Slope ecological restoration data monitoring method based on three-dimensional visualization

Abstract

This invention discloses a data monitoring method for slope ecological restoration based on three-dimensional visualization, relating to the field of three-dimensional visualization monitoring of slope ecological restoration. The specific steps of this method are as follows: simultaneously collecting multi-source data such as topography, vegetation, and ecological factors from the target slope; importing the topographic point cloud data into an engine and processing it to generate a three-dimensional topographic model; matching vegetation data to generate a health heatmap and injecting ecological factor data into the model for annotation; establishing a data update triggering mechanism to drive the engine to refresh synchronously when data is updated, forming a complete closed loop of real-time monitoring; this invention builds a multi-source data synchronous acquisition system with a unified timestamp, achieving deep integration of multi-dimensional data and slope spatial characteristics, intuitively and accurately reflecting the ecological restoration status of the slope; constructing an automatic data update triggering mechanism to form a complete closed loop of monitoring, outputting dynamic analysis reports, timely capturing changes, identifying risks, and improving the intelligence and precision of monitoring.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional visualization monitoring of slope ecological restoration, specifically a method for monitoring slope ecological restoration data based on three-dimensional visualization. Background Technology

[0002] Slope ecological restoration is a core component of ecological governance in geotechnical engineering projects such as highways, railways, and water conservancy. The restoration effect directly impacts the stability of the engineering slope and the quality of ecological environment restoration. With the development of ecological restoration technologies, accurate monitoring of multi-dimensional data, including slope topography, vegetation growth, and soil ecological factors, has become crucial for evaluating restoration effectiveness and optimizing governance plans. Currently, the application of 3D visualization technology in geotechnical engineering monitoring is becoming increasingly widespread, providing visual technical support for slope ecological restoration monitoring. The industry's demand for simultaneous acquisition, spatial fusion, and real-time dynamic display of multi-source monitoring data is also becoming increasingly urgent, driving the upgrade of slope ecological restoration monitoring towards digitalization and intelligence.

[0003] Existing slope ecological restoration monitoring methods mostly employ single-dimensional data acquisition modes. Equipment such as 3D laser scanning, vegetation sensing, and ecological factor monitoring lacks a unified timestamp synchronization triggering mechanism, resulting in spatiotemporal deviations in data acquisition and making it difficult to achieve accurate matching of multi-source data. Point cloud registration relies solely on conventional algorithms without considering slope topographical constraints, leading to insufficient registration accuracy and poor alignment between the 3D topographic model and the actual slope. Vegetation health data is disconnected from the topographic model, and ecological factor monitoring data are mostly discrete point values ​​without spatial visualization annotations, failing to intuitively reflect the ecological status of the entire slope area. Furthermore, monitoring data updates are mostly manually scheduled, lacking an automatically triggered dynamic refresh mechanism, making it difficult to form a real-time monitoring closed loop. Moreover, the ability to fuse and analyze multi-source data is weak, failing to output quantitative evolutionary pattern analysis reports and failing to meet the needs of refined monitoring for slope ecological restoration.

[0004] Existing slope ecological restoration data monitoring technologies suffer from shortcomings in multi-source data synchronization, spatial fusion, and dynamic real-time performance. This makes it difficult to accurately, comprehensively, and in real-time reflect the true state of slope ecological restoration, imposing limitations on restoration effect evaluation and dynamic optimization of treatment plans. Furthermore, monitoring delays can lead to missed opportunities for early warning of slope ecological and geological risks. Therefore, there is an urgent need to overcome the technical bottlenecks of traditional monitoring methods and construct a slope ecological restoration data monitoring method that combines multi-source data synchronous acquisition, precise spatial fusion, and dynamic real-time updates using 3D visualization technology. This method would enable integrated visual monitoring and quantitative analysis of slope topography, vegetation, and soil ecological factors, providing data support and technical assurance for the scientific management of slope ecological restoration. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a data monitoring method for slope ecological restoration based on three-dimensional visualization. This method synchronously collects multi-source slope data with a unified timestamp, constructs a three-dimensional terrain model using a point cloud registration algorithm with terrain feature constraints, establishes a mapping relationship between vegetation data and the model to generate a health heat map, and spatially labels ecological factor data. Through a data update triggering mechanism, the monitoring data is dynamically refreshed in real time, forming a complete closed loop throughout the entire process, and synchronously outputting monitoring results and quantitative analysis reports.

[0006] To solve the above-mentioned technical problems, this invention provides the following technical solution: a method for monitoring slope ecological restoration data based on three-dimensional visualization, the specific steps of which are as follows: S100. Synchronous data acquisition: Data is collected synchronously on the target slope using a unified timestamp. The slope topography point cloud data is obtained through three-dimensional laser scanning, the original band data of vegetation coverage and vegetation health index are obtained through multispectral sensing, and the soil moisture content, soil organic matter and slope data are obtained through ecological factor monitoring. S200, 3D terrain modeling: Import the slope terrain point cloud data into the 3D visualization engine. After point cloud preprocessing, use the point cloud registration algorithm to complete the point cloud spatial alignment. After surface reconstruction processing, generate the slope foundation 3D terrain model. S300, Vegetation Health Heat Map: The original band data of vegetation coverage and vegetation health index are used to obtain the vegetation health index by the slope vegetation health index calculation algorithm. The vegetation health index is matched to the vegetation distribution area of ​​the slope basic three-dimensional terrain model according to the spatial coordinate correspondence, and the vegetation health heat map is generated by the three-dimensional visualization engine. S400, Ecological Factor Labeling: Soil moisture content, soil organic matter and slope data are injected into the corresponding terrain grid of the three-dimensional terrain model of the slope foundation according to spatial coordinates. The three-dimensional visualization engine completes the color differentiation and numerical labeling of the corresponding grid area according to the data values. S500 Real-time Dynamic Refresh: A data update trigger mechanism is established. When the monitoring data of any acquisition link in S100 is updated, the 3D visualization engine is immediately driven to synchronously refresh the 3D topographic model of the slope foundation, the vegetation health heat map, the color differentiation and numerical labeling information of ecological factors. The entire process from S100 to S400 is executed in a loop to form a complete closed loop for real-time monitoring of slope ecological restoration data.

[0007] Furthermore, the unified timestamp is used for synchronous data acquisition. A hardware synchronization trigger mode with BeiDou / GPS dual-system timing is adopted. The three types of acquisition devices, namely three-dimensional laser scanning, multispectral sensing, and ecological factor monitoring, share the same trigger signal source. The time synchronization deviation of a single acquisition is controlled within 50ms, ensuring that the three types of acquired data have a consistent spatiotemporal reference.

[0008] Furthermore, the specific execution process of the point cloud registration algorithm is as follows: Feature points at the top, toe, and abrupt changes in slope gradient in the slope topographic point cloud are extracted to form matching point pairs. A registration error objective function with topographic feature constraints is constructed. The rotation matrix and translation vector are obtained through iterative solution to complete the spatial alignment of the multi-station scan slope point cloud. The objective function for the registration error is: ; in, The objective function for registering slope point clouds is defined; the smaller the function value, the higher the registration accuracy of multi-site point clouds. A 3×3 rotation matrix for registering point clouds of slopes at multiple stations is used to correct spatial angular deviations in the point clouds. A 3×1 translation vector is used to register point clouds of slopes at multiple stations to correct spatial position deviations in the point clouds. The total number of slope topographic feature point pairs participating in the registration; For the first point cloud to be registered One slope topographic feature point; As a reference point in the cloud and One-to-one matching One slope topographic feature point; For reference feature points The three-dimensional coordinate vector after performing a rotation transformation; The centroid coordinates of all slope topographic feature points in the point cloud to be registered; The centroid coordinates of all slope topographic feature points in the reference point cloud; This is a constraint weight coefficient for slope topographic features, with a value range of 0.2-0.8, used to strengthen the matching priority of the core features of slope gradient and aspect; Based on spatially aligned slope topographic point cloud data, a continuous topographic mesh surface is constructed, so that the reconstructed surface fully conforms to the slope, aspect and elevation characteristics of the slope topography, generating a three-dimensional topographic model of the slope base with complete spatial topological relationships.

[0009] Furthermore, the calculation formula for the slope vegetation health index solution algorithm is as follows: ; in, The slope vegetation health index has a value range of [-1, 1]. The higher the value, the higher the coverage of the slope restoration vegetation and the better the physiological health status. The near-infrared spectral reflectance acquired by multispectral sensing; The reflectance of the red band spectrum acquired by multispectral sensing; The short-wave infrared spectral reflectance acquired by multispectral sensing; This is the vegetation coverage weighting coefficient; The vegetation water stress weighting coefficient; satisfies .

[0010] Furthermore, the spatial coordinate correspondence matching specifically involves: establishing a mapping transformation matrix between the multispectral image pixel coordinates and the coordinates of the three-dimensional terrain model of the slope foundation; establishing a one-to-one mapping relationship between each pixel of the multispectral sensor data and the corresponding spatial grid cell of the three-dimensional terrain model of the slope foundation, with the mapping deviation controlled within one grid cell; matching the vegetation data to the corresponding vegetation distribution area of ​​the three-dimensional terrain model of the slope foundation; and after the matching is completed, the three-dimensional visualization engine generates a vegetation health heat map based on the slope vegetation health index calculation results.

[0011] Furthermore, the specific process of injecting soil moisture content, soil organic matter, and slope data into the corresponding terrain grid is as follows: the three-dimensional terrain model of the slope foundation is divided into regular terrain grid units of equal size. Taking the spatial coordinates of the ecological factor monitoring points in S100 as the reference, the soil moisture content, soil organic matter, and slope data of the discrete monitoring points are mapped to the terrain grid units, forming a spatial coverage of discrete monitoring data to the three-dimensional terrain model of the slope foundation, so that each grid unit of the three-dimensional terrain model of the slope foundation has corresponding ecological factor monitoring data.

[0012] Furthermore, the color differentiation and numerical labeling are as follows: For monitoring data of three ecological factors—soil moisture content, soil organic matter, and slope—independent continuous color gradient mapping rules are established, specifically a blue-green-yellow-red continuous gradient color gradient system. Soil moisture content is defined as follows: 15%-25% represents the suitable range for slope restoration vegetation growth; <15% represents the drought range; and >25% represents the excessively wet range. The corresponding color gradients are: below 15% transitions from light blue to dark blue; 15%-25% transitions from light green to dark green; and above 25% transitions from yellow to red. Soil organic matter is defined as <5g / kg, 5... Fertility levels are divided into zones based on weight (g / kg-10g / kg), weight (10g / kg-20g / kg), weight (20g / kg-30g / kg), and weight (>30g / kg). The color gradient transitions from red, yellow, and light green to dark green as the weight increases. Slope levels are divided into safety levels based on gradient (<15°), gradient (15°-30°), gradient (30°-45°), and slope (>45°). The color gradient transitions from dark green, light green, and yellow to red as the weight increases. Different weight zones correspond to different color gradients. Additionally, each terrain grid cell is assigned a triggerable numerical label. When the viewing command is triggered, the label displays the real-time values ​​of the three types of ecological factor monitoring data for the corresponding grid cell.

[0013] Furthermore, the data update triggering mechanism specifically executes as follows: the three types of acquisition devices in S100—three-dimensional laser scanning, multispectral sensing, and ecological factor monitoring—transmit the latest acquired data with a unified timestamp to the three-dimensional visualization engine in real time; the three-dimensional visualization engine verifies each set of received data in real time, and when the difference between the new data from any acquisition device and the data of the same type in the previous frame exceeds a preset threshold, a refresh command is immediately generated; simultaneously, the other two types of acquisition devices are linked to synchronously verify the latest data with the same timestamp, ensuring that all data are synchronous data with the same spatiotemporal reference during refresh, without any data timing misalignment; the preset thresholds are specifically: the slope topography deformation threshold corresponding to three-dimensional laser scanning ≥ 5mm, the vegetation health index change threshold corresponding to multispectral sensing ≥ 0.15, and the soil moisture content change threshold, soil organic matter change threshold ≥ 5%, and slope change threshold ≥ 2g / kg corresponding to ecological factor monitoring ≥ 0.5°.

[0014] Furthermore, the system forms a complete closed loop for real-time monitoring of slope ecological restoration data, synchronously outputs the results of coordinated monitoring of slope ecological restoration, and the three-dimensional visualization engine outputs the three-dimensional dynamic monitoring image of the slope in real time. At the same time, based on the synchronously collected slope topographic point cloud data, vegetation data, and ecological factor data, it generates a quantitative analysis report on the slope topographic deformation trend, vegetation health evolution law, and the linkage and change relationship of ecological factors.

[0015] Compared with existing technologies, this method for monitoring slope ecological restoration data based on three-dimensional visualization has the following advantages: I. This invention establishes a multi-source data synchronous acquisition system with a unified timestamp to achieve precise spatiotemporal matching of various data on slope topography, vegetation, and soil ecological factors. Combined with a point cloud registration algorithm constrained by topographic features, it completes 3D terrain modeling, ensuring a high degree of fit between the basic model and slope topographic features. It establishes a spatial mapping relationship between vegetation data and the 3D model, generating a vegetation health heatmap. This spatially maps discrete ecological factor data to a terrain grid and completes visual annotation, achieving deep integration of multi-dimensional monitoring data with slope spatial features. Breaking away from the limitations of traditional monitoring methods that suffer from data disconnect and discrete presentation, this invention can comprehensively, intuitively, and accurately reflect the overall state of slope ecological restoration, providing practical spatial data support for a comprehensive evaluation of restoration effects.

[0016] Second, this invention constructs an automatic data update triggering mechanism, combined with the linkage data verification of multiple acquisition devices, to achieve real-time dynamic refreshing of monitoring data, and cyclically executes the entire process from data acquisition to visualization, forming a complete closed loop for slope ecological restoration monitoring; relying on a 3D visualization engine, it completes the synchronous integration and analysis of multi-source data, and outputs a quantitative analysis report on the dynamic evolution laws and linkage relationships of slope topography, vegetation, and ecological factors; it timely captures dynamic changes in the slope ecological restoration process, accurately identifies potential ecological and geological risks, and provides real-time and scientific decision-making basis for the dynamic optimization of slope ecological restoration and management schemes, thereby improving the intelligence and precision of monitoring work.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart illustrating the steps of a data monitoring method for slope ecological restoration based on 3D visualization; Figure 2 A flowchart illustrating the synchronous data acquisition process for a slope ecological restoration data monitoring method based on 3D visualization; Figure 3 This is a flowchart illustrating the execution of a slope topographic point cloud registration algorithm based on a 3D visualization-based slope ecological restoration data monitoring method. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below. Example 1:

[0021] Data collection on the slopes of the mining spoil heap was conducted synchronously using a unified timestamp and a hardware synchronization trigger mode with BeiDou / GPS dual-system timing. This ensured that three types of data acquisition equipment—3D laser scanning, multispectral sensing, and ecological factor monitoring—shared the same trigger signal source, guaranteeing that all three types of equipment collected data in the same time dimension and ensuring the spatiotemporal matching of topography, vegetation, and ecological factor data. 3D laser scanning acquired full-topographic point cloud data of the spoil heap slopes, focusing on capturing point cloud information of the unique topographic features formed by the spoil heap's loading, accurately reconstructing the topographic structure and three-dimensional characteristics of the spoil heap slopes. Multispectral sensing collected raw band data of vegetation coverage and vegetation health index in the planting areas of the slope restoration vegetation, specifically capturing the spectral growth information of the restored vegetation. Ecological factor monitoring was conducted at different loading layers and restoration areas of the spoil heap slope, acquiring soil moisture content, soil organic matter, and slope data at each point, accurately recording the differences in ecological and topographic indicators in different areas and loading layers of the spoil heap slope.

[0022] The collected point cloud data of the mine spoil heap slope topography is imported into a 3D visualization engine. After point cloud preprocessing, noise and invalid point cloud data are filtered out to improve the accuracy and usability of the point cloud data. Then, a point cloud registration algorithm is used for point cloud spatial alignment. During the execution of the point cloud registration algorithm, feature points at the top, bottom, abrupt slope changes, and stratification boundaries of the spoil heap slope are extracted to form matching point pairs, making the matching point pairs more consistent with the topographic features of the spoil heap slope. A registration error objective function is constructed and the rotation matrix and translation vector are obtained through iterative solution to complete the spatial alignment of the point cloud of the spoil heap slope from multiple stations. The formula is as follows: ,in, The objective function for registration error of slope point cloud; A 3×3 rotation matrix for registering point clouds of slopes at multiple stations; A 3×1 translation vector registered for multi-station slope point clouds; The total number of slope topographic feature point pairs participating in the registration; For the first point cloud to be registered One slope topographic feature point; As a reference point in the cloud and One-to-one matching One slope topographic feature point; For reference feature points The three-dimensional coordinate vector after performing a rotation transformation; The centroid coordinates of all slope topographic feature points in the point cloud to be registered; The centroid coordinates of all slope topographic feature points in the reference point cloud; Weighting coefficients are constrained for slope topographic features; point cloud data from multiple stations are fused into a unified spatial system, followed by surface reconstruction. Based on the spatially aligned slope topographic point cloud data, a continuous topographic mesh surface is constructed, ensuring that the reconstructed surface fully conforms to the slope, aspect, and elevation characteristics of the mine spoil heap slope, accurately restoring the topographic features of the stratified loading, and finally generating a basic three-dimensional topographic model of the mine spoil heap slope with complete spatial topological relationships, clearly showing the three-dimensional layered structure and overall topographic appearance of the spoil heap slope.

[0023] Based on the original band data of vegetation cover and vegetation health index acquired by multispectral sensing, the vegetation health index of each restoration area of ​​the mine spoil heap slope was calculated using a slope vegetation health index calculation algorithm. The formula is as follows: ,in, The slope vegetation health index; The near-infrared spectral reflectance acquired by multispectral sensing; The reflectance of the red band spectrum acquired by multispectral sensing; The short-wave infrared spectral reflectance acquired by multispectral sensing; This is the vegetation coverage weighting coefficient; The system employs a vegetation water stress weighting coefficient to accurately calculate the growth and health status of vegetation in different restoration zones and different loading layers. Subsequently, it performs spatial coordinate correspondence matching, establishing a mapping transformation matrix between multispectral image pixel coordinates and the coordinates of the slope foundation 3D topographic model. This establishes a one-to-one mapping relationship between each pixel of the multispectral sensor data and the corresponding spatial grid unit of the slope foundation 3D topographic model, enabling precise spatial alignment between multispectral vegetation data and the 3D topographic model. The vegetation health index-related vegetation data is accurately matched to the actual vegetation distribution area of ​​the slope foundation 3D topographic model, with a focus on matching vegetation data in each restoration zone of the spoil heap slope. This ensures the vegetation health index is accurately located in specific restoration areas and loading layers. Finally, a 3D visualization engine generates a vegetation health heat map of the mine spoil heap slope based on the slope vegetation health index calculation results, intuitively presenting the differences in vegetation health distribution in different restoration zones and different loading layers of the spoil heap slope, clearly demonstrating the growth status of the ecologically restored vegetation.

[0024] First, the three-dimensional topographic model of the mine spoil heap slope foundation is divided into regular topographic grid units of equal size. The spatial distribution of these grid units is then optimized based on the topographic characteristics of the spoil heap slope's layered topography, making the grid units more suitable for the layered topographic structure of the spoil heap slope. Using the spatial coordinates of ecological factor monitoring points from the synchronous data acquisition phase as a reference, soil moisture content, soil organic matter, and slope data from discrete monitoring points are mapped to the corresponding topographic grid units. This forms a full-domain spatial coverage of discrete monitoring data to the three-dimensional topographic model of the slope foundation, ensuring that each grid unit of the model possesses corresponding ecological characteristics. Factor monitoring data focuses on achieving complete coverage of ecological factor data in the stratified area of ​​the spoil heap, comprehensively and accurately presenting the ecological factor status of different areas of the spoil heap slope. Subsequently, a 3D visualization engine completes the color differentiation and numerical labeling of the corresponding grid areas based on the data values. Independent continuous color gradient mapping rules are set for three ecological factors—soil moisture content, soil organic matter, and slope—based on a blue-green-yellow-red continuous gradient color system. Soil moisture content is defined as follows: 15%-25% is the suitable range for vegetation growth in slope restoration; below 15% is the arid range; above 25% is the excessively wet range; and low... In the 15% range, the color transitions from light blue to dark blue; in the 15%-25% suitable range, it transitions from light green to dark green; and in the range above 25%, it transitions from yellow to red. Soil organic matter is categorized into fertility ranges: below 5g / kg, 5g / kg-10g / kg, 10g / kg-20g / kg, 20g / kg-30g / kg, and above 30g / kg. The color gradation transitions from red, yellow, and light green to dark green as the value increases. Slope is categorized into safety level ranges: below 15°, 15°-30°, 30°-45°, and above 45°. The color gradation transitions from dark green to light green as the value increases. The color gradient transitions from green and yellow to red, with different numerical ranges corresponding to different color gradients. This allows the numerical changes and regional differences of the three types of ecological factors to be displayed intuitively through color. At the same time, triggerable numerical labels are set for each terrain grid unit. When the viewing command is triggered, the label displays the real-time values ​​of the monitoring data of the three types of ecological factors in the corresponding grid unit. Grid units in the stacked stratification area are additionally labeled with the correlation information of ecological factor data related to the stratification. This allows staff to not only intuitively see the visual distribution of ecological factors, but also to finely query specific values ​​and clearly understand the correlation between stacked stratification and ecological factors.

[0025] A data update triggering mechanism was established for monitoring slopes at mining spoil heaps. Three types of acquisition devices—3D laser scanning, multispectral sensing, and ecological factor monitoring—transmit the latest acquired data with a unified timestamp to the 3D visualization engine in real time, ensuring the real-time nature and continuity of the monitoring data. The sensitivity of data verification was optimized to address potential topographical changes, rapid vegetation growth or withering, and soil factor fluctuations at spoil heap slopes. This allows data verification to promptly capture various data changes at spoil heap slopes. The 3D visualization engine performs real-time verification on each received data set. When new data from any acquisition device is compared with the data of the same type from the previous frame... When the difference in values ​​exceeds a preset threshold, a refresh command is immediately generated. Simultaneously, two other types of data acquisition devices are linked to synchronously verify the latest data with the same timestamp, ensuring the synchronization of various data updates. Then, the 3D visualization engine is driven to synchronously refresh the 3D terrain model of the mine spoil heap slope, the vegetation health heat map, and the color differentiation and numerical annotation information of ecological factors. This ensures that the monitoring images and data of the spoil heap slope always closely match the actual situation. The entire process of synchronous data acquisition, 3D terrain modeling, vegetation health heat map creation, and ecological factor annotation is continuously and cyclically executed, forming a complete closed loop for real-time monitoring of ecological restoration data of the mine spoil heap slope. Figure 1 As shown in the diagram, during closed-loop operation, the 3D visualization engine outputs real-time 3D dynamic monitoring images of the slope, focusing on the topographic and ecological factor changes in the stratified loading area. This allows staff to accurately grasp the key regional status of the spoil heap slope for ecological restoration. Simultaneously, based on synchronously collected slope topographic point cloud data, vegetation data, and ecological factor data, it continuously generates quantitative analysis reports on the topographic deformation trend, vegetation health evolution patterns, and the interconnected changes of ecological factors in the mine spoil heap slope. This systematically sorts out the data change patterns of various types during the spoil heap slope ecological restoration process, providing scientific and accurate data support and analysis basis for evaluating the ecological restoration effect of the spoil heap slope, optimizing subsequent restoration plans, and maintaining the ecological safety of the mine slope.

[0026] This embodiment specifically captures layered terrain and vegetation information during the synchronous data acquisition phase. A point cloud registration algorithm extracts layered feature points to align the point clouds, constructing a 3D terrain model that reconstructs the slack structure. A slope vegetation health index calculation algorithm accurately calculates the health status of vegetation in different zones. Ecological factor labeling achieves full coverage and visualization of layered areas. An optimized real-time dynamic refresh mechanism for verification sensitivity accurately captures subtle slope changes and forms a monitoring closed loop. This application not only enables refined and real-time monitoring of ecological restoration data for spoil heap slopes but also provides scientific analysis and data support for evaluating the ecological restoration effects of such slopes in mining areas, optimizing subsequent plans, and maintaining ecological safety. Example 2:

[0027] Data collection on mountain highway slopes was conducted synchronously using a unified timestamp and a hardware synchronization trigger mode with BeiDou / GPS dual-system timing. This ensured that three types of data acquisition equipment—3D laser scanning, multispectral sensing, and ecological factor monitoring—shared the same trigger signal source, guaranteeing the spatiotemporal consistency of the data collected by all three types of equipment. 3D laser scanning accurately acquired topographic point cloud data of the entire highway slope area, fully reconstructing the three-dimensional topographic features of the highway slope. Multispectral sensing collected raw band data on vegetation coverage and vegetation health index in the vegetation-covered areas of the slope, accurately capturing the raw spectral information of vegetation growth. Ecological factor monitoring equipment was deployed to obtain soil moisture content, soil organic matter, and slope data at various monitoring points within the slope area. Figure 2 As shown, the actual state of key ecological and topographic indicators of the slope is fully recorded.

[0028] The collected point cloud data of mountain highway slope topography is completely imported into a 3D visualization engine. First, point cloud preprocessing is performed to remove invalid point cloud data, improving data validity and accuracy. Then, a point cloud registration algorithm is used to perform spatial alignment of the point clouds. During the algorithm execution phase, feature points at the slope top, slope toe, and abrupt slope changes are extracted to form matching point pairs. A registration error objective function is constructed, and the rotation matrix and translation vector are obtained through iterative solutions, allowing the multi-station scanned point cloud data to form a unified spatial coordinate system, thus completing the spatial alignment of the multi-station scanned highway slope point clouds. Subsequently, surface reconstruction processing is performed. Based on the spatially aligned slope topography point cloud data, a continuous topographic mesh surface is constructed, ensuring that the reconstructed surface fully conforms to the slope, aspect, and elevation characteristics of the mountain highway slope topography. Finally, a basic 3D topographic model of the mountain highway slope with complete spatial topological relationships is generated, such as... Figure 3 As shown, the overall terrain features of the highway slope are accurately and three-dimensionally presented.

[0029] Using raw band data of vegetation coverage and vegetation health index acquired by multispectral sensing as the basis for calculation, the vegetation health index of each area of ​​the mountain highway slope is calculated using a slope vegetation health index calculation algorithm. This accurately verifies the growth and health status of vegetation in different areas. Next, spatial coordinate correspondence matching is carried out to establish a mapping transformation matrix between the pixel coordinates of the multispectral image and the coordinates of the three-dimensional terrain model of the slope foundation. A one-to-one mapping relationship is established between each pixel of the multispectral sensing data and the corresponding spatial grid unit of the three-dimensional terrain model of the slope foundation, so that the vegetation data and the three-dimensional terrain model achieve accurate spatial correspondence. The vegetation data related to the calculated vegetation health index are accurately matched to the actual vegetation distribution area of ​​the three-dimensional terrain model of the slope foundation, accurately locating the spatial position of the vegetation health index in each area. Finally, the three-dimensional visualization engine generates a vegetation health heat map of the mountain highway slope based on the slope vegetation health index calculation results, intuitively presenting the spatial distribution differences and overall situation of the vegetation health status of the highway slope.

[0030] First, the 3D topographic model of the mountain highway slope foundation is divided into regular topographic grid units of equal size, allowing ecological factor data to be accurately located at the specific spatial position of the slope. Using the spatial coordinates of the ecological factor monitoring points in the synchronous data acquisition phase as a reference, the soil moisture content, soil organic matter, and slope data of discrete monitoring points are precisely mapped to the corresponding topographic grid units, forming a full spatial coverage of discrete monitoring data to the 3D topographic model of the slope foundation. This ensures that each grid unit of the model has corresponding ecological factor monitoring data, comprehensively covering the ecological factor monitoring dimensions of the highway slope. Subsequently, the 3D visualization engine completes the color differentiation and numerical labeling of the corresponding grid areas based on the data values. Independent continuous color gradient mapping rules are set for the three ecological factors—soil moisture content, soil organic matter, and slope—based on a blue-green-yellow-red continuous gradient color system. Specifically, a soil moisture content of 15%-25% is considered the suitable range for vegetation growth in slope restoration; below 15% is considered arid; and above 25% is considered excessive. The wet zone transitions from light blue to dark blue for wet zones below 15%, from light green to dark green for suitable zones between 15% and 25%, and from yellow to red for zones above 25%. Soil organic matter is categorized into fertility zones: below 5g / kg, 5g / kg-10g / kg, 10g / kg-20g / kg, 20g / kg-30g / kg, and above 30g / kg. The color gradient transitions from red, yellow, and light green to dark green as the value increases. Slope is categorized into safety level zones: below 15°, 15°-30°, 30°-45°, and above 45°. The color gradient transitions from dark green, light green, and yellow to red as the value increases. Different value zones correspond to different color gradients, allowing the differences in the values ​​of the three types of ecological factors to be visually displayed through color. At the same time, triggerable numerical labels are set for each terrain grid unit. When a viewing command is triggered, the real-time values ​​of the monitoring data of the three types of ecological factors in the corresponding grid unit are displayed, realizing the visualization and refined quantitative query of ecological factor monitoring data.

[0031] A dedicated data update triggering mechanism was established for monitoring mountain highway slopes. Three types of acquisition devices—3D laser scanning, multispectral sensing, and ecological factor monitoring—continuously transmit the latest acquired data with a unified timestamp to the 3D visualization engine in real time, ensuring the real-time transmission of monitoring data. The 3D visualization engine verifies each set of received data in real time, promptly capturing subtle changes in the data. When the difference between the new data from any acquisition device and the value of the same type of data in the previous frame exceeds a preset threshold, a refresh command is immediately generated. At the same time, the other two types of acquisition devices are linked to synchronously verify the latest data with the same timestamp, ensuring the synchronicity and consistency of various data updates. Subsequently, the 3D visualization engine is driven to synchronously refresh the basic 3D terrain model of the mountain highway slope, the vegetation health heat map, and the color differentiation and numerical annotation information of ecological factors, keeping the monitoring screen and data always up-to-date. The entire process of synchronous data acquisition, 3D terrain modeling, vegetation health heat map, and ecological factor annotation is continuously and cyclically executed, forming a complete closed loop for real-time monitoring of ecological restoration data of mountain highway slopes. During closed-loop operation, the 3D visualization engine outputs real-time 3D dynamic monitoring images of the slope, allowing staff to intuitively and dynamically grasp the overall status of ecological restoration of highway slopes. At the same time, based on synchronously collected slope topographic point cloud data, vegetation data, and ecological factor data, it continuously generates quantitative analysis reports on the topographic deformation trend of mountain highway slopes, the evolution law of vegetation health, and the linkage and change relationship of ecological factors. This provides accurate and systematic data support for evaluating the ecological restoration effect of mountain highway slopes, optimizing subsequent restoration plans, and ensuring the safe operation and maintenance of slopes.

[0032] This embodiment applies a 3D visualization-based slope ecological restoration data monitoring method to mountainous highway slopes. It utilizes a dual BeiDou / GPS system to achieve synchronous data acquisition from multiple devices, ensuring the spatiotemporal consistency of topography, vegetation, and ecological factor data. A precise 3D topographic model is constructed using point cloud registration and surface reconstruction algorithms, and a vegetation health heatmap is generated by combining this with a slope vegetation health index calculation algorithm. Furthermore, ecological factors are visualized and labeled using color-gradient mapping, and a data update trigger mechanism enables dynamic updating of data across all dimensions, forming a monitoring closed loop. This process achieves real-time, accurate, and visualized monitoring of highway slope ecological restoration data. The generated quantitative analysis report provides systematic and accurate data support for evaluating the ecological restoration effect, optimizing solutions, and ensuring safe operation and maintenance of such slopes.

[0033] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for monitoring slope ecological restoration data based on three-dimensional visualization, characterized in that, The specific steps of this method are as follows: S100. Synchronous data acquisition: Data is collected synchronously on the target slope using a unified timestamp. The slope topography point cloud data is obtained through three-dimensional laser scanning, the original band data of vegetation coverage and vegetation health index are obtained through multispectral sensing, and the soil moisture content, soil organic matter and slope data are obtained through ecological factor monitoring. S200, 3D terrain modeling: Import the slope terrain point cloud data into the 3D visualization engine. After point cloud preprocessing, use the point cloud registration algorithm to complete the point cloud spatial alignment. After surface reconstruction processing, generate the slope foundation 3D terrain model. S300, Vegetation Health Heat Map: The original band data of vegetation coverage and vegetation health index are used to obtain the vegetation health index by the slope vegetation health index calculation algorithm. The vegetation health index is matched to the vegetation distribution area of ​​the slope basic three-dimensional terrain model according to the spatial coordinate correspondence, and the vegetation health heat map is generated by the three-dimensional visualization engine. S400, Ecological Factor Labeling: Soil moisture content, soil organic matter and slope data are injected into the corresponding terrain grid of the three-dimensional terrain model of the slope foundation according to spatial coordinates. The three-dimensional visualization engine completes the color differentiation and numerical labeling of the corresponding grid area according to the data values. S500 Real-time Dynamic Refresh: A data update trigger mechanism is established. When the monitoring data of any acquisition link in S100 is updated, the 3D visualization engine is immediately driven to synchronously refresh the 3D topographic model of the slope foundation, the vegetation health heat map, the color differentiation and numerical labeling information of ecological factors. The entire process from S100 to S400 is executed in a loop to form a complete closed loop for real-time monitoring of slope ecological restoration data.

2. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S100, the unified timestamp is used to collect data synchronously. The hardware synchronization trigger mode of Beidou / GPS dual system time synchronization is adopted. The three types of acquisition devices, namely three-dimensional laser scanning, multispectral sensing and ecological factor monitoring, share the same trigger signal source.

3. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S200, the specific execution process of the point cloud registration algorithm is as follows: Feature points at the top, toe, and abrupt changes in slope gradient in the slope topographic point cloud are extracted to form matching point pairs. A registration error objective function with topographic feature constraints is constructed. The rotation matrix and translation vector are obtained through iterative solution to complete the spatial alignment of the multi-station scan slope point cloud. The objective function for the registration error is: ； in, The objective function for registration error of slope point cloud; A 3×3 rotation matrix for registering point clouds of slopes at multiple stations; A 3×1 translation vector registered for multi-station slope point clouds; The total number of slope topographic feature point pairs participating in the registration; For the first point cloud to be registered One slope topographic feature point; As a reference point in the cloud and One-to-one matching One slope topographic feature point; For reference feature points The three-dimensional coordinate vector after performing a rotation transformation; The centroid coordinates of all slope topographic feature points in the point cloud to be registered; The centroid coordinates of all slope topographic feature points in the reference point cloud; The weighting coefficients are constrained by the topographic features of the slope. Based on the spatially aligned slope topographic point cloud data, a continuous topographic mesh surface is constructed, so that the reconstructed surface fully conforms to the slope, aspect and elevation characteristics of the slope topography, generating a three-dimensional topographic model of the slope base with complete spatial topological relationships.

4. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S300, the calculation formula for the slope vegetation health index solution algorithm is as follows: ； in, The slope vegetation health index; The near-infrared spectral reflectance acquired by multispectral sensing; The reflectance of the red band spectrum acquired by multispectral sensing; The short-wave infrared spectral reflectance acquired by multispectral sensing; This is the vegetation coverage weighting coefficient; This represents the vegetation water stress weighting coefficient.

5. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S300, the spatial coordinate correspondence matching specifically involves: establishing a mapping transformation matrix between the multispectral image pixel coordinates and the coordinates of the three-dimensional terrain model of the slope foundation; establishing a one-to-one mapping relationship between each pixel of the multispectral sensor data and the corresponding spatial grid cell of the three-dimensional terrain model of the slope foundation; matching the vegetation data to the corresponding vegetation distribution area of ​​the three-dimensional terrain model of the slope foundation; and after the matching is completed, the three-dimensional visualization engine generates a vegetation health heat map based on the slope vegetation health index calculation results.

6. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S400, the specific process of injecting soil moisture content, soil organic matter, and slope data into the corresponding terrain grid is as follows: the three-dimensional terrain model of the slope foundation is divided into regular terrain grid units of equal size. Taking the spatial coordinates of the ecological factor monitoring points in S100 as the reference, the soil moisture content, soil organic matter, and slope data of the discrete monitoring points are mapped to the terrain grid units, forming a spatial coverage of discrete monitoring data to the three-dimensional terrain model of the slope foundation, so that each grid unit of the three-dimensional terrain model of the slope foundation has corresponding ecological factor monitoring data.

7. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S400, the color differentiation and numerical labeling are as follows: independent continuous color gradient mapping rules are set for the monitoring data of three types of ecological factors: soil moisture content, soil organic matter, and slope. Different numerical ranges correspond to different color gradients. At the same time, triggerable numerical labels are set for each terrain grid unit. When the viewing command is triggered, the labels display the real-time values ​​of the three types of ecological factor monitoring data of the corresponding grid unit.

8. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S500, the data update triggering mechanism is specifically executed as follows: the three types of acquisition devices in S100—three-dimensional laser scanning, multispectral sensing, and ecological factor monitoring—transmit the latest acquired data with a unified timestamp to the three-dimensional visualization engine in real time; the three-dimensional visualization engine verifies each set of received data in real time, and when the difference between the new data from any acquisition device and the data of the same type in the previous frame exceeds a preset threshold, a refresh command is immediately generated; at the same time, the other two types of acquisition devices are linked to synchronously verify the latest data with the same timestamp.

9. The method for monitoring slope ecological restoration data based on three-dimensional visualization according to claim 1, characterized in that, In step S500, a complete closed loop for real-time monitoring of slope ecological restoration data is formed, and the results of the linked monitoring of slope ecological restoration are output synchronously. The three-dimensional visualization engine outputs the three-dimensional dynamic monitoring image of the slope in real time. At the same time, based on the synchronously collected slope topographic point cloud data, vegetation data, and ecological factor data, a quantitative analysis report on the slope topographic deformation trend, vegetation health evolution law, and the linkage change relationship of ecological factors is generated.

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