Water and soil conservation monitoring method and system based on three-dimensional laser scanning

Abstract

The invention relates to the technical field of laser monitoring, and provides a water and soil conservation monitoring method and system based on three-dimensional laser scanning. The method comprises the following steps: traversing a monitoring section of a target area to arrange a scanning reference point; performing periodic full-coverage scanning according to a preset frequency by using a ground laser radar to obtain a regional earth surface point cloud data sequence; performing space-time erosion trend analysis based on the point cloud data sequence, and determining a trend factor set; differentiating trend factors, asynchronously adjusting the monitoring frequency, and constructing an asynchronous double-item monitoring frequency group; and distributing the frequency group to each reference point, and implementing asynchronous water and soil conservation monitoring on the monitoring section. The technical problem that a traditional water and soil conservation monitoring method is unreasonable in monitoring resource allocation due to fixed monitoring frequency and cannot accurately capture dynamic change of erosion is solved, and the purposes of optimizing resource allocation through the dynamic differential monitoring frequency, improving monitoring timeliness and erosion evolution capture precision and improving erosion evolution capture efficiency are achieved. The technical effects of intelligence and refinement of water and soil conservation monitoring are achieved.

Description

Technical Field

[0001] The present application relates to the field of laser monitoring technology, and in particular to a soil and water conservation monitoring method and system based on three-dimensional laser scanning. Background Art

[0002] Currently, soil and water conservation monitoring, as a core means of ensuring ecological security and environmental sustainability, has been widely applied in large-scale infrastructure projects such as water conservancy, hydropower, highways, and railways. This is particularly true in representative long-distance linear water conservancy projects, such as the Huaihe River Estuary Waterway Phase II Project. Faced with rugged terrain, sparse vegetation, strong construction disturbances, and long construction cycles, the construction process is prone to serious soil and water loss problems, such as slope failure, soil spillage, and embankment slippage. Traditional soil and water conservation monitoring systems suffer from three major technical bottlenecks: First, fixed-cycle monitoring models fail to adapt to the nonlinear evolution of erosion activity. The same monitoring frequency is used during the active flood season and the stable dry season, resulting in missing data during critical evolution stages or wasted monitoring resources. Second, single-point monitoring equipment struggles to capture the full range of erosion characteristics across a linear project, resulting in monitoring blind spots and data fragmentation. Third, traditional indicator systems are limited to two-dimensional spatial analysis and cannot interpret the spatiotemporal interactions between three-dimensional terrain evolution and soil and water loss. To this end, it is urgent to establish a soil and water conservation monitoring mechanism with high spatial accuracy, temporal continuity and dynamic adaptability, so as to achieve comprehensive perception, trend identification and regulatory feedback of construction disturbance areas, and provide data support and decision-making basis for ecological protection work throughout the entire construction process. 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 lead to 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 the present application provides a soil and water conservation monitoring method based on three-dimensional laser scanning, the method comprising: traversing a monitoring section set of a target area to arrange scanning benchmark points to obtain a scanning benchmark point set; utilizing a ground-based laser radar device to perform periodic full-coverage scanning according to a preset monitoring frequency based on the arranged scanning benchmark point set to obtain a regional surface point cloud data sequence set; performing spatiotemporal erosion trend analysis based on the regional surface point cloud data sequence set to determine a regional spatiotemporal erosion trend factor set; differentiating the regional spatiotemporal erosion trend factor set, and asynchronously adjusting the preset monitoring frequency according to the differentiation result to construct an asynchronous dual-item monitoring frequency group set, wherein each asynchronous dual-item monitoring frequency group includes a maximum adjusted monitoring frequency and a minimum adjusted monitoring frequency; distributing the asynchronous dual-item monitoring frequency group set to the scanning benchmark point set to perform asynchronous soil and water conservation monitoring on the monitoring section set.

[0005] In another aspect of the present disclosure, a soil and water conservation monitoring system based on three-dimensional laser scanning is provided, which comprises: a reference point layout module: traversing a set of monitoring sections of a target area to perform scanning reference point layout, obtaining a set of scanning reference points; a full-coverage scanning module: using a ground laser radar device to perform periodic full-coverage scanning according to a set of laid scanning reference points at a preset monitoring frequency, obtaining a set of regional ground point cloud data sequences; a trend analysis module: performing spatio-temporal erosion trend analysis based on the set of regional ground point cloud data sequences, determining a set of regional spatio-temporal erosion trend factors; an asynchronous adjustment module: differentiating the set of regional spatio-temporal erosion trend factors, and adjusting the preset monitoring frequency asynchronously according to the differentiation result, 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; a soil and water conservation monitoring module: 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.

[0006] The one or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: The above-mentioned soil and water conservation monitoring method based on three-dimensional laser scanning first traverses the monitoring sections in the target area, reasonably lays out a plurality of reference points for laser scanning, and forms a complete set of reference points. Then, by means of a ground laser radar device, the set of reference points is periodically scanned at a set time frequency, so as to obtain a set of ground point cloud data sequences covering the entire region. Subsequently, by processing and analyzing the point cloud data, representative soil erosion change trend factors are identified. In order to improve the response capability and resource utilization efficiency of the monitoring, the trend factors are further differentiated, and the scanning frequency is dynamically adjusted according to the significant degree of erosion change in different regions, to generate an asynchronous monitoring frequency combination combining high frequency and low frequency. Finally, the combination is distributed to the corresponding scanning reference points, realizing targeted and differentiated asynchronous soil and water conservation monitoring of each monitoring section, effectively improving the accuracy and efficiency of the monitoring.

[0007] The above description is only a summary of the technical solutions of the present disclosure. In order to more clearly understand the technical means of the present disclosure, the present disclosure can be implemented in accordance with the content of the specification, and in order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the following specific embodiments of the present disclosure are described. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0009] Figure 1 Schematic diagram of the flow of a soil and water conservation monitoring method based on three-dimensional laser scanning in one embodiment.

[0010] Figure 2 Schematic diagram of a process for performing spatiotemporal erosion trend analysis in a soil and water conservation monitoring method based on three-dimensional laser scanning in one embodiment.

[0011] Figure 3 This is an architecture diagram of a soil and water conservation monitoring system based on three-dimensional laser scanning in one embodiment.

[0012] Explanation of the accompanying symbols: benchmark point layout module 11, full coverage scanning module 12, trend analysis module 13, asynchronous adjustment module 14, soil and water conservation monitoring module 15. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a soil and water conservation monitoring method and system based on three-dimensional laser scanning to solve the technical problems of traditional soil and water conservation monitoring methods that result in unreasonable allocation of monitoring resources and inability to accurately capture dynamic changes in erosion due to fixed monitoring frequencies.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the present application provides a soil and water conservation monitoring method based on three-dimensional laser scanning, the method comprising: The monitoring section set of the target area is traversed to arrange scanning reference points to obtain a scanning reference point set.

[0017] In the embodiments of the present application, in the target monitoring area, first, a plurality of representative monitoring sections are selected according to the pre-planned spatial distribution rules. These monitoring sections are generally cross-sections perpendicular to the terrain trend or contour lines set along the engineering area (such as embankment slope, spoil heap, drainage ditch, etc.), which are used to reflect the characteristics of water and soil erosion of different geomorphic units. Subsequently, each monitoring section is processed by traversal, that is, the spatial position, terrain slope, surface properties (such as whether it is a bare area, vegetation coverage) and other information are analyzed in sequence. A plurality of scanning reference points are arranged at positions with good vision, no obstruction, stable terrain and conducive to laser reflection. The scanning reference points are physical reference points, which can be spatially positioned by GNSS measurement, total station pointing or retroreflector ball identification, and serve as a spatial reference framework for subsequent ground laser radar scanning. After the reference point arrangement of all monitoring sections is completed, a set of uniformly distributed scanning reference points with unique coordinate identifiers is formed, which provides an accurate spatial anchoring basis for subsequent point cloud data collection, and also supports key processing procedures such as data alignment, trend comparison and time series analysis.

[0018] Table 1: Monitoring section scanning reference point arrangement data table Monitoring section number Section length (m) Terrain type Number of reference points Average layout spacing (m) Layout basis D-01 150 Gently sloping hilly area 6 25 Terrain changes gently, evenly distributed D-02 90 Slope gully area 5 18 There are obvious erosion gullies, dense distribution D-03 120 Waste dump body 7 17.1 Loose soil, high-frequency sampling is required D-04 200 River bank gentle slope 8 25 Open field of view, spacing can be appropriately relaxed D-05 100 Sparse vegetation bare land 6 16.7 Lack of feature points, need to encrypt layout As shown in Table 1, the key data of five typical monitoring sections when arranging scanning reference points are shown, including section length, terrain type, reference point arrangement number and spacing, which are used to guide accurate point arrangement, ensure the spatial coverage integrity of point cloud data and the comparability of subsequent analysis.

[0019] Using the ground laser radar device, periodic full-coverage scanning is performed according to the pre-set monitoring frequency based on the arranged scanning reference point set, and a set of regional surface point cloud data sequences is obtained.

[0020] In one embodiment, after the layout of the scanning reference points is completed, the ground laser radar device is used to carry out scanning operation of the ground surface data, relying on the laid scanning reference points as the spatial positioning reference. The laser radar device emits high-frequency laser beams and receives the laser signals reflected from the ground objects, accurately measures the distance between the ground objects and the device based on the time-of-flight (ToF) or phase difference principle, and combines the scanning angle and the device attitude information to construct the spatial point cloud data with three-dimensional coordinate attributes (X, Y, Z) in real time. During the scanning process, each scanning reference point laid is taken as the starting positioning point, and full coverage scanning is carried out according to the unified coordinate system, so as to ensure that there is no blind area and omission in the monitoring area. In order to improve the timeliness and trend capturing ability of the monitoring, the process is repeated periodically according to the preset monitoring frequency (for example, once every three days or once a day), to form a sequence set of regional ground surface point cloud data, which not only retains the ground surface geometric structure, topographic profile and height change information of the monitoring area at each period, but also provides a structured and quantifiable high-precision data basis for subsequent spatio-temporal erosion analysis, trend identification and change judgment.

[0021] Based on the sequence set of regional ground surface point cloud data, spatio-temporal erosion trend analysis is carried out to determine a set of regional spatio-temporal erosion trend factors.

[0022] In one embodiment, first, the sequence set of ground surface point cloud data obtained by the multi-period laser radar scanning is subjected to period-by-period feature extraction according to a preset soil and water erosion feature index, which includes attribute indexes such as ground elevation, slope and bare ground surface area, to describe the erosion state of the ground surface at different time nodes. Subsequently, the extracted data is subjected to trend analysis according to the time sequence, and the factors reflecting the regional erosion change trend are preliminarily extracted. Then, the preliminarily extracted trend factors are subjected to spatial interaction correlation processing according to the distribution positions of the scanning reference points in the actual region and in combination with the topographic trend of the target region, to determine the final set of regional spatio-temporal erosion trend factors, so as to more accurately describe and predict the evolution of soil and water loss in subsequent dynamic monitoring, and lay a high-quality decision basis for intelligent regulation of the dynamic monitoring frequency.

[0023] Further, as shown in Figure 2 The present application provides a set of regional spatio-temporal erosion trend factors determined by carrying out spatio-temporal erosion trend analysis based on the sequence set of regional ground surface point cloud data, which includes: According to the preset water and soil erosion characteristic index, erosion characteristic analysis is performed on the regional surface point cloud data sequence set respectively, and a regional surface water and soil erosion characteristic sequence set is obtained; the regional surface water and soil erosion characteristic sequence set is analyzed in time and space erosion trend according to the order from front to back, and an initial regional time and space erosion trend factor set is determined; according to the positions corresponding to the scanning reference point set, the initial regional time and space erosion trend factor set is interactively associated in combination with the regional topographic trend, and the regional time and space erosion trend factor set is obtained.

[0024] Preferably, first, according to the preset water and soil erosion characteristic index, the regional surface point cloud data sequence collected from the ground laser radar is processed by stages, and each stage of point cloud data is converted into a set of quantifiable erosion characteristic values, such as surface elevation, surface slope, bare surface area, crack area and vegetation coverage, etc., to form a regional surface water and soil erosion characteristic sequence set in time sequence form, which reflects the change process of regional landforms over time. Subsequently, the water and soil erosion characteristic sequence of each monitoring region is compared in time sequence step by step, that is, the trend of the first two time sequence nodes is interacted, and then the subsequent nodes are gradually introduced and the trend information is updated, thereby forming an initial time and space erosion trend factor set reflecting the regional evolution law, which includes but is not limited to elevation drop trend rate, slope increase area density, bare expansion rate, etc., which can preliminarily reflect the dynamic evolution process of surface erosion. Then, in combination with the spatial positions of the scanning reference points and considering the actual topographic trend of the target region (such as the slope direction, drainage path, etc.), the initial trend factor is interactively associated with the spatial neighborhood, and in this process, a neighborhood analysis range is constructed around each reference point to determine whether the trend of other points in the range has an upstream or downstream conduction effect on the point, and if necessary, the trend value is corrected and normalized. Finally, a regional time and space erosion trend factor set with spatial consistency and trend coupling characteristics is formed. Through the above process, not only the time evolution characteristics of surface erosion can be quantitatively extracted, but also the erosion conduction mechanism between different landform areas can be captured, providing more accurate and interpretable basic data for subsequent frequency regulation and risk area identification.

[0025] Further, the preset water and soil erosion characteristic index includes surface elevation, surface slope, bare surface area, crack area and vegetation coverage.

[0026] Optionally, the preset soil and water erosion feature indicators are used to quantitatively extract important surface change information affecting soil erosion from the point cloud data, mainly including surface elevation, surface slope, bare surface area, crack area and vegetation coverage. Among them, the surface elevation reflects the vertical distance of a point on the ground relative to a unified reference surface, which is the basic parameter for identifying topography and judging erosion depth. The elevation value of each point is extracted through the Z-axis coordinate of the point cloud data, and the entire region is interpolated for elevation using rasterization processing (such as 0.5m x 0.5m grid) to generate a digital elevation model (DEM), and the elevation change can be calculated by the difference between adjacent time points of DEM. Slope refers to the degree of inclination of the ground surface relative to the horizontal plane, which is commonly used to measure the potential of slope erosion. Slope is usually obtained by calculating the local gradient of DEM. Bare surface refers to the surface area that lacks vegetation coverage or is obviously disturbed by human activities. It is usually distinguished between bare surface (such as soil, concrete, etc. high reflection area) and non-bare surface (such as vegetation, water, etc. low reflection area) by comparing the reflection intensity characteristics of the point cloud with the set empirical threshold, and all points with reflection intensity greater than or equal to the empirical threshold are screened out, projected onto the horizontal plane to construct a two-dimensional distribution map, and then the total area covered by these bare points is calculated by boundary envelope or grid statistics, and then divided by the total area of the monitoring region to obtain the proportion of bare surface area. Cracks are an important precursor of slope instability, and crack area represents the extent of crack propagation. Crack features are generally identified by local concave structure or abrupt fracture boundary of point cloud (such as based on curvature analysis or voxel scanning). After the crack region boundary is extracted, the crack area is calculated using two-dimensional projection contour integration for monitoring the change trend. Vegetation coverage represents the proportion of a unit area covered by vegetation, which is an important indicator for evaluating the anti-erosion capacity of the ground surface. It is usually classified and identified by combining the echo height characteristics of laser radar or multispectral image, and the NDVI (normalized difference vegetation index) or the proportion of points in the point cloud that are higher than a certain threshold above the ground surface is used to calculate the required vegetation coverage.

[0027] Further, the application provides a spatio-temporal erosion trend analysis of the sequence set of regional surface soil and water erosion features in the order from front to back, to determine an initial set of regional spatio-temporal erosion trend factors, 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 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; and the process is repeated until the last position of the regional surface water and soil erosion feature sequence set is reached to obtain a target trend interaction memory set; and 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.

[0028] Optionally, after obtaining the set of regional surface water and soil erosion feature sequences, for any one of the water and soil erosion feature sequences in the set of regional surface water and soil erosion feature sequences, trend accumulation analysis is gradually performed in time sequence. First, the earliest two time nodes in the sequence are selected, i.e., the first regional surface water and soil erosion feature and the second regional surface water and soil erosion feature, and the selected time nodes are subjected to same-type feature trend analysis and different-type associated feature trend analysis to generate a first trend interaction memory set, wherein each first trend interaction memory in the first trend interaction memory set stores 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 change direction (up, down, or unchanged), change magnitude, and associated change relationship between different types of features (such as slope, elevation, etc.) between the two nodes, and also records the spatial distribution characteristics of the change trend. Subsequently, the third regional surface water and soil erosion feature is introduced and subjected to the same same-type and different-type trend identification as the preceding regional surface water and soil erosion feature, i.e., the second regional surface water and soil erosion feature, and further interaction analysis is performed according to the identification result and the corresponding trend information in the first trend interaction memory set to identify the approximation degree of the two, and a second trend interaction memory set is formed by updating. In this way, the entire feature sequence is gradually processed in time sequence until the last time node, and finally a complete target trend interaction memory set is obtained, which is essentially a continuous memory vector containing time variation trajectories and spatial transmission relationships. In order 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, wherein the neural network model can be constructed based on LSTM (Long Short-Term Memory Network), ConvLSTM (Convolutional Long Short-Term Memory Network), GCN-LSTM (Graph Convolution + LSTM Hybrid Model), etc. Taking ConvLSTM as an example, a neural network model structure is constructed using ConvLSTM, including an input layer, a ConvLSTM unit layer, a convolution fusion layer, and an output layer. Historical regional surface water and soil erosion features (such as elevation change, slope evolution, and bare area ratio) are converted into fixed-size raster map sequences in time sequence to form input data, which are processed by the ConvLSTM unit in the time dimension and subjected to local convolution operations in the spatial dimension to extract and remember spatial trend features evolving over time, which are passed to the next ConvLSTM layer layer by layer.After that, the last hidden state is sent to the convolution fusion layer for channel compression and trend induction. The output layer generates trend factors consistent with the spatial dimension of the monitoring area. The numerical value represents the soil erosion trend intensity of each region. The mean square error (MSE) loss function is used to calculate the error between the predicted trend factor and the actual trend label. The gradient of the loss function to each layer parameter of the model is calculated layer by layer through the back propagation algorithm. The Adam optimizer is used to iteratively optimize the model weights to minimize the overall trend prediction error. Repeat the training process until the maximum number of iterations or the loss convergence threshold is reached. After training, use an independent validation set to evaluate the model and test its fitting accuracy and spatial consistency in predicting the soil erosion trend intensity of different regions. If the validation result meets the preset performance standard, output the neural network model, otherwise, adjust the learning rate, sequence length, or the number of convolution kernels according to the validation feedback to improve the model's ability to extract trend factors and generalization effect. In practical application, the target trend interaction memory set is traversed, and the traversed content is input into the neural network model in turn. The neural network model will evolve according to the learned mapping relationship between time series and neighborhood space, output a set of trend factors, which are used to reflect the soil erosion trend intensity and direction of each region in the monitoring period. Finally, these trend factors are added to the same set to obtain the initial regional spatio-temporal erosion trend factor set, which can be used as a quantitative basis for subsequent dynamic frequency adjustment and high-risk area identification.

[0029] Further, the application provides spatio-temporal erosion trend interaction of the first regional soil and water erosion feature and the second regional soil and water erosion feature in the first two positions of any one of the regional soil and water erosion feature sequence set, 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 soil and water erosion feature and the second regional soil and water erosion feature, including: The first same-type regional soil and water erosion feature trend set is obtained by same-type feature trend analysis of the first regional soil and water erosion feature and the second regional soil and water erosion feature in the first two positions of any one of the regional soil and water erosion feature sequence set. The first different-type associated regional soil and water erosion feature trend set is obtained by different-type associated feature trend analysis of the first regional soil and water erosion feature and the second regional soil and water erosion feature in the first two positions of any one of the regional soil and water erosion feature sequence set. The second regional soil and water erosion feature set, the first same-type regional soil and water erosion feature trend set, and the first different-type associated regional soil and water erosion feature trend set are stored in vectors initially empty, to obtain the first trend interaction memory set.

[0030] Optionally, first, the characteristic values of the first and second regional soil erosion characteristics in any one of the regional soil erosion characteristic sequence set are selected, and then the characteristics are divided according to the characteristic types, and the same type of characteristic trend analysis and different type of characteristic trend analysis are performed. In the same type of characteristic trend analysis, each type of characteristic (such as elevation vs. elevation, slope vs. slope) is compared one by one to determine whether it shows an increasing, decreasing or constant trend in the time dimension. For example, if the slope value increases significantly from the first region to the second region, the dimension trend is determined to be increasing. The trend of all the same type of characteristics is summarized to form a first same type of regional soil erosion characteristic trend set. In the different type of characteristic trend analysis, different types of characteristics are combined and analyzed, such as elevation and bare area, slope and vegetation coverage, etc. For each pair of different type of characteristic combination, the characteristic values of the first and second regional soil erosion characteristics at the two time nodes are extracted to construct two time series with a length of 2, and the Pearson correlation coefficient is used to calculate the correlation of the two different type of characteristics at the first and second time nodes. This correlation is used to roughly estimate the trend direction (such as positive or negative change). Subsequently, all the different type of characteristic 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 trend strengths are stored in the first different type of associated regional soil erosion characteristic trend set as a key component of the trend interaction memory vector construction, providing a basis for subsequent trend aggregation, deduplication and identification. Then, the second regional soil erosion characteristic set, the first same type of trend set and the first different type of associated trend set are stored in an initially empty vector to form a trend interaction memory vector and stored in the first trend interaction memory set to record the current trend state. In order to control the amount of trend data and enhance the discriminability of trend memory, the characteristic values of the third time node are also introduced, and the same same type and different type of identification as described above is performed between the third regional soil erosion characteristics and the second regional soil erosion characteristics stored in the first trend interaction memory set to obtain a second same type of regional soil erosion characteristic trend set and a second different type of associated regional soil erosion characteristic trend set. The cosine similarity is used to calculate the approximation of the second same type of regional soil erosion characteristic trend set, the second different type of associated regional soil erosion characteristic trend set and the first same type of regional soil erosion characteristic trend set, the first different type of associated regional soil erosion characteristic trend set. If the newly identified trend is significantly different from the trend in the memory vector (i.e. the approximation is lower than the approximation threshold), it means that the trend is a potential change point, which should be added to the first trend interaction memory set for updating. If the approximation is high, it means that the trend has been covered by the existing trend, and there is no need for redundant storage.Through the mechanism, the system avoids accumulation of invalid or repetitive trends while dynamically updating trend information, thereby providing a more concise and more representative trend memory basis for subsequent overall trend modeling.

[0031] Further, the application provides that, according to the positions corresponding to the set of scanning reference points, the set of initial regional spatio-temporal erosion trend factors is interactively correlated with the topographic trend of the target region to obtain the set of regional spatio-temporal erosion trend factors, including: According to the topographic trend of the target region, a set of near-neighbor scanning reference point neighborhoods corresponding to the set of scanning reference points is determined according to a preset neighborhood distance bandwidth; in combination with the set of initial regional spatio-temporal erosion trend factors, it is determined whether there is a near-neighbor scanning reference point in the set of near-neighbor scanning reference point neighborhoods that is greater than or equal to the initial regional spatio-temporal erosion trend factor corresponding to the scanning reference point, and if so, it is added to the set of risk near-neighbor scanning reference point neighborhoods; the initial regional spatio-temporal erosion trend factor of the risk near-neighbor scanning reference point in the set of risk near-neighbor scanning reference point neighborhoods is used to correct the initial regional spatio-temporal erosion trend factor of the scanning reference point, to obtain the set of corrected regional spatio-temporal erosion trend factors.

[0032] Optionally, first, according to the topographic trend of the target region and the preset neighborhood distance bandwidth, the near-neighbor region of each scanning reference point is determined. Specifically, according to the position of each scanning reference point, other reference points that are spatially close to it are selected according to the set distance range to form a set of near-neighbor scanning reference point neighborhoods. These near-neighbor points are relatively close to the current reference point in terms of topography and may be affected by similar water and soil erosion. Subsequently, in combination with the set of initial regional spatio-temporal erosion trend factors, it is determined whether the erosion trend factors of these near-neighbor scanning reference points are greater than or equal to the trend factor of the current reference point. If there is a point that meets this condition (i.e., the water and soil erosion trend of these near-neighbor reference points is similar to or more severe than that of the current reference point), it is added to the set of risk near-neighbor scanning reference point neighborhoods. Then, the spatio-temporal erosion trend factors of these risk near-neighbor scanning reference points are used to correct the trend factor of the current reference point. In this process, the spatio-temporal erosion trend factor of each near-neighbor point is calculated by weighting according to the distance from the current reference point. Generally, the closer the point, the greater the weight, and the farther the point, the smaller the weight. Through this weighted calculation, the trend information of the near-neighbor points can be effectively combined to adjust the spatio-temporal erosion trend factor of the current reference point, so that it is more consistent with the actual risk situation. The corrected set of initial regional spatio-temporal erosion trend factors is output as the set of regional spatio-temporal erosion trend factors. This set of regional spatio-temporal erosion trend factors reflects the water and soil loss trend of the current reference point under the consideration of the risk of the surrounding region, which can improve the accuracy and comprehensiveness of risk assessment.

[0033] The set of spatial and temporal erosion trend factors of the regions are differentiated, and the preset monitoring frequency is adjusted asynchronously according to the differentiation result, to construct 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.

[0034] In one embodiment, after obtaining the set of spatial and temporal erosion trend factors of the regions, the set of spatial and temporal erosion trend factors of the regions are first differentiated, i.e., all the spatial and temporal erosion trend factors of the regions are classified according to the significance and type of changes, which reflect different degrees of soil erosion, different types of erosion characteristics (such as changes in elevation, slope, and expansion of bare land surface), and geographical and environmental characteristics of the regions. By differentiating the factors, it can be identified which regions have experienced more severe erosion changes and which regions have experienced relatively mild changes, and then the monitoring needs of different regions can be targeted. Subsequently, the preset monitoring frequency is adjusted asynchronously according to the differentiation result. For regions that have experienced significant changes, the monitoring frequency can be increased to capture the erosion changes more timely, and for regions that have experienced relatively small changes, the monitoring frequency can be reduced to reduce resource waste. Then, the adjusted monitoring frequencies are summarized to form a set of asynchronous double-item monitoring frequency groups, which includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency, to ensure that the system can reasonably allocate monitoring resources while ensuring monitoring accuracy, thereby achieving optimal use and dynamic adjustment of resources.

[0035] Further, the present application provides that the set of spatial and temporal erosion trend factors of the regions are differentiated, and the preset monitoring frequency is adjusted asynchronously according to the differentiation result, to construct a set of asynchronous double-item monitoring frequency groups, comprising: The set of spatial and temporal erosion trend factors of the regions are divided according to the differentiation constraint to obtain K sets of divided regional spatial and temporal erosion factors, wherein K is a positive integer; K maximum divided regional spatial and temporal erosion factors and K minimum divided regional spatial and temporal erosion factors are extracted from the K sets of divided regional spatial and temporal erosion factors; the preset monitoring frequency is adjusted based on the K maximum divided regional spatial and temporal erosion factors and the K minimum divided regional spatial and temporal erosion factors to obtain K maximum adjustment monitoring frequencies and K minimum adjustment monitoring frequencies; and the K maximum adjustment monitoring frequencies and the K minimum adjustment monitoring frequencies are summarized to obtain the set of asynchronous double-item monitoring frequency groups.

[0036] Preferably, first, all the spatio-temporal erosion trend factors in the set of regional spatio-temporal erosion trend factors are determined by experience or using the elbow rule method to determine a division number K, and then K spatio-temporal erosion trend factors are randomly selected from the set of regional spatio-temporal erosion trend factors as initial center points. Subsequently, for each spatio-temporal erosion trend factor, the Euclidean distance between it and the K center points is calculated and compared with the distance constraint in the differentiated division constraint, and the points less than or equal to the distance constraint are divided into the corresponding set, so that the points far away can not be misclassified into similar sets, and then new center point calculation is performed according to the divided set. Generally, the new center point is the mean value of the set. This process is repeated until the center point no longer changes or the preset iteration number is reached, thereby obtaining K divided regional spatio-temporal erosion factor sets, wherein K is a positive integer representing the number of divided regions, and each set represents a region with similar change characteristics. Then, K maximum divided regional spatio-temporal erosion factors and K minimum divided regional spatio-temporal erosion factors are extracted from the K divided regional spatio-temporal erosion factor sets, respectively. The maximum factor represents the most serious soil erosion region, and the minimum factor represents the least change region. In this way, the key high-risk and low-risk regions of soil erosion in the region can be identified. Then, based on the K maximum divided regional spatio-temporal erosion factors and the K minimum divided regional spatio-temporal erosion factors, the preset monitoring frequency is adjusted. Specifically, for the region with significant soil erosion change (i.e., the region where the maximum factor is located), the monitoring frequency needs to be improved to capture the erosion change in time, and for the region with less change (i.e., the region where the minimum factor is located), the monitoring frequency can be reduced to save resources, thereby obtaining K maximum adjusted monitoring frequencies and K minimum adjusted monitoring frequencies. Finally, the K maximum adjusted monitoring frequencies and the K minimum adjusted monitoring frequencies are summarized to form a comprehensive asynchronous double-item monitoring frequency group set, which includes a combination strategy of high-frequency monitoring and low-frequency monitoring. By adjusting the monitoring frequency asynchronously, the system can increase the monitoring frequency in the key region and reduce the monitoring frequency in the stable region, thereby realizing efficient allocation and dynamic adaptation of resources.

[0037] Further, the application comprises: obtaining a standard trend spatio-temporal erosion factor corresponding to the preset monitoring frequency; dividing the K maximum divided regional spatio-temporal erosion factors and the K minimum divided regional spatio-temporal erosion factors by the standard trend spatio-temporal erosion factor, respectively, and multiplying the ratio by the preset monitoring frequency to obtain K maximum adjusted monitoring frequencies and K minimum adjusted monitoring frequencies.

[0038] Optionally, first, a standard trend spatiotemporal erosion factor corresponding to the preset monitoring frequency is obtained, the standard trend spatiotemporal erosion factor is a reference value set based on historical data, and generally represents the water and soil erosion trend in a standard region, which serves as a reference for adjusting the monitoring frequency. Subsequently, for the K maximum partition region spatiotemporal erosion factors and the K minimum partition region spatiotemporal erosion factors, the spatiotemporal erosion factor of each region is calculated by ratio with the standard trend spatiotemporal erosion factor, and the ratio reflects the intensity of the water and soil loss trend of the region relative to the standard trend. Then, the ratio is multiplied by the preset monitoring frequency, and the result obtained represents the actual adjusted monitoring frequency required by each region, thereby obtaining the K maximum adjusted monitoring frequencies and the K minimum adjusted monitoring frequencies, wherein the K maximum adjusted monitoring frequencies correspond to the regions with the most drastic changes, and the monitoring frequency will increase compared with the standard frequency, while the K minimum adjusted monitoring frequencies correspond to the regions with smaller changes, and the monitoring frequency will decrease. This adjustment mechanism ensures that the system can dynamically adjust the monitoring frequency according to the erosion trend intensity of each region, thereby improving the accuracy of monitoring, while avoiding excessive monitoring of regions with small changes.

[0039] The asynchronous dual-item monitoring frequency group set is distributed to the scan reference point set, and asynchronous soil and water conservation monitoring is performed on the monitoring section set.

[0040] In one embodiment, after obtaining the asynchronous dual-item monitoring frequency group set, based on the geographic location of each scan reference point and the water and soil loss trend of the monitoring region to which it belongs, the corresponding adjusted monitoring frequency (high frequency or low frequency) is assigned to the corresponding reference point, so that regions with high monitoring frequency will obtain more monitoring resources, while regions with low monitoring frequency will reduce the monitoring frequency, thereby achieving optimal allocation of resources. Subsequently, the adjusted monitoring frequencies are transmitted to the corresponding monitoring section set, and the system performs asynchronous soil and water conservation monitoring on these sections according to the assigned monitoring frequency, i.e., regions with higher frequency will collect and update data more frequently, while regions with lower frequency will reduce the frequency of monitoring activities, thereby ensuring that the monitoring process is both accurate and efficient. This asynchronous monitoring method can dynamically adjust the monitoring intensity according to the water and soil loss risk of the region, ensuring that sufficient real-time data is obtained in key regions, while avoiding excessive monitoring of regions with small changes, thereby improving monitoring efficiency and reducing unnecessary resource consumption.

[0041] Further, the application provides that, in a preset calibration window, position calibration is performed on the scan reference point set, and the scan reference point set is updated according to the calibration result.

[0042] Preferably, after the monitoring duration reaches the preset calibration window, position calibration will be performed. The preset calibration window refers to a predetermined time period or a specific spatial range in the monitoring period, which is used to ensure the accuracy and precision of the monitoring device. Specifically, within the preset calibration window, each reference point in the scanning reference point set will be traversed, and the position of these reference points will be calibrated through precise positioning techniques such as GNSS positioning, total station measurement, etc. This process ensures that the position data of each reference point is accurate, avoiding position deviation caused by device errors or environmental changes. After calibration is completed, the scanning reference point set will be updated based on the calibration results. If the actual position of some reference points deviates from the original position, their coordinate information will be adjusted according to the calibration results, ensuring that subsequent monitoring data is more accurate. In 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 the reference points, ensuring the accuracy of subsequent soil and water conservation monitoring data and providing a robust data foundation for monitoring analysis.

[0043] In summary, the embodiments of the present application have at least the following technical effects: The embodiments of the present application first traverse the monitoring section set of the target area to lay out scanning reference points, obtaining a scanning reference point set. Then, using a ground laser radar device, periodic full-coverage scanning is performed according to the preset monitoring frequency based on the laid-out scanning reference point set, obtaining a sequence set of regional ground point cloud data. Subsequently, spatio-temporal erosion trend analysis is performed based on the sequence set of regional ground point cloud data, determining a set of regional spatio-temporal erosion trend factors. Then, the set of regional spatio-temporal erosion trend factors is differentiated, and the preset monitoring frequency is adjusted asynchronously based on the differentiation results, constructing a set of asynchronous double-item monitoring frequency groups, wherein each asynchronous double-item monitoring frequency group includes a maximum adjustment monitoring frequency and a minimum adjustment monitoring frequency. Finally, the set of asynchronous double-item monitoring frequency groups is distributed to the scanning reference point set, and asynchronous soil and water conservation monitoring is performed on the monitoring section set. These technical effects collectively solve the technical problem of traditional soil and water conservation monitoring methods that result in unreasonable allocation of monitoring resources due to fixed monitoring frequency, which cannot accurately capture dynamic changes in erosion. The technical effects of optimizing resource allocation, improving monitoring timeliness and erosion evolution capture accuracy, and realizing intelligent and fine-grained soil and water conservation monitoring are achieved.

[0044] Embodiment two, based on the same inventive concept as the three-dimensional laser scanning-based soil and water conservation monitoring method in the preceding embodiments, such as Figure 3As shown, the application provides a soil and water conservation monitoring system based on three-dimensional laser scanning, which comprises: a reference point layout module 11: traversing a set of monitoring sections of a target area to perform scanning reference point layout, obtaining a set of scanning reference points; a full-coverage scanning module 12: using a ground laser radar device to perform periodic full-coverage scanning according to the set of scanning reference points and a preset monitoring frequency, obtaining a set of regional ground point cloud data sequences; a trend analysis module 13: performing 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; an asynchronous adjustment module 14: differentiating the set of regional spatio-temporal erosion trend factors and adjusting the preset monitoring frequency asynchronously according to the differentiation result to construct 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; a soil and water conservation monitoring module 15: distributing the set of asynchronous double-item monitoring frequency groups to the set of scanning reference points to perform asynchronous soil and water conservation monitoring on the set of monitoring sections.

[0045] Further, the trend analysis module 13 is further used to perform the following method: According to a preset soil and water erosion characteristic index, erosion characteristic analysis is performed on the set of regional ground point cloud data sequences respectively to obtain a set of regional ground soil and water erosion characteristic sequences; spatio-temporal erosion trend analysis is performed on the set of regional ground soil and water erosion characteristic sequences in the order from front to back to determine an initial set of regional spatio-temporal erosion trend factors; according to the positions corresponding to the set of scanning reference points, the initial set of regional spatio-temporal erosion trend factors are interactively correlated in combination with the terrain trend of the target area to obtain the set of regional spatio-temporal erosion trend factors.

[0046] Further, the trend analysis module 13 is further used to perform the following method: The preset soil and water erosion characteristic index comprises ground elevation, surface slope, bare ground surface area, crack area and vegetation coverage.

[0047] Further, the trend analysis module 13 is further used to perform the following method: 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; and the process is repeated until the last position of the regional surface water and soil erosion feature sequence set is reached, to obtain a target trend interaction memory set; and 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.

[0048] Further, the trend analysis module 13 is further used to perform the following method: 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 the 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 a first trend interaction memory set.

[0049] Further, the trend analysis module 13 is further used to perform the following method: According to the terrain 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, if so, it is added to a risk near-neighbor scanning reference point neighborhood set; the initial regional time and space erosion trend factor of the risk near-neighbor scanning reference point in the risk near-neighbor scanning reference point neighborhood set is used to correct the initial regional time and space erosion trend factor of the scanning reference point, to obtain a corrected regional time and space erosion trend factor set.

[0050] Further, the asynchronous adjustment module 14 is further configured to execute the following method: The K sets of divided regional spatio-temporal erosion factors are divided according to differentiated division constraints, K sets of maximum divided regional spatio-temporal erosion factors and K sets of minimum divided regional spatio-temporal erosion factors are extracted from the K sets of divided regional spatio-temporal erosion factors respectively, the preset monitoring frequency is adjusted based on the K sets of maximum divided regional spatio-temporal erosion factors and the K sets of minimum divided regional spatio-temporal erosion factors, K sets of maximum adjusted monitoring frequencies and K sets of minimum adjusted monitoring frequencies are obtained, and the K sets of maximum adjusted monitoring frequencies and the K sets of minimum adjusted monitoring frequencies are summarized to obtain the set of asynchronous dual monitoring frequency groups.

[0051] Further, the asynchronous adjustment module 14 is further configured to execute the following method: The standard trend spatio-temporal erosion factor corresponding to the preset monitoring frequency is obtained, the K sets of maximum divided regional spatio-temporal erosion factors and the K sets of minimum divided regional spatio-temporal erosion factors are divided by the standard trend spatio-temporal erosion factor respectively, and the K sets of maximum adjusted monitoring frequencies and the K sets of minimum adjusted monitoring frequencies are obtained by multiplying the preset monitoring frequency by the ratio.

[0052] Further, the soil and water conservation monitoring module 15 is further configured to execute the following method: In a preset calibration window, the set of scanning reference points is traversed for position calibration, and the set of scanning reference points is updated according to the calibration result.

[0053] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0054] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0055] The present application is only an exemplary description of the present application, and any and all modifications, changes, combinations or equivalents within the scope of the present application are considered to be covered by the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalents, the present application intends to include these modifications and changes.

Claims

1. A soil and water conservation monitoring method based on three-dimensional laser scanning, characterized in that: The method comprises: Traversing the monitoring section set of the target area to arrange scanning reference points and obtain a scanning reference point set; Using a ground-based laser radar device, a periodic full-coverage scan is performed according to a preset monitoring frequency based on a set of scanning reference points to obtain a set of regional surface point cloud data sequences. Performing a spatiotemporal erosion trend analysis based on the regional surface point cloud data sequence set to determine a regional spatiotemporal erosion trend factor set; Differentiating the set of regional spatiotemporal erosion trend factors, and asynchronously adjusting the preset monitoring frequency according to the differentiation result, to construct a set of asynchronous dual-item monitoring frequency groups, wherein each asynchronous dual-item monitoring frequency group includes a maximum adjusted monitoring frequency and a minimum adjusted monitoring frequency; The asynchronous dual-item monitoring frequency group set is distributed to the scanning reference point set, and asynchronous soil and water conservation monitoring is performed on the monitoring section set.

2. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 1, characterized in that: Based on the regional surface point cloud data sequence set, a spatiotemporal erosion trend analysis is performed to determine a regional spatiotemporal erosion trend factor set, including: Performing erosion characteristic analysis on the regional surface point cloud data sequence set according to preset soil and water erosion characteristic indicators to obtain a regional surface soil and water erosion characteristic sequence set; Performing spatiotemporal erosion trend analysis on the surface water and soil erosion characteristic sequence set of the region in order from front to back to determine the initial regional spatiotemporal erosion trend factor set; According to the positions corresponding to the set of scanning reference points, the initial set of regional spatiotemporal erosion trend factors is interactively correlated with the topography of the target area to obtain the set of regional spatiotemporal erosion trend factors.

3. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 2, characterized in that: The preset soil and water erosion characteristic indicators include surface elevation, surface slope, exposed surface area, crack area and vegetation coverage.

4. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 3, characterized in that: The spatial and temporal erosion trend of the surface water and soil erosion characteristic sequence set of the region is analyzed in order from front to back, and the initial regional spatial and temporal erosion trend factor set is determined, including: Performing spatiotemporal erosion trend interaction on the first two regional surface water and soil erosion characteristics and the second regional surface water and soil erosion characteristics in any regional surface water and soil erosion characteristic sequence set in the regional surface water and soil erosion characteristic sequence set to determine a first trend interaction memory set, wherein each first trend interaction memory is used to store a characteristic trend between the first regional surface water and soil erosion characteristic and the second regional surface water and soil erosion characteristic; Then, performing spatiotemporal erosion trend interaction on a third regional surface water and soil erosion feature in any regional surface water and soil erosion feature sequence in the regional surface water and soil erosion feature sequence set and a corresponding first trend interactive memory in the first trend interactive memory set to determine a second trend interactive memory set; This process is repeated until the end of the regional surface water and soil erosion characteristic sequence set is reached, and the target trend interactive memory set is obtained. The target trend interactive memory set is traversed to perform spatiotemporal erosion trend analysis to obtain the initial regional spatiotemporal erosion trend factor set.

5. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 4, characterized in that: Performing a spatiotemporal erosion trend interaction on the first two regional surface water and soil erosion characteristics and the second regional surface water and soil erosion characteristics in any regional surface water and soil erosion characteristic sequence set in the regional surface water and soil erosion characteristic sequence set to determine a first trend interaction memory set, wherein each first trend interaction memory is used to store a characteristic trend between the first regional surface water and soil erosion characteristic and the second regional surface water and soil erosion characteristic, including: performing similar feature trend analysis on the first two regional surface water and soil erosion features and the second regional surface water and soil erosion features in any regional surface water and soil erosion feature sequence set in the regional surface water and soil erosion feature sequence set, to obtain a first similar regional surface water and soil erosion feature trend set; performing heterogeneous correlation feature trend analysis on the first two regional surface water and soil erosion features and the second regional surface water and soil erosion features in any regional surface water and soil erosion feature sequence set in the regional surface water and soil erosion feature sequence set to obtain a first heterogeneous correlation regional surface water and soil erosion feature trend set; The second regional surface water and soil erosion feature set, the first similar regional surface water and soil erosion feature trend set and the first heterogeneous associated regional surface water and soil erosion feature trend set are respectively stored in initially empty vectors to obtain a first trend interactive memory set.

6. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 4, characterized in that: According to the positions corresponding to the set of scanning reference points, the initial set of regional spatiotemporal erosion trend factors is interactively correlated with the topography of the target area to obtain the set of regional spatiotemporal erosion trend factors, including: Determining a neighborhood set of neighboring scanning reference points corresponding to the scanning reference point set according to the terrain direction of the target area and a preset neighborhood distance bandwidth; Combined with the initial regional spatiotemporal erosion trend factor set, determine whether there is a neighboring scanning reference point in the neighborhood set of the neighboring scanning reference point whose initial regional spatiotemporal erosion trend factor is greater than or equal to the corresponding scanning reference point. If so, add it to the risk neighboring scanning reference point neighborhood set; The initial regional spatiotemporal erosion trend factors of the scanning reference points are corrected using the initial regional spatiotemporal erosion trend factors of the risk neighbor scanning reference points in the neighborhood set of the risk neighbor scanning reference points to obtain a corrected regional spatiotemporal erosion trend factor set.

7. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 1, characterized in that: Differentiate the regional spatiotemporal erosion trend factor set, and asynchronously adjust the preset monitoring frequency according to the differentiation result to construct an asynchronous dual-item monitoring frequency group set, including: Dividing the regional spatiotemporal erosion trend factor set according to the differentiated distinction constraint to obtain K divided regional spatiotemporal erosion factor sets, where K is a positive integer; Respectively extracting K maximum space-time erosion factors of the divided regions and K minimum space-time erosion factors of the divided regions from the set of K space-time erosion factors of the divided regions; The preset monitoring frequency is adjusted based on the K maximum divided area spatiotemporal erosion factors and the K minimum divided area spatiotemporal erosion factors to obtain K maximum adjusted monitoring frequencies and K minimum adjusted monitoring frequencies, and the K maximum adjusted monitoring frequencies and the K minimum adjusted monitoring frequencies are summarized to obtain the asynchronous dual-item monitoring frequency group set.

8. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 7, characterized in that: include: Obtaining a standard trend spatiotemporal erosion factor corresponding to the preset monitoring frequency; The K maximum divided area spatiotemporal erosion factors and the K minimum divided area spatiotemporal erosion factors are respectively compared with the standard trend spatiotemporal erosion factor, and the ratio is multiplied by the preset monitoring frequency to obtain K maximum adjustment monitoring frequencies and K minimum adjustment monitoring frequencies.

9. The soil and water conservation monitoring method based on three-dimensional laser scanning according to claim 1, characterized in that: In a preset calibration window, the scanning reference point set is traversed to perform position calibration, and the scanning reference point set is updated according to the calibration result.

10. The soil and water conservation monitoring system based on three-dimensional laser scanning is characterized by: The system is used to execute the soil and water conservation monitoring method based on three-dimensional laser scanning according to any one of claims 1 to 9, and the system comprises: Benchmark point layout module: traverses the monitoring section set of the target area to perform scanning benchmark point layout and obtain a scanning benchmark point set; Full coverage scanning module: Utilizes the ground-based laser radar device to perform periodic full coverage scanning according to the set of scanning reference points at a preset monitoring frequency to obtain a set of regional surface point cloud data sequences; Trend analysis module: performing spatiotemporal erosion trend analysis based on the regional surface point cloud data sequence set to determine the regional spatiotemporal erosion trend factor set; Asynchronous adjustment module: differentiates the set of regional spatiotemporal erosion trend factors, and asynchronously adjusts the preset monitoring frequency according to the differentiation result, to construct a set of asynchronous dual-item monitoring frequency groups, wherein each asynchronous dual-item monitoring frequency group includes a maximum adjusted monitoring frequency and a minimum adjusted monitoring frequency; Soil and water conservation monitoring module: distributes the asynchronous dual-item monitoring frequency group set to the scanning reference point set, and performs asynchronous soil and water conservation monitoring on the monitoring section set.

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