A Method and System for Soil and Water Conservation Monitoring Based on Three-Dimensional Laser Scanning

Abstract

This application relates to the field of laser monitoring technology, providing a method and system for soil and water conservation monitoring based on three-dimensional laser scanning. The method includes: establishing scanning reference points across the target area's monitoring sections; periodically performing full-coverage scanning using a ground-based lidar at a preset frequency to acquire regional surface point cloud data sequences; conducting spatiotemporal erosion trend analysis based on the point cloud data sequences to determine a set of trend factors; differentiating trend factors and asynchronously adjusting the monitoring frequency to construct an asynchronous dual-mode monitoring frequency group; and distributing the frequency group to each reference point to implement asynchronous soil and water conservation monitoring of the monitoring sections. This application solves the technical problems of traditional soil and water conservation monitoring methods, which suffer from unreasonable resource allocation and inaccurate capture of dynamic erosion changes due to fixed monitoring frequencies. It achieves the technical effect of optimizing resource allocation through dynamic and differentiated monitoring frequencies, improving monitoring timeliness and accuracy in capturing erosion evolution, and realizing intelligent and refined soil and water conservation monitoring.

Description

Technical Field

[0001] This application relates to the field of laser monitoring technology, specifically to a method and system for soil and water conservation monitoring based on three-dimensional laser scanning. Background Technology

[0002] Currently, soil and water conservation monitoring, as a core means to ensure ecological security and sustainable environmental development, has been widely applied in large-scale infrastructure projects such as water conservancy, hydropower, highways, and railways. Especially in typical long-distance linear water conservancy projects like the second phase of the Huaihe River Estuary Channel project, the characteristics of large topographic relief, sparse vegetation, strong construction disturbance, and long construction periods make it extremely easy to induce serious soil erosion problems such as slope damage, overflow of excavated soil, and dike slippage during construction. Traditional soil and water conservation monitoring systems suffer from three major technical bottlenecks: First, fixed-period monitoring cannot match the nonlinear evolution of erosion activities, using the same monitoring frequency during the active erosion period in the flood season and the stable period in the dry season, resulting in missing data or wasted monitoring resources during key evolution stages; second, single-point monitoring equipment is difficult to capture the erosion characteristics of the entire linear project area, resulting in monitoring blind spots and data fragmentation; third, traditional indicator systems are limited to two-dimensional spatial analysis and cannot analyze the spatiotemporal interaction between three-dimensional topographic evolution and soil erosion. Therefore, it is urgent to build a soil and water conservation monitoring mechanism that combines high spatial accuracy, temporal continuity, and dynamic adaptability, so as to achieve comprehensive perception, trend identification, and control feedback of construction disturbance areas, and provide data support and decision-making basis for ecological protection work throughout the entire process of engineering construction. Summary of the Invention

[0003] This application provides a soil and water conservation monitoring method and system based on three-dimensional laser scanning, aiming to solve the technical problems of traditional soil and water conservation monitoring methods, which suffer from unreasonable allocation of monitoring resources and inability to accurately capture dynamic changes in erosion due to fixed monitoring frequencies.

[0004] The first aspect disclosed in this application provides a method for soil and water conservation monitoring based on three-dimensional laser scanning. The method includes: traversing a set of monitoring sections in a target area to establish scanning reference points, thereby obtaining a set of scanning reference points; using a ground-based lidar device to perform periodic full-coverage scanning according to the established set of scanning reference points and a preset monitoring frequency, thereby obtaining a set of regional surface point cloud data sequences; performing spatiotemporal erosion trend analysis based on the set of regional surface point cloud data sequences to determine a set of regional spatiotemporal erosion trend factors; differentiating the set of regional spatiotemporal erosion trend factors and asynchronously adjusting the preset monitoring frequency according to the differentiation results to construct a set of asynchronous dual-mode monitoring frequency groups, wherein each asynchronous dual-mode monitoring frequency group includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency; and distributing the set of asynchronous dual-mode monitoring frequency groups to the set of scanning reference points to perform asynchronous soil and water conservation monitoring on the set of monitoring sections.

[0005] Another aspect of this application discloses a soil and water conservation monitoring system based on three-dimensional laser scanning. The system includes: a benchmark point deployment module: traversing a set of monitoring sections in the target area to deploy scanning benchmark points, obtaining a set of scanning benchmark points; a full-coverage scanning module: using a ground-based lidar device to perform periodic full-coverage scanning according to the deployed set of scanning benchmark points and a preset monitoring frequency, obtaining a set of regional surface point cloud data sequences; a trend analysis module: performing spatiotemporal erosion trend analysis based on the set of regional surface point cloud data sequences to determine a set of regional spatiotemporal erosion trend factors; an asynchronous adjustment module: differentiating the set of regional spatiotemporal erosion trend factors and asynchronously adjusting the preset monitoring frequency according to the differentiation results, constructing a set of asynchronous dual-mode monitoring frequency groups, wherein each asynchronous dual-mode monitoring frequency group includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency; and a soil and water conservation monitoring module: distributing the set of asynchronous dual-mode monitoring frequency groups to the set of scanning benchmark points to perform asynchronous soil and water conservation monitoring on the set of monitoring sections.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The aforementioned three-dimensional laser scanning-based soil and water conservation monitoring method first traverses the monitoring sections within the target area, rationally deploying multiple reference points for laser scanning to form a complete reference point set. Then, using ground-based lidar equipment, a comprehensive scan is performed periodically at a set time frequency based on these reference points, thereby acquiring a surface point cloud data sequence covering the entire area. Next, by processing and analyzing this point cloud data, representative soil erosion trend factors are identified. To improve monitoring responsiveness and resource utilization efficiency, these trend factors are further differentiated. Based on the significance of erosion changes in different areas, the scanning frequency is dynamically adjusted to generate an asynchronous monitoring frequency combination combining high and low frequencies. Finally, this combination is distributed to the corresponding scanning reference points, achieving targeted and differentiated asynchronous soil and water conservation monitoring of each monitoring section, effectively improving monitoring accuracy and efficiency.

[0008] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a soil and water conservation monitoring method based on three-dimensional laser scanning in one embodiment.

[0011] Figure 2 This is a schematic diagram of the spatiotemporal erosion trend analysis process of a soil and water conservation monitoring method based on three-dimensional laser scanning in one embodiment.

[0012] Figure 3 This is an architecture diagram of a soil and water conservation monitoring system based on three-dimensional laser scanning in one embodiment.

[0013] Explanation of reference numerals in the attached diagram: 11. Benchmark point layout module; 12. Full coverage scanning module; 13. Trend analysis module; 14. Asynchronous adjustment module; 15. Soil and water conservation monitoring module. Detailed Implementation

[0014] This application provides a soil and water conservation monitoring method and system based on three-dimensional laser scanning, which solves the technical problems of traditional soil and water conservation monitoring methods, such as unreasonable allocation of monitoring resources and inability to accurately capture dynamic changes in erosion due to fixed monitoring frequencies.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0017] Example 1, as Figure 1 As shown, this application provides a method for soil and water conservation monitoring based on three-dimensional laser scanning, the method comprising:

[0018] The set of monitoring sections in the target area is traversed to establish scanning reference points, thus obtaining the set of scanning reference points.

[0019] In this embodiment, within the target monitoring area, several representative monitoring sections are first selected according to pre-planned spatial distribution rules. These monitoring sections are generally profiles perpendicular to the terrain or contour lines, set along the engineering area (such as embankment slopes, spoil heaps, drainage ditches, etc.), used to reflect the soil and water erosion characteristics of different geomorphic units. Subsequently, each monitoring section is processed by analyzing its spatial location, terrain slope, and surface attributes (such as whether it is a bare area, vegetation cover, etc.). Several scanning reference points are then deployed at locations with good visibility, no obstructions, stable terrain, and favorable laser reflection. These scanning reference points are physical reference points, spatially positioned using GNSS measurements, total station positioning, or reflective ball marking, serving as a spatial reference frame for subsequent ground-based lidar scanning. After the reference points for all monitoring sections are deployed, a set of spatially uniformly distributed scanning reference points with unique coordinates is formed. This set provides a precise spatial anchoring basis for subsequent point cloud data acquisition and also supports key processing flows such as data alignment, trend comparison, and time series analysis.

[0020] Table 1: Data Table of Monitoring Section Scan Reference Point Layout

[0021] Monitoring section number Cross-sectional length (m) Terrain type Number of reference points Average spacing between installations (m) Layout basis D-01 150 Gentle slope hilly area 6 25 The terrain changes gently and the distribution is even. D-02 90 sloping gully area 5 18 There are obvious erosion gullies, densely distributed D-03 120 spoil heap 7 17.1 The soil is loose and requires high-frequency sampling. D-04 200 Gentle slope of the riverbank 8 25 With a wide field of vision, the spacing can be appropriately widened. D-05 100 Sparse vegetation and bare land 6 16.7 Lacking feature points, densification is required.

[0022] As shown in Table 1, the key data for setting up scanning reference points for five typical monitoring sections are presented, including section length, terrain type, number and spacing of reference points, which are used to guide precise point setting and ensure the spatial coverage integrity of point cloud data and the comparability of subsequent analysis.

[0023] By using a ground-based lidar device to perform periodic full-coverage scanning based on a set of deployed scanning reference points and at a preset monitoring frequency, a set of regional surface point cloud data sequences is obtained.

[0024] In one embodiment, after the deployment of scanning reference points is completed, a ground-based lidar device is used to scan the surface data, relying on the deployed scanning reference points as spatial positioning references. The lidar device emits high-frequency laser beams and receives laser signals reflected from ground objects. Based on the time-of-flight (ToF) or phase difference principle, it accurately measures the distance between the ground objects and the device, and combines the scanning angle and device attitude information to construct spatial point cloud data with three-dimensional coordinate attributes (X, Y, Z) in real time. During the scanning process, each deployed scanning reference point is used as the starting positioning point, and a full-coverage scan is carried out according to a unified coordinate system to ensure that there are no blind spots or omissions in the monitoring area. To improve the timeliness and trend capture capability of monitoring, this process is repeated periodically according to a preset monitoring frequency (e.g., once every three days or once a day), forming a set of regional surface point cloud data sequences. This set not only retains the surface geometry, landform outline, and height change information of the monitoring area at different periods, but also provides a structured, quantifiable, and high-precision data foundation for subsequent spatiotemporal erosion analysis, trend identification, and change judgment.

[0025] Spatiotemporal erosion trend analysis was performed based on the set of surface point cloud data sequences in the region to determine the set of spatiotemporal erosion trend factors in the region.

[0026] In one embodiment, firstly, based on the surface point cloud data sequence obtained from multiple phases of lidar scanning, features are extracted phase by phase according to preset water and soil erosion characteristic indicators. These indicators include attributes such as surface elevation, slope, and exposed surface area, used to characterize the erosion state of the surface at different time points. Subsequently, trend analysis is performed on the extracted data according to the time sequence to initially extract factors reflecting the regional erosion change trend. Then, based on the distribution of the scanning reference points in the actual area and combined with the topographic orientation of the target area, spatial interaction correlation processing is performed on the initially extracted trend factors to determine the final set of regional spatiotemporal erosion trend factors. This allows for a more accurate description and prediction of the evolution of soil erosion in subsequent dynamic monitoring, laying a high-quality decision-making basis for the intelligent control of dynamic monitoring frequency.

[0027] Furthermore, such as Figure 2 As shown, this application provides a method for analyzing spatiotemporal erosion trends based on the set of surface point cloud data sequences in the region, and determining a set of spatiotemporal erosion trend factors in the region, including:

[0028] The erosion characteristics of the regional surface point cloud data sequence set are analyzed according to the preset water and soil erosion characteristic indicators to obtain the regional surface water and soil erosion characteristic sequence set; the spatiotemporal erosion trend of the regional surface water and soil erosion characteristic sequence set is analyzed in the order from front to back to determine the initial regional spatiotemporal erosion trend factor set; based on the location corresponding to the scanning reference point set and combined with the terrain orientation of the target area, the initial regional spatiotemporal erosion trend factor set is interactively correlated to obtain the regional spatiotemporal erosion trend factor set.

[0029] Preferably, the regional surface point cloud data sequence collected from ground-based lidar is first processed in stages according to preset water and soil erosion characteristic indicators. Each stage of point cloud data is converted into a set of quantifiable erosion characteristic values, such as surface elevation, surface slope, exposed surface area, crack area, and vegetation coverage, forming a time-series set of regional surface water and soil erosion characteristic sequences. These characteristics reflect the changes in regional landforms over time. Subsequently, the water and soil erosion characteristic sequences of each monitoring area are compared step by step in chronological order. That is, the first two time-series nodes are first compared for trend interaction, and then subsequent nodes are gradually introduced and the trend information is updated, thereby forming an initial set of spatiotemporal erosion trend factors that reflect the regional evolution law. These factors include, but are not limited to, the rate of elevation decline, the density of areas with increasing slope, and the rate of exposed surface expansion, which can initially reflect the dynamic evolution process of surface erosion. Subsequently, combining the spatial location of the scanning benchmarks and considering the actual topographic orientation of the target area (e.g., slope orientation, drainage paths, etc.), the initial trend factors are subjected to spatial neighborhood interaction correlation processing. During this process, a neighborhood analysis range is constructed near each benchmark point to determine whether the trends of other points within this range have an upstream or downstream transmission effect on that point. If necessary, trend values ​​are corrected and normalized. Ultimately, a set of regional spatiotemporal erosion trend factors with spatial consistency and trend coupling characteristics is formed. Through this process, not only can the temporal evolution characteristics of surface erosion be quantitatively extracted, but the erosion transmission mechanism between different geomorphic areas can also be captured, providing more accurate and interpretable basic data for subsequent frequency regulation and risk area identification.

[0030] Furthermore, this application provides the preset water and soil erosion characteristic indicators, including surface elevation, surface slope, exposed surface area, crack area, and vegetation coverage.

[0031] Optionally, preset water and soil erosion characteristic indicators are used to quantify and extract important surface change information affecting soil erosion from point cloud data. These mainly include surface elevation, surface slope, exposed surface area, crack area, and vegetation cover. Surface elevation reflects the vertical distance of a point on the ground relative to a unified reference surface and is a fundamental parameter for identifying landform undulations and determining erosion depth. The elevation value of each point is extracted using the Z-axis coordinate of the point cloud data, and the elevation of the entire area is interpolated using rasterization (e.g., 0.5m × 0.5m grid) to generate a digital elevation model (DEM). The elevation change can be calculated using the difference between DEM values ​​at adjacent time points. Slope refers to the degree of inclination of the ground surface relative to the horizontal plane and is often used to measure the potential for slope erosion. Slope is usually obtained by calculating the local gradient of the DEM. Exposed surfaces refer to areas lacking vegetation cover or significantly disturbed by human activity. Typically, the reflectance intensity characteristics of point clouds are compared with pre-defined empirical thresholds to distinguish between exposed surfaces (such as high-reflectance areas like soil, rock, and concrete) and non-exposed surfaces (such as low-reflectance areas like vegetation and water bodies). Points with reflectance intensity greater than or equal to the empirical threshold are selected, projected onto a horizontal plane to construct a two-dimensional distribution map, and then the total area covered by these exposed points is calculated using boundary envelope or raster statistical methods. This area is then divided by the total area of ​​the monitored region to obtain the proportion of exposed surface area. Cracks are an important precursor to slope instability. Crack area indicates the degree of crack propagation. Crack characteristics are generally identified through local concave structures or abrupt fracture boundaries in the point cloud (e.g., based on curvature analysis or voxel scanning). After extracting the crack region boundary, the crack area is calculated using two-dimensional projection and contour integration to monitor changing trends. Vegetation coverage represents the proportion of a unit area covered by vegetation and is an important indicator for assessing the land surface's resistance to erosion. It is usually classified and identified by combining the echo height characteristics of lidar or multispectral imagery, and calculated using NDVI (Normalized Difference Vegetation Index) or the proportion of points in point cloud that are above a certain threshold above the land surface, to obtain the required vegetation coverage.

[0032] Furthermore, this application provides a method for analyzing the spatiotemporal erosion trend of the regional surface water and soil erosion characteristic sequence set in chronological order to determine an initial set of regional spatiotemporal erosion trend factors, including:

[0033] For any two regional surface water and soil erosion feature sequences within the set of regional surface water and soil erosion feature sequences, a spatiotemporal erosion trend interaction is performed on the first and second regional surface water and soil erosion features to determine a first trend interaction memory set. Each first trend interaction memory is used to store the feature trend between the first and second regional surface water and soil erosion features. Then, for any three regional surface water and soil erosion feature sequences within the set of regional surface water and soil erosion feature sequences, a spatiotemporal erosion trend interaction is performed with the corresponding first trend interaction memory in the first trend interaction memory set to determine a second trend interaction memory set. This process is repeated until the last element of the set of regional surface water and soil erosion feature sequences is reached to obtain a target trend interaction memory set. The target trend interaction memory set is then traversed to perform spatiotemporal erosion trend analysis to obtain the initial set of regional spatiotemporal erosion trend factors.

[0034] Optionally, after obtaining the set of regional surface water and soil erosion characteristic sequences, trend accumulation analysis is performed step by step in chronological order for the water and soil erosion characteristic sequence of any region in the set. First, the two earliest time nodes in the sequence are selected, namely the surface water and soil erosion characteristics of the first region and the surface water and soil erosion characteristics of the second region. The selected time nodes are then subjected to trend analysis of similar characteristics and trend analysis of dissimilar characteristics to generate a first trend interaction memory set. Each first trend interaction memory in the first trend interaction memory set stores the characteristic trend between the surface water and soil erosion characteristics of the first region and the surface water and soil erosion characteristics of the second region. This includes the direction of change (increasing, decreasing, or unchanged) and magnitude of change of the same characteristic type (such as slope, elevation, etc.) between the two nodes, as well as the correlation between different types of characteristics. It also records the spatial distribution characteristics of this trend. Subsequently, the surface water and soil erosion characteristics of the third region are introduced and compared with the previous surface water and soil erosion characteristics, namely the surface water and soil erosion characteristics of the second region, using the same trend identification methods for similar and dissimilar characteristics. Then, based on the identification results, further interactive analysis is performed with the corresponding trend information in the first trend interaction memory set to identify the similarity between the two, and this is used to update and form the second trend interaction memory set. This process continues sequentially, processing the entire feature sequence until the last time node, ultimately obtaining the complete target trend interaction memory set. This set is essentially a continuous memory vector containing temporal change trajectories and spatial transmission relationships. To extract representative trend factors from the target trend interaction memory set, the target trend interaction memory set is input into a pre-trained neural network model. This neural network model can be built based on LSTM (Long Short-Term Memory), ConvLSTM (Convolutional Long Short-Term Memory), GCN-LSTM (Graph Convolutional + LSTM Hybrid Model), etc. Taking ConvLSTM as an example, a neural network model structure is built using ConvLSTM, including an input layer, ConvLSTM unit layers, convolutional fusion layers, and an output layer. The historical surface water and soil erosion characteristics (such as elevation changes, slope evolution, and exposed area ratio) of the region are converted into a fixed-size raster image sequence in chronological order to form the input data. The raster data of each period is recursively processed in the time dimension by ConvLSTM units, and local convolution operations are performed in the spatial dimension to extract and memorize the spatial trend features that evolve over time, and pass them layer by layer to the next ConvLSTM layer.Next, the hidden state at the last moment is fed into a convolutional fusion layer for channel compression and trend summarization. The output layer generates trend factors consistent with the spatial dimension of the monitored area, with numerical values ​​representing the intensity of soil erosion trends in each area. The mean squared error (MSE) loss function is then used to calculate the error between the trend factors predicted by the model and the actual trend labels. The gradient of the loss function with respect to the parameters of each layer of the model is calculated layer by layer through the backpropagation algorithm. The Adam optimizer is then used to iteratively optimize the model weights, adjusting the parameters to minimize the overall trend prediction error. This training process is repeated until the set maximum number of iterations or the loss convergence threshold is reached. After training, the model is evaluated using an independent validation set to examine its fitting accuracy and spatial consistency in predicting the intensity of soil erosion trends in different areas. If the validation results meet the preset performance standards, the neural network model is output; otherwise, hyperparameters such as the learning rate, sequence length, or number of convolutional kernels are further adjusted based on the validation feedback to improve the model's ability to extract trend factors and its generalization effect. In practical applications, the target trend interactive memory set is traversed, and the traversed content is sequentially input into a neural network model. The neural network model evolves the time series and neighborhood space based on the learned mapping relationship, outputting a set of trend factors to reflect the intensity and direction of soil and water erosion trends in each region within the monitoring period. Finally, these trend factors are added to the same set, resulting in an initial set of regional spatiotemporal erosion trend factors, which can serve as a quantitative basis for subsequent dynamic frequency adjustment and high-risk area identification.

[0035] Furthermore, this application provides a method for performing spatiotemporal erosion trend interaction on the first two regional surface water and soil erosion features and the second regional surface water and soil erosion features within any regional surface water and soil erosion feature sequence set, to determine a first trend interaction memory set. Each first trend interaction memory is used to store the feature trend between the first regional surface water and soil erosion features and the second regional surface water and soil erosion features, including:

[0036] A first set of regional surface water and soil erosion feature trends is obtained by performing a similar feature trend analysis on the first and second regional surface water and soil erosion features within any regional surface water and soil erosion feature sequence in the set of regional surface water and soil erosion feature sequences. A second set of regional surface water and soil erosion feature trends, a first set of regional surface water and soil erosion feature trends, and a third set of regional surface water and soil erosion feature trends are obtained by performing a heterogeneous association feature trend analysis on the first and second regional surface water and soil erosion features within any regional surface water and soil erosion feature sequence in the set of regional surface water and soil erosion feature sequences. The second set of regional surface water and soil erosion feature trends, the first set of regional surface water and soil erosion feature trends, and the first set of regional surface water and soil erosion feature trends are stored in initially empty vectors to obtain a first trend interaction memory set.

[0037] Optionally, firstly, select the feature values ​​of the first two time points within any regional surface water and soil erosion feature sequence (i.e., the first and second regional water and soil erosion features) from the set of regional surface water and soil erosion feature sequences. Then, divide them according to feature type and perform trend analysis of similar features and trend analysis of dissimilar features respectively. In the trend analysis of similar features, compare each feature of the same type (such as elevation to elevation, slope to slope) one by one to determine whether it shows an increasing, decreasing, or unchanged trend in the time dimension. For example, if the slope value increases significantly from the first region to the second region, the trend in this dimension is determined to be increasing. The changing trends of all similar features will be summarized to form the first set of regional water and soil erosion feature trends of the same type. In the heterogeneous correlation feature trend analysis, different types of features are combined for analysis, such as elevation and exposed area, slope and vegetation coverage. For each pair of heterogeneous features, feature values ​​at two time points are extracted from the soil erosion features of the first and second regions, respectively, constructing two time series of length 2. Then, the Pearson correlation coefficient is used to calculate the correlation between the changes of the two heterogeneous features at the first two time points. This correlation is used for a rough estimate of the trend direction (e.g., whether it is a positive or negative change). Subsequently, all heterogeneous feature combinations with significant Pearson correlation coefficients (i.e., the absolute value of the correlation coefficient is greater than the correlation threshold) and their corresponding trend directions and trends are stored in the surface soil erosion feature trend set of the first heterogeneous correlation region. This serves as a key component of the trend interaction memory construction, providing a foundation for subsequent trend aggregation, deduplication, and identification. Afterward, the soil erosion feature set of the second region, the first homogeneous trend set, and the first heterogeneous correlation trend set are stored together in an initially empty vector to form a trend interaction memory vector, which is then stored in the first trend interaction memory set to record the current trend status. To control the amount of trend data and enhance the discriminative power of trend memory, a third time node feature value is introduced. The same same same-class and different-class identification is performed on the feature change trends between the surface water and soil erosion features of the third region and the surface water and soil erosion features of the second region stored in the first trend interaction memory set. This yields a trend set of surface water and soil erosion features of the second-class region and a trend set of surface water and soil erosion features of the second-class region. Then, cosine similarity calculation is used to compare the approximation of the trend sets of surface water and soil erosion features of the second-class region and the second-class region with the trend sets of surface water and soil erosion features of the first-class region and the first-class region. If the newly identified trend differs significantly from the trend in the memory vector (i.e., the approximation is below the approximation threshold), it indicates that the trend is a potential change point and should be added to the first trend interaction memory set for updating. If the approximation is high, it is considered that the trend has been covered by existing trends and does not require redundant storage.Through this mechanism, the system can dynamically update trend information while avoiding the accumulation of invalid or repetitive trends, thus providing a more concise and representative trend memory foundation for subsequent overall trend modeling.

[0038] Furthermore, this application provides a method for interactively associating the initial set of spatiotemporal erosion trend factors with the terrain orientation of the target area based on the locations corresponding to the set of scanning reference points, thereby obtaining the set of spatiotemporal erosion trend factors, including:

[0039] Based on the terrain of the target area, a neighborhood set of nearest scanning reference points corresponding to the set of scanning reference points is determined according to a preset neighborhood distance bandwidth. Combining this with the initial set of regional spatiotemporal erosion trend factors, it is determined whether there are any nearest scanning reference points in the neighborhood set of nearest scanning reference points whose initial regional spatiotemporal erosion trend factors are greater than or equal to those of the scanning reference point. If so, these are added to the risk nearest scanning reference point neighborhood set. The initial regional spatiotemporal erosion trend factors of the scanning reference points are then corrected using the initial regional spatiotemporal erosion trend factors of the risk nearest scanning reference points in the risk nearest scanning reference point neighborhood set, resulting in a corrected set of regional spatiotemporal erosion trend factors.

[0040] Optionally, firstly, based on the terrain orientation of the target area and the preset neighborhood distance bandwidth, the nearest neighbor region for each scanning reference point is determined. Specifically, based on the location of each scanning reference point, other reference points spatially close to it are selected within a set distance range to form a nearest neighbor scanning reference point neighborhood set. These nearest neighbor points are relatively close to the current reference point in terms of terrain and may be affected by similar soil erosion. Subsequently, combined with the initial set of spatiotemporal erosion trend factors for the region, it is determined whether the erosion trend factors of these nearest neighbor scanning reference points are greater than or equal to the trend factor of the current reference point. If there are points that meet this condition (i.e., the soil erosion trend of these nearest neighbor reference points is similar to or more severe than that of the current reference point), they are added to the risk nearest neighbor scanning reference point neighborhood set. Afterward, the trend factor of the current reference point is corrected using the spatiotemporal erosion trend factors of these risk nearest neighbor scanning reference points. In this process, the spatiotemporal erosion trend factor of each nearest neighbor point is weighted according to its distance from the current reference point. Generally, points that are closer are given greater weight, while points that are farther away are given less weight. This weighted calculation effectively combines trend information from neighboring points to adjust the spatiotemporal erosion trend factor of the current benchmark point, making it more closely reflect the actual risk situation. The corrected initial set of regional spatiotemporal erosion trend factors will be output as the regional spatiotemporal erosion trend factor set. This set reflects the soil erosion trend of the current benchmark point, taking into account the risks in the surrounding areas, thus improving the accuracy and comprehensiveness of risk assessment.

[0041] The set of spatiotemporal erosion trend factors in the region is differentiated, and the preset monitoring frequency is asynchronously adjusted according to the differentiation results to construct an asynchronous dual-mode monitoring frequency group set, wherein each asynchronous dual-mode monitoring frequency group includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency.

[0042] In one embodiment, after obtaining the set of regional spatiotemporal erosion trend factors, the set is first differentiated by classifying the spatiotemporal erosion trend factors of all regions according to the significance and type of change. These factors reflect different degrees of soil erosion, different types of erosion characteristics (such as elevation changes, slope changes, and the expansion of exposed surface), and the geographical and environmental characteristics of each region. By differentiating the factors, it is possible to identify which regions have experienced more drastic erosion changes and which regions have experienced more gradual changes, thus addressing the monitoring needs of different regions accordingly. Subsequently, based on the results of these differentiations, the preset monitoring frequency is asynchronously adjusted. For regions with significant changes, the monitoring frequency can be increased to capture erosion changes more promptly, while for regions with minor changes, the monitoring frequency can be reduced to minimize resource waste. Afterward, the adjusted monitoring frequencies are summarized to form an asynchronous dual-mode monitoring frequency set, which includes the maximum and minimum adjusted monitoring frequencies. This ensures that the system can rationally allocate monitoring resources while maintaining monitoring accuracy, thereby achieving optimal resource utilization and dynamic adjustment.

[0043] Furthermore, this application provides a set of factors that differentiate the spatiotemporal erosion trend of the region, and asynchronously adjusts the preset monitoring frequency based on the differentiation results to construct a set of asynchronous dual-monitoring frequency groups, including:

[0044] The set of regional spatiotemporal erosion trend factors is divided according to a differentiated constraint to obtain K sets of regional spatiotemporal erosion factors, where K is a positive integer. The K largest and K smallest regional spatiotemporal erosion factors are extracted from each of the K sets. Based on these K largest and K smallest regional spatiotemporal erosion factors, the preset monitoring frequency is adjusted to obtain K largest and K smallest adjusted monitoring frequencies. These K largest and K smallest adjusted monitoring frequencies are then combined to obtain the asynchronous dual-mode monitoring frequency set.

[0045] Preferably, firstly, for all spatiotemporal erosion trend factors in the set of regional spatiotemporal erosion trend factors, a division number K is determined through empirical judgment or methods such as the elbow rule. Then, K spatiotemporal erosion trend factors are randomly selected from the set of regional spatiotemporal erosion trend factors as initial division center points. Subsequently, for each spatiotemporal erosion trend factor, its distance to the K center points is calculated using Euclidean distance and compared with the distance constraint in the differentiation constraint. Points with a distance less than or equal to this distance constraint are assigned to the corresponding set. This ensures that points that are far apart are not misclassified into similar sets. Then, a new center point is calculated based on the divided sets. Generally, this new center point is the set mean. This process is repeated until the center point no longer changes or the preset number of iterations is reached, thus obtaining K sets of regional spatiotemporal erosion factors, where K is a positive integer representing the number of divided regions, and each set represents a type of region with similar change characteristics. Next, from the set of K regional spatiotemporal erosion factors, K maximum and K minimum regional spatiotemporal erosion factors are extracted. The maximum factor represents the area with the most severe soil erosion, while the minimum factor represents the area with the least change in soil erosion. In this way, key high-risk and low-risk areas of soil erosion within the region can be identified. Then, based on these K maximum and K minimum regional spatiotemporal erosion factors, the preset monitoring frequency is adjusted. Specifically, for areas with significant changes in soil erosion (i.e., areas with the maximum factor), the monitoring frequency needs to be increased to promptly capture erosion changes; for areas with smaller changes (i.e., areas with the minimum factor), the monitoring frequency can be decreased to save resources, thus obtaining the K maximum adjusted monitoring frequency and the K minimum adjusted monitoring frequency. Finally, the K maximum and K minimum adjustment monitoring frequencies are combined to form a comprehensive asynchronous dual-mode monitoring frequency set. This set includes a combination strategy of high-frequency and low-frequency monitoring. By asynchronously adjusting the monitoring frequency, the system can increase the monitoring frequency when monitoring key areas and decrease the monitoring frequency in relatively stable areas, thereby achieving efficient resource allocation and dynamic adaptation.

[0046] Furthermore, this application includes:

[0047] Obtain the standard trend spatiotemporal erosion factor corresponding to the preset monitoring frequency; divide the K largest segmented spatiotemporal erosion factors and the K smallest segmented spatiotemporal erosion factors by the standard trend spatiotemporal erosion factor, and multiply the ratio by the preset monitoring frequency to obtain the K largest adjustment monitoring frequency and the K smallest adjustment monitoring frequency.

[0048] Optionally, the standard trend spatiotemporal erosion factor corresponding to the preset monitoring frequency is first obtained. This standard trend spatiotemporal erosion factor is a baseline value set based on historical data, typically representing the soil and water erosion trend within a standard area, and serves as a reference for adjusting the monitoring frequency. Subsequently, for the K largest and K smallest spatiotemporal erosion factors for different regions, the ratio of each region's spatiotemporal erosion factor to the standard trend spatiotemporal erosion factor is calculated. This ratio reflects the intensity of the soil erosion trend in that region relative to the standard trend. Then, this ratio is multiplied by the preset monitoring frequency. The result represents the adjusted monitoring frequency actually needed for each region, thus obtaining the K largest and K smallest adjusted monitoring frequencies. The K largest adjusted monitoring frequencies correspond to the regions with the most drastic changes, and their monitoring frequencies will be increased compared to the standard frequency. Conversely, the K smallest adjusted monitoring frequencies correspond to regions with less change, and their monitoring frequencies will be decreased. This adjustment mechanism ensures that the system can dynamically adjust the monitoring frequency according to the erosion trend intensity of each region, thereby improving monitoring accuracy while avoiding over-monitoring of areas with little change.

[0049] The asynchronous dual-monitoring frequency group set is distributed to the scanning reference point set to perform asynchronous soil and water conservation monitoring on the monitoring section set.

[0050] In one embodiment, after obtaining the set of asynchronous dual-monitoring frequencies, based on the geographical location of each scanning reference point and the soil erosion trend of its monitoring area, the corresponding adjusted monitoring frequency (high or low frequency) is allocated to the corresponding reference point. This way, areas with higher monitoring frequencies receive more monitoring resources, while areas with lower monitoring frequencies have reduced monitoring frequency, thus achieving optimal resource allocation. Subsequently, these adjusted monitoring frequencies are transmitted to the corresponding set of monitoring sections. On these sections, the system performs asynchronous soil and water conservation monitoring according to the allocated monitoring frequency; that is, areas with higher frequencies will have more frequent data collection and updates, while areas with lower frequencies will have reduced monitoring activity, ensuring that the monitoring process is both accurate and efficient. This asynchronous monitoring method can dynamically adjust the monitoring intensity according to the soil erosion risk of a region, ensuring sufficient real-time data is obtained in key areas while avoiding over-monitoring of areas with minimal changes, thereby improving monitoring efficiency and reducing unnecessary resource consumption.

[0051] Furthermore, this application provides a method for performing position calibration by traversing the set of scanning reference points within a preset calibration window, and updating the set of scanning reference points based on the calibration results.

[0052] Preferably, after the monitoring duration reaches a preset calibration window, position calibration is performed. The preset calibration window refers to a predetermined time period or specific spatial range within the monitoring cycle, used to ensure the accuracy and precision of the monitoring equipment. Specifically, within the preset calibration window, each benchmark point in the scanned benchmark point set is traversed, and the positions of these benchmark points are calibrated using precise positioning techniques (such as GNSS positioning, total station measurement, etc.). This process ensures that the position data of each benchmark point is accurate, avoiding position deviations caused by equipment errors or environmental changes. After calibration, the scanned benchmark point set is updated based on the calibration results. If the actual position of some benchmark points deviates from the original position, their coordinate information is adjusted according to the calibration results, thereby ensuring more accurate subsequent monitoring data. During this process, the magnitude of the deviation and the details of the correction are also recorded to ensure data consistency and reliability. Through this process, the system can maintain high-precision positioning of benchmark points, ensuring the accuracy of subsequent soil and water conservation monitoring data and providing a robust data foundation for monitoring and analysis.

[0053] In summary, the embodiments of this application have at least the following technical effects:

[0054] This application embodiment first traverses the set of monitoring sections in the target area to establish scanning reference points, obtaining a set of scanning reference points. Then, using a ground-based lidar device, periodic full-coverage scanning is performed according to the established set of scanning reference points and a preset monitoring frequency to obtain a set of regional surface point cloud data sequences. Next, spatiotemporal erosion trend analysis is performed based on the set of regional surface point cloud data sequences to determine a set of regional spatiotemporal erosion trend factors. Then, the set of regional spatiotemporal erosion trend factors is differentiated, and the preset monitoring frequency is asynchronously adjusted based on the differentiation results to construct a set of asynchronous dual-mode monitoring frequency groups. Each asynchronous dual-mode monitoring frequency group includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency. Finally, the set of asynchronous dual-mode monitoring frequency groups is distributed to the set of scanning reference points to perform asynchronous soil and water conservation monitoring of the monitoring section set. These technical effects collectively solve the technical problems of unreasonable allocation of monitoring resources and inability to accurately capture dynamic changes in erosion caused by fixed monitoring frequencies in traditional soil and water conservation monitoring methods. This achieves the technical effect of optimizing resource allocation through dynamic and differentiated monitoring frequencies, improving monitoring timeliness and accuracy in capturing erosion evolution, and realizing intelligent and refined soil and water conservation monitoring.

[0055] Example 2, based on the same inventive concept as the soil and water conservation monitoring method based on three-dimensional laser scanning in the previous examples, such as... Figure 3As shown, this application provides a soil and water conservation monitoring system based on three-dimensional laser scanning. The system includes: a reference point deployment module 11: deploying scanning reference points by traversing the monitoring section set of the target area to obtain a set of scanning reference points; a full-coverage scanning module 12: using a ground-based lidar device to perform periodic full-coverage scanning according to the deployed scanning reference point set and a preset monitoring frequency to obtain a set of regional surface point cloud data sequences; a trend analysis module 13: performing spatiotemporal erosion trend analysis based on the set of regional surface point cloud data sequences to determine a set of regional spatiotemporal erosion trend factors; an asynchronous adjustment module 14: differentiating the set of regional spatiotemporal erosion trend factors and asynchronously adjusting the preset monitoring frequency according to the differentiation results to construct a set of asynchronous dual-mode monitoring frequency groups, wherein each asynchronous dual-mode monitoring frequency group includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency; and a soil and water conservation monitoring module 15: distributing the set of asynchronous dual-mode monitoring frequency groups to the set of scanning reference points to perform asynchronous soil and water conservation monitoring on the monitoring section set.

[0056] Furthermore, the trend analysis module 13 is also used to perform the following methods:

[0057] The erosion characteristics of the regional surface point cloud data sequence set are analyzed according to the preset water and soil erosion characteristic indicators to obtain the regional surface water and soil erosion characteristic sequence set; the spatiotemporal erosion trend of the regional surface water and soil erosion characteristic sequence set is analyzed in the order from front to back to determine the initial regional spatiotemporal erosion trend factor set; based on the location corresponding to the scanning reference point set and combined with the terrain orientation of the target area, the initial regional spatiotemporal erosion trend factor set is interactively correlated to obtain the regional spatiotemporal erosion trend factor set.

[0058] Furthermore, the trend analysis module 13 is also used to perform the following methods:

[0059] The preset water and soil erosion characteristic indicators include surface elevation, surface slope, exposed surface area, crack area, and vegetation coverage.

[0060] Furthermore, the trend analysis module 13 is also used to perform the following methods:

[0061] For any two regional surface water and soil erosion feature sequences within the set of regional surface water and soil erosion feature sequences, a spatiotemporal erosion trend interaction is performed on the first and second regional surface water and soil erosion features to determine a first trend interaction memory set. Each first trend interaction memory is used to store the feature trend between the first and second regional surface water and soil erosion features. Then, for any three regional surface water and soil erosion feature sequences within the set of regional surface water and soil erosion feature sequences, a spatiotemporal erosion trend interaction is performed with the corresponding first trend interaction memory in the first trend interaction memory set to determine a second trend interaction memory set. This process is repeated until the last element of the set of regional surface water and soil erosion feature sequences is reached to obtain a target trend interaction memory set. The target trend interaction memory set is then traversed to perform spatiotemporal erosion trend analysis to obtain the initial set of regional spatiotemporal erosion trend factors.

[0062] Furthermore, the trend analysis module 13 is also used to perform the following methods:

[0063] A first set of regional surface water and soil erosion feature trends is obtained by performing a similar feature trend analysis on the first and second regional surface water and soil erosion features within any regional surface water and soil erosion feature sequence in the set of regional surface water and soil erosion feature sequences. A second set of regional surface water and soil erosion feature trends, a first set of regional surface water and soil erosion feature trends, and a third set of regional surface water and soil erosion feature trends are obtained by performing a heterogeneous association feature trend analysis on the first and second regional surface water and soil erosion features within any regional surface water and soil erosion feature sequence in the set of regional surface water and soil erosion feature sequences. The second set of regional surface water and soil erosion feature trends, the first set of regional surface water and soil erosion feature trends, and the first set of regional surface water and soil erosion feature trends are stored in initially empty vectors to obtain a first trend interaction memory set.

[0064] Furthermore, the trend analysis module 13 is also used to perform the following methods:

[0065] Based on the terrain of the target area, a neighborhood set of nearest scanning reference points corresponding to the set of scanning reference points is determined according to a preset neighborhood distance bandwidth. Combining this with the initial set of regional spatiotemporal erosion trend factors, it is determined whether there are any nearest scanning reference points in the neighborhood set of nearest scanning reference points whose initial regional spatiotemporal erosion trend factors are greater than or equal to those of the scanning reference point. If so, these are added to the risk nearest scanning reference point neighborhood set. The initial regional spatiotemporal erosion trend factors of the scanning reference points are then corrected using the initial regional spatiotemporal erosion trend factors of the risk nearest scanning reference points in the risk nearest scanning reference point neighborhood set, resulting in a corrected set of regional spatiotemporal erosion trend factors.

[0066] Furthermore, the asynchronous adjustment module 14 is also used to perform the following method:

[0067] The set of regional spatiotemporal erosion trend factors is divided according to a differentiated constraint to obtain K sets of regional spatiotemporal erosion factors, where K is a positive integer. The K largest and K smallest regional spatiotemporal erosion factors are extracted from each of the K sets. Based on these K largest and K smallest regional spatiotemporal erosion factors, the preset monitoring frequency is adjusted to obtain K largest and K smallest adjusted monitoring frequencies. These K largest and K smallest adjusted monitoring frequencies are then combined to obtain the asynchronous dual-mode monitoring frequency set.

[0068] Furthermore, the asynchronous adjustment module 14 is also used to perform the following method:

[0069] Obtain the standard trend spatiotemporal erosion factor corresponding to the preset monitoring frequency; divide the K largest segmented spatiotemporal erosion factors and the K smallest segmented spatiotemporal erosion factors by the standard trend spatiotemporal erosion factor, and multiply the ratio by the preset monitoring frequency to obtain the K largest adjustment monitoring frequency and the K smallest adjustment monitoring frequency.

[0070] Furthermore, the soil and water conservation monitoring module 15 is also used to perform the following methods:

[0071] In the preset calibration window, the set of scanning reference points is traversed to perform position calibration, and the set of scanning reference points is updated according to the calibration results.

[0072] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0073] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0074] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for monitoring soil and water conservation based on three-dimensional laser scanning, characterized by, The method comprises: traversing a set of monitoring sections of a target region to lay out scanning reference points, and obtaining a set of scanning reference points; periodically performing full-coverage scanning according to a preset monitoring frequency based on the set of laid-out scanning reference points by using a ground laser radar device, and obtaining a set of regional ground point cloud data sequences; performing spatio-temporal erosion trend analysis based on the set of regional ground point cloud data sequences, and determining a set of regional spatio-temporal erosion trend factors; differentially distinguishing the set of regional spatio-temporal erosion trend factors, and asynchronously adjusting the preset monitoring frequency according to a distinguishing result, and constructing a set of asynchronous double-item monitoring frequency groups, wherein each asynchronous double-item monitoring frequency group comprises a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency; distributing the set of asynchronous double-item monitoring frequency groups to the set of scanning reference points, and performing asynchronous soil and water conservation monitoring on the set of monitoring sections; wherein the spatio-temporal erosion trend analysis based on the set of regional ground point cloud data sequences to determine a set of regional spatio-temporal erosion trend factors comprises: performing erosion characteristic analysis on the set of regional ground point cloud data sequences according to preset soil and water erosion characteristic indexes, and obtaining a set of regional ground soil and water erosion characteristic sequences; performing spatio-temporal erosion trend analysis on the set of regional ground soil and water erosion characteristic sequences in a front-to-back order, and determining an initial set of regional spatio-temporal erosion trend factors; interacting the initial set of regional spatio-temporal erosion trend factors according to positions corresponding to the set of scanning reference points and in combination with a terrain trend of the target region, and obtaining the set of regional spatio-temporal erosion trend factors; wherein the differential distinction of the set of regional spatio-temporal erosion trend factors and the asynchronous adjustment of the preset monitoring frequency according to a distinguishing result to construct a set of asynchronous double-item monitoring frequency groups comprises: dividing the set of regional spatio-temporal erosion trend factors according to differential distinction constraints, and obtaining K divided regional spatio-temporal erosion factor sets, wherein K is a positive integer; respectively extracting K maximum divided regional spatio-temporal erosion factors and K minimum divided regional spatio-temporal erosion factors from the K divided regional spatio-temporal erosion factor sets; adjusting the preset monitoring frequency based on the K maximum divided regional spatio-temporal erosion factors and the K minimum divided regional spatio-temporal erosion factors, obtaining K maximum adjustment monitoring frequencies and K minimum adjustment monitoring frequencies, and aggregating the K maximum adjustment monitoring frequencies and the K minimum adjustment monitoring frequencies to obtain the set of asynchronous double-item monitoring frequency groups.

2. The water and soil conservation monitoring method based on three-dimensional laser scanning according to claim 1, wherein, The preset soil and water erosion characteristic indexes comprise ground elevation, surface slope, bare ground surface area, crack area, and vegetation coverage. 3.The water and soil conservation monitoring method based on three-dimensional laser scanning according to claim 2, wherein, The spatio-temporal erosion trend analysis on the set of regional ground soil and water erosion characteristic sequences in a front-to-back order to determine an initial set of regional spatio-temporal erosion trend factors comprises: The first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature in the first two positions in any one of the regional surface water and soil erosion feature sequence set are interacted in time and space erosion trend to determine a first trend interaction memory set, wherein each first trend interaction memory is used to store the feature trend between the first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature. The third regional surface water and soil erosion feature in any one of the regional surface water and soil erosion feature sequence set is interacted in time and space erosion trend with the corresponding first trend interaction memory in the first trend interaction memory set to determine a second trend interaction memory set. Similarly, until the last position of the regional surface water and soil erosion feature sequence set is reached, a target trend interaction memory set is obtained. The target trend interaction memory set is traversed to analyze the time and space erosion trend to obtain the initial regional time and space erosion trend factor set.

4. The water and soil conservation monitoring method based on three-dimensional laser scanning according to claim 3, characterized by, The first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature in the first two positions in any one of the regional surface water and soil erosion feature sequence set are interacted in time and space erosion trend to determine a first trend interaction memory set, wherein each first trend interaction memory is used to store the feature trend between the first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature, including: The first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature in the first two positions in any one of the regional surface water and soil erosion feature sequence set are analyzed in the same feature trend to obtain a first same-type regional surface water and soil erosion feature trend set; The first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature in the first two positions in any one of the regional surface water and soil erosion feature sequence set are analyzed in different-type associated feature trend to obtain a first different-type associated regional surface water and soil erosion feature trend set; The second regional surface water and soil erosion feature set, the first same-type regional surface water and soil erosion feature trend set, and the first different-type associated regional surface water and soil erosion feature trend set are respectively stored in an initially empty vector to obtain the first trend interaction memory set. 5.The water and soil conservation monitoring method based on three-dimensional laser scanning according to claim 3, wherein, According to the positions corresponding to the scanning reference point set, the initial regional time and space erosion trend factor set is interacted and associated with the topography trend of the target region to obtain the regional time and space erosion trend factor set, including: According to the topography trend of the target region, a near-neighbor scanning reference point neighborhood set corresponding to the scanning reference point set is determined according to a preset neighborhood distance bandwidth; In combination with the initial regional time and space erosion trend factor set, it is judged whether there is a near-neighbor scanning reference point in the near-neighbor scanning reference point neighborhood set that is greater than or equal to the initial regional time and space erosion trend factor corresponding to the scanning reference point, and if so, the corresponding near-neighbor scanning reference point is added to a risk near-neighbor scanning reference point neighborhood set; The initial region space-time erosion trend factor of the scanning reference point is corrected by using the initial region space-time erosion trend factor of the risk neighbor scanning reference point in the risk neighbor scanning reference point neighborhood set, to obtain a set of modified region space-time erosion trend factors. 6.The water and soil conservation monitoring method based on three-dimensional laser scanning according to claim 1, wherein, Comprise: Obtain the standard trend space-time erosion factor corresponding to the preset monitoring frequency; Respectively, the K maximum partition region space-time erosion factor and K minimum partition region space-time erosion factor are divided by the standard trend space-time erosion factor, and the ratio is multiplied by the preset monitoring frequency, to obtain K maximum adjustment monitoring frequency and K minimum adjustment monitoring frequency.

7. The water and soil conservation monitoring method based on three-dimensional laser scanning according to claim 1, wherein, In the preset calibration window, the scanning reference point set is traversed for position calibration, and the scanning reference point set is updated according to the calibration result.

8. A water and soil conservation monitoring system based on three-dimensional laser scanning, characterized by, The system is used to execute the soil and water conservation monitoring method based on three-dimensional laser scanning in any one of claims 1-7, and the system comprises: The reference point layout module traverses the monitoring section set of the target area to lay out the scanning reference point, to obtain a scanning reference point set; The full coverage scanning module uses the ground laser radar device to perform periodic full coverage scanning according to the preset monitoring frequency based on the laid scanning reference point set, to obtain a set of region surface point cloud data sequences; The trend analysis module performs space-time erosion trend analysis based on the set of region surface point cloud data sequences, to determine a set of region space-time erosion trend factors; The asynchronous adjustment module differentiates the set of region space-time erosion trend factors, and adjusts the preset monitoring frequency asynchronously according to the differentiation result, to construct a set of asynchronous double monitoring frequency groups, wherein each asynchronous double monitoring frequency group comprises a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency; The soil and water conservation monitoring module distributes the set of asynchronous double monitoring frequency groups to the scanning reference point set, to perform asynchronous soil and water conservation monitoring on the monitoring section set.

Images