Method and system for comprehensive testing of multi-element compound fertilizer efficiency in south double-cropping rice area

Abstract

This invention relates to the field of soil fertility technology, specifically to a method and system for comprehensive detection of the fertilizer efficacy of multi-element compound fertilizers in southern double-cropping rice areas. The method includes the following steps: obtaining the redox potential level to determine the reduction state classification; combining the diffusion constant to determine the nutrient release boundary; analyzing the ion antagonism strength to obtain the effective chelation potential energy; using the xylem pressure difference to determine the vascular bundle node transport conductance parameters; analyzing the release rate and chelation potential energy to determine the theoretical nutrient flux; determining the physiological intensity through calcium ion pulses; comparing deviations; and calculating a score. In this invention, by coupling soil reduction classification, fertilizer release boundary, rhizosphere chelation efficiency, and plant transport conductance multi-dimensional parameters, a correlation model from soil environment to plant physiology is established. Real-time physiological feedback signals are used to perform dynamic weight allocation and normalization processing, improving the matching degree between evaluation results and actual growth response, and achieving high-frequency and accurate quantification of the efficacy of multi-element compound fertilizers.

Description

Technical Field

[0001] This invention relates to the field of soil and fertilizer science, and in particular to a method and system for comprehensive testing of the fertilizer efficacy of multi-element compound fertilizer in the southern double-cropping rice region. Background Technology

[0002] The field of soil fertility science primarily studies the physicochemical properties and biological activity of soil, as well as their interaction with plant nutrient supply. Its core aspects include soil nutrient cycling patterns, fertilizer nutrient form transformation, and soil productivity assessment. Through long-term locational monitoring experiments and laboratory physicochemical analysis, this field systematically reveals the spatiotemporal evolution characteristics of crop nutrient absorption and soil nutrient supply capacity. Currently, multi-element compound fertilizer formulations typically contain a specific ratio of macroelements (such as nitrogen, phosphorus, and potassium) and microelements (such as calcium, magnesium, and silicon), often supplemented with specific coatings or slow-release materials to match the nutrient requirements of double-cropping rice. The complexity of such formulations necessitates sophisticated detection methods. With extremely high spatiotemporal resolution capabilities, the traditional method for comprehensive testing of the fertilizer efficacy of multi-element compound fertilizer in the double-cropping rice region of southern China refers to the method for testing the fertilizer efficacy of multi-element compound fertilizer in the double-cropping rice system of southern China. During the growth and development period of early and late rice, a comparative experiment is conducted in representative fields. Soil samples are collected from the topsoil layer at a depth of 0 to 20 cm using a soil drill, and rice root, stem, leaf, and panicle tissues are collected using scissors. Subsequently, in the laboratory, pretreatment operations such as drying, crushing, and concentrated sulfuric acid digestion are performed. The mass fractions of nitrogen, phosphorus, potassium, and trace elements in the samples are determined using a Kjeldahl nitrogen analyzer, spectrophotometer, or inductively coupled plasma atomic emission spectrometer. Finally, the biomass, number of effective panicles, number of grains per panicle, and final yield data of rice at each growth stage are summarized.

[0003] Traditional methods for detecting fertilizer efficiency in double-cropping rice in southern China rely on manual field sampling and laboratory physicochemical analysis. This involves collecting topsoil samples with a soil drill and performing digestion and boiling to determine nutrient content. However, this process is time-consuming and suffers from significant data lag. It fails to reflect the dynamic evolution of the soil's reduction state during alternating periods of flooding and drying, making it difficult to accurately capture the instantaneous release patterns of fertilizer nutrients in the complex rhizosphere environment. Furthermore, it neglects the limiting effect of vascular bundle transport efficiency on nutrient absorption within the plant. Evaluation indicators are mostly static measurements, lacking correlation analysis between multi-source environmental parameters and physiological signals. This makes it impossible to dynamically adjust weights based on actual transport intensity. Moreover, this static and lagging detection method cannot effectively assess the synergistic and antagonistic release effects of different elements in a specific formula within the actual soil microenvironment. Consequently, the dynamic adjustment, evaluation, and optimization of multi-element compound fertilizer formulas lack real-time and accurate physiological and environmental feedback data. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a comprehensive detection method for the fertilizer effect of multi-element compound fertilizer in the southern double-cropping rice area, comprising the following steps: S1: Obtain the magnitude of soil redox potential, compare the potential distribution during flooding and drying periods and match the water management stage, extract the preset target evolution feature data, and determine the reduction state classification result. S2: Calculate the nutrient release boundary of compound fertilizer, determine the soil moisture content data corresponding to the reduction state classification result, calculate the soil relative humidity coefficient and perform kinetic mapping and parameter weight adjustment in combination with the substrate diffusion constant, and determine the fertilizer nutrient release rate boundary. S3: Analyze the chelation efficiency of rhizosphere ions, determine the concentrations of potassium, calcium, magnesium and ammonium ions collected and calculate the background ion antagonism intensity index, adjust the coefficient ratio of citric acid concentration and stability constant in the same period during the calibration process, and determine the effective chelation potential energy information. S4: Calculate the nutrient transport conductance across nodes, determine the pressure difference between the xylem sap between the first and second nodes from the bottom to obtain the pressure drop across nodes, and combine it with the dry matter growth rate of the panicle to perform ratio calculation to obtain the transport flux coefficient, and determine the vascular bundle node transport conductance parameters. S5: The theoretical nutrient distribution flux is obtained by analyzing the boundary of the fertilizer nutrient release rate, the effective chelation potential energy information and the transport conductance parameters of the vascular bundle nodes. The actual physiological transport intensity is determined by combining the calcium ion pulse frequency of the monitoring nodes. The fertilizer efficiency comprehensive evaluation score is calculated by comparing the degree of deviation between the two and performing normalized weighted processing.

[0005] As a further aspect of the present invention, the reduction state classification results include redox potential level, water saturation, and soil pore permeability; the fertilizer nutrient release rate boundary specifically includes dissolution kinetic constant, nutrient diffusion distance, and saturated interface flux; the effective chelation potential energy information specifically refers to chelation reaction constant, ion coordination ratio, and available phosphorus dissociation rate; the vascular bundle node transport conductance parameters include vascular bundle vessel resistance, xylem pressure gradient, and internode distribution coefficient; and the comprehensive fertilizer efficiency evaluation score specifically includes nutrient balance score, translocation coordination index, and comprehensive fertilizer efficiency weight.

[0006] As a further aspect of the present invention, the step of obtaining the restoration state grading result specifically includes: S101: Collect and analyze the magnitude and fluctuation characteristics of soil redox potential collected by in-situ sensors in the double-cropping rice area, statistically analyze the potential distribution range and dispersion coefficient corresponding to the flooding period and the drying period, calculate the spatiotemporal fluctuation trend and charge coverage attribute of soil redox potential under each sampling period, aggregate the charge flux distribution of multiple depth detection nodes and establish spatial dimension mapping and correlation paths, and generate a potential numerical distribution matrix. S102: Based on the water management records of rice throughout its growth period and constructing potential benchmark interval data, calculate the deviation vector and outlier magnitude of the potential value distribution matrix relative to the potential benchmark interval data, screen the water management stages that match the soil redox potential, associate the corresponding water irrigation and drainage records with attribute identifiers, and perform weighted calculations on the deviation terms to establish a water stage matching factor. S103: For the moisture stage matching factor, extract the target feature information of the potential evolution over time under the corresponding moisture management stage, match the target feature information with the corresponding restoration level attribute association mapping process, calculate the regression parameters of the real-time potential increment relative to the historical dynamic distribution, combine the preset number of restoration level discrimination indicators to determine the level classification, and calculate the classification probability and feature distribution density to generate restoration state classification results.

[0007] As a further aspect of the present invention, the process of constructing potential reference interval data based on water management records throughout the entire rice growth period specifically comprises: The water management records for the entire rice growth period are obtained, and water control commands, water coverage depth, and irrigation / drainage switching time parameters corresponding to the flooding, tillering, heading, and grain-filling stages are extracted. For water control commands, the associated historical soil in-situ potential measurement dataset is retrieved, and the potential balance amplitude and potential drift slope under flooded saturation and dry unsaturation states are analyzed. The periodic fluctuation response magnitude of the potential in each growth stage is extracted. For the potential balance amplitude in each growth stage, the historical statistical mean and variance of the target potential signal are calculated, and the proportional offset of the mean is calculated as the dynamic fluctuation tolerance range. By superimposing the potential balance amplitude and the dynamic fluctuation tolerance range and performing difference calculations, the upper and lower limits of the redox potential distribution under the target water state are determined. The upper and lower limits of the distribution corresponding to each growth stage are aggregated, and a topological correlation mapping process is established between the time axis, water control commands, and potential distribution range. A potential reference trend envelope covering the entire rice growth cycle is generated, and the potential benchmark interval data is constructed.

[0008] As a further aspect of the present invention, the step of obtaining the fertilizer nutrient release rate boundary specifically comprises: S201: Obtain the reduction state classification result, retrieve the soil volumetric water content and soil saturated water holding capacity data corresponding to the reduction state classification result, statistically analyze the water transport characteristic magnitude under each reduction gradient, aggregate the soil pore water distribution vector, and establish the soil water spatiotemporal distribution matrix. S202: Based on the aforementioned soil moisture spatiotemporal distribution matrix, calculate the ratio of soil volumetric water content to soil saturated water holding capacity, generate the soil relative humidity coefficient, perform nonlinear kinetic mapping correlation calculation on substrate concentration and reaction rate, obtain the target substrate diffusion constant, establish the correlation between substrate concentration response gradient and diffusion rate evolution trend, and generate kinetic response characteristic records. S203: Call the dynamic response feature record, perform parameter allocation weight adjustment for soil relative humidity coefficient and target substrate diffusion constant, perform data calibration during partial derivative solution, and perform boundary condition fitting calculation in combination with the nutrient release flux and diffusion curvature of multi-element compound fertilizer particles applied to the target soil to determine the fertilizer nutrient release rate boundary.

[0009] As a further aspect of the present invention, the step of obtaining the effective chelation potential energy information specifically includes: S301: Collects data on potassium ion concentration, calcium ion concentration, magnesium ion concentration and ammonium ion concentration from the rhizosphere microenvironment of double-cropping rice in southern China, calculates the deviation of the activity of each cation from the electroneutrality equilibrium point, performs a weighted summation of multi-component ion concentrations, calculates the ionization equilibrium offset, and generates the background ion antagonism intensity index. S302: Call the background ion antagonism strength index, monitor the citrate molar concentration and ion binding stability constant data in the rhizosphere exudate of the same period, establish a nonlinear mapping relationship between the background ion antagonism strength index and the ligand exchange rate, perform parameter weight allocation adjustment for the energy barrier in the chelation reaction pathway, and perform calibration coefficient ratio correction for the occupancy rate of the active center of the citrate chelation reaction, calculate the reaction kinetic compensation value, and generate chelation calibration weight coefficient. S303: Based on the chelation calibration weighting coefficient, a calibration operation is performed on the molar concentration of citric acid and the ion binding stability constant to obtain the kinetic constant of ion pair formation after calibration. The chelation potential energy distribution threshold under the preset benchmark environment is compared with the chelation potential energy distribution threshold. Multi-factor superposition fitting processing is performed, and normalized energy conversion is performed according to the distribution characteristics of effective chelation site density and ion capture flux. The distribution of intermolecular force energy levels is analyzed and determined to obtain effective chelation potential energy information.

[0010] As a further aspect of the present invention, the process for obtaining the chelation potential energy distribution threshold under the preset benchmark environment is as follows: Soil physicochemical parameter data is acquired, and background conductivity parameters including soil hydrogen ion concentration, organic matter content, water saturation, and target cation are extracted. Records of ideal complexation reactions of citric acid and target cations under equilibrium conditions are retrieved from the soil physicochemical parameter data. Chemical potential energy, complexation equilibrium constant, and molecular orbital energy level parameters corresponding to a unit mole of ligand are extracted. For the ideal complexation reaction records, the Gibbs free energy variable generated by the combination of citric acid functional groups with the concentrations of potassium, calcium, magnesium, and ammonium ions is calculated. An energy correlation mapping process between charge density, coordinating atom distance, and electrostatic attraction is established. The theoretical binding energy level distribution range is determined, and the energy boundary in the binding process of each target cation is statistically analyzed. Discretization interval division is performed on the theoretical binding energy level distribution range. By calculating the probability density distribution curve corresponding to each coordination mode, the energy critical point corresponding to the probability density peak is extracted, and the minimum energy gradient for maintaining an effective chelation reaction is determined. The chelation potential energy distribution threshold under the preset benchmark environment is obtained.

[0011] As a further aspect of the present invention, the step of obtaining the transmission conductance parameter of the vascular bundle node specifically includes: S401: Monitors pressure microsensors deployed inside the vascular tissue of the second and first internodes of rice, acquires xylem sap pressure data corresponding to the second and first internodes, statistically analyzes the pressure fluctuation components and baseline offset values ​​corresponding to each observation period, calculates the pressure difference between the second and first internodes, aggregates the pressure potential energy vectors of multiple detection nodes and establishes the internode pressure transmission mapping process, determines the pressure gradient fluctuation range inside the target vascular tissue, and generates cross-node pressure drop. S402: Based on the cross-node pressure drop, acquire the dry matter growth rate data collected by the ear biomass monitoring equipment, calculate the ratio of dry matter growth rate to cross-node pressure drop, analyze the nutrient flow transmission flux density in the transverse path from the second-to-last internode to the first-to-last internode, establish the proportional mapping process between pressure drop and mass increment, determine the material transport efficiency in the target physiological period, calculate the flow load distribution inside the vascular bundle, and generate the transmission flux coefficient. S403: For the aforementioned transmission flux coefficient, extract the cross-sectional geometric parameters and wall thickness data of the conduit. By analyzing the response strength of the transmission flux coefficient relative to the geometric parameters of the vascular bundle, perform the superposition calculation of rheological resistance and node transport load. Use impedance normalization processing to determine the distribution of node conductance efficiency, establish the correlation mapping process between transport power and flow rate, and generate vascular bundle node transmission conductance parameters by fitting the flow conductance characteristic magnitude at the vascular tissue node.

[0012] As a further aspect of the present invention, the steps for obtaining the comprehensive fertilizer effect evaluation score are specifically as follows: S501: Extract the fertilizer nutrient release rate boundary, the effective chelation potential energy information and the vascular bundle node transport conductance parameters, analyze the coupling correlation ratio between fertilizer flux, rhizosphere chelation efficiency and plant conduction efficiency, perform cascade multiplication operation between nutrient diffusion kinetics and physiological transport conductance, match the target environmental temperature and humidity correction coefficients, and generate theoretical nutrient distribution flux. S502: Collect the target calcium ion pulse frequency at the corresponding monitoring node, extract the energy spectrum characteristics and signal amplitude distribution density of the target calcium ion signal in the frequency domain space, analyze the electrochemical signal fluctuation amplitude and frequency offset corresponding to each pulse waveform, establish the function mapping process between calcium ion oscillation period and physiological transport flux, perform calibration coefficient matching for physiological activity at the target monitoring node, analyze the real-time transport energy level distribution and ion conduction energy level inside the target plant, and obtain the actual physiological transport intensity. S503: Compare the theoretical nutrient distribution flux with the actual physiological transport intensity and calculate the corresponding numerical deviation and vector magnitude. Analyze the matching degree between fertilizer release trajectory and plant absorption needs. Based on the deviation degree, dynamically adjust the weight allocation coefficients of fertilizer nutrient release rate, chelation potential energy and transport conductance, and perform normalization processing to generate a comprehensive fertilizer efficiency evaluation score.

[0013] A comprehensive fertilizer efficacy testing system for multi-element compound fertilizers in southern double-cropping rice areas includes: The redox analysis module obtains the magnitude of soil redox potential, compares the potential distribution during flooding and drying periods and matches the water management stage, extracts preset target evolution feature data, and determines the reduction state classification result. The water and nutrient mapping module calculates the nutrient release boundary of the compound fertilizer, determines the soil moisture content data corresponding to the reduction state classification result, calculates the soil relative humidity coefficient and performs kinetic mapping and parameter weight adjustment in combination with the substrate diffusion constant, and determines the fertilizer nutrient release rate boundary. The ion antagonism analysis module analyzes the chelation efficiency of rhizosphere ions, determines the concentrations of collected potassium, calcium, magnesium and ammonium ions and calculates the background ion antagonism intensity index, adjusts the coefficient ratio of citric acid concentration and stability constant in the same period during the calibration process, and determines the effective chelation potential energy information. The node transport measurement module calculates the nutrient transport conductance across nodes, determines the pressure difference between the xylem sap between the first and second to last nodes to obtain the pressure drop across nodes, and performs ratio calculations based on the dry matter growth rate of the panicle to obtain the transport flux coefficient, and determines the vascular bundle node transport conductance parameters. The fertilizer efficiency analysis and evaluation module obtains the theoretical nutrient distribution flux by analyzing the boundary of the fertilizer nutrient release rate, the effective chelation potential energy information, and the transport conductance parameters of the vascular bundle nodes. It determines the actual physiological transport intensity by combining the calcium ion pulse frequency of the monitoring nodes, and calculates the comprehensive fertilizer efficiency evaluation score by comparing the deviation between the two and performing normalized weighted processing.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a potential benchmark interval is established by correlating the spatiotemporal fluctuation characteristics of soil redox potential with irrigation and drainage records to accurately classify reduction levels. Kinetic mapping is performed using the substrate diffusion constant and soil relative humidity to deconstruct the nutrient release boundary of compound fertilizer in a fluctuating water environment. Combined with rhizosphere cation antagonism index and chelation weight calibration, a quantitative analysis of the effective chelation potential energy of the microenvironment is achieved. The xylem pressure gradient and dry matter growth rate are integrated to calculate the vascular bundle node transport conductance, constructing a physiological characteristic nutrient transport assessment model. By comparing theoretically allocated flux with real-time calcium ion pulse frequency mapping of transport intensity, dynamic normalization of multi-factor weights is achieved, improving the matching degree between evaluation results and actual growth response. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a method for comprehensive testing of the fertilizer efficacy of multi-element compound fertilizer in southern double-cropping rice areas, comprising the following steps: S1: Obtain the magnitude of soil redox potential, compare the potential distribution during flooding and drying periods and match the water management stage, extract the preset target evolution feature data, and determine the reduction state classification result. S2: Calculate the nutrient release boundary of compound fertilizer, determine the soil moisture content data corresponding to the collected reduction state classification results, calculate the soil relative humidity coefficient and combine it with the substrate diffusion constant to perform kinetic mapping and parameter weight adjustment, and determine the fertilizer nutrient release rate boundary. S3: Analyze the chelation efficiency of rhizosphere ions, determine the concentrations of potassium, calcium, magnesium and ammonium ions collected and calculate the background ion antagonism intensity index, adjust the coefficient ratio of citric acid concentration and stability constant in the same period during the calibration process, and determine the effective chelation potential energy information. S4: Calculate the nutrient transport conductance across nodes, determine the pressure difference between the xylem sap between the first and second nodes from the bottom to obtain the pressure drop across nodes, and combine it with the dry matter growth rate of the panicle to perform ratio calculation to obtain the transport flux coefficient, and determine the vascular bundle node transport conductance parameters. S5: The theoretical nutrient distribution flux is obtained by analyzing the boundary of fertilizer nutrient release rate, effective chelation potential energy information and vascular bundle node transport conductance parameters. The actual physiological transport intensity is determined by combining the calcium ion pulse frequency of the monitoring nodes. The comprehensive fertilizer efficiency evaluation score is calculated by normalizing and weighting the two by comparing the degree of deviation between them.

[0020] The reduction state classification results include redox potential level, water saturation, and soil pore permeability. The fertilizer nutrient release rate boundary specifically includes dissolution kinetic constant, nutrient diffusion distance, and saturated interface flux. The effective chelation potential energy information specifically refers to chelation reaction constant, ion coordination ratio, and available phosphorus dissociation rate. The vascular bundle node transport conductance parameters include vascular bundle vessel resistance, xylem pressure gradient, and internode distribution coefficient. The comprehensive fertilizer efficiency evaluation score specifically includes nutrient balance score, translocation coordination index, and comprehensive fertilizer efficiency weight.

[0021] Please see Figure 2 The specific steps for obtaining the restored state classification results are as follows: S101: Collect and analyze the magnitude and fluctuation characteristics of soil redox potential collected by in-situ sensors in the double-cropping rice area, statistically analyze the potential distribution range and dispersion coefficient corresponding to the flooding period and the drying period, calculate the spatiotemporal fluctuation trend and charge coverage attribute of soil redox potential under each sampling period, aggregate the charge flux distribution of multiple depth detection nodes and establish spatial dimension mapping and correlation paths, and generate a potential numerical distribution matrix. In the topsoil of double-cropping rice areas in southern China, an in-situ sensor array consisting of platinum and saturated calomel electrodes was deployed vertically at depths of 5 cm, 15 cm, and 25 cm. Data sampling was performed every 10 minutes to acquire raw soil redox potential signals. The weak voltage signals were then subjected to low-pass filtering using a 10th-order Butterworth filter to remove power frequency interference above 50 Hz and polarization noise during initial sensor insertion. The dynamic fluctuation range of the potential was statistically analyzed during typical flooding stages such as the early rice tillering stage and the drying period. The mean, variance, and skewness coefficient of the potential change curve over 24 consecutive hours were calculated to determine the time-domain dispersion of the potential. For example, in a flooding sampling period, the average potential at a depth of 5 cm was -150 mV with a variance of 25 mV², while the potential during the drying period rose to 320 mV, with a coefficient of variation of 0.12. Further analysis of the potential evolution trend with depth was conducted. Spatial weighted interpolation logic was used to spatially weight the charge flux of different detection nodes, transforming discrete point source data into a three-dimensional spatial vector covering the root activity area. During this process, potential values ​​were aggregated with spatial coordinates and sampling timestamps to construct a numerical matrix containing vertical depth, horizontal span, and time series. The topological relationships of the matrix elements described the soil charge coverage properties. To ensure the accuracy of subsequent analysis, before generating the potential value distribution matrix, normalization was performed by comparing it with historical data from the same period, mapping all voltage values ​​to a standard range of -500 mV to +700 mV. For example, when a step fluctuation of more than 100 mV in potential was detected within 30 minutes, the data point was identified as an outlier, and moving average compensation was performed, taking the average of the previous and next five sampling points as the current replacement value to ensure that the generated matrix accurately describes the redox background of the soil microenvironment. Calculation results show that under strong reducing conditions, the charge distribution density in the core region of the matrix reaches 0.85 microcoulombs per cubic centimeter, while under oxidizing conditions it decreases to 0.05 microcoulombs. By performing gradient calculations on the charge density at different depths, the trend vector of charge flow is extracted. This vector is then fused with the time axis to form a two-dimensional spatiotemporal matrix with dimensions of 144 x 3, where 144 represents the daily sampling frequency and 3 represents the detection depth level. This matrix is ​​the generated potential distribution matrix, providing basic data support for subsequent moisture stage matching.

[0022] S102: Based on the water management records of rice throughout its growth period and the construction of potential benchmark interval data, the deviation vector and outlier magnitude of the potential value distribution matrix relative to the potential benchmark interval data are calculated. Water management stages that match the soil redox potential are screened, and the corresponding water irrigation and drainage records and attribute labels are associated. The deviation items are weighted and calculated to establish a water stage matching factor. Based on water management records throughout the entire rice growth period, irrigation and drainage time parameters for key growth stages, including flooding, greening, tillering, jointing, heading, and grain filling, were retrieved. Data on the water depth at each stage was also retrieved; for example, a 3 cm water layer was required during tillering, while 5 to 7 days of drying were necessary during jointing. Using these management records, soil in-situ potential measurement datasets from the past 5 years were analyzed. The equilibrium amplitude of the potential under flooded saturation conditions (between -200 mV and -100 mV) and the amplitude under desaturated conditions (between 200 mV and 450 mV) were extracted, and the drift slope of the potential due to water loss was calculated. By statistically analyzing the historical mean and variance of the potential signals during each growth period, 15% of the mean was set as the allowable range for dynamic fluctuations. The upper and lower limits of the distribution for each time period were determined through superposition and difference calculations.

[0023] Table 1. Data on baseline ranges of potentials during typical reproductive years

[0024] As shown in Table 1, a potential reference trend envelope covering the entire cycle is generated by establishing a topological mapping between the time axis and the water command. The deviation vector of the potential value distribution matrix relative to this envelope is calculated. If the actual potential measured during the tillering stage is 100 mV, and the upper limit of the reference interval is 50 mV, then the deviation is calculated to be 50 mV. Dividing this deviation by the span of the reference interval, 200 mV, yields an outlier magnitude of 0.25. Based on this, it is determined that the actual water status does not match the management command. By associating real-time water irrigation and drainage attribute identifiers, the deviation is weighted. The calculated deviation vector magnitude is substituted into the reciprocal relationship model to establish a water stage matching factor. This factor is set between 0 and 1, with a value closer to 1 indicating a higher degree of matching. For example, when the measured deviation vector magnitude is 0.05 and the weight coefficient is 0.9, the water stage matching factor is calculated as 0.955 by subtracting the product of the deviation and the weight from 1. When the matching factor is greater than 0.85, the current detected potential dynamics are considered to be a perfect match with the expected water management stage. If the matching factor is less than 0.4, an alarm is triggered, indicating an anomaly in soil permeability or water regulation. Finally, the matched water stage identifier, such as day 5 of the tillering stage, is merged with the deviation vector to form a feature input for determining the restored state.

[0025] S103: For the matching factor of the water stage, extract the target feature information of the potential evolution over time under the corresponding water management stage, match the corresponding restoration level attribute association mapping process for the target feature information, calculate the regression parameters of the real-time potential increment relative to the historical dynamic distribution, combine the preset number of restoration level discrimination indicators to determine the level classification, and calculate the classification probability and feature distribution density to generate restoration state classification results. Based on the calculated water stage matching factor, the characteristic vector of potential evolution over time was extracted within the selected water management stage, focusing on analyzing the slope of potential decline or the rate of potential recovery. In the initial 24 hours of flooding, if the potential rapidly drops from 200 mV to -100 mV, the calculated hourly average increment is -12.5 mV. This real-time increment is compared with the regression parameter in the historical dynamic distribution. This regression parameter is the expected slope value obtained by performing least squares regression analysis on potential data from the same stage over the past three years, set at -10.8 mV per hour. Multiple regression analysis is performed, with regression coefficients set to reflect the sensitivity of potential to water changes. Combined with auxiliary indicators such as soil hydrogen ion concentration and temperature, the corresponding reduction level attributes are matched. The reduction state is divided into four levels: oxidizing, weakly reducing, strongly reducing, and extremely strongly reducing. A Gaussian kernel function is used to smooth the characteristic distribution density, with the bandwidth parameter of the kernel function set to 0.05. By calculating the probability of a current feature point falling into each level, for example, when the potential is stable at -150 mV and the fluctuation variance is less than 5 mV squared, the corresponding probability density function value is calculated. This value is then compared with the sum of the probability densities of all levels, determining the probability of it belonging to the strong reducing level as 0.92. The feature distribution density in the reducing state grading results is statistically calculated. If the potential value corresponding to the density peak is in the extremely strong reducing range, it indicates that the organic matter degradation in the soil is severe and the oxygen consumption rate is extremely fast. By recursively calculating data from three consecutive sampling periods, spurious fluctuations caused by momentary poor sensor contact are eliminated. Finally, a stable reducing state grading result is output, including a code representing the redox potential level (e.g., level 3 represents strong reducing), the corresponding belonging probability of 0.92, and simultaneously measured water saturation and soil porosity parameters. This result provides a prerequisite for boundary fitting of the nutrient release rate of subsequent compound fertilizers.

[0026] Please see Figure 3 The specific steps for obtaining the fertilizer nutrient release rate boundary are as follows: S201: Obtain the reduction state classification results, retrieve the soil volumetric water content and soil saturated water holding capacity data corresponding to the reduction state classification results, statistically analyze the water transport characteristic magnitude under each reduction gradient, aggregate the soil pore water distribution vector, and establish the soil moisture spatiotemporal distribution matrix. The reduction state classification results obtained from the aforementioned determination are obtained. By retrieving the pre-stored soil physicochemical property database, soil volumetric water content and saturated water holding capacity parameters corresponding to different reduction gradients are retrieved. For example, under a strong reduction level, due to long-term flooding leading to soil pores being essentially filled with water, the corresponding soil volumetric water content is retrieved as 0.45 cubic centimeters per cubic centimeter, while the soil saturated water holding capacity is 0.48 cubic centimeters per cubic centimeter. For the clayey red soil or paddy soil unique to double-cropping rice areas, the magnitude of water transport characteristics between micropores and macropores is statistically analyzed, and the infiltration path of water in the rhizosphere microenvironment is described using unsaturated hydraulic conductivity. By aggregating soil moisture sensor readings at depths of 5 cm, 15 cm, and 25 cm, a water distribution vector representing the spatial heterogeneity of water is generated, establishing a soil moisture spatiotemporal distribution matrix covering three-dimensional spatial coordinates and time steps. Each cell in this matrix represents the average water content within a 1 cubic centimeter volume. By calculating the gradient distribution within the matrix, the potential energy gradient of water diffusion from the fertilizer particle surface to the surrounding soil is calculated. If the moisture distribution vector within a certain micro-region exhibits significant anisotropy, tensor decomposition is performed to extract the moisture transport components along the principal direction. For example, if the measured longitudinal diffusion component is 0.82 and the lateral component is 0.18, these weights are used to correct the distribution characteristics of the moisture matrix, providing a physical parameter matrix for accurately describing the nutrient leaching environment.

[0027] S202: Based on the soil moisture spatiotemporal distribution matrix, calculate the ratio of soil volumetric water content to soil saturated water holding capacity, generate the soil relative humidity coefficient, perform nonlinear kinetic mapping correlation calculation on substrate concentration and reaction rate, obtain the target substrate diffusion constant, establish the correlation between substrate concentration response gradient and diffusion rate evolution trend, and generate kinetic response characteristic records. Based on the generated soil moisture spatiotemporal distribution matrix, soil volumetric water content data at the target location is extracted. For example, the value at a fertilizer application depth of 10 cm is selected as 0.4 cubic centimeters per cubic centimeter, and the saturated water holding capacity of the soil at that location is retrieved as 0.5 cubic centimeters per cubic centimeter. The ratio of the two is calculated by dividing 0.4 by 0.5, resulting in a soil relative humidity coefficient of 0.8. For the multi-element compound fertilizer for target detection, its main components are urea, diammonium phosphate, and potassium chloride. In a specific implementation scenario, the specific formula mass ratio of this multi-element compound fertilizer is set as follows: nitrogen (N): phosphorus (P2O5): potassium (K2O) = 18:12:15. It also incorporates available micronutrients, including 2% calcium (Ca), 1.5% magnesium (Mg), and appropriate amounts of zinc (Zn), tailored to the soil characteristics of the double-cropping rice region in southern China. The substrate concentration distribution of each core component under different humidity conditions is analyzed for the above formula. A nonlinear kinetic mapping correlation operation based on substrate consumption rate and nutrient release rate is performed. This operation simulates the mobility of nutrient molecules in soil pore water to obtain the diffusion constant of the target substrate. For example, when the soil relative humidity coefficient is 0.8, a kinetic library file is retrieved, and the substrate diffusion constant of nitrogen under this formulation is found to be 2.5 × 10⁻⁹ square meters per second. By establishing a correlation between the evolution trend of substrate concentration response gradient and diffusion rate, the kinetic response trajectory of multi-element nutrient release over time is recorded.

[0028] Table 2 Characteristic parameters of nutrient release kinetic response

[0029] As shown in Table 2, the evolution rates at different humidity levels are detailed in the kinetic response characteristic record. Analysis revealed that when the substrate concentration reaches 80% of the saturation point, the diffusion rate enters the nonlinear growth region, at which point the response intensity coefficient is between 0.82 and 0.88. This process, through feature matching of historical experimental data, provides a customized kinetic evolution model for compound fertilizers with different formulations.

[0030] S203: Call the dynamic response feature record, perform parameter allocation weight adjustment for soil relative humidity coefficient and target substrate diffusion constant, and perform data calibration during partial derivative solution. Combine the nutrient release flux and diffusion curvature of multi-element compound fertilizer particles applied to the target soil to perform boundary condition fitting calculation and determine the fertilizer nutrient release rate boundary. The generated kinetic response feature records are used to adjust the parameter allocation weights for the soil relative humidity coefficient (0.8) and the target substrate diffusion constant. During the partial derivative calculation, the initial value for the humidity weight is set to 0.6, and the initial value for the diffusion constant weight is set to 0.4. The weights are iteratively corrected by calculating the sum of squared residuals between the measured nutrient concentration and the model prediction, for example, adjusting the humidity weight to 0.65 and the diffusion constant weight to 0.35. Combining the initial particle size of the multi-element compound fertilizer granules (2.5 mm), the nutrient content (45%), and the permeability parameters of the coating material applied to the target soil, kinetic fitting calculations are performed under boundary conditions. The nutrient dissolution flux on the fertilizer particle surface is calculated; for example, the instantaneous nitrogen release flux is measured to be 0.15 mg / m² / s. Curvature analysis of the release flux over time is performed to determine the resistance distribution for nutrient migration from the particle core to the boundary. Using the fitted boundary conditions, the time required for nutrient release to reach its peak and the maximum effective distance for nutrient diffusion are calculated. If the coefficient of determination of the fitted curve is greater than 0.95, the obtained boundary value of fertilizer nutrient release rate is considered highly reliable. For example, for a certain sulfur-coated compound fertilizer, the dissolution kinetic constant was calculated to be 0.045 per day under a soil relative humidity coefficient of 0.8. Multiplying this constant by the particle surface area yields a nutrient release rate boundary value of 5.2 grams per cubic meter per day. This boundary information directly determines the total amount of nutrients that the roots can access, providing crucial input flux data for subsequent analysis of the chelation efficiency of rhizosphere ions.

[0031] Please see Figure 4 The specific steps for obtaining effective chelation potential energy information are as follows: S301: Collects data on potassium ion concentration, calcium ion concentration, magnesium ion concentration and ammonium ion concentration from the rhizosphere microenvironment of double-cropping rice in southern China, calculates the deviation of the activity of each cation from the electroneutrality equilibrium point, performs a weighted summation of multi-component ion concentrations, calculates the ionization equilibrium offset, and generates the background ion antagonism intensity index. During the peak tillering stage of rice, a miniature ion-selective electrode sensor array deployed in the rhizosphere microenvironment was used to collect real-time concentrations of potassium, calcium, magnesium, and ammonium ions within a 5 mm radius of the root surface. Through pre-calibrated voltage-concentration conversion logic, the obtained concentrations were 1.2 mmol / L for potassium, 2.5 mmol / L for calcium, 0.8 mmol / L for magnesium, and 3.1 mmol / L for ammonium. The deviation of each cation activity from the electroneutrality equilibrium point was calculated. This process first calculates the total charge molar concentration of all cations by multiplying each ion concentration by its charge number and summing the results, yielding a total charge concentration of 11 mmol / L. A weighted summation of the multi-component ion concentrations was then performed, with weights of 0.25, 0.3, and 0.2 for potassium, calcium, and magnesium ions, respectively, and a weight of 0.25 for ammonium ions. The shift in ionization equilibrium was calculated, reflecting the degree of charge imbalance in the rhizosphere environment due to nutrient absorption. If the weighted total charge concentration deviates from the initial equilibrium point by more than 15%, a significant ion antagonism is considered to exist. By integrating the deviation, a background ion antagonism intensity index is generated; for example, in this measurement, this index was calculated to be 0.42. This value indicates that cation competition in the environment is relatively intense, significantly inhibiting the chelation reaction of target nutrient elements.

[0032] S302: Call the background ion antagonism strength index, monitor the molar concentration of citrate and the ion binding stability constant data in the rhizosphere exudate of the same period, establish a nonlinear mapping relationship between the background ion antagonism strength index and the ligand exchange rate, perform parameter weight allocation adjustment for the energy barrier in the chelation reaction pathway, and perform calibration coefficient ratio correction for the occupancy rate of the active center of the citrate chelation reaction, calculate the reaction kinetic compensation value, and generate chelation calibration weight coefficient. The generated background ion antagonism strength index of 0.42 was used, and rhizosphere exudate data from the same period were monitored. The molar concentration of citric acid was extracted, and its instantaneous concentration in the root surface microdomain was measured to be 55 μmol / L. The binding stability constant of citric acid and the target ion was retrieved, and the initial logarithmic stability constant was set to 12.5. A nonlinear mapping relationship between the background ion antagonism strength index and the ligand exchange rate was established to simulate the preemptive effect of background cations on the active sites of citric acid. The energy barrier in the chelation reaction pathway was calculated; if the antagonism strength increased from 0.2 to 0.42, the energy barrier was considered to have increased by 25%. Parameter weighting was adjusted, increasing the calibration weight of the citric acid molar concentration from 0.5 to 0.65, and correcting the occupancy rate of the active sites in the citric acid chelation reaction. The compensation value was calculated using a reaction kinetic compensation model, for example, a compensation coefficient of 1.15 was obtained. This coefficient was multiplied by the original chelation rate to generate the final chelation calibration weight coefficient. This coefficient reflects the actual contribution efficiency of rhizosphere exudates to nutrient activation under the current complex ionic background. If the coefficient is below 0.6, it means that a large amount of citric acid is ineffectively consumed by background ions and cannot effectively chelate the target nutrients.

[0033] S303: Based on the chelation calibration weighting coefficient, a calibration operation is performed on the molar concentration of citric acid and the ion binding stability constant to obtain the kinetic constant of ion pair formation after calibration. The chelation potential energy distribution threshold under the preset benchmark environment is compared with the chelation potential energy distribution threshold. Multi-factor superposition fitting processing is performed, and normalized energy conversion is performed according to the distribution characteristics of effective chelation site density and ion capture flux. The distribution of intermolecular force energy levels is analyzed and determined to obtain effective chelation potential energy information. Based on the generated chelation calibration weighting coefficients, a product calibration operation is performed on the citric acid molar concentration and the ion binding stability constant to obtain the calibrated ion pair formation kinetic constant. This constant is then compared with the chelation potential energy distribution threshold under a preset baseline environment, and multi-factor superposition fitting processing is performed. The chelation potential energy distribution threshold under the preset baseline environment is obtained by acquiring soil physicochemical parameter data, extracting background parameters including soil pH 5.5, organic matter content 25 g / kg, and water saturation 85%. Related ideal complexation reaction records are retrieved, and the chemical potential energy corresponding to one mole of ligand is extracted to be -350 kJ.

[0034] Table 3 Reference Table of Ion Chelation Energy Levels and Potential Energy Distribution

[0035] As shown in Table 3, the energy mapping between charge density and coordination center distance was established by calculating the Gibbs free energy variable generated by the binding of citric acid functional groups with each cation. The theoretical binding energy level distribution range in the non-antagonistic state was determined to be between -500 and -100 kJ. The energy boundary was statistically analyzed, and discretization was performed. The probability density peak was extracted by calculating the probability density distribution curve; for example, the energy critical point of -320 kJ was extracted as the potential energy division benchmark. The minimum energy gradient for maintaining effective chelation was determined, and normalized energy conversion was performed based on the rhizosphere effective chelation site density of 45 per square micrometer and the ion capture flux distribution characteristics. The distribution of intermolecular force energy levels was analyzed. If the actually measured energy level was above the potential energy threshold, the chelation potential energy was considered effective. Finally, the effective chelation potential energy information was output, including an energy level score of 85 and an effective site occupancy rate of 0.68.

[0036] Please see Figure 5 The specific steps for obtaining the transmission conductance parameters of the vascular bundle nodes are as follows: S401: Monitors pressure microsensors deployed inside the vascular tissue of the second and first internodes of rice, acquires xylem sap pressure data corresponding to the second and first internodes, statistically analyzes the pressure fluctuation components and baseline offset values ​​corresponding to each observation period, calculates the pressure difference between the second and first internodes, aggregates the pressure potential energy vectors of multiple detection nodes and establishes the internode pressure transmission mapping process, determines the pressure gradient fluctuation range inside the target vascular tissue, and generates cross-node pressure drop. During the rice heading stage, high-precision pressure microsensors were implanted into the vascular bundles of the first and second internodes from the bottom. Real-time monitoring and acquisition of xylem sap pressure data for the first and second internodes from the bottom were conducted, with the baseline pressure measured at 0.85 MPa for the second internode and 0.62 MPa for the first internode. Pressure fluctuation components were statistically analyzed for each observation period, and spike noise caused by drastic fluctuations in instantaneous leaf transpiration rates was removed. The pressure difference between the two internodes was calculated by subtracting 0.62 from 0.85, yielding a pressure difference of 0.23 MPa. Pressure potential energy vectors from multiple detection points were aggregated to establish an internode pressure conduction mapping process, simulating the flow direction and resistance distribution of sap in the xylem vessels. The fluctuation range of the pressure gradient within the target vascular tissue was determined; if the pressure gradient increased from 0.05 MPa per centimeter to 0.12 MPa per centimeter during the midday high-temperature period, it reflected an increase in transpiration pull. The pressure difference was integrated over time to generate the cross-node pressure drop. This pressure difference is the core dynamic indicator driving nutrient transport from the stem to the ear. If the pressure difference is less than 0.1 MPa, the transport capacity is considered insufficient. The cross-node pressure difference value calculated in this study is 0.23 MPa, providing a pressure gradient basis for subsequent calculations of nutrient transport efficiency.

[0037] S402: Based on the cross-node pressure drop, acquire the dry matter growth rate data collected by the ear biomass monitoring equipment, calculate the ratio of dry matter growth rate to cross-node pressure drop, analyze the nutrient flow transmission flux density in the second-to-last internode to the first-to-last internode transmission path, establish the proportional mapping process between pressure drop and mass increment, determine the material transport efficiency in the target physiological period, calculate the flow load distribution inside the vascular bundle, and generate the transmission flux coefficient; Based on the generated cross-node pressure drop of 0.23 MPa, data from an automatic biomass monitoring device deployed in the panicle were retrieved to obtain the dry matter growth rate per unit time. For example, the dry matter growth rate in the panicle was measured to be 15 mg / h during the peak grain-filling stage. The ratio of the dry matter growth rate to the cross-node pressure drop was calculated, and 15 was divided by 0.23 to obtain a transport flux density parameter of 65.2 mg / MPa / h. By establishing a proportional mapping process between pressure drop and mass increment, the transport efficiency of nutrient flow in the translocation path from the second-to-last internode to the first-to-last internode was analyzed. A transport efficiency judgment criterion was set; if the ratio was between 50 and 80, it was considered a normal transport state. The flow load distribution within the vascular bundles was calculated, and the distribution ratio of nutrient solution in different xylem groups was analyzed. Using a flow load distribution model, the spatial distribution characteristics of efficient and inefficient xylems were identified. The calculated transport flux coefficient was 0.65, which reflects the actual mass proportion of nutrients migrating to the panicle under unit pressure. If the coefficient shows a downward trend, combined with ambient temperature data, it can be determined whether high temperatures have obstructed transport pathways. This coefficient provides direct physiological support for quantitatively evaluating the plant's internal nutrient transport capacity, reflecting the plant's efficiency in utilizing nutrients absorbed by the roots.

[0038] S403: For the transmission flux coefficient, extract the cross-sectional geometric parameters and wall thickness data of the conduit. By analyzing the response intensity of the transmission flux coefficient relative to the geometric parameters of the vascular bundle, perform the superposition calculation of rheological resistance and node transport load. Use impedance normalization to determine the distribution of node conduction efficiency, establish the correlation mapping process between transport power and flow rate, and generate the vascular bundle node transmission conductance parameters by fitting the flow conduction characteristic magnitude at the vascular tissue node. For the generated transport flux coefficient of 0.65, the cross-sectional geometric parameters of the conduit were retrieved, yielding an average diameter of 45 micrometers and a wall thickness of 3.2 micrometers. A rheological resistance model was established by analyzing the response intensity of the transport flux coefficient relative to these geometric parameters. This model calculates the friction coefficient of the fluid in the micro-cavity, setting the fluid viscosity coefficient to 1.2 mPa·s. The superposition calculation of rheological resistance and node transport load was performed, and impedance normalization logic was used to unify the complex physical dimensions into a conduction efficiency index between 0 and 1. A correlation mapping between transport power and flow rate was established, fitting the flow conduction characteristic magnitude at the vascular tissue nodes. For example, the calculated node conduction efficiency was 0.78. Through nonlinear regression of the flow conduction characteristics, the node conduction slope was calculated to be 1.25. Finally, the vascular bundle node transport conductance parameter was determined and generated, obtained by multiplying the flux coefficient by the conduction efficiency. The calculated vascular bundle node transport conductance parameter was 0.507. This parameter directly describes the plant's xylem's ability to transport nutrients as a nutrient highway; a higher value indicates less resistance to nutrient flow to the panicle.

[0039] Please see Figure 6 The specific steps for obtaining the comprehensive fertilizer effect evaluation score are as follows: S501: Extract fertilizer nutrient release rate boundary, effective chelation potential energy information and vascular bundle node transport conductance parameters, analyze the coupling correlation ratio between fertilizer flux, rhizosphere chelation efficiency and plant conduction efficiency, perform cascade multiplication operation between nutrient diffusion kinetics and physiological transport conductance, match target environmental temperature and humidity correction coefficients, and generate theoretical nutrient distribution flux. The aforementioned fertilizer nutrient release rate boundary (5.2 g / m³ / day), effective chelation potential energy (85 min), and vascular bundle node transport conductance parameter (0.507) were extracted. The coupling correlation ratio between nutrient flux, rhizosphere chelation efficiency, and plant transport efficiency was analyzed, and a cascaded product operation was performed between nutrient diffusion kinetics and physiological transport conductance. This process simulates the entire chain of nutrient flow from fertilizer particles, through rhizosphere activation, and finally into the panicle via the vascular bundles. Correction coefficients corresponding to the target ambient temperature of 28°C and air humidity of 75% were matched, and the temperature correction coefficient was set to 1.12. Using a dimension conversion operator, the release rate boundary (5.2 g / m³ / day) was first mapped to a baseline nutrient flux from the perspective of a single plant (e.g., set to 60 mg / h). Subsequently, the effective chelation potential energy information (85 min converted to an effective supply coefficient of 0.85) and the transport conductance parameter (0.507) were used as limiting factors on the absorption and transport chain, and a cascaded product operation was performed. Specifically, the baseline nutrient flux of 60 is calculated by multiplying the effective supply coefficient of 0.85, then by the transport conductivity of 0.507, and finally by the temperature correction factor of 1.12 (i.e., 60 × 0.85 × 0.507 × 1.12). The calculated theoretical nutrient distribution flux is 28.98 mg / h. This value represents the maximum theoretical nutrient intake that the ear should receive under ideal conditions, based on the current soil environment and plant physiological characteristics.

[0040] S502: Collect the target calcium ion pulse frequency at the corresponding monitoring node, extract the energy spectrum characteristics and signal amplitude distribution density of the target calcium ion signal in the frequency domain space, analyze the electrochemical signal fluctuation amplitude and frequency offset corresponding to each pulse waveform, establish the function mapping process between calcium ion oscillation period and physiological transport flux, perform calibration coefficient matching for physiological activity at the target monitoring node, analyze the real-time transport energy level distribution and ion conduction energy level inside the target plant, and obtain the actual physiological transport intensity. Ion-selective microprobes deployed at the vascular bundle nodes at the base of the panicle were used to acquire calcium ion pulse frequency signals in real time. The energy spectrum characteristics of the calcium ion signal in the frequency domain were extracted, and the dominant pulse frequency was measured to be 12 Hz, with a signal amplitude distribution density of 0.45 μV per square Hz. The amplitude jumps and frequency shifts in the pulse waveform were analyzed to establish a linear mapping between the calcium ion oscillation period and physiological transport flux. A transport flux of 2.2 mg / h was set for each Hz frequency. Calibration coefficient matching was performed for the physiological activity of the monitoring nodes, with a calibration value of 0.95. The real-time transport energy level distribution within the plant was analyzed; if the high-frequency component accounted for more than 60% of the energy spectrum, the actual transport intensity was considered to be at a high level. By multiplying the dominant frequency of 12 Hz by the flux conversion coefficient of 2.2, and then by the calibration coefficient of 0.95, the actual physiological transport intensity was calculated to be 25.08 mg / h. This value reflects the intensity level of nutrient transport to the panicle actually completed by the plant at this moment.

[0041] S503: Compare the theoretical nutrient distribution flux with the actual physiological transport intensity and calculate the corresponding numerical deviation and vector magnitude. Analyze the matching degree between fertilizer release trajectory and plant absorption needs. Based on the deviation degree, dynamically adjust the weight allocation coefficients of fertilizer nutrient release rate, chelation potential energy and transport conductance, and perform normalization to generate a comprehensive fertilizer efficiency evaluation score. The theoretical nutrient distribution flux of 28.8 mg / h was compared with the actual physiological transport intensity of 25.08 mg / h. The deviation between the two values ​​was calculated by subtracting 25.08 from 28.8 and then dividing by 28.8, yielding a deviation ratio of 0.129. The matching degree between fertilizer release trajectory and plant absorption requirements was analyzed; if the deviation ratio was less than 0.15, it was judged as an excellent match. The weights of various evaluation indicators were dynamically adjusted according to the deviation degree. Since the deviation ratio was low, the initial weight allocation was maintained. Normalization was performed, mapping the deviation ratio to a percentage score, with 1 minus the deviation ratio and multiplied by 100. The calculated base score was 87.1 points. The score was corrected by considering the stability coefficients of environmental humidity and temperature, for example, by multiplying by a correction factor of 1.04. The final comprehensive fertilizer efficiency evaluation score was 90.58 points. This score quantitatively reflects the fertilizer utilization efficiency of the double-cropping rice area in the south under the current water management and fertility conditions. A score higher than 90 indicates that the current compound fertilizer formula and water management strategy have achieved a high degree of synergy.

[0042] Please see Figure 7 A comprehensive fertilizer efficacy testing system for multi-element compound fertilizers in southern double-cropping rice areas includes: The redox analysis module obtains the magnitude of soil redox potential, compares the potential distribution during flooding and drying periods and matches the water management stage, extracts preset target evolution feature data, and determines the reduction state classification result. The water and nutrient mapping module calculates the nutrient release boundary of compound fertilizer, determines the soil moisture content data corresponding to the collected reduction state classification results, calculates the soil relative humidity coefficient and performs kinetic mapping and parameter weight adjustment in combination with the substrate diffusion constant, and determines the fertilizer nutrient release rate boundary. The ion antagonism analysis module analyzes the chelation efficiency of rhizosphere ions, determines the concentrations of collected potassium, calcium, magnesium and ammonium ions and calculates the background ion antagonism intensity index, adjusts the coefficient ratio of citric acid concentration and stability constant in the same period during the calibration process, and determines the effective chelation potential energy information. The node transport measurement module calculates the nutrient transport conductance across nodes, determines the pressure difference between the xylem sap between the first and second to last nodes to obtain the pressure drop across nodes, and performs ratio calculations based on the dry matter growth rate of the panicle to obtain the transport flux coefficient, and determines the vascular bundle node transport conductance parameters. The fertilizer efficiency analysis and evaluation module obtains the theoretical nutrient distribution flux by analyzing the boundary of fertilizer nutrient release rate, effective chelation potential energy information and vascular bundle node transport conductance parameters. It determines the actual physiological transport intensity by combining the calcium ion pulse frequency of the monitoring nodes. The module performs normalization and weighting by comparing the degree of deviation between the two and calculates the comprehensive fertilizer efficiency evaluation score.

[0043] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for comprehensive testing of the fertilizer efficiency of multi-element compound fertilizer in the double-cropping rice area in the south, characterized in that, Includes the following steps: S1: Obtain the magnitude of soil redox potential, compare the potential distribution during flooding and drying periods and match the water management stage, extract the preset target evolution feature data, and determine the reduction state classification result. S2: Calculate the nutrient release boundary of compound fertilizer, determine the soil moisture content data corresponding to the reduction state classification result, calculate the soil relative humidity coefficient and perform kinetic mapping and parameter weight adjustment in combination with the substrate diffusion constant, and determine the fertilizer nutrient release rate boundary. S3: Analyze the chelation efficiency of rhizosphere ions, determine the concentrations of potassium, calcium, magnesium and ammonium ions collected and calculate the background ion antagonism intensity index, adjust the coefficient ratio of citric acid concentration and stability constant in the same period during the calibration process, and determine the effective chelation potential energy information. S4: Calculate the nutrient transport conductance across nodes, determine the pressure difference between the xylem sap between the first and second nodes from the bottom to obtain the pressure drop across nodes, and combine it with the dry matter growth rate of the panicle to perform ratio calculation to obtain the transport flux coefficient, and determine the vascular bundle node transport conductance parameters. S5: The theoretical nutrient distribution flux is obtained by analyzing the boundary of the fertilizer nutrient release rate, the effective chelation potential energy information and the transport conductance parameters of the vascular bundle nodes. The actual physiological transport intensity is determined by combining the calcium ion pulse frequency of the monitoring nodes. The fertilizer efficiency comprehensive evaluation score is calculated by comparing the degree of deviation between the two and performing normalized weighted processing.

2. The method according to claim 1, characterized in that, The reduction state classification results include redox potential level, water saturation, and soil pore permeability. The fertilizer nutrient release rate boundary specifically includes dissolution kinetic constant, nutrient diffusion distance, and saturated interface flux. The effective chelation potential energy information specifically refers to chelation reaction constant, ion coordination ratio, and available phosphorus dissociation rate. The vascular bundle node transport conductance parameters include vascular bundle vessel resistance, xylem pressure gradient, and internode distribution coefficient. The comprehensive fertilizer efficiency evaluation score specifically includes nutrient balance score, translocation coordination index, and comprehensive fertilizer efficiency weight.

3. The method according to claim 1, characterized in that, The specific steps for obtaining the restoration state grading results are as follows: S101: Collect and analyze the magnitude and fluctuation characteristics of soil redox potential collected by in-situ sensors in the double-cropping rice area, statistically analyze the potential distribution range and dispersion coefficient corresponding to the flooding period and the drying period, calculate the spatiotemporal fluctuation trend and charge coverage attribute of soil redox potential under each sampling period, aggregate the charge flux distribution of multiple depth detection nodes and establish spatial dimension mapping and correlation paths, and generate a potential numerical distribution matrix. S102: Based on the water management records of rice throughout its growth period and constructing potential benchmark interval data, calculate the deviation vector and outlier magnitude of the potential value distribution matrix relative to the potential benchmark interval data, screen the water management stages that match the soil redox potential, associate the corresponding water irrigation and drainage records with attribute identifiers, and perform weighted calculations on the deviation terms to establish a water stage matching factor. S103: For the moisture stage matching factor, extract the target feature information of the potential evolution over time under the corresponding moisture management stage, match the target feature information with the corresponding restoration level attribute association mapping process, calculate the regression parameters of the real-time potential increment relative to the historical dynamic distribution, combine the preset number of restoration level discrimination indicators to determine the level classification, and calculate the classification probability and feature distribution density to generate restoration state classification results.

4. The method according to claim 3, characterized in that, The process of constructing potential reference interval data based on water management records throughout the entire rice growth period is as follows: The water management records for the entire rice growth period are obtained, and water control commands, water coverage depth, and irrigation / drainage switching time parameters corresponding to the flooding, tillering, heading, and grain-filling stages are extracted. For water control commands, the associated historical soil in-situ potential measurement dataset is retrieved, and the potential balance amplitude and potential drift slope under flooded saturation and dry unsaturation states are analyzed. The periodic fluctuation response magnitude of the potential in each growth stage is extracted. For the potential balance amplitude in each growth stage, the historical statistical mean and variance of the target potential signal are calculated, and the proportional offset of the mean is calculated as the dynamic fluctuation tolerance range. By superimposing the potential balance amplitude and the dynamic fluctuation tolerance range and performing difference calculations, the upper and lower limits of the redox potential distribution under the target water state are determined. The upper and lower limits of the distribution corresponding to each growth stage are aggregated, and a topological correlation mapping process is established between the time axis, water control commands, and potential distribution range. A potential reference trend envelope covering the entire rice growth cycle is generated, and the potential benchmark interval data is constructed.

5. The method according to claim 3, wherein the method is characterized in that, The specific steps for obtaining the fertilizer nutrient release rate boundary are as follows: S201: Obtain the reduction state classification result, retrieve the soil volumetric water content and soil saturated water holding capacity data corresponding to the reduction state classification result, statistically analyze the water transport characteristic magnitude under each reduction gradient, aggregate the soil pore water distribution vector, and establish the soil water spatiotemporal distribution matrix. S202: Based on the aforementioned soil moisture spatiotemporal distribution matrix, calculate the ratio of soil volumetric water content to soil saturated water holding capacity, generate the soil relative humidity coefficient, perform nonlinear kinetic mapping correlation calculation on substrate concentration and reaction rate, obtain the target substrate diffusion constant, establish the correlation between substrate concentration response gradient and diffusion rate evolution trend, and generate kinetic response characteristic records. S203: Call the dynamic response feature record, perform parameter allocation weight adjustment for soil relative humidity coefficient and target substrate diffusion constant, perform data calibration during partial derivative solution, and perform boundary condition fitting calculation in combination with the nutrient release flux and diffusion curvature of multi-element compound fertilizer particles applied to the target soil to determine the fertilizer nutrient release rate boundary.

6. The method according to claim 5, wherein the method is characterized in that, The specific steps for obtaining the effective chelation potential energy information are as follows: S301: Collects data on potassium ion concentration, calcium ion concentration, magnesium ion concentration and ammonium ion concentration from the rhizosphere microenvironment of double-cropping rice in southern China, calculates the deviation of the activity of each cation from the electroneutrality equilibrium point, performs a weighted summation of multi-component ion concentrations, calculates the ionization equilibrium offset, and generates the background ion antagonism intensity index. S302: Call the background ion antagonism strength index, monitor the citrate molar concentration and ion binding stability constant data in the rhizosphere exudate of the same period, establish a nonlinear mapping relationship between the background ion antagonism strength index and the ligand exchange rate, perform parameter weight allocation adjustment for the energy barrier in the chelation reaction pathway, and perform calibration coefficient ratio correction for the occupancy rate of the active center of the citrate chelation reaction, calculate the reaction kinetic compensation value, and generate chelation calibration weight coefficient. S303: Based on the chelation calibration weighting coefficient, a calibration operation is performed on the molar concentration of citric acid and the ion binding stability constant to obtain the kinetic constant of ion pair formation after calibration. The chelation potential energy distribution threshold under the preset benchmark environment is compared with the chelation potential energy distribution threshold. Multi-factor superposition fitting processing is performed, and normalized energy conversion is performed according to the distribution characteristics of effective chelation site density and ion capture flux. The distribution of intermolecular force energy levels is analyzed and determined to obtain effective chelation potential energy information.

7. The method according to claim 6, wherein the method is characterized in that, The specific process for obtaining the chelation potential energy distribution threshold under the preset benchmark environment is as follows: Soil physicochemical parameter data is acquired, and background conductivity parameters including soil hydrogen ion concentration, organic matter content, water saturation, and target cation are extracted. Records of ideal complexation reactions of citric acid and target cations under equilibrium conditions are retrieved from the soil physicochemical parameter data. Chemical potential energy, complexation equilibrium constant, and molecular orbital energy level parameters corresponding to a unit mole of ligand are extracted. For the ideal complexation reaction records, the Gibbs free energy variable generated by the combination of citric acid functional groups with the concentrations of potassium, calcium, magnesium, and ammonium ions is calculated. An energy correlation mapping process between charge density, coordinating atom distance, and electrostatic attraction is established. The theoretical binding energy level distribution range is determined, and the energy boundary in the binding process of each target cation is statistically analyzed. Discretization interval division is performed on the theoretical binding energy level distribution range. By calculating the probability density distribution curve corresponding to each coordination mode, the energy critical point corresponding to the probability density peak is extracted, and the minimum energy gradient for maintaining an effective chelation reaction is determined. The chelation potential energy distribution threshold under the preset benchmark environment is obtained.

8. The method according to claim 6, characterized in that, The specific steps for obtaining the transmission conductance parameters of the vascular bundle nodes are as follows: S401: Monitors pressure microsensors deployed inside the vascular tissue of the second and first internodes of rice, acquires xylem sap pressure data corresponding to the second and first internodes, statistically analyzes the pressure fluctuation components and baseline offset values ​​corresponding to each observation period, calculates the pressure difference between the second and first internodes, aggregates the pressure potential energy vectors of multiple detection nodes and establishes the internode pressure transmission mapping process, determines the pressure gradient fluctuation range inside the target vascular tissue, and generates cross-node pressure drop. S402: Based on the cross-node pressure drop, acquire the dry matter growth rate data collected by the ear biomass monitoring equipment, calculate the ratio of dry matter growth rate to cross-node pressure drop, analyze the nutrient flow transmission flux density in the transverse path from the second-to-last internode to the first-to-last internode, establish the proportional mapping process between pressure drop and mass increment, determine the material transport efficiency in the target physiological period, calculate the flow load distribution inside the vascular bundle, and generate the transmission flux coefficient. S403: For the aforementioned transmission flux coefficient, extract the cross-sectional geometric parameters and wall thickness data of the conduit. By analyzing the response strength of the transmission flux coefficient relative to the geometric parameters of the vascular bundle, perform the superposition calculation of rheological resistance and node transport load. Use impedance normalization processing to determine the distribution of node conductance efficiency, establish the correlation mapping process between transport power and flow rate, and generate vascular bundle node transmission conductance parameters by fitting the flow conductance characteristic magnitude at the vascular tissue node.

9. The method according to claim 8, wherein the method is characterized in that, The specific steps for obtaining the comprehensive fertilizer efficiency evaluation score are as follows: S501: Extract the fertilizer nutrient release rate boundary, the effective chelation potential energy information and the vascular bundle node transport conductance parameters, analyze the coupling correlation ratio between fertilizer flux, rhizosphere chelation efficiency and plant conduction efficiency, perform cascade multiplication operation between nutrient diffusion kinetics and physiological transport conductance, match the target environmental temperature and humidity correction coefficients, and generate theoretical nutrient distribution flux. S502: Collect the target calcium ion pulse frequency at the corresponding monitoring node, extract the energy spectrum characteristics and signal amplitude distribution density of the target calcium ion signal in the frequency domain space, analyze the electrochemical signal fluctuation amplitude and frequency offset corresponding to each pulse waveform, establish the function mapping process between calcium ion oscillation period and physiological transport flux, perform calibration coefficient matching for physiological activity at the target monitoring node, analyze the real-time transport energy level distribution and ion conduction energy level inside the target plant, and obtain the actual physiological transport intensity. S503: Compare the theoretical nutrient distribution flux with the actual physiological transport intensity and calculate the corresponding numerical deviation and vector magnitude. Analyze the matching degree between fertilizer release trajectory and plant absorption needs. Based on the deviation degree, dynamically adjust the weight allocation coefficients of fertilizer nutrient release rate, chelation potential energy and transport conductance, and perform normalization processing to generate a comprehensive fertilizer efficiency evaluation score.

10. A multi-element compound fertilizer efficiency comprehensive detection system for double cropping rice area in the south, characterized in that, The system is used to implement the comprehensive detection method for the fertilizer effect of multi-element compound fertilizer in the southern double-cropping rice area as described in any one of claims 1-9. The system includes: The redox analysis module obtains the magnitude of soil redox potential, compares the potential distribution during flooding and drying periods and matches the water management stage, extracts preset target evolution feature data, and determines the reduction state classification result. The water and nutrient mapping module calculates the nutrient release boundary of the compound fertilizer, determines the soil moisture content data corresponding to the reduction state classification result, calculates the soil relative humidity coefficient and performs kinetic mapping and parameter weight adjustment in combination with the substrate diffusion constant, and determines the fertilizer nutrient release rate boundary. The ion antagonism analysis module analyzes the chelation efficiency of rhizosphere ions, determines the concentrations of collected potassium, calcium, magnesium and ammonium ions and calculates the background ion antagonism intensity index, adjusts the coefficient ratio of citric acid concentration and stability constant in the same period during the calibration process, and determines the effective chelation potential energy information. The node transport measurement module calculates the nutrient transport conductance across nodes, determines the pressure difference between the xylem sap between the first and second to last nodes to obtain the pressure drop across nodes, and performs ratio calculations based on the dry matter growth rate of the panicle to obtain the transport flux coefficient, and determines the vascular bundle node transport conductance parameters. The fertilizer efficiency analysis and evaluation module obtains the theoretical nutrient distribution flux by analyzing the boundary of the fertilizer nutrient release rate, the effective chelation potential energy information, and the transport conductance parameters of the vascular bundle nodes. It determines the actual physiological transport intensity by combining the calcium ion pulse frequency of the monitoring nodes, and calculates the comprehensive fertilizer efficiency evaluation score by comparing the deviation between the two and performing normalized weighted processing.

Images