A precise pesticide application method and system for soil remediation and a storage medium

Abstract

This invention provides a precise application method, system, and storage medium for soil remediation, including: acquiring gridded soil data of the area to be remediated; obtaining the pollution diffusion weight of each grid cell through spatial correlation calculation, calculating the soil migration coefficient field in combination with soil moisture content, and correcting the pollutant concentration to obtain a corrected concentration distribution; dividing the corrected concentration distribution into zones, calculating the application boundary coefficient between adjacent grid cells, and reorganizing the grid to form an irregular application cell set; calculating the basic application dose based on the corrected concentration distribution of each application cell, calculating the spraying angle coefficient in combination with terrain elevation, and jointly determining the spraying control parameters of each cell; during spraying, matching the equipment trajectory with the cell boundary in real time, and when the trajectory crosses the boundary, performing a smooth transition correction on the control parameters based on the application boundary coefficient, and outputting the corresponding spraying control command.

Description

Technical Field

[0001] This application belongs to the field of pesticide application, and in particular relates to a precision pesticide application method, system and storage medium for soil remediation. Background Technology

[0002] Precision application technology typically divides the area to be remediated into regular grids, obtains the pollutant concentration in each grid by sampling, and then sets the basic dosage for each grid based on the static concentration.

[0003] However, conventional application strategies, when faced with varying actual site environments, neglect the impact of soil moisture content on pollutant migration and diffusion processes. This results in calculated dosages that fail to accurately reflect the actual diffusion trends of pollutants. Conventional methods also rigidly define the remediation area as a regular grid, failing to consider the continuity and boundary characteristics of pollution diffusion between adjacent grids. This hinders the reasonable reorganization of application units based on actual conditions, easily leading to application blind spots or excessive pesticide accumulation at grid boundaries. Furthermore, spraying systems often employ fixed spraying postures, which significantly reduce pesticide adhesion and deep penetration when dealing with undulating terrain. In actual spraying operations, when spraying equipment crosses grid boundaries with different dosages, the lack of a smooth transition correction mechanism combining real-time position trajectory and control parameters leads to control delays in the equipment and disrupts the uniformity of application at boundaries. Therefore, how to comprehensively consider multidimensional soil characteristics and topographical factors to achieve irregular division of application units and smooth control of the operation process is a pressing technical challenge in the field of soil remediation application. Summary of the Invention

[0004] To address the problem that existing technologies fail to comprehensively consider multidimensional soil characteristics and topographical factors, making it difficult to achieve irregular division of application units and smooth control of the operation process.

[0005] In a first aspect, the present invention proposes a precision application method for soil remediation, comprising: Obtain gridded soil data of the area to be remediated, including pollutant concentration information, soil moisture content information, and topographic elevation information; perform spatial correlation calculation on the gridded soil data to obtain the pollution diffusion weight of each grid cell; Based on the pollution diffusion weight and the soil moisture content information, a soil migration coefficient field is calculated, and the pollutant concentration information is corrected using the soil migration coefficient field to obtain a corrected concentration distribution. The corrected concentration distribution is then partitioned, and the application boundary coefficient between adjacent grid cells is calculated. Based on the application boundary coefficient, the grid cells are reorganized into application units to form an irregular application unit set. The base dosage is calculated based on the corrected concentration distribution of each irregular application unit set, and the spraying angle coefficient is calculated in combination with the terrain elevation information. The spraying control parameters of each irregular application unit set are determined by the base dosage and the spraying angle coefficient. During the spraying process, real-time position trajectory data is acquired, and the real-time position trajectory data is matched and calculated with the boundary of the irregular application unit set. When the trajectory of the spraying equipment crosses the boundary, the spraying control parameters are smoothly corrected according to the application boundary coefficient, and the corresponding spraying control command is output.

[0006] In another aspect, the present invention also proposes a precision application system for soil remediation, comprising the following modules: The acquisition module is used to acquire gridded soil data of the area to be remediated. The soil data includes pollutant concentration information, soil moisture content information, and topographic elevation information. Spatial correlation calculation is performed on the gridded soil data to obtain the pollution diffusion weight of each grid cell. The recombination module is used to calculate the soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and to correct the pollutant concentration information through the soil migration coefficient field to obtain the corrected concentration distribution; to perform partitioning processing on the corrected concentration distribution, to calculate the application boundary coefficient between adjacent grid cells, and to recombine the grid cells according to the application boundary coefficient to form an irregular application cell set; The determination module is used to calculate the basic application dose based on the corrected concentration distribution of each of the irregular application unit sets, calculate the spraying angle coefficient in combination with the terrain elevation information, and determine the spraying control parameters of each of the irregular application unit sets through the basic application dose and the spraying angle coefficient. The output module is used to acquire real-time position trajectory data during the spraying process, match the real-time position trajectory data with the boundary of the irregular application unit set, and when the trajectory of the spraying equipment crosses the boundary, perform smooth transition correction on the spraying control parameters according to the application boundary coefficient, and output the corresponding spraying control command.

[0007] This invention integrates multi-dimensional data on pollutant concentration, moisture content, and topographic elevation to construct a soil migration coefficient field for in-depth correction of pollutant concentration, reflecting the actual distribution and migration patterns of pollutants. Based on the application boundary coefficient, the grid is reorganized into an irregular set of application units, ensuring that the operational area division conforms to the actual pollution boundary. By combining the corrected concentration with topographic elevation to calculate the base dosage and spraying angle, quantitative drug administration is achieved for different terrains and pollution levels. During application, a location trajectory matching mechanism is used to smoothly adjust control parameters when the equipment crosses the area boundary, avoiding sudden changes in dosage, over- or under-dosing during cross-boundary application. This ensures soil remediation effectiveness while improving application accuracy and pesticide utilization. Attached Figure Description

[0008] Figure 1 This is a flowchart of the first embodiment. Detailed Implementation

[0009] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0010] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0011] In the first embodiment, the present invention proposes a precision application method for soil remediation, such as... Figure 1 As shown, it includes: S1, Obtain gridded soil data of the area to be remediated, the soil data including pollutant concentration information, soil moisture content information and topographic elevation information; Perform spatial correlation calculation on the gridded soil data to obtain the pollution diffusion weight of each grid cell; Specifically, an aerial survey of the target area to be remediated is conducted using a drone equipped with a multispectral sensor and lidar to obtain topographic elevation information. A soil sampling drill is used to collect samples at preset intervals, and a portable gas chromatography-mass spectrometry (GC-MS) instrument is used to analyze the soil samples and obtain pollutant concentration information. A time-domain reflectometry (TDAR) instrument is used to measure the soil moisture content at each sampling point. The area to be remediated is divided into a fixed-size rectangular grid, and the spatial resolution parameter of the grid data is set, preferably ranging from 0.5m to 2.0m, with a preferred setting of 1m × 1m. The Kriging interpolation algorithm is used to map the collected discrete sampling point data to the center of each rectangular grid, obtaining pollutant concentration information, soil moisture content information, and topographic elevation information for each grid cell, which are then combined to form gridded soil data.

[0012] A target grid cell and its eight adjacent grid cells are selected as a local calculation window. The pollutant concentration difference between the target grid cell and each of its adjacent grid cells, as well as the planar distance between the grid center points, are calculated. The concentration gradient value is obtained by dividing the concentration difference by the planar distance. All concentration gradient values ​​between the target grid cell and its eight surrounding grid cells are then summed with weights determined by distance (e.g., inverse distance weighting) to obtain the local average concentration gradient of that grid cell. The local average concentration gradient of all grid cells across the entire domain is normalized and mapped to the interval 0 to 1, serving as the pollution diffusion weight for the target grid cell.

[0013] In an optional embodiment, the step of performing spatial correlation calculations on the gridded soil data to obtain the pollution diffusion weight of each grid cell includes: A concentration difference feature vector set is constructed using the pollutant concentration difference between grid nodes, and the pollution diffusion weight of each grid cell is calculated based on the concentration difference feature vector set.

[0014] In an optional embodiment, the step of constructing a concentration difference feature vector set using the pollutant concentration differences between grid nodes, and calculating the pollution diffusion weight of each grid cell based on the concentration difference feature vector set, includes: Obtain eight pollutant concentration values ​​between the central grid node and its eight neighboring grid nodes, and subtract the pollutant concentration values ​​of the eight neighboring grid nodes from the pollutant concentration value of the central grid node to generate eight sets of spatial concentration differences. The eight sets of spatial concentration differences at the same central grid node are arranged in a clockwise spatial sequence to generate a one-dimensional feature vector, and the one-dimensional feature vectors of all grid nodes are concatenated sequentially to generate the concentration difference feature vector set. Extract all positive concentration differences from the single-dimensional feature vector corresponding to each central grid node, sum them to obtain the total difference value, and count the number of positive concentration differences. If the count is greater than zero, the sum of the differences is divided by the count to obtain the average difference; if the count is equal to zero, the average difference is set to zero. Divide the mean difference by the pollutant concentration value of the central grid node to obtain the relative diffusion intensity; add a preset smoothing compensation constant to the relative diffusion intensity to generate the pollution diffusion weight of the corresponding grid cell.

[0015] Using each grid point as the center, the concentration values ​​of the surrounding eight grids (up, down, left, right, and four diagonal directions) are obtained. For example, if the concentration of the central node is 100 mg / kg, the concentrations of its eight neighboring nodes are 90, 105, 80, 110, 85, 95, 100, and 90 mg / kg, respectively. Subtracting the neighboring values ​​from the central value yields spatial concentration differences of 10, -5, 20, -10, 15, 5, 0, and 10 mg / kg. These concentration differences are then reorganized into a one-dimensional feature vector [10, -5, 20, -10, 15, 5, 0, 10] in a clockwise direction, and then concatenated to form a global concentration difference feature vector set. A directional positive value screening mechanism is implemented when calculating the pollution diffusion weights, performing statistical calculations only on positive concentration differences. Positive differences of 10, 20, 15, 5, and 10 are extracted from the above feature vectors, and summed to obtain a total difference value of 60. The number of positive values ​​is counted as 5, and the average difference is calculated to be 12. This average difference is divided by the pollutant concentration value of the central grid node. If the central concentration is zero, the average concentration of the eight neighboring nodes is used instead, yielding the dimensionless relative diffusion intensity. To avoid the weights becoming ineffective under extremely small concentration differences and to maintain algorithm smoothness, a preset smoothing compensation constant is used. The preferred range for this constant is typically set to 0.01 to 0.1. Assuming a set value of 0.05, the pollution diffusion weight of the corresponding grid cell is 0.12 + 0.05 = 0.17. This pollution diffusion weight represents the relative net potential energy of pollutant diffusion from the local microenvironment to the surrounding areas.

[0016] Optionally, when dealing with tens of millions of grid nodes or requiring high real-time response for pesticide application, the spatial topology can be reduced from eight-neighborhood to four-neighborhood. That is, only the concentration values ​​in the four orthogonal directions (top, bottom, left, and right) of the central grid are extracted to generate four sets of spatial concentration differences. While sacrificing the minimal accuracy difference in the diagonal directions at the edges, the length of the single-dimensional feature vector and the load of subsequent accumulation operations can be reduced by 50%, improving the solution and response speed of the gradient matrix.

[0017] S2, calculate the soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and correct the pollutant concentration information through the soil migration coefficient field to obtain the corrected concentration distribution; perform partitioning processing on the corrected concentration distribution, calculate the application boundary coefficient between adjacent grid cells, and reorganize the grid cells into application units according to the application boundary coefficient to form an irregular application unit set. Specifically, soil moisture content information is extracted from each grid cell, and this information is divided by the maximum water-holding capacity of the soil in that area to obtain the relative moisture content. The pollution diffusion weight is multiplied by the relative moisture content, and the result is calculated using an exponential function with the natural logarithm as the base, yielding the soil migration coefficient for the corresponding grid cell. The soil migration coefficients of all grid cells are aggregated to form a soil migration coefficient field. The original pollutant concentration information for each grid cell is multiplied by the corresponding soil migration coefficient to calculate the compensation concentration. The original pollutant concentration information is added to the compensation concentration to generate the corrected concentration distribution for the entire area to be remediated.

[0018] Multiple threshold levels for corrected concentrations are set to initially divide the area to be repaired into different concentration zones. The difference in corrected concentration between any two adjacent grid cells at the boundary of the concentration zones is calculated, and this difference is divided by a set standard allowable deviation value to obtain the application boundary coefficient. It is determined whether the application boundary coefficient is less than a preset aggregation threshold. If it is less than the aggregation threshold, it indicates that the application requirements of adjacent grid cells are similar, and the two adjacent grid cells are merged using a region growing algorithm. This process is repeated, traversing all grid cells at the boundaries, breaking the boundaries of the original regular rectangular grids, and merging continuous grid cells with similar application requirements together to form a set of irregular application cells divided according to actual application requirements.

[0019] In an optional embodiment, the step of calculating a soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and correcting the pollutant concentration information using the soil migration coefficient field to obtain a corrected concentration distribution, includes: Extract the percentage value of soil volumetric water content for each grid cell from the soil moisture content information, and subtract the percentage value of soil volumetric water content from the standard saturated volumetric water content constant to obtain the soil drying water loss potential energy. The basic migration coefficient of each grid cell is calculated by multiplying the pollution diffusion weight by the soil drying water loss potential energy and dividing by the soil basic porosity constant. The soil migration coefficient field is generated by interpolating the basic migration coefficient of the global coordinate position according to the regional coordinate system. The original value of the pollutant concentration information in each grid cell is multiplied by the basic migration coefficient at the corresponding position to obtain the migration adjustment scalar. The original value of the pollutant concentration information is added to the migration adjustment scalar, and the results are summarized to form the corrected concentration distribution.

[0020] The soil volumetric water content percentage of the grid cells is obtained using moisture sensors or historical inversion data, typically fluctuating between 10% and 40%, for example, an actual reading of 25%. A standard saturated volumetric water content constant is retrieved from the database; this constant is determined based on local soil geological conditions, with a preferred value of 45%. The soil drying water loss potential energy is calculated to be 0.20, characterizing the remaining space capacity of the soil to absorb water and drive pollutant migration. Using a pre-calibrated soil basic porosity constant, for example, set to 0.40, combined with the pollution diffusion weight calculated in the previous step, assumed to be 0.05, the basic migration coefficient of this grid is calculated to be 0.025. A smooth and continuous soil migration coefficient field is generated from the scattered grid data using Kriging or bilinear interpolation algorithms. Based on this, a concentration correction is performed on the grid. Assuming the original pollutant concentration is 100 mg / kg, the pollutant concentration is multiplied by the basic migration coefficient 0.025, resulting in a migration adjustment scalar of 2.5 mg / kg. The corrected concentration distribution value is 102.5 mg / kg.

[0021] When generating the soil migration coefficient field, a co-kriging interpolation method with topographic constraints or using the runoff direction matrix as a secondary variable can be adopted. That is, the topographic elevation information of the corresponding grid is read simultaneously during the interpolation calculation, and positive gain compensation is artificially assigned to the grids at the bottom of the runoff line, so that the generated migration coefficient field can reflect the aggravating effect of gravity flow on the enrichment of pollutants in low-lying areas, thereby making the corrected concentration distribution more reasonable.

[0022] In an optional embodiment, the step of partitioning the modified concentration distribution and calculating the application boundary coefficient between adjacent grid cells includes: Extract the center coordinate parameters of the first and second grid cells that are in a tangent and shared-edge connected state, and use the center coordinate parameters to calculate the Euclidean distance between the two grid cells. Read the values ​​of the corrected concentration distribution corresponding to the coordinate positions of the first grid cell and the second grid cell, and calculate the absolute change difference after subtracting the values ​​of the two corrected concentration distributions; Divide the absolute variation difference by the Euclidean distance to obtain the scalar value of the distance gradient rate. Through formula The application boundary coefficient at the target boundary is calculated and output, where, The application boundary coefficient is the given application boundary coefficient. The distance gradient rate scalar value, This is a preset reference gradient constant.

[0023] Obtain the center geographic coordinates of the first and second grid cells that are tangent and share edges, for example, coordinate parameters (1.0, 2.0) and (2.0, 2.0) respectively, and calculate their Euclidean distance as 1.0 m. Extract the corrected concentration values ​​corresponding to the two grid cells, for example, 150 mg / kg and 135 mg / kg respectively, and calculate the absolute variation difference between them as 15 mg / kg. Using the above parameters, divide the absolute variation difference by the Euclidean distance to obtain the distance gradient rate scalar value G = 15.0. Set the reference gradient constant. for To mitigate the oscillations caused by extreme concentration jumps in the partitioning algorithm, a mapping compression using the natural logarithm function is employed: substituting into the formula B≈2.77. The calculated target boundary application boundary coefficient is 2.77. This value reflects the severity of the difference in pesticide demand between adjacent grids; a larger boundary coefficient indicates a more severe discontinuity in pollution levels on both sides. This avoids the cutting distortion caused by using a fixed threshold, ensuring the detection of regional differentiation characteristics even with minute changes in floating-point data. Moreover, areas with extremely high pollution concentrations often have soil surface compaction or permeability bottlenecks due to long-term geological evolution. If the equipment immediately pours out the maximum flow rate of pesticide upon entering this area, the soil pores cannot absorb water within milliseconds, leading to surface runoff or water accumulation. This causes the high-concentration pesticide to leak into adjacent low-contamination areas, resulting in secondary pollution.

[0024] In real-world work environments, farmland or sites awaiting restoration may contain hard barriers such as impermeable walls, field ridges, or irrigation ditches. Even if the absolute variation difference between the two sides of such barriers is small, they should still be considered strong boundaries. When calculating the scalar value of the distance gradient rate, a barrier penalty weight matrix can be used in conjunction with the GIS vector base map: when a barrier mapping is detected between the first and second grid cells, the calculated scalar value G of the distance gradient rate is multiplied by a preset blocking penalty coefficient, and then divided by a reference gradient constant for dimensionless transformation. This artificially creates an insurmountable ultra-high pesticide application boundary coefficient B after logarithmic transformation, preventing unreasonable zoning and merging across the irrigation canal isolation zone.

[0025] In an optional embodiment, the step of reorganizing the grid cells according to the application boundary coefficient to form an irregular application cell set includes: Set the boundary cutoff threshold constant; In the area to be repaired, the first undivided grid cell is selected as the initial centroid reference point for the expansion of the region, and the associated application boundary coefficient values ​​of the surrounding adjacent grid boundaries are sequentially retrieved and calculated. When the associated application boundary coefficient value at an adjacent boundary is lower than the boundary cutoff threshold constant, the corresponding adjacent grid cells are merged into the current expanded connected data block; The newly included grid cells are used as new centroid reference points for recursive expansion calculations until the associated application boundary coefficient values ​​corresponding to all adjacent boundaries are greater than or equal to the boundary truncation threshold constant. Then, the expansion process is closed and locked, and the current expanded connected data block is output as a single closed set of the irregular application cells.

[0026] The control terminal inputs a boundary truncation threshold constant, which is set according to the working resolution and control accuracy of the spray nozzle, preferably within the range of 1.5 to 3.5, for example, pre-set to 2.0. The first untreated grid is selected as the initial centroid reference point for regional expansion from the upper left corner of the repaired global map or the location with the highest pollutant concentration, and recursively detected outwards. During the expansion loop, the associated application boundary coefficient values ​​of adjacent grids are read. If a boundary coefficient value of 1.2 is detected on the right, it indicates that the pollution level in that direction is similar; the feature ID of the right grid is merged into the current expanded connected data block and pushed onto the processing stack as a new centroid reference point to continue probing outwards. If a boundary coefficient value of 2.8 is detected below, it is determined that there is a discontinuity in application demand at that boundary, and merging in that direction is stopped. This logic continues recursively, marking grids until the boundary coefficients of all unvisited boundaries around the current connected block reach or exceed 2.0. The processing stack is cleared, the program is closed and locked, and the merged grid set is output as a single closed set of irregular application units. The data block reorganization method ensures that the divided areas match the actual pollution value band outline, avoiding over-spraying or under-spraying of agents at the boundaries caused by conventional rectangular segmentation.

[0027] S3, calculate the basic dosage based on the corrected concentration distribution of each irregular application unit set, calculate the spraying angle coefficient in combination with the terrain elevation information, and determine the spraying control parameters of each irregular application unit set through the basic dosage and the spraying angle coefficient; Specifically, the average value of the corrected concentration distribution of all grid cells within the irregular application unit set is statistically analyzed. This average value is then multiplied by a pre-calibrated pesticide demand constant per unit area to obtain the base pesticide dosage for the irregular application unit set. Simultaneously, the maximum slope of the terrain elevation information within the irregular application unit set is calculated, and the cosine of the maximum slope is used as the spray angle coefficient. The base pesticide dosage is multiplied by the reciprocal of the spray angle coefficient to obtain the target spray flow rate, which is then combined with a preset nozzle working pressure to serve as the spray control parameters for each of the irregular application unit sets.

[0028] In an optional embodiment, calculating the spray angle coefficient based on the terrain elevation information includes: Extract the elevation measurement values ​​of the center point of each grid unit within the irregular application unit set area from the terrain elevation information; The finite difference algorithm is used to calculate the first-order spatial slope differential matrix of the grid center point along the horizontal axis path, and at the same time, the first-order spatial slope differential matrix of the grid center point is calculated along the vertical axis path. Through formula Calculate the slope angle of the true terrain cross section for each grid cell. ,in, These are the elements in the first-order spatial slope differential matrix within the same grid. For the corresponding elements in the vertical first-order spatial slope differential matrix; Through formula The spray angle coefficient of each grid cell is calculated, and the average value of the spray angle coefficients of all grid cells within the irregular application unit set area is obtained to obtain the spray angle coefficient of the irregular application unit set, which is used to compensate for the spray dosage on the actual slope area of ​​the ground.

[0029] By reading digital elevation model data, the elevation value of the center point of each grid within an irregular application unit containing 50 grids is extracted. The finite difference algorithm is applied to calculate the slope derivative in a two-dimensional spatial coordinate system. For example, if a grid advances 1m along the horizontal X-axis, the elevation increases by 0.1m, which is represented by the first-order differential element in the horizontal direction. =0.1; For every 1m advance along the vertical Y-axis, the elevation rises by 0.15m, that is, vertically towards the first-order differential element. =0.15. The actual slope angle of the cross-section at this point is calculated based on the spatial geometric synthesis formula: The grid spraying angle coefficient is calculated using the geometric area projection compensation formula. This indicates that due to the 10.2° slope, the actual three-dimensional exposed surface area of ​​the grid is 1.6% larger than the two-dimensional projected horizontal area. The arithmetic mean of the spraying angle coefficients calculated from the 50 grids within this irregular application set is taken, assuming a mean of 1.020, and used as the global spraying angle coefficient for the entire unit set. When issuing control parameters, the original planar base application dosage, such as 100 liters / acre, is multiplied by 1.020 and applied with a compensation command of 102 liters / acre, thus solving the problem of pesticide concentration dilution caused by the slope projection.

[0030] Alternatively, a three-dimensional irregular triangular mesh surface can be constructed using high-resolution lidar point cloud data. The sum of the true absolute areas of all spatial micro-triangles within the corresponding two-dimensional mesh range can be calculated by iterating through the mesh. This sum of true absolute areas can be divided by the base area of ​​the standard two-dimensional mesh to output the mesh spraying angle coefficient that includes the surface roughness features.

[0031] S4. During the spraying process, real-time position trajectory data is acquired, and the real-time position trajectory data is matched and calculated with the boundary of the irregular application unit set. When the trajectory of the spraying equipment crosses the boundary, the spraying control parameters are smoothly corrected according to the application boundary coefficient, and the corresponding spraying control command is output.

[0032] Specifically, a real-time differential positioning module and an inertial measurement unit mounted on the spraying equipment collect the equipment's current latitude and longitude coordinates and heading angle at a fixed frequency to form real-time position trajectory data. The current coordinates are then compared with the vector boundary points of an irregular set of spraying units stored in the map system using a ray cross-validation method to determine the specific spraying unit the equipment is currently in and the next spraying unit it will enter. When the equipment crosses the boundary between two spraying units, the spraying boundary coefficient between the two units is extracted as a time constant and substituted into a first-order low-pass filter algorithm model to gradually calculate the spraying flow rate from the current spraying unit to the next spraying unit, generating a continuously changing control voltage signal. This control voltage signal is then sent as a spraying control command to the proportional solenoid valve of the spraying equipment via a programmable logic controller to control the real-time spraying volume.

[0033] In an optional embodiment, the step of smoothly transitioning the spraying control parameters according to the application boundary coefficient when the spraying equipment trajectory crosses the boundary includes: The real-time travel coordinates of the spraying equipment are obtained according to a preset cycle, and the real-time travel coordinates are mapped onto the work surface division map for position overlap detection. When the latest travel coordinates are detected to cross the boundary of the current application unit and enter the range of the next adjacent new application unit, the application boundary coefficient at the corresponding boundary position is extracted; Divide the constant by the sum of the drug application boundary coefficient and the preset zero constant to calculate the boundary damping correction factor; The spraying control parameters corresponding to the next adjacent new application unit are retrieved, and the control parameters are multiplied by the boundary damping correction factor to calculate the instantaneous transition spraying dose. The corresponding control command is then output to the servo driver to drive the liquid pump motor, thereby completing the smooth transition correction of the spraying control parameters.

[0034] During operation, the control system's built-in RTK-GPS positioning module reports the current travel coordinates (X, Y) of the drone or spraying tractor in real time according to a high-frequency preset period, for example, set to 100ms. These high-frequency coordinates are mapped onto the underlying spraying unit partitioning layer. Once the logic function detects that two consecutive frames of coordinates fall within units A and B respectively, an out-of-bounds interrupt signal is triggered, and the spraying boundary coefficient shared by both units is quickly retrieved from memory, for example, a value of 3.0. To ensure the safety of the division operation and control the buffer slope, a zero-prevention constant is preset, set to 0.5 in this example. The microprocessor divides the constant 1 by (the sum of the spraying boundary coefficient 3.0 and the zero-prevention constant 0.5) to calculate a boundary damping correction factor of 0.286. Assuming the target spraying control parameter of the newly retrieved spraying unit B is a required pump flow rate of 200mL / s, the target value is extracted and multiplied by the aforementioned boundary damping correction factor to calculate 57.2mL / s. The microprocessor then generates a PWM duty cycle digital instruction containing an instantaneous transition dose of 57.2 mL / s, which is sent to the pump servo driver via the CAN bus. The pump motor does not jump from low flow rate to full load to 200 mL / s at the moment of crossing the boundary. Instead, it starts with damped acceleration at 57.2 mL / s and then smoothly approaches the steady-state target value of 200 mL / s through a ramp function during the control cycle.

[0035] Optionally, after calculating the boundary damping correction factor, a hard saturation limiting module can be added: Min(1.0, boundary damping correction factor) is used to cap the factor, preventing abnormal increases in the factor due to an excessively small boundary coefficient, and ensuring that the instantaneous transition dose does not exceed the target value. For example, when the boundary coefficient B is extremely small, the factor may be greater than 1; after limiting, it is forcibly reset to 1.0, thus directly outputting the target dose and avoiding unnecessary delays. Alternatively, other smooth transition functions can be used instead of the inverse proportional formula, such as a factor expression based on negative exponential decay. ,in In response to the smoothing tuning constant, this expression ensures that the factor is always within the (0,1] interval and is inversely proportional to the boundary coefficient B. That is, the greater the boundary difference, the smaller the factor and the smoother the transition, thus achieving damping gradual control under all operating conditions.

[0036] In a second embodiment, the present invention also proposes a precision application system for soil remediation, comprising the following modules: The acquisition module is used to acquire gridded soil data of the area to be remediated. The soil data includes pollutant concentration information, soil moisture content information, and topographic elevation information. Spatial correlation calculation is performed on the gridded soil data to obtain the pollution diffusion weight of each grid cell. The recombination module is used to calculate the soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and to correct the pollutant concentration information through the soil migration coefficient field to obtain the corrected concentration distribution; to perform partitioning processing on the corrected concentration distribution, to calculate the application boundary coefficient between adjacent grid cells, and to recombine the grid cells according to the application boundary coefficient to form an irregular application cell set; The determination module is used to calculate the basic application dose based on the corrected concentration distribution of each of the irregular application unit sets, calculate the spraying angle coefficient in combination with the terrain elevation information, and determine the spraying control parameters of each of the irregular application unit sets through the basic application dose and the spraying angle coefficient. The output module is used to acquire real-time position trajectory data during the spraying process, match the real-time position trajectory data with the boundary of the irregular application unit set, and when the trajectory of the spraying equipment crosses the boundary, perform smooth transition correction on the spraying control parameters according to the application boundary coefficient, and output the corresponding spraying control command.

[0037] In an optional embodiment, the step of performing spatial correlation calculations on the gridded soil data to obtain the pollution diffusion weight of each grid cell includes: A concentration difference feature vector set is constructed using the pollutant concentration difference between grid nodes, and the pollution diffusion weight of each grid cell is calculated based on the concentration difference feature vector set.

[0038] In an optional embodiment, the step of constructing a concentration difference feature vector set using the pollutant concentration differences between grid nodes, and calculating the pollution diffusion weight of each grid cell based on the concentration difference feature vector set, includes: Obtain eight pollutant concentration values ​​between the central grid node and its eight neighboring grid nodes, and subtract the pollutant concentration values ​​of the eight neighboring grid nodes from the pollutant concentration value of the central grid node to generate eight sets of spatial concentration differences. The eight sets of spatial concentration differences at the same central grid node are arranged in a clockwise spatial sequence to generate a one-dimensional feature vector, and the one-dimensional feature vectors of all grid nodes are concatenated sequentially to generate the concentration difference feature vector set. Extract all positive concentration differences from the single-dimensional feature vector corresponding to each central grid node, sum them to obtain the total difference value, and count the number of positive concentration differences. If the count is greater than zero, the sum of the differences is divided by the count to obtain the average difference; if the count is equal to zero, the average difference is set to zero. Divide the mean difference by the pollutant concentration value of the central grid node to obtain the relative diffusion intensity; add a preset smoothing compensation constant to the relative diffusion intensity to generate the pollution diffusion weight of the corresponding grid cell.

[0039] In an optional embodiment, the step of calculating a soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and correcting the pollutant concentration information using the soil migration coefficient field to obtain a corrected concentration distribution, includes: Extract the percentage value of soil volumetric water content for each grid cell from the soil moisture content information, and subtract the percentage value of soil volumetric water content from the standard saturated volumetric water content constant to obtain the soil drying water loss potential energy. The basic migration coefficient of each grid cell is calculated by multiplying the pollution diffusion weight by the soil drying water loss potential energy and dividing by the soil basic porosity constant. The soil migration coefficient field is generated by interpolating the basic migration coefficient of the global coordinate position according to the regional coordinate system. The original value of the pollutant concentration information in each grid cell is multiplied by the basic migration coefficient at the corresponding position to obtain the migration adjustment scalar. The original value of the pollutant concentration information is added to the migration adjustment scalar, and the results are summarized to form the corrected concentration distribution.

[0040] In an optional embodiment, the step of partitioning the modified concentration distribution and calculating the application boundary coefficient between adjacent grid cells includes: Extract the center coordinate parameters of the first and second grid cells that are in a tangent and shared-edge connected state, and use the center coordinate parameters to calculate the Euclidean distance between the two grid cells. Read the values ​​of the corrected concentration distribution corresponding to the coordinate positions of the first grid cell and the second grid cell, and calculate the absolute change difference after subtracting the values ​​of the two corrected concentration distributions; Divide the absolute variation difference by the Euclidean distance to obtain the scalar value of the distance gradient rate. Through formula The application boundary coefficient at the target boundary is calculated and output, where, The application boundary coefficient is the given application boundary coefficient. The distance gradient rate scalar value, This is a preset reference gradient constant.

[0041] In an optional embodiment, the step of reorganizing the grid cells according to the application boundary coefficient to form an irregular application cell set includes: Set the boundary cutoff threshold constant; In the area to be repaired, the first undivided grid cell is selected as the initial centroid reference point for the expansion of the region, and the associated application boundary coefficient values ​​of the surrounding adjacent grid boundaries are sequentially retrieved and calculated. When the associated application boundary coefficient value at an adjacent boundary is lower than the boundary cutoff threshold constant, the corresponding adjacent grid cells are merged into the current expanded connected data block; The newly included grid cells are used as new centroid reference points for recursive expansion calculations until the associated application boundary coefficient values ​​corresponding to all adjacent boundaries are greater than or equal to the boundary truncation threshold constant. Then, the expansion process is closed and locked, and the current expanded connected data block is output as a single closed set of the irregular application cells.

[0042] In an optional embodiment, calculating the spray angle coefficient based on the terrain elevation information includes: Extract the elevation measurement values ​​of the center point of each grid unit within the irregular application unit set area from the terrain elevation information; The finite difference algorithm is used to calculate the first-order spatial slope differential matrix of the grid center point along the horizontal axis path, and at the same time, the first-order spatial slope differential matrix of the grid center point is calculated along the vertical axis path. Through formula Calculate the slope angle of the true terrain cross section for each grid cell. ,in, These are the elements in the first-order spatial slope differential matrix within the same grid. For the corresponding elements in the vertical first-order spatial slope differential matrix; Through formula The spray angle coefficient of each grid cell is calculated, and the average value of the spray angle coefficients of all grid cells within the irregular application unit set area is obtained to obtain the spray angle coefficient of the irregular application unit set, which is used to compensate for the spray dosage on the actual slope area of ​​the ground.

[0043] In an optional embodiment, the step of smoothly transitioning the spraying control parameters according to the application boundary coefficient when the spraying equipment trajectory crosses the boundary includes: The real-time travel coordinates of the spraying equipment are obtained according to a preset cycle, and the real-time travel coordinates are mapped onto the work surface division map for position overlap detection. When the latest travel coordinates are detected to cross the boundary of the current application unit and enter the range of the next adjacent new application unit, the application boundary coefficient at the corresponding boundary position is extracted; Divide the constant by the sum of the drug application boundary coefficient and the preset zero constant to calculate the boundary damping correction factor; The spraying control parameters corresponding to the next adjacent new application unit are retrieved, and the control parameters are multiplied by the boundary damping correction factor to calculate the instantaneous transition spraying dose. The corresponding control command is then output to the servo driver to drive the liquid pump motor, thereby completing the smooth transition correction of the spraying control parameters.

[0044] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0045] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0046] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0047] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0048] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A precision application method for soil remediation, characterized in that, Includes the following steps: Obtain gridded soil data of the area to be remediated, including pollutant concentration information, soil moisture content information, and topographic elevation information; perform spatial correlation calculation on the gridded soil data to obtain the pollution diffusion weight of each grid cell; The soil migration coefficient field is calculated based on the pollution diffusion weight and the soil moisture content information, and the pollutant concentration information is corrected by the soil migration coefficient field to obtain the corrected concentration distribution. The modified concentration distribution is partitioned, the application boundary coefficient between adjacent grid cells is calculated, and the grid cells are reorganized according to the application boundary coefficient to form an irregular application cell set. The base dosage is calculated based on the corrected concentration distribution of each irregular application unit set, and the spraying angle coefficient is calculated in combination with the terrain elevation information. The spraying control parameters of each irregular application unit set are determined by the base dosage and the spraying angle coefficient. During the spraying process, real-time position trajectory data is acquired, and the real-time position trajectory data is matched and calculated with the boundary of the irregular application unit set. When the trajectory of the spraying equipment crosses the boundary, the spraying control parameters are smoothly corrected according to the application boundary coefficient, and the corresponding spraying control command is output.

2. The method according to claim 1, characterized in that, The step of performing spatial correlation calculations on the gridded soil data to obtain the pollution diffusion weight of each grid cell includes: A concentration difference feature vector set is constructed using the pollutant concentration difference between grid nodes, and the pollution diffusion weight of each grid cell is calculated based on the concentration difference feature vector set.

3. The method according to claim 2, characterized in that, The process of constructing a concentration difference feature vector set using the pollutant concentration differences between grid nodes, and calculating the pollution diffusion weight of each grid cell based on the concentration difference feature vector set, includes: Obtain eight pollutant concentration values ​​between the central grid node and its eight neighboring grid nodes, and subtract the pollutant concentration values ​​of the eight neighboring grid nodes from the pollutant concentration value of the central grid node to generate eight sets of spatial concentration differences. The eight sets of spatial concentration differences at the same central grid node are arranged in a clockwise spatial sequence to generate a one-dimensional feature vector, and the one-dimensional feature vectors of all grid nodes are concatenated sequentially to generate the concentration difference feature vector set. Extract all positive concentration differences from the single-dimensional feature vector corresponding to each central grid node, sum them to obtain the total difference value, and count the number of positive concentration differences. If the count is greater than zero, the sum of the differences is divided by the count to obtain the average difference; if the count is equal to zero, the average difference is set to zero. Divide the mean difference by the pollutant concentration value of the central grid node to obtain the relative diffusion intensity; add a preset smoothing compensation constant to the relative diffusion intensity to generate the pollution diffusion weight of the corresponding grid cell.

4. The method according to claim 1, characterized in that, The process of calculating a soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and then correcting the pollutant concentration information using the soil migration coefficient field to obtain a corrected concentration distribution, includes: Extract the percentage value of soil volumetric water content for each grid cell from the soil moisture content information, and subtract the percentage value of soil volumetric water content from the standard saturated volumetric water content constant to obtain the soil drying water loss potential energy. The basic migration coefficient of each grid cell is calculated by multiplying the pollution diffusion weight by the soil drying water loss potential energy and dividing by the soil basic porosity constant. The soil migration coefficient field is generated by interpolating the basic migration coefficient of the global coordinate position according to the regional coordinate system. The original value of the pollutant concentration information in each grid cell is multiplied by the basic migration coefficient at the corresponding position to obtain the migration adjustment scalar. The original value of the pollutant concentration information is added to the migration adjustment scalar, and the results are summarized to form the corrected concentration distribution.

5. The method according to claim 1, characterized in that, The step of partitioning the corrected concentration distribution and calculating the application boundary coefficient between adjacent grid cells includes: Extract the center coordinate parameters of the first and second grid cells that are in a tangent and shared-edge connected state, and use the center coordinate parameters to calculate the Euclidean distance between the two grid cells. Read the values ​​of the corrected concentration distribution corresponding to the coordinate positions of the first grid cell and the second grid cell, and calculate the absolute change difference after subtracting the values ​​of the two corrected concentration distributions; Divide the absolute variation difference by the Euclidean distance to obtain the scalar value of the distance gradient rate. Through formula The application boundary coefficient at the target boundary is calculated and output, where, The application boundary coefficient is the given application boundary coefficient. The distance gradient rate scalar value, This is a preset reference gradient constant.

6. The method according to claim 1, characterized in that, The step of reorganizing the grid cells according to the application boundary coefficient to form an irregular application cell set includes: Set the boundary cutoff threshold constant; In the area to be repaired, the first undivided grid cell is selected as the initial centroid reference point for the expansion of the region, and the associated application boundary coefficient values ​​of the surrounding adjacent grid boundaries are sequentially retrieved and calculated. When the associated application boundary coefficient value at an adjacent boundary is lower than the boundary cutoff threshold constant, the corresponding adjacent grid cells are merged into the current expanded connected data block; The newly included grid cells are used as new centroid reference points for recursive expansion calculations until the associated application boundary coefficient values ​​corresponding to all adjacent boundaries are greater than or equal to the boundary truncation threshold constant. Then, the expansion process is closed and locked, and the current expanded connected data block is output as a single closed set of the irregular application cells.

7. The method according to claim 5, characterized in that, The calculation of the spraying angle coefficient based on the terrain elevation information includes: Extract the elevation measurement values ​​of the center point of each grid unit within the irregular application unit set area from the terrain elevation information; The finite difference algorithm is used to calculate the first-order spatial slope differential matrix of the grid center point along the horizontal axis path, and at the same time, the first-order spatial slope differential matrix of the grid center point is calculated along the vertical axis path. Through formula Calculate the slope angle of the true terrain cross section for each grid cell. ,in, These are the elements in the first-order spatial slope differential matrix within the same grid. For the corresponding elements in the vertical first-order spatial slope differential matrix; Through formula The spray angle coefficient of each grid cell is calculated, and the average value of the spray angle coefficients of all grid cells within the irregular application unit set area is obtained to obtain the spray angle coefficient of the irregular application unit set, which is used to compensate for the spray dosage on the actual slope area of ​​the ground.

8. The method according to claim 1, characterized in that, When the spraying equipment trajectory crosses the boundary, the spraying control parameters are smoothly corrected according to the application boundary coefficient, including: The real-time travel coordinates of the spraying equipment are obtained according to a preset cycle, and the real-time travel coordinates are mapped onto the work surface division map for position overlap detection. When the latest travel coordinates are detected to cross the boundary of the current application unit and enter the range of the next adjacent new application unit, the application boundary coefficient at the corresponding boundary position is extracted; Divide the constant by the sum of the drug application boundary coefficient and the preset zero constant to calculate the boundary damping correction factor; The spraying control parameters corresponding to the next adjacent new application unit are retrieved, and the control parameters are multiplied by the boundary damping correction factor to calculate the instantaneous transition spraying dose. The corresponding control command is then output to the servo driver to drive the liquid pump motor, thereby completing the smooth transition correction of the spraying control parameters.

9. A precision pesticide application system for soil remediation, characterized in that, Includes the following modules: The acquisition module is used to acquire gridded soil data of the area to be remediated. The soil data includes pollutant concentration information, soil moisture content information, and topographic elevation information. Spatial correlation calculation is performed on the gridded soil data to obtain the pollution diffusion weight of each grid cell. The recombination module is used to calculate the soil migration coefficient field based on the pollution diffusion weight and the soil moisture content information, and to correct the pollutant concentration information through the soil migration coefficient field to obtain the corrected concentration distribution; The modified concentration distribution is partitioned, the application boundary coefficient between adjacent grid cells is calculated, and the grid cells are reorganized according to the application boundary coefficient to form an irregular application cell set. The determination module is used to calculate the basic application dose based on the corrected concentration distribution of each of the irregular application unit sets, calculate the spraying angle coefficient in combination with the terrain elevation information, and determine the spraying control parameters of each of the irregular application unit sets through the basic application dose and the spraying angle coefficient. The output module is used to acquire real-time position trajectory data during the spraying process, match the real-time position trajectory data with the boundary of the irregular application unit set, and when the trajectory of the spraying equipment crosses the boundary, perform smooth transition correction on the spraying control parameters according to the application boundary coefficient, and output the corresponding spraying control command.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1-8.

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