Terrain surveying and mapping system and method of unmanned aerial vehicle

Abstract

The invention discloses a topographic surveying and mapping system and method of an unmanned aerial vehicle, and belongs to the technical field of topographic surveying and mapping. The system comprises the following modules: a data acquisition module which is responsible for acquiring high-precision terrain and environmental parameter data of a target area by using an unmanned aerial vehicle platform; the data fusion and correction module is responsible for fusing multi-source data and meteorological information and eliminating errors among sensors; the multi-dimensional feature analysis and recognition module is used for performing feature extraction and classification on data of each sensor through multi-dimensional feature analysis, and recognizing and separating an environment interference signal and a real building deformation signal; the interference elimination and deformation extraction module is used for carrying out pattern recognition and quantitative analysis on periodic non-deformation interference and accurately extracting real deformation characteristics of a building from complex dynamic noise; the real-time analysis module is used for realizing real-time data processing and feedback and ensuring that high-precision building change information is quickly provided; and the visualization module provides multi-dimensional dynamic visualization display including a building change heat map, an interference distribution map and a time sequence curve.

Description

Technical Field

[0001] This application relates to the technical field of topographic surveying and mapping, and more specifically, to a topographic surveying and mapping system and method for unmanned aerial vehicles (UAVs). Background Art

[0002] With the rapid development of UAV technology and its wide application in various industries, the potential of UAVs in fields such as topographic surveying and mapping, building monitoring, and disaster warning has been gradually explored. Traditional topographic surveying methods mostly rely on manual data collection or the use of ground equipment for measurement. These methods are not only inefficient but also have certain errors, making it difficult to cope with the challenges of complex terrains and dynamic environments. Especially in the surveying and mapping of large-scale and complex terrains, the accuracy and speed of traditional methods often fail to meet the requirements.

[0003] As an aerial platform, UAVs have the advantages of flexibility, low cost, and fast operation, and have been widely used in topographic surveying and mapping. By carrying a variety of high-precision sensors such as light detection and ranging (LiDAR), optical cameras, and infrared sensors, UAVs can quickly collect topographic data of the target area. However, with the explosion of surveying and mapping data volume and the complexity of the terrain environment, how to ensure high-precision data collection and effective fusion, eliminate interference signals, and improve data processing efficiency has become an urgent technical problem to be solved.

[0004] At present, although many UAV surveying and mapping systems can achieve data collection and preliminary processing, they generally have the following problems: First, the fusion of multi-source data is not accurate enough to eliminate the errors between sensors; second, there is a lack of effective identification and processing mechanisms for interference signals that appear in complex terrains and dynamic environments; third, existing systems mostly rely on ground-end data processing and cannot fully utilize the real-time processing capabilities of the UAV platform, resulting in delays in real-time feedback and emergency response; fourth, existing visualization technologies are relatively simple and difficult to intuitively present terrain deformation and its change trends, and cannot provide effective auxiliary support for decision-makers.

[0005] In summary, how to ensure high-precision topographic data collection while using advanced data fusion, interference elimination, deformation extraction, and real-time data processing technologies to improve the overall performance and application value of the UAV topographic surveying and mapping system has become an urgent technical problem to be solved. Summary of the Invention

[0006] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a topographic surveying and mapping system for UAVs in view of the above problems, which is characterized by including the following modules:

[0007] A data collection module, responsible for synchronously collecting high-precision topographic and environmental parameter data of the target area using the UAV platform;

[0008] The data fusion and calibration module is responsible for fusing multi-source data and meteorological information, eliminating the errors between sensors, and ensuring that all data are accurately aligned under a unified spatio-temporal reference framework;

[0009] The multi-dimensional feature analysis and recognition module extracts and classifies the features of each sensor's data through multi-dimensional feature analysis, effectively identifying and separating environmental interference signals and real building deformation signals;

[0010] The interference elimination and deformation extraction module performs pattern recognition and quantitative analysis on periodic non-deformation interferences, and accurately extracts the real building deformation features from complex dynamic noises;

[0011] The real-time analysis module deploys an optimized lightweight data processing algorithm on the UAV platform to achieve real-time processing and feedback of data, ensuring the rapid provision of high-precision building change information;

[0012] The visualization module provides multi-dimensional dynamic visual displays, including building change heat maps, interference distribution maps, and time series curves.

[0013] The purpose of this application also lies in providing a topographic surveying method for UAVs, including the following steps:

[0014] Step 1: Use the UAV platform to synchronously and accurately collect the topographic and environmental parameters of the target area;

[0015] Step 2: Fuse multi-source data and meteorological information, eliminate the errors between sensors, and ensure that all data are accurately aligned under a unified spatio-temporal reference framework;

[0016] Step 3: Extract and classify the features of each sensor's data through multi-dimensional feature analysis, effectively identifying and separating environmental interference signals and real topographic deformation signals;

[0017] Step 4: Perform pattern recognition and quantitative analysis on periodic non-deformation interferences, and accurately extract the real topographic deformation features from complex dynamic noises;

[0018] Step 5: Deploy an optimized lightweight data processing algorithm on the UAV platform to achieve real-time processing and feedback of data, ensuring the rapid provision of high-precision topographic change information in emergency monitoring and disaster warning scenarios;

[0019] Step 6: Provide multi-dimensional dynamic visual displays, including topographic change heat maps, interference distribution maps, and time series curves.

[0020] Further, Step 1 includes the following steps:

[0021] Select a suitable multi-rotor UAV platform and configure an RTK-GPS positioning system to ensure that the positioning accuracy of the flight path reaches the centimeter level;

[0022] Preset the optimal flight altitude, speed, and overlap rate to meet the requirements of mapping data acquisition;

[0023] Equip with a high-precision LiDAR system to collect terrain point cloud data, and select appropriate laser pulse frequencies and scanning angles to achieve high-resolution characterization of complex terrains;

[0024] Integrate a multi-spectral camera to collect surface reflectance data covering visible light, near-infrared, and short-wave infrared bands, and accurately identify environmental features such as surface materials, vegetation coverage, and humidity distribution by recording reflectance differences in different bands;

[0025] Deploy a micro-meteorological station module to record temperature, humidity, air pressure, wind speed, and wind direction parameters in real time;

[0026] Design a data synchronous acquisition framework, adopt a unified time reference and precise timestamp marking to ensure the synchronization of sensor data with millisecond-level accuracy;

[0027] Based on the preset flight mission and real-time data analysis, dynamically adjust the adaptive sampling strategy, increase the sampling density in complex terrain areas, and moderately reduce the sampling frequency in flat areas to balance data quality and processing efficiency, and maximize the use of the flight time and payload capacity of the drone.

[0028] Furthermore, the drone platform has the following parameters: the maximum flight altitude is 250 meters, the cruising speed is 12 - 18 m / s, the maximum flight time is not less than 40 minutes, and the wind resistance reaches level 5;

[0029] The horizontal positioning accuracy of the RTK-GPS positioning system carried by the drone is better than ±2.5 cm, and the vertical positioning accuracy is better than ±5 cm;

[0030] The point cloud acquisition density of the LiDAR system is not less than 80 points per square meter, the ranging accuracy is better than ±3 cm, and the scanning angle range is 360° horizontal × 60° vertical;

[0031] The spatial resolution of the multi-spectral camera is not less than 5 cm / pixel, and the spectral resolution covers 5 bands, including blue light, green light, red light, near-infrared, and short-wave infrared;

[0032] The measurement accuracy of the micro-meteorological station module: temperature ±0.5°C, relative humidity ±3%, air pressure ±0.8 hPa, wind speed ±0.5 m / s, wind direction ±8°.

[0033] Furthermore, step 2 includes the following steps:

[0034] Convert all sensor data to the local coordinate system, and use feature point matching and spatial interpolation methods to correct the geometric position offset caused by GPS drift and attitude deviation;

[0035] The Network Time Protocol is adopted to eliminate the time delay in data acquisition of each sensor, and combined with the UAV flight trajectory information, the spatio-temporal inconsistency caused by platform movement is corrected to ensure that all data points have accurate four-dimensional spatio-temporal tags;

[0036] Through control point verification and comparison of data in overlapping areas, the errors between sensors are quantified, including position offset, attitude angle error and scale deformation, and high-precision alignment of multi-source data is achieved based on the inverse transformation algorithm;

[0037] Analyze the influence of temperature, humidity and air pressure changes on sensor performance, and combine real-time environmental parameters during flight to dynamically compensate for measurement deviations caused by atmospheric refraction, thermal expansion and humidity changes;

[0038] Adopt statistical analysis and spatial consistency test methods to identify and mark outliers caused by equipment failures, signal interference or environmental mutations, and at the same time use neighborhood smoothing technology to repair data missing and abnormal areas;

[0039] According to the spatial sampling density and accuracy characteristics of different sensors, design an adaptive weight allocation strategy to ensure the overall data consistency while retaining high-frequency details, and finally generate a unified high-precision terrain and environmental parameter dataset.

[0040] Furthermore, the spatial error threshold for feature point matching is set to be less than 5 cm for correcting geometric position offsets; the synchronization accuracy of the Network Time Protocol is better than 0.5 ms to ensure the time tag consistency of all data points; the distribution density of control point verification is not less than 4 per square kilometer, and the point position accuracy is better than ±1.5 cm; the temperature compensation parameter is set to a measurement deviation of ±0.08 mm / m caused by a 1°C change; the humidity compensation parameter is set to a measurement deviation of ±0.05 mm / m caused by a 10% change in relative humidity; the air pressure compensation parameter is set to a measurement deviation of ±0.03 mm / m caused by a 10 hPa change in air pressure; the standard deviation threshold for outlier identification is set to 3σ, and data points outside this range are marked as potential outliers.

[0041] Furthermore, Step 3 includes the following steps:

[0042] Apply multi-scale wavelet transform to decompose point cloud data and multi-spectral images, extract spatio-temporal features in different frequency domains, and identify the inherent frequency patterns and abnormal fluctuations of terrain structures;

[0043] Use pre-annotated terrain feature samples for training to achieve accurate surface cover classification and segmentation, and distinguish fixed ground objects from temporary environmental elements through multi-temporal data comparison;

[0044] Decompose the complex terrain change signal into independent components from different sources, identify and quantify seasonal variations, human interferences, instrument noises, and real geological movements, and achieve the blind separation and feature reconstruction of multi-source signals;

[0045] Perform semantic annotation on the identified terrain change signals, distinguish different types of terrain changes, and evaluate their development stages and potential risk levels;

[0046] Implement spatio-temporal correlation analysis, explore the causal relationship between terrain changes and environmental parameters, and distinguish temporary changes induced by the environment from persistent geological movements through conditional probability inference;

[0047] Integrate the classification results of multiple features, optimize the reliability of deformation analysis, and finally generate a high-confidence separation result map of terrain deformation and environmental interference.

[0048] Further, step 4 includes the following steps:

[0049] Convert the time-series terrain data to the frequency domain space, extract the characteristic frequencies and amplitude information, accurately identify the periodic patterns, and quantify various periodic interferences;

[0050] Based on the identified periodic interferences and real-time observation data, construct the state space equations, and use the recursive optimal estimation algorithm to suppress random noises and systematic errors while ensuring the complete retention of the non-periodic real terrain change signals;

[0051] Decompose the complex terrain change signal into a finite number of intrinsic mode functions and singular components, and through modal energy analysis and statistical significance testing, extract the terrain change patterns with physical meanings while effectively filtering out random noises;

[0052] Use differential interferometric measurement and point cloud comparison analysis to quantify the terrain elevation changes at different spatial scales, and combine the spatial context information and geological prior knowledge to distinguish local minor deformations from large-scale geological movements;

[0053] Combine Bayesian inference and Markov random field theory, and use the time continuity and spatial correlation constraints to identify the abnormal change regions that do not conform to the surrounding environment and historical trends;

[0054] Integrate blind source separation, sparse representation, and low-rank matrix decomposition techniques to maximize the suppression of noises and various interferences while retaining the amplitude and morphological integrity of terrain changes, and finally generate a terrain deformation feature map with a high signal-to-noise ratio.

[0055] Further, step 5 includes the following steps:

[0056] Compress the data processing algorithm into an edge version with low resource occupancy and fast inference speed, while maintaining the core functions, reduce the computational complexity by more than 80%;

[0057] Design a streaming data processing architecture, decompose the terrain analysis task into sub-tasks that can be executed in parallel, and realize the real-time transfer of sensor data acquisition, preprocessing, feature extraction and deformation analysis;

[0058] Build a multi-granularity analysis framework according to the urgency of data processing and computational resource constraints, and dynamically adjust the algorithm accuracy and computational complexity;

[0059] Deploy the primary analysis and early warning tasks with high real-time requirements on the drone edge computing platform, while offload the computationally intensive in-depth analysis and historical data comparison tasks to the cloud server;

[0060] Combine the preset risk threshold and expert rules to automatically generate hierarchical early warning information, and mark the spatial distribution and development trend of high-risk areas;

[0061] Ensure that key monitoring results can be transmitted to the ground command center in a timely manner under limited bandwidth conditions.

[0062] Further, step 6 includes the following steps:

[0063] Build a 3D terrain change heat map rendering engine, combine high-precision digital elevation models with terrain deformation data, and adopt an adaptive color mapping and transparency encoding strategy to visually display the spatial distribution and intensity level of terrain changes;

[0064] Map the identified various non-deformation interference sources to independent layers to achieve differential display of different interference types and support multi-dimensional cross-analysis;

[0065] Generate deformation history curves with high temporal resolution for key monitoring points and areas of interest, and overlay the time series data of environmental parameters to reveal the temporal coupling relationship between terrain changes and external factors;

[0066] Combine geological disaster sensitivity analysis and vulnerability assessment, calculate and visualize the potential impact range and loss estimation, and support multi-scenario simulation and emergency response decision-making;

[0067] Deeply integrate the terrain monitoring results with high-definition images and 3D models to build an interactive virtual-real combined scene to achieve more intuitive environmental perception and analysis;

[0068] Design a collaborative analysis and knowledge sharing platform, integrate monitoring data, analysis results and expert knowledge, and provide multi-level information display and collaborative interpretation functions.

[0069] Compared with the prior art, the present application has the following beneficial effects:

[0070] Through multi-source data fusion, interference elimination, deformation extraction, real-time processing, and multi-dimensional visualization display, this application accurately monitors and analyzes the changes in buildings and terrain, improving the accuracy, real-time performance, and intelligent level of topographic surveying and mapping. Description of the Drawings

[0071] Figure 1 It is a schematic structural diagram of a topographic surveying and mapping system of an unmanned aerial vehicle disclosed in an embodiment of this application.

[0072] Figure 2 It is a schematic flow diagram of a topographic surveying and mapping method of an unmanned aerial vehicle disclosed in an embodiment of this application. Detailed Embodiments

[0073] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The described embodiments are some, but not all, of the embodiments of the present invention.

[0074] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0075] The embodiments described below with reference to the drawings and directional terms are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0076] As Figure 1 shown, the topographic surveying and mapping system of an unmanned aerial vehicle includes the following modules:

[0077] A data acquisition module, responsible for synchronously acquiring high-precision topographic and environmental parameter data of the target area using the unmanned aerial vehicle platform;

[0078] A data fusion and calibration module, responsible for fusing multi-source data and meteorological information, eliminating errors between sensors, and ensuring that all data is accurately aligned under a unified spatio-temporal reference framework;

[0079] A multi-dimensional feature analysis and recognition module, which performs feature extraction and classification on the data of each sensor through multi-dimensional feature analysis, and effectively identifies and separates environmental interference signals and real building deformation signals;

[0080] An interference elimination and deformation extraction module, which performs pattern recognition and quantitative analysis on periodic non-deformation interference, and accurately extracts the real deformation characteristics of buildings from complex dynamic noise;

[0081] The real-time analysis module deploys an optimized lightweight data processing algorithm on the UAV platform to achieve real-time data processing and feedback, ensuring the rapid provision of high-precision building change information;

[0082] The visualization module provides multi-dimensional dynamic visualization displays, including building change heat maps, interference distribution maps, and time-series curves.

[0083] In summary, the building mapping system based on UAV remote sensing technology realizes high-precision and real-time building change monitoring by integrating multiple modules. The data acquisition module uses the UAV platform to synchronously collect topographic and environmental data, providing high-precision data for subsequent analysis; the data fusion and calibration module ensures unified alignment of data and improves data accuracy by eliminating sensor errors; the multi-dimensional feature analysis and recognition module effectively extracts and classifies sensor data, accurately distinguishing building deformation and environmental interference signals; the interference elimination and deformation extraction module accurately identifies and quantitatively analyzes periodic interference, extracting the true deformation characteristics of the building; the real-time analysis module realizes rapid data processing and feedback through lightweight algorithms, providing timely information on building changes; the visualization module enhances the intuitiveness and operability of monitoring results by dynamically displaying building change heat maps and interference distribution maps. The entire system improves the accuracy, real-time performance, and interference recognition ability of building mapping, significantly enhancing the reliability of building deformation monitoring and the visualization analysis ability.

[0084] As Figure 2 shown, the purpose of this application is also to provide a topographic mapping method for UAVs, including the following steps:

[0085] Step 1, use the UAV platform to synchronously and highly accurately collect the topographic and environmental parameters of the target area;

[0086] Step 2, fuse multi-source data and meteorological information, eliminate the errors between sensors, and ensure that all data are accurately aligned under a unified spatio-temporal reference framework;

[0087] Step 3, perform feature extraction and classification on the data of each sensor through multi-dimensional feature analysis, effectively identifying and separating environmental interference signals and true topographic deformation signals;

[0088] Step 4, perform pattern recognition and quantitative analysis on periodic non-deformation interference, and accurately extract the true deformation characteristics of the terrain from complex dynamic noise;

[0089] Step 5, deploy an optimized lightweight data processing algorithm on the UAV platform to achieve real-time data processing and feedback, ensuring the rapid provision of high-precision topographic change information in emergency monitoring and disaster warning scenarios;

[0090] Step 6, provide multi-dimensional dynamic visualization displays, including topographic change heat maps, interference distribution maps, and time-series curves.

[0091] In step 1, the UAV platform is equipped with high-precision sensor equipment, such as LiDAR, high-resolution camera, multi-spectral camera and GNSS positioning system, and takes advantage of the stable platform during flight to collect comprehensive and accurate data of the target area. During the flight, the UAV can obtain real-time information on various environmental parameters such as terrain, vegetation coverage, water distribution, etc. in the area through preset route planning, ensuring the time synchronization and spatial continuity of data collection. The advantage of this technology lies in its flexibility and efficiency. It can be quickly deployed in complex or difficult-to-reach areas. The collected data not only has a high spatial resolution, but also can form a multi-dimensional and multi-angle terrain information data set due to the collaborative work of multiple sensors. This high-precision data collection method can not only reduce the subjective errors caused by traditional manual surveys, but also greatly improve the real-time and accuracy of the data. The UAV collection technology also uses advanced positioning and attitude control to keep the sensor in a stable state during flight, ensuring that the data will not be distorted due to jitter or external interference during the acquisition process. By preprocessing the raw data collected by the sensor and removing noise and outliers, the signal-to-noise ratio and effectiveness of the data can be further improved. This step has significant technical effects, especially in the fields of emergency rescue, natural disaster monitoring, environmental protection, etc. It has great application prospects. It can provide decision makers with accurate terrain and environmental information in a short time, helping to respond quickly and make scientific decisions. More importantly, through the simultaneous collection of terrain data and environmental parameters, a three-dimensional, multi-dimensional regional environmental panorama can be constructed, thereby providing a solid data foundation and reliable spatiotemporal reference for subsequent data fusion, feature extraction and change monitoring.

[0092] Step 2 forms a unified and standardized data set by performing multi-source data fusion on data from different sensors, different platforms, and even different acquisition time periods. First, advanced algorithms are used to preprocess the acquired data. Through techniques such as data filtering, calibration, and registration, errors caused by differences in acquisition angles, time intervals, and hardware accuracies among different sensors are eliminated. The topographic, environmental, and meteorological data collected by the sensors are aligned in time and space to achieve seamless connection between different data. By using a unified spatio-temporal reference framework, not only can the precise matching of each data source in the time and space dimensions be ensured, but also the dynamic changes within the region can be monitored and compared in real time. This technology makes full use of the complementary information among multi-source data. Through algorithm optimization, systematic errors in positioning, calibration, etc. of each sensor can be eliminated, improving the reliability and accuracy of the overall data. Especially in complex environments and adverse meteorological conditions, by fusing meteorological information, factors such as wind speed, temperature, and humidity are corrected in real time, effectively compensating for the impact of environmental changes on the measurement accuracy of the sensors. At the same time, the data fusion technology also has high fault tolerance and robustness, so that even if some data is missing or abnormal, it will not have an obvious impact on the accuracy of the overall data. Through multi-level and multi-scale data matching and alignment, the finally formed unified data set provides a solid foundation for subsequent feature extraction, pattern recognition, and deformation analysis, enabling quantitative comparison and joint analysis of data from different sources in the same reference system, thus achieving precise monitoring of terrain changes and environmental dynamics in the target area and meeting the actual application requirements of high-demand scenarios such as disaster early warning and geological disaster assessment.

[0093] In step 3, an advanced multi-dimensional feature analysis technique is adopted to deeply mine and extract multi-dimensional features such as space, frequency, and time in the data collected by the unmanned aerial vehicle, so as to achieve a detailed classification and discrimination of various signals existing in the data. First of all, mathematical tools such as statistical analysis, Fourier transform, and wavelet transform are used to decompose and reconstruct the main signals, noises, and potential patterns in the data. By setting a variety of feature indicators, such as texture, gradient, shape, and dynamic change characteristics, it is possible to identify the abnormal fluctuations caused by environmental interference, and at the same time separate the information reflecting the real terrain deformation. Further, machine learning algorithms, such as support vector machine (SVM), random forest, and deep neural network, are used to classify and label the extracted features. This process not only improves the ability to identify signals in complex backgrounds, but also maintains a high recognition accuracy in the case of high data dimensions and large noise interference. The advantage of using multi-dimensional feature analysis is that it can consider both local details and overall trends in the data, so as to achieve an accurate distinction between terrain change signals and environmental noises. Especially in scenarios of regional terrain mutation or local anomaly monitoring, this technology can timely capture minute deformation characteristics, providing key information for subsequent disaster early warning and geological disaster assessment. In addition, the continuous iteration and optimization of feature extraction and classification algorithms have better adaptability and robustness when facing data under different environments and acquisition conditions, ensuring that high-efficiency and stable monitoring performance can still be maintained in complex and changeable application scenarios. Through the application of multi-dimensional feature analysis technology, the entire data processing process has been systematized and standardized, and then the accurate separation of real information and interference signals in sensor data has been achieved.

[0094] In step 4, an in-depth analysis is carried out on the periodic non-deformation interference signal. By establishing a mathematical model and pattern recognition algorithm for the interference signal, the periodic noise in the environment is distinguished from the real terrain deformation signal. Periodic non-deformation interference is often caused by equipment vibration, external mechanical movement, or meteorological periodic fluctuations. These signals exhibit obvious periodic characteristics in the frequency domain. Through methods such as Fourier transform, wavelet analysis, and autoregressive model, the periodic components existing in the data can be quantitatively decomposed and spectrum analyzed. First, all signal components with obvious periodic characteristics in the data are identified, and techniques such as template matching and spectrum analysis are used to compare and correct these components, thus effectively removing the periodic interference unrelated to the real terrain deformation. The quantitative analysis technique further models and compensates the interference signal by extracting the noise intensity, frequency change, and phase information, making the subsequent deformation feature extraction more accurate. At the same time, by combining the time series information of multi-source data, the change law of the interference signal in different time periods can be dynamically monitored, and a complete interference signal database can be constructed, so as to predict and compensate the possible periodic errors in future data acquisition in advance. This technique not only theoretically solves the interference problem of periodic noise on terrain deformation analysis, but also in practical applications, through data filtering and reconstruction, effectively extracts the real signal from complex dynamic noise. In this way, not only the accuracy of the data processing result is ensured, but also the robustness and stability in a high-noise environment are greatly improved. Through the pattern recognition and quantitative analysis of the periodic non-deformation interference signal, an accurate and interference-free terrain change model can finally be provided, providing a scientific basis for disaster warning, environmental monitoring, and engineering construction, and ensuring that every key decision is based on real and reliable data.

[0095] Step 5 focuses on integrating advanced data processing algorithms onto the UAV platform, ensuring real-time processing and analysis while collecting data, thereby providing immediate response information for emergency monitoring and disaster warning. The optimization goal of lightweight data processing algorithms is to balance the computational complexity of the algorithms and the real-time requirements. Through algorithm compression, model pruning, and edge computing technologies, high-speed data operations can be achieved even with limited computing resources on the UAV platform. Real-time data processing requires the algorithms to not only have efficient data preprocessing, feature extraction, and model inference capabilities, but also the algorithm response time must be controlled within the millisecond level to promptly feedback the monitoring results. For this purpose, multi-threaded parallel computing and adaptive sampling technologies are introduced in the algorithm design, enabling the data stream to maintain the minimum delay in all aspects of transmission, processing, and feedback. At the same time, considering the complex environment and huge data volume in emergency scenarios, incremental learning and online update mechanisms are also adopted, which can continuously optimize and adjust the model parameters during the data collection process, improving the overall adaptability to dynamic changing scenarios. By deploying the data processing algorithms on the UAV platform, not only the time and bandwidth pressure for data transmission to the backend central processing are reduced, but also the real-time performance of on-site decision-making is greatly improved, providing reliable support for emergency rescue and disaster warning. Under the tight coupling of hardware and software, this solution realizes the seamless connection of data collection, processing, and feedback, enabling each flight mission to generate high-precision terrain change reports and transmit them to the command center in real time through wireless communication technology, so that decision-makers can obtain accurate and detailed information in a timely manner, formulate response measures in a timely manner, and ensure the safety of people's lives and property.

[0096] Step 6 transforms the multi-dimensional data that has undergone real-time processing and analysis into an intuitive graphical interface through advanced visualization techniques, providing users with a comprehensive and multi-angle monitoring result display platform. First, by generating a terrain change heat map, the deformation information within the region is presented in the form of color gradients, enabling users to intuitively observe which areas have undergone significant changes and which parts are in a stable state. At the same time, the interference distribution map helps users distinguish the true terrain deformation signals from environmental noise in the data by marking the spatial positions and intensities of interference signals, thus providing a reference for subsequent analysis and decision-making. The time-series curve shows the dynamic change process of the data within the monitoring region over time. Through the real-time updated data curve, it can reflect the trend and periodic characteristics of terrain changes. This multi-dimensional dynamic visualization display is based on advanced data rendering techniques and interactive graphical interface designs, enabling complex data to be presented in a simple and intuitive manner. Users can quickly obtain key information through various forms such as graphs, charts, and maps. At the same time, it also supports interactive operations, allowing users to zoom in, zoom out, switch perspectives and time periods, and deeply analyze the data changes in specific regions or at specific time points, enhancing the flexibility and depth of data analysis. The entire visualization module not only achieves the efficient integration and display of data technically, but also conducts fine design in terms of user experience, ensuring that users at different levels and with different backgrounds can quickly understand the information conveyed by the data. Whether it is scientific researchers, emergency decision-makers, or the general public, they can obtain an intuitive perception of terrain changes and environmental dynamics through this platform, providing a highly valuable reference basis for scientific research, urban planning, and disaster emergency response. In addition, the dynamic display of multi-dimensional data also promotes the transformation of the data analysis process from traditional static reports to an interactive and real-time response monitoring mode, greatly enhancing the application effect of monitoring and the decision-making support ability.

[0097] In summary, the UAV-based mapping data acquisition and analysis method achieves high-precision monitoring of the entire process from data acquisition to result display through multiple steps and multiple technical means. Each step fully demonstrates the advantages of advanced sensing technologies, data fusion, pattern recognition, and real-time processing, ensuring both the accuracy of data and the improvement of processing efficiency and application flexibility. From synchronous acquisition to multi-source data fusion, then to multi-dimensional feature extraction and interference elimination, and finally to real-time feedback and dynamic visualization display, each link is closely connected, jointly constructing a complete and efficient monitoring system. This method not only has extremely high application value in disaster warning and emergency rescue, but also provides new technical ideas and implementation paths for geographic information systems and environmental monitoring, and will surely play a greater role in future urban planning, ecological protection, and resource management and other fields.

[0098] Furthermore, Step 1 includes the following steps:

[0099] Select a suitable multi-rotor UAV platform and configure an RTK-GPS positioning system to ensure that the positioning accuracy of the flight path reaches the centimeter level;

[0100] Preset the optimal flight altitude, speed, and overlap rate to meet the requirements of mapping data collection;

[0101] Equip with a high-precision LiDAR system to collect terrain point cloud data, and select appropriate laser pulse frequencies and scanning angles to achieve high-resolution characterization of complex terrains;

[0102] Integrate a multi-spectral camera to collect surface reflectance data, covering the visible, near-infrared, and short-wave infrared bands. By recording the reflectance differences in different bands, accurately identify environmental features such as surface materials, vegetation coverage, and humidity distribution;

[0103] Deploy a micro-meteorological station module to record temperature, humidity, air pressure, wind speed, and wind direction parameters in real time;

[0104] Design a data synchronous acquisition framework, using a unified time reference and precise timestamp marking to ensure the synchronization of sensor data with millisecond-level accuracy;

[0105] Based on the preset flight mission and real-time data analysis, dynamically adjust the adaptive sampling strategy, increasing the sampling density in complex terrain areas and moderately reducing the sampling frequency in flat areas to balance data quality and processing efficiency, and maximize the use of the UAV's flight time and payload capacity.

[0106] In summary, by precisely configuring the UAV platform and high-precision sensors, centimeter-level positioning accuracy and an efficient data acquisition strategy are achieved, ensuring high-resolution and accuracy in terrain mapping. RTK-GPS improves the accuracy of the flight path, while optimized flight parameters (altitude, speed, overlap rate) ensure the integrity and consistency of data coverage. High-precision LiDAR combined with appropriate laser pulse frequencies and scanning angles enables fine characterization of complex terrains, while the multi-spectral camera accurately analyzes surface materials, vegetation coverage, and humidity distribution through reflectance data in different bands. The integrated micro-meteorological station records environmental parameters in real time, providing meteorological compensation information and improving data reliability. In addition, the data synchronous acquisition framework uses a unified time reference and high-precision timestamps to ensure the spatio-temporal consistency of sensor data, and optimizes data acquisition under different terrain conditions through an adaptive sampling strategy, improving data quality while enhancing flight efficiency. Overall, this solution maximizes the UAV's payload capacity and endurance, while ensuring high-precision mapping data collection, and realizes intelligent and dynamic data optimization and management.

[0107] Furthermore, the drone platform has the following parameters: the maximum flight altitude is 250 meters, the cruising speed is 12 - 18 m / s, the maximum flight time is not less than 40 minutes, and the wind resistance capacity reaches level 5;

[0108] The horizontal positioning accuracy of the RTK - GPS positioning system carried by the drone is better than ±2.5 cm, and the vertical positioning accuracy is better than ±5 cm;

[0109] The point cloud acquisition density of the LiDAR system is not less than 80 points per square meter, the ranging accuracy is better than ±3 cm, and the scanning angle range is 360° horizontal × 60° vertical;

[0110] The spatial resolution of the multispectral camera is not less than 5 cm / pixel, and the spectral resolution covers 5 bands, including blue light, green light, red light, near - infrared, and short - wave infrared;

[0111] The measurement accuracy of the micro - meteorological station module: temperature ±0.5°C, relative humidity ±3%, air pressure ±0.8 hPa, wind speed ±0.5 m / s, wind direction ±8°.

[0112] Furthermore, step 2 includes the following steps:

[0113] Convert all sensor data to the local coordinate system, and use feature point matching and spatial interpolation methods to correct the geometric position offset caused by GPS drift and attitude deviation;

[0114] Adopt the Network Time Protocol to eliminate the time delay in data acquisition of each sensor, and combine the drone flight trajectory information to correct the spatio - temporal inconsistency caused by platform movement, ensuring that all data points have accurate four - dimensional spatio - temporal tags;

[0115] Quantify the errors between sensors, including position offset, attitude angle error, and scale deformation, through control point verification and overlapping area data comparison, and achieve high - precision alignment of multi - source data based on the inverse transformation algorithm;

[0116] Analyze the influence of temperature, humidity, and air pressure changes on sensor performance, and dynamically compensate for the measurement deviation caused by atmospheric refraction, thermal expansion, and humidity changes during flight by combining real - time environmental parameters;

[0117] Adopt statistical analysis and spatial consistency test methods to identify and mark outliers caused by equipment failures, signal interference, or environmental mutations, and at the same time use neighborhood smoothing technology to repair data missing and abnormal areas;

[0118] Design an adaptive weight allocation strategy according to the spatial sampling density and accuracy characteristics of different sensors, ensure the overall data consistency while retaining high - frequency details, and finally generate a unified high - precision terrain and environmental parameter dataset.

[0119] Furthermore, analyze the influence of temperature, humidity, and air pressure changes on the sensor performance, and combine real-time environmental parameters during flight to dynamically compensate for measurement deviations caused by atmospheric refraction, thermal expansion, and humidity changes, including the following steps:

[0120] For the temperature data collected in real-time during flight, use the formula ΔS T = S0·α T ·(T - T0) to compensate for thermal expansion error and adjust the measured value to S T = S0 - ΔS T , where ΔS T is the measurement deviation caused by temperature; S0 is the original measured value, i.e., the measurement data before any compensation; α T is the temperature compensation coefficient, indicating the sensitivity of the sensor to temperature changes; T is the current temperature during flight; T0 is the standard temperature during calibration; S T is the sensor measurement value after temperature compensation;

[0121] For the real-time humidity data, use the formula ΔS H = S0·α H ·(H - H0) to correct the deviation caused by humidity changes and obtain S H = S0 - ΔS H , where ΔS H is the humidity compensation amount, indicating the sensor measurement deviation caused by humidity changes; α H is the humidity sensitivity coefficient, indicating the response degree of the sensor to humidity changes; H is the current ambient humidity; H0 is the standard humidity during calibration; S H is the sensor measurement value after humidity compensation;

[0122] Utilize the real-time air pressure and, according to the formula ΔS P = S0·α P ·(P - P0) to eliminate the influence of air pressure changes and the corrected measured value is S P = S0 - ΔS P , where ΔS P is the air pressure compensation amount, indicating the sensor measurement deviation caused by air pressure changes; α P is the air pressure sensitivity coefficient, indicating the response degree of the sensor to air pressure changes; P is the current ambient air pressure; P0 is the standard air pressure during calibration; S P is the sensor measurement value after air pressure compensation;

[0123] By considering the combined influence of air pressure, temperature, and humidity, use the formula to dynamically compensate for refraction error and the corrected value is S ref = S0 - ΔS ref , where ΔS ref$\Delta$ is the atmospheric refraction compensation amount, representing the measurement deviation caused by the atmospheric refraction effect; $\beta$ is the atmospheric refraction correction coefficient, representing the response degree of the sensor to atmospheric refraction; $S$ ref is the sensor measurement value after atmospheric refraction compensation;

[0124] Comprehensively compensate for the influences of temperature, humidity, air pressure and atmospheric refraction, and calculate the finally compensated sensor output using the following formula: where $S$ comp is the sensor measurement value after comprehensive compensation.

[0125] Furthermore, the spatial error threshold for feature point matching is set to be less than 5 cm for correcting geometric position offsets; the synchronization accuracy of the Network Time Protocol is better than 0.5 ms to ensure the time mark consistency of all data points; the distribution density of control point verification is not less than 4 per square kilometer, and the point position accuracy is better than ±1.5 cm; the temperature compensation parameter is set to a measurement deviation of ±0.08 mm / m caused by a 1°C change; the humidity compensation parameter is set to a measurement deviation of ±0.05 mm / m caused by a 10% change in relative humidity; the air pressure compensation parameter is set to a measurement deviation of ±0.03 mm / m caused by a 10 hPa change in air pressure; the standard deviation threshold for outlier identification is set to 3σ, and data points outside this range are marked as potential outliers.

[0126] Furthermore, step 3 includes the following steps:

[0127] Apply multi-scale wavelet transform to decompose the point cloud data and multi-spectral images, extract spatio-temporal features in different frequency domains, and identify the inherent frequency patterns and abnormal fluctuations of the terrain structure;

[0128] Use pre-annotated terrain feature samples for training to achieve accurate surface cover classification and segmentation, and distinguish fixed ground objects and temporary environmental elements through multi-temporal data comparison;

[0129] Decompose the composite terrain change signal into independent components from different sources, identify and quantify seasonal changes, human interference, instrument noise and real geological movements, and achieve blind separation and feature reconstruction of multi-source signals;

[0130] Semantically annotate the identified terrain change signals, distinguish different types of terrain changes, and evaluate their development stages and potential risk levels;

[0131] Implement spatio-temporal correlation analysis, explore the causal relationship between terrain changes and environmental parameters, and distinguish temporary changes induced by the environment and continuous geological movements through conditional probability inference;

[0132] Integrate various feature classification results, optimize the reliability of deformation analysis, and finally generate a high-confidence separation result map of terrain deformation and environmental interference.

[0133] In summary, by applying multi-scale wavelet transform to deeply analyze point cloud data and multi-spectral images, spatio-temporal features in different frequency domains are extracted to accurately identify the inherent frequency patterns and abnormal fluctuations of the terrain structure, thereby effectively analyzing complex terrain changes. Training is carried out using pre-labeled terrain feature samples to achieve the classification and segmentation of land cover. At the same time, through the comparison of multi-temporal data, fixed ground objects and temporary environmental elements are distinguished. In addition, through blind source separation technology, the composite terrain change signal is decomposed into independent components from different sources to identify seasonal changes, human interference, instrument noise, and real geological movements, enhancing the accuracy of data processing. On this basis, semantic annotation and potential risk assessment are performed on the terrain change signal. By combining spatio-temporal correlation analysis to explore the causal relationship between terrain changes and environmental parameters, conditional probability inference is used to further distinguish temporary changes from persistent geological movements. Finally, by integrating the classification results of multiple features, the reliability of deformation analysis is optimized, and a high-confidence terrain deformation and environmental interference separation result map is generated, ensuring high precision and high credibility in terrain change analysis.

[0134] Furthermore, the decomposition level of the multi-scale wavelet transform is set to 4 layers, covering spatial scale features from 0.5 meters to 40 meters; the classification accuracy requirement for land cover reaches over 85%, and the minimum unit area for segmentation is no more than 2 square meters; the time intervals for the comparison of multi-temporal data are set to four levels: 12 hours, 24 hours, 7 days, and 30 days; the number of independent components for signal decomposition is set to 3 - 6, adaptively adjusted according to the signal-to-noise ratio; the cycle range for seasonal changes is set to 30 - 365 days, and the characteristic frequency range for human interference is 1 - 30 days; the confidence threshold for spatio-temporal correlation analysis is set to 90%, and the correlation coefficient threshold is 0.7.

[0135] Furthermore, spatio-temporal correlation analysis is implemented to explore the causal relationship between terrain changes and environmental parameters, and conditional probability inference is used to distinguish temporary changes induced by the environment from persistent geological movements, including the following steps:

[0136] Based on the formula P(D(t),E(t)) = P(D(t)|E(t))P(E(t)), a joint probability distribution model of terrain changes and environmental parameters is established, where P(D(t),E(t)) represents the joint probability distribution of terrain change D(t) and environmental parameter E(t) at time t; P(D(t)|E(t)) represents the conditional probability of the occurrence of terrain change D(t) given the environmental parameter E(t); P(E(t)) represents the marginal probability distribution of the environmental parameter E(t), that is, the probability of the occurrence of the environmental parameter at a certain moment t;

[0137] Using A formula is used to calculate the conditional probability of terrain change under different environmental conditions, quantifying the direct impact of environmental factors on terrain change;

[0138] The terrain change D(t) is decomposed into a long-term trend T(t) and a short-term fluctuation N(t), i.e., D(t) = T(t) + N(t), and their respective characteristics are extracted through time series analysis;

[0139] Using Analyze the relationship between the short-term fluctuation N(t) and the environmental parameter E(t) to determine whether the terrain change is environmentally induced. Among them, P(N(t)|E(t)) represents the conditional probability of the occurrence of the short-term fluctuation N(t) under the given environmental parameter E(t); σ is the standard deviation of the environmental parameter E(t), reflecting the degree of fluctuation of the environmental parameter; μ E (t) is the mean value of the environmental parameter E(t) at time t, representing the average level of the environmental parameter at a certain moment;

[0140] Comprehensively analyze the interaction between the terrain and environmental parameters in each region, use statistical tests to verify the causal relationship, and mark and conduct risk assessment on abnormal regions.

[0141] Furthermore, step 4 includes the following steps:

[0142] Convert the time-series terrain data to the frequency domain space, extract characteristic frequencies and amplitude information, accurately identify periodic patterns, and quantify various periodic interferences;

[0143] Based on the identified periodic interferences and real-time observation data, construct a state space equation set, and use the recursive optimal estimation algorithm to suppress random noise and systematic errors while ensuring the complete retention of the non-periodic true terrain change signal;

[0144] Decompose the complex terrain change signal into a finite number of intrinsic mode functions and singular components, and through modal energy analysis and statistical significance testing, extract terrain change patterns with physical meanings while effectively filtering out random noise;

[0145] Use differential interferometric measurement and point cloud comparison analysis to quantify the terrain elevation change at different spatial scales, and combine spatial context information and geological prior knowledge to distinguish local micro-deformations from large-scale geological movements;

[0146] Combining Bayesian inference and Markov random field theory, use time continuity and spatial correlation constraints to identify abnormal change regions that do not conform to the surrounding environment and historical trends;

[0147] Integrate blind source separation, sparse representation, and low-rank matrix decomposition techniques to maximize the suppression of noise and various interferences while retaining the amplitude and morphological integrity of the terrain change, and finally generate a terrain deformation feature map with a high signal-to-noise ratio.

[0148] In summary, by converting the time-series terrain data into the frequency domain space, the characteristic frequencies and amplitude information are accurately extracted, thereby effectively identifying and quantifying the periodic interference signals and improving the accuracy of data analysis. Based on the identified periodic interference, a state-space equation set is constructed, and the recursive optimal estimation algorithm is used to suppress the random noise and systematic errors while ensuring the integrity of the true terrain change signals. In addition, the method adopts the modal decomposition technology to decompose the complex terrain change signals into a finite number of intrinsic mode functions and singular components. Through the modal energy analysis and statistical significance test, the terrain change patterns with physical meanings are extracted, and the random noise is effectively filtered out. In terms of spatial analysis, by combining differential interferometric measurement and point cloud comparison analysis, the terrain elevation changes at different scales are accurately quantified, and with the help of geological prior knowledge, the local minor deformations and large-scale geological movements are distinguished. Further, using Bayesian inference and Markov random field theory, through the constraints of time continuity and spatial correlation, the abnormal change areas are accurately identified, enhancing the stability of terrain change detection. Finally, by integrating the blind source separation, sparse representation, and low-rank matrix decomposition technologies, while retaining the integrity of the terrain deformation amplitude and morphology, various types of interference are maximally suppressed, and a terrain deformation feature map with a high signal-to-noise ratio is generated, thus ensuring the high precision, high stability, and high reliability of terrain change analysis.

[0149] Furthermore, the noise suppression ability of the recursive optimal estimation algorithm reaches above 15 dB, while ensuring that the retention rate of non-periodic signals is not less than 90%; the threshold setting of the intrinsic mode function decomposition is that the characteristic energy accounts for not less than 80% of the total energy; the minimum detectable deformation amount of the differential interferometric measurement is 8 mm, and the elevation change detection accuracy of the point cloud comparison analysis is better than 1.5 cm; the prior probability model of Bayesian inference is constructed based on the historical data of the past 120 days, and the spatial correlation radius of the Markov random field is set to 20 - 80 m; the rank parameter r of the low-rank matrix decomposition is set to 8% - 12% of the matrix dimension to ensure the efficiency of signal sparse representation.

[0150] Furthermore, by using differential interferometric measurement and point cloud comparison analysis, the terrain elevation changes at different spatial scales are quantified, and combined with the spatial context information and geological prior knowledge, the local minor deformations and large-scale geological movements are distinguished, including the following steps:

[0151] By calculating the phase difference Δφ of the radar echo, according to the formula the elevation change of the ground surface is quantified, where Δh DInSAR is the elevation change amount calculated by differential interferometric measurement; λ is the wavelength of the radar signal; θ is the angle between the radar beam and the ground;

[0152] By calculating the elevation difference in the continuous time point cloud data Evaluate the local elevation changes, and analyze the overall trend of deformation using the mean and standard deviation to obtain detailed local deformation information, where Δh PC is the elevation change calculated based on the point cloud data; is the elevation value of the i-th point measured at time t + 1; is the elevation value of the i-th point measured at time t;

[0153] By weighted fusion of DInSAR and point cloud data, the formula is expressed as: Δh fusion (x, y) = w1·Δh DInSAR (x, y) + w2·Δh PC (x, y), where Δh fusion (x, y) is the elevation change at the coordinate (x, y) after fusing DInSAR and point cloud data; w1 and w2 are the weighted coefficients of DInSAR and point cloud data respectively; Δh DInSAR (x, y), Δh PC (x, y) are the elevation changes calculated by DInSAR and point cloud data respectively at the coordinate (x, y);

[0154] The large-scale deformation is obtained through Δh large (x, y) = G σ *Δh fusion (x, y), while the local deformation is Δh local (x, y) = Δh fusion (x, y) - Δh large (x, y), effectively distinguishing the deformation characteristics of different scales, where Δh large (x, y) represents the large-scale topographic change at the spatial position (x, y); Δh local (x, y) represents the small-scale topographic change at the spatial position (x, y);

[0155] According to the geological prior knowledge, set the threshold T large of the large-scale deformation and the threshold T local of the local small deformation. If Δh large (x, y) exceeds T large , it is determined as a large-scale geological movement; if Δh local (x, y) exceeds T local , it is a local small deformation.

[0156] Furthermore, step 5 includes the following steps:

[0157] Compress the data processing algorithm into an edge version with low resource occupancy and fast inference speed, while maintaining the core functions, reducing the computational complexity by more than 80%;

[0158] Design a streaming data processing architecture, decompose terrain analysis tasks into sub-tasks that can be executed in parallel, and achieve real-time transfer of sensor data acquisition, preprocessing, feature extraction, and deformation analysis;

[0159] Build a multi-granularity analysis framework based on the urgency of data processing and computational resource constraints, and dynamically adjust the algorithm accuracy and computational complexity;

[0160] Deploy primary analysis and early warning tasks with high real-time requirements on the drone edge computing platform, while offload computationally intensive in-depth analysis and historical data comparison tasks to the cloud server;

[0161] Combine preset risk thresholds and expert rules to automatically generate hierarchical early warning information, and mark the spatial distribution and development trend of high-risk areas;

[0162] Ensure that key monitoring results can be transmitted to the ground command center in a timely manner under limited bandwidth conditions.

[0163] In summary, by optimizing the data processing algorithm, the computational complexity is reduced by more than 80%, making it applicable to resource-constrained drone edge computing platforms, thus achieving efficient and low-power data analysis. Adopting a streaming data processing architecture, decompose terrain analysis tasks into sub-tasks that can be executed in parallel, enabling real-time transfer of sensor data acquisition, preprocessing, feature extraction, and deformation analysis, and improving the system response speed. The multi-granularity analysis framework dynamically adjusts the algorithm accuracy and computational complexity according to the task urgency and computational resource constraints to ensure efficient operation in different application scenarios. In addition, this method adopts a hierarchical computing strategy, deploying primary analysis and early warning tasks with high real-time requirements on the drone side, while offloading computationally intensive in-depth analysis and historical data comparison tasks to the cloud server, thus taking into account both computational efficiency and analysis accuracy. In terms of data application, combine preset risk thresholds and expert rules to automatically generate hierarchical early warning information, mark the spatial distribution and development trend of high-risk areas, and ensure the reliability of disaster monitoring.

[0164] Further, step 6 includes the following steps:

[0165] Build a 3D terrain change heat map rendering engine, combine high-precision digital elevation models with terrain deformation data, and adopt an adaptive color mapping and transparency encoding strategy to intuitively display the spatial distribution and intensity level of terrain changes;

[0166] Map various identified non-deformation interference sources to independent layers to achieve differential display of different interference types and support multi-dimensional cross-analysis;

[0167] Generate deformation history curves with high temporal resolution for key monitoring points and regions of interest, and overlay the time-series data of environmental parameters to reveal the temporal coupling relationship between topographic changes and external factors;

[0168] Combine geological hazard sensitivity analysis and vulnerability assessment, calculate and visualize the potential impact range and loss estimation to support multi-scenario simulation and emergency response decision-making;

[0169] Deeply integrate the topographic monitoring results with high-definition images and 3D models to construct an interactive scene that combines virtual and real elements, and achieve more intuitive environmental perception and analysis;

[0170] Design a collaborative analysis and knowledge sharing platform, integrate monitoring data, analysis results and expert knowledge, and provide multi-level information display and collaborative interpretation functions.

[0171] In summary, by constructing a 3D topographic change heatmap rendering engine, combining high-precision digital elevation models and topographic deformation data, and adopting an adaptive color mapping and transparency encoding strategy, the spatial distribution and intensity level of topographic changes are intuitively displayed, improving the data visualization effect. At the same time, non-deformation interference sources are mapped to independent layers to support the differential display and multi-dimensional cross-analysis of different interference types, enhancing the ability to identify complex environmental interferences. For key monitoring points and regions of interest, generate deformation history curves with high temporal resolution and overlay the time-series data of environmental parameters to reveal the temporal coupling relationship between topographic changes and external factors. In addition, this method combines geological hazard sensitivity analysis and vulnerability assessment, calculates and visualizes the potential impact range and loss estimation, providing data support for multi-scenario simulation and emergency response. In terms of data fusion, deeply integrate the topographic monitoring results with high-definition images and 3D models to construct an interactive scene that combines virtual and real elements, enhancing the intuitiveness of environmental perception and analysis. Finally, design a collaborative analysis and knowledge sharing platform, integrate monitoring data, analysis results and expert knowledge, support multi-level information display and collaborative interpretation, enhance the application value of topographic monitoring data, and provide intelligent support for disaster warning and emergency decision-making.

[0172] Furthermore, the spatial resolution of the 3D topographic change heatmap is 1 meter, the display accuracy of the change amount is millimeter-level, and 10-level color scales are used to represent the deformation intensity;

[0173] The interference source layer is divided into 5 categories: vegetation change, hydrological change, meteorological impact, human activities, and sensor error, and each category is identified by a different legend;

[0174] The sampling interval of the high-temporal-resolution deformation history curve is 2 hours, and the time span can be adjusted to 24 hours, 7 days, 30 days, and 180 days;

[0175] The spatial resolution of the potential impact range assessment is 10 meters, and the uncertainty range of the loss estimation is controlled within ±25%;

[0176] The virtual-real interactive scenario supports stepless zoom from 1:1 to 1:5000, and the geometric accuracy of the 3D model is better than 15 cm;

[0177] The collaborative analysis platform supports the simultaneous access of no less than 15 user terminals, and the data response delay is less than 3 seconds.

[0178] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A topographic mapping system for a drone, characterized in that, It includes the following modules: The data acquisition module is responsible for synchronously acquiring high-precision terrain and environmental parameter data of the target area using the UAV platform; The data fusion and calibration module is responsible for fusing multi-source data and meteorological information, eliminating the errors between sensors, and ensuring that all data are accurately aligned under a unified spatio-temporal reference framework; The multi-dimensional feature analysis and recognition module extracts and classifies the features of each sensor data through multi-dimensional feature analysis, and effectively identifies and separates environmental interference signals and real building deformation signals; The interference elimination and deformation extraction module performs pattern recognition and quantitative analysis on periodic non-deformation interference, and accurately extracts the real deformation features of the building from complex dynamic noise; The real-time analysis module deploys an optimized lightweight data processing algorithm on the UAV platform to achieve real-time processing and feedback of data, and ensures the rapid provision of high-precision building change information; The visualization module provides multi-dimensional dynamic visualization displays, including building change heat maps, interference distribution maps, and time series curves.

2. The topographic mapping method of the unmanned aerial vehicle is implemented based on the topographic mapping system of the unmanned aerial vehicle described in claim 1, and is characterized in that, It includes the following steps: Step 1: Synchronously and highly precisely acquire the terrain and environmental parameters of the target area using the UAV platform; Step 2: Fuse multi-source data and meteorological information, eliminate the errors between sensors, and ensure that all data are accurately aligned under a unified spatio-temporal reference framework; Step 3: Extract and classify the features of each sensor data through multi-dimensional feature analysis, and effectively identify and separate environmental interference signals and real terrain deformation signals; Step 4: Perform pattern recognition and quantitative analysis on periodic non-deformation interference, and accurately extract the real deformation features of the terrain from complex dynamic noise; Step 5: Deploy an optimized lightweight data processing algorithm on the UAV platform to achieve real-time processing and feedback of data, and ensure the rapid provision of high-precision terrain change information in emergency monitoring and disaster warning scenarios; Step 6: Provide multi-dimensional dynamic visualization displays, including terrain change heat maps, interference distribution maps, and time series curves.

3. The topographic mapping method of the unmanned aerial vehicle according to claim 2, wherein, Step 1 includes the following steps: Select a suitable multi-rotor UAV platform and configure an RTK-GPS positioning system to ensure that the positioning accuracy of the flight path reaches the centimeter level; Preset the optimal flight height, speed, and overlap rate to meet the requirements of mapping data acquisition; Carry a high-precision LiDAR system to collect terrain point cloud data, and select appropriate laser pulse frequencies and scanning angles to achieve high-resolution characterization of complex terrains; Integrate a multi-spectral camera to collect surface reflectance data, covering visible light, near-infrared, and short-wave infrared bands, and accurately identify environmental features such as surface materials, vegetation coverage, and humidity distribution by recording the reflection differences in different bands; Deploy a micro meteorological station module to record temperature, humidity, air pressure, wind speed, and wind direction parameters in real time; Design a data synchronous acquisition framework, adopt a unified time reference and accurate timestamp marking to ensure the synchronization of each sensor data with a millisecond-level accuracy; Based on the preset flight mission and real-time data analysis, dynamically adjust the adaptive sampling strategy, increase the sampling density in complex terrain areas, and moderately reduce the sampling frequency in flat areas to balance data quality and processing efficiency, and maximize the use of the flight time and payload capacity of the UAV.

4. The topographic mapping method of the drone according to claim 3, characterized in that, The drone platform has the following parameters: the maximum flight altitude is 250 meters, the cruising speed is 12 - 18 m / s, the maximum flight time is not less than 40 minutes, and the wind resistance ability reaches level 5; The horizontal positioning accuracy of the RTK - GPS positioning system carried by the drone is better than ±2.5 cm, and the vertical positioning accuracy is better than ±5 cm; The point cloud acquisition density of the LiDAR system is not less than 80 points per square meter, the ranging accuracy is better than ±3 cm, and the scanning angle range is 360° horizontal × 60° vertical; The spatial resolution of the multispectral camera is not less than 5 cm / pixel, and the spectral resolution covers 5 bands, including blue light, green light, red light, near - infrared, and short - wave infrared; The measurement accuracy of the micro - meteorological station module: temperature ±0.5°C, relative humidity ±3%, air pressure ±0.8 hPa, wind speed ±0.5 m / s, wind direction ±8°.

5. The topographic mapping method of the drone according to claim 2, characterized in that, Step 2 includes the following steps: Convert all sensor data to the local coordinate system, and use feature - point matching and spatial interpolation methods to correct the geometric position offset caused by GPS drift and attitude deviation; Use the Network Time Protocol to eliminate the time delay in the acquisition of each sensor's data, and combine the drone's flight trajectory information to correct the spatio - temporal inconsistency caused by platform movement, ensuring that all data points have accurate four - dimensional spatio - temporal tags; Quantify the errors between sensors, including position offset, attitude angle error, and scale deformation, through control - point verification and data comparison in the overlapping area, and achieve high - precision alignment of multi - source data based on the inverse transformation algorithm; Analyze the influence of temperature, humidity, and air pressure changes on sensor performance, and dynamically compensate for the measurement deviation caused by atmospheric refraction, thermal expansion, and humidity changes during flight by combining real - time environmental parameters; Use statistical analysis and spatial consistency test methods to identify and mark outliers caused by equipment failures, signal interference, or environmental mutations, and at the same time use neighborhood smoothing technology to repair data missing and abnormal areas; According to the spatial sampling density and accuracy characteristics of different sensors, design an adaptive weight allocation strategy to ensure the overall data consistency while retaining high - frequency details, and finally generate a unified high - precision terrain and environmental parameter dataset.

6. The topographic mapping method of the unmanned aerial vehicle according to claim 5, characterized in that, The spatial error threshold for feature - point matching is set to be less than 5 cm for correcting geometric position offset; the synchronization accuracy of the Network Time Protocol is better than 0.5 ms to ensure the time - tag consistency of all data points; the distribution density of control - point verification is not less than 4 per square kilometer, and the point - position accuracy is better than ±1.5 cm; the temperature compensation parameter is set to ±0.08 mm / m of measurement deviation caused by each °C change; the humidity compensation parameter is set to ±0.05 mm / m of measurement deviation caused by every 10% change in relative humidity; the air - pressure compensation parameter is set to ±0.03 mm / m of measurement deviation caused by every 10 hPa change in air pressure; the standard - deviation threshold for outlier identification is set to 3σ, and data points outside this range are marked as potential outliers.

7. The topographic mapping method of the unmanned aerial vehicle according to claim 2, characterized in that Step 3 includes the following steps: Apply multi - scale wavelet transform to decompose the point cloud data and multispectral images, extract spatio - temporal features in different frequency domains, and identify the inherent frequency patterns and abnormal fluctuations of the terrain structure; Train using pre-annotated topographic feature samples to achieve accurate land cover classification and segmentation, and distinguish fixed features from temporary environmental elements through multi-temporal data comparison; Decompose the complex topographic change signal into independent components from different sources, identify and quantify seasonal changes, human interference, instrument noise, and real geological movements, and achieve blind separation and feature reconstruction of multi-source signals; Semantically annotate the identified topographic change signals, distinguish different types of topographic changes, and evaluate their development stages and potential risk levels; Implement spatio-temporal correlation analysis to explore the causal relationship between topographic changes and environmental parameters, and distinguish temporary changes induced by the environment from persistent geological movements through conditional probability inference; Integrate the classification results of multiple features, optimize the reliability of deformation analysis, and finally generate a high-confidence separation result map of topographic deformation and environmental interference.

8. The topographic mapping method of the unmanned aerial vehicle according to claim 2, characterized in that Step 4 includes the following steps: Convert the time-series topographic data to the frequency domain space, extract characteristic frequencies and amplitude information, accurately identify periodic patterns, and quantify various periodic interferences; Based on the identified periodic interferences and real-time observation data, construct a state space equation set, and use the recursive optimal estimation algorithm to suppress random noise and systematic errors while ensuring the complete retention of non-periodic real topographic change signals; Decompose the complex topographic change signal into a finite number of intrinsic mode functions and singular components, and extract physically meaningful topographic change patterns through modal energy analysis and statistical significance tests while effectively filtering out random noise; Use differential interferometric measurement and point cloud comparison analysis to quantify topographic elevation changes at different spatial scales, and combine spatial context information and geological prior knowledge to distinguish local minor deformations from large-scale geological movements; Combine Bayesian inference and Markov random field theory, and use time continuity and spatial correlation constraints to identify abnormal change regions that do not conform to the surrounding environment and historical trends; Integrate blind source separation, sparse representation, and low-rank matrix decomposition techniques to maximize the suppression of noise and various interferences while retaining the amplitude and morphological integrity of topographic changes, and finally generate a topographic deformation feature map with a high signal-to-noise ratio.

9. The topographic mapping method of the unmanned aerial vehicle according to claim 2, characterized in that, Step 5 includes the following steps: Compress the data processing algorithm into an edge version with low resource occupancy and fast inference speed, and reduce the computational complexity by more than 80% while maintaining the core functions; Design a streaming data processing architecture, decompose the topographic analysis task into sub-tasks that can be executed in parallel, and achieve real-time transfer of sensor data acquisition, preprocessing, feature extraction, and deformation analysis; Build a multi-granularity analysis framework based on the urgency of data processing and computational resource constraints, and dynamically adjust the algorithm accuracy and computational complexity; Deploy primary analysis and early warning tasks with high real-time requirements on the drone edge computing platform, while offloading computationally intensive in-depth analysis and historical data comparison tasks to the cloud server; Automatically generate hierarchical early warning information in combination with preset risk thresholds and expert rules, and mark the spatial distribution and development trend of high-risk areas; Ensure that key monitoring results can be transmitted to the ground command center in a timely manner under limited bandwidth conditions.

10. The topographic mapping method of the unmanned aerial vehicle according to claim 2, characterized in that, Step 6 includes the following steps: Build a 3D terrain change heat map rendering engine. Combine high-precision digital elevation models with terrain deformation data, and adopt an adaptive color mapping and transparency encoding strategy to intuitively display the spatial distribution and intensity level of terrain changes; Map various identified non-deformation interference sources to independent layers to achieve differential display of different interference types and support multi-dimensional cross-analysis; Generate deformation history curves with high temporal resolution for key monitoring points and regions of interest, and overlay the time-series data of environmental parameters to reveal the temporal coupling relationship between terrain changes and external factors; Combine geological disaster sensitivity analysis and vulnerability assessment, calculate and visualize the potential impact range and loss estimation, and support multi-scenario simulation and emergency response decision-making; Deeply integrate terrain monitoring results with high-definition images and 3D models to build an interactive scene that combines virtual and real, and achieve more intuitive environmental perception and analysis; Design a collaborative analysis and knowledge sharing platform, integrate monitoring data, analysis results and expert knowledge, and provide multi-level information display and collaborative interpretation functions.

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