A soil sampling method and system for forestry engineering mapping

Abstract

The application provides a soil sampling method and system for forestry engineering mapping, which divides a target region in space through multi-source heterogeneous data in the target region, obtains each divided region, determines the sampling state and traffic attribute of each divided region, and constructs an influence atlas of path planning of a soil sampling device in the target region; determines a coordinated adjustment path of the soil sampling device in the target region through the influence atlas, the sampling state and traffic attribute of each divided region; guides the soil sampling device to perform soil sampling based on the coordinated adjustment path; in the sampling process, multi-source heterogeneous data of a region corresponding to a remaining sampling point in the target region is collected in real time to adjust the coordinated adjustment path in real time until soil sampling of the target region is completed. According to the application, the soil sampling region can be coordinately sampled based on the dynamic correlation of each dimension data in the soil sampling region of the forestry engineering mapping.

Description

Technical Field

[0001] This application relates to the field of soil sampling technology, and more specifically, to a soil sampling method and system for forestry engineering surveying. Background Technology

[0002] Soil sampling is a fundamental step in precision agriculture, environmental assessment, and scientific research. It aims to systematically collect representative soil samples to analyze their physical properties, chemical composition, and pollutant content. Its core lies in following scientific standards, using professional tools to collect soil samples at specific depths according to pre-set grids or points, and ensuring that the samples are uncontaminated and can accurately reflect the overall condition of the sampling area.

[0003] Soil sampling operations in forestry engineering surveying often face challenges such as complex terrain, uneven vegetation cover, and the coexistence of static obstacles (dead branches, rocks, gullies) and dynamic obstacles (wild animals, temporary work facilities) in the target area. Traditional soil sampling methods often rely on manually pre-set paths, simply referencing basic geographic information without systematically integrating multi-dimensional heterogeneous data such as terrain, vegetation, soil, and obstacles. This results in a lack of scientific and targeted path planning, easily leading to paths traversing prohibited areas and difficult-to-access sections. Furthermore, traditional methods cannot perceive the dynamic changes in the target area environment in real time during sampling. When the sampling status and accessibility of the remaining sampling points change due to obstacle movement or sudden changes in soil conditions, it is difficult to quickly adjust the sampling path. This not only reduces sampling efficiency and increases equipment wear and tear but may also affect the accuracy of sampling data due to unreasonable paths. Therefore, it fails to meet the requirements of forestry engineering surveying for precise, efficient, and safe soil sampling. Thus, how to conduct collaborative sampling of soil sampling areas based on the dynamic correlation of various dimensions of data in forestry engineering surveying has become a challenge for the industry. Summary of the Invention

[0004] This application provides a soil sampling method and system for forestry engineering surveying, which can perform collaborative sampling of soil sampling areas based on the dynamic correlation of data in various dimensions of soil sampling areas in forestry engineering surveying.

[0005] In a first aspect, this application provides a soil sampling method for forestry engineering surveying, comprising the following steps: Within the target area of ​​forestry engineering surveying, multiple sensors mounted on soil sampling equipment are used to collect multi-source heterogeneous data on the target area, including topography, vegetation, soil information, and static and dynamic obstacles. Based on the multi-source heterogeneous data, the target area is spatially divided to obtain each divided area, and the sampling status and access attributes of each divided area during the sampling process are determined. Based on the correlation between the data in the multi-source heterogeneous data, an impact map of the soil sampling equipment when performing path planning in the target area is constructed. By coordinating the sampling path of the soil sampling device in the target area based on the influence map, the sampling status of each divided area and the access attributes, a coordinated adjustment path of the soil sampling device in the target area is obtained. The soil sampling device is guided to perform soil sampling at various sampling points in the target area based on the coordinated adjustment path. During the sampling process, multiple sensors mounted on the soil sampling equipment collect multi-source heterogeneous data of the remaining sampling points in the target area in real time, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed.

[0006] In some embodiments, the target region is spatially divided based on the multi-source heterogeneous data to obtain each divided region, specifically including: The multi-source heterogeneous data is preprocessed to obtain preprocessed multi-source heterogeneous data; Extract multi-source heterogeneous features from the preprocessed multi-source heterogeneous data; Based on the aforementioned multi-source heterogeneous features, the target region is spatially divided to obtain various divided regions.

[0007] In some embodiments, determining the sampling status and access attributes of each segmented region during the sampling process specifically includes: Retrieve the regional heterogeneous features of each partitioned region from the multi-source heterogeneous features corresponding to the multi-source heterogeneous data; Obtain the threshold data of feature parameters of the target region during the sampling process; By comparing the regional heterogeneous features of each segmented region one by one using the threshold data of the feature parameters, the sampling status and access attributes of each segmented region during the sampling process can be obtained.

[0008] In some embodiments, constructing an impact map of the soil sampling device when performing path planning within a target area based on the correlation between various data in the multi-source heterogeneous data specifically includes: Determine the relationships between the data in the multi-source heterogeneous data; Obtain the path planning data of the soil sampling device during sampling; Based on the aforementioned correlations and the path planning data, an impact map is constructed for the soil sampling equipment to perform path planning within the target area.

[0009] In some embodiments, the sampling path of the soil sampling device in the target area is coordinated and adjusted based on the influence map, the sampling status of each divided region, and the accessibility attributes, to obtain the coordinated adjustment path of the soil sampling device in the target area. Specifically, this includes: Obtain the preset sampling path of the soil sampling device in the target area; Based on the impact map, the sampling status of each divided region, and the traffic attributes, determine each risk path and each alternative path in the target region; By coordinating the sampling paths of the soil sampling device in the target area through various risk paths and alternative paths, a coordinated adjustment path for the soil sampling device in the target area is obtained.

[0010] In some embodiments, guiding the soil sampling device to perform soil sampling at various sampling points in the target area based on the coordinated adjustment path specifically includes: The collaborative adjustment path is input into the soil sampling device; Soil sampling is performed at each sampling point in the target area using the soil sampling device according to the coordinated adjustment path.

[0011] In some embodiments, during the sampling process, multi-source heterogeneous data corresponding to the remaining sampling points within the target area are collected in real time by multiple sensors mounted on the soil sampling device, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed. Specifically, this includes: During the sampling process, multiple sensors mounted on the soil sampling equipment are used to collect multi-source heterogeneous data in real time from the areas corresponding to the remaining sampling points in the target area, thus obtaining real-time multi-source heterogeneous data. The collaborative adjustment path is adjusted in real time based on real-time collected multi-source heterogeneous data to obtain the adjusted collaborative adjustment path. The adjusted collaborative adjustment path continues to guide the soil sampling device to sample the target area until soil sampling of the target area is completed.

[0012] Secondly, this application provides a soil sampling system for forestry engineering surveying, comprising: The data acquisition module is used to collect multi-source heterogeneous data, including topography, vegetation, soil information, and static and dynamic obstacles, within the target area of ​​forestry engineering surveying and mapping through various sensors mounted on soil sampling equipment. The processing module is used to spatially divide the target area based on the multi-source heterogeneous data to obtain each divided area, and determine the sampling status and access attributes of each divided area during the sampling process. Based on the correlation between the data in the multi-source heterogeneous data, the module constructs the influence map of the soil sampling device when performing path planning in the target area. The processing module is also used to coordinately adjust the preset sampling path of the soil sampling device in the target area based on the influence map, the sampling status of each divided area and the access attributes, so as to obtain the coordinated adjustment path of the soil sampling device in the target area. The processing module is also used to guide the soil sampling device to perform soil sampling at each sampling point in the target area based on the collaborative adjustment path; The execution module is used to collect multi-source heterogeneous data of the remaining sampling points in the target area in real time through various sensors mounted on the soil sampling equipment during the sampling process, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed.

[0013] Thirdly, this application provides a computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described soil sampling method for forestry engineering mapping.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described soil sampling method for forestry engineering surveying.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The soil sampling method and system for forestry engineering surveying provided in this application firstly collects multi-source heterogeneous data, including topography, vegetation, soil information, and static and dynamic obstacles, within the target area of ​​the forestry engineering surveying using multiple sensors mounted on a soil sampling device. Based on this multi-source heterogeneous data, the target area is spatially divided into various regions, and the sampling status and accessibility of each region during the sampling process are determined. An influence map is constructed based on the correlation between the various data in the multi-source heterogeneous data to guide the soil sampling device's path planning within the target area. The preset sampling path of the soil sampling device in the target area is then coordinated and adjusted using the influence map, the sampling status of each region, and its accessibility, resulting in a coordinated adjustment path for the soil sampling device within the target area. Based on this coordinated adjustment path, the soil sampling device is guided to sample soil at various sampling points within the target area. During the sampling process, multiple sensors mounted on the soil sampling device collect multi-source heterogeneous data in real time for the areas corresponding to the remaining sampling points within the target area, allowing for real-time adjustment of the coordinated adjustment path until soil sampling of the target area is completed.

[0016] Therefore, this application, during the soil sampling process, utilizes multiple sensors to collect multi-source heterogeneous data on terrain, vegetation, soil, and static and dynamic obstacles, breaking the limitations of a single data dimension in sampling planning. Based on this multi-source heterogeneous data, spatial division is performed, and the sampling status and accessibility attributes of each area are determined. Simultaneously, a path planning influence map is constructed by combining the correlations between data, achieving effective correlation and integration of multi-dimensional data. This provides a quantitative basis for regional division and path optimization in collaborative sampling, solving the problem that isolated data from different dimensions cannot support collaborative decision-making. By collaboratively adjusting the preset sampling path using the influence map and regional attributes, the sampling path balances sampling needs with equipment accessibility, ensuring the orderly conduct of collaborative sampling. The collaboratively adjusted path guides the sampling equipment to perform sampling, ensuring the implementation of the collaborative sampling scheme. During the sampling process, multi-source heterogeneous data of the remaining sampling point areas are collected in real time, and the path is dynamically adjusted. The dynamic correlation of multi-dimensional data is used to address environmental changes during the sampling process, avoiding problems such as low sampling efficiency and equipment obstruction caused by initial data lag. Ultimately, efficient collaborative sampling of the entire sampling area is achieved. Using the scheme proposed in this application, collaborative sampling of soil sampling areas can be carried out based on the dynamic correlation of data in various dimensions of soil sampling areas in forestry engineering surveying. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a soil sampling method for forestry engineering mapping according to some embodiments of this application; Figure 2This is an exemplary flowchart illustrating the determination of a partitioned region according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of a collaborative adjustment path according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a soil sampling system for forestry engineering surveying, as shown in some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device that implements a soil sampling method for forestry engineering surveying, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a soil sampling method for forestry engineering surveying according to some embodiments of this application. The soil sampling method for forestry engineering surveying mainly includes the following steps: In step 101, within the target area of ​​the forestry engineering survey, multiple sensors mounted on the soil sampling equipment are used to collect multi-source heterogeneous data including topography, vegetation, soil information, and static and dynamic obstacles within the target area.

[0020] It should be noted that the multi-source heterogeneous data in this application refers to datasets with diverse sources and significantly different data structures and forms, collected from different types of sensors mounted on soil sampling equipment. These datasets reflect key operational environment and resource information within the target area of ​​forestry engineering surveying, including topographic features, vegetation growth and distribution, basic physicochemical properties of the soil surface, location distribution of static obstacles, and real-time movement patterns of dynamic obstacles. The multi-source heterogeneous data includes topographic point cloud data collected by lidar sensors, regional geographic coordinate data obtained by GPS positioning sensors, vegetation reflectance spectral values ​​captured by vegetation spectral analyzers, obstacle images and dynamic target videos captured by high-definition visual cameras, and basic physicochemical parameters of the soil surface temperature, humidity, and pH value collected by soil surface temperature and humidity sensors.

[0021] In step 102, the target area is spatially divided based on the multi-source heterogeneous data to obtain each divided area, and the sampling status and access attributes of each divided area during the sampling process are determined. Based on the correlation between the data in the multi-source heterogeneous data, an influence map of the soil sampling device when performing path planning in the target area is constructed.

[0022] In some embodiments, reference Figure 2The figure is an exemplary flowchart of determining the partitioned region in some embodiments of this application. In this embodiment, the target region is spatially partitioned based on the multi-source heterogeneous data to obtain each partitioned region, which can be achieved by the following steps: In step 1021, the multi-source heterogeneous data is preprocessed to obtain preprocessed multi-source heterogeneous data; In step 1022, multi-source heterogeneous features are extracted from the preprocessed multi-source heterogeneous data; In step 1023, the target region is spatially divided based on the multi-source heterogeneous features to obtain various divided regions.

[0023] In specific implementation, the multi-source heterogeneous data is preprocessed to obtain the preprocessed multi-source heterogeneous data. This can be achieved in the following way: preprocessing operations are performed on the collected multi-source heterogeneous data. Gaussian filtering algorithm is used to denoise the terrain point cloud data collected by lidar. Nearest neighbor interpolation is used to complete some missing parameters collected by soil sensor. Data deduplication algorithm is used to remove duplicate obstacle images captured by visual camera. At the same time, geographic coordinate labels are uniformly added to all heterogeneous data based on GPS positioning data to complete data format normalization and obtain preprocessed multi-source heterogeneous data. Other methods can also be used in other embodiments, which are not limited here.

[0024] In addition, in specific implementation, the extraction of multi-source heterogeneous features from the preprocessed multi-source heterogeneous data can be achieved in the following ways: the slope parameter in the terrain features is calculated using a slope calculation model, the vegetation coverage parameter in the vegetation features is extracted using the pixel dichotomy method, the pH value and humidity mean parameters in the soil features are statistically calculated using the arithmetic mean method, and the static obstacle distribution density and dynamic obstacle activity frequency parameters in the obstacle features are calculated using the density statistics method, thus forming multi-source heterogeneous features that include four major categories: terrain, vegetation, soil, and obstacles. Other methods can also be used in other embodiments, which are not limited here.

[0025] In addition, in specific implementation, the target region is spatially divided based on the multi-source heterogeneous features to obtain each divided region. This can be achieved in the following way: First, the target region is divided into regular grid units with a preset precision using a grid partitioning method. Then, the K-means clustering algorithm is used, with the extracted multi-source heterogeneous features as the clustering index. The number of clusters is determined according to the area of ​​the target region and the sampling requirements, and the cluster centers are initialized. The Euclidean distance between the feature vector of each grid unit and the cluster center is calculated. The grid units with the smallest distance are grouped into the same class. The cluster centers are iteratively updated until the convergence condition is met. Finally, grid units of the same type are merged and the boundary is smoothed and calibrated to obtain each divided region. Other methods can also be used in other embodiments, which are not limited here.

[0026] It should be noted that the multi-source heterogeneous features in this application represent a set of features of different dimensions extracted from multi-source heterogeneous data. These features reflect the core operational environment attributes of the local space within the target area of ​​forestry engineering surveying, including topographic conditions, vegetation growth status, basic soil physicochemical properties, and the distribution and activity patterns of obstacles. The divided regions represent spatial units with clear boundaries and similar attributes within the target area. These regions reflect the types of local areas with similar operational conditions within the target area and can provide a spatial dimension basis for sampling status determination, access attribute division, and path planning.

[0027] In some embodiments, determining the sampling status and access attributes of each segmented region during the sampling process can be achieved using the following steps: Retrieve the regional heterogeneous features of each partitioned region from the multi-source heterogeneous features corresponding to the multi-source heterogeneous data; Obtain the threshold data of feature parameters of the target region during the sampling process; By comparing the regional heterogeneous features of each segmented region one by one using the threshold data of the feature parameters, the sampling status and access attributes of each segmented region during the sampling process can be obtained.

[0028] In specific implementation, the regional heterogeneous features of each divided region can be retrieved from the multi-source heterogeneous features corresponding to the multi-source heterogeneous data in the following way: through the data management module built into the soil sampling device, according to the unique geographic code of each divided region, the regional heterogeneous features corresponding to each region are retrieved from the stored multi-source heterogeneous feature set. Specifically, it covers seven core parameters: average terrain slope, terrain flatness deviation, vegetation coverage, average soil pH, average soil moisture, static obstacle distribution density, and dynamic obstacle activity frequency. This ensures that the feature parameters of each divided region correspond one-to-one with the geographical range without overlap or confusion. Other methods can also be used in other embodiments, which are not limited here.

[0029] In addition, in specific implementation, the characteristic parameter threshold data of the target area during the sampling process can be obtained in the following way: based on the industry standard requirements of the "Technical Specification for Forestry Soil Sampling", and combined with the mechanical operation performance parameters of the soil sampling equipment, the preset characteristic parameter threshold data is obtained. The sampling state threshold is set based on the equipment's maximum climbing ability of 25°, the upper limit of vegetation coverage corresponding to the effective working space of the sampler of 30%, the suitable pH range of 6.0-7.5 for most effective analysis of forestry soil samples, the suitable soil moisture sampling range of 20%-35%, the maximum density of static obstacles that the equipment can avoid of 5 per 100㎡, and the upper limit of dynamic obstacle activity frequency that does not affect operational efficiency of 1 time per hour. The passage attribute threshold is set based on the upper limit of terrain flatness deviation of 5cm corresponding to stable equipment passage, the upper limit of static obstacle density that the equipment can pass at low speed of 8 per 100㎡, and the operational safety threshold corresponding to occasional dynamic obstacle activity. Finally, the following is adopted: The weighted comprehensive judgment method assigns weights based on the degree of influence of various characteristic parameters on sampling operations and equipment accessibility. Among them, the average terrain slope and the deviation of terrain flatness have the greatest impact on equipment operation safety, each assigned a weight of 25%. Vegetation coverage and static obstacle distribution density have a significant impact on sampling operability, each assigned a weight of 15%. The average soil pH, average soil moisture, and frequency of dynamic obstacle activity have a secondary impact on sampling efficiency and sample quality, each assigned a weight of 10%. The aforementioned terrain slope threshold, vegetation coverage threshold, soil pH threshold, soil moisture threshold, static obstacle distribution density threshold, and dynamic obstacle activity frequency threshold related to sampling status determination, as well as the terrain flatness deviation threshold, static obstacle distribution density threshold, and dynamic obstacle activity frequency threshold related to accessibility attribute determination, are used as characteristic parameter threshold data of the target area in the sampling process. Other methods can be used in other embodiments, which are not limited here.

[0030] In addition, in specific implementation, the sampling status and access attributes of each segmented region can be obtained by comparing the regional heterogeneous features of each segmented region one by one with the corresponding thresholds in the feature parameter threshold data. The following method can be used: compare the seven regional heterogeneous feature parameters of each segmented region with the corresponding thresholds in the feature parameter threshold data one by one. Parameters that meet the threshold requirements are given positive scores according to their corresponding weights. Parameters that exceed the threshold range are deducted corresponding weight scores according to the degree of deviation. Then, the final score of each parameter is multiplied by its preset weight coefficient. Finally, the weighted score of all parameters is calculated. By accumulating the scores, the comprehensive score of the sampling status and the comprehensive score of the passage attribute of the area can be obtained respectively. According to the score range judgment result, the area with a comprehensive score of sampling status ≥ 80 points is judged as sampleable, the area with a score of 60-79 points is judged as not sampled temporarily, and the area with a score < 60 points is judged as prohibited from sampling. The area with a comprehensive score of passage attribute ≥ 85 points is judged as passable, the area with a score of 70-84 points is judged as slow, and the area with a score < 70 points is judged as prohibited. This completes the determination of the sampling status and passage attribute of all divided areas. Other methods can be used in other embodiments, which are not limited here.

[0031] It should be noted that the regional heterogeneity features in this application reflect the unique topographic conditions, vegetation growth status, soil physicochemical properties, and obstacle distribution and activity patterns of each subdivided area, forming the basic environment for sampling operations; the characteristic parameter threshold data reflect the feasible boundary conditions for soil sampling operations and equipment access; the sampling status represents the feasibility results of sampling in each subdivided area of ​​the target region, reflecting whether each subdivided area is suitable for carrying out soil sampling operations and the suitability of the operations; the accessibility attributes represent the accessibility results of each subdivided area of ​​the target region for soil sampling equipment, reflecting the safety and efficiency level of soil sampling equipment access in each subdivided area.

[0032] In some embodiments, constructing an impact map of the soil sampling device for path planning within a target area based on the correlation between various data in the multi-source heterogeneous data can be achieved through the following steps: Determine the relationships between the data in the multi-source heterogeneous data; Obtain the path planning data of the soil sampling device during sampling; Based on the aforementioned correlations and the path planning data, an impact map is constructed for the soil sampling equipment to perform path planning within the target area.

[0033] In specific implementation, determining the correlation between various data in the multi-source heterogeneous data can be achieved in the following way: The preprocessed multi-source heterogeneous data is classified and archived into four categories: terrain, vegetation, soil, and obstacles. Then, based on support and confidence calculation methods, the correlation between different categories of data is quantitatively analyzed. First, the classified terrain, vegetation, soil, and obstacle data are discretized, defining antecedents and consequents for correlation rules such as "terrain slope > 20° indicates increased traffic resistance" and "vegetation coverage > 50% indicates increased sampling time." Then, the proportion of data entries in all datasets that simultaneously contain both the antecedent and consequent of the rule is counted to obtain the support of the rule. The proportion of data entries containing both the antecedent and consequent of the rule is counted to obtain the confidence of the rule. Finally, the calculated support and confidence are compared with preset thresholds. Association rules that meet the threshold requirements are selected and classified into three levels of association strength: strong, medium, and weak. Specifically, terrain slope data and equipment passage resistance data are considered strongly positively correlated if the support is ≥0.6 and the confidence level is ≥0.8; vegetation coverage data and sampling operation time data are considered moderately positively correlated if the support is ≥0.5 and the confidence level is ≥0.7; soil physicochemical parameter data and sampling point priority data are considered moderately positively correlated if the support is ≥0.4 and the confidence level is ≥0.6; static obstacle distribution density data and path detour distance data are considered strongly positively correlated if the support is ≥0.7 and the confidence level is ≥0.85; and dynamic obstacle activity frequency data and path adjustment frequency data are considered strongly positively correlated if the support is ≥0.55 and the confidence level is ≥0.75. This clarifies the association types and strength levels among various data types. Other methods can be used in other embodiments, which are not limited here.

[0034] In addition, in specific implementation, the path planning data of the soil sampling equipment during sampling is obtained, which specifically includes equipment parameters, sampling point parameters, and planning target parameters. Among them, the equipment parameters are a maximum climbing angle of 25°, a minimum turning radius of 3 meters, a rated travel speed of 0.8 m / s, and an obstacle avoidance distance of 1.5 meters. The sampling point parameters are the geographic coordinates of the preset sampling points, sampling depth requirements, sample quantity requirements, and sampling priority ranking. The planning target parameters are minimizing the total path length, full coverage of sampling points, equipment operation safety factor ≥ 0.9, and operation efficiency ≥ 8 sampling points / hour. Other methods can be used to achieve this in other embodiments, which are not limited here.

[0035] In addition, in specific implementation, the influence map of the soil sampling device when performing path planning in the target area based on the aforementioned correlation and the path planning data can be constructed in the following way: using the grid division unit of the target area as the spatial carrier of the map, the analytic hierarchy process (AHP) is used to construct an influence weight judgment model. Path planning optimization is set as the target layer, the correlation of four types of data (topography, vegetation, soil, and obstacles) is set as the criterion layer, and each grid unit is set as the scheme layer. The importance of each correlation in the criterion layer is compared pairwise using the 1-9 scaling method, a judgment matrix is ​​constructed, and a consistency check is performed. After the check is passed, the weights of the obstacle data correlation, topography data correlation, and vegetation data correlation are calculated. The results of association weights and soil data association weights are then combined with the association strength levels of various data types and the equipment constraints and sampling task requirements in the path planning data. Each grid cell is assigned a quantitative influence value of "association strength level × influence weight × constraint rule". Constraint information such as "forced avoidance when slope > 25°" and "temporary suspension of passage in high-frequency activity areas of dynamic obstacles" are simultaneously labeled. Finally, through two-dimensional matrix visualization technology, the quantitative influence values ​​and constraint information of all grid cells are integrated and presented to generate a path planning influence map that can intuitively reflect the constraints of various data associations on path planning. Other methods can be used in other embodiments, which are not limited here.

[0036] It should be noted that the association relationships in this application represent the association types and strong, medium, and weak association strength levels between multi-source heterogeneous data categories, reflecting the degree of interaction and influence of the corresponding operational environment elements on the operation and path planning of soil sampling equipment; the path planning data represents the basic data set supporting the path planning of soil sampling equipment, reflecting the boundary of equipment operation capacity, specific requirements of sampling tasks, and the goal orientation of path planning; the influence map reflects the quantitative constraint degree and constraint rules of the association effects of various operational environment elements on the path planning of soil sampling equipment, and can be used to plan and adjust the sampling path of soil sampling equipment.

[0037] In step 103, the sampling path of the soil sampling device in the target area is adjusted in a coordinated manner based on the influence map, the sampling status of each divided area and the access attributes, so as to obtain the coordinated adjustment path of the soil sampling device in the target area.

[0038] In some embodiments, reference Figure 3 The figure is an exemplary flowchart for determining the coordinated adjustment path in some embodiments of this application. In this embodiment, the sampling path of the soil sampling device in the target area is coordinated and adjusted by the influence map, the sampling status of each divided area and the access attributes. The coordinated adjustment path of the soil sampling device in the target area can be obtained by the following steps: In step 1031, the preset sampling path of the soil sampling device in the target area is obtained; In step 1032, each risk path and each alternative path in the target area are determined based on the influence map, the sampling status of each divided region, and the traffic attributes. In step 1033, the preset sampling path of the soil sampling device in the target area is coordinated and adjusted through each risk path and each alternative path to obtain the coordinated adjustment path of the soil sampling device in the target area.

[0039] It should be noted that the preset sampling path in this application refers to the pre-planned route for soil sampling equipment operation before coordinating adjustments based on the influence map of path planning, the sampling status of each divided area, and the accessibility attributes. It reflects the initial planning intent of achieving full coverage of sampling points and a relatively short total path length, while also embodying the preliminary consideration of the efficiency of completing the sampling task. The preset sampling path includes the geographic coordinates of all preset sampling points, the connection order of path segments between each sampling point, the actual length and direction of each path segment, and the basic operational constraints set during the initial planning, such as the order of sampling point operations and the range of equipment travel speed.

[0040] In specific implementation, determining each risk path and each alternative path in the target area based on the influence map, the sampling status of each divided area, and the accessibility attributes can be achieved in the following way: Based on the sampling status and accessibility attributes, risk assessment is carried out in combination with the quantitative influence value and constraint rules corresponding to each path segment in the influence map. Path segments that directly pass through prohibited sampling areas or restricted access areas are identified as risk paths, and path segments that pass through temporarily unsampled areas or slow-moving areas and whose influence value exceeds the preset safety threshold are identified as medium-risk paths. Then, using the well-known geographic information processing technology of spatial buffer analysis, a buffer zone is set with each type of risk path segment as the center and according to the safe avoidance distance of the soil sampling equipment. In the passable area outside the buffer zone, multiple potential access routes that do not cross the risk area are planned in combination with the terrain flatness and obstacle distribution. These routes are used as alternative paths. Other methods can also be used in other embodiments, which are not limited here.

[0041] In addition, in specific implementation, the soil sampling equipment's preset sampling path in the target area is coordinated and adjusted through various risk paths and alternative paths. The coordinated adjustment path of the soil sampling equipment in the target area can be achieved in the following way: with the core optimization objectives of full coverage of sampling points, compliance with operational safety standards, and the shortest total path length, high and medium risk path segments in the preset sampling path are first eliminated. Then, the planned alternative paths are connected to the breakpoints of the original path. Next, the connection order of all path nodes is iteratively adjusted, redundant and overlapping path segments with the same direction are merged, and missing sampling point connection paths in the sampleable area are added. After the initial path adjustment is completed, a comprehensive feasibility verification is carried out on the entire adjusted path. The verification content includes whether the path completely avoids all prohibited sampling and access areas, whether the terrain slope of the area traversed by the path meets the maximum climbing ability requirements of the equipment, whether the turning radius of the path meets the minimum turning radius limit of the equipment, and whether it covers all sampleable preset sampling points in the target area. After confirming that all verification items meet the requirements, the coordinated adjustment path of the soil sampling equipment in the target area is finally obtained. Other methods can also be used in other embodiments, which are not limited here.

[0042] It should be noted that, in this application, the risk path refers to a pre-set path segment in the target area where there are operational hazards, reflecting the degree to which the pre-set path restricts the safety of soil sampling equipment operation, sampling efficiency, and task completion quality; the alternative path refers to multiple potential alternative routes planned in the passable area outside the risk area, reflecting feasible solutions to avoid path risks and ensure operational continuity; the collaborative adjustment path refers to the final operation route formed after removing the risk path segment from the pre-set path, incorporating the alternative path, and optimizing the node sequence and feasibility verification, reflecting a soil sampling equipment operation planning scheme that takes into account full coverage of sampling points, equipment operation safety, and optimal total path length.

[0043] In step 104, the soil sampling device is guided to perform soil sampling at each sampling point in the target area based on the coordinated adjustment path.

[0044] In some embodiments, guiding the soil sampling device to perform soil sampling at various sampling points in the target area based on the coordinated adjustment path can be achieved by the following steps: The collaborative adjustment path is input into the soil sampling device; Soil sampling is performed at each sampling point in the target area using the soil sampling device according to the coordinated adjustment path.

[0045] In practice, the first step is to transmit the coordinated adjustment path data, which includes the geographical coordinates of sampling points, the connection order of path segments, the travel constraint rules for each area, and the work priority of sampling points, to the navigation control module of the soil sampling equipment via a data interface. Coordinate calibration technology is used to match the geocoding of the path data with the coordinate system of the equipment's positioning system. A deviation correction algorithm is then used to eliminate coordinate system errors, ensuring that the positioning accuracy of the path data reaches the centimeter level, meeting the positioning requirements of the sampling operation. Subsequently, the BeiDou positioning and navigation system of the soil sampling equipment is activated to obtain the equipment's current location information in real time and dynamically match it with the coordinated adjustment path. This guides the equipment to travel according to the work priority of the sampling points planned in the path. During the journey, the equipment's environmental perception module collects data such as the terrain slope and obstacle distribution of the area in real time, automatically adjusting its travel status according to the path's constraint rules, maintaining its position within passable areas. The device operates at a rated speed, reducing speed and increasing obstacle monitoring frequency in slow-moving areas. When the device reaches a preset sampling point, the positioning system triggers a precise coordinate positioning program. After confirming that the sampling point's position deviation is within the allowable range, the device's sampling control module controls the sampler to complete soil sample collection according to the preset values ​​of soil physicochemical parameters for that area, following the sampling depth and sampling frequency specified in the Forestry Soil Sampling Technical Specifications. Simultaneously, data such as sampling time, sample number, soil moisture, and pH value are associated with the sampling point coordinates and stored in the device's local database. If sudden dynamic obstacles or changes in terrain conditions are encountered during the journey, the device automatically calls pre-stored alternative paths for local path fine-tuning. After the sampling work at all preset sampling points is completed, the device automatically generates a sampling task execution list, summarizing the actual path execution status and sample collection data to complete the entire soil sampling process based on collaborative path adjustment.

[0046] In step 105, during the sampling process, multiple sensors mounted on the soil sampling equipment collect multi-source heterogeneous data of the area corresponding to the remaining sampling points in the target area in real time, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed.

[0047] In some embodiments, during the sampling process, multi-source heterogeneous data corresponding to the remaining sampling points within the target area are collected in real time by multiple sensors mounted on the soil sampling device, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed. This can be achieved by the following steps: During the sampling process, multiple sensors mounted on the soil sampling equipment are used to collect multi-source heterogeneous data in real time from the areas corresponding to the remaining sampling points in the target area, thus obtaining real-time multi-source heterogeneous data. The collaborative adjustment path is adjusted in real time based on real-time collected multi-source heterogeneous data to obtain the adjusted collaborative adjustment path. The adjusted collaborative adjustment path continues to guide the soil sampling device to sample the target area until soil sampling of the target area is completed.

[0048] In practice, during the sampling process, multiple sensors mounted on the soil sampling equipment collect multi-source heterogeneous data in real time from the areas corresponding to the remaining sampling points within the target area. Based on this real-time heterogeneous data, the collaborative adjustment path is adjusted in real time. First, mean filtering technology is used to average five consecutive sets of data for each parameter to reduce noise. Then, the data is compared parameter by parameter with previously stored historical regional data and path influence maps. Deviation thresholds of ±3° terrain, ±5% soil, and ±2 obstacles per 100㎡ are set to determine whether the sampling status and accessibility of the remaining sampling point areas have changed significantly. If adjustment is needed, spatial buffer analysis technology is immediately used, centered on the risk area and within 1.5 meters of the equipment. A 2-meter radius buffer zone is generated based on the standard safety distance. 3-5 alternative paths are planned outside the buffer zone. Prioritizing safety, ensuring full coverage of sampling points, and minimizing path increments, the remaining sampling point operation sequence and path connections are optimized. The path slope is verified to be ≤25° and the turning radius ≥3 meters to obtain the adjusted collaborative adjustment path. Subsequently, the adjusted collaborative adjustment path is loaded into the navigation control module. Combined with the BeiDou positioning calibration device position, the device is guided to continue sampling along the new path. The above process of "sensor acquisition, data processing and comparison, and path adjustment as needed" is repeated. In case of sensor failure, a backup sensor is activated to use the most recent valid data until all sampling point operations are completed. Sampling data and path adjustment records are archived simultaneously to ensure continuous and controllable operation.

[0049] In another aspect, in some embodiments, this application provides a soil sampling system for forestry engineering surveying, with reference to... Figure 4 The figure is a schematic diagram of the structure of a soil sampling system for forestry engineering surveying according to some embodiments of this application. The soil sampling system 400 for forestry engineering surveying includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to collect multi-source heterogeneous data including topography, vegetation, soil information and static and dynamic obstacles in the target area of ​​forestry engineering surveying and mapping through a variety of sensors mounted on the soil sampling equipment. Processing module 402, in this application, is used to spatially divide the target area based on the multi-source heterogeneous data, obtain each divided area, determine the sampling status and access attributes of each divided area during the sampling process, and construct an influence map of the soil sampling device when performing path planning in the target area based on the correlation between the data in the multi-source heterogeneous data. It should be noted that the processing module 402 in this application is also used to coordinately adjust the preset sampling path of the soil sampling device in the target area based on the influence map, the sampling status of each divided area and the access attributes, so as to obtain the coordinated adjustment path of the soil sampling device in the target area. Additionally, it should be noted that the processing module 402 in this application is also used to guide the soil sampling device to perform soil sampling at each sampling point in the target area based on the collaborative adjustment path. The execution module 403 in this application is mainly used to collect multi-source heterogeneous data of the area corresponding to the remaining sampling points in the target area in real time through various sensors mounted on the soil sampling equipment during the sampling process, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed.

[0050] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to acquire the code and execute the above-described soil sampling method for forestry engineering mapping.

[0051] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device implementing a soil sampling method for forestry engineering mapping, according to some embodiments of this application. The soil sampling method for forestry engineering mapping in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0052] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0053] The communication bus 502 can be used to transmit information between the aforementioned components.

[0054] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0055] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0056] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0057] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0058] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0059] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described soil sampling method for forestry engineering surveying.

[0060] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0061] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A soil sampling method for forestry engineering surveying, characterized in that, Includes the following steps: Within the target area of ​​forestry engineering surveying, multiple sensors mounted on soil sampling equipment are used to collect multi-source heterogeneous data on the target area, including topography, vegetation, soil information, and static and dynamic obstacles. Based on the multi-source heterogeneous data, the target area is spatially divided to obtain each divided area, and the sampling status and access attributes of each divided area during the sampling process are determined. Based on the correlation between the data in the multi-source heterogeneous data, an impact map of the soil sampling equipment when performing path planning in the target area is constructed. By coordinating the sampling path of the soil sampling device in the target area based on the influence map, the sampling status of each divided area and the access attributes, a coordinated adjustment path of the soil sampling device in the target area is obtained. The soil sampling device is guided to perform soil sampling at various sampling points in the target area based on the coordinated adjustment path. During the sampling process, multiple sensors mounted on the soil sampling equipment collect multi-source heterogeneous data of the remaining sampling points in the target area in real time, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed.

2. The method as described in claim 1, characterized in that, Based on the aforementioned multi-source heterogeneous data, the target region is spatially divided, resulting in each divided region specifically including: The multi-source heterogeneous data is preprocessed to obtain preprocessed multi-source heterogeneous data; Extract multi-source heterogeneous features from the preprocessed multi-source heterogeneous data; Based on the aforementioned multi-source heterogeneous features, the target region is spatially divided to obtain various divided regions.

3. The method as described in claim 1, characterized in that, Determining the sampling status and access attributes of each segmented region during the sampling process specifically includes: Retrieve the regional heterogeneous features of each partitioned region from the multi-source heterogeneous features corresponding to the multi-source heterogeneous data; Obtain the threshold data of feature parameters of the target region during the sampling process; By comparing the regional heterogeneous features of each segmented region one by one using the threshold data of the feature parameters, the sampling status and access attributes of each segmented region during the sampling process can be obtained.

4. The method as described in claim 1, characterized in that, Constructing an impact map of the soil sampling device's path planning within the target area based on the correlations between various data points in the multi-source heterogeneous data specifically includes: Determine the relationships between the data in the multi-source heterogeneous data; Obtain the path planning data of the soil sampling device during sampling; Based on the aforementioned correlations and the path planning data, an impact map is constructed for the soil sampling equipment to perform path planning within the target area.

5. The method as described in claim 1, characterized in that, By coordinating the sampling path of the soil sampling device in the target area based on the influence map, the sampling status of each divided region, and the accessibility attributes, the specific coordinated adjustment path of the soil sampling device in the target area includes: Obtain the preset sampling path of the soil sampling device in the target area; Based on the impact map, the sampling status of each divided region, and the traffic attributes, determine each risk path and each alternative path in the target region; By coordinating the sampling paths of the soil sampling device in the target area through various risk paths and alternative paths, a coordinated adjustment path for the soil sampling device in the target area is obtained.

6. The method as described in claim 1, characterized in that, The process of guiding the soil sampling device to perform soil sampling at various sampling points in the target area based on the aforementioned coordinated adjustment path specifically includes: The collaborative adjustment path is input into the soil sampling device; Soil sampling is performed at each sampling point in the target area using the soil sampling device according to the coordinated adjustment path.

7. The method as described in claim 1, characterized in that, During the sampling process, multiple sensors mounted on the soil sampling equipment collect multi-source heterogeneous data in real time from the areas corresponding to the remaining sampling points within the target area. This data is used to adjust the collaborative adjustment path in real time until soil sampling of the target area is completed. Specifically, this includes: During the sampling process, multiple sensors mounted on the soil sampling equipment are used to collect multi-source heterogeneous data in real time from the areas corresponding to the remaining sampling points in the target area, thus obtaining real-time multi-source heterogeneous data. The collaborative adjustment path is adjusted in real time based on real-time collected multi-source heterogeneous data to obtain the adjusted collaborative adjustment path. The adjusted collaborative adjustment path continues to guide the soil sampling device to sample the target area until soil sampling of the target area is completed.

8. A soil sampling system for forestry engineering surveying, characterized in that, include: The data acquisition module is used to collect multi-source heterogeneous data, including topography, vegetation, soil information, and static and dynamic obstacles, within the target area of ​​forestry engineering surveying and mapping through various sensors mounted on soil sampling equipment. The processing module is used to spatially divide the target area based on the multi-source heterogeneous data to obtain each divided area, and determine the sampling status and access attributes of each divided area during the sampling process. Based on the correlation between the data in the multi-source heterogeneous data, the module constructs the influence map of the soil sampling device when performing path planning in the target area. The processing module is also used to coordinately adjust the preset sampling path of the soil sampling device in the target area based on the influence map, the sampling status of each divided area and the access attributes, so as to obtain the coordinated adjustment path of the soil sampling device in the target area. The processing module is also used to guide the soil sampling device to perform soil sampling at each sampling point in the target area based on the collaborative adjustment path; The execution module is used to collect multi-source heterogeneous data of the remaining sampling points in the target area in real time through various sensors mounted on the soil sampling equipment during the sampling process, so as to adjust the collaborative adjustment path in real time until the soil sampling of the target area is completed.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the soil sampling method for forestry engineering mapping as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the soil sampling method for forestry engineering surveying as described in any one of claims 1 to 7.

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