A three-dimensional model construction method and system for topographic mapping

Abstract

The application provides a three-dimensional model construction method and system for topographic mapping, which comprises the following steps: calling a terrain model in a terrain database, and dividing a detection area according to the terrain model; generating a flight path of an unmanned aerial vehicle according to a key detection position, wherein the unmanned aerial vehicle is provided with an image acquisition device, and information of the detection area is acquired according to an identification instruction, mapping information is generated, and a surface image model is generated; comparing a detection area with environmental information, historical information and action response respectively according to a plurality of detection areas in the surface image model and a deviation generation strategy to generate multidimensional deviation data; acquiring the surface image model, acquiring a detection area type, respectively calculating the credibility of the multidimensional deviation data according to the detection area type, generating a terrain change item according to the multidimensional deviation data; and updating each detection area in the terrain model according to the terrain change item to obtain a terrain three-dimensional model.

Description

Technical Field

[0001] This invention relates to the technical field of topographic mapping, and more specifically to a method for constructing a three-dimensional model for topographic mapping. Background Technology

[0002] The purpose of topographic surveying is to investigate and plan land resources, and to achieve scientific management and optimized utilization of land resources through the collection and processing of topographic information. Currently, topographic surveying mainly uses drones to measure the terrain and obtain information on terrain distribution, changes, and quality. Patent application CN117351166A, a land management surveying system based on big data, relates to land surveying technology, including a model building model and a model correction model. The model correction model divides the detection target into several monitoring areas, obtains actual surveying data of the area to be verified based on manual surveying, obtains actual feature vector parameters, obtains the position information corresponding to the marker points from the constructed 3D land model, and obtains 3D reconstruction feature vectors. The similarity parameter of the feature vector parameters obtains the accuracy index of the initial 3D model corresponding to each monitoring area. Based on the verification results, the area to be corrected is obtained, and model correction instructions are generated simultaneously. The current topographic surveying method requires manual verification of the surveying data of the detection area to ensure the accuracy of the model components and to obtain the trend of terrain changes. This requires a lot of manpower and cannot obtain topographic surveying parameters in a timely manner. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a three-dimensional model construction method for terrain mapping, so as to overcome the above-mentioned defects in the existing technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for constructing a 3D model for topographic mapping, including

[0006] The information acquisition step is used to acquire detection information, retrieve the terrain model from the terrain database based on the detection information, and divide the detection area based on the terrain model.

[0007] The detection and acquisition steps involve obtaining key detection locations for each detection area based on the detection area, generating a drone flight path based on the key detection locations, and using an image acquisition device on the drone to collect information from the detection area according to the recognition instructions, generating mapping information, and generating a surface image model.

[0008] The multi-dimensional deviation analysis step involves comparing several detection areas in the surface image model with environmental information, historical information, and action response using a deviation generation strategy to generate multi-dimensional deviation data. The multi-dimensional deviation data includes historical information deviation values, environmental information deviation values, action response deviation values, etc.

[0009] The feature analysis step is used to obtain the surface image model and the detection area type. Based on the detection area type, the confidence of the multi-dimensional deviation data is calculated according to the confidence calculation strategy, and the terrain change item is generated based on the multi-dimensional deviation data.

[0010] The model correction step involves updating each detection area in the terrain model based on the terrain change terms to obtain a three-dimensional terrain model.

[0011] Preferably, the detection and acquisition step includes a key detection location identification sub-step. This sub-step is used to acquire the calibration objects within the detection area, acquire the location of the calibration objects, acquire key detection points, acquire the location information and mapping cost of each key detection point, calculate the comprehensive benefit, prioritize each key detection point according to the comprehensive benefit, select key detection points according to the priority ranking result, and generate the UAV flight path.

[0012] Preferably, the identification instructions include generating different detection instructions based on the type of detection area to be acquired, such as soil area, vegetation area, rock area, etc.

[0013] Preferably, when the detection area is a soil area, the drone generates a soil detection command. The drone is equipped with a soil detector. When the detection area is a soil area, the drone is driven to approach the soil and insert the soil detector into the soil to obtain soil moisture and soil hardness.

[0014] Preferably, when the detection area type is a vegetated area, the UAV generates a vegetation detection command. The UAV is equipped with an infrared scanning device and a blower. Based on the image information captured by the image acquisition device, the location of the vegetation is determined, a first infrared image is acquired, and multiple detection points are marked. After blowing air onto the detection points using the blower, a second infrared image is acquired. Based on the first and second infrared images, the vegetation height and density are analyzed and determined, and combined with the image information, mapping information is generated.

[0015] Preferably, the deviation generation strategy includes a historical information deviation step, which is used to obtain image information of the same detection area at several times in the historical database, perform feature analysis, determine the feature position deviation and color deviation, and generate historical information deviation values.

[0016] Preferably, the deviation generation strategy includes an environmental information deviation step, which is used to acquire environmental information, retrieve historical data under the same environmental conditions from the historical database for comparison, generate image deviation, and acquire the equipment deviation of the UAV under the same environmental conditions. Based on the environmental information deviation and the equipment deviation, a comprehensive analysis is performed to obtain the environmental information deviation value.

[0017] Preferably, the deviation generation strategy includes an action response deviation step, which is used to obtain detection instructions and has a preset detection instruction database. According to different detection instructions, the corresponding action response deviation value is obtained by indexing the detection instruction database.

[0018] Preferably, the feasibility calculation strategy includes

[0019] The parameter acquisition steps involve acquiring the detection region type and corresponding multi-dimensional deviation data for each region. The detection region is configured with different confidence levels for the multi-dimensional deviation data according to the detection region type, and the corresponding confidence levels are obtained by indexing.

[0020] The feasibility weight adjustment step is used to obtain the credibility of multi-dimensional deviation data. A preset deviation value threshold range is set. Multi-dimensional deviation data is obtained separately and compared with the corresponding deviation threshold range. If any set of multi-dimensional deviation data is outside the deviation threshold range, the feasibility of the current multi-dimensional deviation data is zero, and the test is repeated.

[0021] A 3D model building system for terrain mapping, including

[0022] The information acquisition module is used to acquire detection information, retrieve the terrain model from the terrain database based on the detection information, and divide the detection area according to the terrain model.

[0023] The detection and acquisition module acquires the key detection positions of each detection area according to the detection area, and generates the flight path of the UAV based on the key detection positions. The UAV is equipped with image acquisition equipment and infrared scanning equipment, and collects information of the detection area according to the recognition instructions, generates mapping information, and generates a surface image model.

[0024] The multi-dimensional deviation analysis module generates multi-dimensional deviation data by comparing the detection areas with environmental information, historical information, and action response based on several detection areas in the surface map image model and through a deviation generation strategy. The multi-dimensional deviation data includes historical information deviation values, environmental information deviation values, action response deviation values, etc.

[0025] The feature analysis module is used to acquire surface image models and detect region types. Based on the detection region types, it calculates the confidence level of multi-dimensional deviation data and generates terrain change items based on the multi-dimensional deviation data.

[0026] The model correction module updates each detection area in the terrain model according to the terrain change items to obtain a three-dimensional terrain model.

[0027] The beneficial effects of this invention are as follows: It can retrieve detailed terrain models from a terrain database and rationally divide the detection area, providing a clear framework for subsequent data acquisition. The detection and acquisition step ensures comprehensive coverage of each detection area by determining key detection locations and planning the UAV flight path. The image acquisition equipment and other professional detection equipment on the UAV collect information according to recognition instructions, acquiring rich mapping information and generating high-precision surface image models. These operations make data acquisition more comprehensive and accurate, providing a reliable data foundation for subsequent analysis and model building. The multi-dimensional deviation analysis step uses a deviation generation strategy to compare the detection area from multiple dimensions such as environmental information, historical information, and action response, generating multi-dimensional deviation data. This multi-dimensional analysis method can deeply explore the causes and characteristics of terrain changes, not only discovering changes in terrain over time (historical information deviation), but also considering the impact of environmental factors on terrain (environmental information deviation), and deviations during the execution of detection actions (action response deviation). By analyzing this deviation data, the stability and change trend of the terrain can be more accurately assessed. The feature analysis step uses a credibility calculation strategy to calculate the credibility of the multi-dimensional deviation data according to the detection area type, making the analysis results more reliable. By evaluating the reliability of deviation data from different types of detection areas, more valuable data can be selected, reducing the impact of outliers on the analysis results. Generating topographic change items based on multi-dimensional deviation data clearly reflects changes in the terrain in various aspects, providing strong support for the research and prediction of terrain changes. The model correction step updates each detection area in the terrain model based on the topographic change items, ultimately obtaining a three-dimensional terrain model. This real-time update mechanism enables the terrain model to reflect actual terrain changes in a timely manner, providing accurate terrain information for fields such as geological disaster early warning, land resource management, and ecological environment monitoring. For example, in geological disaster early warning, a timely updated terrain model can more accurately predict the probability and impact range of disasters such as landslides and debris flows, thereby enabling the implementation of effective preventative measures. Attached Figure Description

[0028] Figure 1 This is a flowchart of the present invention;

[0029] Figure 2 This is a step diagram of the present invention;

[0030] Figure 3 This is the multi-dimensional deviation data relationship diagram of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0034] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings:

[0035] like Figure 1-3 As shown, this invention provides a method for constructing a three-dimensional model for terrain mapping, including...

[0036] The information acquisition step is used to obtain detection information. Based on the detection information, a terrain model is retrieved from the terrain database, and the detection area is divided according to the terrain model. Multi-source detection information is collected. This information covers basic geographic data of the monitoring area, such as latitude and longitude range and altitude; environmental data, including real-time temperature, humidity, light intensity, precipitation, and wind direction and force; and time information, i.e., the specific date and time of the monitoring task. Based on the collected detection information, the system performs a precise search in the terrain database to retrieve a terrain model matching the monitoring area. The terrain database stores a large amount of terrain data from different regions and periods. This data exists in the form of digital models and may contain detailed information such as contour lines, slope, and landform types. The system uses Geographic Information System (GIS) technology to scientifically divide the monitoring area according to the retrieved terrain model. During the division, the complexity of the terrain is fully considered, and areas with large terrain undulations and diverse landform types are divided into separate detection areas. At the same time, based on the potential risk level, areas prone to geological disasters (such as landslides and debris flows) are designated as key detection areas. This method of categorization not only improves the targeting and efficiency of monitoring but also ensures closer attention to high-risk areas.

[0037] The detection and data acquisition process involves acquiring key detection locations for each detection area and generating a drone flight path based on these locations. The drone, equipped with image acquisition devices, collects information from the detection area according to recognition commands, generating mapping information and a surface image model. For each detection area, the system utilizes spatial analysis algorithms, combined with the characteristics of the terrain model, to determine key detection locations. These locations are typically situated at critical nodes of terrain change, such as mountain peaks, valleys, steep slope edges, and areas potentially prone to geological hazards. Based on the determined key detection locations, a path planning algorithm generates the optimal flight path for the drone. During the planning process, drone flight performance limitations, such as maximum flight altitude, speed, and endurance, are fully considered to ensure the feasibility of the flight path. Simultaneously, obstacle avoidance planning is implemented to prevent potential collisions between drones, ensuring drone safety during flight. The drone, equipped with high-definition cameras and multispectral cameras, flies along the preset flight path. Upon reaching a key detection location, it collects information from the detection area according to preset recognition commands. These commands can include the identification of specific landmarks or the extraction of specific terrain features. After preliminary processing, the collected data is used to generate mapping information, including image data and geographic coordinate information.

[0038] The multi-dimensional deviation analysis step involves generating multi-dimensional deviation data based on several detection areas in the surface image model, comparing these areas using environmental information, historical information, and action response data through a deviation generation strategy. This multi-dimensional deviation data includes historical information deviation values, environmental information deviation values, and action response deviation values. The generated surface image model is then compared with historical data stored in the terrain database. Image matching algorithms are used to extract and match features from images from different periods, calculating historical information deviation values. For example, by comparing images of the same area from different years, changes in terrain, such as increases or decreases in vegetation cover and changes in land use types, are analyzed. Real-time environmental information is also incorporated into the surface image model analysis. Environmental data analysis algorithms are used to assess the impact of environmental factors on terrain, generating environmental information deviation values. For example, during periods of high precipitation, the impact of soil moisture changes on terrain stability is analyzed; during periods of high wind, the movement of surface dust is observed; and data collected after a drone performs specific actions (such as pressing soil or emitting sound waves) is compared with theoretically expected results. Finally, an action response analysis algorithm is used to calculate action response deviation values. For example, by comparing actual images of soil after it has been pressed by a drone with theoretical images, changes in the soil's physical properties such as hardness and elasticity can be determined.

[0039] The feature analysis step is used to acquire a surface image model and determine the detection region type. Based on the detection region type, the credibility of multi-dimensional deviation data is calculated using a credibility calculation strategy, and a terrain change item is generated based on the multi-dimensional deviation data. Each detection region is categorized according to the terrain model and surface image model. Detection region types can include mountains, plains, water areas, and forests. Different types of detection regions may have different patterns and influencing factors of terrain change, thus requiring different analysis methods. For different types of detection regions, a credibility calculation strategy is used to evaluate the credibility of the multi-dimensional deviation data. The credibility calculation strategy can consider various factors, such as the reliability of the data source, the time interval of data acquisition, and the magnitude of data changes. Based on the multi-dimensional deviation data and its credibility, a feature analysis algorithm is used to generate a terrain change item. The terrain change item can include changes in terrain displacement, deformation, erosion, and deposition.

[0040] The model correction steps involve updating each detected area in the terrain model based on terrain change parameters to obtain a 3D terrain model. A model update algorithm is employed to integrate terrain change parameters into the original terrain model, enabling dynamic correction. For example, in areas where landslides have occurred, the slope and elevation information in the terrain model are updated promptly. After updating each detected area, a 3D modeling algorithm is used to integrate the updated detected areas, generating a new 3D terrain model. This new 3D terrain model visually reflects the latest terrain changes, providing accurate decision support for geological disaster early warning and land resource management.

[0041] The detection and acquisition process includes a key detection location identification sub-step. This sub-step acquires markers within the detection area, determines their locations, and identifies key detection points. It obtains the location information and mapping cost of each key detection point, calculates the overall benefit, prioritizes the key detection points based on the overall benefit, and selects the key detection points based on the priority ranking to generate the UAV flight path. In this stage, the UAV first searches for and identifies pre-set markers within the detection area using image recognition and positioning technology. These markers typically possess significant visual features, such as unique shapes and colors, and commonly include specially designed signs and stakes with special textures, enabling the UAV to quickly and accurately identify them in complex environments.

[0042] The identification commands include generating different detection commands based on the type of the detection area being acquired, such as soil areas, vegetation areas, and rock areas. During the drone's monitoring of the detection area, the generation of identification commands is based on the accurate determination of the detection area type. The system first uses the image acquisition equipment onboard the drone (such as a high-definition camera or multispectral camera) to acquire initial image information of the detection area. Through advanced image recognition algorithms, these images are analyzed and processed to extract feature information such as color, texture, and shape. Based on this feature information, the detection area is divided into different types, such as soil areas, vegetation areas, and rock areas. Once the detection area type is determined, the system generates specific detection commands that match it according to preset rules and algorithms, ensuring that the drone can perform accurate detection operations for the characteristics of different areas.

[0043] When the detection area is identified as a soil area, the drone generates a soil detection command. Equipped with a soil analyzer, the drone approaches the soil and inserts the analyzer to obtain soil moisture and hardness data. To ensure effective soil detection, the drone is equipped with a specialized soil analyzer. Upon receiving the command, the drone uses its flight control system to precisely approach the soil surface. Then, using its robotic arm or other device, it inserts the soil analyzer into the soil. The analyzer contains multiple sensors. A moisture sensor accurately measures soil moisture content, while a hardness sensor applies pressure and measures the soil's reaction force to determine hardness. This acquired soil moisture and hardness data serves as crucial mapping information for subsequent soil condition analysis and assessment, such as determining whether soil moisture content is suitable for crop growth or evaluating soil stability to prevent geological disasters.

[0044] When the detection area is a vegetated area, the drone generates a vegetation detection command. Equipped with an infrared scanning device and a blower, the drone determines the vegetation location based on images captured by the image acquisition device, obtains a first infrared image, and marks multiple detection points. After blowing air onto the detection points, a second infrared image is acquired. Based on the first and second infrared images, the vegetation height and density are analyzed, and mapping information is generated by combining the image information. In this case, the onboard infrared scanning device and blower play a crucial role. First, based on images captured by the image acquisition device, image recognition and analysis technology accurately determines the specific location of the vegetation within the detection area. Then, the infrared scanning device is activated to scan the vegetated area and acquire a first infrared image. This first infrared image reflects the radiation characteristics of the vegetation in the infrared band, providing basic data for subsequent analysis. After acquiring the first infrared image, the system marks multiple detection points in the vegetated area according to certain rules. The selection of these monitoring points typically considers factors such as the uniformity of vegetation distribution and its growth status. After calibration, the air-blowing device is activated to blow air onto each monitoring point. The purpose of air blowing is to induce certain dynamic changes in the vegetation to better acquire its structural information. After air blowing, a second infrared image is acquired using an infrared scanning device. By comparing the first and second infrared images, the changes in infrared radiation of the vegetation before and after air blowing are analyzed. Based on these changes, the height and density of the vegetation can be inferred. For example, by analyzing features such as changes in vegetation shadows and the amplitude of leaf swaying in the infrared images, combined with relevant algorithms and models, the average height of the vegetation can be calculated; by statistically analyzing the infrared radiation intensity of the vegetation per unit area, the density of the vegetation can be estimated; finally, by combining the image information captured by the image acquisition device and the analysis results of the infrared images, mapping information about the vegetation area is generated. This mapping information not only includes vegetation height and density data, but may also include other relevant information such as vegetation species and growth status, providing strong data support for ecological environment monitoring and forestry resource management.

[0045] The deviation generation strategy includes a historical information deviation step, which acquires image information of the same detection area at several times from the historical database, performs feature analysis, determines feature position deviation and color deviation, and generates historical information deviation values. It accesses the historical database and, based on the geographic coordinates and terrain features of the current detection area, accurately locates and acquires image information of the same detection area at several different times. The selection of these times comprehensively considers the time span and data representativeness, such as including images from different time periods like the past week, month, or year, to comprehensively reflect the changes in the area over time. Advanced image feature extraction algorithms are used to process the acquired images from different times. During feature extraction, various features in the images are identified, such as geometric features like the edges, shapes, and textures of objects like trees, rocks, and buildings, as well as color features like soil and vegetation. Then, a feature matching algorithm is used to match the same features in images from different times. After feature matching, the positions of the same features in images from different times are compared. The offset of the feature position is calculated, such as the difference in coordinates of a tree between two images at different times, thus obtaining the feature position deviation. Simultaneously, the changes in color features at different times are analyzed, such as variations in soil color depth and differences in vegetation hue, to determine color deviation. Feature location deviation and color deviation are quantified, and a historical information deviation value is calculated based on preset weighting coefficients. For example, the weight of feature location deviation might be set to 0.6, and the weight of color deviation to 0.4. The final historical information deviation value is obtained through weighted summation, reflecting the degree of change of the current detection area relative to its historical state.

[0046] The deviation generation strategy includes an environmental information deviation step, which acquires environmental information and compares it with historical data under the same environmental conditions retrieved from a historical database to generate image deviation. It also acquires the environmental information deviation affecting the UAV's equipment under the same environmental conditions. Based on the environmental information deviation and equipment deviation, a comprehensive analysis is performed to obtain the environmental information deviation value. Real-time collection of environmental information for the current detection task, including temperature, humidity, light intensity, wind speed, wind direction, and precipitation, is conducted. This information can be collected through various environmental sensors mounted on the UAV or from external data sources such as nearby weather stations. Based on the acquired environmental information, historical data under the same environmental conditions (or with acceptable differences in environmental conditions) is searched and retrieved from the historical database. For example, if the current environment is 25°C, 60% humidity, and sunny, images and related data under similar temperature, humidity, and weather conditions are searched in the historical database. The currently acquired image is compared and analyzed with the retrieved historical images. Using image comparison algorithms, differences between images are calculated, such as pixel-level differences and structural similarity, thus obtaining the image deviation. These image deviations can reflect changes in terrain, vegetation, etc., under the same environmental conditions. Simultaneously, the impact of environmental information on the UAV equipment is considered. For example, in high humidity environments, the electronic equipment of a drone may be subject to interference, leading to a decrease in image acquisition accuracy; strong winds may affect the drone's flight stability, thus impacting the accuracy of data acquisition. By analyzing and testing the equipment's performance under different environmental conditions, the potential deviations that the equipment may produce under different environmental conditions are pre-determined, i.e., equipment deviation. Image deviation and equipment deviation are then considered comprehensively. Based on their relative importance, appropriate weights are set for weighted calculation, ultimately obtaining the environmental information deviation value. For example, if image deviation is considered to have a greater impact on the results, the image deviation weight might be set to 0.7, and the equipment deviation weight to 0.3. The environmental information deviation value, obtained through weighted calculation, reflects the degree of influence of environmental factors on the current detection results.

[0047] The deviation generation strategy includes a motion response deviation step, which is used to acquire detection commands and has a pre-set detection command database. Based on different detection commands, the system indexes the database to retrieve the corresponding motion response deviation value. When the UAV performs a detection task, the system acquires the current detection commands. These commands are pre-set according to the type of detection area and the monitoring target, such as soil detection commands for soil areas and vegetation detection commands for vegetation areas. A pre-established detection command database exists, storing various detection commands and their corresponding motion response deviation values. The system performs precise indexing in the detection command database based on the acquired detection commands to find the corresponding motion response deviation value. For example, if the current detection command is to detect soil hardness, the system finds the motion response deviation value related to the soil hardness detection command in the database. Once the corresponding motion response deviation value is found in the database, it is extracted as the motion response deviation value under the current detection command. This deviation value is determined based on previous experimental, test, or empirical data, reflecting the possible difference between the actual motion response and the ideal state when executing the detection command. In this way, the results of the drone executing detection commands can be evaluated and corrected, improving the accuracy and reliability of the detection data.

[0048] Feasibility calculation strategies, including

[0049] The parameter acquisition steps involve obtaining the detection area type and corresponding multi-dimensional deviation data for each region. Different confidence levels are configured for the multi-dimensional deviation data based on the detection area type, and the corresponding confidence levels are indexed to obtain the data. After completing data collection and multi-dimensional deviation analysis for each detection area, the system first clarifies the detection area type for each region, such as soil, vegetation, or rock. Simultaneously, it collects the corresponding multi-dimensional deviation data for that region, covering aspects such as historical information deviation values, environmental information deviation values, and action response deviation values. For example, for a soil region, it might obtain historical information deviation values ​​resulting from changes in soil color and location in historical comparisons, environmental information deviation values ​​resulting from comparing current environmental conditions with historical environmental conditions, and action response deviation values ​​between the actual and expected responses when performing soil detection actions. Based on different detection area types, the system pre-configures different confidence levels for the multi-dimensional deviation data. This configuration is based on an understanding of the characteristics of different regions and past experience data. For example, for soil areas, due to the relatively complex nature of soil and its influence by multiple factors, the reliability of historical information deviation data might be set to 0.6, the reliability of environmental information deviation data to 0.7, and the reliability of action response deviation data to 0.8. For vegetation areas, the reliability configuration might differ due to the characteristics of vegetation growth and change. By establishing an indexing mechanism, the system can quickly and accurately obtain the reliability corresponding to the multi-dimensional deviation data for each detection area.

[0050] The feasibility weighting adjustment step is used to obtain the credibility of multi-dimensional deviation data. A preset deviation value threshold range is established. Multi-dimensional deviation data is obtained separately and compared with the corresponding deviation threshold range. If any set of multi-dimensional deviation data is outside the deviation threshold range, the feasibility of the current multi-dimensional deviation data is zero, and the system is re-detected. The credibility of multi-dimensional deviation data for each detection area is obtained, and this credibility will serve as an important basis for subsequent feasibility calculations. Simultaneously, the system presets deviation value threshold ranges for different types of multi-dimensional deviation data. These threshold ranges are determined based on long-term monitoring experience, relevant field standards, and reasonable expectations of terrain changes. For example, for historical information deviation values, the preset threshold range may be within a specific percentage range, such as ±10%; for environmental information deviation values, different environmental factors may have different threshold ranges, such as a relatively small threshold for deviation caused by temperature changes, while a relatively large threshold for deviation caused by humidity changes. The system extracts multi-dimensional deviation data for each detection area and compares it with the corresponding deviation threshold range one by one. For example, for historical information deviation values ​​of a certain detection area, it is determined whether they are within the preset historical information deviation value threshold range; the same operation is performed for environmental information deviation values ​​and action response deviation values. If any set of multi-dimensional deviation data is outside its corresponding deviation threshold range, it indicates that the set of deviation data has an anomaly, possibly due to data acquisition errors, environmental interference, or other unknown factors. In this case, the system determines the feasibility of the current multi-dimensional deviation data to be zero and triggers a re-detection mechanism. During re-detection, detection parameters may be adjusted, detection equipment may be replaced, or detection methods may be changed to ensure that more accurate and reliable data is obtained, thereby improving the accuracy of subsequent analysis and model construction. If all multi-dimensional deviation data are within the corresponding deviation threshold range, the feasibility of the data in the detection area is further calculated based on credibility and other relevant factors, providing a more valuable reference for subsequent terrain change analysis and model correction.

[0051] A 3D model building system for terrain mapping, including

[0052] The information acquisition module is used to acquire detection information, retrieve the terrain model from the terrain database based on the detection information, and divide the detection area according to the terrain model.

[0053] The detection and acquisition module acquires the key detection locations of each detection area according to the detection area, and generates the drone flight path based on the key detection locations. The drone is equipped with image acquisition equipment and infrared scanning equipment, and collects information from the detection area according to the recognition instructions, generates mapping information, and generates a surface image model.

[0054] The multi-dimensional deviation analysis module generates multi-dimensional deviation data by comparing the detection areas with environmental information, historical information, and action response based on several detection areas in the surface map image model and through a deviation generation strategy. The multi-dimensional deviation data includes historical information deviation values, environmental information deviation values, and action response deviation values.

[0055] The feature analysis module is used to acquire surface image models and detect region types. Based on the detection region types, it calculates the confidence level of multi-dimensional deviation data and generates terrain change items based on the multi-dimensional deviation data.

[0056] The model correction module updates each detection area in the terrain model based on the terrain change items to obtain a 3D terrain model.

[0057] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A three-dimensional model construction method for topographic mapping, characterized by, Comprising An information acquisition step for acquiring detection information, retrieving a terrain model in a terrain database according to the detection information, and dividing a detection area according to the terrain model; A detection collection step for acquiring key detection positions of each detection area according to the detection area, generating a UAV flight path according to the key detection positions, carrying an image collection device on the UAV, collecting information of the detection area according to the identification instruction, generating surveying and mapping information, and generating a surface image model; A multi-dimensional deviation analysis step for comparing the detection area from environmental information, historical information, and action response according to a plurality of detection areas in the surface image model and through a deviation generation strategy to generate multi-dimensional deviation data, wherein the multi-dimensional deviation data includes a historical information deviation value, an environmental information deviation value, and an action response deviation value; A feature analysis step for acquiring the surface image model, acquiring a detection area type, calculating the credibility of the multi-dimensional deviation data according to the detection area type through a credibility calculation strategy, and generating a terrain change item according to the multi-dimensional deviation data; The credibility calculation strategy includes: A parameter acquisition step for acquiring the detection area type of each area and the corresponding multi-dimensional deviation data, configuring different credibility for the multi-dimensional deviation data according to the detection area type, and acquiring the corresponding credibility through index acquisition; A credibility weight adjustment step for acquiring the credibility of the multi-dimensional deviation data, presetting a deviation threshold range, acquiring the multi-dimensional deviation data, and comparing it with the corresponding deviation threshold range, if any group of multi-dimensional deviation data is outside the deviation threshold range, the current multi-dimensional deviation data credibility is zero, and re-detection is performed; A model correction step for updating each detection area in the terrain model according to the terrain change item to obtain a terrain three-dimensional model.

2. The method of claim 1, wherein, The detection collection step is provided with a key detection position identification sub-step for acquiring a calibration object in the detection area, acquiring the position of the calibration object, acquiring key detection points, acquiring the position information and surveying and mapping cost of each key detection point, calculating the comprehensive benefit, prioritizing each key detection point according to the comprehensive benefit, selecting the key detection point according to the priority sorting result, and generating the UAV flight path.

3. The method of claim 1, wherein, The identification instruction includes generating different detection instructions according to the detection area type, and the detection area type includes soil area, vegetation area, and rock area.

4. The method of claim 3, wherein, When the detection area type is soil area, the UAV generates soil detection instructions, the UAV is equipped with a soil detector, when the detection area is soil area, the UAV is driven to approach the soil and insert the soil detector into the soil to acquire soil moisture and soil hardness.

5. The method of claim 1, wherein, When the detection area type is a vegetation area, the UAV generates a vegetation detection instruction, the UAV is provided with an infrared scanning device and a blowing device, a vegetation position is determined according to image information captured by the image capturing device, a first infrared image is acquired, a plurality of detection points are calibrated, and a second infrared image is acquired after the detection points are blown by the blowing device, the vegetation height and the vegetation density are analyzed and judged according to the first infrared image and the second infrared image, and the surveying and mapping information is generated in combination with the image information.

6. The method of claim 1, wherein, The deviation generation strategy includes a historical information deviation step for acquiring image information of the same detection area at several time points in a historical database, performing feature analysis, judging the feature position deviation and the color deviation, and generating a historical information deviation value.

7. The method of claim 1, wherein, The deviation generation strategy includes an environment information deviation step for acquiring environment information, calling historical data under the same environment condition in the historical database for comparison to generate image deviation, and acquiring equipment deviation of the UAV under the same environment condition, and comprehensively analyzing and acquiring an environment information deviation value according to the environment information deviation and the equipment deviation.

8. The method for constructing a three-dimensional model for topographic mapping according to claim 3, wherein, The deviation generation strategy includes an action response deviation step for acquiring a detection instruction and preconfiguring a detection instruction database, indexing corresponding action response deviation values in the detection instruction database according to different detection instructions.

9. A three-dimensional model building system for topographic mapping, characterized by, Comprise An information acquisition module is configured to acquire detection information, call a terrain model in a terrain database according to the detection information, and divide detection areas according to the terrain model; A detection acquisition module is configured to acquire key detection positions of each detection area according to the detection areas, generate a UAV flight path according to the key detection positions, provide the UAV with an image capturing device and an infrared scanning device, acquire information of the detection areas according to a recognition instruction, generate surveying and mapping information, and generate a surface image model; A multi-dimensional deviation analysis module is configured to compare detection areas from environment information, historical information, and action responses according to a plurality of detection areas in the surface image model by using a deviation generation strategy to generate multi-dimensional deviation data, wherein the multi-dimensional deviation data includes a historical information deviation value, an environment information deviation value, and an action response deviation value; A feature analysis module is configured to acquire a surface image model, acquire a detection area type, calculate a credibility of the multi-dimensional deviation data according to the detection area type, and generate a terrain change item according to the multi-dimensional deviation data; The credibility calculation strategy comprises: A parameter acquisition step is configured to acquire a detection area type and corresponding multi-dimensional deviation data of each area, and the multi-dimensional deviation data is respectively configured with different credibilities according to the detection area type, and the corresponding credibility is acquired by indexing. The credibility weight adjusting step is used to acquire the credibility of the multi-dimension deviation data, and is provided with a deviation value threshold range, respectively acquires the multi-dimension deviation data, and respectively compares with the corresponding deviation threshold range, if any one group of multi-dimension deviation data is out of the deviation threshold range, the credibility of the current multi-dimension deviation data is zero, and re-detection is performed; The model correction module updates each detection region in the terrain model according to the terrain change term to obtain a terrain three-dimensional model.

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