Metal mine area terrain updating method based on laser radar

Abstract

The invention relates to the technical field of surveying and mapping and geographic information, and discloses a metal mine area terrain updating method based on a laser radar. The method comprises the following steps: fusing a satellite SAR remote sensing image, an unmanned aerial vehicle orthoimage and a mining area production operation plan, and constructing a topographic change probability grid model; based on the model, identifying a high-probability change area and generating a target laser radar scanning task; and the unmanned aerial vehicle platform executes high-precision scanning, denoising, registration and ground point extraction are performed after point cloud data are obtained, and the three-dimensional digital terrain reference model is locally updated. The system comprises a multi-source data acquisition module, a probability modeling module, a dynamic task planning module, a laser scanning execution module and a model updating module. Through on-demand accurate scanning and closed-loop automatic updating, the terrain monitoring efficiency, accuracy and timeliness are improved, and resource consumption is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of surveying and geographic information technology, and particularly relates to a metal mine area terrain updating method based on a laser radar. BACKGROUND

[0002] With the continuous expansion of the development scale of metal mineral resources, the mine area terrain frequently changes due to operations such as blasting, excavation, transportation and natural subsidence. In order to ensure safety in production, optimize mining planning and environmental monitoring, it is urgent to update and survey the mine area terrain with high frequency and high precision. Laser radar has become a core technical means for mine area terrain surveying due to its advantages of active detection, all-weather operation and high spatial resolution.

[0003] However, the traditional laser radar-based terrain updating method generally adopts a full-coverage scanning strategy with fixed flight lines or preset paths. Whether the terrain changes or not, the same density of data collection is performed on the entire mine area. This passive full-scene scanning mode not only wastes a large amount of computing resources and energy, but also prolongs the data acquisition and processing cycle, making it difficult to meet the stringent requirements of real-time and efficiency in modern smart mines.

[0004] The adaptive terrain updating method based on laser radar focuses on the closed-loop integration of terrain change perception and scanning behavior control. This direction aims to identify the terrain change area in real time and dynamically adjust the flight or driving parameters of the laser radar platform accordingly, realizing intelligent allocation of scanning resources in space and time dimensions. The basic principle is to use historical terrain data as a reference to quickly compare with newly acquired point clouds, generate a probability distribution map reflecting potential changes, and use it to drive the decision engine of the scanning system, thereby significantly reducing the overall operation load while ensuring the surveying accuracy of key areas.

[0005] In the prior art, terrain updating usually relies on post-offline processing, that is, after completing the full-area scanning, the change detection and model reconstruction are performed through post-processing software, which cannot realize feedback adjustment during data acquisition. Even if some systems introduce a preliminary change detection mechanism, they often lack deep coupling with the motion control of the scanning platform, making it impossible to adjust the scanning density or path in real time according to the change probability. In addition, the existing methods do not effectively combine edge computing capabilities for on-site data verification and quality evaluation, making it difficult to complete the whole process of detection-decision-execution-verification in a single task.

[0006] The above defects make the existing laser radar terrain updating scheme have bottlenecks in efficiency, response speed and resource utilization, especially in large-scale and high-dynamic metal mine area application scenarios, making it difficult to support intelligent and autonomous surveying requirements, thus an adaptive terrain updating method that can realize the integration of perception, decision and execution is urgently needed. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a laser radar-based metal mine area terrain updating method to overcome the problems of low efficiency, resource waste and delayed response to sudden terrain changes caused by full-coverage repeated scanning or manually specified area scanning when monitoring the terrain of a vast mine area in the prior art.

[0008] To solve the above technical problems, the present application provides a laser radar-based metal mine area terrain updating method, which fuses low-resolution remote sensing monitoring data and mine production operation plans to construct a terrain change probability model for the entire mine area, accurately guides high-resolution laser radar scanning resources to target scanning operations in areas that have actually changed, and realizes closed-loop updating of the three-dimensional digital terrain reference model after scanning is completed.

[0009] The present application provides a laser radar-based metal mine area terrain updating method, which comprises:

[0010] Obtaining multi-source heterogeneous monitoring data covering the entire metal mine area, the multi-source heterogeneous monitoring data including periodically obtained satellite synthetic aperture radar remote sensing image data, unmanned aerial vehicle photogrammetry orthophoto data, and real-time obtained mine digital production operation plan data;

[0011] Based on the three-dimensional digital terrain reference model of the historical period, the multi-source heterogeneous monitoring data is subjected to spatio-temporal registration and fusion processing, and a terrain change probability grid model covering the metal mine area is established, each grid cell in the terrain change probability grid model having a probability value indicating the change of the terrain of the cell;

[0012] According to the terrain change probability grid model, identify and extract the grid cell cluster with a probability value higher than the preset change determination threshold, and generate one or more target area polygons to be scanned;

[0013] For each target area polygon to be scanned, generate a laser radar scanning task instruction containing its geographic coordinates, range, and scanning accuracy requirement, and issue the task instruction to the laser radar scanning execution platform;

[0014] The laser radar scanning execution platform autonomously navigates to the location of the target area polygon to be scanned according to the received task instruction, and performs high-resolution three-dimensional laser scanning to obtain the current three-dimensional point cloud data of the area;

[0015] The obtained current three-dimensional point cloud data is processed and seamlessly fused into the three-dimensional digital terrain reference model of the historical period to generate an updated three-dimensional digital terrain reference model of the current period.

[0016] As an embodiment of the present application, the acquisition of multi-source heterogeneous monitoring data specifically includes:

[0017] Through the interface with the satellite data service provider, time-series synthetic aperture radar interferometry data sets covering the mining area range with a specific revisit period are acquired in a timely manner;

[0018] The unmanned aerial vehicle aerial survey platform is scheduled to fly periodically along the preset route over the mining area to collect high-resolution optical images, and digital orthophoto maps are generated through photogrammetry technology;

[0019] The application program interface is used to access the mining area production management information system to obtain structured production operation plans containing the time, spatial position, and influence range of blasting, mining, and soil removal operations within the next monitoring period.

[0020] As an embodiment of the present application, the establishment of the terrain change probability grid model specifically includes:

[0021] First, the satellite synthetic aperture radar remote sensing image data is subjected to coherence change detection processing, and the interference coherence coefficient values between two consecutive period images are calculated to generate an initial change intensity map representing the fine deformation of the ground surface;

[0022] Second, the current unmanned aerial vehicle aerial survey orthophoto image data is pixel-level registered with the digital orthophoto map of the previous period, and is input into a pre-trained twin convolutional neural network for difference analysis. The network is trained to distinguish between real terrain changes and temporary ground cover changes such as vehicles and equipment, and outputs a ground feature change classification mask identified as mining, filling, or stable;

[0023] Third, the mining area digital production operation plan data is analyzed, the operation area specified in the plan is mapped to the geographic coordinate system, and a prior change probability value is assigned according to the operation type to form a prior probability map of the operation plan;

[0024] Finally, a Bayesian probability fusion framework is established, the prior probability map of the operation plan is taken as prior information, the initial change intensity map and the ground feature change classification mask are taken as observation evidence, pixel-level probability update calculation is performed, and finally the terrain change probability grid model is generated.

[0025] As an embodiment of the present application, the generation of the polygon of the target area to be scanned according to the terrain change probability grid model specifically includes:

[0026] A global change determination threshold is applied to the terrain change probability grid model for binary processing, and the grid with a probability value higher than the threshold is marked as a change grid, and the grid with a probability value less than the threshold is marked as a stable grid.

[0027] performing a density-based spatial clustering of noise algorithm on all changed grids to aggregate spatially adjacent changed grids into one or more changed region clusters;

[0028] calculating the minimum bounding polygon of each changed region cluster and taking the polygon as the target region polygon to be scanned.

[0029] As an embodiment of the present application, the generating and issuing of the laser radar scanning task instruction specifically comprises:

[0030] dynamically setting a scanning point cloud density index for each target region polygon to be scanned according to the area size, terrain complexity and average probability value in the terrain change probability grid model;

[0031] querying a real-time state database of the laser radar scanning execution platform to obtain the current position, remaining power and available load information of each platform;

[0032] constructing a multi-objective optimization function, with the optimization objective being to minimize the total completion time of all scanning tasks and the constraint condition being the endurance and load limit of the platform;

[0033] solving the multi-objective optimization function by using a genetic algorithm or an ant colony optimization algorithm to obtain an optimal task allocation scheme and flight path planning of each platform, generating a machine executable task description file and issuing it to the corresponding laser radar scanning execution platform through a wireless data link.

[0034] As an embodiment of the present application, the laser radar scanning execution platform is a multi-rotor unmanned aerial vehicle equipped with a high-precision laser radar scanner, an inertial navigation system and a real-time kinematic differential positioning module.

[0035] As an embodiment of the present application, the processing of the obtained current three-dimensional point cloud data and fusion into the three-dimensional digital terrain reference model specifically comprises:

[0036] performing denoising filtering on the laser radar scanning original point cloud data to eliminate noise points caused by atmospheric aerosols or aircraft vibration;

[0037] using the airborne inertial navigation system data and differential correction data of the ground base station to solve the absolute geographic coordinates of the point cloud, realizing accurate geographic registration of the point cloud;

[0038] classifying the point cloud into ground points and non-ground points and extracting pure ground point cloud;

[0039] in the three-dimensional digital terrain reference model of the historical period, marking and deleting the region data covered by the target region polygon to be scanned.

[0040] The irregular triangle network construction algorithm or the Kriging interpolation algorithm is used to inlay the extracted pure ground point cloud into the deleted area of the three-dimensional digital terrain reference model, local accurate updating of the model is realized, and a three-dimensional digital terrain reference model of a current period is formed.

[0041] According to another aspect of the present application, a metal mine area terrain updating system based on a laser radar is also provided, which comprises:

[0042] A multi-source heterogeneous data acquisition module is configured to acquire periodic satellite synthetic aperture radar remote sensing image data, unmanned aerial vehicle photogrammetry orthographic image data and real-time mine area digitized production operation plan data covering the entire metal mine area;

[0043] A terrain change probability modeling module is connected with the multi-source heterogeneous data acquisition module and is configured to perform spatio-temporal registration and fusion processing on the acquired multi-source heterogeneous monitoring data based on a three-dimensional digital terrain reference model of a historical period, and establish a terrain change probability grid model covering the metal mine area;

[0044] A dynamic scanning task planning module is connected with the terrain change probability modeling module and is configured to identify and generate one or more to-be-scanned target area polygons according to the terrain change probability grid model, and plan laser radar scanning task instructions containing geographic coordinates, scanning parameters and an execution platform for the to-be-scanned target area polygons;

[0045] A high-precision laser radar scanning execution module is wirelessly connected with the dynamic scanning task planning module and is configured to receive and execute the scanning task instructions, perform three-dimensional laser scanning on the specified to-be-scanned target area polygons, and acquire current three-dimensional point cloud data of the area;

[0046] A three-dimensional terrain model updating module is connected with the high-precision laser radar scanning execution module and is configured to process the acquired current three-dimensional point cloud data, seamlessly fuse the current three-dimensional point cloud data into the three-dimensional digital terrain reference model of the historical period, generate an updated three-dimensional digital terrain reference model of a current period, and provide the updated model to the terrain change probability modeling module as a reference for a next period.

[0047] As an embodiment of the present application, the terrain change probability modeling module internally comprises:

[0048] A synthetic aperture radar data processing unit is configured to perform coherence change detection processing on satellite remote sensing image data;

[0049] An optical image analysis unit internally has a twin convolutional neural network and is configured to perform difference analysis on unmanned aerial vehicle orthographic image data and output a feature change classification mask;

[0050] a production plan analysis unit for converting the structured production operation plan into a priori change probability map;

[0051] a Bayesian probability fusion unit for fusing the outputs of the three units to generate a final terrain change probability raster model.

[0052] As an embodiment of the present application, the dynamic scanning task planning module internally includes:

[0053] a change area extraction unit for threshold processing and spatial clustering of the terrain change probability raster model to generate a polygon of the target area to be scanned;

[0054] a task parameter and resource matching unit with a multi-objective optimization algorithm solver built-in, for generating an optimal task allocation scheme and path planning according to the attributes of the area to be scanned and the real-time state of the scanning platform.

[0055] Compared with the prior art, the present application has the beneficial effects that:

[0056] 1. By constructing a terrain change probability model, the scanning resources are changed from indiscriminate full-area coverage to efficient on-demand precise investment, reducing the time, energy consumption and data storage cost of invalid scanning operations, and greatly improving the overall operation efficiency of terrain updating;

[0057] 2. The two heterogeneous data sources of remote sensing images and mine production plans are fused, and the probability is fused through the Bayesian framework, which not only can detect the terrain changes that have occurred, but also can predict the planned operation area, realize the comprehensive and accurate identification of expected changes and unexpected changes, and improve the accuracy and reliability of change detection;

[0058] 3. An automated closed-loop operation process from change detection, task planning, data acquisition to model updating is established, which maximizes the reduction of manual intervention, ensures the timeliness and continuity of terrain data updating, and provides high-time decision support for mine safety production, dynamic adjustment of mining and stripping plans, and accurate measurement of earthwork volume;

[0059] 4. Through dynamic scanning task planning and resource optimization allocation, efficient collaborative scheduling of multiple laser radar scanning execution platforms is realized, ensuring that all change areas can be monitored and covered in the shortest time under limited scanning resources, further improving the response speed and processing capacity of the system. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is the overall technical scheme architecture diagram of the metal mine terrain updating method based on laser radar proposed by the present application;

[0061] Figure 2 is the core principle framework diagram of the terrain change probability grid model construction in the application;

[0062] Figure 3 is the logical flow framework diagram of the multi-source heterogeneous monitoring data fusion and spatio-temporal registration in the application;

[0063] Figure 4 is the logical flow framework diagram of the dynamic scanning task planning and multi-platform collaborative scheduling in the application;

[0064] Figure 5 is the multi-level interaction relationship and data flow diagram of the high-precision laser radar scanning execution and three-dimensional terrain model closed-loop update in the application; DETAILED DESCRIPTION

[0065] Please refer to Figures 1 to 5 The application provides a metal mine area terrain updating method based on a laser radar, aiming to solve the technical problems of quickly and accurately identifying and positioning the terrain change area in a vast metal mine area, and realizing dynamic optimization allocation of scanning resources.

[0066] The method fuses low-resolution remote sensing monitoring data and mine production operation plans to construct a terrain change probability model of the whole mine area, so as to accurately guide high-resolution laser radar scanning resources to target scanning operation in the area actually changed, and realize closed-loop update of the three-dimensional digital terrain reference model after scanning.

[0067] The method comprises the following steps:

[0068] S1, acquiring multi-source heterogeneous monitoring data covering the whole metal mine area;

[0069] S2, based on the three-dimensional digital terrain reference model of the historical period, performing spatio-temporal registration and fusion processing on the multi-source heterogeneous monitoring data, and establishing a terrain change probability grid model covering the metal mine area;

[0070] S3, according to the terrain change probability grid model, identifying and extracting a grid cell cluster with a probability value higher than a preset change judgment threshold, and generating one or more scanning target area polygons;

[0071] S4, for each scanning target area polygon, generating a laser radar scanning task instruction containing its geographic coordinates, range, scanning accuracy requirement, and issuing the task instruction to a laser radar scanning execution platform;

[0072] S5, the laser radar scanning execution platform autonomously navigates to a position where the target region polygon to be scanned is located according to the received task instruction, and performs high-resolution three-dimensional laser scanning to obtain current three-dimensional point cloud data of the region;

[0073] S6, the obtained current three-dimensional point cloud data is processed and seamlessly fused into the historical period three-dimensional digital terrain reference model to generate an updated three-dimensional digital terrain reference model of the current period.

[0074] In step S1, multi-source heterogeneous monitoring data covering the entire metal mining area is obtained, including periodically obtained satellite synthetic aperture radar remote sensing image data, unmanned aerial vehicle photogrammetry orthophoto data, and real-time obtained mining area digitized production operation plan data.

[0075] Specifically, through a data interface established with a satellite data service provider, a time series synthetic aperture radar interferometric measurement data set covering the mining area range is obtained at regular intervals, the data set has a fixed revisit period, usually 7 to 14 days, and a spatial resolution of 5 to 10 meters, which is used to capture millimeter-level deformation information on the ground.

[0076] At the same time, one or more unmanned aerial vehicle photogrammetry platforms equipped with high-resolution optical cameras are dispatched to periodically fly over the mining area according to the preset flight route, the flight height is set to 300 to 500 m, the heading overlap rate is greater than 80%, the lateral overlap rate is greater than 60%, and optical images with a ground sampling distance of better than 0.1 m are collected, and the current digital orthophoto map is generated through photogrammetry software.

[0077] In addition, the application program interface is accessed to the mining area production management information system to obtain real-time structured production operation plan data, which includes the start and end time, center geographic coordinates, influence radius, operation type and estimated earthwork volume of all blasting, mining and earth removal operations in the future monitoring period.

[0078] In step S2, based on the three-dimensional digital terrain reference model of the historical period, the multi-source heterogeneous monitoring data is processed for spatio-temporal registration and fusion, and a terrain change probability grid model covering the metal mining area is established. The spatial resolution of the terrain change probability grid model is 10 m, and each grid unit stores a floating point value between 0 and 1, indicating the probability of terrain change in the current monitoring period. The specific process of establishing the model includes four sub-steps.

[0079] First, the satellite synthetic aperture radar remote sensing image data is processed for coherence change detection. Select two consecutive monitoring periods to obtain synthetic aperture radar image pairs, and calculate the interference coherence coefficient, as follows:

[0080] ;

[0081] wherein, and respectively represent the complex radar echo signals of the first period and the second period, denotes the mean operation within the local window. The value of the coherence coefficient The lower the value of the coherence coefficient, the greater the change in the surface scattering characteristics of the region, and thus the inference of the existence of terrain disturbance.

[0082] The calculated coherence coefficient map is normalized to map the initial change intensity map, with a pixel value range of 0-1.

[0083] Secondly, the current UAV photogrammetry orthographic image data and the digital orthographic image map of the last period are pixel-level registered. The affine transformation model based on feature points is used, the scale-invariant feature conversion algorithm is selected to extract stable corner points in the two periods of images, the random sample consensus algorithm is used to remove the mis-matching point pairs, and finally the optimal affine transformation matrix is solved to realize sub-pixel level alignment.

[0084] The aligned two-period images are input into a pre-trained twin convolutional neural network for difference analysis. The network consists of two weight-shared encoder branches and a contrast measurement head, the encoder uses a deep residual network structure, and the output feature dimension is 512.

[0085] The network uses a large number of labeled samples for supervised learning in the training stage, and the labels are divided into three categories: mining area, dumping area, and stable area. The network output is the probability distribution of each pixel belonging to a class, and the class with the maximum probability is taken as the final classification result to generate a mask of ground feature change classification. The mask effectively distinguishes permanent terrain changes caused by mining activities from non-terrain changes caused by transport vehicles, temporary equipment, etc.

[0086] The mining area digital production operation plan data is parsed again. The spatial influence range of each operation record in the plan is projected into a unified geographic coordinate system to form a series of circular or polygonal regions. According to the operation type, assign a prior change probability value: assign 0.95 to blasting operation, 0.9 to open-pit mining, 0.85 to dumping operation, and 0.6 to other auxiliary operations. For the area not covered by any operation plan, assign a basic background probability value of 0.1. After superimposing all regions, the operation plan prior probability map is formed.

[0087] Finally, a Bayesian probability fusion framework is established, taking the operation plan prior probability map as prior information, and the initial change intensity map and the ground feature change classification mask as observation evidence, to perform pixel-level probability update calculation. Let be the prior probability, be the likelihood function, is the posterior probability.

[0088] For each grid cell, if the topographic feature change classification mask determines mining or filling, the likelihood function takes the value of the pixel value corresponding to the initial change intensity map; if it determines stability, the likelihood function takes the value of its complement. The posterior probability calculation formula is as follows:

[0089]

[0090] wherein, is a normalization constant. Through this formula, the final terrain change probability grid model is generated by pixel-by-pixel calculation.

[0091] In step S3, according to the terrain change probability grid model, a cluster of grid cells with a probability value higher than a preset change determination threshold is identified and extracted to generate one or more target area polygons to be scanned. The preset change determination threshold is set to 0.4, which is determined by historical data backtracking verification, ensuring that the missed detection rate is less than 5% while controlling the false alarm rate within an acceptable range.

[0092] The terrain change probability grid model is binarized by applying the threshold, and the grid with a probability value higher than 0.4 is marked as a change grid, and the rest is marked as a stable grid. Then, a density-based spatial clustering noise application algorithm is performed on all change grids.

[0093] The core parameters of the algorithm include the neighborhood radius and the minimum number of points . The neighborhood radius is set to 30m, corresponding to three grid cells; the minimum number of points is set to 5. The algorithm traverses all change grids and aggregates change grids with a spatial distance less than the neighborhood radius and connectivity into a change region cluster.

[0094] For each change region cluster, its convex hull or minimum area bounding rectangle is calculated, and further simplified to a polygon with less than 20 vertices as a target area polygon to be scanned. The vertex coordinates of the polygon are represented in the world geodetic coordinate system with centimeter-level accuracy.

[0095] In step S4, for each target area polygon to be scanned, a laser radar scanning task instruction containing its geographic coordinates, range, and scanning accuracy requirement is generated, and the task instruction is sent to the laser radar scanning execution platform. The process of generating the task instruction includes dynamically setting the scanning parameters and multi-platform task allocation.

[0096] First, according to the area size, terrain complexity and average probability value of each target area polygon to be scanned in the terrain change probability grid model, a scanning point cloud density index is dynamically set for it.

[0097] ​For areas with an area less than 10000 m2, the point cloud density is set to 200 points / m2; for areas with an area between 10000 m2 and 50000 m2, it is set to 100 points / m2; and for areas with an area greater than 50000 m2, it is set to 50 points / m2.

[0098] The complexity of the terrain is evaluated by calculating the standard deviation of the elevations within the polygon. If the standard deviation is greater than 5m, the point cloud density is increased by 20% based on the original value. If the average probability value is higher than 0.7, the point cloud density is increased by 10%.

[0099] Secondly, the real-time status database of the laser radar scanning execution platform is queried. This database records the current position, remaining battery percentage, maximum endurance time, current load status, and communication link quality of all available platforms. Assuming there are three laser radar scanning execution platforms in the system, numbered 1, 2, and 3. Platform 1 is located at the east side of the mine area charging station with 90% remaining battery, maximum endurance time of 45 minutes; Platform 2 is currently executing the remaining tasks from the previous period and is expected to return in 10 minutes; Platform 3 is located at the west side of the parking lot with 75% remaining battery and a maximum endurance time of 35 minutes.

[0100] Subsequently, a multi-objective optimization function is constructed. The optimization objective is to minimize the total completion time of all scanning tasks, with the following constraints: the single flight time of each platform is less than 90% of its maximum endurance time; the total area covered by each platform's single task is less than its load capacity upper limit; all scanning areas must be assigned and assigned only once.

[0101] The optimization variables are the task-platform assignment matrix and the flight path sequence of each platform. An improved genetic algorithm is used to solve the optimization problem. The population size is set to 100, the crossover probability is 0.8, the mutation probability is 0.1, and the iteration number is 200 generations. The fitness function considers the total flight time, platform energy consumption balance, and task urgency weight. After solving, the optimal task allocation scheme is obtained: Platform 1 is responsible for two high-probability small-area regions, Platform 2 is responsible for a large-area region after returning, and Platform 3 is responsible for a medium-area complex terrain region.

[0102] For each platform, a detailed flight path plan is generated, including takeoff point, waypoint sequence, scanning height, scanning speed, and return strategy. Finally, the above information is packaged into a machine executable task description file and sent to the corresponding laser radar scanning execution platform through a 400MHz wireless data link.

[0103] In step S5, the laser radar scanning execution platform autonomously navigates to the location of the target region polygon to be scanned according to the received task instructions, and performs high-resolution three-dimensional laser scanning to obtain the current three-dimensional point cloud data of the region. The laser radar scanning execution platform is a multi-rotor unmanned aerial vehicle equipped with a high-precision laser radar scanner, an inertial navigation system, and a real-time differential positioning module.

[0104] The laser radar scanner is a 16-line mechanical rotary type, with a ranging accuracy of ±2 cm, a maximum ranging of 200 m, and a scanning frequency of 10 Hz. The inertial navigation system includes a three-axis gyroscope and a three-axis accelerometer, with a zero offset stability better than 0.1° / h. The real-time differential positioning module receives correction signals from three ground reference stations deployed in the mine area, achieving centimeter-level positioning accuracy.

[0105] After receiving the task instructions, the platform first performs self-checking to confirm that the battery voltage, communication link, and sensor status are normal.

[0106] Subsequently, the platform autonomously takes off according to the planned path and flies along the preset waypoints. Before entering the target region polygon boundary, the laser radar scanner is started 50 m away from the boundary, and the inertial navigation system and differential positioning module data are recorded synchronously.

[0107] During scanning, the platform maintains a constant height of 50 m, and the flight speed is set to 5 m / s to ensure that the point cloud density meets the task requirements. The scanning coverage is strictly limited within the polygon boundary, and data outside the boundary is automatically discarded.

[0108] After scanning is completed, the platform returns to the designated landing point along the planned return path and uploads the raw point cloud data package through the wireless link. The data package includes the timestamp, raw laser echo intensity, angle encoder reading, inertial navigation raw data, and differential positioning solution results.

[0109] In step S6, the obtained current three-dimensional point cloud data is processed and seamlessly fused into the historical period three-dimensional digital terrain reference model to generate an updated three-dimensional digital terrain reference model for the current period. The processing process includes four sub-steps.

[0110] First, the laser radar scanning raw point cloud data is denoised and filtered. A statistical outlier removal algorithm is used to calculate the average distance of each point from its nearest neighbor. If the distance is greater than the global mean plus twice the standard deviation, the point is determined to be a noise point and is removed. This step effectively removes abnormal points caused by atmospheric suspended dust, flying birds, or aircraft vibration.

[0111] Secondly, the point cloud is solved in absolute geographic coordinates by using airborne inertial navigation system data and differential correction data of ground base station. Through tight coupling Kalman filtering algorithm, the precise three-dimensional coordinates of each laser point in the world coordinate system are solved by fusing laser radar ranging, inertial attitude and differential positioning position information. The geographic registration error of the solved point cloud is controlled within 5 cm in horizontal direction and within 10 cm in elevation direction.

[0112] Thirdly, the ground points and non-ground points of the point cloud are classified. The gradual morphological filtering algorithm is adopted, the window size is set from 0.5 m to 10 m gradually, and the ground points are iteratively separated. For steep areas with a slope greater than 45°, a plane fitting method based on normal vector is used for correction. Finally, the pure ground point cloud is extracted, and non-ground objects such as vegetation, vehicles and equipment are removed.

[0113] Finally, in the three-dimensional digital terrain reference model of the historical period, the region data covered by the polygon of the target region to be scanned is marked and deleted. The reference model is stored in the form of irregular triangle net, and the coordinates and normal vector of each triangle are recorded. The deletion operation quickly locates the affected triangle set through spatial indexing and removes it from the model to form a hollow area.

[0114] Subsequently, the irregular triangle net construction algorithm is used to reconstruct the local triangle net by taking the extracted pure ground point cloud and the boundary points of the hollow area as input. The newly constructed triangle net and the original model are seamlessly spliced at the boundary, and the normal vector is continuous without cracks or overlaps.

[0115] The updated model is the three-dimensional digital terrain reference model of the current period, which has higher spatial resolution and accuracy than remote sensing data, and can be directly used for earthwork calculation, slope stability analysis and mining plan adjustment. The model is stored in the database and used as the historical reference for the next monitoring period, which is called in step S2, thereby forming a complete closed-loop updating process.

[0116] The above method steps constitute the core technical scheme of the present application. On this basis, the present application further provides a metal mine area terrain updating system based on laser radar, which is used to support the automatic execution of the above method. The system includes a multi-source heterogeneous data acquisition module, a terrain change probability modeling module, a dynamic scanning task planning module, a high-precision laser radar scanning execution module and a three-dimensional terrain model updating module.

[0117] The multi-source heterogeneous data acquisition module is deployed in the mine area data center server and connected to the satellite data service provider interface, the unmanned aerial vehicle ground control station and the production management information system through a dedicated line. The module has a built-in timing task scheduler that triggers data pulling operations at preset intervals and performs preliminary format verification and metadata extraction on the original data to ensure data integrity and timeliness.

[0118] The terrain change probability modeling module is connected with the multi-source heterogeneous data acquisition module, receives the preliminary processed remote sensing image, orthographic image and production plan data. The module internally integrates a synthetic aperture radar data processing unit, an optical image analysis unit, a production plan analysis unit and a Bayesian probability fusion unit.

[0119] The synthetic aperture radar data processing unit calls an interferometric measurement professional library to perform coherence calculation and phase unwrapping; the optical image analysis unit loads a pre-trained twin convolutional neural network model and runs in a graphics processor accelerated environment; the production plan analysis unit converts the job plan into a geospatial object through a structured analysis engine; and the Bayesian probability fusion unit performs pixel-level probability updating under a unified grid calculation framework to output a terrain change probability grid model.

[0120] The dynamic scanning task planning module is connected with the terrain change probability modeling module and receives the probability grid model as input. The module internally includes a change area extraction unit and a task parameter and resource matching unit. The change area extraction unit calls a spatial clustering algorithm library to perform threshold segmentation and clustering analysis; and the task parameter and resource matching unit maintains a real-time platform state table and embeds a multi-objective optimization algorithm solver to support switching of genetic algorithm and ant colony optimization algorithm. The module outputs a standardized task instruction file and issues it to each laser radar scanning execution platform through a secure encryption channel.

[0121] The high-precision laser radar scanning execution module is composed of multiple unmanned aerial vehicle platforms carrying laser radars, and each platform is equipped with an embedded flight controller. The controller has a built-in task analysis engine that can analyze the issued task instructions and drive the flight control system to perform autonomous navigation and scanning operations. The platform maintains bidirectional communication with the ground station through a 400MHz data link to transmit state information and point cloud data in real time.

[0122] The three-dimensional terrain model updating module is deployed on a high-performance computing cluster and receives raw point cloud data from each platform. The module integrates a point cloud processing pipeline to sequentially perform denoising, registration, classification and model fusion operations. The processing results are written into a three-dimensional geographic information system database and services are provided to other business systems in the mining area through an application programming interface.

[0123] In summary, the present application realizes intelligent prediction, accurate positioning and efficient updating of terrain changes in a metal mining area by constructing a multi-level perception system from macro remote sensing to micro laser radar, combined with prior knowledge of production plans. The entire process does not require human intervention, improving the timeliness, accuracy and resource utilization efficiency of terrain monitoring, and providing a solid data foundation for intelligent mine construction.

[0124] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it should be taken in its broadest possible sense. This is especially so where "comprising", "including", "containing", "characterized by" or the like are used as subordinating conjunctions. In this context, "comprising" is intended to mean that the embodiments can include a combination of one or more elements and / or features, including those not expressly described, without necessarily relying on any of the other features or elements not expressly described. In addition, it is to be understood that the described embodiments include any alterative or equivalent implementations or substitutions for one or more of the elements or features described. Any such alternatives or equivalents are intended to be encompassed by the description of the described embodiments.

[0125] While the embodiments of the application have been shown and described herein, it is to be understood that the application is not limited to these embodiments. Rather, many modifications, changes, alternatives and variations will be apparent to those skilled in the art once the teachings of the present disclosure have been known. It is therefore contemplated that the application shall cover any and all such modifications, changes, alternatives and variations as fall within the true spirit and scope of the present application.

Claims

1. A method for updating a metal mine area terrain based on a laser radar, characterized by, The method comprises the following steps: acquiring multi-source heterogeneous monitoring data covering the entire metal mining area, the multi-source heterogeneous monitoring data comprising satellite synthetic aperture radar remote sensing image data acquired periodically, unmanned aerial vehicle aerial survey orthographic image data, and real-time acquired mining area digitized production operation plan data; based on a historical period three-dimensional digital terrain reference model, performing spatio-temporal registration and fusion processing on the multi-source heterogeneous monitoring data, establishing a terrain change probability grid model covering the metal mining area, each grid cell in the terrain change probability grid model having a probability value indicating terrain change of the cell; according to the terrain change probability grid model, identifying and extracting a grid cell cluster with a probability value higher than a preset change determination threshold, and generating one or more to-be-scanned target area polygons; for each to-be-scanned target area polygon, generating a laser radar scanning task instruction containing its geographic coordinates, range, and scanning accuracy requirement, and issuing the task instruction to a laser radar scanning execution platform; the laser radar scanning execution platform autonomously navigates to the position of the to-be-scanned target area polygon according to the received task instruction, and performs high-resolution three-dimensional laser scanning to acquire current three-dimensional point cloud data of the area; processing the acquired current three-dimensional point cloud data and seamlessly fusing it into the historical period three-dimensional digital terrain reference model to generate an updated three-dimensional digital terrain reference model of the current period.

2. The laser-radar-based metal-mining district terrain updating method according to claim 1, characterized by, The acquisition of multi-source heterogeneous monitoring data covering the entire metal mining area comprises: through an interface with a satellite data service provider, acquiring a time-sequenced synthetic aperture radar interferometric data set covering the mining area range with a specific revisit period; scheduling an unmanned aerial vehicle aerial survey platform to periodically fly over the mining area according to a preset route, collecting high-resolution optical images, and generating a digital orthographic image map through photogrammetry; through an application program interface, accessing a mining area production management information system to acquire structured production operation plans containing the time, spatial location, and influence range of blasting, mining, and soil removal operations in the future monitoring period.

3. The laser-radar-based metal district terrain updating method according to claim 1, characterized by, The establishment of the terrain change probability grid model covering the metal mining area comprises: performing coherence change detection processing on the satellite synthetic aperture radar remote sensing image data, calculating the interference coherence coefficient value between two consecutive period images to generate an initial change intensity map representing subtle ground deformation; performing pixel-level registration on the current unmanned aerial vehicle aerial survey orthographic image data and the digital orthographic image map of the previous period, and inputting them into a pre-trained twin convolutional neural network for difference analysis, the network being trained to distinguish between real terrain changes and temporary ground cover changes such as vehicles and equipment, and outputting a ground object change classification mask identified as mining, filling, or stable; analyzing the mining area digitized production operation plan data, mapping the operation areas specified in the plan to the geographic coordinate system, and assigning a priori change probability value according to the operation type to form an operation plan a priori probability map; A Bayesian probability fusion framework is established, the job plan prior probability map is taken as prior information, the initial change intensity map and the feature change classification mask are taken as observation evidence, pixel-level probability updating calculation is performed, and finally the terrain change probability grid model is generated.

4. The laser-radar-based metal district terrain update method according to claim 3, characterized by, The satellite synthetic aperture radar remote sensing image data is subjected to coherence change detection processing, including: Synthetic aperture radar images of two continuous monitoring periods are selected, and an interference coherence coefficient is calculated, and the formula is: ； with the complex radar echo signals of the first and second cycle, respectively, denotes a mean operation over a local window; The calculated coherence coefficient map is normalized and mapped into an initial change intensity map, and the pixel value range is 0-1.

5. The laser-radar-based metal district terrain update method according to claim 3, characterized by, The current unmanned aerial vehicle photogrammetry orthographic image data and the digital orthographic image map of the last period are pixel-level registered, and are input into a pre-trained twin convolutional neural network for difference analysis, including: An affine transformation model based on feature points is used to perform sub-pixel level alignment of the two images; The aligned two images are input into a twin convolutional neural network composed of two encoder branches sharing weights and a contrast metric head, and the encoder adopts a deep residual network structure; The network outputs the probability distribution of the category to which each pixel belongs, and the category with the maximum probability is taken as the final classification result to generate a feature change classification mask.

6. The laser-radar-based metal district terrain updating method according to claim 3, characterized by, The mine digital production job plan data is analyzed, the job area clearly indicated in the plan is mapped into a geographic coordinate system, and a prior change probability value is assigned according to the job type to form a job plan prior probability map, including: The spatial influence range of each job record is projected into a unified geographic coordinate system to form a circular or polygonal area; After superimposing all areas, the job plan prior probability map is formed.

7. The laser-radar-based metal district terrain updating method according to claim 3, characterized by, A Bayesian probability fusion framework is established for pixel-level probability updating calculation, including: Let be the prior probability, be the likelihood function, be the posterior probability; If the feature change classification mask determines mining or filling, the likelihood function takes the pixel value of the initial change intensity map; if it is determined to be stable, the likelihood function takes the complement value thereof; The posterior probability is calculated pixel by pixel through the formula: ； The terrain change probability grid model is generated.

8. The laser-radar-based metal district terrain updating method according to claim 7, characterized by, According to the terrain change probability grid model, a target area polygon to be scanned is generated, including: A global change determination threshold is applied to the terrain change probability grid model for binary processing, and the grid with a probability value higher than the threshold is marked as a change grid, and the grid with a probability value less than the threshold is marked as a stable grid; A density-based spatial clustering noise application algorithm is performed on all change grids to aggregate spatially adjacent change grids into one or more change area clusters; The minimum enclosing polygon of each change area cluster is calculated, and the polygon is taken as the target area polygon to be scanned.

9. The laser-radar-based metal district terrain updating method according to claim 1, characterized by, A laser radar scanning task instruction is generated and issued, including: According to the area size, terrain complexity and average probability value of each target area polygon to be scanned in the terrain change probability grid model, a scanning point cloud density index is dynamically set for it; The real-time state database of the laser radar scanning execution platform is queried to obtain the current position, remaining power and available load information of each platform; A multi-objective optimization function is constructed, whose optimization objective is to minimize the total completion time of all scanning tasks, and the constraint condition is the endurance and load limit of the platform; The genetic algorithm or ant colony optimization algorithm is used to solve the multi-objective optimization function, to obtain the optimal task allocation scheme and flight path planning of each platform, and to generate a machine executable task description file, which is transmitted to the corresponding laser radar scanning execution platform through a wireless data link.

10. The laser-radar-based metal district terrain updating method according to claim 1, characterized by, The obtained current three-dimensional point cloud data is processed and fused into a three-dimensional digital terrain reference model, including: The original laser radar scanning point cloud data is denoised and filtered to remove noise points caused by atmospheric suspended matter or aircraft vibration; The point cloud is solved for absolute geographic coordinates using airborne inertial navigation system data and differential correction data from ground stations, to achieve accurate geographic registration of the point cloud; The point cloud is classified into ground points and non-ground points, and the pure ground point cloud is extracted; In the three-dimensional digital terrain reference model of the historical period, the region data covered by the polygon of the target region to be scanned is marked and deleted; The extracted pure ground point cloud is inlaid into the deleted area of the three-dimensional digital terrain reference model using an irregular triangle network construction algorithm or a Kriging interpolation algorithm, to realize local accurate updating of the model and form a three-dimensional digital terrain reference model of the current period.

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