High-precision surveying and mapping method based on surveying and mapping aerial photography

Abstract

The invention discloses a high-precision surveying and mapping method based on surveying and mapping aerial photography, and relates to the technical field of surveying and mapping engineering. S2, selecting aerial photography equipment; s3, making a flight plan; s4, arranging ground control points; s5, aerial photography data acquisition; s6, data preprocessing and image splicing; s7, performing aerial triangulation and model reconstruction; s8, performing multi-sensor data fusion; s9, data analysis and evaluation; and S10, outputting and applying the result. According to the high-precision surveying and mapping method based on surveying and mapping aerial photography, the high-precision real-time image correction and enhancement technology is combined, and the efficiency and precision of a surveying and mapping task can be remarkably improved; the flight path can be quickly optimized through quantum calculation, and the breadth and quality of image acquisition are ensured; real-time image correction and enhancement process image data in the flight process, the workload of post-processing is reduced, and the surveying and mapping process is more intelligent, efficient and flexible.

Description

Technical Field

[0001] The invention relates to the technical field of surveying and mapping engineering, and in particular to a high-precision surveying and mapping method based on surveying and mapping aerial photography. Background Art

[0002] High-precision surveying and mapping methods based on surveying and mapping aerial photography mainly use platforms such as drones and fixed-wing aircraft to obtain large-area surface images, and are widely used in geographic information systems, urban planning, environmental monitoring and other fields. Traditional surveying and mapping methods rely on fixed flight paths and control point layouts, combined with post-image correction and enhancement technology to complete surveying and mapping tasks. However, under complex terrain and diverse environmental conditions, these technologies face problems such as low efficiency, limited accuracy, and long data processing cycles. Especially in large-scale, high-precision surveying and mapping tasks, the limitations of traditional technologies are particularly prominent.

[0003] Disadvantages of existing technology Fixed flight path planning: Traditional methods usually preset fixed flight paths and cannot adapt to changes in terrain and environment during flight in real time, resulting in uneven image coverage and reduced data quality.

[0004] The burden of post-processing is heavy: the geometric correction and enhancement processing of images mainly rely on post-processing, which increases the data processing cycle and affects the surveying and mapping efficiency.

[0005] Unable to dynamically adapt to complex terrain: In areas with complex terrain, the density of traditional control points cannot be adjusted dynamically, resulting in insufficient surveying and mapping accuracy, especially in areas with large elevation differences. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a high-precision surveying and mapping method based on surveying and mapping aerial photography to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a high-precision surveying and mapping method based on surveying and mapping aerial photography, comprising the following steps: S1. Surveying and mapping mission planning Clarify the specific objectives of the survey, the required accuracy, the size of the coverage area, and the final output format, and plan the task based on terrain features, weather conditions, and sunshine conditions to ensure that the images obtained meet the survey accuracy requirements; S2. Selection of aerial photography equipment Equipment options include high-resolution digital cameras, multispectral sensors, and lidar; After the equipment is selected, calibration is performed, including geometric calibration and radiation calibration, which is used to eliminate systematic errors in the data; S3. Flight plan preparation Develop a detailed flight plan based on flight altitude, route spacing, overlap of adjacent photos, and flight speed, and optimize the flight plan path based on quantum computing; The parameters are adjusted according to the complexity of the terrain, the required resolution and the characteristics of the equipment to ensure that each photo can be stitched accurately; S4. Ground control point layout Lay out ground control points in the survey area as a benchmark for subsequent data processing; The layout of ground control points is distributed throughout the survey area based on the dynamic terrain adaptation flight strategy; S5. Aerial photography data collection After the flight plan is completed, perform aerial photography data collection, and during the flight, monitor the flight status and sensor data quality in real time; S6. Data preprocessing and image stitching Perform data preprocessing while collecting data, and stitch image data after data collection is completed; Image stitching uses the overlapping areas between adjacent images to achieve accurate stitching through image matching technology to generate continuous orthophotos or stereo models; S7. Aerial triangulation and model reconstruction After the image stitching is completed, the aerial triangulation technology is used to perform high-precision geometric correction on the image and reconstruct the three-dimensional terrain model; Aerial triangulation derives the precise position and attitude of each image by identifying common feature points in multiple images and combining them with the known coordinates of ground control points to perform mathematical operations; S8, Multi-sensor data fusion Use multi-sensor data fusion technology to combine data from different sensors; For example, the precise height information from LiDAR is combined with the high-resolution images from an optical camera to generate a highly accurate and detailed 3D model; S9. Data analysis and evaluation Conduct data analysis and accuracy assessment to verify whether the surveying and mapping results meet the expected standards; S10. Output and application of results Convert the surveying and mapping results into the final output format and apply them.

[0008] Further optimizing the technical solution, in step S1, the output format of the surveying and mapping includes a topographic map, a digital elevation model, and an orthophoto; Mission planning includes selecting the appropriate flight path, photography altitude, and required aerial photography equipment and sensors.

[0009] To further optimize the technical solution, in step S3, the flight path is optimized based on quantum computing to address the performance bottleneck problem in complex terrain or large-scale mapping tasks, specifically including the following process: Construct a quantum flight path optimization model, using quantum bits to represent different choices of flight paths based on the properties of quantum superposition and quantum entanglement; Assume the flight path is represented by a set of parameters Indicates that each parameter Corresponding to a key point on the path; through quantum computing, the optimization objective of the flight path is expressed as: in, is the quantum optimization function of the flight path, which represents the comprehensive evaluation value of flight efficiency; is the key point on the path The relevant single optimization objective function; It is a global optimization function that considers the overall characteristics of the path, such as the smoothness and coverage of the path; and is the weight coefficient, which controls the relative importance of different optimization objectives; In order to ensure the continuity of the flight path in the entire measurement area, a quantum entanglement path coherence evaluation model is set up, assuming that the coherence between paths is expressed by the following formula: in, is the coherence evaluation function of the path; Is a waypoint The wave function represents the state of the point; is the entanglement coefficient between the path points, controlling the coherence between the path points; The coherence between path points is calculated to evaluate and adjust the smoothness and continuity of the path.

[0010] To further optimize the technical solution, in step S4, the dynamic terrain adaptation flight strategy includes the following process: Real-time terrain perception: During flight, the system uses low-resolution preliminary terrain data to perceive terrain changes in the flight area in real time; Dynamically adjust flight altitude: The system automatically adjusts the aircraft's flight altitude based on the terrain. In flat areas, the aircraft maintains a lower flight altitude to obtain detailed ground information. In mountainous or steep terrain, the flight altitude will increase to ensure safety and wide coverage. Optimize the density of control points: Through machine learning algorithms, the system analyzes the complexity of the terrain and deploys more ground control points in complex areas to improve the accuracy of surveying and mapping data; in areas with simpler terrain, the number of control points is reduced.

[0011] To further optimize the technical solution, in step S6, the sensor data, flight status information and environmental factors are combined to perform preprocessing while the data is collected, so as to reduce the complex post-processing steps of the data, and a real-time geometric correction model and a real-time image enhancement model are provided; The real-time geometric correction model dynamically adjusts the geometric deformation of the image according to changes in flight status and environmental conditions; The real-time image enhancement model improves the visual effects of images in real time, such as denoising, sharpening and color adjustment.

[0012] To further optimize this technical solution, the real-time geometric correction model is as follows: in, Represents the original image point Real-time correction function; represents the corrected image point; is the horizontal correction, based on the speed of the aircraft , Tilt Angle , Flight altitude And other relevant data are calculated; It is the correction amount in the vertical direction, similar to the horizontal direction, taking into account the attitude and altitude of the aircraft.

[0013] To further optimize this technical solution, the real-time image enhancement model is as follows: in, Is the original image The enhancement function of is the enhanced image; is the image enhancement factor based on the light intensity , Noise Level , sharpening factor Make adjustments.

[0014] To further optimize the technical solution, in step S8, when the multi-sensor data is fused, the ground features in the aerial image are automatically identified and classified through a deep learning model of an artificial intelligence system, such as a convolutional neural network; The AI ​​system automatically separates different types of features in large-scale data processing and generates specific geographic information layers based on user needs; Combined with multi-sensor fusion data, the artificial intelligence system intelligently corrects and enhances the image, identifying complex shadow effects and cloud obstructions.

[0015] Further optimizing the technical solution, in step S9, the accuracy assessment includes checking the accuracy of geographic coordinates, the accuracy of elevation, and the coherence of image stitching; By comparing with known high-precision reference data, the error of the surveying and mapping results is calculated, and necessary adjustments or reprocessing are made based on the evaluation results to ensure the reliability and accuracy of the surveying and mapping results.

[0016] To further optimize the technical solution, in step S10, the output surveying and mapping results include generating topographic maps, orthophotos, and three-dimensional terrain models of various scales, and performing analysis and report preparation according to user needs; The output surveying and mapping results meet user needs and industry standards, and are directly applied to multiple fields including engineering design, land planning, and environmental monitoring.

[0017] Compared with the prior art, the present invention provides a high-precision surveying and mapping method based on surveying and mapping aerial photography, which has the following beneficial effects: This high-precision surveying and mapping method based on surveying and mapping aerial photography can significantly improve the efficiency and accuracy of surveying and mapping tasks by setting up quantum computing to optimize flight path planning and dynamic terrain adaptation flight strategies, combined with high-precision real-time image correction and enhancement technology; quantum computing can quickly optimize the flight path to ensure the breadth and quality of image acquisition; real-time image correction and enhancement processes image data during flight, reducing the workload of post-processing, and realizing precise geometric correction and visual optimization in complex terrain, making the surveying and mapping process more intelligent, efficient and flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the process of a high-precision surveying and mapping method based on surveying and mapping aerial photography proposed by the present invention; Figure 2 A schematic diagram of a process for optimizing flight path planning in a high-precision surveying and mapping method based on surveying and mapping aerial photography proposed by the present invention; Figure 3A schematic flow chart of a dynamic terrain adaptation flight strategy in a high-precision surveying and mapping method based on surveying and mapping aerial photography proposed by the present invention; Figure 4 This is a specific implementation step diagram of the dynamic terrain adaptation flight strategy in the high-precision surveying and mapping method based on surveying and mapping aerial photography proposed by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example: See also Figure 1 , a high-precision surveying and mapping method based on surveying and mapping aerial photography, comprising the following steps: S1. Surveying and mapping mission planning Clarify the specific objectives of the survey, the required accuracy, the size of the coverage area, and the final output format, and plan the task based on factors such as terrain features, weather conditions, and sunshine conditions to ensure that the acquired images meet the surveying and mapping accuracy requirements.

[0021] In this embodiment, the output formats of the surveying and mapping include topographic maps, digital elevation models, and orthophotos; Mission planning includes selecting the appropriate flight path, photography altitude, and required aerial photography equipment and sensors.

[0022] S2. Selection of aerial photography equipment Equipment options include high-resolution digital cameras, multispectral sensors, and lidar.

[0023] After the equipment is selected, calibration is performed, which includes geometric calibration (such as lens distortion correction) and radiation calibration (such as spectral response calibration) to eliminate systematic errors in the data.

[0024] S3. Flight plan preparation A detailed flight plan is made based on flight altitude, route spacing, overlap of adjacent photos (usually 60%-80% forward overlap and 30%-60% lateral overlap) and flight speed, and the flight plan path is optimized based on quantum computing.

[0025] Parameters are adjusted based on terrain complexity, required resolution, and equipment characteristics to ensure that each photo can be stitched accurately.

[0026] like Figure 2As shown, in this embodiment, in step S3, the flight path is optimized based on quantum computing to address the performance bottleneck problem in complex terrain or large-scale mapping tasks, which specifically includes the following process: Construct a quantum flight path optimization model, using quantum bits to represent different choices of flight paths based on the properties of quantum superposition and quantum entanglement; Assume the flight path is represented by a set of parameters Indicates that each parameter Corresponding to a key point on the path (such as waypoint position, altitude, speed, etc.). Through quantum computing, the optimization goal of the flight path is expressed as: in, is the quantum optimization function of the flight path, which represents the comprehensive evaluation value of flight efficiency.

[0027] is the key point on the path A single optimization objective function is associated, such as the height or speed of a waypoint.

[0028] It is a global optimization function that takes into account the overall characteristics of the path, such as the smoothness and coverage of the path.

[0029] and is the weight coefficient, which controls the relative importance of different optimization objectives.

[0030] In quantum computing, qubits can represent multiple path choices simultaneously in a superposition state. Through quantum algorithms (such as quantum annealing or quantum approximate optimization algorithms), Perform a global search to find This model can quickly evaluate and optimize flight paths in complex terrain, thereby reducing flight time and improving data collection coverage and quality.

[0031] In order to ensure the continuity of the flight path in the entire measurement area, a quantum entanglement path coherence evaluation model is set up. It is assumed that the coherence between the paths can be expressed by the following formula: in, is the coherence evaluation function of the path.

[0032] Is a waypoint The wave function represents the state of the point.

[0033] is the entanglement coefficient between the path points, controlling the coherence between the path points.

[0034] By calculating the coherence between waypoints, the smoothness and continuity of the path can be evaluated and adjusted. Quantum computing allows us to consider the mutual influence between multiple waypoints at the same time, ensuring that the final selected path is not just a simple combination of individual optimal points, but an overall optimal coherent path. In this way, the ineffective flight distance between waypoints can be minimized, thereby improving the overall efficiency of the flight mission.

[0035] In practical applications, these two formula models can be used in combination. First, multiple candidate paths are quickly calculated through the quantum flight path optimization model, and then the quantum entanglement path coherence evaluation model is used to evaluate the coherence of these candidate paths and select the optimal coherent path. This process can be completed in a very short time with the support of quantum computing, and even in the face of complex terrain or large-scale mapping tasks, it can ensure efficient and high-precision path planning.

[0036] S4. Ground control point layout Ground control points are laid out in the survey area as a benchmark for subsequent data processing.

[0037] The layout of ground control points is distributed throughout the survey area based on a dynamic terrain adaptation flight strategy.

[0038] like Figure 3 As shown, in this embodiment, the dynamic terrain adaptation flight strategy includes the following processes: Real-time terrain perception: During flight, the system uses low-resolution preliminary terrain data to perceive terrain changes in the flight area in real time; Dynamically adjust flight altitude: The system automatically adjusts the aircraft's flight altitude based on the terrain. In flat areas, the aircraft maintains a lower flight altitude to obtain detailed ground information. In mountainous or steep terrain, the flight altitude will increase to ensure safety and wide coverage. Optimize the density of control points: Through machine learning algorithms, the system analyzes the complexity of the terrain and deploys more ground control points in complex areas to improve the accuracy of surveying and mapping data; in areas with simpler terrain, the number of control points is reduced.

[0039] like Figure 4 As shown, in this embodiment, the implementation steps of the dynamic terrain adaptation flight strategy are as follows: Terrain data input: Before the flight begins, the system extracts preliminary low-resolution terrain information from remote sensing data and establishes a terrain model as the preliminary basis for flight parameters.

[0040] Flight mission planning: During the flight mission planning stage, the system generates a preliminary flight path based on the terrain model and develops a basic plan for the layout of control points.

[0041] Real-time data update during flight: When performing surveying and mapping tasks, the aircraft uses sensors (such as LiDAR and photogrammetry systems) to collect terrain data in real time, and dynamically updates the terrain model in combination with the preliminary terrain model.

[0042] Path and control point adjustments: Based on the updated terrain model, the aircraft adjusts the flight path in real time according to changes in terrain, increases or decreases the number of control points, and optimizes flight coverage in key areas.

[0043] Automated terrain analysis: Through machine learning models, the system analyzes the complexity of the terrain and makes adjustments. For example, in steep mountains or river valleys, the system will identify areas with large height differences and increase mapping control points; in relatively flat areas, the system will reduce the layout of control points to optimize flight efficiency.

[0044] Through real-time perception and automatic adjustment, the problem of surveying and mapping accuracy in complex terrain is solved, and the risk of insufficient deployment of traditional control points is avoided. The system can intelligently adapt to changes in the environment and provide flexible and accurate surveying and mapping solutions.

[0045] S5. Aerial photography data collection After the flight plan is completed, aerial photography data collection is carried out. During the flight, the flight status and sensor data quality are monitored in real time.

[0046] In this embodiment, the data in each flight mission is ensured to be of high quality. The data collection process may be affected by external factors such as wind speed, airflow, and light changes, so an emergency plan is required to adjust the flight path and altitude to ensure continuous and clear images.

[0047] S6. Data preprocessing and image stitching Data preprocessing is performed while data is being collected, and image data splicing is performed after data collection is completed.

[0048] Image stitching utilizes the overlapping areas between adjacent images and achieves precise stitching through image matching technology to generate continuous orthophotos or stereo models.

[0049] In this embodiment, in step S6, sensor data, flight status information (such as flight altitude, speed, tilt angle, etc.) and environmental factors (such as light intensity, cloud distribution) are combined to perform preprocessing while data is collected to reduce the complex post-processing steps of the data, and a real-time geometric correction model and a real-time image enhancement model are provided; The real-time geometric correction model dynamically adjusts the geometric deformation of the image according to changes in flight status and environmental conditions; The real-time image enhancement model improves the visual effects of images in real time, such as denoising, sharpening and color adjustment.

[0050] The real-time geometric correction model is as follows: in, Represents the original image point Real-time correction function.

[0051] Represents the corrected image point.

[0052] is the horizontal correction, based on the speed of the aircraft , Tilt Angle , Flight altitude And other relevant data are calculated.

[0053] It is the correction amount in the vertical direction, similar to the horizontal direction, taking into account the attitude and altitude of the aircraft.

[0054] During the acquisition process, the sensor will record the flight status in real time, such as tilt angle and speed. This information will be passed to the correction algorithm to calculate the real-time horizontal and vertical corrections. and Each image point will be corrected according to these parameters when it is collected, ensuring that all images maintain geometric consistency when finally stitched together, reducing errors caused by aircraft instability.

[0055] The real-time image enhancement model is as follows: in, Is the original image The enhancement function of .

[0056] is the enhanced image.

[0057] is the image enhancement factor based on the light intensity , Noise Level , sharpening factor Make adjustments.

[0058] Light intensity :The ambient light intensity data collected by the sensor in real time is used to adjust the brightness and contrast of the image.

[0059] Noise Level : The noise level in the image is calculated in real time based on environmental conditions (such as wind speed, weather) and sensor status. This parameter is used to dynamically adjust the denoising process.

[0060] Sharpness Factor : Used to enhance image edges to ensure that details are preserved during image stitching. When the flight altitude or speed changes, the sharpening process can be more flexible to avoid over-sharpening or under-sharpening.

[0061] During the flight, the light intensity sensor and noise detection algorithm will monitor the flight environment in real time. and noise level , enhancement function Automatically adjust the brightness, contrast and noise of the image. This enhanced processing ensures the stability of image quality under complex weather conditions. In addition, the system dynamically adjusts the sharpness factor according to the current flight speed and altitude. , to prevent over-sharpening during high-speed flight.

[0062] S7. Aerial triangulation and model reconstruction After the image stitching is completed, the aerial triangulation technology is used to perform high-precision geometric correction on the images and reconstruct the three-dimensional terrain model.

[0063] Aerial triangulation identifies common feature points in multiple images and combines them with the known coordinates of ground control points to perform mathematical operations to derive the precise position and attitude of each image.

[0064] In this embodiment, a high-precision digital terrain model (DTM) or digital elevation model (DEM) can be constructed in this way.

[0065] S8, Multi-sensor data fusion Multi-sensor data fusion technology is used to combine data from different sensors.

[0066] For example, the precise height information of LiDAR is combined with the high-resolution images of an optical camera to generate a highly accurate and detailed 3D model.

[0067] In this embodiment, in step S8, when multi-sensor data is fused, the deep learning model of the artificial intelligence system, such as convolutional neural network (CNN), can automatically identify and classify the features of objects in the aerial image (such as buildings, roads, vegetation, water bodies, etc.). In large-scale data processing, the system automatically separates different types of objects and generates specific geographic information layers according to user needs. Combined with multi-sensor fusion data, the artificial intelligence system can also perform intelligent correction and enhancement on the image, and identify details that are difficult to handle with traditional methods, such as complex shadow effects, cloud cover, etc. This automated processing method not only reduces the need for manual intervention, but also greatly improves the efficiency of image analysis, ensuring the generation of high-precision surveying and mapping results in a short time.

[0068] S9. Data analysis and evaluation Conduct data analysis and accuracy assessment to verify whether the surveying and mapping results meet the expected standards.

[0069] In this embodiment, the accuracy assessment includes checking the accuracy of geographic coordinates, the accuracy of elevation, and the consistency of image stitching; By comparing with known high-precision reference data, the error of the surveying and mapping results is calculated, and necessary adjustments or reprocessing are made based on the evaluation results to ensure the reliability and accuracy of the surveying and mapping results.

[0070] S10. Output and application of results Convert the surveying and mapping results into the final output format and apply them.

[0071] In this embodiment, the output surveying and mapping results include generating topographic maps, orthophotos, and three-dimensional terrain models of various scales, and performing analysis and report preparation according to user needs; The output surveying and mapping results meet user needs and industry standards, and are directly applied to multiple fields including engineering design, land planning, and environmental monitoring.

[0072] The beneficial effects of the present invention are: This high-precision surveying and mapping method based on surveying and mapping aerial photography can significantly improve the efficiency and accuracy of surveying and mapping tasks by setting up quantum computing to optimize flight path planning and dynamic terrain adaptation flight strategies, combined with high-precision real-time image correction and enhancement technology; quantum computing can quickly optimize the flight path to ensure the breadth and quality of image acquisition; real-time image correction and enhancement processes image data during flight, reducing the workload of post-processing, and realizing precise geometric correction and visual optimization in complex terrain, making the surveying and mapping process more intelligent, efficient and flexible.

[0073] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-precision surveying and mapping method based on surveying and mapping aerial photography, characterized in that: The following steps are involved: S1. Surveying and mapping mission planning Define the specific objectives of the survey, the required accuracy, the size of the coverage area, and the final output format, and plan the task based on terrain features, weather conditions, and sunshine conditions; S2. Selection of aerial photography equipment Equipment options include high-resolution digital cameras, multispectral sensors, and lidar; After the equipment is selected, calibration is performed, which includes geometric calibration and radiation calibration; S3. Flight plan preparation Develop a detailed flight plan based on flight altitude, route spacing, overlap of adjacent photos, and flight speed, and optimize the flight plan path based on quantum computing; and adjust the parameters according to the terrain complexity, required resolution, and equipment characteristics; S4. Ground control point layout Lay out ground control points in the survey area as a benchmark for subsequent data processing; The layout of ground control points is distributed throughout the survey area based on the dynamic terrain adaptation flight strategy; S5. Aerial photography data collection After the flight plan is completed, perform aerial photography data collection, and during the flight, monitor the flight status and sensor data quality in real time; S6. Data preprocessing and image stitching Perform data preprocessing while collecting data, and stitch image data after data collection is completed; Image stitching uses the overlapping areas between adjacent images to achieve accurate stitching through image matching technology to generate continuous orthophotos or stereo models; S7. Aerial triangulation and model reconstruction After the image stitching is completed, the aerial triangulation technology is used to perform high-precision geometric correction on the image and reconstruct the three-dimensional terrain model; Aerial triangulation derives the precise position and attitude of each image by identifying common feature points in multiple images and combining them with the known coordinates of ground control points to perform mathematical operations; S8, Multi-sensor data fusion Use multi-sensor data fusion technology to combine data from different sensors; For example, the precise height information from LiDAR is combined with the high-resolution images from an optical camera to generate a highly accurate and detailed 3D model; S9. Data analysis and evaluation Conduct data analysis and accuracy assessment to verify whether the surveying and mapping results meet the expected standards; S10. Output and application of results Convert the surveying and mapping results into the final output format and apply them.

2. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1, characterized in that: In step S1, the output format of the surveying and mapping includes topographic maps, digital elevation models, and orthophotos; Mission planning includes selecting the appropriate flight path, photography altitude, and required aerial photography equipment and sensors.

3. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1 is characterized in that: In step S3, the flight path is optimized based on quantum computing to address performance bottlenecks in complex terrain or large-scale mapping tasks, specifically including the following process: Construct a quantum flight path optimization model, using quantum bits to represent different choices of flight paths based on the properties of quantum superposition and quantum entanglement; Assume the flight path is represented by a set of parameters Indicates that each parameter Corresponding to a key point on the path; through quantum computing, the optimization objective of the flight path is expressed as: in, is the quantum optimization function of the flight path, which represents the comprehensive evaluation value of flight efficiency; is the key point on the path The relevant single optimization objective function; It is a global optimization function that considers the overall characteristics of the path, such as the smoothness and coverage of the path; and is the weight coefficient, which controls the relative importance of different optimization objectives; In order to ensure the continuity of the flight path in the entire measurement area, a quantum entanglement path coherence evaluation model is set up, assuming that the coherence between paths is expressed by the following formula: in, is the coherence evaluation function of the path; Is a waypoint The wave function represents the state of the point; is the entanglement coefficient between the path points, controlling the coherence between the path points; The coherence between path points is calculated to evaluate and adjust the smoothness and continuity of the path.

4. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1 is characterized in that: In step S4, the dynamic terrain adaptation flight strategy includes the following process: Real-time terrain perception: During flight, the system uses low-resolution preliminary terrain data to perceive terrain changes in the flight area in real time; Dynamically adjust flight altitude: The system automatically adjusts the aircraft's flight altitude based on the terrain. In flat areas, the aircraft maintains a lower flight altitude to obtain detailed ground information. In mountainous or steep terrain, the flight altitude will increase. Optimize the density of control points: Through machine learning algorithms, the system analyzes the complexity of the terrain and deploys more ground control points in complex areas to improve the accuracy of surveying and mapping data; in areas with simpler terrain, the number of control points is reduced.

5. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1, characterized in that: In step S6, the sensor data, flight status information and environmental factors are combined to perform preprocessing while the data is collected, and a real-time geometric correction model and a real-time image enhancement model are set; The real-time geometric correction model dynamically adjusts the geometric deformation of the image according to changes in flight status and environmental conditions; The real-time image enhancement model improves the visual effects of images in real time, such as denoising, sharpening and color adjustment.

6. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 5, characterized in that: The real-time geometric correction model is as follows: in, Represents the original image point Real-time correction function; represents the corrected image point; is the horizontal correction, based on the speed of the aircraft , Tilt Angle , Flight altitude And other relevant data are calculated; It is the correction amount in the vertical direction, similar to the horizontal direction, taking into account the attitude and altitude of the aircraft.

7. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 5, characterized in that: The real-time image enhancement model is as follows: in, Is the original image The enhancement function of is the enhanced image; is the image enhancement factor based on the light intensity , Noise Level , sharpening factor Make adjustments.

8. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1, characterized in that: In step S8, when the multi-sensor data is fused, the features of the objects in the aerial image are automatically identified and classified through a deep learning model of the artificial intelligence system, such as a convolutional neural network; The AI ​​system automatically separates different types of features in large-scale data processing and generates specific geographic information layers based on user needs; Combined with multi-sensor fusion data, the artificial intelligence system intelligently corrects and enhances the image, identifying complex shadow effects and cloud obstructions.

9. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1, characterized in that: In step S9, the accuracy assessment includes checking the accuracy of geographic coordinates, the accuracy of elevation, and the consistency of image stitching; By comparing with known high-precision reference data, the error of the surveying and mapping results is calculated, and necessary adjustments or reprocessing are made based on the evaluation results.

10. The high-precision surveying and mapping method based on surveying and mapping aerial photography according to claim 1, characterized in that: In step S10, the output surveying and mapping results include topographic maps of various scales, orthophotos, and three-dimensional terrain models, and are analyzed and reported according to user needs.

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