A method for constructing 3D scenes of power transmission lines by fusing multi-source terrain data

Abstract

This invention discloses a method for constructing a 3D scene of a power transmission line by fusing multi-source terrain data, comprising the following steps: S1: Acquiring multi-source terrain data within the corridor of the target power transmission line, the multi-source terrain data including at least laser point cloud data, aerial photogrammetry data, and satellite remote sensing digital elevation model data; S2: Preprocessing the multi-source terrain data, including coordinate system unification, data registration, and outlier removal, to obtain a standardized multi-source terrain dataset; S3: Constructing an adaptive weighted fusion model, dynamically calculating the fusion weight of each data source within each terrain unit based on the inherent attributes of each data source and its data performance in different terrain regions. This invention achieves precision, intelligence, and practicality in 3D scene construction through innovative designs such as standardized data preprocessing, an adaptive weighted fusion model, and integration of engineering analysis and visualization.

Description

Technical Field

[0001] This invention relates to the field of model building, specifically to a method for constructing a three-dimensional scene of a power transmission line by fusing multi-source terrain data. Background Technology

[0002] 3D scene construction of transmission lines is a core technological support for the entire lifecycle of transmission line planning, design, construction, and operation and maintenance. Its core objective is to reconstruct the topography of the transmission line corridor based on accurate terrain data, and combine it with 3D models of transmission towers, conductors, and other equipment to achieve visualization and quantitative analysis of functions such as line layout optimization, safety distance verification, and risk and hazard prediction. The accuracy and practicality of this technology directly determine the safety (e.g., avoiding conductor contact with terrain and geological disaster risks), economy (e.g., reducing on-site survey costs and construction rework), and operation and maintenance efficiency (e.g., quickly locating potential hazards) of transmission line construction. Especially in transmission line projects that traverse complex terrains such as plains, mountains, and steep slopes, the requirements for the completeness, accuracy, and engineering adaptability of terrain data are extremely high.

[0003] Existing 3D scene construction technologies for power transmission lines struggle to meet the high precision and practicality requirements of complex scenarios. The main issues are: either relying on single terrain data (such as satellite remote sensing DEMs or aerial photogrammetry data), failing to balance high precision (e.g., the advantages of laser point clouds) and wide coverage (e.g., the advantages of satellite remote sensing); or using multi-source data but lacking a reasonable fusion scheme, with fixed weighting models failing to adapt to the differences in various terrain regions, resulting in large fusion data errors and low conformity to actual terrain; inconsistent coordinate systems and spatial misalignments among different data sources, coupled with the failure to effectively remove outliers, making direct fusion calculations difficult and further impacting the accuracy of subsequent digital elevation models and 3D scenes; the determination of basic weights often relies on subjective judgment, failing to consider the inherent attributes of the data sources (spatial resolution, elevation accuracy) and actual performance; and the impact of terrain complexity and local data consistency on weights is not taken into account, leading to the misinterpretation of "optimal data sources." The advantages of traditional 3D scenes cannot be fully utilized, and the scientific nature of the fusion results is insufficient. Traditional 3D scenes mostly only realize the visualization of terrain and equipment, without integrating core engineering needs such as wind deflection simulation, sag calculation, and geological disaster risk zone analysis. They cannot provide direct data support for line design, construction verification, and operation and maintenance decisions, and are difficult to transform into practical engineering value. Key steps (such as weight calculation and data consistency assessment) lack clear quantitative formulas and standardized processes, resulting in large differences in results when different projects or implementers operate, which is not conducive to technology promotion and cross-scenario application. Therefore, a method for constructing 3D scenes of transmission lines by fusing multi-source terrain data is proposed. Summary of the Invention

[0004] The present invention solves the above-mentioned technical problems through the following technical solution, and the present invention includes the following steps: S1: Acquire multi-source terrain data within the corridor of the target transmission line. The multi-source terrain data shall include at least laser point cloud data, aerial photogrammetry data, and satellite remote sensing digital elevation model data. S2: Preprocess the multi-source terrain data, including coordinate system I, data registration and outlier removal, to obtain a standardized multi-source terrain dataset; S3: Construct an adaptive weighted fusion model that dynamically calculates the fusion weight of each data source within each terrain unit based on the inherent attributes of each data source and the data performance in different terrain regions. S4: Based on the adaptive weighted fusion model, a weighted fusion calculation is performed on the standardized multi-source terrain dataset to generate a fused high-precision digital elevation model; S5: Based on the fused high-precision digital elevation model and combined with the 3D model of the transmission line equipment, a 3D scene of the transmission line is constructed.

[0005] Furthermore, the data registration in step S2 is specifically as follows: Using laser point cloud data as a reference, aerial photogrammetry data and satellite remote sensing digital elevation model data are registered to the same spatial coordinate system through feature point matching and affine transformation algorithms.

[0006] Furthermore, step S3 involves constructing an adaptive weight fusion model, specifically including: S31: Divide the transmission line corridor into multiple terrain units according to a regular grid; S32: For each data source, determine its basic weights based on its spatial resolution and elevation accuracy; S33: For each terrain unit, calculate its terrain complexity factor; S34: For each data source within each terrain unit, evaluate its local data consistency within that unit; S35: For each terrain unit, the final fusion weight of each data source in that unit is adaptively calculated by combining the basic weight, terrain complexity factor and local data consistency.

[0007] Furthermore, the terrain complexity factor Dj in step S33 is obtained in the following way: Calculate the standard deviation of the elevation values ​​of all grid points within the j-th terrain unit. Average of slope values Then, these two parameters are linearly combined and normalized to obtain the terrain complexity factor Dj, i.e.: ; Where α and β are adjustment coefficients, and the denominator is the maximum value of this linear combination of all terrain units in the entire region.

[0008] Furthermore, the local data consistency in step S34 is evaluated in the following way: Calculate the deviation between the elevation value of the current data source in the j-th terrain cell and the average elevation of all data sources in the same terrain cell; the reciprocal of this deviation or the value of a negative correlation function is used as a measure of local data consistency.

[0009] Furthermore, in step S35, the final fusion weights The calculation formula is: ; in, Let be the base weight of the i-th data source, and Dj be the terrain complexity factor of the j*-th terrain unit. Let be the local data consistency metric for the i-th data source in the j-th terrain unit. This is a weight adjustment function based on terrain complexity and local consistency, where n is the total number of data sources.

[0010] Furthermore, the basic weight The determination method is as follows: based on the metadata of the data source and the sampling inspection results, its spatial resolution and elevation accuracy are scored, and the score results are normalized and used as the basic weight of the data source.

[0011] Furthermore, step S5 also includes: Based on the fused high-precision digital elevation model, at least one of the following is performed: wind deflection simulation, sag calculation, or geological disaster risk zone analysis. The analysis results are then integrated into the 3D scene of the transmission line for visualization.

[0012] Compared with the prior art, the present invention has the following advantages: This method for constructing a three-dimensional scene of a power transmission line by fusing multi-source terrain data integrates three core types of data: laser point cloud (high precision), aerial photogrammetry (wide coverage), and satellite remote sensing DEM (large area). Through standardized preprocessing of coordinate system I, feature point matching + affine transformation registration, and outlier removal, it eliminates data format differences and errors, maximizes the complementary advantages of each data source, improves the integrity and reliability of terrain data, and adapts to the data source coverage requirements of different power transmission line corridors (remote, complex terrain, etc.).

[0013] An innovative dynamic weighting model is constructed, which integrates basic weights, terrain complexity factors, and local data consistency. First, terrain units are divided into grids according to rules. Then, terrain complexity is quantified by elevation standard deviation and average slope. Local consistency is assessed by combining the deviation between the data source and the overall elevation mean. Finally, the data source weights of each terrain unit are accurately calculated using formulas. This solves the problem that traditional fixed weights cannot adapt to complex terrains, making the fusion results more consistent with actual terrain characteristics and generating a more accurate digital elevation model.

[0014] The 3D scene built on the high-precision elevation model not only integrates the 3D model of transmission line equipment for visualization, but also supports core engineering needs such as wind deflection simulation (optimizing line safety spacing), sag calculation (adapting to different meteorological conditions), and geological disaster risk zone analysis (avoiding hidden dangers in advance). It transforms the data fusion results into a direct basis for construction design and operation and maintenance decisions, reduces on-site survey costs, and improves the scientificity and efficiency of transmission line planning, construction, and operation and maintenance.

[0015] The entire process from data acquisition, preprocessing, model building to scene output is standardized. The basic weights are determined through metadata and sampling inspection scores. The terrain complexity, local consistency, and final weights are all supported by clear calculation formulas. The technology is highly replicable and can be extended to the construction of 3D scenes of transmission lines in different regions and at different voltage levels, making the system more worthy of promotion and use. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0017] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0018] like Figure 1 As shown, this embodiment provides a technical solution: a method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion, comprising the following steps: S1: Acquire multi-source terrain data within the corridor of the target transmission line. The multi-source terrain data shall include at least laser point cloud data, aerial photogrammetry data, and satellite remote sensing digital elevation model data. S2: Preprocess the multi-source terrain data, including coordinate system I, data registration and outlier removal, to obtain a standardized multi-source terrain dataset; S3: Construct an adaptive weighted fusion model that dynamically calculates the fusion weight of each data source within each terrain unit based on the inherent attributes of each data source and the data performance in different terrain regions. S4: Based on the adaptive weighted fusion model, a weighted fusion calculation is performed on the standardized multi-source terrain dataset to generate a fused high-precision digital elevation model; S5: Based on the fused high-precision digital elevation model and combined with the 3D model of the transmission line equipment, a 3D scene of the transmission line is constructed.

[0019] The data registration in step S2 is as follows: Using laser point cloud data as a reference, aerial photogrammetry data and satellite remote sensing digital elevation model data are registered to the same spatial coordinate system through feature point matching and affine transformation algorithms; Using high-precision laser point cloud data as the registration benchmark, it provides a stable and accurate reference for aerial photogrammetry data and satellite remote sensing digital elevation model data, reducing registration errors from the source.

[0020] By employing feature point matching and affine transformation algorithms, we can quickly identify common features of multi-source data and establish corresponding relationships, efficiently solve the problem of spatial position deviation between different data sources, and achieve accurate alignment of multi-source data in the same spatial coordinate system.

[0021] By unifying the spatial coordinate system, the format barriers and spatial misalignments of multi-source terrain data are eliminated, enabling the standardized dataset to meet the prerequisites for fusion computation, and directly ensuring the effectiveness and accuracy of subsequent adaptive weight fusion model computation.

[0022] Step S3 involves constructing an adaptive weight fusion model, specifically including: S31: Divide the transmission line corridor into multiple terrain units according to a regular grid; S32: For each data source, determine its basic weights based on its spatial resolution and elevation accuracy; S33: For each terrain unit, calculate its terrain complexity factor; S34: For each data source within each terrain unit, evaluate its local data consistency within that unit; S35: For each terrain unit, the final fusion weight of each data source in that unit is adaptively calculated by combining the basic weight, terrain complexity factor and local data consistency.

[0023] The terrain complexity factor Dj in step S33 is obtained in the following way: Calculate the standard deviation of the elevation values ​​of all grid points within the j-th terrain unit. Average of slope values Then, these two parameters are linearly combined and normalized to obtain the terrain complexity factor Dj, i.e.: ; Where α and β are adjustment coefficients, and the denominator is the maximum value of this linear combination of all terrain units in the entire region; In step S34, local data consistency is evaluated in the following ways: Calculate the deviation between the elevation value of the current data source in the j-th terrain cell and the average elevation of all data sources in the same terrain cell; the reciprocal of this deviation or the value of a negative correlation function is used as a measure of local data consistency.

[0024] Final fusion weights in step S35 The calculation formula is: ; in, Let be the base weight of the i-th data source, and Dj be the terrain complexity factor of the j*-th terrain unit. Let be the local data consistency metric for the i-th data source in the j-th terrain unit. This is a weight adjustment function based on terrain complexity and local consistency, where n is the total number of data sources; By dividing the transmission line corridor into independent terrain units using a regular grid, the problem of a one-size-fits-all approach with fixed global weights is avoided. This allows the data source weights for each local area to be tailored to its own terrain characteristics, adapting to mixed terrain scenarios such as plains and mountains. A three-dimensional evaluation system is constructed, consisting of basic weights, terrain complexity factors, and local data consistency. This system considers both the inherent attributes of the data source (spatial resolution, elevation accuracy) and the objective characteristics of the terrain (steepness, elevation fluctuation) and the local performance of the data (deviation from the overall mean). This provides multiple quantitative bases for weight allocation, rather than a single-dimensional judgment. High-precision data sources (such as laser point clouds) receive higher weights in areas with complex terrain and high data consistency, while broad-coverage, low-cost data sources (such as satellite remote sensing DEMs) play a supplementary role in simpler terrains, achieving dynamic adaptation of "higher weights for better data sources and lower weights for inferior data sources." From terrain unit division to final weight calculation, each step has a clear process and quantitative indicators. Basic weights are determined through scoring normalization, and terrain complexity and local consistency have specific calculation logics, eliminating ambiguous judgment steps and facilitating technology promotion and result verification.

[0025] If the target transmission line corridor traverses three continuous terrain sections: "plain-gentle slope mountain-steep slope mountain", it is divided into three independent terrain units (denoted as j=1,2,3) using a 100m×100m regular grid, involving two types of core data sources (denoted as i=1,2): Data source i=1: Laser point cloud data (high spatial resolution, excellent elevation accuracy, suitable for complex terrain); Data source i=2: Satellite remote sensing digital elevation model (DEM) data (wide coverage, low acquisition cost, suitable for simple shapes).

[0026] Step 1: Divide the terrain into units (S31): After the grid is divided according to the rules, the core characteristics of the three terrain units are clear: j=1 is plain terrain (flat terrain with little elevation fluctuation), j=2 is gentle slope mountain (moderate slope with some elevation fluctuation), and j=3 is steep slope mountain (large slope with drastic elevation fluctuation).

[0027] Step 2: Determine the basic weights (S32): Based on the data source metadata and sampling inspection results, the resolution and elevation accuracy of the two types of data sources are comprehensively scored, and the score results are normalized and used as the basic weight. The laser point cloud data (i=1) received a comprehensive score of 85 points, based on the basic weight. ; The satellite remote sensing DEM data (i=2) has a comprehensive score of 55 points, based on the fundamental weight. .

[0028] Step 3: Calculate the terrain complexity factor Dj (S33): The adjustment coefficients are set as α=0.5 (weighting the standard deviation of the equilibrium elevation) and β=0.5 (weighting the mean slope). The terrain complexity factor is calculated using the following formula: ; in, Let sj be the standard deviation of the elevation values ​​of all grid points within the j-th terrain unit (reflecting the degree of elevation fluctuation), and sj be the average slope values ​​of all grid points within the j-th terrain unit (reflecting the steepness of the terrain). The denominator is the sum of all terrain units within the entire corridor. The maximum value.

[0029] Detailed calculation process: For j=1 (plain): Substituting into ; For j=2 (gentle slope mountain): Substituting into ; For j=3 (steep mountain slope): Substituting into ; Global maximum value ,therefore: .

[0030] Step 4: Assess local data consistency (S34): By calculating the deviation between the elevation values ​​of each data source within the terrain unit and the mean elevation value of all data sources, the negative correlation function value of the deviation is taken (simplified as...). As a measure of consistency, the smaller the deviation, the higher the consistency. The closer the value is to 1: j=1 (plain): The deviation of the laser point cloud from the mean is 0.8m, the deviation of the satellite remote sensing DEM is 1.5m, and the global maximum deviation is 5.2m. Therefore ; j=2 (gentle slope mountain): laser point cloud deviation is 1.2m, satellite remote sensing DEM deviation is 3.1m, therefore ; j=3 (steep slope mountainous area): laser point cloud deviation is 1.8m, satellite remote sensing DEM deviation is 5.2m, therefore .

[0031] Step 5: Calculate the final fusion weights (S35): Set weight adjustment function (The more complex the terrain and the higher the data consistency, the larger the adjustment coefficient). The final weight is calculated using the following formula: ; Where n=2 is the total number of data sources, and k is the index of the data source.

[0032] Detailed calculation process (taking steep mountain slope with j=3 as an example): First, calculate the adjustment function value: ; Substitute into the weight formula: ; Final weight results for other terrain units: j=1 (plain): (Satellite remote sensing DEMs still have some weight, leveraging their wide coverage advantage); j=2 (gentle slope mountainous area): (Increased weight of laser point cloud to adapt to more complex terrain).

[0033] It is evident that as terrain complexity increases, the weight of high-precision laser point clouds gradually increases until steep slopes and mountains completely dominate the fusion calculation, perfectly adapting to the data needs of different terrains.

[0034] Basic weights The determination method is as follows: based on the metadata of the data source and the sampling inspection results, its spatial resolution and elevation accuracy are scored, and the score results are normalized and used as the basic weight of the data source. Focusing on core performance indicators of the data source (spatial resolution and elevation accuracy), this approach replaces subjective judgment with clearly defined scoring rules, providing a unified quantitative basis for basic weights and avoiding human bias. Combining metadata (theoretical performance) and sampling verification (actual performance) in a dual-dimensional scoring process respects the inherent attributes of the data source while correcting for discrepancies between theory and practice, ensuring that basic weights better align with real-world application scenarios. The scoring results from different dimensions are converted into weight values ​​in the 0-1 range, resolving the issue of the inability to directly integrate "spatial resolution" and "elevation accuracy" due to their different dimensions, laying the foundation for subsequent coupled calculations with terrain complexity and local consistency. With clearly defined and controllable scoring rules, sampling verification methods, and normalization formulas, consistent basic weight results can be obtained across different projects and personnel, facilitating technology promotion and cross-scenario application.

[0035] We continue to use the previous two types of data sources (i=1: laser point cloud data; i=2: satellite remote sensing DEM data) and three terrain units (j=1: plain; j=2: gentle slope mountain; j=3: steep slope mountain) to ensure the continuity of the examples.

[0036] Set scoring criteria: A scoring system is designed for the two core indicators, "spatial resolution" and "elevation accuracy," with a total score of 100 points. Each indicator accounts for 50 points (the weights can be adjusted according to engineering needs; here, equal weights are set for simplified calculation). Spatial resolution score: The higher the resolution (unit: m), the higher the score, with a maximum score of 50 points; Elevation accuracy score: The smaller the error (unit: cm), the higher the score, with a maximum score of 50 points.

[0037] Obtain metadata and sampling inspection results: By querying theoretical performance through data source metadata and combining it with on-site sampling verification (randomly selecting 100 ground points to verify accuracy), the sub-scores for the two types of data sources were obtained: Data source i=1 (laser point cloud): The metadata shows a spatial resolution of 0.5m, and the sampling inspection shows no obvious deviation, scoring 45 points. The metadata shows an elevation error of ≤5cm, and the actual average error during sampling is 3.2cm, resulting in a score of 40. Total score: 45 + 40 = 85 points.

[0038] Data source i=2 (satellite remote sensing DEM): The metadata shows a spatial resolution of 30m, and the sampling inspection shows no obvious deviation, so it scores 30 points. The metadata shows an elevation error of ≤30cm, and the actual average error during sampling is 22.5cm, resulting in a score of 25. Total score: 30 + 25 = 55 points.

[0039] Normalized calculation of basic weights: The basic weights are calculated using the following normalization formula: ; Where Si is the total score of the i-th data source and n is the total number of data sources (here n=2). This is the sum of the scores from all data sources.

[0040] Detailed calculation process: Calculate the sum of scores from all data sources: point; Substitute into the formula to calculate the basic weights: Laser point cloud (i=1): ; Satellite remote sensing DEM (i=2): .

[0041] This basic weight objectively reflects the fact that "the core performance of laser point cloud data is better than that of satellite remote sensing DEM data". It can be directly used as the initial input of the adaptive weight fusion model and further coupled with the terrain complexity factor Dj and local data consistency Cij to finally obtain dynamic weights that are adapted to different terrain units. This ensures that the "superior data source" first obtains a reasonable initial weight in the whole scene and then dynamically adjusts it according to the local situation.

[0042] Step S5 also includes: Based on the fused high-precision digital elevation model, at least one of wind deflection simulation, sag calculation or geological hazard risk zone analysis is performed, and the analysis results are integrated into the three-dimensional scene of the transmission line for visualization. By integrating high-precision digital elevation models (DEMs) with the core engineering requirements of transmission lines, this approach supports key applications such as wind deflection simulation, sag calculation, and geological hazard risk analysis. It avoids limiting 3D scenes to mere "visual displays," directly providing data support for construction design and operation and maintenance decisions. Based on the fused high-precision DEM (error ≤ 0.5m), it replaces traditional on-site surveys or low-precision data calculations, reducing analytical biases caused by terrain errors. For example, sag calculation deviations can be reduced to the engineering allowable range (±0.3m), avoiding risks such as conductors touching the terrain or insufficient safety clearance due to wind deflection. The analysis results (such as sag curves and geological hazard risk zone boundaries) are overlaid with the 3D scene, intuitively presenting the spatial relationship between "terrain-line-analysis results," eliminating the need for professionals to interpret complex data and facilitating collaboration among design, construction, and operation and maintenance teams. The analysis type can be flexibly selected according to the engineering stage (sag calculation in the design stage, geological hazard analysis in the site selection stage, and wind deflection simulation in the operation and maintenance stage), adapting to the needs of the entire transmission line lifecycle.

[0043] The target transmission line corridor contains three terrain units (j=1: plain, j=2: gentle slope mountain, j=3: steep slope mountain). Based on the previous process, a fused high-precision DEM (elevation error ≤0.5m) has been generated. The line uses 220kV transmission conductors, and sag calculations are performed on typical spans. The results are integrated into the 3D scene visualization.

[0044] Sag is the amount of sag of a transmission line conductor between two towers. Its magnitude directly affects the safe distance between the conductor and the terrain and buildings, and needs to be accurately calculated by combining the terrain elevation difference (obtained from DEM), meteorological conditions, and conductor parameters. This case adopts the commonly used catenary approximation sag formula in engineering (applicable to small and medium spans).

[0045] Determine the calculation parameters (based on DEM and engineering specifications): Basic engineering parameters: Conductor type: LGJ-400 / 35 (steel core aluminum stranded wire), unit length weight g=1.487N / m, cross-sectional area A=425.24mm², elastic modulus E=65GPa; Meteorological conditions: Standard temperature t0 = 20℃, no ice and no wind (engineering design benchmark condition); Extracting key terrain parameters from DEM (selecting one typical span for each of the three terrain units): j=1 (plain): The distance between the two towers (span) L1=300m, and the foundation elevation of both towers is 100m (flat terrain, low elevation difference). m); j=2 (gentle slope mountainous area): span L2=250m, tower A foundation elevation 120m, tower B foundation elevation 150m (topographic elevation difference) m); j=3 (steep slope mountainous terrain): span L3=200m, tower A foundation elevation 180m, tower B foundation elevation 250m (topographic elevation difference) m).

[0046] Sag calculation formula and calculation process: For medium and small ranges, when the terrain elevation difference When, the approximate sag formula for a horizontal parabola is used; when When using the corrected formula for the oblique parabola, the details are as follows: The formula for the sag of a horizontal parabola (applicable to plains with j=1) ): ; Corrected sag formula for oblique parabolic curves (applicable to mountainous terrain with j=2 and j=3) ): ; in, For the horizontal stress of the conductor (engineering design value, take...) MPa), denoted as sag under horizontal span, and f as the corrected sag under actual terrain elevation difference.

[0047] Step 1: Calculate the foundation sag coefficient corresponding to the horizontal stress. Horizontal stress of conductor Pa, substituting the core parameters of the horizontal parabola formula: ; Step 2: Calculate the sag of each terrain element j=1 (plain): Substituting into the formula for a horizontal parabola: ; Because the terrain is flat, the actual sag is consistent with the horizontal sag, that is... m; j=2 (gentle slope mountainous area): m( First calculate the horizontal sag, then make corrections: Horizontal sag m, Corrected actual sag:

[0048] j=3 (steep mountain slope): m( (This needs to be corrected.) Horizontal sag m, Corrected actual sag: ; Analysis results integration and visualization: The sag calculation results (conductor sag curve) of the three terrain units and the safety distance threshold (according to the 220kV line specification, the minimum safe distance between the conductor and the ground is ≥6m) are overlaid with the three-dimensional scene: Plain j=1: Visual display shows conductor sag of 0.140m, and the lowest point of the conductor is 8.2m from the ground (DEM elevation + tower height - sag), which meets safety requirements; Gentle slope mountain j=2: The visual display shows the guide line descending along the terrain slope, with the lowest point 7.5m away from the slope surface, avoiding the collision risk of gentle slope convex terrain; Steep slope mountainous terrain j=3: Visualized traverse shows that due to the large elevation difference in the terrain, the drooping curve is offset to the lower elevation side. After precise calculation through DEM, the distance between the lowest point and the steep slope ground is 6.8m, which just meets the safety threshold and avoids the traverse touching the mountain due to terrain errors.

[0049] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0050] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," 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 the present invention. 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0051] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for constructing a three-dimensional scene of a power transmission line through multi-source terrain data fusion, characterized in that, Includes the following steps: S1: Acquire multi-source terrain data within the corridor of the target transmission line. The multi-source terrain data shall include at least laser point cloud data, aerial photogrammetry data, and satellite remote sensing digital elevation model data. S2: Preprocess the multi-source terrain data, including coordinate system I, data registration and outlier removal, to obtain a standardized multi-source terrain dataset; S3: Construct an adaptive weighted fusion model that dynamically calculates the fusion weight of each data source within each terrain unit based on the inherent attributes of each data source and the data performance in different terrain regions. S4: Based on the adaptive weighted fusion model, a weighted fusion calculation is performed on the standardized multi-source terrain dataset to generate a fused high-precision digital elevation model; S5: Based on the fused high-precision digital elevation model and combined with the 3D model of the transmission line equipment, a 3D scene of the transmission line is constructed.

2. The method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 1, characterized in that: The data registration in step S2 is as follows: Using laser point cloud data as a reference, aerial photogrammetry data and satellite remote sensing digital elevation model data are registered to the same spatial coordinate system through feature point matching and affine transformation algorithms.

3. A method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 2, characterized in that: Step S3 involves constructing an adaptive weight fusion model, specifically including: S31: Divide the transmission line corridor into multiple terrain units according to a regular grid; S32: For each data source, determine its basic weights based on its spatial resolution and elevation accuracy; S33: For each terrain unit, calculate its terrain complexity factor; S34: For each data source within each terrain unit, evaluate its local data consistency within that unit; S35: For each terrain unit, the final fusion weight of each data source in that unit is adaptively calculated by combining the basic weight, terrain complexity factor and local data consistency.

4. A method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 3, characterized in that: The terrain complexity factor Dj in step S33 is obtained in the following way: Calculate the standard deviation of the elevation values ​​of all grid points within the j-th terrain unit. Average of slope values Then, these two parameters are linearly combined and normalized to obtain the terrain complexity factor Dj.

5. A method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 4, characterized in that: In step S34, local data consistency is evaluated in the following ways: Calculate the deviation between the elevation value of the current data source in the j-th terrain cell and the average elevation of all data sources in the same terrain cell; the reciprocal of this deviation or the value of a negative correlation function is used as a measure of local data consistency.

6. A method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 5, characterized in that: Final fusion weights in step S35 The calculation formula is: ； in, Let be the base weight of the i-th data source, and Dj be the terrain complexity factor of the j*-th terrain unit. Let be the local data consistency metric for the i-th data source in the j-th terrain unit. This is a weight adjustment function based on terrain complexity and local consistency, where n is the total number of data sources.

7. A method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 6, characterized in that: Basic weights The determination method is as follows: based on the metadata of the data source and the sampling inspection results, its spatial resolution and elevation accuracy are scored, and the score results are normalized and used as the basic weight of the data source.

8. A method for constructing a three-dimensional scene of a power transmission line based on multi-source terrain data fusion according to claim 7, characterized in that: Step S5 also includes: Based on the fused high-precision digital elevation model, at least one of wind deflection simulation, sag calculation or geological hazard risk zone analysis is performed, and the analysis results are integrated into the three-dimensional scene of the transmission line for visualization.

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