Isotope tracer based data analysis system for soil and water erosion

Abstract

The present application relates to the technical field of soil erosion monitoring, and discloses a soil erosion data analysis system based on isotope tracing. The system comprises an isotope collection module, a spatial division module, an erosion evaluation module and a dynamic analysis module. The isotope collection module is used to obtain initial isotope characteristic values of water and soil samples in a monitoring area; the spatial division module divides the monitoring area into a plurality of monitoring units according to terrain parameters; the erosion evaluation module is associated with the first two modules, and generates an initial erosion grade according to the initial isotope characteristic value distribution state of the water and soil samples in each monitoring unit; and the dynamic analysis module is associated with the erosion evaluation module, calls reference isotope characteristic values of a historical isotope database to obtain a characteristic difference degree, adjusts the erosion grade determination threshold of the monitoring unit according to the characteristic difference degree, and updates the initial erosion grade according to the adjusted threshold. The system can improve the accuracy and dynamics of soil erosion data analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water and soil loss monitoring, in particular to a water and soil loss data analysis system based on isotope tracing. BACKGROUND

[0002] As a prominent problem in the field of ecological environment, the monitoring and evaluation of water and soil loss has important value for regional ecological protection and land resource management. Traditional water and soil loss data analysis methods rely on runoff plot observation, erosion pin method, tracer labeling and other means, which have certain limitations in practical application.

[0003] Runoff plot observation calculates the loss amount by setting a standard plot and periodically measuring runoff and sediment volume, but this method is greatly affected by natural conditions such as topography and climate, and has limited monitoring range, making it difficult to reflect the spatial heterogeneity of water and soil loss in large areas. Although the erosion pin method can directly obtain the erosion depth of the soil surface, it cannot dynamically track the water and soil loss process, and the data acquisition period is long and the timeliness is insufficient.

[0004] In the tracer labeling method, traditional physical or chemical tracers are easily disturbed by environmental factors, and their migration rules in soil deviate from the actual water and soil loss process, limiting the accuracy of the monitoring results. At the same time, existing monitoring systems lack fine division of the monitoring area, making it difficult to accurately evaluate the water and soil loss characteristics of different land units, and in the determination of loss grade, the threshold value is mostly dependent on empirical values, lacking dynamic association with historical data, resulting in insufficient objectivity and adaptability of the evaluation results. SUMMARY

[0005] The purpose of the present application is to provide a water and soil loss data analysis system based on isotope tracing to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides a water and soil loss data analysis system based on isotope tracing, which comprises:

[0007] The isotope collection module is configured to obtain the initial isotope characteristic value of the water and soil sample in the monitoring area;

[0008] The spatial division module is configured to divide the monitoring area into a plurality of monitoring units according to the topographic parameters;

[0009] The loss evaluation module is associated with the isotope collection module and the spatial division module, respectively, and the loss evaluation module is configured to generate the initial loss grade of the monitoring unit according to the initial isotope characteristic value distribution state of the water and soil sample of each monitoring unit;

[0010] The dynamic analysis module is associated with the loss evaluation module, and is configured to call reference isotope characteristic values of a historical isotope database according to the initial loss grade, and obtain a characteristic difference degree;

[0011] The dynamic analysis module is further configured to adjust a loss grade determination threshold of the monitoring unit according to the characteristic difference degree.

[0012] The dynamic analysis module is further configured to update the initial loss grade according to the adjusted loss grade determination threshold.

[0013] Preferably, when the loss evaluation module is configured to generate the initial loss grade of the monitoring unit, the loss evaluation module is further configured to extract an initial isotope characteristic value range of the water and soil samples in the same monitoring unit.

[0014] The loss evaluation module is further configured to compare the initial isotope characteristic value range with a preset loss fluctuation threshold interval.

[0015] When the initial isotope characteristic value range is lower than a lower limit of the loss fluctuation threshold interval, the loss evaluation module calls a preset stable loss grade as the initial loss grade.

[0016] When the initial isotope characteristic value range is within the loss fluctuation threshold interval, the loss evaluation module generates a dispersion coefficient according to the position distribution of the water and soil samples.

[0017] When the initial isotope characteristic value range is higher than an upper limit of the loss fluctuation threshold interval, the loss evaluation module activates an abnormal loss analysis program.

[0018] Preferably, when the dynamic analysis module is configured to obtain the characteristic difference degree, the dynamic analysis module is further configured to extract a historical isotope characteristic value mean of the same monitoring unit in the historical isotope database.

[0019] The dynamic analysis module is further configured to calculate an absolute deviation amount of the initial isotope characteristic value from the historical isotope characteristic value mean.

[0020] The dynamic analysis module is further configured to map the absolute deviation amount to a preset difference degree quantification scale.

[0021] Preferably, when the dynamic analysis module is configured to adjust the loss grade determination threshold of the monitoring unit, the dynamic analysis module is further configured to establish an inverse correlation rule between the characteristic difference degree and the loss grade determination threshold.

[0022] When the characteristic difference degree reaches a first difference critical value, the dynamic analysis module starts a first-level threshold decay mechanism.

[0023] When the feature difference reaches a second difference threshold, the dynamic analysis module initiates a secondary threshold decay mechanism.

[0024] Preferably, when the dynamic analysis module is configured to update the initial loss grade, the dynamic analysis module is further configured to re-compare the adjusted loss grade determination threshold with the initial isotope feature value range;

[0025] When the initial isotope feature value range is higher than the adjusted loss grade determination threshold, the dynamic analysis module raises the initial loss grade;

[0026] When the initial isotope feature value range is lower than the adjusted loss grade determination threshold, the dynamic analysis module maintains the initial loss grade.

[0027] Preferably, the system further comprises a spatial correlation correction module associated with the loss evaluation module, the spatial correlation correction module is configured to obtain initial loss grades of adjacent monitoring units;

[0028] The spatial correlation correction module is further configured to calculate a spatial influence weight according to a terrain connectivity parameter;

[0029] The spatial correlation correction module is further configured to fuse the initial loss grades of adjacent monitoring units based on the spatial influence weight to generate a corrected loss grade.

[0030] Preferably, when the spatial correlation correction module is configured to calculate the spatial influence weight, the spatial correlation correction module is further configured to extract elevation drop data of the monitoring unit boundary;

[0031] The spatial correlation correction module is further configured to identify the relationship of adjacent units on the water flow direction path;

[0032] The spatial correlation correction module is further configured to generate an erosion conduction coefficient according to the elevation drop and the water flow path length.

[0033] Preferably, when the spatial correlation correction module is configured to generate the corrected loss grade, the spatial correlation correction module is further configured to screen adjacent monitoring units with erosion conduction coefficients exceeding a conduction threshold;

[0034] The spatial correlation correction module is further configured to normalize the erosion conduction coefficient to a spatial influence weight;

[0035] The spatial correlation correction module is further configured to perform weighted fusion calculation on the initial loss grades of the target unit and adjacent units.

[0036] Preferably, the system further comprises a provenance tracking module associated with the isotope collection module, the provenance tracking module being configured to cluster the initial isotope characteristic values of the water and soil samples according to initial isotope characteristic values of the water and soil samples;

[0037] The provenance tracking module is further configured to match the isotope characteristic fingerprint library of different strata.

[0038] The provenance tracking module is further configured to determine the material source proportion of the water and soil loss.

[0039] Preferably, when the provenance tracking module is configured to determine the material source proportion, the provenance tracking module is further configured to construct a similarity matrix of the initial isotope characteristic values and the stratum characteristic fingerprints.

[0040] The provenance tracking module is further configured to iteratively optimize the matching residual of the similarity matrix.

[0041] The provenance tracking module is further configured to output the material source contribution rate that meets the residual convergence condition.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] The initial isotope characteristic values of the water and soil samples in the monitoring area are obtained by the isotope collection module, and the stable physical and chemical properties of isotopes and their resistance to environmental interference can provide more reliable basic data for water and soil loss monitoring. As natural or artificial tracer substances, the distribution and migration of isotopes in water and soil can directly reflect the process and intensity of water and soil loss. Compared with traditional tracers, isotopes can more truly reflect the actual situation, thereby improving the accuracy of monitoring data.

[0044] The space division module divides the monitoring area into a plurality of monitoring units according to the terrain parameters, achieving fine segmentation of the monitoring area. The water and soil loss characteristics of different landform units are significantly different. By such division, each monitoring unit can be analyzed separately, avoiding the problem of masking local water and soil loss characteristics due to overall analysis of the region, so that the subsequent loss evaluation can better fit the actual situation of each unit, improving the spatial relevance of the evaluation.

[0045] The loss evaluation module generates an initial loss level based on the distribution state of the initial isotope characteristic values, directly associating the isotope characteristics with the loss level, and breaking through the limitations of relying on experience judgment or a single index in traditional evaluation methods. The distribution of isotope characteristic values can comprehensively reflect the cumulative effect and dynamic change of water and soil loss, and the initial level division based thereon is more scientific, laying a reasonable foundation for subsequent dynamic adjustment.

[0046] The dynamic analysis module calls the reference isotope characteristic value of the historical isotope database and calculates the characteristic difference degree, compares the current monitoring data with the historical data, and can capture the dynamic change trend in the process of water and soil loss. The loss level determination threshold of the monitoring unit is adjusted based on the characteristic difference degree, so that the threshold setting is no longer fixed and unchanged, but can be dynamically adapted according to the actual isotope characteristic change, thereby enhancing the adaptability and objectivity of the threshold.

[0047] The initial loss level is updated according to the adjusted threshold, so that the evaluation result of the loss level can be dynamically updated with the change of the monitoring data, and the dynamic tracking of the water and soil loss level is realized. The dynamic updating mechanism can timely reflect the change of the water and soil loss condition, compared with the traditional fixed period evaluation, and can better reflect the timeliness of the water and soil loss process, so as to timely find the area where the water and soil loss is intensified or relieved, and facilitate to take corresponding measures according to the change. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The timing diagram of the water and soil loss data analysis system based on isotope tracing according to the present application is shown in the figure;

[0049] Figure 2 The flowchart for generating the initial loss level of the loss evaluation module is shown in the figure;

[0050] Figure 3 The flowchart for adjusting the loss level determination threshold of the dynamic analysis module is shown in the figure;

[0051] Figure 4 The flowchart for generating the corrected loss level of the spatial correlation correction module is shown in the figure;

[0052] Figure 5 The flowchart for determining the material source proportion of the source tracing module is shown in the figure. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Please refer to Figure 1 The present application provides a water and soil loss data analysis system based on isotope tracing, which comprises:

[0055] The isotope collection module is responsible for obtaining initial isotope characteristic values of water and soil samples in the monitoring area. The spatial division module divides the monitoring area into several monitoring units based on terrain parameters such as slope, elevation, and land cover type. The loss evaluation module is associated with the isotope collection module and the spatial division module, and generates initial loss levels of the monitoring units according to the distribution state of the initial isotope characteristic values of the water and soil samples in each monitoring unit. The dynamic analysis module is associated with the loss evaluation module, and calls the reference isotope characteristic values of the historical isotope database according to the initial loss levels to obtain the characteristic difference degree. The dynamic analysis module further adjusts the loss level judgment threshold of the monitoring unit according to the characteristic difference degree, and updates the initial loss level based on the adjusted threshold. The historical isotope database stores the past collected isotope characteristic value data for reference by the dynamic analysis module.

[0056] Embodiment 1: refer to Figure 2 When generating the initial loss level of the monitoring unit, the loss evaluation module performs the following operation process. The isotope collection module obtains the initial isotope characteristic values of the water and soil samples in the monitoring area, which are determined by a mass spectrometer and recorded as a numerical sequence. The spatial division module divides the monitoring area into several monitoring units based on terrain grid data, and the area range of each unit is dynamically determined by the slope change rate and the land cover type. The loss evaluation module extracts the initial isotope characteristic values of all water and soil samples in the same monitoring unit, calculates the difference between the maximum and minimum values as the initial isotope characteristic value range. The range reflects the dispersion degree of isotope distribution in the unit.

[0057] The loss evaluation module compares the initial isotope characteristic value range with the preset loss fluctuation threshold interval. The loss fluctuation threshold interval is set by analyzing the change rule of the isotope range in historical soil and water loss events, and includes a lower threshold and an upper threshold. When the initial isotope characteristic value range is lower than the lower limit of the loss fluctuation threshold interval, the loss evaluation module calls the pre-stored stable loss level parameter in the system, and directly uses it as the initial loss level of the monitoring unit. The stable loss level corresponds to a state of highly uniform isotope distribution, indicating that the water and soil loss process tends to be stable.

[0058] When the initial isotope characteristic value range is within the loss fluctuation threshold interval, the loss evaluation module starts spatial dispersion analysis. The system reads the geographic coordinate data of all water and soil samples in the monitoring unit, and calculates the spatial distribution dispersion coefficient of the sample points. The dispersion coefficient is generated by the ratio of the statistical variance of the sample coordinates to the unit area, and quantifies the variation intensity of the isotope characteristic values in space. The loss evaluation module queries the pre-set loss level mapping table according to the dispersion coefficient value to match the corresponding initial loss level. The mapping table divides the dispersion coefficient into multiple interval segments, and each interval corresponds to a loss level.

[0059] When the initial isotope characteristic value range is higher than the upper limit of the loss fluctuation threshold interval, the loss evaluation module activates the abnormal loss analysis program. The program first checks the sample collection time and environmental parameters to exclude abnormal data caused by heavy rain events or human interference. If the check is passed, the system starts the high-precision resampling mode, encrypts the sampling points in the target monitoring unit, and obtains the supplementary isotope characteristic value data set. The loss evaluation module recalculates the range based on the supplementary data, and if it is still above the upper limit of the threshold interval, it outputs a high-risk loss level identifier and marks it as a unit to be verified.

[0060] When obtaining the feature difference degree, the dynamic analysis module performs the following operation process. The system retrieves the historical data records of the same monitoring unit from the historical isotope database according to the initial loss level provided by the loss evaluation module. The time span of the historical data is set by the system configuration parameters, and by default covers the isotope monitoring results of the last three hydrological years. The dynamic analysis module calculates the arithmetic mean of the historical isotope characteristic values as the reference benchmark value.

[0061] The dynamic analysis module traverses all water and soil samples in the current monitoring unit, calculates the absolute deviation of the initial isotope characteristic value of each sample from the historical benchmark value. The deviation is obtained by taking the absolute value of the difference between the two, forming a deviation data set. The system inputs the deviation data set into the preset difference quantization scale for mapping processing. The difference quantization scale uses a piecewise function design: when the deviation is below the set threshold, it is directly linearly mapped to the [0, 0.3] interval; when the deviation exceeds the set threshold, it is compressed and mapped to the [0.3, 1] interval using a logarithmic function. The final output feature difference degree is the weighted average of the mapping values of all samples in the unit, and the weight is determined by the spatial position weight coefficient of the sample in the unit.

[0062] During the calculation of the feature difference degree, the system automatically detects the integrity of the historical data. If the historical records of the target unit are insufficient, the system expands the search space to retrieve historical data from adjacent units and generates virtual historical benchmark values through spatial interpolation algorithms. For newly added units that are monitored for the first time, the system calls the terrain similarity model to match unit data with similar terrain parameters in the historical database as a substitute benchmark. The feature difference degree calculation result is updated in real time to the dynamic analysis cache area for subsequent threshold adjustment module calls.

[0063] The entire implementation process uses a hierarchical verification mechanism. When the isotope characteristic value range is at the critical point of the threshold interval, the system automatically starts the review program: recalibrates the instrument measurement error range, verifies the terrain division boundary accuracy, and if necessary, triggers the manual review process. All intermediate calculation results are recorded in the operation log to support full-process backtracking analysis.

[0064] Example 2: see Figure 3, the dynamic analysis module performs the following operation process when adjusting the loss level determination threshold of the monitoring unit. The system receives the feature difference degree value calculated in embodiment 1, which reflects the deviation of the current monitoring unit isotope feature from the historical baseline. The basic threshold parameter preset in the module is stored in the system configuration file, which is determined by analyzing the corresponding relationship between the loss level and the isotope change in long-term monitoring data. The dynamic analysis module establishes a mathematical correlation model between the feature difference degree and the loss level determination threshold, which uses a nonlinear response function to make the threshold adjustment amplitude and the difference degree change present a gradual response characteristic.

[0065] When the feature difference degree value reaches the first difference critical value preset by the system, the first level response of the threshold adjustment mechanism is triggered. The first difference critical value is set based on historical data analysis, corresponding to a moderate degree of isotope feature change. The system reads the loss level determination threshold of the current unit and applies a preset first attenuation coefficient for numerical adjustment. The attenuation coefficient is obtained by machine learning algorithm training on historical optimization cases, which can automatically fine-tune the specific value according to the terrain characteristics of the unit area. The adjusted new threshold is immediately updated to the dynamic parameter library of the monitoring unit and marked as a temporary adjustment state.

[0066] If the feature difference degree continues to increase and reaches the second difference critical value, the system starts a more intense second level threshold attenuation mechanism. The second difference critical value corresponds to a significant isotope feature variation, at which time the system uses a composite adjustment strategy: first calculate the preliminary adjustment amount based on the current feature difference degree, and then make a second correction combined with the recent loss level change trend of the unit. The adjustment record of the threshold of the adjacent unit is referred to in the correction process to ensure spatial continuity. The final determined second level adjustment threshold will overwrite the first level adjustment result and trigger the system warning notification function.

[0067] The dynamic analysis module performs the following operation process when updating the initial loss level. The system obtains the adjusted new determination threshold and recompares it with the initial isotope feature value range of the monitoring unit. The comparison process uses a floating interval matching algorithm, which allows a buffer transition zone to be set near the threshold boundary to avoid frequent level jumps due to small value fluctuations. When the initial isotope feature value range continues to be higher than the adjusted determination threshold, the system starts the loss level promotion program.

[0068] The grade promotion operation follows a preset progressive rule: first, check the time interval between the current grade and the previous grade, if the interval is too short, start the stability verification; then evaluate the duration of the difference exceeding the threshold, only the unit that meets the minimum duration requirement will be confirmed to upgrade. The upgrade range is determined by the proportion of the difference exceeding the threshold, using a stepwise growth mode, no more than two grade spans per upgrade. All grade change operations generate detailed change logs, recording the threshold parameters, difference values and timestamp information before and after adjustment.

[0069] For the case where the initial isotope characteristic value difference is lower than the adjusted threshold, the system performs a grade maintenance operation. This operation is not simply a status quo, but includes a dynamic monitoring mechanism: continuously track the relative change trend of the difference and the threshold, and trigger a warning when the difference is reduced to the warning range. Units in the maintenance state will be marked as observation objects, and their data sampling frequency will be automatically increased to detect potential trends in a timely manner. The system regularly generates maintenance status analysis reports, summarizing the stable duration of each unit and the related parameter fluctuation.

[0070] The entire threshold adjustment and grade update process uses a closed-loop control mechanism. After each threshold modification, the system automatically starts a verification period during which it closely monitors the isotope characteristic change trend of the unit. If it finds that the adjusted threshold deviates systematically from the actual situation, it triggers a parameter calibration program. The calibration process refers to other environmental parameters monitored at the same period, such as rainfall, vegetation coverage, etc., for multi-factor collaborative correction. All adjustment operations leave complete operation traces, supporting parameter rollback and effect tracing.

[0071] The system establishes an independent threshold adjustment history file for each monitoring unit, recording the time, amplitude and trigger conditions of each modification. These data are used to improve the system's adaptive learning ability, and by analyzing long-term adjustment records, the setting of difference threshold and the response curve of decay mechanism are continuously optimized. For units in special geological conditions, the system supports manual intervention mode, allowing experienced values to override automatic adjustment results, but all intervention operations require additional detailed explanation notes.

[0072] During extreme weather events or sudden environmental changes, the system starts an emergency response mode. In this mode, the difference threshold is temporarily relaxed, the threshold adjustment response speed is accelerated, and the grade adjustment range is appropriately expanded. After the emergency mode ends, the system automatically performs data review to verify the reasonableness of all adjustment operations during the period, and makes necessary corrections. The entire process ensures that the system can respond to sudden situations in a timely manner while maintaining the stability of long-term monitoring.

[0073] Example 3: see Figure 4The spatial correlation correction module performs a terrain-driven erosion grade correction process. The system first obtains initial erosion grade data of each monitoring unit from the erosion assessment module and establishes a spatial topology database. The database records the boundary coordinates and adjacent relationships of all monitoring units, and the adjacent relationship is defined as a unit pair that shares a boundary length exceeding a preset minimum value. The module reads digital elevation model data, extracts the elevation point set of each monitoring unit boundary, and calculates the elevation drop of adjacent unit boundaries. For any two adjacent units, the elevation drop is determined through the following process: sampling points are selected at equal intervals on the common boundary line, the absolute value of the elevation difference of each point is calculated, and the weighted average value of the elevation differences of all sampling points is taken as the final drop value, and the weight is determined by the length proportion of the boundary segment where the sampling point is located.

[0074] The system identifies the adjacent unit relationship on the water flow direction path. Based on the digital elevation model, the D8 single flow direction algorithm is used to calculate the water flow direction of each grid. Starting from the outlet of the target monitoring unit, the runoff path is tracked along the water flow direction, and all the sequences of monitoring units passing through are recorded. The sequence constitutes the downstream conduction chain of the target unit, and the upstream contribution unit set is analyzed in reverse. The water flow path length is obtained by accumulating the distances of the center points of the grids passing through, and the three-dimensional spatial distance calculation is used instead of the plane projection distance to consider the influence of the actual surface curvature.

[0075] The spatial correlation correction module generates an erosion conduction coefficient based on the elevation drop and the water flow path length. For a unit pair with a water flow path correlation, the conduction coefficient is calculated according to the following formula:

[0076] ;

[0077] Wherein: represents the erosion conduction coefficient from unit to unit , is the elevation drop (unit: meters) from unit to unit , is the water flow path length (unit: meters), is the surface erosion resistance factor (dimensionless). The surface erosion resistance factor is obtained by interpreting remote sensing images, and is assigned according to the combination of vegetation coverage, soil type and land use type, with a value range of 0.1 (bare bedrock) to 1.0 (loose cultivated soil). This coefficient quantifies the erosion material transport intensity of upstream units to downstream units, and the higher the coefficient value, the more significant the spatial influence.

[0078] The module automatically constructs a spatial influence network of the monitoring units. The network nodes are the monitoring units, the directed edges represent the water flow direction path relationship, and the edge weight is the erosion conduction coefficient. The system screens effective connections with a conduction coefficient exceeding a preset conduction threshold. The threshold is dynamically adjusted according to the average erosion intensity in the region. For units without a direct water flow path but close in spatial distance, the Euclidean distance weight is supplemented to form a complete spatial correlation matrix.

[0079] The spatial influence weight is generated through normalization processing. For a target unit , the conduction coefficients of all adjacent units are collected, and the weight is calculated:

[0080] ;

[0081] wherein: is the total number of adjacent units having effective spatial correlation with the unit . The weight reflects the relative contribution of each adjacent unit to the correction influence on the target unit, and the sum of all weights is 1. For units located at the watershed ridge position, when there is no effective adjacent unit, the system automatically assigns a spatial independence identifier to it, skipping the correction process.

[0082] The corrected loss grade is generated through spatial weighted fusion. Let the initial loss grade of the target unit be , the grades of its adjacent units be , and the corresponding spatial influence weight be , then the corrected loss grade is calculated as:

[0083] ;

[0084] wherein: is the self-weight coefficient of the target unit (default 0.6), is the influence decay factor of the adjacent unit (default 0.4). The decay factor is dynamically adjusted according to the water flow direction: 0.6 when the adjacent unit is located upstream of the target unit, 0.2 when it is located downstream, and 0.4 when it is horizontally adjacent. The calculation is iteratively propagated in the spatial correlation network. After the corrected grade of each unit is output, the network node values are updated immediately, and three rounds of iteration are performed to stabilize the correction results.

[0085] The system sets special terrain processing rules. For units with water conservancy projects (such as dams and terraces), virtual buffer units are automatically inserted to block the conduction path; for areas prone to landslides and landslides, additional terrain mutation factors are added to strengthen spatial influence. All correction calculations are performed in independent threads, supporting real-time interruption and manual intervention, and the correction results are compared with the original data to trigger an expert review mechanism when the difference exceeds the set threshold.

[0086] The entire spatial correction process generates a visual analysis atlas. The atlas uses different colors to represent the difference between the initial grade and the corrected grade, uses arrow thickness to represent the spatial influence weight, and uses contour lines to display the terrain features. This atlas supports dynamic playback of historical correction processes to help identify trends in spatial conduction patterns. The corrected erosion grade data is stored in the spatial correlation database, with version timestamp and correction parameter set.

[0087] Example 4: When generating the corrected erosion grade, the spatial correlation correction module performs the following operation process. The system first filters the adjacent monitoring units whose erosion conduction coefficients exceed the preset conduction threshold. The conduction threshold is dynamically adjusted according to the historical erosion data of the region. Taking a monitoring area in a certain watershed as an example, the system identifies that there is effective spatial correlation between target unit A and adjacent units B, C, and D, with conduction coefficients of 0.85, 0.62, and 0.41, respectively. The conduction threshold is set to 0.4, so unit D is included in the calculation but marked as a weakly correlated unit. The system assigns a spatial influence weight to each effective adjacent unit. The weight calculation uses a normalization method to convert the conduction coefficients of each unit into a relative proportion. See Table 1.

[0088] Table 1: Spatial influence weight distribution table for example units.

[0089] Monitoring unit Conductivity coefficient Spatial influence weight B 0.85 0.45 C 0.62 0.33 D 0.41 0.22

[0090] The weight distribution process takes into account the location characteristics of the terrain. Unit B is located in the positive upstream direction of target unit A, and there is a steep slope between the two units, so the conduction coefficient is the highest; unit C is located on the lateral slope, and the conduction path is tortuous; unit D is located in the downstream gentle slope area, and the influence is the lowest. The system automatically identifies this spatial pattern and preserves the terrain feature information during weight distribution, which is used for subsequent correction calculation logic verification.

[0091] The correction calculation adopts a hierarchical weighting strategy. The initial loss level of target unit A is 3 (moderate loss), and the initial levels of adjacent units B, C, and D are 4, 2, and 1 respectively. The system first calculates the weighted influence value of the adjacent units: the contribution value of unit B is 4 x 0.45 = 1.8, the contribution value of unit C is 2 x 0.33 = 0.66, and the contribution value of unit D is 1 x 0.22 = 0.22, for a total of 2.68. Then, the target unit's self-weighting coefficient of 0.6 is applied, retaining 60% of the original level (3 x 0.6 = 1.8) of the weight, and the final corrected level is 1.8 + 2.68 = 4.48, rounded to 4.

[0092] The system sets up a level transition protection mechanism. When the difference between the corrected result and the initial level exceeds two levels, a review verification process is started. For example, if the initial level is 1 and the corrected result is 4, the system will check the reasonableness of the conduction coefficient calculation and confirm the accuracy of the water flow path identification, and if necessary, request manual intervention. All cases of significant adjustment are recorded in the special event log, including the level values before and after adjustment, the conduction coefficient, and the weight distribution details.

[0093] For special terrain areas, the correction process adds supplementary rules. In terrace distribution areas, the system automatically reduces the conduction coefficient between adjacent units; in gully development zones, the weight proportion of lateral units is increased. These rules are implemented through a geographic knowledge base, and the system automatically matches the corresponding correction strategy based on the geomorphological type label of the unit. Manual annotation of special areas such as collapse bodies or artificial fill areas is also supported, and units in these areas will use customized weight distribution schemes.

[0094] The correction result output includes multi-dimensional verification information. In addition to the final loss level, the system also generates a correction component analysis report, which shows the specific contribution value of each adjacent unit. Taking unit A as an example, the report indicates that the level increase is mainly due to the strong influence of upstream unit B (contribution degree 45%), and also notes the inhibitory effect of unit C (its 2 level lowers the overall correction value). This transparent calculation process facilitates user understanding of the specific action mechanism of spatial correlation.

[0095] The system implements a dynamic weight adjustment function. When consecutive monitoring periods show that the correction result of a unit continuously deviates from the initial level, the weight optimization program is automatically started. This program analyzes the stability of historical conduction coefficients and performs weight smoothing on adjacent relationships with excessive fluctuations. For example, if the conduction coefficient of unit B to unit A decreases from 0.85 to 0.5 in the last three monitoring periods, the system will update the current weight using a moving average value to avoid drastic fluctuations in the correction result.

[0096] The data visualization module synchronously updates the spatial correlation network graph. The graph is centered on the target unit, and arrows of different thickness represent the weight proportion of each adjacent unit, with the arrow color reflecting the conduction coefficient size. Users can view the correction calculation details of any unit through interactive operations, including a three-dimensional simulation demonstration of the conduction path. The visualization system also supports comparing the correction results of different time sections to help identify the timing characteristics of the spatial influence pattern.

[0097] The entire correction process establishes a complete quality traceability chain. From the conduction coefficient calculation and weight distribution to the final grade correction, the operation parameters and intermediate results of each step are stored in the spatial correlation database. The database records the operation time, algorithm version, and parameter source, supporting the calculation process backtracking at any historical time point. When the system detects an update to the terrain data, it automatically marks the affected units that need to recalculate the conduction coefficient, ensuring the timeliness of the spatial correlation model.

[0098] For large-scale monitoring areas, the system uses a distributed computing architecture. The spatial correlation network is divided into several subnets, and the correction calculation of each subnet is performed in parallel on independent server nodes, with the grade data of boundary units synchronized through a message queue. This architecture design significantly improves processing efficiency, and in a region containing 5000 monitoring units, the complete correction process can be completed within 20 minutes. All computing nodes share a unified spatial weight knowledge base, ensuring the consistency of calculation standards between regions.

[0099] The application strategy of the correction results supports multiple mode configurations. The system provides conservative, standard, and aggressive three correction intensity options: the conservative mode limits the grade adjustment to no more than one level; the standard mode allows two-level adjustment but requires additional verification; the aggressive mode allows three-level adjustment for high conduction coefficient relationships. Users can select the appropriate mode according to the monitoring purpose and risk tolerance, and the calculation results of different modes are stored separately and labeled with clear application scenario suggestions.

[0100] The system regularly generates spatial correlation analysis reports. The report analyzes the conduction coefficient distribution characteristics of each region, identifies abnormal high-value areas and isolated units, and analyzes the spatial pattern of weight distribution. These analysis results are used to optimize the design of the monitoring network, such as adding temporary monitoring points in areas with excessively high conduction coefficients or adjusting the unit division boundaries in areas with spatial correlation fractures. The report also includes stability evaluation of the correction effect, which evaluates the reliability of the spatial correlation model by comparing the differences in correction results between consecutive periods.

[0101] Example 5: Refer to Figure 5 The traceability tracking module performs the quantitative analysis process of the source of soil erosion materials. The system receives the initial isotope characteristic value data set provided by the isotope collection module, which includes the multi-dimensional isotope ratio of all water and soil samples in the monitoring area, such as δ¹³C, 87 Sr / 86Sr, etc. combination feature vector. The module first standardizes the sample data to eliminate system bias caused by sampling depth or seasonal factors, generating a comparable isotope feature matrix.

[0102] Sample clustering analysis uses an improved density peak algorithm. The algorithm automatically determines the optimal number of clusters, avoiding the subjectivity of presetting the number of categories. The calculation process includes three core stages: first, calculate the local density value of each sample, reflecting the density of the sample distribution in its neighborhood; then calculate the minimum distance between the sample and the higher density sample to identify potential cluster centers; finally, select the cluster center through the decision graph interaction, and assign all samples to the corresponding group. The clustering results are marked as G1 to Gk, and each group represents a candidate set of material sources with similar isotope characteristics.

[0103] The system calls the stratigraphic isotope feature fingerprint library for matching. The fingerprint library stores the standard isotope features of different geological units, including the feature fingerprint vectors of typical strata such as bedrock, weathering crust, and paleosol layer. The matching process uses the dynamic time warping algorithm to solve the longitudinal variation problem of stratum fingerprints caused by geological processes. For each sample group, calculate the similarity between its centroid vector and each stratum fingerprint, and consider both the absolute difference and the consistency of the trend of the feature values in the similarity evaluation.

[0104] Material source proportion calculation is achieved through a mixing model inversion. The system constructs a sample-stratum similarity matrix, with rows corresponding to samples, columns corresponding to stratum types, and element values representing the feature matching degree of the sample and the stratum. The inversion model expresses the actual observed isotope feature values as a linear combination of stratum fingerprints, and determines the contribution proportion of each stratum by solving the combination coefficients. The inversion process introduces the constraint condition of stratum geological exposure area to avoid results that contradict field geological surveys.

[0105] Iterative optimization uses a gradient descent method with adaptive step size. In the initialization, equal contribution proportions are assigned to each stratum, and the residual sum of squares between the theoretical isotope feature values and the measured values is calculated. In each iteration, the contribution proportion is adjusted to make the residual decrease along the gradient direction. The step size is dynamically adjusted according to the current residual change rate: the step size is increased to accelerate convergence when the residual decreases rapidly, and the step size is decreased to improve stability when the residual oscillates. The optimization termination condition is set with double standards: the residual change rate is less than the threshold or the maximum number of iterations is reached.

[0106] The contribution rate output includes uncertainty evaluation. The system performs Monte Carlo simulation to generate perturbed data sets within the measurement error range of sample feature values, and repeats the inversion calculation 1000 times. The distribution characteristics of the contribution proportions of each stratum are statistically analyzed, and the median value is output as the final contribution rate, with the 5%-95% percentile interval as the confidence range. For strata with a contribution rate below 5%, the system automatically classifies them as "trace source" category and does not participate in the main contribution analysis.

[0107] The result visualizes a three-dimensional provenance map. The spatial dimension shows the material source composition of each sampling point, with a pie chart representing the contribution proportion of each stratum; the stratum dimension displays the contribution characteristics at different depths, revealing the vertical distribution law of material sources; the time dimension shows the dynamic change process of material sources before and after the rainy season through animation. Users can interactively filter specific strata and highlight the spatial areas where the contribution rate exceeds 50%.

[0108] The system sets geological logic verification rules. When the inversion result shows that the contribution rate of a certain stratum is abnormally high, it automatically checks the actual outcrop situation of the stratum around the sampling point. If there is no corresponding outcrop on the geological map, spatial interpolation correction is triggered: the contribution rate of the stratum in the adjacent area is adjusted by distance weighting. At the same time, the logical consistency of the contribution rates of different strata is detected, for example, the contribution proportion of alluvial layer and bedrock layer should meet the rules of sedimentology.

[0109] The provenance result is analyzed in coordination with the loss level. The system establishes an association matrix between the loss level and the main material source type, and identifies the risk threshold of material loss of a specific stratum. When the high loss level area overlaps with the peak area of the contribution rate of the easy-eroded stratum, the key prevention area is generated. All analysis results are stored in the provenance knowledge base, supporting multi-dimensional retrieval according to time, space or stratum type.

[0110] The module ensures the repeatability of the whole process. From data input to result output, the parameter setting and intermediate results of each processing step are recorded in the provenance log. The log is stored in a structured manner, supporting on-demand playback of the calculation process of any step. Users can adjust the clustering parameters or fingerprint matching weights to re-execute the analysis, and the system automatically compares the differences in results under different parameters to help determine the optimal parameter combination.

[0111] For complex geological areas, the system provides a hierarchical provenance mode. In the vertical sequence development area, samples are grouped by collection depth for independent analysis; in the tectonic fracture zone, a fault influence factor is added to correct the fingerprint matching process; in the human activity area, a virtual fingerprint of artificial fill layer is added to participate in the calculation. These extended functions are activated by configuration switches to meet the provenance needs of different scenarios.

[0112] The system regularly updates the stratum fingerprint library. When new geological drilling data is added, the isotopic characteristics of the core samples are automatically extracted to supplement the fingerprint library; when systematic deviations are found between the fingerprint library and the measured data, the fingerprint library calibration program is started. The calibration process preserves historical versions of fingerprints, supporting longitudinal comparison of provenance results. All fingerprint update operations must pass the stratum type consistency check to ensure the accuracy of the geological meaning.

[0113] The report generation adopts a modular design. The basic report contains stratigraphic contribution rate statistics and spatial distribution maps; the extended module can add special chapters such as material loss flux estimation and key stratum loss trend analysis. The report supports custom output granularity, from single-point detailed tracing to regional summary statistics, which can be flexibly configured. All chart data are accompanied by metadata explanations, listing the calculation method and data source.

[0114] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0115] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous further modifications and changes can be apparent to one skilled in the art without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. An isotope tracing based soil erosion data analysis system, characterized in that, The method comprises the following steps: The isotope collection module is configured to obtain the initial isotope characteristic value of the water and soil sample in the monitoring area; The space division module is configured to divide the monitoring area into several monitoring units according to the terrain parameters; The loss evaluation module is associated with the isotope collection module and the space division module, respectively, and the loss evaluation module is configured to generate the initial loss level of the monitoring unit according to the initial isotope characteristic value distribution state of the water and soil sample in each monitoring unit; The dynamic analysis module is associated with the loss evaluation module, and the dynamic analysis module is configured to call the reference isotope characteristic value of the historical isotope database according to the initial loss level to obtain the characteristic difference degree; The dynamic analysis module is further configured to adjust the loss level judgment threshold of the monitoring unit according to the characteristic difference degree; The dynamic analysis module is further configured to update the initial loss level according to the adjusted loss level judgment threshold; The space correlation correction module is associated with the loss evaluation module, and the space correlation correction module is configured to obtain the initial loss level of the adjacent monitoring unit; The space correlation correction module is further configured to calculate the spatial influence weight according to the terrain connectivity parameters; The space correlation correction module is further configured to fuse the initial loss levels of the adjacent monitoring units based on the spatial influence weight to generate the corrected loss level; When the space correlation correction module is configured to calculate the spatial influence weight, the space correlation correction module is further configured to extract the elevation difference data of the monitoring unit boundary; The space correlation correction module is further configured to identify the relationship of adjacent units on the water flow direction path; The space correlation correction module is further configured to generate the erosion conduction coefficient according to the elevation difference and the water flow path length, and for the unit pair that has the water flow path correlation, the conduction coefficient is calculated according to the following formula: ; wherein, Ci,j represents the erosion conductance coefficient from cell i to cell j, hi,j is the elevation fall from cell i to cell j, li,j is the flow path length, is the surface resistance factor; When the space correlation correction module is configured to generate the corrected loss level, the space correlation correction module is further configured to screen the adjacent monitoring units whose erosion conduction coefficient exceeds the conduction threshold; The spatial correlation correction module is further configured to normalize the erosion conduction coefficients to spatial influence weights, collecting for a target cell i the conduction coefficients of all its neighboring cells j , calculate weights : ; Wherein, n is the total number of adjacent units that have effective spatial correlation with unit i; The spatial correlation correction module is further configured to perform a weighted fusion calculation on the initial churn levels of the target cell and the adjacent cells, assuming that the initial churn level of the target cell i is the level of the j adjacent cells of the target cell is the corresponding spatial influence weight is and the corrected churn level is calculated as ; wherein, is a self-weighting coefficient of the target cell, default 0.6, is an influence attenuation factor of the adjacent cell, the attenuation factor is dynamically adjusted according to the water flow direction: 0.6 when the adjacent cell is located upstream of the target cell, 0.2 when the adjacent cell is located downstream of the target cell, and 0.4 when the adjacent cell is horizontally adjacent to the target cell.

2. The isotope tracing based water erosion data analysis system of claim 1, wherein, When the loss evaluation module is configured to generate the initial loss level of the monitoring unit, the loss evaluation module is further configured to extract the initial isotope characteristic value range of the water and soil sample in the same monitoring unit; The loss evaluation module is further configured to compare the initial isotope characteristic value range with the preset loss fluctuation threshold interval; When the initial isotope characteristic value range is lower than the lower limit of the loss fluctuation threshold interval, the loss evaluation module calls the preset stable loss level as the initial loss level; When the initial isotope characteristic value range is within the loss fluctuation threshold interval, the loss evaluation module generates the dispersion coefficient according to the position distribution of the water and soil sample; When the initial isotope characteristic value range is higher than the upper limit of the loss fluctuation threshold interval, the loss evaluation module activates the abnormal loss analysis program.

3. The isotope-tag based soil erosion data analysis system of claim 2, wherein, The dynamic analysis module is configured to obtain the feature difference degree, comprising: the dynamic analysis module is further configured to extract the mean value of the historical isotope feature values of the same monitoring unit in the historical isotope database; The dynamic analysis module is further configured to calculate the absolute deviation of the initial isotope feature value from the mean value of the historical isotope feature values; The dynamic analysis module is further configured to map the absolute deviation to a preset difference quantization scale.

4. The isotope-tag based soil erosion data analysis system of claim 3, wherein, The dynamic analysis module is configured to adjust the loss level determination threshold of the monitoring unit, comprising: the dynamic analysis module is further configured to establish an inverse association rule between the feature difference degree and the loss level determination threshold; When the feature difference degree reaches a first difference threshold, the dynamic analysis module starts a first threshold decay mechanism; When the feature difference degree reaches a second difference threshold, the dynamic analysis module starts a second threshold decay mechanism.

5. The isotope-tag based soil erosion data analysis system of claim 4, wherein, The dynamic analysis module is configured to update the initial loss level, comprising: the dynamic analysis module is further configured to re-couple the adjusted loss level determination threshold with the initial isotope feature value range; When the initial isotope feature value range is higher than the adjusted loss level determination threshold, the dynamic analysis module raises the initial loss level; When the initial isotope feature value range is lower than the adjusted loss level determination threshold, the dynamic analysis module maintains the initial loss level.

6. The isotope tracing based water erosion data analysis system of claim 1, wherein, Further comprising a trace tracking module associated with the isotope collection module, the trace tracking module is configured to cluster the initial isotope feature values of the water and soil samples; The trace tracking module is further configured to match the isotope feature fingerprint library of different strata; The trace tracking module is further configured to determine the material source proportion of the water and soil loss.

7. The isotope tracing based water erosion data analysis system of claim 6, wherein, The trace tracking module is configured to determine the material source proportion, comprising: the trace tracking module is further configured to construct a similarity matrix of the initial isotope feature values and the stratum feature fingerprints; The trace tracking module is further configured to iteratively optimize the matching residual of the similarity matrix; The trace tracking module is further configured to output the material source contribution rate that meets the residual convergence condition.

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