameters;

[0070] Dynamic monitoring of the water absorption process of organic fertilizer samples was conducted based on rainfall and weight parameters to obtain dynamic water absorption data.

[0071] Temperature gradient water release analysis was performed on the water absorption dynamic data to obtain temperature water release data;

[0072] Based on temperature-induced water release data and water absorption dynamic data, a cyclic water absorption and release stability test was conducted to obtain cyclic stability data;

[0073] Moisture availability data is obtained by evaluating the moisture release data based on the humidity response curve set and the temperature moisture release data.

[0074] A dynamic graph of water exchange was constructed based on water availability data and cycle stability data.

[0075] In this embodiment of the invention, an organic fertilizer sample pretreated to an air-dried state (e.g., equilibrated for 24 hours at 25°C and 50% relative humidity) is placed in a cylindrical container with a filter at the bottom to allow free drainage. This container is then placed on a microgravity sensor (electronic balance) with an accuracy of 0.001 grams. A simulated rainfall device is connected, capable of precisely controlling rainfall intensity, for example, by adjusting the nozzle flow rate and height to achieve three different simulated rainfall intensities: 2 mm / h (light), 5 mm / h (moderate), and 10 mm / h (heavy). Before starting the simulated rainfall, the initial mass m of the sample is recorded. The simulated rainfall device is activated, a specific rainfall intensity is set, and timing begins. During the rainfall, the mass change m(t) of the sample is continuously recorded in real time using a microgravity sensor, for example, every 30 seconds. The duration Δt of the rainfall and the total rainfall V are also recorded. (V) =Rainfall intensity × Rainfall area × Δt). After the rainfall stops, the change in sample mass over time is recorded until drainage essentially stops. The recorded rainfall intensity, duration, total rainfall, and sample mass data corresponding to time are compiled into a dataset called "Rainfall Parameters and Weight Parameters".

[0076] Using a dataset of rainfall and weight parameters, calculate the water absorption of the sample at different time points t: Δm(t) = m(t) - m Plot the change in water absorption or water absorption rate (the percentage of water absorbed relative to the initial dry weight) over time. Analyze the shape of the water absorption curves under different rainfall intensities: for example, the initial water absorption rate. and reaching saturation water absorption capacity The required time. Saturation water absorption can be determined by the mass change when the water absorption curve reaches its plateau. Calculate the time required for a sample to reach its saturation water absorption (or a preset saturation percentage, such as 75%) under a specific rainfall intensity. These time series water absorption / absorption rate curves, initial water absorption rate, saturated water absorption, and time required to reach a specific saturation level constitute "water absorption dynamic data".

[0077] Take organic fertilizer samples that have reached saturation water absorption in simulated rainfall experiments (or prepare samples with the same high moisture content). Place the samples in an environmental chamber where temperature and humidity can be precisely controlled. Set different environmental temperature gradients, such as 15°C, 25°C, and 35°C, and maintain a constant relative humidity (e.g., 50%). Place the samples on microgravity sensors at each temperature environment (or remove the samples at different time points and weigh them using a balance). Record the change in sample mass over time. This change reflects the process of moisture loss from the sample due to evaporation and desorption. Mass changes were continuously monitored at each temperature condition until the sample mass stabilized or reached a preset low moisture content. The sample mass was calculated at different time points. Water release ,in This refers to the sample mass at the start of water release. The amount of water released or the remaining water content (the percentage of remaining water relative to the initial dry weight) is plotted as a curve over time. These water release time series curves obtained under different temperature conditions, along with the corresponding water release rates, constitute "temperature-based water release data."

[0078] Design a standard water absorption-release cycle process. One cycle includes an absorption phase (e.g., immersing the sample in water until saturation, or subjecting it to a simulated rainfall of standard intensity) and a release phase (e.g., air-drying at 25°C and 50% relative humidity to an initial air-dried state, or drying in a 60°C oven to constant weight). Repeat the water absorption-release cycle on the organic fertilizer sample multiple times, for example, performing 5 or 10 complete cycles. At the end of each cycle (water absorption saturation and water release completion), measure and record key hydraulic parameters. For example, record the maximum water holding capacity after each saturation absorption. And the minimum moisture content after each water release and drying. ,in Indicates the number of cycles. Based on water absorption dynamic data, compare the time required to reach saturation under different numbers of cycles. or initial water absorption rate Based on temperature-dependent water release data, the time required to release 50% of the water at the standard temperature (25℃) was compared for different cycles. Analyze the trends of these parameters with the number of cycles, and calculate the decay rate of the maximum water holding capacity (( ) / (×100%) or the rate of change of water absorption rate. These quantitative data reflecting the changes in the hydraulic properties of organic fertilizer during repeated wet-drying processes, such as the cyclic decay of maximum water holding capacity, changes in water absorption / release rates, and structural stability indicators (such as changes in dispersibility after soaking, although only hydraulic property data need to be considered here), constitute "cyclic stability data".

[0079] A set of humidity response curves was obtained using humidity gradient adsorption-desorption measurements. This set of curves reflects the water content that organic fertilizer can adsorb and retain at different equilibrium relative humidities, indirectly reflecting the water retention capacity of different pore sizes and binding sites. For example, the water absorption at high relative humidity mainly corresponds to capillary condensation and free water in macropores and mesopores, while the water absorption at low relative humidity mainly corresponds to adsorbed water and strongly bound water in micropores. Combined with temperature-induced water release data, the relationship between the dynamic process of water release from the sample at a specific temperature (e.g., 25°C) and the water content corresponding to different equilibrium points on the humidity response curve was analyzed. For example, the percentage of total absorbed water released during the water release process was calculated when the water content decreased to the equilibrium water content corresponding to 85% relative humidity; or the release rate was calculated at which water content range (corresponding to which humidity range on the humidity curve) the release rate changed significantly. This helps to distinguish between easily released "effective" water (such as free water in macropores, corresponding to the high humidity equilibrium point) and difficult-to-release "ineffective" or strongly bound water (such as adsorbed water in micropores, corresponding to the low humidity equilibrium point). The "availability" or "releaseability" of residual moisture in a sample is quantified at different temperatures and release times. These quantification results, such as the proportion of water released in 24 hours at a specific temperature relative to the total absorbed water, and the humidity range on the humidity curve corresponding to the residual moisture, constitute "moisture availability data".

[0080] This project integrates dynamic water absorption data, temperature-release water data (after assessing water availability), water availability data, and cycle stability data. A comprehensive visualization or quantitative model is constructed to represent the dynamic water exchange behavior of organic fertilizer. This can be a set of graphs containing multiple curves: for example, one set of graphs shows water absorption curves under different rainfall intensities; another set shows water release curves at different temperatures, with key equilibrium points on the humidity response curve overlaid as a reference; another set of graphs can show the changes in key hydraulic parameters (such as maximum water absorption, 24-hour water release ratio, and time required to reach semi-dryness) with the number of wet-dry cycles. Water availability data (such as the proportion of "available" water) and cycle stability data (such as the cycle decay rate of maximum water holding capacity) are labeled as additional information or parameters in the graphs. The resulting "dynamic water exchange graph" is a comprehensive data set and visualization that fully reflects the water absorption rate, water retention capacity, water release characteristics, water availability, and performance stability of organic fertilizer under simulated natural conditions, as well as repeated wet-dry cycles.

[0081] Preferably, the hydraulic parameter response analysis in step S2 specifically involves:

[0082] Humidity sensitivity analysis was performed based on the set of humidity response curves to obtain humidity sensitivity data;

[0083] Extract water absorption kinetic parameters from the moisture exchange dynamic diagram based on the humidity response curve set;

[0084] The water absorption efficiency is evaluated based on the water absorption kinetic parameters to obtain the water absorption efficiency index.

[0085] The water retention capacity was measured based on the dynamic diagram of water exchange to obtain water retention characteristic data;

[0086] The moisture buffering capacity is calculated based on the humidity response curve set and water retention characteristic data to obtain the moisture buffering index.

[0087] Structural adaptability assessment was performed on the humidity response curve set and the dynamic diagram of moisture exchange to obtain structural adaptability data;

[0088] By integrating humidity sensitivity data, water absorption efficiency index, moisture buffering index, and structural adaptation data, a set of hydraulic characteristic parameters is obtained;

[0089] The hydraulic response index is obtained by comprehensively constructing the response index based on the set of hydraulic characteristic parameters.

[0090] In this embodiment of the invention, a set of humidity response curves is used, which describes the equilibrium moisture content data points of organic fertilizer samples under a relative humidity gradient of 10% to 95%. The moisture absorption curves or desiccation curves are analyzed to calculate the rate of change of moisture content with relative humidity. The entire humidity range can be divided into several intervals (e.g., 10-30%, 30-60%, 60-95%), and the average humidity sensitivity S within each interval is calculated. =ΔMoisture content / Δrelative humidity Δ moisture content It is the change in equilibrium moisture content within this humidity range, Δrelative humidity. This refers to the width of the humidity range. Alternatively, the humidity response curve can be mathematically fitted, for example using the GAB (Guggenheim-Anderson-de Boer) model or a polynomial model. The derivative of the fitted function with respect to relative humidity, dW / dRH, can then be calculated. This derivative represents the humidity sensitivity at a specific relative humidity point or range. Humidity sensitivity data can be expressed as the average sensitivity value across different humidity ranges, or as a function or table describing how sensitivity changes with humidity.

[0091] While water absorption kinetic parameters are primarily obtained from the water absorption curves in the water exchange dynamics diagram, combining them with a set of humidity response curves helps to understand the driving forces behind the water absorption process. Key information is extracted from the water absorption dynamics data, particularly the curve of sample mass increasing over time (e.g., at a standard rainfall intensity of 5 mm / h). A kinetic model is then fitted to this curve to quantify the water absorption rate. Commonly used models include pseudo-first-order kinetic models. Or a pseudo-second-order dynamic model: ,in yes Water absorption at any given time (unit mass of water / unit mass of dry fertilizer). It is the equilibrium water absorption capacity (maximum water absorption capacity). and This is the adsorption rate constant. It is obtained by fitting the experimental data using linear or nonlinear regression methods. , or Parameters such as: Initial water absorption rate It can be estimated from the initial slope of the curve, or calculated through a model (e.g., a pseudo-first-order model v). pseudo-secondary model The parameters obtained from these fittings, along with the calculated initial water absorption rate, constitute the water absorption kinetic parameters.

[0092] Using extracted water absorption kinetic parameters and dynamic data, the ability of organic fertilizer to rapidly absorb water is evaluated. The time required to reach a certain water absorption ratio, such as the maximum water absorption capacity, is calculated. 50% ) or 75% (Time required) or The shorter the time, the higher the water absorption efficiency. The cumulative water absorption over a standard time period (e.g., within 10 minutes of the start of rainfall) or the average water absorption rate per unit time (e.g., within the first 5 minutes) can also be calculated. Comparing water absorption kinetic parameters under different rainfall intensities assesses the organic fertilizer's response to varying rainfall levels. Water absorption efficiency indicators can be one or more quantifiable values, such as those for standard rainfall intensities (5 mm / hour). The value, or the ratio of the initial water absorption rate to the maximum theoretical water absorption rate.

[0093] Utilize water release curve data from water exchange dynamics graphs. These curves depict the change in moisture content of organic fertilizer samples over time under simulated drying conditions (e.g., at different temperatures). Determine key water retention capacity indicators. For example, calculate the residual moisture content of the sample after simulated rainfall has ceased and it has been fully drained (e.g., 24 hours after drainage), which is analogous to the field capacity of soil. Calculate the moisture content retained by the sample after drying for a certain period (e.g., 48 hours) under standard drying conditions (e.g., 25°C, 50% relative humidity), reflecting its ability to resist evaporative water loss. Combine with a set of humidity response curves to determine the moisture content that organic fertilizer can retain at lower relative humidities (e.g., 10% or 20% relative humidity, corresponding to strongly bound water that is difficult for plants to absorb), reflecting its ability to retain non-available water, but a high total water retention can prolong drying time. Water retention characteristic data can include saturated moisture content after drainage, residual moisture content after standard drying, and equilibrium moisture content corresponding to the low relative humidity point.

[0094] By integrating humidity response curve sets and water retention characteristic data, the ability of organic fertilizers to maintain relatively stable moisture content under fluctuating water supply is assessed. Materials with strong moisture buffering capacity exhibit less drastic response to changes in environmental humidity or water supply. The area of ​​the hygroscopic-dehumidification hysteresis loop in the humidity response curve set is calculated. A larger hysteresis loop area indicates a greater difference in moisture content during the hygroscopic and dehumidification processes within the same humidity variation range. This is generally related to the material's ability to store and release moisture in wet-dry cycles and can be considered as an aspect of moisture buffering capacity. The morphology of the water release curves (from the water exchange dynamics diagram) is analyzed, particularly the slope within the moisture content range associated with plant available water. A gentler slope indicates a longer drying time or a greater humidity drop during the moisture content decrease, reflecting better moisture buffering capacity. The moisture buffering index can be based on the hysteresis loop area, the average slope of the water release curves within a key moisture content range (e.g., the range from moisture content after drainage to moisture content after standard drying), or a comprehensive score combining both.

[0095] Using cyclic stability data from a water exchange dynamic graph, combined with a set of humidity response curves, the stability of the water absorption, retention, and release characteristics of organic fertilizer during repeated hygroscopic and dehumidification cycles was evaluated. If the organic fertilizer structure undergoes significant degradation during the wet-dry cycle (e.g., pore collapse, particle disintegration), its hydraulic properties will change with the number of cycles. Key hydraulic parameters measured at different cycle numbers were compared, such as the percentage difference Δ between the maximum water absorption after the first cycle and the maximum water absorption after the tenth cycle. = ×100%. Calculate the rate of change of residual moisture content after standard drying, or the time required to reach semi-dryness, with the number of cycles. These rates of change or percentage decreases reflect the "water stability" of the organic fertilizer structure and its adaptability to changes in wet and dry conditions. Structural adaptability data can be one or more indicators, such as the rate of change of key hydraulic parameters after multiple cycles, or a structural stability score calculated based on these rates of change.

[0096] Collect all the quantitative hydraulic characteristic indicators obtained from the aforementioned calculations: humidity sensitivity data (e.g., sensitivity values ​​or average sensitivity in different humidity ranges), water absorption kinetic parameters (e.g., or ), water absorption efficiency index (e.g., This includes data on water retention characteristics (e.g., moisture content after drainage, moisture content after standard drying, and low RH moisture content), moisture buffering indices (e.g., hysteresis loop area, critical interval water release slope), and structural adaptation data (e.g., cyclic change rate of key parameters). These values ​​are integrated into a structured dataset, such as a vector or a row in a table, where each element or column represents a specific hydraulic characteristic parameter. This dataset constitutes the complete "hydraulic characteristic parameter set" for the organic fertilizer sample.

[0097] Based on various indicators in the hydraulic characteristic parameter set, a comprehensive "hydraulic response index" reflecting the overall hydraulic regulation capacity of organic fertilizer is constructed. The parameters in the hydraulic characteristic parameter set are weighted according to their importance in different agricultural application scenarios. For example, in sandy soil improvement requiring rapid infiltration and avoidance of surface runoff, water absorption efficiency and initial water absorption rate have higher weights; in arid regions requiring long-term water retention, water retention capacity and water buffering capacity have higher weights. Parameters are standardized (e.g., scaled to 0-100 points) to eliminate dimensional differences. Three core response indices are calculated: 1. Water absorption rate coefficient: calculated by weighted combination of initial water absorption rate, water absorption rate constant, ... 1. Calculation of relevant humidity sensitivity parameters. 2. Water retention capacity value: calculated by weighted combination of maximum water absorption, moisture content after drainage, moisture content after standard drying, and moisture content at low RH. 3. Water release regulation factor: calculated by weighted combination of water release curve slope, water buffer index (hysteresis loop area, water release slope in critical intervals), and structural adaptation data. The final "hydraulic response index" includes these three quantitative parameters, which comprehensively reflect the organic fertilizer's ability and characteristics in water absorption, retention, and release, providing input for subsequent soil matching analysis.

[0098] Preferably, the structural complementarity assessment in step S3 specifically includes:

[0099] A pore size difference analysis was performed on the organic fertilizer structure spectrum and the soil improvement demand spectrum to obtain a pore size complementary interval table.

[0100] The connectivity matching degree of the organic fertilizer structure spectrum and the soil improvement demand spectrum was calculated to obtain a connectivity matching degree table;

[0101] Based on the connectivity matching degree table and the aperture complementarity interval table, the aperture distribution after mixing is predicted, and an aperture improvement prediction table is obtained.

[0102] Based on the aperture improvement prediction table, the surface area change is estimated to obtain the surface property change table;

[0103] The complementarity score table is calculated based on the aperture complementarity interval table, connectivity matching degree table, and surface property variation table.

[0104] Soil compatibility was graded based on the complementarity score table to obtain the structural complementarity score table.

[0105] In this embodiment of the invention, pore size distribution data (e.g., volume percentage of micropores <1μm, mesopores 1-10μm, and macropores >10μm) from an organic fertilizer structure spectrum and pore size distribution data for different soil types and key pore size ranges requiring improvement from a soil amendment demand spectrum are loaded. For example, sandy soils typically lack micropores and have few mesopores, resulting in poor water demand; clay soils are typically rich in micropores but have poor macropores and connectivity, leading to poor aeration and permeability; loam soils have a relatively balanced structure but defects within a specific particle size range. For each target soil type, the pore size distribution of the organic fertilizer is compared with its corresponding soil pore size distribution. The pore size ranges in the organic fertilizer pore size distribution that can effectively fill or bridge the defects in the target soil pore structure are identified. For example, for sandy soils, micropores below 1μm and mesopores of 1-10μm in the organic fertilizer are considered complementary pore sizes because they can fill the macropores between sand grains, increasing water-holding space. For clay, macropores larger than 10 μm and mesopores of 1-10 μm in organic fertilizer are considered complementary pore sizes because they help form new macropore channels, improving aeration and water permeability. The percentage or absolute volume of pores in organic fertilizer within these complementary pore size ranges is quantified. These quantified results, such as "for sandy soil, the percentage of <10 μm pore volume in organic fertilizer is X%", and "for clay, the percentage of >1 μm pore volume in organic fertilizer is Y%", are compiled into a table listing different soil types, their corresponding complementary pore size ranges, and their contributions. This table is the "Complementary Pore Size Range Table".

[0106] This study utilizes spatial connectivity indices (e.g., maximum connected domain volume ratio, average tortuosity) from organic fertilizer structure maps and pore network connectivity characteristics and required connectivity aspects in different soil types from soil amendment demand maps (e.g., sandy soil has high connectivity but large channels, while clay soil has poor connectivity and tortuous channels). It assesses how the pore network of organic fertilizer interacts with the pore network of the soil, thereby affecting overall connectivity. Connectivity matching indices are calculated: for example, if organic fertilizer has a highly connected micro-mesopore network, this is beneficial for improving water-holding connectivity in sandy soil (preventing rapid water loss and forming capillary connections). If organic fertilizer has a highly connected macropore network, this is beneficial for improving aeration and drainage connectivity in clay soil. Matching calculations can consider the degree of fit between the organic fertilizer connectivity index and soil connectivity defects, as well as the possibility that the organic fertilizer particles, after being added to the soil, will form new connection channels with the existing soil pore network. For example, a matching score can be calculated to reflect how the pore connectivity characteristics of organic fertilizer (high or low, and which pore size ranges have strong connectivity) help solve the connectivity problems of the target soil. These matching scores or quantitative indicators can be compiled into a table listing different soil types and their corresponding organic fertilizer connectivity matching scores; this table is called the "connectivity matching table".

[0107] Based on the contribution of organic fertilizer to the pore size of different soils determined by the pore size complementarity interval table and the structural integration potential reflected by the connectivity matching degree table, the changes in pore size distribution after mixing organic fertilizer with target soil are predicted. A structural mixing model is established, which considers the processes of organic fertilizer particles dispersing into the soil matrix, filling the original pores of the soil, maintaining their own porosity, and forming new aggregates with soil particles. The model input includes the original pore size distribution of the soil, the pore size distribution of the organic fertilizer, a preset mixing ratio (e.g., adding one unit mass or volume of organic fertilizer to one unit mass or volume of soil), and an "integration efficiency" parameter affected by the connectivity matching degree. For example, the predicted percentage of pore volume in a newly formed (or improved) pore size range after mixing = the original pore volume percentage of that range in the soil + the percentage of pore volume of organic fertilizer in that range × the integration efficiency factor. The integration efficiency factor is determined based on the connectivity matching degree; the higher the matching degree, the closer the factor is to 1, indicating that the organic fertilizer pores can be more effectively integrated and improve the soil structure. Calculate the predicted total porosity after mixing, as well as the predicted volume percentage of different pore size ranges (micropores, mesopores, and macropores). Compile these predictions, such as "after mixing with sand, the volume of micropores and mesopores increases to X%", or "after mixing with clay, the volume of macropores increases to Y%, and the total porosity increases to Z%", into a table listing the predicted pore size distribution and total porosity after mixing organic fertilizer with different soil types. This table is the "Porosity Improvement Prediction Table".

[0108] The change in specific surface area of ​​the soil-organic fertilizer mixture was estimated using post-mixing pore size distribution data from the pore size improvement prediction table. The specific surface area of ​​soil and organic fertilizer is closely related to their pore size distribution, with micropores contributing the majority of the specific surface area. Based on the predicted pore volume ratios of different pore size ranges after mixing, and the average specific surface area data of soil and organic fertilizer within these pore size ranges (from the pore parameter table and soil database), the total specific surface area of ​​the mixture was estimated. For example, the total specific surface area ≈ ∑ (Volume percentage of pore size range i after mixing × specific surface area of ​​the material corresponding to that range). Consider the impact of agglomerate formation or destruction during mixing on the effective specific surface area, which is related to the predicted degree of structural improvement. Calculate the change or rate of change of the total specific surface area after mixing relative to the original specific surface area of ​​the soil. Organize these estimates, such as "specific surface area increases by A% after mixing with sand" or "specific surface area increases by B% after mixing with clay," into a table listing the predicted specific surface area and its changes after mixing organic fertilizer with different soil types. This table is the "Surface Property Change Table."

[0109] Quantitative data from the pore size complementarity interval table, connectivity matching degree table, and surface property change table are integrated to calculate the total structural complementarity score of organic fertilizer for each soil type. Each indicator is assigned a weight reflecting its importance in improving specific soil structure (weight values ​​can be derived from the key indicator weights set in the soil improvement demand spectrum). For example, for sandy soils, the complementarity of micro and mesopores has a high weight, as does the increase in specific surface area; for clay soils, the complementarity of macropores has a high weight, as does connectivity matching degree. The data for each indicator are standardized (e.g., scaled to 0-100 points) and then weighted and summed.

[0110] Complementarity score = w ×(Aperture Complement Standardized Score) + w ×(Standardized score of connectivity matching) + w ×(Standardized score of change in specific surface area) + ...

[0111] Where w ,w ,w These are the weights of the corresponding indicators, and ∑w =1. Calculate the complementarity score between each target soil type and the organic fertilizer. Compile these scores into a table, listing different soil types and their corresponding structural complementarity scores; this table is the "Complementarity Score Table".

[0112] Based on the values ​​in the complementarity score table, the structural complementarity of organic fertilizers for different soil types is graded or converted into a final score. For example, a matching level can be set according to the score range: 80-100 points for high matching, 60-79 points for medium matching, 40-59 points for low matching, and less than 40 points for no matching. Alternatively, the scores in the complementarity score table can be directly used as the final structural complementarity score. The grading results or final scores are then compiled into a table, typically listing soil types and their corresponding structural complementarity scores (e.g., 0-100 points). This table is the "structural complementarity score table," which quantifies the potential of organic fertilizers to improve the microstructure of different soil types.

[0113] Preferably, the hydraulic synergy calculation in step S3 specifically involves:

[0114] Infiltration characteristics matching analysis was performed on the hydraulic response index and the soil amendment demand spectrum to obtain infiltration coordination data;

[0115] A synergistic evaluation of water-holding characteristics was conducted on the hydraulic response index and the soil amendment demand spectrum to obtain water-holding gain data.

[0116] A synergistic analysis of water release regulation was conducted on the hydraulic response index and the soil amendment demand spectrum to obtain uniform water release data.

[0117] Hydraulic synergy index table is obtained by performing comprehensive hydraulic calculations on infiltration coordination data, water retention gain data, and water release uniformity data.

[0118] Structural-hydraulic linkage analysis was performed on the hydraulic synergy index table and the structural complementarity score table to obtain structural-hydraulic linkage data.

[0119] Environmental adaptability assessment was conducted based on structural hydraulic linkage data and hydraulic synergy index table to obtain environmental adaptability data;

[0120] A hydraulic synergy index map was constructed based on the hydraulic synergy index table, structural hydraulic linkage data, and environmental adaptability data.

[0121] In this embodiment of the invention, the water absorption rate coefficient in the hydraulic response index and the infiltration characteristics data of different soil types and the required infiltration rate in the soil amendment demand spectrum (e.g., sandy soil infiltration is too fast and needs to be slowed down, while clay soil infiltration is too slow and needs to be accelerated) are used to analyze whether the rapid water absorption capacity of organic fertilizer matches the soil's water absorption capacity and produces a synergistic effect. For example, for clay with a low infiltration rate, if the organic fertilizer has a high water absorption rate coefficient, it can quickly absorb water entering the soil surface, reduce surface runoff, and help guide water downwards, thereby accelerating the overall infiltration process and demonstrating a high degree of matching. For sandy soil with an extremely high infiltration rate, if the water absorption rate of the organic fertilizer is too high, water will quickly become saturated inside the organic fertilizer aggregates, while the soil matrix will still drain water rapidly, reducing the synergistic effect; in this case, a moderate or slightly slower water absorption rate is more suitable, helping to form a more stable distribution of water between the organic fertilizer and sand particles. A quantitative index, such as an "infiltration coordination score," is calculated, which is based on the degree of matching between the organic fertilizer water absorption rate coefficient and the target soil infiltration rate. For example, the score can be calculated using a function that takes the organic fertilizer water absorption rate coefficient and the target soil infiltration rate as inputs and outputs a score from 0 to 100. This score table is known as "infiltration coordination data," reflecting the synergistic potential of organic fertilizer in improving the infiltration performance of various soil types.

[0122] This study utilizes the water retention capacity and release regulation factors in the hydraulic response index, along with water holding characteristics and available water requirements for different soil types from the soil amendment demand spectrum (e.g., sandy soils have low total water holding capacity and require improvement, while clay soils have low available water and require increase). It assesses how the water retention capacity of organic fertilizer synergizes with soil water holding capacity to increase total water holding and optimize the proportion of available water. For sandy soils, the high water retention capacity of organic fertilizer significantly increases overall water holding, directly compensating for the deficiencies of sandy soils and demonstrating high synergy. For clay soils, although their total water holding capacity is not low, a large amount of water is trapped in micropores and ineffective. In this case, while the water retention capacity of organic fertilizer is important, its release regulation factors (especially whether it can release water within the available water range of plants) are more crucial. If organic fertilizer can effectively store water and release it under moderate humidity, it can increase the available water content of clay soil, demonstrating high synergy. A quantitative index, such as a "water holding gain score," is calculated, which comprehensively considers the theoretical contributions of the water retention capacity of organic fertilizer and its release regulation factors to the total water holding and available water content of different soil types. For example, the score can be calculated using a weighted average: Water-holding gain score = w_capacity × Standardized water-holding capacity value + w_water release × Standardized water release control factor, where the weight w is determined based on the soil type's requirements for total water holding capacity and available water. This score table is the "water-holding gain data," reflecting the synergistic potential of organic fertilizers in improving the water retention and effective utilization of various soil types.

[0123] This study utilizes water release regulators from the hydraulic response index and the water release characteristics and water supply stability requirements of different soil types within the soil amendment demand spectrum (e.g., sandy soils dry quickly, requiring extended water supply time, while clay soils release water slowly, requiring accelerated water release). It assesses how the water release pattern of organic fertilizer synergizes with the soil water release pattern to provide a more stable and plant-absorbable water supply. If the water release regulators of organic fertilizer indicate relatively slow and uniform water release (e.g., a flat water release curve and high water buffering capacity), this demonstrates high synergy for extending the effective water supply time in sandy soils. For clay soils, if organic fertilizer promotes the release of water trapped in micropores (e.g., by improving pore connectivity) or releases water within the plant's available water range, it helps improve the water release characteristics of clay soils, demonstrating synergy. A quantitative index, such as a "water release uniformity score," is calculated based on the degree of matching between the organic fertilizer water release regulators and the soil's demand for stable water supply. For example, the score can be evaluated based on the theoretical optimization effect of the water release regulators on the soil water release curve morphology. This scoring table, known as "water release uniformity data," reflects the synergistic potential of organic fertilizers in optimizing various soil moisture release patterns.

[0124] By integrating infiltration coordination data, water retention gain data, and water release uniformity data, a comprehensive "hydraulic synergy index" is calculated for each soil type. Based on the relative importance weights of infiltration, water retention, and water release characteristics for different soil types as defined in the soil amendment demand spectrum, the previously calculated scores are weighted and summed. For example, for sandy soil, water retention gain and water release uniformity have higher weights, while infiltration coordination has a relatively lower weight; for clay soil, infiltration coordination and water release uniformity have higher weights, while water retention gain (focusing on total amount) has a relatively lower weight. Each score is standardized to 0-100 points. The hydraulic synergy index I_synergy = w_infiltration × infiltration coordination score + w_water retention × water retention gain score + w_water release × water release uniformity score, where w_infiltration + w_water retention + w_water release = 1, and the weights are dynamically adjusted according to soil type. The calculated hydraulic synergy indices for each soil type are compiled into a table, which is the "hydraulic synergy index table".

[0125] It is recognized that the improvement of soil structure by organic fertilizers can, in turn, affect its hydraulic properties. Using a hydraulic synergy index table and a structural complementarity score table, the enhancing effect of structural improvement on hydraulic synergy is analyzed. For example, if organic fertilizer has a high structural complementarity score for a certain type of soil, it means that it can effectively increase macropores and improve connectivity, which will promote the movement and distribution of water in the soil, thereby enhancing the performance of the organic fertilizer's own hydraulic properties (such as water absorption and release) and improving the overall hydraulic synergy effect. A structure-hydraulic linkage model is established, which adjusts the hydraulic synergy index based on the structural complementarity score. For example, a "linkage gain factor" F_linkage = f(structural complementarity score) is calculated, where f can be a linear or nonlinear increasing function; the higher the structural complementarity score, the larger the linkage gain factor (F_linkage ≥ 1). The "linkage hydraulic score" I_linkage = hydraulic synergy index × F_linkage is calculated. Alternatively, a more refined model can predict the impact of structural improvement on specific hydraulic parameters (such as saturated hydraulic conductivity and effective water content range), and then recalculate the hydraulic synergy index. The calculated linkage gain factor or linkage hydraulic score data are compiled into a table, which is called "structural hydraulic linkage data".

[0126] Combining structural hydraulic linkage data (or directly using the hydraulic synergy index table, if the linkage analysis has been integrated), the hydraulic synergy index table, and the implicit or explicit environmental factors in the soil improvement demand spectrum regarding the requirements for improvement effects (e.g., stronger water retention and release capacity required in arid environments, and stronger infiltration and drainage capacity required in flood-prone environments), the hydraulic synergy effect of organic fertilizer under specific environmental conditions (such as simulated drought and simulated waterlogging) is evaluated. For example, for sandy soil used in arid regions, if the linked hydraulic score of organic fertilizer is high and its high score mainly comes from water retention and slow water release characteristics, its environmental adaptability score is high. For clay soil used in humid regions, if the linked hydraulic score of organic fertilizer is high and its high score mainly comes from improved infiltration and aeration characteristics, its environmental adaptability score is high. Introducing environmental pressure factors (such as drought index and waterlogging index), the performance of organic fertilizer hydraulic synergy under these environmental pressures is calculated to obtain an "environmental adaptability score." This score can be an adjustment to the linked hydraulic score or an independent indicator. These scores are compiled into a table, which constitutes the "environmental adaptability data."

[0127] By integrating the hydraulic synergy index table, structural hydraulic linkage data, and environmental adaptability data, a final "hydraulic synergy index map" is constructed. Depending on application requirements, the final hydraulic synergy index can be a simple hydraulic synergy index, a linked hydraulic score considering structural linkage, or a final score further incorporating environmental adaptability assessment. For example, the final hydraulic synergy index = linked hydraulic score × environmental adaptability correction factor. The final hydraulic synergy index (normalized to 0-100 points) corresponding to each soil type is compiled into a table. This data is visualized, for example, by creating a bar chart showing the final hydraulic synergy index of organic fertilizer on different soil types, or a radar chart showing the scores of organic fertilizer in multiple dimensions such as infiltration, water retention, water release, and environmental adaptability. This collection of quantitative scores and visual charts constitutes the "hydraulic synergy index map," which comprehensively reflects the synergistic potential of organic fertilizer in improving water properties under different soil types and environmental conditions.

[0128] Preferably, step S4 specifically includes:

[0129] Step S41: Design the fertilizer application gradient based on the soil-fertilizer adaptation matrix to obtain the fertilizer application effect curve;

[0130] Step S42: Analyze the impact of environmental factors based on the fertilizer application rate effect curve to obtain the environmental response adjustment factor;

[0131] Step S43: Calculate the improvement index based on the environmental response adjustment factor and the fertilizer application rate effect curve to obtain the improvement effect index set;

[0132] Step S44: Generate a soil improvement expectation report based on the improvement effect index set.

[0133] In this embodiment of the invention, a soil-fertilizer compatibility matrix is ​​loaded. This matrix quantifies the compatibility of organic fertilizer with different soil types and includes preliminary optimal application ratio recommendations for various soil types. Based on the scores of this compatibility matrix, representative target soil types are selected for analysis. For example, three soil types with organic fertilizer compatibility scores of 90 (high compatibility), 70 (medium compatibility), and 50 (low compatibility) are selected. A set of fertilizer application rate gradients is designed for each representative soil type. For example, the weight mixing ratio of organic fertilizer to soil is set to 1%, 3%, 5%, 8%, and 12%. Typical bulk density data for these representative soil types (e.g., sandy soil bulk density 1.6 g / cm³, clay soil bulk density 1.4 g / cm³, loam soil bulk density 1.3 g / cm³) and preset tillage layer depths (e.g., 0-20 cm) are obtained. The weight mixing ratios are converted into actual application rates per unit area (tons / hectare). The calculation method is as follows: Application rate (tons / hectare) = Mixing ratio (%) × Tillage depth (meters) × Soil bulk density (tons / cubic meter) × 10000 (square meters / hectare). For example, a 1% mixing ratio in sandy soil with a bulk density of 1.6 tons / cubic meter and a depth of 20 cm would result in an application rate of approximately 1% × 0.2 meters × 1.6 tons / cubic meter × 10000 square meters / hectare = 3.2 tons / hectare. Based on the optimal application ratio recommendations contained in the adaptation matrix, ensure that the designed fertilization gradient covers a value close to the recommended value; for example, if the recommended value is 5%, then a gradient of 5% becomes a critical node. Calculate the theoretical increase in organic matter content caused by adding organic fertilizer to the soil under different fertilization gradients, assuming the organic matter content of the organic fertilizer is known (e.g., 60% dry basis organic matter content). Based on a pore size improvement prediction model, this study extends the prediction to different fertilization rates to forecast the theoretical changes in soil pore structure (porosity and pore size distribution) under various fertilization rate gradients. For example, it predicts the trend of increasing total porosity after mixing with increasing fertilization rate. A relationship model is established between fertilization rate and the predicted degree of structural improvement (e.g., increase in total porosity, increase in the proportion of macropore volume), which typically shows a saturation trend. The predicted structural improvement rate is combined with the hydraulic characteristic parameters of organic fertilizer (derived from the hydraulic response index) to estimate the theoretical values ​​of soil moisture retention capacity and aeration improvement under different fertilization rates. These theoretical improvement effect values ​​(e.g., percentage increase in moisture retention rate) are plotted on the ordinate, and fertilization rate on the abscissa to create a "fertilization rate effect curve" for each representative soil type. This curve shows the trend of improvement effect with fertilization rate. Simultaneously, combining the cost of organic fertilizer and the expected economic benefits of improvement (e.g., estimating water-saving costs per unit increase in moisture retention), the economic benefits under different fertilization rates are analyzed to determine the optimal economic application rate range (usually the application rate range before the benefit growth tends to plateau).

[0134] The fertilizer application rate effect curve, which predicts the improvement effect under standard or hypothetical environmental conditions, is used. Environmental factors (such as rainfall patterns, temperature variations, and evaporation intensity) are introduced as moderating variables to analyze their impact on the predicted results of the fertilizer application rate effect curve. Long-term meteorological data under typical environmental conditions are extracted from meteorological databases, distinguishing between arid regions (annual rainfall <250 mm, high evaporation), semi-arid regions (annual rainfall 250-500 mm, evaporation > rainfall), and humid regions (annual rainfall >500 mm, evaporation < rainfall), and considering the temperature range and variation patterns of different seasons. A soil moisture-air-temperature dynamic model is established to simulate the moisture content, temperature, and gas exchange processes of different soil types (with different amounts of organic fertilizer applied) under different meteorological conditions. This model is used to simulate soils under different fertilizer application rate gradients, with different meteorological data sequences as input. The simulation results were analyzed to quantify the impact of different environmental factors (such as the intensity of a single heavy rainfall, the number of consecutive drought days, daily average temperature, daily temperature range, and potential evaporation) on the improvement effects of organic fertilizer (such as water retention duration and aeration recovery rate). For example, under high evaporation conditions, the actual effect of water retention improvement was lower than the predicted value under moderate evaporation conditions; under continuous rainy weather, the importance of aeration improvement became more prominent. These impacts were quantified as "environmental response adjustment factors," which can be multiplicative coefficients or additive correction terms used to adjust the predicted improvement effects under baseline (standard environment) conditions. For example, for the water retention improvement rate, the adjustment factor was less than 1 under high evaporation conditions, indicating that the actual effect was weakened; for the aeration improvement, the adjustment factor was less than 1 under high humidity conditions, indicating that the improvement effect was limited. These adjustment factors can be constructed into a table or a set of functions to describe the regulatory effects under different combinations of environmental parameters; this table or set of functions is the "environmental response adjustment factor."

[0135] Using environmental response modifiers and fertilizer application rate effect curves (representing the baseline effect under standard conditions), a set of regression equations for the improvement effect is established. This set of equations uses fertilizer application rate and key environmental parameters (or their corresponding modifiers) as independent variables, and the predicted improvement indicators (water retention improvement rate, aeration improvement degree, and drought resistance enhancement index) as dependent variables. The equations can be linear or nonlinear, for example: Improvement indicator = β +β ×Fertilizer application rate + β ×Environmental factors +β ×Environmental factors +...+ε, or including interaction terms and quadratic terms. First, using fertilization rate-effect curves or regression equations, calculate the baseline values ​​of water retention improvement rates under different fertilization rate gradients under standard environmental conditions. Organize these baseline values ​​into a "water baseline effect table". Then, apply environmental response modifiers to correct the water baseline effect table, predicting the water retention improvement rate under different combinations of environmental conditions (e.g., the performance of a specific fertilization rate in arid, humid, or different temperature ranges). Organize these corrected predictions into an "environmental correction effect matrix", where rows represent fertilization rate gradients, columns represent different environmental scenarios, and cell values ​​represent the predicted water retention improvement rate. Similarly, based on existing soil aeration data, organic fertilizer structural characteristics (from structural spectra), and fertilization rate (from structural change predictions from fertilization rate-effect curves), calculate the improvement in soil aeration under different fertilization rates (e.g., increase in saturated hydraulic conductivity, increase in gas diffusion coefficient, converted to aeration per unit time). The improvement in aeration also needs to be corrected based on environmental humidity conditions (from rainfall / evaporation-related parameters in the environmental response modifiers), as soil moisture is a key environmental factor affecting aeration. The corrected aeration improvement predictions are compiled into an "Aeration Improvement Table." Combining the water retention capacity and water release control factors in the hydraulic response index, and the plant available water range data in the soil amendment demand spectrum, the number of days the soil moisture content remains within the plant available water range under simulated no-rainfall conditions with different fertilization amounts is predicted. This is calculated by simulating the process of soil moisture decreasing over time through evaporation and plant absorption, and comparing the difference in the number of days the moisture content is below the permanent wilting point before and after adding organic fertilizer, resulting in a "Drought Resistance Enhancement Index." These indices are compiled into a "Drought Resistance Index Table." The prediction results from the environmental correction effect matrix, the aeration improvement table, and the drought resistance index table are compared and validated with small-scale controlled trials or existing field data. Based on the validation results, the regression equation, modifiers, or prediction models are optimized and corrected, for example, by adjusting the regression coefficients or introducing additional correction terms to improve prediction accuracy. The final set of verified and corrected prediction results, including values ​​such as water retention improvement rate, aeration improvement degree, and drought resistance enhancement index under different fertilization amounts and environmental conditions, constitutes the "improvement effect index set".

[0136] This study utilizes a set of soil improvement effect indicators, which contains quantitative predictions of the effects of organic fertilizer on various soil improvement indicators under different application rates and environmental conditions. Based on the data in this set, visualization charts are generated, such as a 3D surface plot showing the trend of water retention improvement rate with fertilizer application rate and a key environmental factor (e.g., annual rainfall); bar charts or line graphs are also created to compare the soil improvement effects of the organic fertilizer on different soil types under standard application rates. An economic benefit analysis is conducted by combining the economic input costs (organic fertilizer price, transportation, and application costs) and expected economic benefits (e.g., estimated yield increase and water and fertilizer cost reduction based on predicted improvement effects) under different application rate gradients, calculating the input-output ratio and payback period for different schemes. Based on the decomposition characteristics and structural stability data of the organic fertilizer (from cycle stability), the decay curve of the soil improvement effect over time is predicted, the duration of the improvement effect is estimated, and optimal reapplication cycles are recommended. For specific soil problems (such as desertification, salinization, and compaction) and the needs of local major crops, customized application recommendations are provided based on predicted improvement effects. These recommendations include the range of recommended fertilization amounts (considering a balance between economic benefits and improvement effects), the optimal application time (e.g., before sowing, during the crop growth period), and the application method (e.g., full-layer mixing, hole application). All the above analysis results, predicted data, charts, economic analysis, and recommendations are integrated into a clearly structured, richly illustrated document. This document, known as the "Soil Improvement Expected Report," provides farmers, agricultural enterprises, or soil remediation projects with scientific and quantitative decision-making support, guiding them to rationally select and apply organic fertilizers to achieve optimal soil improvement effects and economic benefits.

[0137] Preferably, the calculation of the improvement index in step S43 is specifically as follows:

[0138] A set of regression equations for the improvement effect was established based on the environmental response modifier and the fertilizer application rate effect curve.

[0139] Based on the regression equations for the improvement effect, the baseline value was calculated to obtain the moisture baseline effect table.

[0140] Based on the water baseline effect table and environmental response adjustment factors, environmental conditions are corrected to obtain the environmental correction effect matrix;

[0141] The ventilation improvement table was calculated based on the environmental correction effect matrix;

[0142] Based on the ventilation improvement table, drought resistance capacity was assessed to obtain the drought resistance capacity index table;

[0143] The effects of the environmental correction effect matrix, the ventilation improvement table, and the drought resistance index table were verified and corrected to obtain a set of improvement effect indicators.

[0144] In this embodiment of the invention, fertilizer application rate effect curve data (the relationship between fertilizer application rate gradient and predicted improvement effect under standard conditions) and environmental response regulation factor data obtained in step S42 (quantifying the regulatory effect of different environmental factors on the improvement effect) are used. A mathematical model is established, with fertilizer application rate and key environmental factors (or their quantitative representation, such as environmental factor scores or regulation factor values) as independent variables, and the predicted improvement indicators (water retention improvement rate, aeration improvement degree, and drought resistance enhancement index) as dependent variables. A multiple regression analysis method is used to fit regression equations based on simulated data (or combined with existing small-scale experimental data). For example, three regression equations are established for water retention improvement rate (ΔM), aeration improvement degree (ΔA), and drought resistance enhancement index (ΔD):

[0145] 1. ΔM=β +β ×Fertilizer application rate + β ×Environmental factors +β ×Environmental factors +β ×Fertilizer application rate ×Environmental factors +...

[0146] 2. ΔA = γ +γ ×Fertilizer application rate + γ ×Environmental factors +γ ×Environmental factors +γ ×Fertilizer application rate ×Environmental factors +...

[0147] 3. ΔD=δ +δ ×Fertilizer application rate + δ ×Environmental factors +δ ×Environmental factors +δ ×Fertilizer application rate ×Environmental factors +...

[0148] The fertilizer application rate is the amount applied per unit area (tons / hectare or weight percentage); environmental factors Environmental factors These are quantitative parameters derived from environmental response modulators, representing specific environmental conditions (e.g., annual rainfall, average temperature, evaporation intensity index); β ,γ ,δ These are regression coefficients, determined through fitting; the multiplication sign is indicated by "×". Environmental factors. Environmental factors Environmental factors such as humidity are the main environmental factors affecting ventilation; environmental factors Environmental factors The main environmental factors affecting drought resistance (such as the drought index) are the "regression equations for improvement effect".

[0149] Use the water retention improvement rate regression equation from the established set of regression equations for the improved effect. Define the environmental factor variables as values ​​representing standard or baseline environmental conditions (e.g., define environmental factors...). The average annual rainfall value for a certain region, environmental factors (Inputs include multi-year average temperature values, etc.). Input the fertilization gradient values ​​designed in step S41. Calculate the ΔM value predicted by the regression equation for the water retention improvement rate under these fertilization gradients. These calculation results represent the predicted improvement in soil water retention capacity brought about by different fertilization amounts under standard environmental conditions. Compile these fertilization gradients and their corresponding predicted water retention improvement rate ΔM values ​​into a table, which is the "Water Baseline Effect Table".

[0150] Using the "Water Baseline Effect Table" and the "Environmental Response Adjustment Factor," for each fertilization gradient, the actual water retention improvement rate is predicted to deviate from the baseline value under different specific environmental scenarios (e.g., high evaporation drought environment, low evaporation humid environment, different temperature ranges) based on the environmental response adjustment factor. The values ​​in the water baseline effect table are corrected using the environmental response adjustment factor. The correction method depends on the definition of the adjustment factor; for example, if the adjustment factor F_env is a multiplicative coefficient, then the corrected ΔM_correction = baseline ΔM × F_env; if it is an additive correction term A_env, then ΔM_correction = baseline ΔM + A_env. For the different environmental scenarios analyzed in step S42, the ΔM value corresponding to each fertilization amount in the water baseline effect table is corrected. The corrected predicted water retention improvement rate values ​​are organized into a table, where rows represent fertilization gradients, columns represent different environmental scenarios (or combinations of key environmental parameters), and the values ​​in the cells are the predicted percentage of water retention improvement rate. This table is the "Environmental Correction Effect Matrix."

[0151] The established aeration improvement regression equation and environmental correction effect matrix are used (although aeration calculations primarily rely on structural changes and humidity predictions, the environmental correction effect matrix provides humidity information under different environmental conditions). The fertilization gradient and values ​​of environmental factors related to aeration are input (e.g., air content at predicted average soil moisture or maximum water holding capacity under different environmental scenarios, which can be obtained from environmental response modifier analysis or separate soil moisture dynamics models). The aeration improvement ΔA is calculated using the aeration improvement regression equation under different fertilization rates and related environmental conditions. Aeration improvement is typically expressed as the increase in soil gas diffusion coefficient or the percentage increase in air content at a specific water potential, which can be converted to the volume of air passing through a unit area per unit time (cm³ / min). The calculated aeration improvement values ​​under different combinations of fertilization rates and environmental conditions are compiled into a table, which is the "Aeration Improvement Table".

[0152] The drought resistance enhancement index regression equation, along with an aeration improvement table (indirectly affecting root water uptake efficiency) and an environmental correction effect matrix (providing dynamic predictions of water resources under different environmental conditions), were used. Inputs included fertilization gradients and values ​​of environmental factors related to drought resistance (e.g., drought duration, evaporation intensity, and average temperature, derived from environmental response modifiers). The drought resistance enhancement index ΔD was calculated using the drought resistance enhancement index regression equation under different fertilization rates and drought-related environmental conditions. The drought resistance enhancement index represents the extension of the soil's effective water state for plants under conditions without effective rainfall after organic fertilizer application. The calculated drought resistance enhancement index values ​​under different combinations of fertilization rates and environmental conditions were compiled into a table, which is the "Drought Resistance Index Table".

[0153] All prediction results tables (environmental correction effect matrix, aeration improvement table, drought resistance index table) were compared with independent validation data. Validation data came from small-scale pot experiments or micro-plot field trials conducted on selected representative soil types with designed fertilization gradients. In these experiments, soil moisture retention curves, saturated hydraulic conductivity, gas diffusion coefficient, plant available water range, and plant growth and water use under simulated drought stress were measured after the addition of organic fertilizer, and the time it took for the soil to maintain adequate moisture was recorded. The measured improvement effect data were quantitatively compared with the predicted values ​​under corresponding fertilization rates and environmental conditions, and the prediction error (e.g., relative error, root mean square error) was calculated. If the prediction error exceeded a preset tolerance range (e.g., ±15%), the prediction model needed to be corrected. Correction may include adjusting the coefficients of the regression equation, reassessing the values ​​of environmental response moderating factors, or optimizing the underlying model driving the regression equation (e.g., pore size improvement model, hydraulic synergy model). The correction process was iterative until the error between the predicted and measured values ​​was reduced to an acceptable level. The final set of verified and corrected prediction results includes precise quantitative predictions of the organic fertilizer's effects on the moisture retention improvement rate, aeration improvement degree, and drought resistance enhancement index of various target soils under different fertilization amounts and key environmental conditions. These data together constitute the final output "improvement effect index set".

[0154] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0155] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for evaluating organic fertilizer, characterized in that, Includes the following steps: Step S1: Extract pore and moisture characteristics from the organic fertilizer sample to obtain the organic fertilizer structure spectrum; Step S2: Perform humidity gradient adsorption-desorption measurement on organic fertilizer samples to obtain a set of humidity response curves; perform dynamic water exchange measurement on organic fertilizer samples based on the humidity response curve set to obtain a dynamic water exchange graph; calculate humidity sensitivity based on the humidity response curves, extract water absorption kinetic parameters from the dynamic water exchange graph, including initial water absorption rate, saturated water absorption capacity, and rate constant; evaluate water absorption efficiency, which is the time required to reach 75% saturation; determine water retention capacity, including moisture content after drainage and residual moisture content after standard drying; calculate water buffering capacity, including hysteresis loop area and critical interval water release slope; evaluate structural adaptability, including the cycle change rate of key parameters; integrate quantitative indicators into a set of hydraulic characteristic parameters, and perform weighted combination of initial water absorption rate, water absorption rate constant, and the time required to reach 75% saturation. The water absorption rate coefficient was calculated using relevant humidity sensitivity parameters; the water retention capacity was calculated by weighted combination of maximum water absorption, water content after drainage, water content after standard drying, and water content at low RH; the water release control factor was calculated by weighted combination of water release curve slope, moisture buffer index, and structural adaptation data, where the moisture buffer index includes the hysteresis loop area and the water release slope in the critical interval; and a hydraulic response index was constructed based on the water absorption rate coefficient, water retention capacity, and water release control factor. Step S3: Obtain and extract soil amendment demand spectra from typical soil samples of different types; evaluate the structural complementarity of the soil amendment demand spectra and organic fertilizer structure spectra to obtain a structural complementarity scoring table; perform infiltration characteristic matching analysis on the hydraulic response index and soil amendment demand spectra to obtain infiltration coordination data; conduct synergistic evaluation of water retention characteristics on the hydraulic response index and soil amendment demand spectra to obtain water retention gain data; conduct synergistic analysis of water release regulation on the hydraulic response index and soil amendment demand spectra to obtain water release uniformity data; perform hydraulic synergistic comprehensive calculation on the infiltration coordination data, water retention gain data, and water release uniformity data to obtain a hydraulic synergistic index table; conduct structural-hydraulic linkage analysis on the hydraulic synergistic index table and the structural complementarity scoring table to obtain structural-hydraulic linkage data; wherein, the structural-hydraulic linkage analysis specifically includes: Establish a structure-hydraulic linkage model. This model adjusts the hydraulic synergy index based on the structural complementarity score and calculates a linkage gain factor F_linkage. The calculation formula is: F_linkage = f(structural complementarity score). The function f is a linear or nonlinear increasing function. The higher the structural complementarity score, the larger the linkage gain factor. The linkage gain factor is not less than 1. Organize the calculated linkage gain factor or linkage hydraulic score data into structural-hydraulic linkage data. By integrating the hydraulic synergy index table, structural hydraulic linkage data, and environmental adaptability data, a final hydraulic synergy index map is constructed; based on the hydraulic synergy index map and the structural complementarity scoring table, a soil-fertilizer compatibility matrix is ​​constructed. Step S4: Analyze the impact of environmental factors and fertilization rate based on the soil-fertilizer adaptation matrix to obtain the fertilization rate effect curve and environmental response adjustment factor. Using fertilization rate and key environmental parameters or their corresponding adjustment factors as independent variables, and the predicted improvement index as the dependent variable, calculate the baseline values ​​of water retention improvement rate under different fertilization rate gradients under standard environmental conditions using the fertilization rate effect curve. Organize the baseline values ​​of water retention improvement rate into a water baseline effect table. Apply the environmental response adjustment factor to correct the water baseline effect table, predict the corresponding water retention improvement rate under different environmental conditions, and organize the corrected predicted values ​​into an environmental correction effect matrix. In this matrix, rows represent fertilization rate gradients, columns represent different environmental scenarios, and cell values ​​represent the predicted water retention improvement rate. Calculate the aeration improvement table based on the environmental correction effect matrix. Based on the aeration improvement table, drought resistance capacity is assessed to obtain the drought resistance capacity index table; the effects of the environmental correction effect matrix, the aeration improvement table, and the drought resistance capacity index table are verified and corrected to obtain the improvement effect index set; a soil improvement expectation report is generated based on the improvement effect index set. The fertilizer application rate effect curve and environmental response regulation factor obtained are as follows: Based on the pre-set pore size improvement prediction model, the theoretical change range of soil pore structure under each fertilization gradient is predicted, a relationship model between fertilization amount and predicted structural improvement degree is established, the predicted structural improvement range is combined with the hydraulic characteristic parameters of organic fertilizer, the theoretical values ​​of soil moisture retention capacity and aeration improvement under different fertilization amounts are estimated, and the fertilization amount effect curve is plotted. Environmental factors were introduced as moderating variables to analyze their impact on the prediction results of the fertilizer application effect curve. A dynamic model of soil moisture-air-temperature was established and used to simulate soils under different fertilizer application gradients. Different meteorological data sequences were input, and the simulation results were analyzed to quantify the degree of influence of different environmental factors on the improvement effect of organic fertilizer. The degree of influence was quantified into an environmental response moderating factor.

2. The organic fertilizer evaluation method according to claim 1, characterized in that, Step S1 is as follows: Step S11: Analyze the water-bound state of the organic fertilizer sample using a low-field nuclear magnetic resonance spectrometer to obtain the water-bound spectrum; Step S12: Construct three-dimensional pores of the organic fertilizer sample based on the moisture binding spectrum to obtain a set of original pore images; Step S13: Extract structural features from the original pore image set to obtain a pore parameter table; Step S14: Combine the pore parameter table and the moisture binding spectrum to construct a spectral diagram, and obtain the organic fertilizer structure spectrum.

3. The organic fertilizer evaluation method according to claim 1, characterized in that, The dynamic water exchange determination in step S2 is specifically as follows: Rainfall simulation and weight recording were performed on organic fertilizer samples to obtain rainfall and weight parameters; Dynamic monitoring of the water absorption process of organic fertilizer samples was conducted based on rainfall and weight parameters to obtain dynamic water absorption data. Temperature gradient water release analysis was performed on the water absorption dynamic data to obtain temperature water release data; Based on temperature-induced water release data and water absorption dynamic data, a cyclic water absorption and release stability test was conducted to obtain cyclic stability data; Moisture availability data is obtained by evaluating the moisture release data based on the humidity response curve set and the temperature moisture release data. A dynamic graph of water exchange was constructed based on water availability data and cycle stability data.

4. The organic fertilizer evaluation method according to claim 1, characterized in that, The structural complementarity assessment in step S3 specifically involves: A pore size difference analysis was performed on the organic fertilizer structure spectrum and the soil improvement demand spectrum to obtain a pore size complementary interval table. The connectivity matching degree of the organic fertilizer structure spectrum and the soil improvement demand spectrum was calculated to obtain a connectivity matching degree table; Based on the connectivity matching degree table and the aperture complementarity interval table, the aperture distribution after mixing is predicted, and an aperture improvement prediction table is obtained. Based on the aperture improvement prediction table, the surface area change is estimated to obtain the surface property change table; The complementarity score table is calculated based on the aperture complementarity interval table, connectivity matching degree table, and surface property variation table. Soil compatibility was graded based on the complementarity score table to obtain the structural complementarity score table.

5. An organic fertilizer evaluation system, characterized in that, For performing the organic fertilizer evaluation method as described in claim 1, the organic fertilizer evaluation system comprises: The microstructure characterization module is used to extract pore and moisture characteristics from organic fertilizer samples to obtain organic fertilizer structure maps. The hydraulic characteristic assessment module is used to measure the humidity gradient adsorption and desorption of organic fertilizer samples to obtain a set of humidity response curves; to measure the dynamic water exchange of organic fertilizer samples based on the set of humidity response curves to obtain a dynamic water exchange graph; and to analyze the hydraulic parameter response based on the set of humidity response curves and the dynamic water exchange graph to obtain the hydraulic response index. The soil compatibility analysis module is used to acquire and extract soil improvement demand spectra from typical soil samples of different types; to evaluate the structural complementarity of the soil improvement demand spectra and organic fertilizer structure spectra, and to obtain a structural complementarity scoring table; to calculate hydraulic synergy based on the soil improvement demand spectra, the structural complementarity scoring table and the hydraulic response index, and to obtain a hydraulic synergy index map; and to construct a soil-fertilizer compatibility matrix based on the hydraulic synergy index map and the structural complementarity scoring table. The soil improvement benefit prediction module is used to analyze the impact of environmental and fertilization amounts based on the soil-fertilizer adaptation matrix, and obtain the fertilization amount effect curve and environmental response adjustment factor. Based on the environmental response adjustment factor and the fertilization amount effect curve, the module calculates the improvement index and predicts the effect, and obtains the expected soil improvement report.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the organic fertilizer evaluation method as described in any one of claims 1 to 4.

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