A three-dimensional model construction method for topographic surveying

Abstract

The application relates to a three-dimensional model construction method for topographic surveying, which is applied to the field of topographic surveying and comprises the following steps: acquiring ground influencing factors at the current moment, and analyzing ground features of a to-be-surveyed mountain at the current moment according to the ground influencing factors; acquiring point cloud data collected at the current moment, and classifying the point cloud data according to the ground features to determine ground point cloud of the to-be-surveyed mountain; establishing an irregular triangle net, and performing interpolation processing on the irregular triangle net according to the distribution of the ground point cloud to obtain a three-dimensional mountain model of the to-be-surveyed mountain. When there is ground point cloud data influenced by ground influencing factors, the ground features are quickly confirmed, ground point cloud data irrelevant to the ground is efficiently removed, ground point cloud error judgment caused by ground influencing factors is avoided, data recognition probability is reduced, data error is reduced, and model precision is improved. The method is more flexible for different terrains and environments, and reduces environmental error caused by existing topographic surveying.

Description

Technical Field

[0001] This application relates to the field of topographic surveying technology, and in particular to a method for constructing a three-dimensional model for topographic surveying. Background Technology

[0002] Traditional two-dimensional geological data representation methods may fail to fully and accurately express the boundary conditions of complex geological phenomena and the various geological structures contained within a geological body. Three-dimensional models, on the other hand, can intuitively reproduce the spatial distribution of geological units and their interrelationships, maximizing the intuitiveness and accuracy of geological analysis. Therefore, to improve the accuracy of topographic surveys, the construction of three-dimensional models is currently widely adopted.

[0003] Because the amount of data required to build a 3D model is very large, satellite remote sensing technology is generally relied upon for data acquisition. However, since satellites are far from the ground, their data acquisition resolution is low, and they cannot effectively distinguish certain terrain features, resulting in significant errors. This inevitably leads to some inaccuracies in the model after it is built. Summary of the Invention

[0004] This application provides a method for constructing a three-dimensional model for topographic surveying, so as to improve the accuracy of the three-dimensional model for topographic surveying.

[0005] Firstly, this application provides a method for constructing a three-dimensional model for topographic surveying.

[0006] Obtain the ground influencing factors at the current moment, and analyze the ground characteristics of the mountain at the time of the test based on the ground influencing factors;

[0007] The point cloud data collected at the current moment is acquired, and the point cloud data is classified according to the ground characteristics to determine the ground point cloud of the mountain to be measured.

[0008] An irregular triangular network is established, and based on the distribution of the ground point cloud, the irregular triangular network is interpolated to obtain a three-dimensional mountain model of the mountain to be measured.

[0009] This solution, when considering ground-related factors, analyzes and determines the characteristics of the current ground based on the specific situation. Utilizing these characteristics, point cloud data can be classified. This not only categorizes regular ground point clouds but also quickly identifies point cloud data affected by ground-related factors, classifying it accordingly. This efficiently removes point cloud data irrelevant to the ground and avoids misclassification due to ground-related factors, reducing the probability of data identification, minimizing data errors, and improving model accuracy. This approach is also more flexibly applicable to different terrains and environments, reducing environmental errors caused by existing terrain surveys.

[0010] Optionally, obtaining the ground influencing factors at the current moment includes:

[0011] Acquire the mountain information and meteorological data of the mountain to be measured; the mountain information includes altitude information and geological information;

[0012] Based on the meteorological data and the altitude information, determine the meteorological impact of the meteorological data on the meteorological conditions of each area of ​​the mountainous region to be measured;

[0013] Based on the meteorological influences and geological information, determine whether there are any areas of change in the mountain area to be tested;

[0014] If the aforementioned change area exists, then the surface influencing factors of the change area are determined based on the meteorological influences.

[0015] This approach acquires mountain information and meteorological data for the target mountain area, analyzes the meteorological impact of the data on different regions of the mountain, thereby determining whether there are areas of variation. It then analyzes whether significant differences from typical ground conditions exist under different meteorological conditions. If so, it identifies the ground influencing factors based on the corresponding meteorological impacts. This approach uses diverse meteorological data for analysis, not limited to a single condition, allowing for more precise identification of ground influencing factors and a more comprehensive understanding of these factors. This enables more accurate filtering of point cloud data and reduces errors.

[0016] Optionally, acquiring the point cloud data collected at the current moment includes:

[0017] Based on the mountain information, the optimal surveying tool for the mountain to be surveyed was determined.

[0018] The surveying type of the optimal surveying tool is retrieved, the surveying characteristics of the optimal surveying tool are determined, and a surveying route is planned based on the surveying characteristics and the mountain information.

[0019] Control the optimal surveying tool to perform surveying according to the surveying route, and acquire the surveying data of the optimal surveying tool;

[0020] The survey data is preprocessed, and the preprocessed survey data is then converted to obtain point cloud data.

[0021] This approach selects surveying tools based on the terrain information of the mountainous area to be surveyed, making surveying more flexible. The use of multiple surveying tools also enables more efficient mountain surveying, avoiding situations where a single tool is unsuitable for the terrain and results in poor surveying quality. Furthermore, by planning surveying routes based on the characteristics of the optimal surveying tools, surveying efficiency is improved, mitigating the drawbacks of fixed routes that cannot provide comprehensive surveying.

[0022] Optionally, when the optimal mapping tool is a drone, the drone has at least one built-in camera. The preprocessing and data conversion of the mapping data to obtain point cloud data includes:

[0023] Obtain the internal parameters of the camera;

[0024] The camera coordinate system of the survey data is determined based on the internal parameters.

[0025] The mapping data is filtered to obtain several reliable data points;

[0026] Analyze the aforementioned reliable data to determine the scanning location of the reliable data;

[0027] The aforementioned reliable data are filtered to determine whether there are co-located, out-of-direction data at the same scanning position;

[0028] If it is determined that there is co-positional and out-of-direction data, then the co-positional and out-of-direction data is matched to obtain the corresponding dataset;

[0029] Based on the images of the drone body and the environment captured by the camera, the real-time mapping attitude of the drone is determined;

[0030] Based on the real-time mapping posture, the camera coordinate system, and the scanning position, the dataset and other reliable data are transformed to obtain point cloud data.

[0031] With this solution, using drones for surveying and mapping has the advantages of high flexibility and rapid deployment, and can quickly obtain a large amount of surveying and mapping data. Filtering the surveying and mapping data can remove noise and outliers in the data and improve the reliability of the data. When there are data with the same position but opposite directions, through matching, a more complete and accurate data set can be obtained, which helps to eliminate redundancy and conflicts in the data and improve the quality of the data. In addition, using the body images and environmental images captured by the camera, the real-time surveying and mapping attitude of the drone can be determined. The data deviation caused by the attitude change of the drone can be corrected, and the accuracy and consistency of the surveying and mapping can be improved.

[0032] Optionally, when the best surveying and mapping tool is a drone, the drone is equipped with at least one camera inside. Planning the surveying and mapping route according to the surveying and mapping characteristics and the mountain information includes:

[0033] Determine the problem area for surveying and mapping of the to-be-surveyed mountain according to the mountain information;

[0034] Analyze the key surveying and mapping methods for the problem area for surveying and mapping according to the problem area for surveying and mapping and the surveying and mapping characteristics;

[0035] Determine the equipment addition items of the drone according to the key surveying and mapping methods, and adjust the drone according to the equipment addition items;

[0036] Plan the key surveying and mapping route for the problem area for surveying and mapping based on the key surveying and mapping methods and the attribute information of the equipment addition items;

[0037] Obtain the surveying and mapping sequence of the to-be-surveyed mountain according to the position of the problem area for surveying and mapping, and obtain the surveying and mapping route of the to-be-surveyed mountain according to the surveying and mapping sequence and the key surveying and mapping route.

[0038] With this solution, the problem area for surveying and mapping can be accurately located through the mountain information, and key analysis can be carried out on the problem area for surveying and mapping to determine the key surveying and mapping methods and plan the key surveying and mapping route, making the surveying and mapping more comprehensive, improving the surveying and mapping efficiency, and reducing the consumption of unnecessary surveying and mapping work. In addition, by using the high mobility and flexibility of the drone and combining appropriate equipment addition items, multi-device linkage can be achieved to meet various surveying and mapping needs and improve the surveying and mapping efficiency.

[0039] Optionally, classifying the point cloud data to determine the ground point cloud of the to-be-surveyed mountain includes:

[0040] Traverse the point cloud data to determine the key point cloud, and extract the attribute information of the key point cloud;

[0041] Determine the similarity between the surrounding point cloud and the key point cloud according to the attribute information;

[0042] Based on the similarity, the point cloud data is segmented to obtain several point cloud regions;

[0043] Establish a regular grid in each point cloud region, and assign elevation values ​​to the grid points in the regular grid;

[0044] Based on the elevation values, a digital elevation model is generated;

[0045] Based on the digital elevation model, obtain the three-dimensional point cloud coordinates of the point cloud data in each point cloud region;

[0046] Based on the three-dimensional point cloud coordinates, determine the slope of each point cloud within the corresponding area;

[0047] Based on the slope, determine whether the corresponding area belongs to the ground area;

[0048] If it falls within the range, the point cloud data with a slope higher than the ground threshold will be removed to obtain the ground point cloud in the corresponding area; the ground threshold is a preset value used to determine whether the terrain is flat.

[0049] This scheme establishes a regular grid and assigns an elevation value to each point within the grid, facilitating the conversion of discrete point cloud data into a continuous terrain surface representation. Furthermore, establishing a digital elevation model enables terrain visualization, slope analysis, and watershed analysis. By comparing slope with preset ground thresholds, it's possible to determine which areas belong to the ground and which do not. This helps remove non-ground point clouds (such as buildings and vegetation), improving the accuracy of terrain data. By setting appropriate thresholds, flat and rugged areas can be distinguished, providing a basis for subsequent terrain analysis.

[0050] Optionally, the similarity between the surrounding point cloud and the key point cloud is calculated using the following formula based on the attribute information:

[0051]

[0052] Where D represents the distance between the key point cloud and the surrounding point cloud; the three-dimensional coordinates of the key point cloud are (x1, y1, z1); and the three-dimensional coordinates of any surrounding point cloud are (x2, y2, z2).

[0053] This solution utilizes an algorithm to achieve efficient and rapid distance calculation, improving computational efficiency and reducing the error rate of manual calculations. Based on the calculation results and the algorithm itself, the similarity between the surrounding point cloud and the key point cloud is determined, thereby quickly and accurately determining the similarity between the two.

[0054] Secondly, this application provides a three-dimensional model construction device for terrain surveying, comprising:

[0055] The feature analysis module is used to obtain the ground influencing factors at the current time, and analyze the ground characteristics of the mountain at the time of the test based on the ground influencing factors;

[0056] The ground point cloud determination module is used to acquire the point cloud data collected at the current moment, classify the point cloud data according to the ground characteristics, and determine the ground point cloud of the mountain to be measured.

[0057] The model building module is used to establish an irregular triangular network and, based on the distribution of the ground point cloud, to perform interpolation processing on the irregular triangular network to obtain a three-dimensional mountain model of the mountain to be measured.

[0058] Optionally, the feature analysis module is specifically used for:

[0059] Acquire the mountain information and meteorological data of the mountain to be measured; the mountain information includes altitude information and geological information;

[0060] Based on the meteorological data and the altitude information, determine the meteorological impact of the meteorological data on the meteorological conditions of each area of ​​the mountainous region to be measured;

[0061] Based on the meteorological influences and geological information, determine whether there are any areas of change in the mountain area to be tested;

[0062] If the aforementioned change area exists, then the surface influencing factors of the change area are determined based on the meteorological influences.

[0063] Optionally, the ground point cloud determination module is specifically used for:

[0064] Based on the mountain information, the optimal surveying tool for the mountain to be surveyed was determined.

[0065] The surveying type of the optimal surveying tool is retrieved, the surveying characteristics of the optimal surveying tool are determined, and a surveying route is planned based on the surveying characteristics and the mountain information.

[0066] Control the optimal surveying tool to perform surveying according to the surveying route, and acquire the surveying data of the optimal surveying tool;

[0067] The survey data is preprocessed, and the preprocessed survey data is then converted to obtain point cloud data.

[0068] Optionally, when the optimal mapping tool is a drone, the drone has at least one built-in camera, and the ground point cloud determination module is specifically used for:

[0069] Obtain the internal parameters of the camera;

[0070] The camera coordinate system of the survey data is determined based on the internal parameters.

[0071] The mapping data is filtered to obtain several reliable data points;

[0072] Analyze the aforementioned reliable data to determine the scanning location of the reliable data;

[0073] The aforementioned reliable data are filtered to determine whether there are co-located, out-of-direction data at the same scanning position;

[0074] If it is determined that there is co-positional and out-of-direction data, then the co-positional and out-of-direction data is matched to obtain the corresponding dataset;

[0075] Based on the images of the drone body and the environment captured by the camera, the real-time mapping attitude of the drone is determined;

[0076] Based on the real-time mapping posture, the camera coordinate system, and the scanning position, the dataset and other reliable data are transformed to obtain point cloud data.

[0077] Optionally, when the optimal mapping tool is a drone, the drone has at least one built-in camera, and the ground point cloud determination module is further used for:

[0078] Based on the mountain information, the mapping problem area of ​​the mountain to be surveyed is determined;

[0079] Based on the surveying problem area and the surveying characteristics, analyze the key surveying methods for the surveying problem area;

[0080] Based on the aforementioned key mapping method, determine the additional equipment items for the UAV, and adjust the UAV accordingly.

[0081] Based on the key surveying methods and the attribute information of the equipment additions, a key surveying route for the surveying problem area is planned;

[0082] Based on the location of the survey problem area, the surveying sequence of the mountain to be surveyed is obtained, and based on the surveying sequence and the key surveying route, the surveying route of the mountain to be surveyed is obtained.

[0083] Optionally, the ground point cloud determination module is further used for:

[0084] Traverse the point cloud data, determine the key point cloud, and extract the attribute information of the key point cloud;

[0085] Based on the attribute information, determine the similarity between the surrounding point cloud and the key point cloud;

[0086] Based on the similarity, the point cloud data is segmented to obtain several point cloud regions;

[0087] Establish a regular grid in each point cloud region, and assign elevation values ​​to the grid points in the regular grid;

[0088] Based on the elevation values, a digital elevation model is generated;

[0089] Based on the digital elevation model, obtain the three-dimensional point cloud coordinates of the point cloud data in each point cloud region;

[0090] Based on the three-dimensional point cloud coordinates, determine the slope of each point cloud within the corresponding area;

[0091] Based on the slope, determine whether the corresponding area belongs to the ground area;

[0092] If it falls within the range, the point cloud data with a slope higher than the ground threshold will be removed to obtain the ground point cloud in the corresponding area; the ground threshold is a preset value used to determine whether the terrain is flat.

[0093] Optionally, the ground point cloud determination module is further used for:

[0094]

[0095] Where D represents the distance between the key point cloud and the surrounding point cloud; the three-dimensional coordinates of the key point cloud are (x1, y1, z1); and the three-dimensional coordinates of any surrounding point cloud are (x2, y2, z2).

[0096] Thirdly, this application provides an electronic device, including: a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method of the first aspect.

[0097] Fourthly, this application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method of the first aspect.

[0098] The present application adopts the above technical solutions and has the following advantages: When considering the ground influence factors, the characteristics of the current ground can be analyzed and determined according to the specific situation at present. By using the ground characteristics, the point cloud data can be classified. Not only can the conventional ground point clouds be classified, but when there is ground point cloud data affected by ground influence factors, it can also be quickly confirmed according to the ground characteristics and thus classified into the ground point cloud type. This can efficiently remove the point cloud data unrelated to the ground, and can also avoid the situation of misjudgment of ground point clouds caused by ground influence factors, reduce the probability of data identification, reduce data errors, and improve the model accuracy. This method can also be more flexibly applicable to different terrains and environments, reducing the environmental errors caused by existing topographic surveys. Analyzing based on a variety of meteorological data, not limited to a single condition, can more accurately determine the ground influence factors, thus obtaining more comprehensive ground influence factors, achieving more accurate screening of point cloud data, and reducing errors. Selecting surveying and mapping tools according to the mountain information of the待测山地 (to be determined specific mountain name) makes the surveying and mapping more flexible. At the same time, the participation of multiple surveying and mapping tools can more efficiently achieve mountain surveying and mapping, avoiding the situation where a single tool cannot adapt to the terrain and resulting in poor surveying and mapping effects. In addition, planning the surveying and mapping route according to the surveying and mapping characteristics of the best surveying and mapping tool can improve the surveying and mapping efficiency and reduce the drawback that the inherent route cannot comprehensively survey the area. Using drones for surveying and mapping has the advantages of high flexibility and rapid deployment, and can quickly obtain a large amount of surveying and mapping data. Filtering the surveying and mapping data can remove the noise and outliers in the data and improve the reliability of the data. When there are co-located and opposite-direction data, through matching, a more complete and accurate data set can be obtained, which helps to eliminate the redundancy and conflicts in the data and improve the data quality. In addition, by using the body image and environmental image captured by the camera, the real-time surveying and mapping attitude of the drone can be determined. The data deviation caused by the change of the drone's attitude can be corrected, improving the accuracy and consistency of the surveying and mapping. The problem area of the surveying and mapping can be accurately located through the mountain information, and the problem area of the surveying and mapping can be analyzed key points, determining the key surveying and mapping method and planning the key surveying and mapping route, making the surveying and mapping more comprehensive, improving the surveying and mapping efficiency, and reducing the consumption of unnecessary surveying and mapping work. In addition, by using the high mobility and flexibility of the drone and combining with appropriate equipment addition items, multi-device linkage can be achieved to meet various surveying and mapping needs and improve the surveying and mapping efficiency. Establishing a digital elevation model can achieve terrain visualization, slope analysis, watershed analysis, etc. By comparing the slope with the preset ground threshold, it can be determined which areas belong to the ground area and which areas do not belong to the ground area. This helps to remove non-ground point clouds (such as buildings, vegetation, etc.) and improve the accuracy of terrain data. By setting appropriate thresholds, flat areas and rugged areas can be distinguished, providing a basis for subsequent terrain analysis. Using algorithms to achieve efficient and rapid distance calculation can improve the calculation efficiency and reduce the error rate of manual calculation.Based on the calculation results and the algorithm itself, the similarity between the surrounding point cloud and the key point cloud is determined, thereby quickly and accurately determining the similarity between the surrounding point cloud and the key point cloud. Attached Figure Description

[0099] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0100] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0101] Figure 2 A flowchart illustrating a method for constructing a three-dimensional model for terrain surveying, provided in one embodiment of this application;

[0102] Figure 3 A schematic diagram of a three-dimensional model building device for terrain surveying provided in an embodiment of this application;

[0103] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0104] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0105] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0106] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0107] Because the amount of data required to build a 3D model is very large, satellite remote sensing technology is generally relied upon for data acquisition. However, since satellites are far from the ground, their data acquisition resolution is low, and they cannot effectively distinguish certain terrain features, resulting in significant errors. This inevitably leads to some inaccuracies in the model after it is built.

[0108] When certain terrain features are affected by external environmental factors, resulting in changes that differ from typical ground conditions, data obtained from remote sensing satellites may not be sufficient for precise identification. In such cases, manual differentiation by professionals may be necessary, or the changes may be directly ignored. Manual screening would impact the survey progress and is also subject to significant errors, unlike the efficient and comprehensive screening capabilities of intelligent equipment. Conversely, ignoring changes directly would introduce substantial errors into the survey, affecting the final results.

[0109] Based on this, this application provides a method for constructing a 3D model for topographic surveying. It acquires ground influencing factors at the current moment, analyzes the current ground characteristics, and uses these characteristics to classify point cloud data. This not only categorizes regular ground point clouds but also allows for rapid identification of point cloud data affected by ground influencing factors, classifying them into specific ground point cloud types. This efficiently removes point cloud data irrelevant to the ground and avoids misclassification of ground point clouds due to ground influencing factors, reducing the probability of data identification, minimizing data errors, and improving model accuracy. This method is also more flexibly applicable to different terrains and environments, reducing environmental errors inherent in existing topographic surveying methods.

[0110] Figure 1 This is a schematic diagram illustrating an application scenario provided by this application. When surveying certain terrains, such as mountainous terrain, is required, the solution provided in this application can be used. This solution is mounted on any server, retrieves ground influencing factors stored in the server's database, determines the current terrain characteristics, and can also use surveying tools such as drones to perform terrain surveying, obtain point cloud data, and send it to the server. Utilizing the terrain characteristics, the obtained point cloud data is analyzed to determine the ground point cloud, and then an irregular triangular network is established. Based on the distribution of the ground point cloud, interpolation processing is performed on the irregular triangular network to obtain a 3D mountain model. This efficiently removes point cloud data irrelevant to the ground, avoids misjudgments of ground point clouds due to ground influencing factors, reduces the probability of data identification, minimizes data errors, and improves model accuracy. This method is also more flexibly applicable to different terrains and environments, reducing environmental errors caused by existing terrain surveys.

[0111] For specific implementation details, please refer to the following examples.

[0112] Figure 2 This is a flowchart illustrating a method for constructing a 3D model for terrain surveying, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above-described scenarios. Figure 2 As shown, the method includes:

[0113] S201. Obtain the ground influencing factors at the current time, and analyze the ground characteristics of the mountain at the time of the test based on the ground influencing factors.

[0114] Ground-level influencing factors can be considered as external factors that cause changes to the ground that differ from normal ground conditions. For example, meteorological factors like snow: when it snows, the ground may appear white when there is snow accumulation, and gray when there is no snow. In complex and undeveloped terrain, the ground may appear white when there is snow accumulation, and irregularly colored soil when there is no snow accumulation. This is because complex terrain may also be accompanied by other factors such as leaves, sand, and gravel, leading to changes in the ground surface.

[0115] The mountainous terrain to be surveyed is the subject of this survey. Ground features can be considered as characteristics that differ from typical ground features due to current surface influencing factors, such as a white color or a mottled green color (possibly caused by leaves scattered in different locations).

[0116] S202. Obtain the point cloud data collected at the current moment, and classify the point cloud data according to the ground characteristics to determine the ground point cloud of the mountain to be measured.

[0117] Point cloud data can be considered as data obtained by taking pictures of the terrain or other forms of terrain display through certain devices or other means, and then converting the data.

[0118] As can be seen from step S201 above, the ground features may be manifested in the ground color being different from that of the regular ground. At this time, the characteristics of each point cloud can be determined based on the differences between the colors, and thus the point clouds with the same characteristics can be classified into a category, thereby obtaining the ground point cloud of the mountain to be tested from these categories.

[0119] S203. Establish an irregular triangular network and, based on the distribution of ground point clouds, perform interpolation processing on the irregular triangular network to obtain a three-dimensional mountain model of the mountain to be measured.

[0120] When constructing an irregular triangular network (ITN), a suitable partitioning algorithm can be determined based on the area of ​​the mountain to be measured. This algorithm is then used to perform triangulation and construct the ITN. Specifically, when constructing the ITN, construction requirements can be preset, such as minimizing the variance between the three angles of the constructed triangle to make the resulting triangle as close as possible to an equilateral triangle.

[0121] After obtaining the irregular triangular network, the interpolation method is used to refine the network, determine whether there are other terrain points in the corresponding triangles, and then remove them to improve accuracy, thus obtaining a more accurate and less erroneous 3D mountain model.

[0122] The solution provided in this application, when considering ground-related factors, allows for the analysis and determination of current ground characteristics based on the specific circumstances. These characteristics are then used to classify point cloud data. This not only categorizes conventional ground point clouds but also allows for the rapid identification and classification of point cloud data affected by ground-related factors, thus efficiently removing point cloud data irrelevant to the ground and avoiding misclassifications due to these factors. This reduces the probability of data identification errors, minimizes data inaccuracies, and improves model accuracy. Furthermore, this approach is more flexibly applicable to different terrains and environments, reducing environmental errors inherent in existing terrain surveys.

[0123] In some embodiments, mountain information and meteorological data of the mountain to be measured can be obtained, wherein the mountain information includes altitude information and geological information; based on the meteorological data and altitude information, the meteorological impact of the meteorological data on each area of ​​the mountain to be measured is determined; based on the meteorological impact and geological information, it is determined whether there are changing areas in the mountain to be measured; if there are changing areas, the surface influencing factors of the changing areas are determined based on the meteorological impact.

[0124] Mountain information can include geographical location and extent, topographic features, geological information, vegetation and ecosystems, among which geographical location and extent and topographic features can reflect the height information of the mountain to be measured.

[0125] In mountainous terrain, many potential natural phenomena are closely related to weather. For example, rainfall can trigger mudslides, and strong winds can cause fallen leaves and gravel to tumble down, leading to localized changes in the mountainous terrain. Therefore, this embodiment focuses on the impact of meteorological data on the terrain. It should be noted that the meteorological data obtained in this embodiment is not solely based on the current meteorological data for the specific mountain being measured, but rather on historical meteorological data. This allows for a more comprehensive understanding of the changes in different terrains under the influence of varying meteorological data, resulting in more accurate and convincing results.

[0126] When a mountain is located at different altitudes, meteorological data will change, and the impact on the terrain will also vary. For example, the temperature at the foot of the mountain may be 15 degrees Celsius, while the temperature at the summit may be -5 degrees Celsius. Based on meteorological data and altitude information, the meteorological impact on different areas of the mountain under test is determined. Then, combining the meteorological impact with the geological information of different areas within the mountain under test, the changes in each geological area are determined. If there are significant changes, this area can be identified as a change area, which differs from the normal ground surface.

[0127] The solution provided in this embodiment acquires mountain information and meteorological data of the mountain to be measured, analyzes the meteorological impact of the meteorological data on various areas of the mountain to be measured, thereby determining whether there are areas of variation, and further analyzing whether there might be situations that differ significantly from the normal ground under different meteorological conditions. If so, the ground influencing factors are determined based on the corresponding meteorological impacts. This solution uses a variety of meteorological data for analysis, and is not limited to a single condition, which can more accurately determine the ground influencing factors, thereby obtaining a more comprehensive set of ground influencing factors, achieving more accurate screening of point cloud data, and reducing errors.

[0128] In some embodiments, the optimal surveying tool for the mountain to be surveyed can be determined by analyzing the mountain information; the surveying type of the optimal surveying tool can be retrieved, the surveying characteristics of the optimal surveying tool can be determined, and a surveying route can be planned based on the surveying characteristics and mountain information; the optimal surveying tool can be controlled to conduct surveying according to the surveying route, and the surveying data of the optimal surveying tool can be obtained; the surveying data can be preprocessed, and the preprocessed surveying data can be converted to obtain point cloud data.

[0129] In this embodiment, the surveying tools are not limited; multiple options are available, and even combinations of multiple tools can be implemented to achieve better surveying results. Since there are various surveying tools, after obtaining mountain information, based on factors such as the terrain, topography, and geology of the mountain to be surveyed, an analysis is conducted to determine which tool is most suitable for the current mountain and selects it as the optimal surveying tool.

[0130] The surveying type of a surveying tool can be set according to its attributes. For example, tool A is suitable for low-altitude flight surveying, while tool B is suitable for fixed-point surveying. The optimal surveying tool's characteristics are determined based on the corresponding surveying type, and the surveying route is planned accordingly.

[0131] The solution provided in this embodiment selects surveying tools based on the terrain information of the mountain to be surveyed, making surveying more flexible. The use of multiple surveying tools also enables more efficient mountain surveying, avoiding situations where a single tool cannot adapt to the terrain, resulting in poor surveying results. Furthermore, by planning surveying routes based on the characteristics of the optimal surveying tool, surveying efficiency is improved, reducing the drawbacks of fixed routes being unable to provide comprehensive surveying.

[0132] In some embodiments, when the optimal mapping tool is a drone, the drone has at least one built-in camera, which can acquire the camera's internal parameters; based on the internal parameters, the camera coordinate system of the mapping data is determined; the mapping data is filtered to obtain several reliable data sets; the reliable data sets are analyzed to determine the scanning positions of the reliable data sets; the reliable data sets are filtered to determine whether there are co-located, anisotropic data sets at the same scanning position; if co-located, anisotropic data sets are determined, they are matched to obtain the corresponding dataset; based on the drone's fuselage image and environmental image captured by the camera, the drone's real-time mapping attitude is determined; based on the real-time mapping attitude, camera coordinate system, and scanning position, the dataset and other reliable data are transformed to obtain point cloud data.

[0133] When the optimal surveying tool is a drone, calibrating the drone allows us to obtain the camera's internal parameters. These parameters include the camera's focal length, optical center, and camera distortion parameters. Using these parameters, a three-dimensional coordinate system can be established with the camera's optical center as the far point and the camera's optical axis as the Z-axis. This system serves as the camera coordinate system for the surveying data.

[0134] Reliable data can be considered as the data remaining after filtering survey data to remove data caused by noise, interference or other factors.

[0135] Using the camera coordinate system obtained above, the camera coordinates of each reliable data point are analyzed, and the scanning position of the corresponding reliable data point is determined based on these camera coordinates and the basis for establishing this camera coordinate system.

[0136] Co-located data can be considered as data from different sources appearing at the same location. This difference in source can be attributed to differences in direction or differences in reception time.

[0137] When co-located and anisotropic data exist, it indicates that there may be ground obstructions at this location, affecting the mapping of the ground. In this case, the co-located and anisotropic data can be matched to achieve data registration, thereby obtaining the corresponding dataset. This dataset can contain co-located and anisotropic data corresponding to different angles at the same location.

[0138] In a specific implementation, features can be extracted from co-located and anisotropic data. Based on the extracted feature points, corresponding descriptors are calculated. Then, a descriptor matching algorithm is used to determine the similarity between descriptors and whether different feature points match. If they match, they can be considered as co-located and anisotropic data at the same scanning position, thus obtaining the corresponding dataset.

[0139] A drone's fuselage image can be captured by mounting a camera with a robotic arm on the drone. When an image of the fuselage is needed, the robotic arm extends, allowing the camera to capture a complete image of the drone's fuselage. In a specific implementation, a coordinate system can be established based on the robotic arm's position. This coordinate system, along with the fuselage image, can then be used to help analyze and determine the drone's attitude.

[0140] Environmental images can be obtained by taking pictures using the cameras used in the drone mapping process. In specific implementations, each image taken by the camera used in the drone mapping process can be considered an environmental image.

[0141] Since the attitude of the drone may be constantly changing during the mapping process, the images of the drone body and the environment can be acquired continuously in real time.

[0142] By utilizing attitude estimation techniques, feature point analysis is performed on the obtained fuselage and environmental images over a continuous time period to determine feature point changes, thereby estimating the UAV's attitude changes and obtaining the UAV's real-time mapping attitude. In specific implementations, deep learning models can also be used to analyze the images to predict the UAV's real-time mapping attitude.

[0143] The following steps can be taken to perform data transformation:

[0144] 1. Determine the coordinate system, which may include a camera coordinate system, a world coordinate system, and a UAV attitude coordinate system. The camera coordinate system is constructed as described in the above embodiments. The world coordinate system can select the camera optical axis perpendicular to the camera coordinate system as the Z-axis; select a direction parallel to the X-axis of the camera coordinate system as the X-axis; and select a direction parallel to the Y-axis of the camera coordinate system as the Y-axis. The UAV attitude coordinate system is established based on the real-time measured attitude obtained above.

[0145] 2. Based on the obtained real-time mapping attitude, determine the attitude data, including pitch angle, roll angle, yaw angle or quaternion, etc., and establish an attitude compensation model based on the UAV attitude data and the definition of the camera coordinate system to achieve attitude compensation.

[0146] 3. Coordinate system transformation: Transform the data in the camera coordinate system to the world coordinate system.

[0147] The solution provided in this embodiment utilizes UAVs for surveying, offering advantages such as high flexibility and rapid deployment, enabling the quick acquisition of large amounts of surveying data. Filtering the surveying data removes noise and outliers, improving data reliability. When identical or dissimilar data points appear, matching yields a more complete and accurate dataset, helping to eliminate redundancy and conflicts, and improving data quality. Furthermore, using camera images of the UAV's fuselage and surrounding environment, the real-time surveying attitude of the UAV can be determined. This corrects data deviations caused by changes in UAV attitude, improving surveying accuracy and consistency.

[0148] In some embodiments, when the optimal surveying tool is a drone, the drone has at least one built-in camera, which can determine the surveying problem area of ​​the mountain to be surveyed based on mountain information; analyze the key surveying methods for the surveying problem area based on the surveying problem area and surveying characteristics; determine the additional equipment items for the drone based on the key surveying methods, and adjust the drone based on the additional equipment items; plan the key surveying route for the surveying problem area based on the attribute information of the key surveying methods and the additional equipment items; obtain the surveying sequence of the mountain to be surveyed based on the location of the surveying problem area, and obtain the surveying route of the mountain to be surveyed based on the surveying sequence and the key surveying route.

[0149] Problem areas in surveying can be considered as areas that are difficult to survey, areas with dense vegetation cover, or areas with overly complex terrain. The delineation of these areas can be based on the biological complexity of the terrain or on the location of the terrain.

[0150] Mapping characteristics can include features such as terrain undulation, vegetation cover, and geological structure. The key mapping method can be considered the optimal method for mapping the problem area, which may include different mapping technologies such as high-resolution photography, laser scanning, and stereo imaging. If the optimal mapping tool is a drone, but the optimal mapping method is laser scanning, then a lidar device can be considered an additional equipment option. Since drones can be directly equipped with lidar, this additional equipment option can be adjusted to be mounted on the drone. If it cannot be directly mounted on the drone, it can be directly set at the location of the problem area.

[0151] Based on the attribute information of the added equipment, the surveying range and surveying method of the equipment are determined, thereby identifying the key surveying route for this surveying problem area. This key surveying route can comprehensively cover the surveying problem area.

[0152] Through the solution provided in this embodiment, the problem area of surveying and mapping can be accurately located based on the mountain information, and the problem area of surveying and mapping can be analyzed key points, determining the key surveying and mapping methods and planning the key surveying and mapping routes, making the surveying and mapping more comprehensive, improving the surveying and mapping efficiency, and reducing the consumption of unnecessary surveying and mapping work. In addition, by using the high mobility and flexibility of the unmanned aerial vehicle and combining with appropriate equipment addition items, multi-device linkage can be achieved to meet various surveying and mapping requirements and improve the surveying and mapping efficiency.

[0153] In some embodiments, the point cloud data can be traversed to determine the key point clouds, and the attribute information of the key point clouds can be extracted; according to the attribute information, the similarity between the surrounding point clouds and the key point clouds can be determined; according to the similarity, the point cloud data can be segmented to obtain several point cloud regions; a regular grid can be established in each point cloud region, and elevation values can be assigned to the grid points in the regular grid; based on the elevation values, a digital elevation model can be generated; according to the digital elevation model, the three-dimensional point cloud coordinates of the point cloud data in each point cloud region can be obtained; according to the three-dimensional point cloud coordinates, the slope of each point cloud in the corresponding region can be determined; according to the slope, it can be determined whether the corresponding region belongs to the ground region; if so, the point cloud data with a slope higher than the ground threshold can be removed to obtain the ground point cloud in the corresponding region; the ground threshold is a preset value for judging whether the terrain is flat; if not, all the point cloud data in the corresponding region can be removed.

[0154] The key point cloud can be regarded as the point cloud data that can clearly represent the point cloud type. This key point cloud is not limited to only one of a type, and any point cloud data that can clearly represent the point cloud type can be regarded as the key point cloud.

[0155] The surrounding point cloud is the point cloud data surrounding the key point cloud.

[0156] The attribute information of the key point cloud can include the three-dimensional coordinates, color information, normal direction, etc. of the key point cloud. Through these attribute information, a similarity measurement standard can be set, so as to assign different weights to different attribute information, and thus calculate the similarity between the surrounding point cloud and the key point cloud according to the corresponding measurement standard and weight.

[0157] The point cloud region can be divided according to the similarity result. The surrounding point cloud with a higher similarity to the key point cloud can be considered to belong to the same point cloud type as the key point cloud, and at this time, it can be divided into the same point cloud region. Otherwise, it can be divided into other regions.

[0158] The assignment of elevation values can be achieved by calculating the average elevation, median elevation or other statistics of the point cloud data around the grid points.

[0159] The ground threshold can be set based on the mountain information of the mountain to be measured and the ground height of other locations. In some implementations, the part of the point cloud belonging to the ground can be determined first based on the key point cloud, and then the ground threshold can be set based on the slope of the part of the point cloud belonging to the ground.

[0160] The solution provided in this embodiment establishes a regular grid and assigns an elevation value to each point in the grid, which helps to convert discrete point cloud data into a continuous terrain surface representation. Furthermore, establishing a digital elevation model enables terrain visualization, slope analysis, and watershed analysis. By comparing the slope with a preset ground threshold, it is possible to determine which areas belong to the ground and which do not. This helps to remove non-ground point clouds (such as buildings and vegetation), improving the accuracy of terrain data. By setting appropriate thresholds, flat and rugged areas can be distinguished, providing a basis for subsequent terrain analysis.

[0161] In some embodiments, the similarity between the surrounding point cloud and the key point cloud is calculated using the following formula based on attribute information:

[0162]

[0163] Where D represents the distance between the key point cloud and the surrounding point clouds; the three-dimensional coordinates of the key point cloud are (x1, y1, z1); and the three-dimensional coordinates of any surrounding point cloud are (x2, y2, z2).

[0164] In geometric space, the straight-line distance between two points is the most intuitive way to measure similarity or difference. When two points are very close, the straight-line distance between them is small, which usually means that they are similar in some feature or attribute. Therefore, after determining the distance between the keypoint cloud and the surrounding point clouds using the above formula, the distances between each surrounding point cloud and the keypoint cloud are sorted. The closer the surrounding point cloud is to the keypoint cloud, the higher its similarity to the keypoint cloud.

[0165] The solution provided in this embodiment utilizes an algorithm to achieve efficient and rapid distance calculation, improving computational efficiency and reducing the error rate of manual calculation. Based on the calculation results and the algorithm itself, the similarity between the surrounding point cloud and the key point cloud is determined, thereby quickly and accurately determining the similarity between the two.

[0166] Figure 3 A schematic diagram of a three-dimensional model building device for terrain surveying provided in an embodiment of this application is shown below. Figure 3 As shown, the 3D model building device 300 for terrain surveying in this embodiment includes: a feature analysis module 301, a ground point cloud determination module 302, and a model building module 303.

[0167] The feature analysis module 301 is used to acquire the ground influencing factors at the current time, and analyze the ground characteristics of the mountain at the time to be measured based on the ground influencing factors.

[0168] The ground point cloud determination module 302 is used to acquire the point cloud data collected at the current moment, and classify the point cloud data according to the ground characteristics to determine the ground point cloud of the mountain to be measured.

[0169] The model building module 303 is used to establish an irregular triangular network and, based on the distribution of the ground point cloud, to perform interpolation processing on the irregular triangular network to obtain a three-dimensional mountain model of the mountain to be measured.

[0170] In some embodiments, the feature analysis module 301 is specifically used for:

[0171] Acquire the mountain information and meteorological data of the mountain to be measured; the mountain information includes altitude information and geological information;

[0172] Based on the meteorological data and the altitude information, determine the meteorological impact of the meteorological data on the meteorological conditions of each area of ​​the mountainous region to be measured;

[0173] Based on the meteorological influences and geological information, determine whether there are any areas of change in the mountain area to be tested;

[0174] If the aforementioned change area exists, then the surface influencing factors of the change area are determined based on the meteorological influences.

[0175] In some embodiments, the ground point cloud determination module 302 is specifically used for:

[0176] Based on the mountain information, the optimal surveying tool for the mountain to be surveyed was determined.

[0177] The surveying type of the optimal surveying tool is retrieved, the surveying characteristics of the optimal surveying tool are determined, and a surveying route is planned based on the surveying characteristics and the mountain information.

[0178] Control the optimal surveying tool to perform surveying according to the surveying route, and acquire the surveying data of the optimal surveying tool;

[0179] The survey data is preprocessed, and the preprocessed survey data is then converted to obtain point cloud data.

[0180] In some embodiments, when the optimal mapping tool is a drone, the drone has at least one built-in camera, and the ground point cloud determination module 302 is specifically used for:

[0181] Obtain the internal parameters of the camera;

[0182] The camera coordinate system of the survey data is determined based on the internal parameters.

[0183] The mapping data is filtered to obtain several reliable data points;

[0184] Analyze the aforementioned reliable data to determine the scanning location of the reliable data;

[0185] The aforementioned reliable data are filtered to determine whether there are co-located, out-of-direction data at the same scanning position;

[0186] If it is determined that there is co-positional and out-of-direction data, then the co-positional and out-of-direction data is matched to obtain the corresponding dataset;

[0187] Based on the images of the drone body and the environment captured by the camera, the real-time mapping attitude of the drone is determined;

[0188] Based on the real-time mapping posture, the camera coordinate system, and the scanning position, the dataset and other reliable data are transformed to obtain point cloud data.

[0189] In some embodiments, when the optimal mapping tool is a drone, the drone has at least one built-in camera, and the ground point cloud determination module 302 is further configured to:

[0190] Based on the mountain information, the mapping problem area of ​​the mountain to be surveyed is determined;

[0191] Based on the surveying problem area and the surveying characteristics, analyze the key surveying methods for the surveying problem area;

[0192] Based on the aforementioned key mapping method, determine the additional equipment items for the UAV, and adjust the UAV accordingly.

[0193] Based on the key surveying methods and the attribute information of the equipment additions, a key surveying route for the surveying problem area is planned;

[0194] Based on the location of the survey problem area, the surveying sequence of the mountain to be surveyed is obtained, and based on the surveying sequence and the key surveying route, the surveying route of the mountain to be surveyed is obtained.

[0195] In some embodiments, the ground point cloud determination module 302 is further configured to:

[0196] Traverse the point cloud data, determine the key point cloud, and extract the attribute information of the key point cloud;

[0197] Based on the attribute information, determine the similarity between the surrounding point cloud and the key point cloud;

[0198] Based on the similarity, the point cloud data is segmented to obtain several point cloud regions;

[0199] Establish a regular grid in each point cloud region, and assign elevation values ​​to the grid points in the regular grid;

[0200] Based on the elevation values, a digital elevation model is generated;

[0201] Based on the digital elevation model, obtain the three-dimensional point cloud coordinates of the point cloud data in each point cloud region;

[0202] Based on the three-dimensional point cloud coordinates, determine the slope of each point cloud within the corresponding area;

[0203] Based on the slope, determine whether the corresponding area belongs to the ground area;

[0204] If it falls within the range, the point cloud data with a slope higher than the ground threshold will be removed to obtain the ground point cloud in the corresponding area; the ground threshold is a preset value used to determine whether the terrain is flat.

[0205] In some embodiments, the ground point cloud determination module 302 is further configured to:

[0206]

[0207] Where D represents the distance between the key point cloud and the surrounding point cloud; the three-dimensional coordinates of the key point cloud are (x1, y1, z1); and the three-dimensional coordinates of any surrounding point cloud are (x2, y2, z2).

[0208] The apparatus of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0209] Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application, such as... Figure 4 As shown, the electronic device 400 of this embodiment may include a memory 401 and a processor 402.

[0210] The memory 401 stores a computer program that can be loaded by the processor 402 and execute the methods described in the above embodiments.

[0211] The processor 402 and the memory 401 are connected, for example, via a bus.

[0212] Optionally, the electronic device 400 may also include a transceiver. It should be noted that in practical applications, the transceiver is not limited to one, and the structure of the electronic device 400 does not constitute a limitation on the embodiments of this application.

[0213] Processor 402 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 402 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0214] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0215] The memory 401 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0216] The memory 401 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 402. The processor 402 is used to execute the application code stored in the memory 401 to implement the content shown in the foregoing method embodiments.

[0217] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0218] The electronic device in this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0219] This application also provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the methods described in the above embodiments.

[0220] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for constructing a three-dimensional model for terrain surveying, characterized in that, include: Obtain the ground influencing factors at the current moment, and analyze the ground characteristics of the mountain at the time of the test based on the ground influencing factors; The point cloud data collected at the current moment is acquired, and the point cloud data is classified according to the ground characteristics to determine the ground point cloud of the mountain to be measured. An irregular triangular network is established, and based on the distribution of the ground point cloud, the irregular triangular network is interpolated to obtain a three-dimensional mountain model of the mountain to be measured. The step of classifying the point cloud data to determine the ground point cloud of the mountain to be measured includes: Traverse the point cloud data, determine the key point cloud, and extract the attribute information of the key point cloud; Based on the attribute information, determine the similarity between the surrounding point cloud and the key point cloud; Based on the similarity, the point cloud data is segmented to obtain several point cloud regions; Establish a regular grid in each point cloud region, and assign elevation values ​​to the grid points in the regular grid; Based on the elevation values, a digital elevation model is generated; Based on the digital elevation model, obtain the three-dimensional point cloud coordinates of the point cloud data in each point cloud region; Based on the three-dimensional point cloud coordinates, determine the slope of each point cloud within the corresponding area; Based on the slope, determine whether the corresponding area belongs to the ground area; If it falls within the range, the point cloud data with a slope higher than the ground threshold will be removed to obtain the ground point cloud in the corresponding area; the ground threshold is a preset value used to determine whether the terrain is flat.

2. The method according to claim 1, characterized in that, The acquisition of ground influencing factors at the current moment includes: Acquire the mountain information and meteorological data of the mountain to be measured; the mountain information includes altitude information and geological information; Based on the meteorological data and the altitude information, determine the meteorological impact of the meteorological data on the meteorological conditions of each area of ​​the mountainous region to be measured; Based on the meteorological influences and geological information, determine whether there are any areas of change in the mountain area to be tested; If the aforementioned change area exists, then the surface influencing factors of the change area are determined based on the meteorological influences.

3. The method according to claim 2, characterized in that, The acquisition of point cloud data collected at the current moment includes: Based on the mountain information, the optimal surveying tool for the mountain to be surveyed was determined. The surveying type of the optimal surveying tool is retrieved, the surveying characteristics of the optimal surveying tool are determined, and a surveying route is planned based on the surveying characteristics and the mountain information. Control the optimal surveying tool to perform surveying according to the surveying route, and acquire the surveying data of the optimal surveying tool; The survey data is preprocessed, and the preprocessed survey data is then converted to obtain point cloud data.

4. The method according to claim 3, characterized in that, When the optimal surveying tool is a drone, the drone has at least one built-in camera. The surveying data is preprocessed and transformed to obtain point cloud data, including: Obtain the internal parameters of the camera; The camera coordinate system of the survey data is determined based on the internal parameters. The mapping data is filtered to obtain several reliable data points; Analyze the aforementioned reliable data to determine the scanning location of the reliable data; The aforementioned reliable data are filtered to determine whether there are co-located, out-of-direction data at the same scanning position; If it is determined that there is co-positional and out-of-direction data, then the co-positional and out-of-direction data is matched to obtain the corresponding dataset; Based on the images of the drone body and the environment captured by the camera, the real-time mapping attitude of the drone is determined; Based on the real-time mapping posture, the camera coordinate system, and the scanning position, the dataset and other reliable data are transformed to obtain point cloud data.

5. The method according to claim 4, characterized in that, When the optimal surveying tool is a drone, the drone has at least one built-in camera. The process of planning the surveying route based on the surveying characteristics and the mountainous terrain information includes: Based on the mountain information, the mapping problem area of ​​the mountain to be surveyed is determined; Based on the surveying problem area and the surveying characteristics, analyze the key surveying methods for the surveying problem area; Based on the aforementioned key mapping method, determine the additional equipment items for the UAV, and adjust the UAV accordingly. Based on the key surveying methods and the attribute information of the equipment additions, a key surveying route for the surveying problem area is planned; Based on the location of the survey problem area, the surveying sequence of the mountain to be surveyed is obtained, and based on the surveying sequence and the key surveying route, the surveying route of the mountain to be surveyed is obtained.

6. The method according to claim 1, characterized in that, The similarity between the surrounding point cloud and the key point cloud, based on the attribute information, is calculated using the following formula: ； in, This represents the distance between the key point cloud and the surrounding point cloud; the three-dimensional coordinates of the key point cloud are... The three-dimensional coordinates of any surrounding point cloud are: .

7. A three-dimensional model construction device for terrain surveying, characterized in that, include: The feature analysis module is used to obtain the ground influencing factors at the current time, and analyze the ground characteristics of the mountain at the time of the test based on the ground influencing factors; The ground point cloud determination module is used to acquire the point cloud data collected at the current moment, classify the point cloud data according to the ground characteristics, and determine the ground point cloud of the mountain to be measured. The model building module is used to establish an irregular triangular network and, based on the distribution of the ground point cloud, to perform interpolation processing on the irregular triangular network to obtain a three-dimensional mountain model of the mountain to be measured. When classifying the point cloud data and determining the ground point cloud of the mountain to be measured, the ground point cloud determination module is specifically used for: Traverse the point cloud data, determine the key point cloud, and extract the attribute information of the key point cloud; Based on the attribute information, determine the similarity between the surrounding point cloud and the key point cloud; Based on the similarity, the point cloud data is segmented to obtain several point cloud regions; Establish a regular grid in each point cloud region, and assign elevation values ​​to the grid points in the regular grid; Based on the elevation values, a digital elevation model is generated; Based on the digital elevation model, obtain the three-dimensional point cloud coordinates of the point cloud data in each point cloud region; Based on the three-dimensional point cloud coordinates, determine the slope of each point cloud within the corresponding area; Based on the slope, determine whether the corresponding area belongs to the ground area; If it falls within the range, the point cloud data with a slope higher than the ground threshold will be removed to obtain the ground point cloud in the corresponding area; the ground threshold is a preset value used to determine whether the terrain is flat.

8. An electronic device, characterized in that, include: Memory and processor; The memory is used to store program instructions; The processor is configured to call and execute program instructions in the memory to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program; when the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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