Organic fertilizer evaluation method and system and storage medium

Abstract

The invention relates to the technical field of agricultural organic fertilizer evaluation, in particular to an organic fertilizer evaluation method and system and a storage medium. The method comprises the following steps: extracting pore and moisture characteristics of an organic fertilizer sample to obtain an organic fertilizer structure spectrogram; carrying out humidity gradient adsorption and desorption determination on the organic fertilizer sample to obtain a humidity response curve set; performing dynamic moisture exchange measurement on the organic fertilizer sample according to the humidity response curve set to obtain a moisture exchange dynamic graph; performing hydraulic parameter response analysis based on the humidity response curve set and the moisture exchange dynamic graph to obtain a hydraulic response index; obtaining and extracting soil improvement demand spectrums from different types of typical soil samples; and performing structural complementarity evaluation on the soil improvement demand spectrum and the organic fertilizer structure spectrum to obtain a structural complementarity score table. According to the method, the pertinence of soil improvement and the resource utilization efficiency are remarkably improved by analyzing accurate matching of the organic fertilizer and different soil types and quantitative improvement effect prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural organic fertilizer evaluation, and in particular to an organic fertilizer evaluation method, system and storage medium. Background Art

[0002] The existing organic fertilizer evaluation system mainly relies on macroscopic physical and chemical indicators and field test results, ignoring the key microscopic mechanisms of organic fertilizer's impact on soil structure and water dynamics, and is unable to reveal the essential process of organic fertilizer's soil improvement. At the same time, due to the lack of systematic analysis of the microstructure and hydraulic properties of organic fertilizer, existing methods are difficult to accurately predict the specific effects of organic fertilizer in different soil matrices (such as sand, clay, loam, etc.), resulting in unstable or lower-than-expected improvement effects in actual applications. In addition, insufficient predictive capabilities prevent farmers and soil remediation projects from accurately selecting the most suitable organic fertilizer products based on specific soil problems. They can only rely on experience or simple experiments to make choices, which reduces the targetedness and efficiency of soil improvement and increases resource waste and economic costs.

[0003] In summary, existing technologies have problems such as lack of micro-mechanism analysis, inaccurate prediction of improvement effects, and inaccurate soil-fertilizer matching guidance that need to be addressed urgently. Summary of the Invention

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

[0005] To achieve the above object, a method for evaluating organic fertilizer comprises the following steps: Step S1: extracting pore and water characteristics of the organic fertilizer sample to obtain an organic fertilizer structure spectrum; Step S2: performing a humidity gradient adsorption / desorption measurement on the organic fertilizer sample to obtain a humidity response curve set; performing a dynamic water exchange measurement on the organic fertilizer sample according to the humidity response curve set to obtain a water exchange dynamic graph; performing a hydraulic parameter response analysis based on the humidity response curve set and the water exchange dynamic graph to obtain a hydraulic response index; Step S3: obtaining and extracting soil improvement demand spectra from typical soil samples of different types; evaluating the structural complementarity of the soil improvement demand spectra and the organic fertilizer structure spectrum to obtain a structural complementarity score table; calculating hydraulic synergy based on the soil improvement demand spectra, the structural complementarity score table, and the hydraulic response index to obtain a hydraulic synergy index diagram; and constructing a soil-fertilizer adaptation matrix based on the hydraulic synergy index diagram and the structural complementarity score table; Step S4: Analyze the impact of environment and fertilizer application amount based on the soil-fertilizer adaptation matrix to obtain the fertilizer application amount effect curve and the environmental response adjustment factor; calculate the improvement index and predict the effect based on the environmental response adjustment factor and the fertilizer application amount effect curve to obtain the soil improvement expectation report.

[0006] This study uses low-field nuclear magnetic resonance (NMR) and microcomputed tomography (MCU) techniques to quantitatively characterize organic fertilizer pore structure parameters (pore size distribution, porosity, and connectivity) and their interaction with water (proportions of different bound water states) at the microscopic level. This systematic microscopic characterization transcends the limitations of traditional physicochemical indices, constructing a unique "structural hydraulic fingerprint" of organic fertilizer, providing fundamental data for a deeper understanding of the physical mechanisms by which it improves soil quality. Obtaining a structural spectrum of organic fertilizer allows assessment of its inherent properties beyond superficial analysis to gain insight into its inherent potential to influence soil moisture and aeration. Dynamic vapor sorption and simulated rainfall / drying experiments quantify the organic fertilizer's water absorption rate, water retention capacity, water release regulation characteristics, and stability during wet-dry cycles under non-equilibrium conditions. These dynamic hydraulic property data directly reflect the actual hydraulic behavior of organic fertilizer under simulated field conditions, complementing the shortcomings of traditional static physicochemical indices such as total water content. By analyzing hydraulic parameter responses, the complex dynamic behavior is refined into a quantitative hydraulic response index, providing a key, dynamic performance indicator for subsequent matching analysis with soil properties, making the evaluation of the hydraulic function of organic fertilizers more accurate and comprehensive. The structural and hydraulic properties of organic fertilizers are systematically matched to the structural defects and water requirements of different soil types. Through pore size difference analysis and connectivity matching calculation, the complementary improvement potential of organic fertilizers on soil structure is quantified, generating a structural complementarity score table. Through infiltration, water retention, and water release synergy analysis, the synergistic regulation ability of organic fertilizers with soil water properties is quantified, generating a hydraulic synergy index diagram that takes into account the enhancing effect of structural improvements on hydraulic synergy. The resulting soil-fertilizer compatibility matrix intuitively expresses the compatibility of organic fertilizers for various soil types through quantitative scores, overcoming the "one-size-fits-all" drawbacks of traditional evaluation methods. It enables precise matching guidance between organic fertilizers and target soils, significantly improving the targetedness and effectiveness of soil improvement programs. The soil-fertilizer compatibility is then converted into a prediction of specific improvement effects under different fertilizer application rates and environmental conditions. By designing a fertilizer application gradient and analyzing the impact of environmental factors, a quantitative relationship model (a set of regression equations) was established between fertilizer application rate, environmental factors, and improvement effects. This model can predict the specific improvements in soil moisture retention, aeration, and drought resistance achieved by organic fertilizers under different application scenarios (different fertilizer application rates and different regional climates). The resulting soil improvement forecast report not only provides quantitative predictions and optimal application rate recommendations, but also includes economic benefit analysis and long-term maintenance plans. This provides a scientific and reliable basis for decision-making for farmers and soil remediation projects, avoids blind fertilization and wasteful resources, and improves the efficiency and economic benefits of agricultural production and soil remediation.

[0007] Therefore, the present invention provides an organic fertilizer evaluation method. By establishing a systematic evaluation process of "microstructure characterization-hydraulic property evaluation-soil adaptation analysis-improvement benefit prediction", low-field nuclear magnetic resonance and micro CT scanning and other technologies are used to obtain the microstructure characteristics and hydraulic dynamic characteristics of organic fertilizer, and construct the "hydraulic fingerprint" of organic fertilizer. Through the principles of structural complementarity and hydraulic synergy, the precise matching of organic fertilizer with different soil types and quantitative improvement effect prediction are achieved, providing a scientific decision-making basis for farmers and soil remediation projects, and significantly improving the targeted nature of soil improvement and resource utilization efficiency.

[0008] Preferably, the present invention also provides an organic fertilizer evaluation system for executing the organic fertilizer evaluation method described above, the organic fertilizer evaluation system comprising: The microstructure characterization module is used to extract the pore and moisture characteristics of organic fertilizer samples and obtain the organic fertilizer structure spectrum; The hydraulic characteristics evaluation module is used to perform moisture gradient adsorption and desorption measurements on organic fertilizer samples to obtain a moisture response curve set; perform dynamic moisture exchange measurements on organic fertilizer samples based on the moisture response curve set to obtain a moisture exchange dynamic graph; and perform hydraulic parameter response analysis based on the moisture response curve set and the moisture exchange dynamic graph to obtain a hydraulic response index. The soil adaptation analysis module is used to obtain and extract soil improvement demand spectra from typical soil samples of different types; evaluate the structural complementarity of the soil improvement demand spectra and the organic fertilizer structure spectrum to obtain a structural complementarity score table; calculate hydraulic synergy based on the soil improvement demand spectrum, structural complementarity score table, and hydraulic response index to obtain a hydraulic synergy index diagram; and construct a soil-fertilizer adaptation matrix based on the hydraulic synergy index diagram and structural complementarity score table; The improvement benefit prediction module is used to analyze the impact of environment and fertilizer amount based on the soil-fertilizer adaptation matrix, and obtain the fertilizer amount effect curve and environmental response adjustment factor; based on the environmental response adjustment factor and fertilizer amount effect curve, the improvement index calculation and effect prediction are carried out to obtain the soil improvement expectation report.

[0009] This organic fertilizer evaluation system integrates and automates core evaluation processes, including organic fertilizer microstructure characterization, hydraulic property assessment, soil adaptation analysis, and improvement benefit prediction. This system not only achieves scientific quantification of the entire process, from organic fertilizer characteristics to soil improvement effect prediction, but also overcomes the limitations of traditional methods that rely on experience and macro-indicators, significantly improving the efficiency and accuracy of evaluation. The system provides quantitative results such as the organic fertilizer structure spectrum, hydraulic response index, and soil-fertilizer adaptation matrix, providing a scientific basis for accurately identifying the advantages and disadvantages of organic fertilizers. In particular, the soil adaptation analysis module can evaluate structural complementarity and hydraulic synergy based on the characteristics of organic fertilizers and the needs of different soils, achieving a precise match between organic fertilizers and target soils and avoiding blind application. The improvement benefit prediction module further combines fertilizer application rate and environmental factors to predict specific improvement effects, providing optimized application recommendations and economic benefit analysis, providing reliable decision-making support for farmers and soil remediation projects. Ultimately, this helps improve the targetedness and efficiency of soil improvement, reducing resource waste and economic costs.

[0010] Preferably, a computer-readable storage medium stores a computer program, and when the computer program is executed, the organic fertilizer evaluation method as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Schematic diagram of the steps of an organic fertilizer evaluation method.

[0012] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0014] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0015] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0016] In the embodiment of the present invention, reference Figure 1 FIG. 5 is a flow chart showing the steps of the organic fertilizer evaluation method of the present invention. In this example, the organic fertilizer evaluation method comprises the following steps: Step S1: extracting pore and water characteristics of the organic fertilizer sample to obtain an organic fertilizer structure spectrum; In an embodiment of the present invention, the relaxation time distribution of water molecules in different binding states in an organic fertilizer sample is measured by a low-field nuclear magnetic resonance instrument, the ratio of strongly bound water, weakly bound water and free water is distinguished and quantified, and a water binding spectrum is generated. Subsequently, a micro-computed tomography scanner is used to perform high-resolution three-dimensional scanning imaging of the organic fertilizer sample to obtain a set of original grayscale images of its internal pore structure. The grayscale image is subjected to adaptive threshold binarization processing to distinguish between the solid matrix and the pore space, and then a morphological operation is performed to remove noise and optimize the pore boundary. The binary pore image is subjected to a three-dimensional connected domain analysis to identify each independent pore area and calculate its geometric parameters such as volume, surface area, shape factor, and the number and volume distribution of pores of different sizes. The connectivity of the pore network is analyzed to obtain a pore parameter table. Finally, a correlation matrix between water binding state and pore characteristics was established, and a weighted fusion algorithm was used to integrate the data of the water binding spectrum and pore parameter table to construct a three-dimensional coordinate system (pore size distribution rate, bound water ratio, spatial connectivity). The fused data was mapped into a set of characteristic points, and polynomial fitting was performed to generate a set of characteristic curves. The statistical parameters of the characteristic point sets and curves were calculated, and finally a structural spectrum of organic fertilizer was formed to quantitatively describe the microstructure and water binding characteristics of organic fertilizer.

[0017] Step S2: performing a humidity gradient adsorption / desorption measurement on the organic fertilizer sample to obtain a humidity response curve set; performing a dynamic water exchange measurement on the organic fertilizer sample according to the humidity response curve set to obtain a water exchange dynamic graph; performing a hydraulic parameter response analysis based on the humidity response curve set and the water exchange dynamic graph to obtain a hydraulic response index; In this embodiment of the present invention, first, an absolute dry organic fertilizer sample is placed in a dynamic vapor sorption instrument. The equilibrium moisture content and time required to reach equilibrium are measured at each humidity point under a relative humidity gradient of 10%-95%. Moisture absorption and dehumidification isotherms and hysteresis loops are plotted to generate a set of humidity response curves. Next, the air-dried organic fertilizer sample is placed on a microgravity sensor to simulate rainfall processes of varying intensities (2, 5, and 10 mm / hour). The sample's mass change is recorded in real time to monitor water absorption dynamics. After the rainfall ceases, the high-moisture content sample is transferred to different temperatures (15, 25, and 35°C). The mass change is continuously monitored, the water release dynamics are measured, and a dry-wet cyclic stability test is conducted to evaluate the changes in key hydraulic parameters with the number of cycles. The effectiveness of the released water is evaluated in conjunction with the humidity response curve set. Finally, a water exchange dynamics diagram is constructed, containing information on water absorption, water release, and cyclic stability. Finally, the humidity sensitivity was calculated based on the humidity response curve, and the water absorption kinetic parameters (such as initial water absorption rate, saturated water absorption capacity, and rate constant) were extracted from the moisture exchange dynamic diagram. The water absorption efficiency (such as the time required to reach 75% saturation) was evaluated, and the water retention capacity (such as moisture content after drainage and residual moisture content after standard drying) was measured. The moisture buffering capacity (such as the hysteresis loop area and the water release slope in the critical interval) was calculated, and the structural adaptability (such as the cyclic change rate of key parameters) was evaluated. These quantitative indicators were integrated into a hydraulic characteristic parameter set, and standardized weighted calculations were performed according to preset weights to construct a comprehensive hydraulic response index consisting of three quantitative parameters: water absorption rate coefficient, water retention capacity value, and water release control factor.

[0018] Step S3: obtaining and extracting soil improvement demand spectra from typical soil samples of different types; evaluating the structural complementarity of the soil improvement demand spectra and the organic fertilizer structure spectrum to obtain a structural complementarity score table; calculating hydraulic synergy based on the soil improvement demand spectra, the structural complementarity score table, and the hydraulic response index to obtain a hydraulic synergy index diagram; and constructing a soil-fertilizer adaptation matrix based on the hydraulic synergy index diagram and the structural complementarity score table; In an embodiment of the present invention, first, data such as pore size distribution, pore connectivity, and water retention curves of various types of soil are extracted from a standardized database containing the microstructure and hydraulic properties of typical soil samples such as sand, clay, and loam. The key indicators and weights that need to be improved for each type of soil are determined in combination with agronomic needs to form a soil improvement demand spectrum. Next, the pore size distribution and connectivity data in the organic fertilizer structure spectrum are compared with the soil structure characteristics in the soil improvement demand spectrum, and the complementary contribution of organic fertilizers in different pore size ranges and the matching degree of pore network and soil network are analyzed. The pore size distribution and specific surface area changes after mixing are predicted, and the structural complementarity score is calculated based on these structural complementarities and improvement potentials to obtain a structural complementarity score sheet. Then, using the water absorption rate, water retention capacity, and water release control factors 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 fertilizers on infiltration, water retention, and water release with different soil types were analyzed. The infiltration coordination, water retention gain, and water release uniformity were quantified, and a comprehensive hydraulic synergy index was calculated. At the same time, the enhancing effect of structural complementarity on hydraulic synergy was considered, and the hydraulic synergy performance in different environments was evaluated, ultimately constructing a hydraulic synergy index map. Finally, the quantitative scores from the structural complementarity score table and the hydraulic synergy index map were weighted and integrated to construct a two-dimensional fitness evaluation system, generating a soil-fertilizer fitness matrix. This matrix uses a quantitative score of 0-100 to intuitively represent the comprehensive fitness of the organic fertilizer for various typical soil types in terms of structural improvement, water regulation, and aeration enhancement, and provides preliminary recommendations for the optimal application ratio.

[0019] Step S4: Analyze the impact of environment and fertilizer application amount based on the soil-fertilizer adaptation matrix to obtain the fertilizer application amount effect curve and the environmental response adjustment factor; calculate the improvement index and predict the effect based on the environmental response adjustment factor and the fertilizer application amount effect curve to obtain the soil improvement expectation report.

[0020] In one embodiment of the present invention, representative soil types are first selected based on the fitness scores in the soil-fertilizer adaptation matrix. Different gradients of organic fertilizer application rates (calculated by weight or tons / hectare) are designed. Combined with soil bulk density and tillage layer depth, soil organic matter increments and theoretical pore structure changes at different fertilizer application rates are predicted. The hydraulic properties of the organic fertilizer are then used to estimate preliminary improvement effects. A fertilizer rate-effect curve, which shows the relationship between fertilizer application rate and improvement effect, is plotted, and the economic benefits of different fertilizer rates are analyzed. Next, a soil moisture-air-temperature dynamic model is developed by extracting environmental data such as rainfall, temperature, and evaporation from a meteorological database for representative regions. This model simulates the changes in soil moisture and air pressure under different environmental conditions with different fertilizer application rates, quantifies the impact of environmental factors on the improvement effect, and generates an environmental response adjustment factor. Then, using the fertilizer rate-effect curve (baseline effect) and the environmental response adjustment factor, a multivariate regression equation system is established to calculate predicted values for soil moisture retention improvement rate, aeration improvement, and drought resistance enhancement index under different combinations of fertilizer application rates and environmental conditions. This model is then validated and corrected using small-scale test data to obtain a calibrated improvement effect index set. Finally, based on the set of improvement effect indicators, a visual chart is drawn to show the relationship between improvement effect, fertilizer application amount, and environmental factors, an economic benefit analysis is conducted, the duration of the improvement effect is estimated and the re-application cycle is proposed, customized application recommendations are provided for specific soil problems and crop needs, and all analysis results are integrated into a illustrated soil improvement expectation report to provide scientific decision-making guidance for practical applications.

[0021] Preferably, step S1 is specifically: Step S11: analyzing the water binding state of the organic fertilizer sample by a low-field nuclear magnetic resonance spectrometer to obtain a water binding spectrum; Step S12: constructing the pores of the organic fertilizer sample in three dimensions according to the water binding spectrum to obtain a set of original pore images; Step S13: extracting structural features from the pore original image set to obtain a pore parameter table; Step S14: constructing a spectrum of the pore parameter table and the water binding spectrum to obtain an organic fertilizer structure spectrum.

[0022] In the present embodiment, approximately 1 gram of dried organic fertilizer sample was carefully placed into a 10 mm diameter glass sample tube for nuclear magnetic resonance (NMR) and gently compacted using a pressure rod to ensure that the sample was evenly filled in the sample tube and at an appropriate height. The sample tube containing the sample was placed in the probe of a low-field nuclear magnetic resonance (LF-NMR) instrument operating at a frequency of 20 MHz. T was set. Pulse sequence parameters required for relaxation time measurement, for example, setting the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence, echo time (τ ) was set to 2 milliseconds, the number of echoes (NECH) was set to 1024, and the repetition time (TR) was set to 4 seconds to allow sufficient recovery of the proton longitudinal magnetization. The instrument was started and T Relaxation measurement collects the curve data of the water proton signal intensity decaying over time in the sample. The collected signal decay curve reflects the difference in the relaxation rate of water protons in different environments. The collected T The relaxation decay data is processed to decompose the complex decay curve into a distribution spectrum of different relaxation time components. According to the tightness of the binding of water molecules in organic fertilizers to the solid matrix, the relaxation time distribution spectrum is divided into different regions. For example, signals with a relaxation time of less than 10 milliseconds are classified as strongly bound water, signals with a relaxation time between 10 milliseconds and 100 milliseconds are classified as weakly bound water, and signals with a relaxation time greater than 100 milliseconds are classified as free water. The integral area of the signal intensity in each relaxation time region is calculated to obtain the relative content or percentage of each bound water molecule. These data are organized and visualized to generate a "water binding spectrum", which is usually manifested as one or more peaks. The position of the peak represents the main relaxation time, and the area of the peak represents the relative abundance of the corresponding bound water molecules.

[0023] Prepare the organic fertilizer sample into a standard cylindrical shape, for example, by using a mold to compress the sample into a cylindrical block with a diameter of 5 mm and a height of 5 mm. Secure the prepared sample to the sample stage of a micro-computed tomography (Micro-CT) scanner. Set scanning parameters, such as the X-ray tube voltage to 80 kilovolts, the current to 100 microamperes, and the spatial resolution to 5 microns, to ensure a detailed image of the pore structure. Set the scanning angle range from 0° to 360°, with a step angle of less than 1°, to capture X-ray projection images of the sample at different angles. Start the Micro-CT instrument and scan, acquiring hundreds or thousands of 2D projection images at different angles. Process the acquired 2D projection images using filtered back projection (FBP) or other 3D reconstruction algorithms, such as iterative reconstruction algorithms, to reconstruct 3D grayscale image volume data of the organic fertilizer sample. Different grayscale values in this grayscale image represent density differences at different locations in the sample, thereby distinguishing between the solid matrix and the pore space. Based on the water binding spectrum data obtained in step S11, the pore size ranges corresponding to different water binding states are inferred (for example, assuming that micropores primarily 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 a grayscale threshold range) are used to attempt to preliminarily label or distinguish pore regions corresponding to different water binding 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 "pore raw image set," which serves as the basis for subsequent structural feature extraction.

[0024] The original pore image set (3D grayscale image volume data) obtained in step S12 is loaded. The grayscale image is processed using an adaptive threshold segmentation algorithm. This algorithm automatically determines a threshold based on the grayscale distribution of the local region, converting the grayscale image into a binary image, where 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 (erosion followed by dilation) to remove isolated noise points (such as scanning artifacts or tiny solid particles mistakenly identified as pores) and closing (dilation followed by erosion) to fill tiny voids within pores and smooth pore boundaries. A connected domain analysis algorithm is used to analyze the pore pixels or voxels in the binary image, identifying and labeling all interconnected pore regions. Each individual pore or pore network is considered a connected domain. For each identified connected domain, calculate its volume (by counting the number of voxels contained and multiplying it by the unit voxel volume), surface area (by counting the number of voxels bounding the connected domain or using surface meshing methods), and shape factors (such as sphericity and skewness, reflecting pore geometry). Calculate the size distribution of all identified pores. For example, pores can be divided into different size intervals based on diameter or equivalent spherical diameter (e.g., micropores <0.1 μm, mesopores 0.1-10 μm, and macropores >10 μm). The number of pores in each size interval and the proportion of the total pore volume to the overall pore volume are calculated to obtain pore size distribution data. The total porosity is calculated by calculating the proportion of the total pore volume to the total sample volume. Skeleton extraction algorithms (such as thinning algorithms) are applied to the pore network, simplifying it into a one-dimensional skeleton or graph structure. The skeleton connectivity (e.g., number of nodes, number of branches, Euler number) and channel tortuosity (e.g., 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 such as pore size distribution, total porosity, specific surface area, pore connectivity (such as connectivity index) and channel tortuosity obtained from the above calculations are organized into a "pore parameter table".

[0025] A correspondence matrix is established between water binding states (derived from the water binding spectrum) and pore characteristics (derived from the pore parameter table). Based on physical principles or empirical models, this matrix quantifies the relationship between different pore size ranges and connectivity characteristics and the ability to accommodate different water binding states. For example, an element in the matrix represents the weight of a specific pore size interval's contribution to strongly bound water. Using a weighted fusion algorithm, the proportions of each type of bound water (strongly bound water, weakly bound water, and free water) in the water binding spectrum are combined with key indicators from the pore parameter table (such as the pore volume fraction in a specific pore size interval and the pore connectivity index) according to preset weight coefficients. For example, a composite index can be calculated to reflect the potential influence of large pores (which influence free water) and high connectivity on the overall hydraulic behavior. A three-dimensional coordinate system is constructed, with the X-axis representing a quantitative indicator of the organic fertilizer's pore size distribution characteristics (e.g., the pore volume fraction within a specific pore size range or the weighted average pore size), the Y-axis representing a quantitative indicator of the organic fertilizer's bound water fraction (e.g., the total ratio of strongly bound water to weakly bound water), and the Z-axis representing a quantitative indicator of the organic fertilizer's spatial connectivity (e.g., the pore network connectivity index). The integrated data is projected onto this three-dimensional coordinate system to form a characteristic point cloud representing the combination of the organic fertilizer's microstructure and water characteristics. Using polynomial fitting or other curve fitting methods, the characteristic point cloud is fitted on various two-dimensional projection planes or in three-dimensional space to generate characteristic curves or surfaces reflecting the relationship between pore size distribution, bound water fraction, and spatial connectivity. The area under these characteristic curves or their morphological parameters (e.g., slope and curvature) are calculated. These parameters further quantify the structural properties of the organic fertilizer. The final "organic fertilizer structure spectrum" is a visual expression of quantitative data of three key dimensions: pore size distribution rate, bound water ratio and spatial connectivity, and their interrelationships. It comprehensively reflects the microstructural characteristics of organic fertilizer and its potential for regulating water.

[0026] It is particularly important that the structural feature extraction in step S13 is specifically as follows: Perform image binarization processing on the original pore image set to obtain a binary pore image; Perform morphological optimization on the binary pore image to obtain the optimized pore image; Marking the connected regions of the optimized pore image to obtain a marked connected image; Calculate the geometric parameters of the marked connected image to obtain the pore geometry table; Perform size distribution statistics on the pore geometry table to obtain a pore size distribution table; Perform connectivity analysis on the pore size distribution table and the marked connected image to obtain the pore parameter table; In this embodiment of the present 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, solid skeleton voxels have higher grayscale values, while pore space voxels (containing air or low-density fillers) have lower grayscale values. To distinguish the solid skeleton from the pore space, the grayscale image needs to be converted into a binary image. An adaptive threshold segmentation algorithm is then used for processing. This algorithm does not use a single global threshold. Instead, it determines the optimal segmentation threshold for each region based on grayscale statistics of local image regions (such as the local mean 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 is calculated within each region (for example, using the local Otsu algorithm or an algorithm based on the local mean minus a constant). The grayscale value of each voxel is then compared to a threshold for its local region: voxels with grayscale values below the threshold are labeled as pores (e.g., assigned a value of 1), while voxels with grayscale values above or equal to the threshold are labeled as solid (e.g., assigned a value of 0). This processing results in a three-dimensional binary image volume data, where voxel values are either 0 or 1, clearly representing the distribution of pore space and solid skeleton. This data is known as a "binary pore image."

[0027] 3D morphological operations are applied to the binary pore image to remove minor noise generated during the scanning process (such as isolated misclassified voxels) and smooth pore boundaries, making the pore morphology more realistic. A 3D opening operation (erosion followed by dilation) is first performed. The erosion operation slides a 3D structuring element (e.g., a 3×3×3 voxel cube) 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 there is any overlap between the structuring element and the pore region, the central voxel becomes a pore. The dilation operation expands the pore region, restoring the pore size shrunk by erosion, but does not restore noise points completely eliminated by erosion. A 3D closing operation (dilation followed by erosion) is then performed to fill small voids within the pores or connect broken pore channels, smoothing the pore boundaries. The resulting optimized binary image volume data shows more continuous and smooth pore regions with significantly reduced noise; this data is referred to as the "optimized pore image."

[0028] 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 relationship between voxels, for example, 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 performed to find all unlabeled pore voxels that are 26-connected to it and label 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 increasing in value). The resulting 3D image volume data is composed of solid voxels with a value of 0, and voxels belonging to different connected pore regions are assigned their corresponding unique labels. This data is called the "labeled connected image."

[0029] Traverse the labeled connected image and calculate the corresponding geometric parameters for each unique pore connected domain label. For the connected domain with label i: 1. Volume (V ): Statistics of this tag The number of voxels N contained , multiplied by the unit voxel volume v_voxel set by Micro-CT scanning. =N ×v_voxel. 2. Surface area (S ): Estimate the surface area of the connected domain. One method is to count the number of connected domains belonging to the label How many voxels in S are adjacent to solid voxels (voxels with label 0). Multiply the number of adjacent voxel faces by the area of the unit voxel face a_face. ≈ the number of adjacent solid voxel faces × a_face. A more accurate method is to use the voxel data of the connected domain to generate a surface mesh (such as using the Marching Cubes algorithm) and then calculate the surface area of the mesh. 3. Shape factor (F ): Calculate the index reflecting the degree to which the pore shape deviates from the sphere. Commonly used shape factors include sphericity The sphericity value is between 0 and 1, and the sphere = 1, the more irregular or flatter the shape, the smaller the sphericity. Calculate geometric parameters such as volume, surface area, and sphericity for each connected domain and store this data in a table. Each row corresponds to a connected domain label, and the columns contain its volume, surface area, sphericity, and other values. This table is called the "pore geometry table."

[0030] Based on the volume V in the pore geometry table , calculate the equivalent spherical diameter (ESD) of each pore-connected domain ), ESD =(6V / π) Define a series of pore size intervals (e.g., micropores: ESD < 1 μm; mesopores: 1 μm ≤ ESD < 10 μm; macropores: ESD ≥ 10 μm). Traverse the pore geometry table and select the pores according to the ESD of each pore. Classify them into corresponding pore size intervals. Count the number of pores in each pore size interval and the total volume of these pores (the volume of all pores in this interval V Sum the total volume of pores in each pore size interval. Calculate the percentage of the total volume of all pores. Organize these statistical results, including the number of pores, total volume, and volume percentage for each size interval, into a table or histogram. This data is called a "pore size distribution table."

[0031] The pore size distribution table and the labeled connectivity image are combined to calculate parameters describing the overall connectivity of the pore network. First, the connected domain with the largest volume (or the largest number of voxels) is identified from the labeled connectivity image. This typically represents the most dominant, interconnected pore network in the sample. The volume of this largest connected domain is calculated as a percentage of the total volume of all pores, which serves as an index of pore network connectivity. A higher index indicates a more continuous pore network and smoother water and air transport within it. A three-dimensional skeleton extraction algorithm can also be applied to the optimized pore image to simplify the pore network into a one-dimensional skeleton line. The skeleton's topological structure can then 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 channel (the average value of the ratio of the skeleton path length to the straight-line distance between its endpoints). The contents of the pore size distribution table (volume percentage of each size interval), total porosity (the percentage of the total volume of all pores to the total volume of the sample, calculated by counting the proportion of pore voxels in the binary pore image to the total voxels), total specific surface area (the sum of all pore surface areas divided by the sample volume or mass), pore network connectivity index, average tortuosity and other quantitative indicators are integrated into a table. This table is the final "pore parameter table", which comprehensively summarizes the microscopic pore structure characteristics of the organic fertilizer sample.

[0032] It is particularly important that the spectrum synthesis construction in step S14 is specifically as follows: The correlation matrix of pore parameter table and water binding spectrum is constructed to obtain water-pore correlation matrix; Perform parameter fusion calculation on the water hole correlation matrix to obtain a parameter fusion table; Establish a coordinate system based on the parameter fusion table to obtain a three-dimensional coordinate frame; Map the data points of the parameter fusion table according to the three-dimensional coordinate frame to obtain a feature point set; Perform curve fitting on the feature point set to obtain a feature curve group; Calculate the spectrum parameters of the feature point set to obtain the organic fertilizer structure spectrum; In an embodiment of the present invention, a correlation matrix is constructed based on the pore size distribution data in the pore parameter table (for example, the pore volume percentage of different pore size intervals such as <1μm, 1-10μm, and >10μm) and the bound water ratio data in the water binding spectrum (the ratio of strongly bound water, weakly bound water, and free water). This matrix is intended 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 intervals (for example, m=3) and n represents the number of water binding states (for example, n=3). The matrix element A Indicates the The pore size range (such as <1μm pores) The association weight or contribution degree of each water binding state (such as strongly bound water) is given. For example, based on capillary condensation and adsorption theory, micropores (<1μm) are highly associated with strongly bound water and some weakly bound water, mesopores (1-10μm) are associated with weakly bound water and some free water, and macropores (>10μm) are mainly associated with free water. These association weights (A The value can be determined based on known physical models, empirical data, or by regression analysis of a series of standard samples. For example, A is high (micropores-strongly bound water), A is a high value (macropore-free water), A is the median value (mesopores - weakly bound water). This matrix A is the "water-pore correlation matrix", which establishes a quantitative connection between pore structure and water binding properties.

[0033] Using the water-pore correlation matrix A, the pore size distribution data in the pore parameter table, and the bound water ratio data in the water binding spectrum, parameter fusion calculations are performed. The goal is to generate a quantitative index that better represents the comprehensive hydraulic structure of organic fertilizers. 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 Reflects the importance of the pore size to the overall hydraulic function. Calculate a weighted bound water ratio index Y_water, which comprehensively reflects the relative importance of water in different binding states, Y_water=∑ (ratio of bound water state j × weight v ), weight v This reflects the degree of influence of the bound water state on soil water retention or release. In addition, the spatial connectivity index Z_conn in 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, as well as more detailed fusion indicators based on the water-pore correlation matrix (for example, the calculation of "the proportion of micropore volume associated with strongly bound water" = the volume percentage of pores with a diameter <1μm × A + Volume percentage of pore size 1-10 μm × A +...), organized into a table with each row representing the data of an organic fertilizer sample and the columns containing the fused parameter values. This table is called the "Parameter Fusion Table." These fused parameters represent a comprehensive quantitative description of the structure and moisture properties of the organic fertilizer.

[0034] Based on the key fusion parameters in the parameter fusion table (for example, 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 raw fusion parameter values in the parameter fusion table can be normalized to a range of 0 to 100 to facilitate comparison and visualization. A linear scaling method can be used for normalization: parameter value_normalized = (original value - minimum value) / (maximum value - minimum value) × 100. This three-dimensional coordinate system, along with its axis definitions and numerical ranges, constitutes the "three-dimensional coordinate framework," providing the spatial foundation for subsequent data visualization and spectrum construction.

[0035] Each row of data in the parameter fusion table (corresponding to an organic fertilizer sample or its specific fusion feature) is mapped to the established three-dimensional coordinate framework. The three key fusion parameter values (normalized X_struct, Y_water, Z_conn) in each row of the parameter fusion table constitute the (x, y, z) coordinates of a point in space. For example, if the fusion parameters of an organic fertilizer sample are standardized to (85, 72, 91), then a point is drawn in the three-dimensional coordinate system with an X-axis coordinate of 85, a Y-axis coordinate of 72, and a Z-axis coordinate of 91. The fusion parameter values of all samples or features are converted to corresponding three-dimensional spatial points. These points together form a "feature point set" that describes the combined characteristics of the organic fertilizer structure and water properties.

[0036] The characteristic 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 each two-dimensional plane. For example, on the XY plane, a polynomial fit is performed on the point set consisting of (X_struct, Y_water) (for example, using the least squares method to fit a quadratic polynomial Y_water = aX_struct² + bX_struct + c), resulting in a characteristic curve that reflects the relationship between the pore size distribution fusion index and the bound water ratio fusion index. Similarly, a curve reflecting the relationship between X_struct and Z_conn is fitted on the XZ plane, and a curve reflecting the relationship between Y_water and Z_conn is fitted on the YZ plane. These three two-dimensional fitted curves or their mathematical expressions (for example, polynomial coefficients) together constitute a "characteristic curve set," which, in a simplified form, expresses the inherent connections between the different dimensional characteristics of the organic fertilizer sample.

[0037] A set of representative quantitative parameters are extracted from the feature point set and the feature curve group, which together constitute the "organic fertilizer structure spectrum". These parameters may include: 1. Statistical parameters of the feature point set: for example, the coordinates of the center of gravity of the feature point set ( ), represents the central position of the organic fertilizer sample in the three-dimensional space of the structure spectrum; the standard deviation of the feature point set along each coordinate axis (σ ,σ ,σ ), reflecting the variability or distribution range of the sample's structural and water properties. 2. Characteristic curve parameters: For example, the area under the fitted curve in the XY plane reflects the overall strength of the correlation between pore size distribution and bound water ratio; the average slope of the fitted curve in the XZ plane reflects the degree of influence of pore structure changes on connectivity; and the morphological characteristic parameters of the fitted curve in the YZ plane (such as peaks and valleys) reflect the nonlinear relationship between bound water ratio and connectivity. 3. Specific spectrum indicators: For example, a comprehensive structural index can be defined, combining X_struct and Z_conn; a comprehensive water index can be defined, combining Y_water and water availability data (although this data comes from S2, it can be included during spectrum construction). The quantitative parameters calculated above, such as the centroid coordinates, standard deviation, area under the curve, and average slope, are combined to form a structured data table or vector. This data set is the "organic fertilizer structural spectrum" of the organic fertilizer sample. It is a high-dimensional quantitative representation that captures key information about the organic fertilizer's microstructure and water binding properties, and can be used for subsequent soil matching and improvement effect prediction.

[0038] Preferably, the dynamic water exchange determination in step S2 is specifically as follows: Conduct rainfall simulation and weight record on organic fertilizer samples to obtain rainfall parameters and weight parameters; The water absorption process of the organic fertilizer sample is dynamically monitored according to the rainfall parameters and weight parameters to obtain the water absorption dynamic data; Perform temperature gradient water release analysis on the water absorption dynamic data to obtain temperature water release data; According to the temperature water release data and water absorption dynamic data, a cyclic water absorption and release stability test is performed to obtain cyclic stability data; The moisture effectiveness data is obtained by evaluating the temperature water release data according to the humidity response curve set; A water exchange dynamics diagram was constructed based on the water availability data and the cyclic stability data.

[0039] In an embodiment of the present invention, an organic fertilizer sample that has been pre-treated to an air-dried state (e.g., balanced at 25°C and 50% relative humidity for 24 hours) is placed in a cylindrical container with a filter at the bottom to allow free drainage, and the container is placed on a microgravity sensor (electronic balance) with an accuracy of 0.001 grams. A simulated rainfall device is connected, which can accurately control the rainfall intensity, for example, by adjusting the flow rate and height of the nozzle to achieve three different simulated rainfall intensities of 2 mm / hour (light), 5 mm / hour (moderate), and 10 mm / hour (heavy). Before starting the simulated rainfall, the initial mass of the sample, m, is recorded. Start the simulated rainfall device, set a specific rainfall intensity, and start timing. During the rainfall process, the mass change m(t) of the sample is continuously recorded in real time by the microgravity sensor, for example, the mass data is recorded every 30 seconds. Record the duration Δt of the rainfall and the total rainfall V (V = rainfall intensity × rainfall area × Δt). After the rainfall stops, continue recording the changes in sample mass over time until drainage essentially stops. The recorded rainfall intensity, duration, total rainfall, and sample mass data corresponding to time are organized into a "rainfall parameters and weight parameters" dataset.

[0040] Using the rainfall parameter and weight parameter data set, calculate the water absorption of the sample at different time points t Δm(t)=m(t)-m . Plot the water absorption or water absorption rate (water absorption as a percentage of initial dry weight) over time. Analyze the shape of the water absorption curve under different rainfall intensities: for example, the initial water absorption rate , and reach saturated water absorption The time required. The saturated water absorption can be determined by the mass change when the water absorption curve reaches the plateau. Calculate the time required for the sample to reach its saturated water absorption (or a preset saturation ratio, such as 75%) under a specific rainfall intensity. These time series of water absorption / water absorption rate curves, initial water absorption rate, saturated water absorption and time required to reach a specific saturation constitute the "water absorption dynamic data".

[0041] Take an organic fertilizer sample that has reached saturated water absorption during a simulated rainfall experiment (or prepare a sample with the same high moisture content). Place the sample 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 sample on a microgravity sensor under each temperature environment (or remove the sample at different time points and weigh it on a balance). Record the change in sample mass over time. , which reflects the process of the sample losing water due to evaporation and desorption. Under each temperature condition, the mass change is continuously monitored until the sample mass tends to be stable or reaches the preset low moisture content. Calculate the sample at different time points Water release ,in is the sample mass at the start of water release. The amount of water released, or the residual moisture content (the percentage of residual water to the initial dry weight), is plotted over time. These water release time series curves and corresponding water release rates obtained under different temperature conditions constitute "temperature water release data."

[0042] Design a standard water absorption and release cycle process. A cycle includes a water absorption stage (for example, immersing the sample in water until saturated, or conducting a simulated rainfall of standard intensity) and a water release stage (for example, air drying to the initial air-dry state at 25°C and 50% relative humidity, or drying in a 60°C oven to constant weight). Repeat multiple water absorption and release cycles on the organic fertilizer sample, for example, perform 5 or 10 complete cycles. At the end of each cycle (water absorption saturation state and water release end state), measure and record key hydraulic characteristic parameters. For example, record the maximum water holding capacity after each saturated water absorption. , and the minimum moisture content after each water release and drying ,in Indicates the number of cycles. Based on the water absorption dynamic data, the time required to reach saturation under different cycle numbers is compared. or initial water absorption rate Based on the temperature water release data, the time required to release 50% of the water at standard temperature (25°C) under different cycle times is compared. Analyze the changing trend 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 in water absorption rate. These quantitative data reflecting the changes in the hydraulic properties of organic fertilizers during repeated drying and wetting cycles, such as the cyclic decay of maximum water holding capacity, changes in water absorption / release rates, and structural stability indicators (such as changes in dispersion after soaking, although only the hydraulic properties data are of interest here), constitute "cyclic stability data."

[0043] A set of humidity response curves is obtained using humidity gradient sorption / desorption measurements. These curves reflect the amount of water that an 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, water uptake at high relative humidity primarily corresponds to capillary condensation and free water in macropores and mesopores, while water uptake at low relative humidity primarily corresponds to adsorbed water and strongly bound water in micropores. Combined with temperature-dependent water release data, the relationship between the dynamic process of water release from a sample at a specific temperature (e.g., 25°C) and the corresponding water content at different equilibrium points on the humidity response curve is analyzed. For example, the percentage of the total absorbed water released during the water release process can be calculated by the time the sample's moisture content drops to the equilibrium moisture content corresponding to 85% relative humidity. Alternatively, the moisture content range (corresponding to the humidity range on the humidity curve) in which the release rate significantly changes can be determined. This helps distinguish between "available" water that is easily released (such as free water in macropores, corresponding to high humidity equilibrium points) and "ineffective" or strongly bound water that is difficult to release (such as adsorbed water in micropores, corresponding to low humidity equilibrium points). Quantify the "availability" or "releasability" of the remaining water in a sample at different temperatures and water release times. These quantitative results, such as the proportion of water released within 24 hours at a specific temperature to the total absorbed water, and the humidity range on the humidity curve where the remaining water corresponds, constitute "water availability data."

[0044] Integrate water absorption dynamics data, temperature-dependent water release data (after assessing water availability), water availability data, and cyclic stability data. Build a comprehensive visualization or quantitative model representing the dynamic water exchange behavior of organic fertilizers. 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 of graphs shows water release curves under different temperatures, with key equilibrium points on the humidity response curve superimposed for reference; another set of graphs can show how key hydraulic parameters (such as maximum water absorption, 24-hour water release ratio, and time to semi-dryness) change with the number of dry-wet cycles. Water availability data (such as the "effective" water fraction) and cyclic stability data (such as the cyclic decay rate of maximum water holding capacity) are annotated as additional information or parameters in the graphs. The resulting "water exchange dynamics graph" is a comprehensive data set and visualization that comprehensively reflects the water absorption rate, water retention capacity, water release characteristics, water availability, and performance stability of organic fertilizers under simulated natural conditions during repeated dry-wet cycles.

[0045] Preferably, the hydraulic parameter response analysis in step S2 is specifically as follows: Conduct humidity sensitivity analysis based on the humidity response curve set to obtain humidity sensitivity data; Extract water absorption kinetic parameters from the moisture exchange dynamics diagram based on the set of humidity response curves; The water absorption efficiency is evaluated according to the water absorption kinetic parameters to obtain the water absorption efficiency index; The water retention capacity is measured according to the water exchange dynamic diagram to obtain water retention characteristic data; The moisture buffering capacity is calculated based on the humidity response curve set and the water retention characteristic data to obtain the moisture buffering index; The structural adaptability is evaluated on the humidity response curve set and the water exchange dynamic diagram to obtain the structural adaptability data; Integrate moisture sensitivity data, water absorption efficiency index, moisture buffer index and structural adaptation data to obtain a hydraulic characteristic parameter set; The response index is comprehensively constructed based on the hydraulic characteristic parameter set to obtain the hydraulic response index.

[0046] In the embodiment of the present invention, a set of humidity response curves is used, which describes the equilibrium moisture content data points of organic fertilizer samples at a relative humidity gradient of 10% to 95%. The moisture absorption curve or dehumidification curve is analyzed to calculate the rate of change of moisture content with relative humidity. The entire humidity range can be divided into several intervals (for example, 10-30%, 30-60%, 60-95%), and the average humidity sensitivity S in each interval is calculated. =Δ moisture content / ΔRelative humidity , where Δ moisture content is the change in equilibrium moisture content within the humidity range, Δ relative humidity The width of the humidity range can also be determined by mathematically fitting the humidity response curve, for example using the Guggenheim-Anderson-de Boer (GAB) model or a polynomial model. The derivative of the fitted function with respect to relative humidity, dW / dRH, is then calculated. This derivative represents the humidity sensitivity at a specific relative humidity point or range. Humidity sensitivity data can be expressed as average sensitivity values within different humidity ranges, or as a function or table describing how sensitivity varies with humidity.

[0047] While water absorption kinetic parameters are primarily derived from water absorption curves in water exchange dynamic diagrams, incorporating a set of humidity response curves can help understand the driving forces of the water absorption process. Key information can be extracted from the water absorption dynamics data, particularly the time-dependent mass increase curve (e.g., under a standard rainfall intensity of 5 mm / h). Kinetic models can be fitted to this curve to quantify the water absorption rate. Commonly used models include pseudo-first-order kinetic models: Or the pseudo-second-order kinetic model: ,in yes Water absorption at each moment (unit mass of water / unit mass of dry fertilizer), is the equilibrium water absorption (maximum water absorption), and is the adsorption rate constant. The experimental data are fitted by linear or nonlinear regression method to obtain 、 or Initial water absorption rate It can be estimated from the initial slope of the curve or calculated by a model (e.g., pseudo-first-order model v , pseudo-secondary model ). These fitted parameters and the calculated initial water absorption rate constitute the water absorption kinetic parameters.

[0048] Using the extracted water absorption kinetic parameters and water absorption dynamic data, we can evaluate the ability of organic fertilizer to absorb water quickly and calculate the time required to reach a certain water absorption ratio, such as reaching the maximum water absorption. 50% ( ) or 75% ( ) the time required. or The shorter the time, the higher the water absorption efficiency. It is also possible to calculate the cumulative water absorption within a standard time (for example, within 10 minutes after the start of rainfall), or the average water absorption rate per unit time (for example, within the first 5 minutes). Comparing the water absorption dynamics parameters under different rainfall intensities can evaluate the response of organic fertilizers to different rainfall intensities. The water absorption efficiency index can be one or more quantitative values, such as the water absorption efficiency under standard rainfall intensity (5 mm / hour). value, or the ratio of the initial water absorption rate to the maximum theoretical water absorption rate.

[0049] Utilize water-release curve data from water exchange dynamics diagrams. These curves describe the temporal change in moisture content of organic fertilizer samples under simulated drying conditions (e.g., at different temperatures). Key water-retention capacity indicators can be determined. For example, the residual moisture content of a sample after simulated rainfall has ceased and the sample has fully drained (e.g., 24 hours after drainage) can be calculated. This is analogous to the field capacity of the soil. The moisture content retained by the sample after drying under standard drying conditions (e.g., 25°C, 50% relative humidity) for a specified period of time (e.g., 48 hours) can be calculated. This reflects the sample's ability to resist evaporative water loss. Combined with a set of moisture response curves, the moisture content that the organic fertilizer can retain at lower relative humidity (e.g., 10% or 20% relative humidity, corresponding to strongly bound water that is difficult for plants to absorb) can be determined. This reflects the fertilizer's ability to retain non-available water, although high total water retention can extend 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 a low relative humidity point.

[0050] A collection of moisture response curves and water retention data are combined to assess the ability of organic fertilizers to maintain relatively stable moisture content during fluctuations in water supply. Materials with strong moisture buffering capacity exhibit less dramatic moisture content responses to changes in ambient humidity or water supply. The area of the moisture absorption-desorption hysteresis loop is calculated for the moisture response curve collection. A larger hysteresis loop area indicates a greater difference in moisture content between moisture absorption and desorption within the same range of humidity. This is generally related to the material's ability to store and release water during wetting and drying cycles and can be considered an indicator of moisture buffering capacity. The morphology of the water release curve (derived from a water exchange dynamics diagram) is analyzed, particularly its slope within the moisture content range relevant to plant available water. A flatter slope indicates a longer drying period or a greater humidity drop during a moisture content decrease, reflecting a better moisture buffering capacity. The moisture buffering index can be based on the hysteresis loop area, the average slope of the water release curve within a critical moisture content range (e.g., from drained moisture content to standard dried moisture content), or a composite score combining both.

[0051] Utilize the cyclic stability data from the water exchange dynamics diagram, combined with a set of humidity response curves, to assess the stability of the water absorption, water retention, and water release characteristics of organic fertilizers during repeated cycles of moisture absorption and dehumidification. If the organic fertilizer structure undergoes significant degradation during wet-dry cycles (e.g., pore collapse, particle disintegration), its hydraulic properties will change with the number of cycles. Compare key hydraulic parameters measured at different numbers of cycles, for example, the percentage difference between the maximum water absorption after the first cycle and the maximum water absorption after the tenth cycle (Δ = ×100%. Calculate the rate of change in the residual moisture content after standard drying or the time required to reach semi-dryness as a function of the number of cycles. These rates of change or decay percentages reflect the "water stability" of the organic fertilizer structure and its ability to adapt to changes in dryness and wetness. 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.

[0052] Collect all the quantitative hydraulic property indicators obtained from the above calculations: moisture sensitivity data (e.g., sensitivity values or average sensitivity in different moisture intervals), water absorption dynamics parameters (e.g., or ), water absorption efficiency indicators (e.g. ), water retention data (e.g., moisture content after drainage, moisture content after standard drying, low RH moisture content), moisture buffering indicators (e.g., hysteresis loop area, water release slope in the critical interval), and structural adaptation data (e.g., cyclic change rate of key parameters). These values are combined into a structured data set, such as a vector or a row of a table, in which each element or column represents a specific hydraulic characteristic parameter. This data set is the complete "hydraulic characteristic parameter set" of the organic fertilizer sample.

[0053] Based on the various indicators in the hydraulic characteristic parameter set, a comprehensive "hydraulic response index" reflecting the overall hydraulic regulation ability of organic fertilizer is constructed. Each parameter in the hydraulic characteristic parameter set is weighted according to its importance in different agricultural application scenarios. For example, in sandy soil improvement that requires rapid infiltration and avoidance of surface runoff, water absorption efficiency and initial water absorption rate have higher weights; in applications in arid areas that require long-term water retention, water retention capacity and moisture buffering capacity have higher weights. The parameters are standardized (for example, scaled to 0-100 points) to eliminate dimensional differences. Calculate three core response indices: 1. Water absorption rate coefficient: by weighted combination of initial water absorption rate, water absorption rate constant, 1. Calculation of the water retention capacity and related moisture sensitivity parameters. 2. Water retention capacity: A weighted combination of maximum water absorption, water content after drainage, water content after standard drying, and water content at low relative humidity. 3. Water release control factor: A weighted combination of the slope of the water release curve, water buffer indicators (hysteresis loop area, critical interval water release slope), and structural adaptation data. The final "hydraulic response index" includes these three quantitative parameters, which comprehensively reflect the ability and characteristics of organic fertilizers in water absorption, retention, and release, providing input for subsequent soil matching analysis.

[0054] Preferably, the structural complementarity assessment in step S3 is specifically: The aperture difference analysis was conducted on the organic fertilizer structure spectrum and soil improvement demand spectrum to obtain the aperture complementary interval table; The connectivity matching degree of the organic fertilizer structure spectrum and the soil improvement demand spectrum is calculated to obtain a connectivity matching degree table; The pore size distribution after mixing is predicted based on the connectivity matching table and the pore size complementary interval table to obtain an improved pore size prediction table; The surface area change is estimated based on the improved pore size prediction table to obtain a surface property change table; Complementarity score table is calculated based on the aperture complementary interval table, connectivity matching table and surface property change table; Soil adaptability was graded according to the complementarity score table to obtain the structural complementarity score table.

[0055] In this embodiment, pore size distribution data (e.g., the volume percentage of micropores <1μm, mesopores 1-10μm, and macropores >10μm) from the organic fertilizer structure spectrum is loaded along with pore size distribution data for different soil types and key pore size ranges requiring improvement from the soil improvement needs spectrum. For example, sandy soils typically lack micropores and have a small amount of mesopores, resulting in poor water demand; clay soils typically have abundant micropores but poor macropores and connectivity, resulting in poor aeration and water permeability; and loam soils have a relatively balanced structure but exhibit defects within specific particle size ranges. For each target soil type, the pore size distribution of the organic fertilizer is compared with the pore size distribution of the target soil. This identifies pore size ranges within the organic fertilizer pore size distribution that can effectively fill or bridge pore structure defects in the target soil. For example, for sandy soils, micropores below 1μm and mesopores 1-10μm in the organic fertilizer are considered complementary pore sizes because they can fill the macropores between sand particles, increasing water holding capacity. For clay soils, macropores larger than 10 μm and mesopores between 1 and 10 μm in organic fertilizer are considered complementary pore sizes because they contribute to the formation of new macropore channels, improving air and water permeability. The pore volume percentage or absolute volume of organic fertilizer within these complementary pore size ranges is quantified. These quantitative results, such as "For sandy soil, the volume of pores <10 μm in organic fertilizer is X%" and "For clay soil, the volume of pores >1 μm in organic fertilizer is Y%," are organized into a table listing different soil types, their corresponding complementary organic fertilizer pore size ranges, and their contributions. This table is called the "Aperture Complementary Range Table."

[0056] Using spatial connectivity indices (e.g., maximum connected domain volume fraction, average tortuosity) from the organic fertilizer structure spectrum and the pore network connectivity characteristics and connectivity aspects of different soil types in need of improvement (e.g., sandy soils have high connectivity but large channels, while clays have poor connectivity and tortuosity) from the soil improvement needs spectrum, we assess how the pore network of the organic fertilizer interacts with the soil pore network, thereby affecting overall connectivity. Connectivity matching metrics are calculated: For example, if the organic fertilizer has a highly connected micro-mesoporous network, this is beneficial for improving the water-retention connectivity of sandy soils (preventing rapid water loss and forming capillary connections). If the organic fertilizer has a highly connected macroporous network, this is beneficial for improving the aeration and drainage connectivity of clay soils. The matching calculation considers the degree of fit between the organic fertilizer connectivity index and the soil connectivity defects, as well as the possibility that the organic fertilizer particles, after being added to the soil, will form new connecting channels between their own pore network and the existing soil pore network. For example, a matching score is calculated to reflect how the pore connectivity characteristics of organic fertilizer (high or low, and which pore size ranges have strong connectivity) contribute to addressing the connectivity issues of the target soil. These matching scores or quantitative indicators are organized into a table that lists different soil types and their corresponding organic fertilizer connectivity matching scores. This table is called the "Connectivity Matching Table."

[0057] Based on the pore size contribution of organic fertilizer to different soils, as determined by the pore size complementarity table, and the structural integration potential, as reflected by the connectivity matching table, the pore size distribution changes after mixing organic fertilizer with the target soil were predicted. A structural mixing model was developed that accounted for the processes of organic fertilizer particles dispersing into the soil matrix, filling existing soil pores, maintaining their own pores, and forming new aggregates with soil particles. The model inputs included the original soil pore size distribution, the organic fertilizer pore size distribution, a predetermined mixing ratio (e.g., unit mass or volume of organic fertilizer added to unit mass or volume of soil), and an "integration efficiency" parameter influenced by the connectivity matching. For example, the predicted percentage of pore volume newly formed (or improved) in a specific pore size range after mixing = the original percentage of soil pore volume in that range + the percentage of organic fertilizer pore volume in that range × the integration efficiency factor. The integration efficiency factor is determined based on the connectivity matching; higher matching results in a factor closer to 1, indicating that the organic fertilizer pores are more effectively integrated into and improve the soil structure. Calculate the predicted total porosity after mixing, as well as the volume percentages of different pore size ranges (micropores, mesopores, and macropores). Organize these predictions, such as "After mixing with sand, the volume of micropores and mesopores increases to X%," and "After mixing with clay, the volume of macropores increases to Y%, and the total porosity increases to Z%." Compile these predictions into a table listing the predicted pore size distribution and total porosity after mixing organic fertilizers with different soil types. This table is called the "Pore Size Improvement Prediction Table."

[0058] The change in specific surface area of the soil-organic fertilizer mixture was estimated using the pore size distribution data after mixing 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 intervals 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, total specific surface area ≈ ∑ (The volume percentage of pore size interval i after mixing × the specific surface area of the material corresponding to this interval). Consider the impact of the formation or destruction of aggregates during the mixing process 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. These estimated results, such as "After mixing with sand, the specific surface area increases by A%", "After mixing with clay, the specific surface area increases by B%", are organized into a table that lists the predicted specific surface area and its changes after mixing organic fertilizers with different soil types. This table is the "surface property change table".

[0059] The quantitative data from the pore size complementarity table, connectivity matching table, and surface property change table are integrated to calculate the total score of the structural complementarity of organic fertilizer for each soil type. Each indicator is assigned a weight that reflects its importance to improving the specific soil structure (the weight value can be derived from the key indicator weights set in the soil improvement demand spectrum). For example, for sandy soil, the complementarity of micropores and mesopores is highly weighted, as is the increase in specific surface area; for clay, the complementarity of macropores is highly weighted, as is the connectivity matching. The data for each indicator is standardized (for example, scaled to a score of 0-100) and then weighted summed: Complementarity score = w ×(aperture complement normalized score) + w ×(connectivity matching normalized score) + w ×(normalized score of specific surface area change)+... where w ,w ,w is the weight of the corresponding indicator, and ∑w = 1. Calculate the complementarity score for each target soil type and the organic fertilizer. Organize these scores into a table that lists different soil types and their corresponding structural complementarity scores. This table is called the "Complementarity Score Table."

[0060] 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 score range is set to correspond to a level of adaptability: 80-100 points for high adaptability, 60-79 points for medium adaptability, 40-59 points for low adaptability, and less than 40 points for unsuitability. Alternatively, the scores in the complementarity score table are directly used as the final structural complementarity score. The grading results or final scores are organized into a table, typically listing the soil type and the corresponding structural complementarity score (for example, 0-100 points). This table, known as the "structural complementarity score table," quantifies the potential of organic fertilizers to improve the microstructure of different soil types.

[0061] Preferably, the hydraulic synergy calculation in step S3 is specifically as follows: The infiltration characteristics matching analysis was conducted on the hydraulic response index and the soil improvement demand spectrum to obtain the infiltration coordination data; The water holding characteristics of the hydraulic response index and soil improvement demand spectrum were evaluated in synergy to obtain water holding gain data; The hydraulic response index and soil improvement demand spectrum were analyzed in a coordinated manner to obtain uniform water release data. The hydraulic synergy index table is obtained by performing a comprehensive calculation of the infiltration coordination data, water retention gain data and water release uniformity data; Conduct structural-hydraulic linkage analysis on the hydraulic synergy index table and the structural complementarity score table to obtain structural-hydraulic linkage data; Conduct environmental adaptability assessment based on structural hydraulic linkage data and hydraulic synergy index table to obtain environmental adaptability data; A hydraulic synergy index graph is constructed based on the hydraulic synergy index table, structural hydraulic linkage data, and environmental adaptability data.

[0062] In one embodiment of the present invention, the water absorption rate coefficient from the hydraulic response index, along with infiltration characteristics and infiltration rate requirements for different soil types (e.g., sandy soils with excessively rapid infiltration need to be slowed, while clay soils with excessively slow infiltration need to be accelerated) is used to analyze whether the rapid water absorption capacity of organic fertilizers matches the soil's ability to absorb water, generating a synergistic effect. For example, for clay soils with a low infiltration rate, organic fertilizers with a high water absorption rate coefficient can rapidly absorb water entering the soil surface, reducing surface runoff and helping to channel water downward, thereby accelerating the overall infiltration process and demonstrating a high degree of compatibility. For sandy soils with extremely high infiltration rates, excessively high water absorption rates can lead to rapid saturation of organic fertilizer aggregates while the soil matrix continues to drain rapidly, reducing the synergistic effect. In this case, a moderate or slightly slower water absorption rate is more suitable, promoting a more stable distribution of water between the organic fertilizer and sand particles. A quantitative indicator, such as an "infiltration coordination score," is calculated based on the degree of compatibility between the organic fertilizer's water absorption rate coefficient and the soil's target infiltration rate. For example, a score can be calculated using a function that takes as input the water absorption rate coefficient of the organic fertilizer and the target infiltration rate of the soil, and outputs a score from 0 to 100. This score table is called the "infiltration coordination data" and reflects the synergistic potential of organic fertilizers in improving the infiltration performance of various soil types.

[0063] Using the water-holding capacity and water-release control factors from the Hydraulic Response Index, along with water-holding characteristics and available water requirements for different soil types from the Soil Improvement Needs Spectrum (e.g., sandy soils with low total water-holding capacity need to be improved, while clay soils with low available water need to be increased), we assess how the water-holding capacity of organic fertilizers synergizes with soil water-holding capacity to increase total water holding capacity and optimize the available water content. For sandy soils, a high water-holding capacity of organic fertilizers can significantly increase overall water holding capacity, directly compensating for the sand's shortcomings and demonstrating high synergy. For clay soils, while their total water holding capacity is high, much of the water is trapped in micropores and ineffective. In this case, while the water-holding capacity of organic fertilizers is important, their water-release control factors (particularly their ability to release water within the plant-available water range) are even more crucial. If organic fertilizers can effectively store and release water at moderate humidity levels, they can increase the available water content of clay soils, demonstrating high synergy. A quantitative metric, such as a "water-holding gain score," is calculated, which comprehensively considers the theoretical contributions of the water-holding capacity and water-release control factors of organic fertilizers to the total water holding capacity and available water content of different soil types. For example, a score can be calculated using a weighted average: Water Holding Gain Score = w_Capacity × Normalized Water Holding Capacity Value + w_Water Release × Normalized Water Release Control Factor, where the weight w is determined based on the soil type's total water holding and available water requirements. This score, known as the "Water Holding Gain Data," reflects the synergistic potential of organic fertilizers in improving water retention and effective water use in various soil types.

[0064] Using the water-release modifiers from the hydraulic response index and the water-release characteristics and water-supply stability requirements of different soil types (e.g., sandy soils dry quickly and require extended water supply, while clay soils release water slowly and require accelerated water supply) from the soil improvement demand spectrum, we assess how the water-release pattern of organic fertilizers synergizes with the soil water-release pattern to provide a more stable and plant-friendly water supply. If the organic fertilizer's water-release modifiers indicate relatively slow and uniform water release (e.g., a flat water-release curve and high water buffering capacity), this indicates a high degree of synergy in extending the effective water supply period for sandy soils. For clay soils, if the organic fertilizer promotes the release of trapped micropore water (e.g., by improving pore connectivity) or releases water within the plant-available water range, it can help improve the clay soil's water-release characteristics, demonstrating synergy. A quantitative metric, such as a "water-release uniformity score," is calculated based on the degree to which the organic fertilizer's water-release modifiers match the soil's need for a stable water supply. For example, the score could be based on the theoretical optimization of the soil water-release curve morphology by the water-release modifiers. This score sheet is the “water release uniformity data”, which reflects the synergistic potential of organic fertilizers in optimizing the water release pattern of various soil types.

[0065] Infiltration coordination data, water holding gain data, and water release uniformity data are integrated to calculate a comprehensive "Hydraulic Synergy Index" for each soil type. The previously calculated scores are weighted and summed based on the relative importance of infiltration, water holding, and water release characteristics for each soil type as specified in the Soil Improvement Requirement Spectrum. For example, for sandy soils, water holding gain and water release uniformity are weighted more highly, while infiltration coordination is weighted less highly. For clay soils, infiltration coordination and water release uniformity are weighted more highly, while water holding gain (focusing on total volume) is weighted less highly. Each score is normalized to a scale of 0-100. The Hydraulic Synergy Index (I_Syss) is calculated as: w_Infiltration × Infiltration Coordination Score + w_Water Holding × Water Holding Gain Score + w_Water Release × Water Release Uniformity Score, where w_Infiltration + w_Water Holding + w_Water Release = 1, and the weights are dynamically adjusted based on soil type. The calculated Hydraulic Synergy Index for each soil type is organized into a table, referred to as the "Hydraulic Synergy Index Table."

[0066] Recognize that the improvement of soil structure by organic fertilizers can in turn affect its hydraulic properties. Use the Hydraulic Synergy Index table and the Structural Complementarity Score table to analyze the enhancing effect of structural improvements on hydraulic synergy. For example, if organic fertilizer has a high Structural Complementarity score for a particular soil, it means it effectively increases macropores and improves connectivity. This promotes water movement and distribution within the soil, thereby enhancing the organic fertilizer's inherent hydraulic properties (such as water absorption and release) and improving overall hydraulic synergy. Develop a structural-hydraulic linkage model that adjusts the hydraulic synergy index based on the Structural Complementarity score. For example, calculate a "linkage gain factor," F_linkage = f(Structural Complementarity Score). The function f can be a linear or nonlinear increasing function; higher Structural Complementarity Score results in a larger linkage gain factor (F_linkage ≥ 1). Calculate the "linkage hydraulic score," I_linkage = Hydraulic Synergy Index × F_linkage. Alternatively, a more sophisticated model can predict the impact of structural improvements 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 factors or linkage hydraulic score data are organized into a table, which is the "structural hydraulic linkage data".

[0067] Combine structural hydraulic linkage data (or directly use the hydraulic synergy index table if linkage analysis is integrated), the hydraulic synergy index table, and the environmental factors implicit or explicit in the soil improvement demand spectrum (e.g., stronger water retention and release capacity in arid environments, stronger infiltration and drainage capacity in waterlogged environments). Evaluate the hydraulic synergy of organic fertilizers under specific environmental conditions (e.g., simulated drought or simulated waterlogging). For example, for sandy soils used in arid areas, if the organic fertilizer has a high hydraulic linkage score, primarily due to its water retention and slow water release properties, then its environmental adaptability score is high. For clay soils used in humid areas, if the organic fertilizer has a high hydraulic linkage score, primarily due to improved infiltration and aeration properties, then its environmental adaptability score is high. Incorporate environmental stress factors (e.g., drought index, waterlogging index) to calculate the hydraulic synergy performance of the organic fertilizer under these environmental stresses, resulting in an "environmental adaptability score." This score can be an adjustment to the hydraulic linkage score or a standalone indicator. Compile these scores into a table, which becomes the "environmental adaptability data."

[0068] The hydraulic synergy index table, structural hydraulic linkage data, and environmental adaptability data are integrated to construct a final "hydraulic synergy index map." Depending on application requirements, the final hydraulic synergy index can be a simple hydraulic synergy index, a linkage hydraulic score that takes structural linkage into account, or a final score that further incorporates environmental adaptability assessments. For example, the final hydraulic synergy index = linkage hydraulic score × environmental adaptability correction factor. The final hydraulic synergy index for each soil type (normalized to a 0-100 scale) is tabulated. This data can be visualized, for example, by creating a bar chart showing the final hydraulic synergy index for organic fertilizers across different soil types, or a radar chart showing the scores of organic fertilizers across multiple dimensions, such as infiltration, water retention, water release, and environmental adaptability. This combination of quantitative scores and visual charts is the "hydraulic synergy index map," which comprehensively reflects the synergistic potential of organic fertilizers in improving water properties across different soil types and environmental conditions.

[0069] Preferably, step S4 is specifically: Step S41: performing a fertilizer application gradient design based on the soil-fertilizer adaptation matrix to obtain a fertilizer application effect curve; Step S42: performing environmental factor impact analysis based on the fertilizer application rate effect curve to obtain an environmental response adjustment factor; Step S43: Calculating improvement indicators based on the environmental response adjustment factor and the fertilizer application rate effect curve to obtain an improvement effect indicator set; Step S44: Generate a soil improvement expectation report based on the improvement effect indicator set.

[0070] In an embodiment of the present invention, a soil-fertilizer adaptation matrix is loaded, which quantifies the adaptability of organic fertilizer to different soil types and includes preliminary optimal application ratio recommendations for each type of soil. Based on the scores of the adaptation matrix, representative target soil types are selected for analysis. For example, three soil types with organic fertilizer adaptability scores of 90 points (high adaptability), 70 points (medium adaptability), and 50 points (low adaptability) are selected. A set of fertilizer application 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%, respectively. Typical bulk density data for these representative soil types are obtained (for example, sandy soil bulk density of 1.6 g / cm3, clay bulk density of 1.4 g / cm3, and loam bulk density of 1.3 g / cm3) and preset tillage layer depths (for example, 0-20 cm). The weight mixing ratio is converted into the actual application rate per unit area (tons / hectare). The calculation method is: Application rate (tons / hectare) = Mixing ratio (%) × Depth of tillage layer (meters) × Soil bulk density (tons / m³) × 10,000 (m² / hectare). For example, a 1% mixing ratio in sandy soil with a bulk density of 1.6 tons / m³ and a depth of 20 cm would result in an application rate of approximately 1% × 0.2 meters × 1.6 tons / m³ × 10,000 m² / hectare = 3.2 tons / hectare. Based on the optimal application rate recommendations included in the adaptation matrix, ensure that the designed fertilizer rate gradient covers the recommended value. For example, if the recommended value is 5%, the 5% gradient becomes a critical node. Calculate the theoretical organic matter content increase caused by the addition of organic fertilizer to the soil at different fertilizer rate gradients, assuming the organic matter content of the organic fertilizer is known (for example, 60% organic matter content on a dry basis). Based on the pore size improvement prediction model, but generalized to different fertilization rates, the theoretical magnitude of change in soil pore structure (porosity, pore size distribution) is predicted for each fertilization rate gradient. For example, the trend of increasing total porosity after mixing with increasing fertilization rate is predicted. A relationship model is established between fertilization rate and the predicted degree of structural improvement (e.g., increase in total porosity, increase in macropore volume fraction), which generally exhibits a saturation trend. The predicted structural improvement magnitude is combined with the hydraulic properties of organic fertilizer (derived from the hydraulic response index) to estimate theoretical values for improvements in soil water retention capacity and aeration at different fertilization rates. A "fertilizer rate-response curve" is plotted for each representative soil type, with these theoretical improvement values (e.g., percentage increase in water retention) plotted on the vertical axis and fertilizer rate on the horizontal axis. This curve shows the trend of improvement effect as fertilization rate changes. Furthermore, the economic benefits of different fertilization rates are analyzed, taking into account the cost of organic fertilizer and the expected economic benefits of the improvement (e.g., estimating the water savings cost per unit of increased water retention). The optimal economic rate range (typically, the range of rates before the benefit growth levels off) is determined.

[0071] Fertilizer rate-effect curves, which predict the improvement effect under standard or hypothetical environmental conditions, were used. Environmental factors (such as rainfall patterns, temperature fluctuations, and evaporation intensity) were introduced as moderating variables to analyze their impact on the predicted results of the fertilizer rate-effect curves. Long-term meteorological data under typical environmental conditions were extracted from a meteorological database. For example, these data were distinguished 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). The temperature ranges and variations across seasons were also considered. A soil moisture-air-temperature dynamic model was developed to simulate the moisture content, temperature, and gas exchange processes of different soil types (with varying rates of organic fertilizer) under different meteorological conditions. This model was used to simulate soils under different fertilizer rate gradients, using different meteorological data series as input. Simulation results were analyzed to quantify the impact of various environmental factors (such as the intensity of a single heavy rainfall event, the number of consecutive dry days, daily average temperature, diurnal temperature range, and potential evaporation) on the improvement effects of organic fertilizers (such as water retention duration and aeration recovery rate). For example, under high evaporation conditions, the actual effect of improved water retention was lower than the predicted value under moderate evaporation conditions; while under continuous rainy weather, the importance of improved aeration became more pronounced. These impacts were quantified as "environmental response adjustment factors," which can be multiplicative coefficients or additive corrections and are used to adjust the predicted improvement effects under baseline (standard environmental) conditions. For example, for improved water retention, an adjustment factor of less than 1 under high evaporation conditions indicates a weakened actual effect; for improved aeration, an adjustment factor of less than 1 under high humidity conditions indicates a limited improvement effect. These adjustment factors can be constructed as a table or a set of functions to describe the adjustment effects under different combinations of environmental parameters. This table or set of functions is referred to as the "environmental response adjustment factors."

[0072] Using the environmental response adjustment factor and the fertilizer rate effect curve (representing the baseline effect under standard environment), a regression equation system for the improvement effect is established. The equation system uses the fertilizer rate and key environmental parameters (or their corresponding adjustment factors) as independent variables and the predicted improvement index (water retention improvement rate, ventilation improvement, drought resistance enhancement index) as the dependent variable. The equation form can be linear or nonlinear, for example: Improvement index = β +β × amount of fertilizer applied + β ×Environmental factors +β ×Environmental factors +...+ε, or including interaction terms and quadratic terms. First, using fertilizer rate-effect curves or regression equations, baseline values for water retention improvement rates under standard environmental conditions for different fertilizer rate gradients are calculated. These baseline values are organized into a "baseline water effect table." Next, the baseline water effect table is modified using environmental response adjustment factors to predict water retention improvement rates under different environmental condition combinations (e.g., the performance of a specific fertilizer rate in arid regions, humid regions, or across temperature ranges). These modified predictions are organized into an "environmental correction effect matrix," with rows representing fertilizer rate gradients, columns representing different environmental scenarios, and cell values representing predicted water retention improvement rates. Similarly, based on existing soil aeration data, organic fertilizer structural characteristics (from the structural spectrum), and fertilizer rate (from the structural change predictions from the fertilizer rate-effect curve), the degree of improvement in soil aeration (e.g., increase in saturated hydraulic conductivity and increase in gas diffusion coefficient, converted to air flow per unit time) at different fertilizer rates is calculated. Improved aeration also requires correction based on ambient humidity (derived from the rainfall / evaporation-related parameters in the environmental response modifiers), as soil moisture is a key environmental factor influencing aeration. The corrected aeration improvement predictions are compiled into an "Aeration Improvement Table." Combining the water retention capacity and water release modifiers from the hydraulic response index with the plant-available water range data from the soil improvement requirement spectrum, the number of days the soil moisture remains within the plant-available water range at different fertilizer application rates under simulated no-rainfall conditions is predicted. This is calculated by simulating the loss of soil moisture over time due to evaporation and plant uptake, and comparing the number of days the soil moisture remains below the permanent wilting point before and after the addition of organic fertilizer. This yields the "Drought Resistance Enhancement Index." These indices are compiled into a "Drought Resistance Index Table." The predictions from the environmental correction effect matrix, the aeration improvement table, and the drought resistance index table are then compared with small-scale controlled experiments or existing field data for validation. Based on these validation results, the regression equation, modifiers, or prediction model are optimized and modified, such as by adjusting the regression coefficients or introducing additional correction terms, to improve prediction accuracy. The final set of verified and revised prediction results, including the corresponding values of water retention improvement rate, aeration improvement, drought resistance enhancement index under different fertilizer application rates and environmental conditions, constitutes the "improvement effect indicator set".

[0073] Using a set of improvement indicators, which contains quantitatively predicted values of various soil improvement indicators of organic fertilizers at different fertilizer application rates and environmental conditions, visualization charts are created based on the data in the improvement indicator set. For example, a three-dimensional surface plot is generated to show the trend of water retention improvement as a function of fertilizer application rate and a key environmental factor (such as annual rainfall). Bar charts or line graphs are also created to compare the improvement effects of organic fertilizers on different soil types at standard fertilizer application rates. An economic benefit analysis is conducted by combining the economic input costs (organic fertilizer price, transportation, and application costs) and the expected economic benefits (e.g., estimated yield increase and reduced water and fertilizer costs based on the predicted improvement effects) at different fertilizer application rates. Indicators such as the input-output ratio and payback period are calculated for different scenarios. Based on the decomposition characteristics and structural stability data of organic fertilizers (derived from cyclic stability), the decay curve of soil improvement effects over time is predicted, the expected duration of the improvement effects is estimated, and optimal reapplication intervals are recommended. Based on the predicted improvement effects and specific soil issues (such as desertification, salinization, and compaction) and the needs of key local crops, customized application recommendations are provided. These include recommended fertilizer application rates (balancing economic benefits and improvement effects), optimal application times (e.g., before sowing, during the crop growth period), and application methods (e.g., full-layer mixed application, hole application). All of these analysis results, forecast data, charts, economic analysis, and recommendations are integrated into a clearly structured, illustrated document. This document, the "Soil Improvement Expectation Report," provides farmers, agricultural enterprises, or soil remediation projects with a scientific and quantitative basis for decision-making, guiding their rational selection and application of organic fertilizers to achieve optimal soil improvement and economic benefits.

[0074] Preferably, the improvement index calculation in step S43 is specifically as follows: According to the environmental response regulating factors and the fertilizer application rate effect curve, the regression equations of the improvement effect were established; The baseline value is calculated based on the improved effect regression equation group to obtain the moisture baseline effect table; According to the moisture baseline effect table and the environmental response adjustment factor, the environmental condition is corrected to obtain the environmental correction effect matrix; The ventilation improvement table was calculated based on the environmental correction effect matrix; Drought resistance was evaluated based on the aeration improvement table to obtain a drought resistance index table; The environmental correction effect matrix, aeration improvement table and drought resistance index table were verified and revised to obtain an improved effect indicator set.

[0075] In this embodiment of the present invention, the fertilizer application rate-effect curve data (the relationship between the fertilizer application rate gradient and the predicted improvement effect under a standard environment) and the environmental response regulation factor data obtained in step S42 (quantifying the regulatory effect of different environmental factors on the improvement effect) are used to establish a mathematical model, using the fertilizer application rate and key environmental factors (or their quantitative representations, such as environmental factor scores or regulation factor values) as independent variables and the predicted improvement indicators (water retention improvement rate, aeration improvement degree, drought resistance enhancement index) as dependent variables. A multiple regression analysis method is used to fit regression equations based on simulated data (or in combination with existing small-scale test data). For example, three regression equations are established for the water retention improvement rate (ΔM), aeration improvement degree (ΔA), and drought resistance enhancement index (ΔD): 1. ΔM=β +β × amount of fertilizer applied + β ×Environmental factors +β ×Environmental factors +β × Fertilizer amount × Environmental factors +... 2. ΔA=γ +γ × amount of fertilizer applied + γ ×Environmental factors +γ ×Environmental factors +γ × Fertilizer amount × Environmental factors +... 3. ΔD=δ +δ × amount of fertilizer applied + δ ×Environmental factors +δ ×Environmental factors +δ × Fertilizer amount × Environmental factors +... Among them, the amount of fertilizer applied is the amount applied per unit area (tons / hectare or weight percentage); environmental factors , environmental factors etc. are quantitative parameters derived from environmental response regulators that represent specific environmental conditions (e.g., annual rainfall, average temperature, evaporation intensity index); β ,γ ,δ is the regression coefficient, determined by fitting; the multiplication sign is represented by "×". Environmental factors , environmental factors The main environmental factors that affect ventilation (such as humidity); environmental factors , environmental factors etc. are the main environmental factors that affect drought resistance (such as drought index). This set of equations is the "modified effect regression equations".

[0076] Use the water retention improvement rate regression equation in the established improvement effect regression equation group. Set the environmental factor variable to represent the value under standard or baseline environmental conditions (for example, set the environmental factor is the average rainfall value in a certain area over many years, and the environmental factors =( ...

[0077] Utilizing the "Baseline Water Effect Table" and the "Environmental Response Adjustment Factor," for each fertilizer rate gradient, the environmental response adjustment factor is used to predict how the actual water retention improvement rate will deviate from the baseline value under different specific environmental scenarios (e.g., high-evaporation arid environment, low-evaporation humid environment, and different temperature ranges). The environmental response adjustment factor is applied to correct the values in the Baseline Water Effect Table. The correction method depends on the definition of the adjustment factor. For example, if the adjustment factor F_env is a multiplication coefficient, then the corrected ΔM_corrected = Baseline ΔM × F_env; if it is an additive correction term A_env, then ΔM_corrected = Baseline ΔM + A_env. Based on the different environmental scenarios analyzed in step S42, the ΔM value corresponding to each fertilizer rate in the Baseline Water Effect Table is corrected and calculated. The corrected predicted water retention improvement rate values are organized into a table, where rows represent fertilizer rate gradients, columns represent different environmental scenarios (or key environmental parameter combinations), and cell values represent the predicted water retention improvement rate percentages. This table is the "Environmental Correction Effect Matrix."

[0078] Using the established aeration improvement regression equation and an environmental correction effect matrix (although aeration calculations primarily rely on structural changes and moisture predictions, the environmental correction effect matrix provides information on moisture under different environmental conditions), the fertilizer rate gradient and values of environmental factors related to aeration (for example, predicted average soil moisture under different environmental scenarios or air content at maximum water holding capacity, which can be obtained from environmental response modifier analysis or a separate soil water dynamics model) are input. Using the aeration improvement regression equation, the aeration improvement ΔA is calculated for each fertilizer rate and associated environmental conditions. Aeration improvement is typically expressed as an increase in soil gas diffusion coefficient or a percentage increase in air content at a specific water potential, which can be converted to air volume per unit area per unit time (cm³ / min). The calculated aeration improvement values for different combinations of fertilizer rates and environmental conditions are compiled into a table, which is referred to as the "aeration improvement table."

[0079] Using the established drought tolerance enhancement index regression equation, along with a table of improved aeration (indirectly affecting root water uptake efficiency) and an environmental correction effect matrix (providing predictions of water dynamics under different environmental conditions), a fertilizer rate gradient and values of environmental factors related to drought tolerance (e.g., drought duration, evaporation intensity, and average temperature, derived from the environmental response modifier) were input. Using the drought tolerance enhancement index regression equation, the drought tolerance enhancement index (ΔD) was calculated for different fertilizer rates and drought-related environmental conditions. The drought tolerance enhancement index represents the number of days that the soil maintains plant-available water in the absence of effective rainfall after organic fertilizer application. The calculated drought tolerance enhancement index values for different combinations of fertilizer rates and environmental conditions were compiled into a table, which is referred to as the "Drought Resistance Index Table."

[0080] All prediction result tables (environmental modification effect matrix, aeration improvement table, and drought tolerance index table) were compared with independent validation data. Validation data were obtained from small-scale pot experiments or micro-plot field experiments conducted on selected representative soil types using a designed fertilizer rate gradient. In these experiments, soil water 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. The duration of soil maintenance of optimal water conditions was also recorded. The measured modification effect data were quantitatively compared with the predicted values for the corresponding fertilizer rate and environmental conditions, and the prediction error (e.g., relative error, root mean square error) was calculated. If the prediction error exceeded a pre-defined tolerance (e.g., ±15%), the prediction model was revised. This revision could include adjusting regression coefficients, re-evaluating the values of environmental response modifiers, or optimizing the underlying model driving the regression equation (e.g., aperture modification model, hydraulic synergy model). The revision process was iterative until the error between the predicted and measured values was reduced to an acceptable level. The final verified and revised prediction result set includes the precise quantitative prediction values of the organic fertilizer's water retention improvement rate, aeration improvement, and drought resistance enhancement index for various target soils under different fertilizer application rates and key environmental conditions. These data together constitute the final output "improvement effect indicator set."

[0081] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0082] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present 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 present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating organic fertilizer, characterized in that, The following steps are involved: Step S1: extracting pore and water characteristics of the organic fertilizer sample to obtain an organic fertilizer structure spectrum; Step S2: performing a humidity gradient adsorption / desorption measurement on the organic fertilizer sample to obtain a humidity response curve set; performing a dynamic water exchange measurement on the organic fertilizer sample according to the humidity response curve set to obtain a water exchange dynamic graph; performing a hydraulic parameter response analysis based on the humidity response curve set and the water exchange dynamic graph to obtain a hydraulic response index; Step S3: obtaining and extracting soil improvement demand spectra from typical soil samples of different types; evaluating the structural complementarity of the soil improvement demand spectra and the organic fertilizer structure spectrum to obtain a structural complementarity score table; calculating hydraulic synergy based on the soil improvement demand spectra, the structural complementarity score table, and the hydraulic response index to obtain a hydraulic synergy index diagram; and constructing a soil-fertilizer adaptation matrix based on the hydraulic synergy index diagram and the structural complementarity score table; Step S4: Analyze the impact of the environment and fertilizer application amount based on the soil-fertilizer adaptation matrix to obtain a fertilizer application amount effect curve and an environmental response adjustment factor; calculate improvement indicators and predict effects based on the environmental response adjustment factor and the fertilizer application amount effect curve to obtain a soil improvement expectation report.

2. the fertilizer evaluation method according to claim 1, is characterized in that, Step S1 is specifically as follows: Step S11: analyzing the water binding state of the organic fertilizer sample by a low-field nuclear magnetic resonance spectrometer to obtain a water binding spectrum; Step S12: constructing the pores of the organic fertilizer sample in three dimensions according to the water binding spectrum to obtain a set of original pore images; Step S13: extracting structural features from the pore original image set to obtain a pore parameter table; Step S14: constructing a spectrum of the pore parameter table and the water binding spectrum to obtain an organic fertilizer structure spectrum.

3. organic fertilizer evaluation method according to claim 1, is characterized in that, The dynamic water exchange determination in step S2 is specifically as follows: Conduct rainfall simulation and weight record on organic fertilizer samples to obtain rainfall parameters and weight parameters; The water absorption process of the organic fertilizer sample is dynamically monitored according to the rainfall parameters and weight parameters to obtain the water absorption dynamic data; Perform temperature gradient water release analysis on the water absorption dynamic data to obtain temperature water release data; According to the temperature water release data and water absorption dynamic data, a cyclic water absorption and release stability test is performed to obtain cyclic stability data; The moisture effectiveness data is obtained by evaluating the temperature water release data according to the humidity response curve set; A water exchange dynamics diagram was constructed based on the water availability data and the cyclic stability data.

4. organic fertilizer evaluation method according to claim 1, is characterized in that, The hydraulic parameter response analysis in step S2 is specifically as follows: Conduct humidity sensitivity analysis based on the humidity response curve set to obtain humidity sensitivity data; Extract water absorption kinetic parameters from the moisture exchange dynamics diagram based on the set of humidity response curves; The water absorption efficiency is evaluated according to the water absorption kinetic parameters to obtain the water absorption efficiency index; The water retention capacity is measured according to the water exchange dynamic diagram to obtain water retention characteristic data; The moisture buffering capacity is calculated based on the humidity response curve set and the water retention characteristic data to obtain the moisture buffering index; The structural adaptability is evaluated on the humidity response curve set and the water exchange dynamic diagram to obtain the structural adaptability data; Integrate moisture sensitivity data, water absorption efficiency index, moisture buffer index and structural adaptation data to obtain a hydraulic characteristic parameter set; The response index is comprehensively constructed based on the hydraulic characteristic parameter set to obtain the hydraulic response index.

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

6. organic fertilizer evaluation method according to claim 1, is characterized in that, The hydraulic synergy calculation in step S3 is specifically as follows: The infiltration characteristics matching analysis was conducted on the hydraulic response index and the soil improvement demand spectrum to obtain the infiltration coordination data; The water holding characteristics of the hydraulic response index and soil improvement demand spectrum were evaluated in synergy to obtain water holding gain data; The hydraulic response index and soil improvement demand spectrum were analyzed in a coordinated manner to obtain uniform water release data. The hydraulic synergy index table is obtained by performing a comprehensive calculation of the infiltration coordination data, water retention gain data and water release uniformity data; Conduct structural-hydraulic linkage analysis on the hydraulic synergy index table and the structural complementarity score table to obtain structural-hydraulic linkage data; Conduct environmental adaptability assessment based on structural hydraulic linkage data and hydraulic synergy index table to obtain environmental adaptability data; A hydraulic synergy index graph is constructed based on the hydraulic synergy index table, structural hydraulic linkage data, and environmental adaptability data.

7. organic fertilizer evaluation method according to claim 1, is characterized in that, Step S4 is specifically as follows: Step S41: performing a fertilizer application gradient design based on the soil-fertilizer adaptation matrix to obtain a fertilizer application effect curve; Step S42: performing environmental factor impact analysis based on the fertilizer application rate effect curve to obtain an environmental response adjustment factor; Step S43: Calculating improvement indicators based on the environmental response adjustment factor and the fertilizer application rate effect curve to obtain an improvement effect indicator set; Step S44: Generate a soil improvement expectation report based on the improvement effect indicator set.

8. the organic fertilizer evaluation method according to claim 7, is characterized in that, The improvement index calculation in step S43 is specifically as follows: According to the environmental response regulating factors and the fertilizer application rate effect curve, the regression equations of the improvement effect were established; The baseline value is calculated based on the improved effect regression equation group to obtain the moisture baseline effect table; According to the moisture baseline effect table and the environmental response adjustment factor, the environmental condition is corrected to obtain the environmental correction effect matrix; The ventilation improvement table was calculated based on the environmental correction effect matrix; Drought resistance was evaluated based on the aeration improvement table to obtain a drought resistance index table; The environmental correction effect matrix, aeration improvement table and drought resistance index table were verified and revised to obtain an improved effect indicator set.

9. An organic fertilizer evaluation system, characterized in that: For executing the organic fertilizer evaluation method according to claim 1, the organic fertilizer evaluation system comprises: The microstructure characterization module is used to extract the pore and moisture characteristics of organic fertilizer samples and obtain the organic fertilizer structure spectrum; The hydraulic characteristics evaluation module is used to perform moisture gradient adsorption and desorption measurements on organic fertilizer samples to obtain a moisture response curve set; perform dynamic moisture exchange measurements on organic fertilizer samples based on the moisture response curve set to obtain a moisture exchange dynamic graph; and perform hydraulic parameter response analysis based on the moisture response curve set and the moisture exchange dynamic graph to obtain a hydraulic response index. The soil adaptation analysis module is used to obtain and extract soil improvement demand spectra from typical soil samples of different types; evaluate the structural complementarity of the soil improvement demand spectra and the organic fertilizer structure spectrum to obtain a structural complementarity score table; calculate hydraulic synergy based on the soil improvement demand spectrum, structural complementarity score table, and hydraulic response index to obtain a hydraulic synergy index diagram; and construct a soil-fertilizer adaptation matrix based on the hydraulic synergy index diagram and structural complementarity score table; The improvement benefit prediction module is used to analyze the impact of the environment and fertilizer amount based on the soil-fertilizer adaptation matrix, and obtain the fertilizer amount effect curve and environmental response adjustment factor; based on the environmental response adjustment factor and the fertilizer amount effect curve, the improvement index calculation and effect prediction are carried out to obtain the soil improvement expectation report.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the organic fertilizer evaluation method according to any one of claims 1 to 8 is implemented.

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