GNSS multi-path error dynamic suppression method and system based on three-dimensional environment modeling

Abstract

The invention discloses a GNSS multi-path error dynamic suppression method and system based on three-dimensional environment modeling, and relates to the civil engineering safety monitoring cross technical field, and the method comprises the steps: receiving multi-source three-dimensional point cloud data, carrying out the fusion of the multi-source three-dimensional point cloud data, and obtaining the fused three-dimensional point cloud data, the fusion process comprises coarse registration, fine registration and redundant point cloud de-duplication; inputting the fused three-dimensional point cloud data into a pre-established triangular mesh model to perform triangular mesh reconstruction and optimization, and outputting to obtain a three-dimensional environment model; the method comprises the following steps: acquiring a satellite signal propagation path, performing analysis based on a geometrical relationship between the satellite signal propagation path and a three-dimensional environment model to obtain a satellite signal path analysis result, and performing adaptive distribution of observation value weights and positioning settlement based on the satellite signal path analysis result to realize dynamic suppression of multipath errors.

Description

Technical Field

[0001] This invention relates to the field of cross-disciplinary technology of civil engineering safety monitoring, specifically a GNSS multipath error dynamic suppression method and system based on three-dimensional environment modeling. Background Technology

[0002] In the safety monitoring of deep foundation pit engineering, GNSS technology, with its advantages of non-contact, all-weather, and high-frequency data acquisition, has become a core tool for monitoring the horizontal displacement of support structures and surface settlement. However, in complex urban environments, foundation pit construction sites experience two typical types of multipath interference: 1) Mixed path interference, where direct signals and interference signals reflected by structures such as support piles and diaphragm walls are received simultaneously, leading to carrier phase superposition errors; 2) Complete obstruction interference, where direct satellite signals are completely blocked by surrounding buildings or construction machinery, and the receiver only captures distorted signals after multiple reflections. Engineering measurements show that pseudorange errors caused by complete obstruction interference can reach centimeter-level, and phase errors can reach millimeter-level. Especially in areas with dense tower cranes and steel supports, such errors can mask the true deformation trend and even cause a 15-30 minute delay in early warning of stress changes in the support structure, seriously threatening construction safety.

[0003] In existing technologies, multipath suppression mainly relies on choke coil antennas or post-processing filtering algorithms, but these have significant drawbacks in dynamic foundation pit scenarios: 1) Choke coil antennas are large and costly, making them difficult to deploy densely in confined foundation pits; 2) Traditional wavelet transform and Kalman filtering algorithms are based on static environment assumptions and cannot respond in real time to changes in the reflection environment caused by the removal of support structures and earthwork excavation. Taking a deep foundation pit project of a subway as an example, traditional methods failed to detect the periodic full occlusion of GPS L2 signals by the tower crane steel frame, resulting in regular fluctuations of ±6.3mm in horizontal displacement data. The period of this fluctuation was completely synchronized with the tower crane's rotation operation, severely interfering with deformation trend analysis. These problems expose the core bottlenecks of existing technologies: coarse environmental modeling, inefficient occlusion detection, and lagging dynamic response. Summary of the Invention

[0004] To address the shortcomings mentioned in the background section, the present invention aims to provide a method and system for dynamic suppression of GNSS multipath errors based on three-dimensional environment modeling.

[0005] Firstly, the objective of this invention can be achieved through the following technical solution: a dynamic suppression method for GNSS multipath error based on three-dimensional environment modeling, the method comprising the following steps:

[0006] Receive multi-source 3D point cloud data, fuse the multi-source 3D point cloud data to obtain fused 3D point cloud data, wherein the fusion process includes coarse registration, fine registration and deduplication of redundant point clouds;

[0007] The fused 3D point cloud data is input into a pre-established triangular mesh model for triangular mesh reconstruction and optimization, and the output is a 3D environment model.

[0008] The satellite signal propagation path is obtained, and the geometric relationship between the satellite signal propagation path and the three-dimensional environment model is analyzed to obtain the satellite signal path analysis results. Based on the satellite signal path analysis results, the observation weights are adaptively allocated and the positioning calculation is performed to achieve dynamic suppression of multipath errors.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the multi-source 3D point cloud data is acquired through multiple devices, including a stand-alone 3D laser scanner, a handheld 3D laser scanner, and an UAV-borne lidar.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the coarse registration is to initially align the point cloud to the same coordinate system by quickly estimating the global transformation matrix, including rotation and translation;

[0011] The fine registration achieves millimeter-level alignment accuracy by optimizing the local correspondence between point clouds, thus eliminating residual errors after coarse registration.

[0012] The redundant point cloud deduplication eliminates redundant data generated by multi-source point cloud fusion while retaining millimeter-level geometric accuracy of key structures. The redundant data includes repeated scan points and afterimages of dynamic objects.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of reconstructing the triangular mesh: adopting an improved Delaunay subdivision algorithm, introducing edge constraints and dynamic size control, optimizing model complexity while preserving sharp structural features, and adopting an adaptive subdivision strategy to balance model accuracy and storage overhead.

[0014] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: during the reconstruction of the triangulation network, an octree spatial index is constructed, and a depth-optimized octree is adopted for the dense support piles and dynamic changes in construction machinery. Through node depth control and patch capacity constraints, the ray tracing efficiency of hundreds of millions of triangular patches is improved to the second level while ensuring millimeter-level spatial resolution.

[0015] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the analysis based on the geometric relationship between the satellite signal propagation path and the three-dimensional environment model includes complete signal path occlusion detection and rapid detection of adjacent reflective surfaces;

[0016] The signal path complete obstruction detection process: based on the station coordinates P calculated by the GNSS receiver.rcv With satellite position P sat Construct the direct signal path vector Octree spatial indexing is used to accelerate ray and triangular mesh collision detection. The process recursively traverses from the root node, performing detection only within child nodes intersecting the ray path. The algorithm determines whether a ray intersects with a triangular mesh, and its criteria are as follows:

[0017]

[0018] Where T is the vector from the ray origin to the vertex of the triangle, D is the ray direction vector, and E1 and E1 are the edge vectors of the triangular mesh; if t≥0 and u,v∈[0,1], then the path is determined to intersect the triangle and is marked as a direct path occlusion.

[0019] The rapid detection process for adjacent reflective surfaces is as follows: For cases where the triangular facet does not intersect with the triangular facet but is adjacent to the path, a cylindrical buffer region centered on the signal path is defined. An octree spatial index is used to quickly filter triangular facets that fall into the cylindrical buffer region. The bounding boxes of child nodes are recursively checked from the root node of the octree to see if they intersect with the buffer region. The intersecting nodes are traversed. Within the selected nodes, the shortest distance from the triangular facet to the signal path is calculated. If the shortest distance is less than r, it is marked as an adjacent facet. A maximum adjacent facet threshold is set. When the number of adjacent facets counted reaches the maximum adjacent facet threshold, the search is terminated early.

[0020] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of adaptively allocating observation weights based on satellite signal path analysis results, comprising:

[0021] For satellite signals with adjacent reflective surfaces, an adaptive weighted model is constructed that comprehensively considers the number and distance of adjacent surfaces:

[0022]

[0023] In the formula, ω ele For traditional elevation angles, N is assigned a weight. near N represents the number of adjacent faces. max =10 is the threshold for the maximum number of nearest neighbors, d k d is the shortest distance from the k-th neighboring surface to the signal path. th =0.5m is the radius of influence, and the exponential term in the model is exp(-d k / d th It is used to quantify the interference intensity of a single facet, and the weighting coefficient decreases as the number and distance of neighboring faces increase.

[0024] Secondly, in order to achieve the above objectives, this invention discloses a GNSS multipath error dynamic suppression system based on three-dimensional environment modeling, comprising:

[0025] The data processing module is used to receive multi-source 3D point cloud data, fuse the multi-source 3D point cloud data, and obtain fused 3D point cloud data. The fusion process includes coarse registration, fine registration, and deduplication of redundant point clouds.

[0026] The triangulation reconstruction module is used to reconstruct and optimize the triangulation based on the fused 3D point cloud data input into a pre-established triangular mesh model, and output a 3D environment model.

[0027] The dynamic suppression module is used to acquire the satellite signal propagation path, analyze the geometric relationship between the satellite signal propagation path and the three-dimensional environment model to obtain the satellite signal path analysis results, and adaptively allocate observation weights and positioning calculations based on the satellite signal path analysis results to achieve dynamic suppression of multipath errors.

[0028] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs the GNSS multipath error dynamic suppression method based on three-dimensional environment modeling as described above.

[0029] In another aspect of the present invention, in order to achieve the above-mentioned objective, a computer-readable storage medium is disclosed, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is loaded and executed by a processor, the GNSS multipath error dynamic suppression method based on three-dimensional environment modeling as described above is employed.

[0030] The beneficial effects of this invention are:

[0031] This invention establishes an environmental model around a monitoring station using three-dimensional laser scanning point cloud data. Combined with an adaptive weight adjustment and iterative optimization mechanism, it effectively solves the technical bottlenecks of traditional static models, such as insufficient suppression of multipath interference in complex environments, inaccurate positioning accuracy due to dynamic changes in the environment, and divergence in solutions caused by misuse of obstructed signals.

[0032] To address multipath error elimination, this method employs a complete occlusion signal removal mechanism and a dynamic weighting model for adjacent reflective surfaces, which can adaptively suppress more than 60% of multipath interference (experiments verify that the multipath error amplitude is reduced from ±5.2mm to ±2mm). Combined with weight iterative optimization, the planar positioning accuracy of monitoring stations in complex scenarios is improved by more than 65%, with a convergence accuracy of ±1.4mm, which is significantly better than the traditional fixed weight model.

[0033] This method exhibits strong environmental adaptability, supporting stable solutions in scenarios with high incidence of shading and reflection, such as urban canyons and densely supported structures, through real-time signal quality assessment and dynamic weight allocation. The environmental model update and multipath reanalysis modules can be flexibly activated according to engineering needs, balancing algorithm efficiency and accuracy. The generated millimeter-level deformation monitoring data can provide reliable technical support for real-time safety assessment, disaster early warning, and emergency decision-making in high-risk scenarios such as deep foundation pits in subways, slope engineering, and bridge structures. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0036] Figure 2 This is a schematic diagram of the technical process of the present invention;

[0037] Figure 3 This is a plan view of the experimental foundation pit deformation monitoring of this invention;

[0038] Figure 4 This is a schematic diagram of the device used in this invention;

[0039] Figure 5 This is a schematic diagram of the scanned point cloud of the present invention;

[0040] Figure 6 This is a schematic diagram of the triangular mesh generated by the present invention;

[0041] Figure 7 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1:

[0044] like Figure 1 As shown, the GNSS multipath error dynamic suppression method based on 3D environment modeling is characterized by the following steps:

[0045] S101: Receive multi-source 3D point cloud data, fuse the multi-source 3D point cloud data to obtain fused 3D point cloud data, wherein the fusion process includes coarse registration, fine registration and deduplication of redundant point clouds.

[0046] The multi-source 3D point cloud data was acquired through multiple devices, including a stand-alone 3D laser scanner, a handheld 3D laser scanner, and an UAV-borne lidar.

[0047] Specifically, this includes: a station-mounted 3D laser scanner: deployed within a 20-meter radius of the monitoring station to acquire millimeter-level point clouds (accuracy ±1mm, point density ≥800 points / m²) of key structures such as support piles and continuous walls. 2 It is suitable for static high-precision modeling, but is limited by the blind zone of the field of view, and the operation is relatively complex.

[0048] Handheld 3D laser scanner: To address the issues of high-altitude blind spots and complex deployment of airborne lidar, a portable scanner is used for flexible supplementary measurements. It can quickly penetrate into complex areas such as densely packed support piles and update regional point cloud data (accuracy ±1cm), thus compensating for the insufficient spatial coverage of airborne and station-based equipment.

[0049] UAV-borne lidar: It can cover a 200-meter radius around the monitoring station with a flight height of 50 meters, and generate point cloud data of high-altitude obstructions (tower crane boom, nearby buildings) with vertical accuracy ±3cm and horizontal accuracy ±5cm, which solves the problem of rapid data collection over a large area. However, it is affected by vegetation and temporary obstructions and requires ground supplementary measurements.

[0050] In practical applications, equipment combinations can be flexibly selected according to the engineering stage: in the early stage of construction, a combination of gantry-type and machine-mounted equipment is used to quickly establish a global benchmark model; during the excavation stage, handheld equipment is introduced to focus on tracking dynamic areas; during the support structure installation period, gantry-type equipment is mainly used to ensure the modeling accuracy of key structures.

[0051] In complex foundation pit monitoring scenarios, single scanning devices, limited by their inherent characteristics, cannot meet the requirements for high-precision modeling across the entire area. Multi-source point cloud fusion integrates the spatial perception advantages of different devices to construct a comprehensive 3D model covering everything from the ground to high altitudes, including static structures and dynamic objects. Multi-source point cloud fusion overcomes the inherent limitations of single data sources and, through spatial benchmark unification and precision complementarity, provides millimeter-level realistic environmental representation for subsequent satellite signal path analysis, ensuring spatial consistency and reliability in multi-path error identification. Multi-source point cloud fusion is achieved through the following process:

[0052] 1) Coarse registration: When there is a large initial positional deviation between point clouds acquired from different viewpoints or devices, the point clouds are initially aligned to the same coordinate system by quickly estimating the global transformation matrix (rotation, translation). Considering the characteristics of complex foundation pit monitoring scenarios, this invention adopts a matching algorithm based on local features (such as intrinsic shape signature algorithm, scale-invariant feature transformation algorithm, etc.), which has a relatively fast running speed (typical scene matching time is less than 10 minutes), and strong robustness to noise and partial occlusion, with an average coarse matching error within 10 cm.

[0053] 2) Fine Registration: Building upon coarse registration, this invention optimizes the local correspondence between point clouds to achieve millimeter-level alignment accuracy, eliminating residual errors from coarse registration and ensuring high geometric consistency among multi-source point clouds. This provides a precise data foundation for subsequent 3D modeling and path analysis. The invention employs an improved iterative nearest-point algorithm, introducing strategies such as normal vector constraints, dynamic weights, and robust kernel functions to achieve fine matching of multi-source point cloud data. In typical scenarios, the matching time is less than 20 minutes, and the matching accuracy is higher than 3 mm.

[0054] 3) Redundant Point Cloud Deduplication: By eliminating redundant data generated by multi-source point cloud fusion (such as duplicate scan points and afterimages of dynamic objects), the amount of invalid data is reduced while preserving the millimeter-level geometric accuracy of key structures. Considering the characteristics of the construction environment, this invention employs voxel filtering to achieve redundant point cloud deduplication, optimizing efficiency by 40%–60%. Based on the actual site conditions and accuracy requirements, curvature constraint filtering technology is used to repair feature blurring, ensuring the millimeter-level detail integrity (0.15 rad / cm) of high-curvature areas such as the edges of support piles and joints of continuous walls. 2 ).

[0055] S102: Based on the fused 3D point cloud data, input into the pre-established triangular mesh model to perform triangular mesh reconstruction and optimization, and output a 3D environment model;

[0056] To address the complex environment of foundation pits (support piles, diaphragm walls, construction machinery) and the need for multipath suppression, this phase constructs a high-precision triangular mesh model to accurately characterize the spatial morphology of the reflecting surface. Through hierarchical spatial partitioning, the ray tracing efficiency of hundreds of millions of facets is improved to the millisecond level, supporting the real-time requirements of satellite signal obstruction detection. These two elements work together to construct a geometric benchmark that combines accuracy and efficiency, providing core technical support for the dynamic suppression of multipath errors in complex construction scenarios.

[0057] Triangulation Reconstruction: By constructing a high-precision triangular mesh model, the three-dimensional spatial morphology of structures such as retaining piles and continuous walls is accurately represented, providing a geometric benchmark for multipath analysis of satellite signals. An improved Delaunay subdivision algorithm is employed, introducing edge constraints and dynamic size control to optimize model complexity while preserving sharp structural features, ensuring millimeter-level detail integrity (error ≤ 3mm). For irregular surfaces such as vegetation and construction machinery, an adaptive subdivision strategy is used to balance model accuracy and storage overhead, providing a high-fidelity 3D environment model for multipath reflection path tracing.

[0058] Octree Spatial Index Construction: By establishing a hierarchical spatial index structure, efficient management and rapid retrieval of triangular mesh models in complex foundation pit scenarios are achieved, providing real-time geometric computation support for multipath reflection analysis. Addressing challenges such as dense support piles and dynamic changes in construction machinery, a depth-optimized octree is adopted. Through node depth control (8 levels) and patch capacity constraints, ray tracing efficiency for hundreds of millions of triangular patches is improved to the second level while maintaining millimeter-level spatial resolution, providing a high-response 3D environmental benchmark for dynamic suppression of multipath errors.

[0059] The analysis based on the geometric relationship between the satellite signal propagation path and the three-dimensional environment model includes complete signal path obstruction detection and rapid detection of adjacent reflective surfaces;

[0060] 1) Detection of complete signal path obstruction: Based on the station coordinates P calculated by the GNSS receiver rcv With satellite position P sat Construct the direct signal path vector An octree spatial index is used to accelerate ray and triangular mesh collision detection. A recursive traversal is performed starting from the root node, with precise detection only occurring within child nodes intersecting the ray path. The algorithm determines whether a ray intersects with a triangular mesh, and its criteria are as follows:

[0061]

[0062] Where T is the vector from the ray's origin to the vertex of the triangle, D is the ray's direction vector, and E1 and E2 are the edge vectors of the triangular mesh. If t ≥ 0 and u, v ∈ [0, 1], then the path is determined to intersect the triangle, and is marked as a direct path occlusion.

[0063] 2) Rapid Detection of Adjacent Reflecting Facets: For cases where the facet does not intersect with the triangular facet but is adjacent to the path, a cylindrical buffer region (radius r = 0.5m) centered on the signal path is defined. An octree spatial index is used to quickly filter triangular faces that may fall into this region. The bounding boxes of child nodes are recursively checked from the octree root node to see if they intersect the buffer region, traversing the intersecting nodes. Within the filtered nodes, the shortest distance from the triangular facet to the signal path is calculated. If the shortest distance is less than r, it is marked as an adjacent facet. A maximum adjacent facet threshold of 10 is set; the search terminates early when the number of adjacent faces reaches the threshold to avoid invalid calculations.

[0064] By using hierarchical spatial partitioning of octrees and a buffer region coarse screening strategy, more than 80% of non-overlapping child nodes are skipped, reducing redundant computation by 60%, improving single-ray processing efficiency by 7.5 times, and reducing detection time from 15 seconds of full scene traversal to less than 2 seconds.

[0065] S103: Obtain the satellite signal propagation path, analyze the geometric relationship between the satellite signal propagation path and the three-dimensional environment model to obtain the satellite signal path analysis results, and adaptively allocate observation weights and position calculations based on the satellite signal path analysis results to achieve dynamic suppression of multipath errors.

[0066] Based on satellite signal path analysis results, the observation weights are dynamically allocated and a robust positioning model is solved to suppress the impact of multipath errors on the coordinates of the monitoring station and achieve millimeter-level positioning accuracy improvement in complex obstruction scenarios.

[0067] 1) Completely Obstructed Signal Removal: For satellite signals determined to be completely obstructed by the direct path (such as by tower crane booms or support piles), their observations are directly removed to avoid introducing uncorrectable reflection path errors. Engineering cases show that this type of signal removal can reduce horizontal positioning errors by 26% and elevation errors by 42%.

[0068] 2) Near-reflector signal weighting model

[0069] For satellite signals with adjacent reflective surfaces, an adaptive weighted model is constructed that comprehensively considers the number and distance of adjacent surfaces:

[0070]

[0071] In the formula, ω ele For traditional elevation angles, N is assigned a weight. near N represents the number of adjacent faces. max =10 is the threshold for the maximum number of nearest neighbors, d k d is the shortest distance from the k-th neighboring surface to the signal path. th =0.5m is the radius of influence. The exponential term in the model is exp(-d k / dth This is used to quantify the interference intensity of a single facet (the closer the distance, the greater the impact). The weighting coefficient decreases as the number and distance of neighboring faces increase. By normalizing and accumulating the influence of neighboring faces, we can avoid excessive weight decay caused by too many neighboring faces, while retaining the suppression effect of the main interference source.

[0072] 3) Robust solution and iterative optimization

[0073] Based on the GNSS observation equations, effective satellite signals are calculated. First, completely obstructed satellite observations are removed. Then, the weights of each signal are calculated using a nearby reflector weighting model; low weight values ​​directly reflect the degree of multipath interference. During the calculation process, an adaptive weight matrix is ​​constructed, and iterative optimization gradually converges the coordinate results to a stable state. If the coordinate offset of the monitoring station exceeds the allowable range after calculation, environmental model updates and multipath interference reanalysis can be triggered according to actual needs, forming a dynamic closed-loop optimization mechanism. Taking a subway foundation pit project as an example, after one iteration, the stability of the positioning results is significantly improved, with a planar convergence accuracy of ±1.1 mm, a 73% improvement compared to traditional methods. The triggering conditions for environmental model updates and multipath analysis can be flexibly adjusted according to engineering accuracy requirements, ensuring the algorithm's practicality in complex environments.

[0074] Specifically, the following examples further illustrate the solution of the present invention: In a deep foundation pit safety monitoring project for a subway, the team constructed a complete process from data acquisition to location calculation based on multi-source data fusion and adaptive multipath correction technology. First, 12 GNSS monitoring stations were deployed in key areas around the foundation pit (including the support structure, adjacent building connections, and settlement-sensitive areas), using Huace H3 receivers (…). Figure 4 (Left), of which 4 monitoring stations use the multipath suppression method in this patented technology.

[0075] I. Multi-source 3D environmental data acquisition

[0076] Using the Oslei R8+ handheld laser scanner ( Figure 3 (Right) A high-density scan was performed within a 200-meter radius of the monitoring station, accumulating approximately 80 million raw point clouds. To address the overlapping point clouds caused by the handheld device's mobile scanning, an improved voxel filtering algorithm was used for deduplication, compressing the effective point cloud to 46 million points (partial point cloud data can be found in...). Figure 4 ).

[0077] III. Spatial Triangulation Modeling and Optimization

[0078] Based on this, an improved Delaunay meshing algorithm was used to construct a triangulated mesh. By introducing surface constraints (maximum triangle side length ≤ 5cm, included angle between normals of adjacent facets ≤ 15°) to optimize the mesh topology, approximately 120 million triangular facets were ultimately generated (see some examples of the triangulated mesh). Figure 5 ), and build an octree spatial index to support fast nearest neighbor retrieval.

[0079] IV. Satellite Signal Path Analysis

[0080] In the satellite signal path analysis phase, continuous observation data from August 10th to 15th, 2024, were selected, and signal obstruction was analyzed in 30-second windows. Data showed that the average number of visible satellites in the sky above the foundation pit was 18 (GPS: 8-10, BDS: 6-8, Galileo: 4-6), of which an average of 3.2 satellites were completely obstructed by the foundation pit support structure, resulting in a signal failure of ≥12 satellites per day (after removal, the effective satellite count was ≥12). Using an octree index and a triangular network model, the reflection path of each satellite was monitored in real time: when the satellite elevation angle was <35°, triangular facets within a 5-meter range along the signal propagation path were automatically retrieved to identify potential reflecting surfaces (such as metal support frames and concrete walls). Results showed that due to changes in satellite constellation distribution and intensive construction machinery activity, the peak number of satellites affected by reflecting surfaces around some monitoring stations reached 9, accounting for approximately 40% of the visible satellite count.

[0081] V. Adaptive Weight Allocation and Localization Solution

[0082] Based on reflector detection, the weights of observations are dynamically adjusted according to the model in the technical solution. For the distance between the satellite signal path and the reflector, an exponential decay model is used to calculate the weight coefficients: when the distance between the signal path and the reflector is less than 0.5 meters (this threshold is calibrated using field measurement data), the weight of the carrier phase observation decreases exponentially with decreasing distance; for satellites without reflection risk, the original weights are maintained. Error sequences are separated using a single-difference multipath error extraction method combined with Empirical Mode Decomposition (EMD). Comparative analysis shows that the root mean square (RMS) of the multipath error amplitude at the monitoring station using the patented technology is 1.8 mm, a 48.6% reduction compared to traditional stations (3.5 mm), consistent with the decay trend predicted by the model. Ultimately, the horizontal positioning error RMS at the four stations using the patented technology was optimized from 3.2 mm to 2.1 mm, and the vertical error decreased from 5.6 mm to 3.2 mm, showing a significant improvement in accuracy compared to conventional stations. The higher improvement rate in the vertical direction is consistent with the characteristic that multipath effects are sensitive to elevation angles, further verifying the model's ability to suppress multipath errors.

[0083] Example 2: To achieve the above objective, based on Example 1, as follows... Figure 7As shown, this invention discloses a GNSS multipath error dynamic suppression system based on three-dimensional environment modeling, comprising:

[0084] Data processing module 11 is used to receive multi-source 3D point cloud data, fuse the multi-source 3D point cloud data, and obtain fused 3D point cloud data. The fusion process includes coarse registration, fine registration, and deduplication of redundant point clouds.

[0085] The triangulation reconstruction module 12 is used to reconstruct and optimize the triangulation based on the fused 3D point cloud data input into the pre-established triangular mesh model, and output a 3D environment model.

[0086] The dynamic suppression module 13 is used to obtain the satellite signal propagation path, analyze the geometric relationship between the satellite signal propagation path and the three-dimensional environment model to obtain the satellite signal path analysis result, and adaptively allocate the observation weight and positioning calculation based on the satellite signal path analysis result to realize the dynamic suppression of multipath error.

[0087] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0088] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0089] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0090] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A dynamic suppression method for GNSS multipath error based on 3D environment modeling, characterized in that, The method includes the following steps: Receive multi-source 3D point cloud data, fuse the multi-source 3D point cloud data to obtain fused 3D point cloud data, wherein the fusion process includes coarse registration, fine registration and deduplication of redundant point clouds; The fused 3D point cloud data is input into a pre-established triangular mesh model for triangular mesh reconstruction and optimization, and the output is a 3D environment model. The satellite signal propagation path is obtained, and the geometric relationship between the satellite signal propagation path and the three-dimensional environment model is analyzed to obtain the satellite signal path analysis results. Based on the satellite signal path analysis results, the observation weights are adaptively allocated and the positioning calculation is performed to achieve dynamic suppression of multipath errors.

2. The GNSS multipath error dynamic suppression method based on three-dimensional environment modeling according to claim 1, characterized in that, The multi-source 3D point cloud data is acquired through multiple devices, including a stand-alone 3D laser scanner, a handheld 3D laser scanner, and an UAV-borne lidar.

3. The GNSS multipath error dynamic suppression method based on three-dimensional environment modeling according to claim 1, characterized in that, The coarse registration is to initially align the point cloud to the same coordinate system by quickly estimating the global transformation matrix, including rotation and translation. The fine registration achieves millimeter-level alignment accuracy by optimizing the local correspondence between point clouds, thus eliminating residual errors after coarse registration. The redundant point cloud deduplication eliminates redundant data generated by multi-source point cloud fusion while retaining millimeter-level geometric accuracy of key structures. The redundant data includes repeated scan points and afterimages of dynamic objects.

4. The GNSS multipath error dynamic suppression method based on three-dimensional environment modeling according to claim 1, characterized in that, The process of reconstructing the triangulation network involves: using an improved Delaunay subdivision algorithm, introducing edge constraints and dynamic size control, optimizing model complexity while preserving sharp structural features, and employing an adaptive subdivision strategy to balance model accuracy and storage overhead.

5. The GNSS multipath error dynamic suppression method based on three-dimensional environment modeling according to claim 4, characterized in that, During the reconstruction of the triangulation network, an octree spatial index is constructed. For dense support piles and dynamic changes in construction machinery, a depth-optimized octree is adopted. Through node depth control and patch capacity constraints, the ray tracing efficiency of hundreds of millions of triangular patches is improved to the second level while ensuring millimeter-level spatial resolution.

6. The GNSS multipath error dynamic suppression method based on three-dimensional environment modeling according to claim 1, characterized in that, The analysis based on the geometric relationship between the satellite signal propagation path and the three-dimensional environment model includes complete signal path obstruction detection and rapid detection of adjacent reflective surfaces; The signal path complete obstruction detection process: based on the station coordinates P calculated by the GNSS receiver. rcv With satellite position P sat Construct the direct signal path vector Octree spatial indexing is used to accelerate ray and triangular mesh collision detection. The process recursively traverses from the root node, performing detection only within child nodes intersecting the ray path. The algorithm determines whether a ray intersects with a triangular mesh, and its criteria are as follows: Where T is the vector from the ray origin to the vertex of the triangle, D is the ray direction vector, and E1 and E1 are the edge vectors of the triangular mesh; if t≥0 and u,v∈[0,1], then the path is determined to intersect the triangle and is marked as a direct path occlusion. The rapid detection process for adjacent reflective surfaces is as follows: For cases where the triangular facet does not intersect with the triangular facet but is adjacent to the path, a cylindrical buffer region centered on the signal path is defined. An octree spatial index is used to quickly filter triangular facets that fall into the cylindrical buffer region. The bounding boxes of child nodes are recursively checked from the root node of the octree to see if they intersect with the buffer region. The intersecting nodes are traversed. Within the selected nodes, the shortest distance from the triangular facet to the signal path is calculated. If the shortest distance is less than r, it is marked as an adjacent facet. A maximum adjacent facet threshold is set. When the number of adjacent facets counted reaches the maximum adjacent facet threshold, the search is terminated early.

7. The GNSS multipath error dynamic suppression method based on three-dimensional environment modeling according to claim 1, characterized in that, The process of adaptively allocating observation weights based on satellite signal path analysis results includes: For satellite signals with adjacent reflective surfaces, an adaptive weighted model is constructed that comprehensively considers the number and distance of adjacent surfaces: In the formula, ω ele For traditional elevation angles, N is assigned a weight. near N represents the number of adjacent faces. max =10 is the threshold for the maximum number of nearest neighbors, d k d is the shortest distance from the k-th neighboring surface to the signal path. th =0.5m is the radius of influence, and the exponential term in the model is exp(-d k / d th It is used to quantify the interference intensity of a single facet, and the weighting coefficient decreases as the number and distance of neighboring faces increase.

8. A GNSS multipath error dynamic suppression system based on three-dimensional environment modeling, characterized in that, include: The data processing module is used to receive multi-source 3D point cloud data, fuse the multi-source 3D point cloud data, and obtain fused 3D point cloud data. The fusion process includes coarse registration, fine registration, and deduplication of redundant point clouds. The triangulation reconstruction module is used to reconstruct and optimize the triangulation based on the fused 3D point cloud data input into a pre-established triangular mesh model, and output a 3D environment model. The dynamic suppression module is used to acquire the satellite signal propagation path, analyze the geometric relationship between the satellite signal propagation path and the three-dimensional environment model to obtain the satellite signal path analysis results, and adaptively allocate observation weights and positioning calculations based on the satellite signal path analysis results to achieve dynamic suppression of multipath errors.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs the GNSS multipath error dynamic suppression method based on three-dimensional environment modeling as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs the GNSS multipath error dynamic suppression method based on three-dimensional environment modeling as described in any one of claims 1 to 7.

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