Soil organic matter content real-time analysis method and system

Abstract

The invention relates to the technical field of soil data analysis, in particular to a soil organic matter content real-time analysis method and system.The method comprises the following steps of obtaining a soil sample, cutting the soil sample into equal units, collecting the organic matter concentration and the change rate of the organic matter concentration, calculating the slope fluctuation amplitude, marking the starting point of a non-uniform area, and injecting a soil decomposition reagent; recording the organic matter release and residual mass, judging the soil stability according to the residual rate, and distinguishing a stable region from an unstable region to obtain a soil organic matter content stability analysis result. According to the method, the time and space two-dimensional sequence is constructed, continuous tracking of dynamic changes of the organic matter is enhanced, abnormal region boundaries are recognized through slope fluctuation and direction reversal, the local mutation recognition precision is improved, staged residual rate data are obtained in combination with the action of various reagents, and the recognition capacity of response differences of different organic components is enhanced; stability distinguishing is achieved based on the fluctuation amplitude, and accurate evaluation and detection of the soil organic matter content are supported.

Description

Technical Field

[0001] The present invention relates to the technical field of soil data analysis, and particularly relates to a real-time analysis method and system for soil organic matter content. Background Art

[0002] The technical field of soil data analysis includes related methods and systems for collecting, processing, and analyzing various physical, chemical, and biological parameters in soil. The core content is to establish a basic data platform for agriculture, ecological environment protection, and resource management based on the accurate collection and digital processing of soil information. Soil data analysis widely involves aspects such as soil component detection, nutrient distribution analysis, humidity and temperature monitoring, and pollution source identification, and is usually achieved through methods such as sensor collection, data conversion, and digital modeling.

[0003] Among them, the real-time analysis method for soil organic matter content refers to a technical solution for regularly or continuously detecting the organic matter components in soil samples, covering the field collection of soil samples, the extraction and processing of organic substances, and the quantitative analysis of the content. Specifically, based on the collection of soil samples, chemical reagents are used for the separation reaction of organic substances, and by measuring the mass, concentration, or color change of the reaction products, the content of organic matter in the soil is determined. This method usually combines a temperature control device, a reaction vessel, and a data collection unit to standardize the entire analysis process, and through the comparison of sample data at each time point, the real-time data of the soil organic matter content is obtained.

[0004] In the existing real-time analysis process of soil organic matter content, in terms of data acquisition, it usually relies on single-time point or regular detection methods, lacking a continuous description of the changes in soil organic matter content over time and space, resulting in difficulty in effectively identifying the rapid fluctuation characteristics of organic matter in local areas, and there is a risk of omission in identification in scenarios with strong regional heterogeneity or short-term drastic changes. In terms of defining abnormal regions, traditional methods rely more on single-point concentration value comparison, lacking a dynamic analysis basis from the perspective of trend changes, resulting in blurred boundaries of heterogeneous regions and insufficient positioning accuracy. In the process of component reaction identification, conventional methods are mostly based on the single extraction amount, ignoring the decomposition response differences of organic matter components to various chemical reagents, and unable to establish a mapping relationship between reaction behavior and component types, affecting the depth of analysis of the structural stability of complex organic matter in soil. At the same time, the lack of systematic tracking and data recording of the stage changes during the reaction process limits the ability to evaluate the tolerance and release potential of soil organic matter at different reaction stages, and is not conducive to accurately predicting the evolution trend of soil quality and its impact on agriculture and the ecosystem. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a real-time analysis method for soil organic matter content.

[0006] To achieve the above object, the present invention adopts the following technical solution: A real-time analysis method for soil organic matter content, comprising the following steps: S1: Obtain soil samples in the target area, divide them into equal spatial units based on a three-dimensional grid, and collect the organic matter concentration values and concentration change rate values in real time and arrange them to generate real-time spatial content sequence values; S2: According to the real-time spatial content sequence values, calculate the concentration slope values between the front and rear groups of cells respectively, count the differences between adjacent slope values, and mark the direction change situation to obtain the content slope fluctuation amplitude data; S3: According to the inversion positions of the content slope fluctuation amplitude data, extract the average values of the soil organic matter concentrations in the front and rear spatial units, combine the concentration median of the real-time spatial content sequence values, and judge whether the deviation ratio between the average value of the organic matter concentration and the median exceeds the deviation threshold. Mark the positions that meet the conditions as the starting points of the non-uniform regions, and construct regional boundaries between consecutive mutation points to obtain the content deviation region intervals; S4: Inject the soil structure decomposition reagent into the samples in the content deviation region intervals in sequence, record the mass of the organic matter released at each stage and the residual mass after the reaction, calculate the residual rate at each stage and arrange them according to time to obtain the stage-by-stage organic matter content residual rate data.

[0007] As a further solution of the present invention, the real-time spatial content sequence values include concentration time series distribution, spatial positioning information, and concentration change rate. The content slope fluctuation amplitude data includes slope difference amplitude, direction change annotation information, and fluctuation continuity. The content deviation region intervals include the starting points of non-uniform regions, the deviation ratio of the average concentration, and the regional boundary range. The stage-by-stage organic matter content residual rate data includes stage residual rate sequences, release amounts at each stage, and reaction residual masses.

[0008] As a further solution of the present invention, the specific steps of S1 are: S111: Based on the three-dimensional grid established by obtaining soil samples in the target area, according to the position indexes of each spatial unit in the grid division coordinate system, collect the real-time soil organic matter concentration values per unit volume for each spatial unit in turn, and combine the time stamp records to determine the time points corresponding to the spatial units to obtain the unit spatial concentration value sequence; S112: According to the unit spatial concentration value sequence, call the continuous time points corresponding to the spatial units for concentration data collection, count the concentration change rate values for each spatial unit, and integrate the rate values with the position indexes and collection time series of the spatial units to obtain the concentration change rate distribution values; S113: Based on the unit space concentration value sequence and the concentration change rate distribution value, arrange the concentration and rate joint sequence in sequence according to the position index of the space unit. Combine the time continuity, the concentration change amplitude value, the rate mean value, and the volatility to analyze the concentration time-space perturbation, and establish the real-time space content sequence value.

[0009] As a further solution of the present invention, the specific steps of S2 are as follows: S211: Based on the real-time space content sequence value, according to the arrangement order of the space units, calculate the difference value of the content values of two adjacent space units at the same time point, and combine the corresponding space distance to statistically calculate the concentration change gradient between each group of space units to obtain the adjacent unit slope value sequence; S212: According to the adjacent unit slope value sequence, calculate the difference between the front and rear slope values, and perform normalization processing on the difference in combination with the average space distance and the direction variability. Use the formula: ; Calculate the slope change fluctuation amplitude value , where and are the concentration slope values of two adjacent positions respectively, is the sum of the squares of the distances under multiple paths between the two positions, is the average distance difference between the basic space units, represents the direction variability amplitude of the slope difference change; S213: According to the slope change fluctuation amplitude value, integrate the fluctuation amplitude and the direction change characteristics of each position, perform aggregation statistics on the data of different time sequences and space units, and establish the content slope fluctuation amplitude data.

[0010] As a further solution of the present invention, the specific steps of S3 are as follows: S311: According to the continuous change direction recorded in the content slope fluctuation amplitude data, identify and screen all positions where the direction reversal occurs. Combine the soil organic matter concentration values of the previous space unit and the subsequent space unit corresponding to the reversal position, and calculate the organic matter concentration mean value of the front and rear space units at each reversal position to generate the reversal point concentration mean value control value; S312: Based on the reversal point concentration mean value control value, combine the concentration median value in the real-time space content sequence value, compare the offset ratio between each concentration mean value and this median value, and compare the offset ratio with the preset organic matter concentration offset threshold. Use the formula: ; Calculate the offset ratio , determine whether it exceeds the offset threshold, and mark the spatial positions that meet the conditions as the starting points of non-uniform regions, obtaining a non-uniform starting point marking record, where is the average concentration of the spatial units before and after the inflection point, is the median concentration in the real-time spatial content sequence value, is the amplitude of the slope change at the inflection point, is the difference in the organic matter concentration of the unit where the inflection point is located, is the average distance difference between two adjacent inflection points; S313: According to the non-uniform starting point marking record, establish regional boundaries between consecutive mutation points, and sequentially integrate the corresponding spatial units from the starting point to the end point, and collect to obtain a content offset region interval.

[0011] As a further solution of the present invention, the specific steps of S4 are: S411: According to the spatial positions involved in the content offset region interval, sequentially inject a structural decomposition reagent into the soil sample, and record the corresponding mass of the released organic matter after each type of reagent reaction is completed, obtaining a stage release mass value; S412: Based on the stage release mass value, weigh the soil sample after each stage of reaction to obtain the corresponding residual mass value, and combine it with the initial sample mass, and use the following formula to calculate the organic matter residual rate:

[0012] Calculate the residual rate corresponding to each stage , and arrange them in the time sequence of each reaction stage, and establish stage-by-stage organic matter content residual rate data, where is the initial mass of the sample, is the mass of the organic matter released in the stage, is the residual mass after the reaction, is the average reaction coefficient of the reagent used in the stage; S413: According to the stage-by-stage organic matter content residual rate data, match the reagent types and reaction sequences of each stage, and perform structured integration and annotation according to time nodes to obtain stage-by-stage organic matter content residual rate data.

[0013] As a further solution of the present invention, the method further includes S5: According to the stage-by-stage organic matter content residual rate data, calculate the fluctuation amplitude of the content of various organic matters, determine whether the maximum fluctuation amplitude is lower than the residual fluctuation threshold, mark the region not greater than the residual fluctuation threshold as the stable region, and mark the region greater than the residual fluctuation threshold as the unstable region, obtaining the soil organic matter content stability analysis result; The soil organic matter content stability analysis result includes a stable region, an unstable region, and a fluctuation amplitude determination value.

[0014] As a further solution of the present invention, the specific steps of S5 are as follows: S511: Based on the residual rate data of the phased organic matter content, extract the residual rate values of various organic matters in consecutive phases, construct a sequence of changes in organic matter content in chronological order, calculate the difference set of residual rate values between adjacent phases respectively, obtain the change interval of each type of organic matter, and get the fluctuation amplitude of each type of organic matter content; S512: According to the fluctuation amplitude of each type of organic matter content, identify the maximum fluctuation amplitude of each type of organic matter in the corresponding area, compare the maximum fluctuation amplitude with the set residual fluctuation threshold, judge whether the stability condition is satisfied, record the marked state of whether the condition is satisfied, and obtain the regional stability marked value; S513: According to the regional stability marked value, classify the areas that meet the stability condition as stable areas, and classify the areas that do not meet the stability condition as unstable areas, and integrate the corresponding area numbers and type labels to establish the analysis result of the stability of soil organic matter content.

[0015] A real-time analysis system for soil organic matter content, comprising: The content acquisition module is divided into equal space units based on a three-dimensional grid, and collects the concentration and change rate of organic matter per unit volume in soil samples at grid point coordinates, and combines them to generate real-time spatial content sequence values; The slope analysis module calculates the slope and obtains the adjacent difference according to the real-time spatial content sequence value, records the direction change, and generates content slope fluctuation amplitude data; The offset identification module determines the inversion position and calculates the front and back concentrations according to the content slope fluctuation amplitude data, judges whether the offset ratio between the average value of the organic matter concentration and the median of the real-time spatial content sequence value exceeds the offset threshold, and constructs a content offset area interval; The reagent reaction module injects a soil structure decomposition reagent into the content offset area interval, records the release amount and residual amount in each phase, and obtains the phased organic matter content residual rate data; The stability evaluation module calculates the fluctuation amplitude of each type of organic matter content according to the phased organic matter content residual rate data, compares the residual fluctuation threshold to judge the stable area, and obtains the analysis result of the stability of soil organic matter content.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the spatial unit division based on a three-dimensional grid and the simultaneous acquisition of the concentration value and the concentration change rate within a unit volume, the spatial refinement degree and the time-dynamic accuracy of soil organic matter analysis are improved. By combining the acquisition time sequence and the spatial position index to construct a multi-dimensional sequence, it effectively supports the continuous judgment of the migration trend and local aggregation phenomenon of organic matter. The slope difference statistics and direction annotation processing strengthen the ability to identify fluctuation patterns, enabling the micro-scale concentration changes to have identifiable features in the macro sequence. By constructing an offset criterion system based on the reversed position and the concentration median, the starting point and mutation boundary of the non-uniform region are identified, realizing the high-precision boundary construction of the local abnormal region, avoiding information dilution and misjudgment. Relying on the sequential action of multiple types of structure decomposition reagents, a stage sequence of reaction release and residual mass is constructed, strengthening the identification accuracy of the response characteristics of different types of organic substances. Furthermore, a change curve of the reaction residue ratio is established to explore the stability characteristics of organic matter under complex decomposition mechanisms. By screening the stable and unstable regions through the fluctuation amplitude, it provides a basis for the risk grading and precise intervention of the soil state, realizing the multi-dimensional comprehensive discrimination of the behavior mechanism of soil organic matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the main process flow chart of the present invention; Figure 2 is the process flow chart of step S1 of the present invention; Figure 3 is the process flow chart of step S2 of the present invention; Figure 4 is the process flow chart of step S3 of the present invention; Figure 5 is the process flow chart of step S4 of the present invention; Figure 6 is the process flow chart of step S5 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0020] Please refer to Figure 1 , a real-time analysis method for soil organic matter content, comprising the following steps: S1: Obtain soil samples in the target area, divide them into equal space units based on a three-dimensional grid, collect the organic matter concentration value and the concentration change rate value per unit volume in real time for each unit, and arrange them according to the collection time sequence and spatial position index to generate a real-time spatial content sequence value; S2: According to the real-time spatial content sequence value, calculate the concentration slope value between the front and rear groups of cells respectively, count the difference between adjacent slope values, and mark the direction change situation to obtain the content slope fluctuation amplitude data; S3: According to the inversion position of the continuous change direction in the content slope fluctuation amplitude data, extract the average value of the soil organic matter concentration of the front and rear spatial units corresponding to the inversion position, and combine the concentration median of the real-time spatial content sequence value as a reference to judge whether the deviation ratio of the average value of the organic matter concentration to the median exceeds the deviation threshold. Mark the position that meets the conditions as the starting point of the non-uniform area, and construct a regional boundary between the continuous mutation points to obtain the content deviation area interval; S4: According to the samples involved in the content deviation area interval, inject soil structure decomposition reagents (including oxidants, acidic reagents, alkaline reagents, chelating / complexing agents, enzyme preparations, etc.) in sequence, record the mass of organic matter released at each stage and the residual mass after the reaction, calculate the residual rate at each stage and arrange it according to time to obtain the phased organic matter content residual rate data; S5: According to the phased organic matter content residual rate data, calculate the fluctuation amplitude of various organic matter contents, judge whether the maximum fluctuation amplitude is lower than the residual fluctuation threshold, mark the area not greater than the threshold as the stable area, and mark the area greater than the threshold as the unstable area to obtain the analysis result of the stability of the soil organic matter content.

[0021] The real-time spatial content sequence values include the concentration time-series distribution, spatial positioning information, and concentration change rate. The content slope fluctuation amplitude data includes the slope difference amplitude, direction change annotation information, and fluctuation continuity. The content offset region interval includes the starting point of the non-uniform region, the concentration mean offset ratio, and the regional boundary range. The phased organic matter content residue rate data includes the stage residue rate sequence, the release amount of each stage, and the reaction residue mass. The analysis result of the soil organic matter content stability includes the stable area, the unstable area, and the fluctuation amplitude determination value.

[0022] Please refer to Figure 2 , and the S1 step is as follows: S111: Based on the three-dimensional grid established by obtaining soil samples in the target area, according to the position index of each spatial unit in the grid division coordinate system, the soil organic matter concentration value per unit volume is collected in real time for each spatial unit in turn. Combining the timestamp record, the time point corresponding to the spatial unit is determined to obtain the unit space concentration value sequence; Obtain soil samples in the target area. The operation of constructing a three-dimensional grid needs to accurately locate and delimit the spatial boundary in the actual plot through GPS coordinates. Set the side length of the grid unit to 10 meters, and use the Geographic Information System (GIS) to divide the position numbers of each unit space. On this basis, use a portable soil sampling device to collect soil samples within the corresponding depth (such as 0-20 cm) at the grid nodes. The sampling time is recorded as a timestamp accurate to the second, for example, 12:45:32 on March 5, 2025. Subsequently, each sample is sent to the laboratory for the determination of the total organic carbon (TOC) concentration. The measured values are such as 3.47 g / kg, 2.89 g / kg, 3.15 g / kg, etc. The obtained data is matched and stored with the corresponding spatial unit number and time point to form the initial structure of the unit space concentration data. To ensure the measurement accuracy, each unit is set to repeat sampling 3 times, and its average value is calculated as the concentration value of the unit corresponding to the time point. For example, if the three measured values of grid unit A at time point 1 are 3.2, 3.4, and 3.3 g / kg, calculate its average value as (3.2 + 3.4 + 3.3) / 3 = 3.3 g / kg, and record it as the unit volume concentration value at time 1. All units are organized in a matrix structure according to the time series, and the specific format is shown in Table 1.

[0023] Table 1 Initial data table of unit space concentration

[0024] As shown in Table 1, the table lists the sampling results at a certain time point during the previous sampling process, reflecting the concentration sampling and measured average values of different grid spatial units. The data corresponds one by one with the position and time point, providing the basic data structure for subsequent calculations, and finally obtaining the unit space concentration value sequence.

[0025] S112: According to the sequence of unit space concentration values, call the corresponding continuous time points of the space unit for concentration data collection, statistically calculate the concentration change rate values under each space unit, and integrate the rate values with the position index and collection time series of the space unit to obtain the concentration change rate distribution value; Based on the sequence of unit space concentration values, perform a difference operation on the average concentration measurement values of each space unit at two adjacent time points, and further combine the time difference to obtain the concentration change rate value. For example, the average concentration values of a certain space unit B at time points 1 and 2 are 3.3 g / kg and 3.7 g / kg respectively, and the sampling time difference is 48 minutes, that is, 0.8 hours. Then the concentration change rate is (3.7 - 3.3) / 0.8 = 0.5 g / kg / h. All space units repeat such operations and organize them into a rate sequence according to their time series. Each rate value is reserved to two decimal places, and the average value and range statistics of the rates within a continuous time period for the same unit are performed. If there is a situation where the sampling intervals of a certain unit are unequal, standardization processing needs to be carried out before calculation. Time intervals less than 10 minutes are not included in the sequence calculation, and paragraphs exceeding 60 minutes need to be split into two segments, and the rate values are recorded separately for each segment, and then integrated uniformly. For example, if there are three segments of data for space unit C from 1 to 3, which are (3.0 g / kg, 3.4 g / kg, 3.2 g / kg) with sampling times of 12:00, 12:20, and 13:10 respectively, and the time interval between 2 and 3 is 50 minutes, but the concentration drops from 3.4 to 3.2, the rate is (3.2 - 3.4) / 0.83 = -0.24 g / kg / h. The negative value represents a concentration decrease, and such rate values are marked separately in the database when recording. After all rate values are summarized, they form a two-dimensional rate distribution array, sorted by space unit and time point, and finally the concentration change rate distribution value is obtained.

[0026] S113: Based on the sequence of unit space concentration values and the concentration change rate distribution value, arrange the concentration and rate joint sequence in turn according to the position index of the space unit, and combine the time continuity, concentration change amplitude value, rate average value and volatility to analyze the concentration time-space disturbance and establish a real-time space content sequence value; Combined with the unit space concentration value sequence and the concentration change rate distribution value, it is necessary to perform a unified spatio-temporal joint sequence rearrangement on them. According to the numbering order of the grid space units, a combined data group of the concentration and change rate of each unit at each time point is constructed. For example, the concentration sequence of unit D is 3.2, 3.4, 3.1 g / kg, and the corresponding rates are 0.3, -0.5, -0.2 g / kg / h. After obtaining the data group, according to the time change trend, a combined perturbation measurement is carried out using the fluctuation amplitude calculation method. If the concentration value at a certain time point deviates from the daily mean of the whole region by more than 1.5 times the standard deviation, it is marked as a high fluctuation point. Here, the offset threshold is set to 1.2 g / kg. From the statistical experience of past data, if the average concentration of a certain region in March is 3.0 g / kg and the standard deviation is 0.15, then the threshold is 3.0 + 1.5×0.15 = 3.225 g / kg. Points exceeding this value are perturbation abnormal points. Combining the rate mean and rate volatility in the rate distribution, the rate volatility is defined as the ratio of the range of rates in three consecutive time periods to their mean. For example, if the three rate values are 0.2, 0.8, 0.4 g / kg / h, then the volatility is (0.8 - 0.2) / ((0.2 + 0.8 + 0.4) / 3) ≈ 0.75. If the volatility is higher than 1, it is judged as a high-risk point of perturbation. Finally, according to the concentration deviation degree, rate fluctuation level, and time distribution density, perturbation level labels are assigned to each data point, and the overall sequence structure is reconstructed according to the time sequence, and finally a real-time spatial content sequence value is established.

[0027] Please refer to Figure 3 , step S2 is as follows: S211: Based on the real-time spatial content sequence value, according to the arrangement order of the spatial units, the difference between the content values of two adjacent spatial units at the same time point is calculated, and combined with the corresponding spatial distance, the concentration change gradient between each group of spatial units is statistically calculated to obtain the adjacent unit slope value sequence; Based on the real-time spatial content sequence values, first divide the measurement area into regular spatial grids, obtain the organic matter concentration values at the same moment in each spatial unit, number the spatial units in a fixed order, construct unit pairs according to the sequence order, such as unit 1 and unit 2, unit 2 and unit 3, etc., and collect the concentration values of each pair of units at the same time point. Subsequently, combined with the actual spatial distance, perform the slope value calculation operation. For example, if the concentration of unit 1 is 8.3 mg / kg, the concentration of unit 2 is 10.5 mg / kg, and the distance between the two is 4 m, then the slope value is (10.5 - 8.3) / 4 = 0.55 mg / kg·m⁻¹. Similar calculations are performed for all adjacent units to form a sequence of slope values of adjacent units. In practice, the spatial distance usually takes a fixed value such as 2 m or 5 m, and the concentration is obtained based on the real-time sampling data of soil samples. To ensure accuracy, it is recommended that the sampling distance does not exceed 5 m. For example, in a certain agricultural area in Sichuan in March 2024, when sampling the organic matter in the soil, the concentration values collected from the front and back cells are 7.2 mg / kg and 9.1 mg / kg respectively, and the distance is 3 m, then the slope is (9.1 - 7.2) / 3 = 0.63 mg / kg·m⁻¹. All slope value calculations are performed in the above manner. When the concentration decreases, the slope is negative, and it is recorded without taking the absolute value to retain the information on the change direction. The collected result data is listed as follows.

[0028] Table 2 Example Table of Slope Value Calculation

[0029] As shown in Table 2, there are positive and negative slope changes between some units, and all data are arranged in order to form a complete sequence, which serves as the basis for subsequent fluctuation determination.

[0030] S212: According to the sequence of slope values of adjacent units, calculate the difference between the two adjacent slope values, and perform normalization processing on the difference in combination with the average spatial distance and the direction variability. Use the formula: ; Calculate the fluctuation amplitude value of the slope change , where, and are the concentration slope values at two adjacent positions respectively, is the sum of the squares of the distances under multiple paths between the two positions, is the average distance difference between basic spatial units, represents the direction variability amplitude of the slope difference change; For the sequence of adjacent unit slope values, for the two slope values formed by each group of three consecutive cells, such as the slope between cells 1-2 and 2-3, calculate their slope difference to obtain the amplitude of slope change. Then, combine the spatial path distance and direction change amount of these two units to construct an evaluation formula for the amplitude value. During this process, select a set of data within the sample area. Assume that the slope values before and after are 0.55 and -0.225 mg / kg·m⁻¹ respectively, and the difference is |-0.225 - 0.55| = 0.775 mg / kg·m⁻¹. If the sum of the squares of the spatial path is 49 m², the average distance difference is 1.2 m, and the direction variation amplitude (sign jump) is 1.

[0031] According to the formula, substitute the parameters as follows: 、 、 、 and calculate as follows: ; ; ; ; The slope fluctuation amplitude value is 1.0945, indicating that there is a medium-intensity direction jump at this point. When the fluctuation amplitude value exceeds a certain threshold, it is considered that a directional jump occurs. This threshold can be set to 1.0, which is set based on the three-standard-deviation rule on the basis of estimating the mean and standard deviation of 100 groups of samples in the normal area. That is, if the mean is 0.65 and the standard deviation is 0.12, then the threshold is 0.65 + 3×0.12 = 1.01. Using 1.0 as the judgment benchmark, Table 3 gives the fluctuation amplitude results under different parameter combinations.

[0032] Table 3 Calculation Results of Slope Fluctuation Amplitude

[0033] Referring to Table 3, different spatial combinations and direction change conditions have obvious effects on the fluctuation amplitude results. The comprehensiveness of the participating terms in the formula improves the coverage ability of the judgment.

[0034] S213: According to the slope change fluctuation amplitude value, integrate the fluctuation amplitude and direction change characteristics of each position, perform aggregation statistics on the data of different time series and spatial units, and establish the content slope fluctuation amplitude data; According to the fluctuation amplitude value of the slope change, arrange all calculation results in the order of spatial index, aggregate the fluctuation values of all cells at the same sampling moment, and further record the corresponding direction change type, that is, whether the positive and negative signs of the slope are reversed. In actual operation, pair the F-value array recorded by the system with the symbol change sequence. For example, if the F-value array under a certain sequence is [0.32, 0.68, 1.12, 0.94] and the corresponding symbol change marks are [0, 1, 1, 0], it means that there are two direction reversals. The system will perform merger statistics on this result, respectively record the distribution of the fluctuation amplitude intervals, such as less than 0.5, between 0.5 and 1.0 in the middle, and greater than 1.0 in three intervals, and respectively count the direction jump frequencies. Through this method, the quantitative collation and direction marking of the content change trend are completed, and then the content slope fluctuation amplitude data is formed.

[0035] Please refer to Figure 4 , step S3 is as follows: S311: According to the continuous change directions recorded in the content slope fluctuation amplitude data, identify and screen all positions where direction reversals occur. Combine the soil organic matter concentration values of the previous and next spatial units corresponding to the reversal positions, and calculate the average organic matter concentration of the front and back spaces at each reversal position to generate a comparison value of the average concentration at the reversal points; According to the direction change sequence in the content slope fluctuation amplitude data, first extract the slope change directions of each group of spatial units item by item. The extraction method is to calculate the product of the slope signs of adjacent two items. If the product is less than zero, it is judged as a direction reversal, and then mark the position number of the direction reversal point through position indexing. For example, for the direction reversal point numbered 1, the spatial units before and after its position are spatial unit A and spatial unit B respectively. Then, extract the soil organic matter concentration values of spatial unit A and B at this time point respectively, and calculate their average values. The organic matter concentration of unit A is 12.3 mg / kg, and that of unit B is 14.2 mg / kg, and the average concentration M corresponding to the reversal point is 13.25 mg / kg. Similarly, for the reversal point numbered 2, the concentrations of the front and back units are 15.6 mg / kg and 16.4 mg / kg respectively, and the calculated average concentration is 16.0 mg / kg. This operation is repeated until all reversal points are processed to form a list of the average concentrations of the front and back spatial units corresponding to a group of reversal positions. On this basis, the data structure is regularized to form a comparison value of the average concentration at the reversal points.

[0036] S312: Based on the comparison value of the average concentration at the reversal points, combine the median concentration value in the real-time spatial content sequence value, compare the offset ratio between each average concentration and this median value, and compare the offset ratio with the preset organic matter concentration offset threshold. Use the formula: ; Calculate the offset ratio , determine whether it exceeds the offset threshold, and mark the spatial positions that meet the conditions as the starting points of non-uniform regions to obtain non-uniform starting point marking records, where is the average concentration of the spatial units before and after the inversion point, is the median concentration in the real-time spatial content sequence values, is the amplitude of the slope change at the inversion point, is the difference in the organic matter concentration of the unit where the inversion point is located, is the difference in the average distance between two adjacent inversion points; Compare the average concentration reference value at the inversion point with the median concentration value calculated from the real-time spatial content sequence values. Assume that the median concentration G is 14.0 mg / kg, and determine whether the offset ratio between the average concentration M and the median G at each inversion point exceeds the offset threshold.

[0037] Table 4 Inversion Point Parameter Calculation Table

[0038] According to the data in Table 41, calculate the offset ratio P of inversion point 1 as: The first term ; The second term ; Obtain: ; And so on, the P of inversion point 2 is: ; ; ; Then calculate for inversion point 3: ; ; ; The offset threshold is set to 0.09. The basis for setting it is that the degree of deviation between the change in soil organic matter concentration at the reversal point and the overall median concentration must have a certain degree of identification significance. The setting of the offset threshold mainly refers to the upper and lower limits of the change ratio interval composed of the upper and lower quantile difference intervals of the median concentration G in the spatial area in the entire area, and is constrained by the square average magnitude of the slope fluctuation. The 10% variation range of G is used as the reference basis. Combined with the mean fitting range of the slope change A and the concentration difference B, the final offset threshold is set to 0.09. This value shows a slow downward trend with the increase of the overall median concentration G, and a linear growth trend with the average increase of the slope change value A. If P> offset threshold, it is marked as the starting point of the non-uniform area. Therefore, reversal point 2 (P=0.1550) and reversal point 3 (P=0.0948) are marked to obtain the non-uniform starting point mark record.

[0039] S313: establishing a region boundary between consecutive mutation points according to the non-uniform starting point mark record, and integrating corresponding spatial units in sequence from the starting point to the end point to obtain a content deviation region interval; According to the marked non-uniform starting point mark value, each reversal point that meets the conditions is taken as the starting position, and the next reversal point that meets the conditions is further tracked as the end position to form the boundary of the non-uniform area. Then, the spatial units are grouped within the boundary to form a complete area range, and the spatial structure units are prepared for subsequent classification analysis. If reversal point 2 is located at position numbered 24 and reversal point 3 is located at position numbered 30, then the non-uniform area interval is from unit 24 to unit 30. The spatial unit information within this interval is encapsulated to obtain the content offset area interval.

[0040] See also Figure 5 , step S4 is: S411: injecting structural decomposition reagents into the soil samples in sequence according to the spatial positions involved in the content deviation region interval, and recording the corresponding mass of organic matter released after the reaction of each type of reagent is completed to obtain the stage release mass value; According to the spatial positions involved in the content deviation region intervals, the specific coordinates of each spatial unit should be extracted from the existing content deviation region intervals first, and the corresponding soil samples should be extracted from the database according to the coordinates. This process can be achieved through number matching. For example, if region 1 contains position points P1 to P5, then the sample numbers S001 to S005 corresponding to P1 to P5 are extracted and classified into the experimental treatment sequence. Subsequently, structural decomposition reagents are injected in a preset order, including oxidants, acidic reagents, alkaline reagents, chelating agents, and enzyme preparations. The injection order of each type of reagent strictly corresponds to reaction stages T1 to T5. The reaction duration for each stage is uniformly set to 30 minutes, and the injection volume is uniformly 10 milliliters per sample. Suppose in stage T1, the mass of the released organic matter is 3.6 grams after injecting the oxidant, and the initial mass of the corresponding sample is 10 grams. The release amount is recorded as 3.6 grams. After entering stage T2, the acidic reagent is injected, and the recorded release amount is 2.1 grams, and so on. After all the reactions are completed, the organic matter release data for the five stages are obtained. During this process, an electronic balance is used to record the residual mass of the samples after each stage, which are 6.4 grams after T1, 4.3 grams after T2, 2.9 grams after T3, 1.5 grams after T4, and 1.0 grams after T5. The release amount for each stage can be verified by subtracting the residual value from the initial value, as shown in Table 5: Table 5 Release and Residual Mass of Soil Samples at Each Stage

[0041] As shown in Table 5, the released amount of organic matter and the residual mass are traceable after each stage, indicating that the accuracy of the recording process is guaranteed. The injection order and ratio of this type of reagent are carried out according to the standard experimental procedure. The released amount recorded after each stage will be used in the subsequent calculation of the residual rate to obtain the stage release mass value.

[0042] S412: Based on the stage release mass value, weigh the soil samples after each stage reaction to obtain the corresponding residual mass value, and combine it with the initial sample mass. Use the following formula to calculate the organic matter residual rate: ; Calculate the residual rate corresponding to each stage , and arrange them in the chronological order of each reaction stage to establish the stage-by-stage organic matter content residual rate data, where is the initial mass of the sample, is the mass of the organic matter released in the stage, is the residual mass after the reaction, is the average reaction coefficient of the reagent used in the stage; According to the stage release mass value, the release amount and residual mass at each stage in Table 5 can be used as formula parameters to calculate the residual rate stage by stage. The residual rate is expressed as the adjusted proportion of the remaining organic matter in the current stage to the initial mass, so as to reflect the combined influence between the reaction efficiency and the residual capacity.

[0043] The reagent reaction coefficient Z (obtained by quantifying the reagent reaction intensity, and the quantification standard is: oxidant 1.0, acidic reagent 0.8, alkaline reagent 0.6, chelating agent 0.5, enzyme 0.3). For example, when calculating the residual rate of the data in the second stage, substituting the data: Q = 10g, E = 2.1g, H = 4.3g, Z = 0.8, the calculation process is as follows: The first term: ; The second term: ; The third term: ; The final result: ; The result shows that the residual rate in the second stage is 1.5984, indicating that the residual proportion in this stage is relatively low, the organic matter is released sufficiently, which can more comprehensively depict the dynamic change trend of the residual rate and obtain the residual rate data of the organic matter content at each stage.

[0044] S413: According to the residual rate data of the organic matter content at each stage, match the reagent types and reaction sequences at each stage, and perform structured integration and annotation according to the time nodes to obtain the residual rate data of the organic matter content at each stage; According to the residual rate data of the organic matter content at each stage, the mapping relationship between the residual rate sequence at each stage and the corresponding time points can be established, reflecting the collaborative structure between the reagent injection order and the organic matter release. The specific operation is to integrate the residual rate values into a one-dimensional array [1.14, 1.5984,...] in sequence according to the five stages from T1 to T5, and correspond it to the reagent injection type table, and mark the stage time points [30, 60, 90, 120, 150] (unit: minute). If there are multiple soil samples processed synchronously, an independent residual rate time series is generated for each sample and summarized into a unified structure to construct a two-dimensional structure R_matrix, whose columns are time points and rows represent different samples. Through the matrix form, the residual change differences of different samples can be further compared, and finally the residual rate data of the organic matter content at each stage is established.

[0045] Please refer to Figure 6 , and the steps of S5 are: S511: Based on the residual rate data of organic matter content in stages, extract the residual rate values of various organic matters in consecutive stages, construct a sequence of organic matter content changes in chronological order, calculate the difference set of residual rates between adjacent stages respectively, obtain the change interval of each type of organic matter, and get the fluctuation amplitude of each type of organic matter content. Based on the residual rate data of organic matter content in stages, it is necessary to extract the residual rate values of various organic matters in each stage. First, retrieve the residual mass of soil organic matter and the initial sample mass corresponding to each reagent action stage from the original experimental records, and form a time series by calculating the residual rate values. For example, for a soil sample, the residual masses after three-stage reactions are 4.2 g, 3.1 g, and 2.4 g respectively, and the initial mass is 5.0 g. Then the residual rates of each stage are 4.2 / 5.0 = 0.84, 3.1 / 5.0 = 0.62, and 2.4 / 5.0 = 0.48 respectively. Construct a content change sequence based on this, perform a difference operation on this sequence, and obtain the change amount of the residual rate between adjacent stages. That is, the fluctuation amplitude from stage 1 to 2 is 0.84 - 0.62 = 0.22, and the fluctuation amplitude from stage 2 to 3 is 0.62 - 0.48 = 0.14. At the same time, consider different types of organic matters and perform the same operation. If there are five types of organic matters participating in the test in the experiment, then five time series need to be independently processed for each type and the difference operation is performed. For example, the residual rates of each stage corresponding to type a, type b, type c, type d, and type e are shown in Table 6: Table 6 Time series table of residual rates of various organic matters

[0046] As shown in Table 6, further perform the difference operation between stages for each type of organic matter. Taking type a as an example, the fluctuation values are 0.17 and 0.14 in turn. After taking their absolute values, they are stored in the fluctuation set. That is, the fluctuation set of type a is {0.17, 0.14}. Then process the fluctuation sets of type b to type e in turn, and thus form a structured fluctuation data set. In actual operation, the calculation of the residual rate depends on the integrity of the weighing data and the reagent injection sequence, and it is necessary to strictly synchronize the time nodes to ensure the validity of the difference. Interference factors such as missing items and outliers should be avoided during the data construction process. Finally, complete the extraction of the change amount of each type of organic matter under stage action and establish the fluctuation amplitude of each type of organic matter content.

[0047] S512: According to the fluctuation amplitude of each type of organic matter content, identify the maximum fluctuation amplitude of each type of organic matter in the corresponding area, compare the maximum fluctuation amplitude with the set residual fluctuation threshold, judge whether the stability condition is satisfied, record the marked state of whether the condition is satisfied, and obtain the regional stability marked value. According to the fluctuation range of the content of various organic substances, it is necessary to extract the maximum value in the fluctuation set for each category and use it to represent the fluctuation range of this category of organic substances in the corresponding area. Combining the set residual fluctuation threshold to judge the regional stability. First, set the residual fluctuation threshold T. The empirical setting value range is between 0.15 and 0.20. Here, take T = 0.18 as an example. If the maximum fluctuation value of category a is 0.22, then because 0.22 > 0.18, the area it belongs to is initially judged as an unstable area. If the maximum fluctuation of category b is 0.13, then because 0.13 < 0.18, it is marked as a stable area. This judgment needs to be based on the joint decision of all organic substances in the area. Suppose there are categories a, b, and c in a certain area, and their maximum fluctuations are 0.22, 0.13, and 0.35 respectively. Then this area is judged as an unstable area. Here, it is also necessary to introduce the aggregation item of the residual rate data as an auxiliary decision-making basis, such as the average residual rate and the median residual rate, etc., to reflect the regional consistency through numerical differences. Further explain the calculation process of the aggregated residual rate. Taking categories a and b as examples, the stage residual rate sets are {0.83, 0.66, 0.52} and {0.91, 0.85, 0.78} respectively. The average residual rate of category a is (0.83 + 0.66 + 0.52) / 3 ≈ 0.67, and the median value is 0.66. The average residual rate of category b is 0.85, and the median value is 0.85. Through these aggregated indicators, regional characteristic parameters are formed, and then combined with the threshold T to judge the stability attribute. At the same time, introduce the number of samples a, the number of types d, etc. as regional structural parameters, and compare the stability data distribution in a quantitative way. For example, d = 5 types, a = 12 samples. When the aggregated fluctuation range exceeds the threshold and shows a discrete trend, then finally mark this area as an unstable area, and finally establish the regional stability marking value.

[0048] S513: According to the regional stability marking value, classify the areas that meet the stability conditions as stable areas, and classify the areas that do not meet the stability conditions as unstable areas, and integrate the corresponding area numbers and type labels to establish the soil organic matter content stability analysis result; According to the regional stability marking value, perform the marking status classification operation on all areas. If the fluctuation range of the area does not exceed the residual fluctuation threshold in all participating organic matter types, then this area is classified as a stable area. If the fluctuation value of any organic matter exceeds the threshold range, it is marked as an unstable area. During the classification process, it is necessary to map the area identification based on the number and spatial index. For example, the area numbers are 101, 102, 103, etc. The first two are in a stable state and 103 is judged as an unstable area. Then output their stability status labels {101: stable, 102: stable, 103: unstable} respectively. Finally, integrate the three fields of all area numbers, spatial indexes, and stability status labels to establish a structured output data set, and further form the soil organic matter content stability analysis result. This data result can be used for subsequent spatial visualization mapping and regional evolution trend extraction analysis.

[0049] A real-time analysis system for soil organic matter content, comprising: The content acquisition module is divided into equal space units based on a three-dimensional grid, and collects the concentration and change rate of organic matter per unit volume in soil samples at grid point coordinates, and combines them to generate real-time spatial content sequence values; The slope analysis module calculates the slope and obtains the adjacent difference according to the real-time spatial content sequence values, records the direction change, and generates content slope fluctuation amplitude data; The offset recognition module determines the inversion position according to the content slope fluctuation amplitude data, calculates the concentrations before and after, and judges whether the offset ratio between the average value of the organic matter concentration and the median of the real-time spatial content sequence values exceeds the offset threshold, and constructs a content offset region interval; The reagent reaction module injects a soil structure decomposition reagent into the content offset region interval, records the release amount and residual amount at each stage, and obtains stage organic matter content residual rate data; The stability evaluation module calculates the fluctuation amplitude of various organic matter contents according to the stage organic matter content residual rate data, compares the residual fluctuation threshold to judge the stable region, and obtains the soil organic matter content stability analysis result.

[0050] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A real-time analysis method for soil organic matter content, characterized in that, It includes the following steps: S1: Obtain soil samples in the target area, cut them into equal spatial units based on a three-dimensional grid, collect the organic matter concentration values and concentration change rate values in real time and arrange them to generate real-time spatial content sequence values; S2: According to the real-time spatial content sequence values, calculate the concentration slope values between the front and back groups of cells respectively, count the differences between adjacent slope values, and mark the direction change situation to obtain content slope fluctuation amplitude data; S3: According to the reversal positions of the content slope fluctuation amplitude data, extract the average values of the soil organic matter concentrations in the front and back spatial units, combine with the concentration median of the real-time spatial content sequence values, judge whether the deviation ratio between the average value of the organic matter concentration and the median exceeds the deviation threshold, mark the positions that meet the conditions as the starting points of the non-uniform areas, and construct regional boundaries between consecutive mutation points to obtain the content deviation area intervals; S4: Inject soil structure decomposition reagents into the samples in the content deviation area intervals in sequence, record the mass of organic matter released at each stage and the residual mass after the reaction, calculate the residual rate at each stage and arrange them according to time to obtain stage-by-stage organic matter content residual rate data.

2. The real-time analysis method for soil organic matter content according to claim 1, wherein The real-time spatial content sequence values include concentration time series distribution, spatial positioning information, and concentration change rate. The content slope fluctuation amplitude data includes slope difference amplitude, direction change annotation information, and fluctuation continuity. The content deviation area intervals include the starting points of non-uniform areas, concentration average value deviation ratio, and regional boundary ranges. The stage-by-stage organic matter content residual rate data includes stage residual rate sequences, release amounts at each stage, and reaction residual masses.

3. The real-time analysis method for soil organic matter content according to claim 1, characterized in that The specific steps of S1 are as follows: S111: Based on the three-dimensional grid established for obtaining soil samples in the target area, according to the position indexes of each spatial unit in the grid division coordinate system, collect the real-time soil organic matter concentration values per unit volume for each spatial unit in turn, and combine with the time stamp record to determine the time points corresponding to the spatial units to obtain a unit spatial concentration value sequence; S112: According to the unit spatial concentration value sequence, call the consecutive time points corresponding to the spatial units for concentration data collection, count the concentration change rate values for each spatial unit, and integrate the rate values with the position indexes and collection time sequences of the spatial units to obtain concentration change rate distribution values; S113: Based on the unit spatial concentration value sequence and the concentration change rate distribution values, arrange the concentration and rate combined sequences in turn according to the position indexes of the spatial units, combine with time continuity, concentration change amplitude values, rate average values and volatility, analyze the concentration time-space perturbation, and establish real-time spatial content sequence values.

4. The real-time analysis method for soil organic matter content according to claim 1, characterized in that, The specific steps of S2 are as follows: S211: Based on the real-time spatial content sequence values, calculate the differences between the content values of adjacent two spatial units at the same time point according to the arrangement order of the spatial units, combine with the corresponding spatial distances, and count the concentration change gradients between each group of spatial units to obtain an adjacent unit slope value sequence; S212: Calculate the difference between the two adjacent slope values according to the adjacent unit slope value sequence, and normalize the difference by combining the average spatial distance and the direction variability, using the formula: ； Calculate the fluctuation amplitude value of the slope change , where and are the concentration slope values at two adjacent positions respectively, is the sum of the squares of the distances under multiple paths between the two positions, is the average distance difference between the basic space units, represents the directional variation amplitude of the slope difference change; S213: Integrate the fluctuation amplitude and the direction change characteristics at each position according to the slope change fluctuation amplitude value, perform aggregation statistics on the data of different time series and spatial units, and establish the content slope fluctuation amplitude data.

5. The real-time analysis method for soil organic matter content according to claim 1, characterized in that The specific steps of S3 are as follows: S311: Identify and screen all positions where the direction reversal occurs according to the continuous change direction recorded in the content slope fluctuation amplitude data, and calculate the average concentration of organic matter in the front and rear spatial units at each reversal position by combining the soil organic matter concentration values of the previous spatial unit and the subsequent spatial unit corresponding to the reversal position, so as to generate the average concentration reference value at the reversal point; S312: Based on the average concentration reference value at the reversal point, combine the median concentration value in the real-time spatial content sequence value, compare the offset ratio between each average concentration and this median value, and compare the offset ratio with the preset organic matter concentration offset threshold, using the formula: ； Calculation of shift ratio , determine whether it exceeds the offset threshold, and mark the spatial position that meets the condition as the starting point of the non-uniform region to obtain the non-uniform starting point marking record, where is the average concentration of the spatial units before and after the inflection point, is the median concentration in the real-time spatial content sequence value, is the amplitude of the slope change at the inflection point, is the difference in the organic matter concentration of the unit where the inflection point is located, is the average distance difference between two adjacent inflection points; S313: Establish regional boundaries between continuous mutation points according to the non-uniform starting point marker record, and integrate the corresponding spatial units in sequence from the starting point to the end point to collect and obtain the content offset region interval.

6. The real-time analysis method for soil organic matter content according to claim 1, wherein The specific steps of S4 are as follows: S411: Inject the structural decomposition reagent into the soil sample in sequence according to the spatial positions involved in the content offset region interval, and record the mass of the released organic matter after the reaction of each type of reagent is completed to obtain the stage release mass value; S412: Weigh the soil sample after the reaction of each stage based on the stage release mass value to obtain the corresponding residual mass value, and calculate the organic matter residual rate by combining the initial sample mass using the following formula: ； Calculate the residual rate of organic matter corresponding to each stage , and arrange them in the chronological order of the time of each reaction stage to establish the data of the residual rate of organic matter content at each stage. Among them, is the initial mass of the sample, is the mass of organic matter released in the stage, is the residual mass after the reaction, is the average reaction coefficient of the reagent used in the stage; S413: Match the reagent type and reaction sequence of each stage according to the stage organic matter content residual rate data, and perform structured integration and annotation according to the time node to obtain the stage organic matter content residual rate data.

7. The real-time analysis method for soil organic matter content according to claim 1, characterized in that The method further includes S5: Calculate the fluctuation amplitude of the content of various organic matters according to the stage organic matter content residual rate data, determine whether the maximum fluctuation amplitude is lower than the residual fluctuation threshold, mark the area with a fluctuation amplitude not greater than the residual fluctuation threshold as the stable area, and mark the area with a fluctuation amplitude greater than the residual fluctuation threshold as the unstable area to obtain the soil organic matter content stability analysis result; The soil organic matter content stability analysis result includes the stable area, the unstable area, and the fluctuation amplitude determination value.

8. The real-time analysis method for soil organic matter content according to claim 7, characterized in that, Specifically, the steps of S5 are as follows: S511: Based on the stage organic matter content residual rate data, extract the residual rate values of various organic matters in continuous stages, construct the organic matter content change sequence in chronological order, calculate the difference set of the residual rate values between adjacent stages respectively, obtain the change interval of each type of organic matter, and get the fluctuation amplitude of the content of various organic matters; S512: Identify the maximum fluctuation amplitude of each type of organic matter in the corresponding area according to the fluctuation amplitude of the content of each type of organic matter, compare the maximum fluctuation amplitude with the set residual fluctuation threshold, determine whether the stability condition is met, record the marked state of whether the condition is met, and obtain the regional stability marked value; S513: According to the regional stability marked value, classify the areas that meet the stability condition as stable areas, and classify the areas that do not meet the stability condition as unstable areas, and integrate the corresponding area numbers and type labels to establish the analysis result of the stability of the soil organic matter content.

9. A real-time analysis system for soil organic matter content, characterized in that, The system is used to execute the method according to any one of claims 1-8, and includes: The content acquisition module is divided into equal space units based on a three-dimensional grid, and collects the concentration and change rate of organic matter per unit volume in the soil sample at the grid point coordinates, and combines them to generate a real-time spatial content sequence value; The slope analysis module calculates the slope and obtains the adjacent difference according to the real-time spatial content sequence value, records the direction change, and generates the content slope fluctuation amplitude data; The offset identification module determines the inversion position and calculates the front and rear concentrations according to the content slope fluctuation amplitude data, judges whether the offset ratio of the average organic matter concentration to the median of the real-time spatial content sequence value exceeds the offset threshold, and constructs the content offset area interval; The reagent reaction module injects the soil structure decomposition reagent into the content offset area interval, records the release amount and residual amount at each stage, and obtains the stage-by-stage organic matter content residual rate data; The stability evaluation module calculates the fluctuation amplitude of the content of each type of organic matter according to the stage-by-stage organic matter content residual rate data, compares the residual fluctuation threshold to judge the stable area, and obtains the analysis result of the stability of the soil organic matter content.

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