A terrain surveying and mapping method and system based on aerial surveying and mapping technology

Abstract

The present invention provides a terrain surveying and mapping method and system based on aerial surveying and mapping technology, which relates to the field of data processing technology. The method comprises: the state estimation value is the estimated distance between the drone and the target area, and the steps are continuously repeated to capture the radio wave ranging data of the entire target area; the radio wave ranging data is transmitted to the ground processing system in real time through an encryption algorithm; the ground processing system pre-processes the radio wave ranging data to obtain the pre-processed radio wave ranging data; according to the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the drone, a three-dimensional space positioning solution is performed to generate the three-dimensional coordinate point cloud data of the target area; according to the three-dimensional coordinate point cloud data of the target area, a grid model is constructed, and smoothing and texture mapping are performed to generate a three-dimensional terrain model. The present invention improves the efficiency and accuracy of terrain surveying and mapping.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a terrain surveying and mapping method and system based on aerial surveying and mapping technology. Background Art

[0002] Traditional topographic surveying and mapping methods mainly include manual field measurement and satellite remote sensing mapping, etc. These methods have played an important role in topographic surveying and mapping in the past, but with the continuous advancement of technology and the increase in application needs, they have gradually exposed some inherent defects and shortcomings.

[0003] For example, although manual field measurement can provide relatively accurate data, the measurement process is time-consuming and labor-intensive, inefficient, and poorly adaptable to complex terrain and harsh environments. In addition, manual measurement also has certain safety hazards, especially when measuring in dangerous areas such as mountains and canyons, the safety of personnel is difficult to be fully guaranteed.

[0004] Secondly, although satellite remote sensing mapping has the advantages of wide coverage and short data acquisition cycle, its resolution and accuracy are often limited by multiple factors such as satellite orbit altitude, sensor performance and atmospheric conditions. Therefore, in scenarios where high-precision terrain data is required, satellite remote sensing mapping may not meet the needs. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a terrain surveying and mapping method and system based on aerial surveying and mapping technology, which can quickly capture terrain data of the target area, which not only improves the efficiency and accuracy of terrain surveying and mapping, but also reduces surveying and mapping costs and safety risks.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a terrain surveying and mapping method based on aerial surveying and mapping technology comprises:

[0008] Based on the drone’s flight path, the radio wave ranging device transmits a signal to the target area;

[0009] Receive the reflected signal reflected from the target terrain area and calculate the time difference between the reflected signal and the transmitted signal;

[0010] Convert the time difference into an observed value of the distance. Based on the observed value, update the state estimate at the current moment. The state estimate is the estimated distance between the drone and the target area. Repeat the steps to capture the radio wave ranging data of the entire target area.

[0011] The radio wave ranging data is transmitted to the ground processing system in real time through an encryption algorithm;

[0012] The ground processing system pre-processes the radio wave ranging data to obtain pre-processed radio wave ranging data;

[0013] Based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, three-dimensional spatial positioning is solved to generate three-dimensional coordinate point cloud data of the target area;

[0014] According to the three-dimensional coordinate point cloud data of the target area, a grid model is constructed, and smoothing and texture mapping are performed to generate a three-dimensional terrain model.

[0015] Further, according to the flight path of the drone, the radio wave ranging device transmits a signal to the target area, including:

[0016] According to the flight route of the drone, ant individuals are defined, where each ant represents a signal transmission strategy of a radio wave ranging device. The signal transmission strategy includes transmission direction, transmission intensity and transmission time.

[0017] According to the individual ants, the pheromone concentration used to indicate the advantages and disadvantages of different emission strategies is determined;

[0018] According to the pheromone concentration, the solution space is constructed. The solution space consists of all possible emission points on the flight path of the drone. Each point may be the current location where the ant chooses to emit a signal.

[0019] Each ant selects the next launch point according to a certain probability based on the current position and the pheromone concentration in the solution space; each time the ant selects a launch point, the pheromone concentration at the launch point is updated according to the quality of the launch point;

[0020] When all ants have completed a round of launch point selection, the performance of the launch strategy represented by each ant is evaluated to obtain the evaluation results;

[0021] According to the evaluation results, the pheromone update rule is adjusted, and the ant path selection and evaluation optimization stages are repeated until the preset number of iterations is reached to obtain the final radio wave ranging device emission strategy, which includes the final emission point sequence, emission intensity and emission time;

[0022] According to the final radio wave ranging device transmission strategy, the radio wave ranging device transmits a signal to the target area.

[0023] Further, receiving a reflected signal reflected from the target terrain area and calculating a time difference between the reflected signal and the transmitted signal include:

[0024] After transmitting the signal, the radio wave ranging device captures the reflected signal;

[0025] Initialize the gradient descent parameters and calculate the estimated difference between the received reflected signal and the transmitted signal;

[0026] Define a loss function to measure the accuracy of the estimation error;

[0027] According to the gradient information of the loss function, the estimated difference is adjusted, and this process is repeated until the preset number of iterations is reached to obtain the final time difference.

[0028] Furthermore, the radio wave ranging data is transmitted to the ground processing system in real time through an encryption algorithm, including:

[0029] Generate an initial encryption key according to the symmetric encryption algorithm;

[0030] Optimizing the initial encryption key to obtain an optimized key;

[0031] Encrypting the radio wave ranging data using the optimized key to obtain encrypted ranging data;

[0032] Encapsulating the encrypted ranging data into a data packet format for transmission to obtain an encapsulated data packet;

[0033] Send the encapsulated data packet to the ground processing system.

[0034] Further, the initial encryption key is optimized to obtain an optimized key, including:

[0035] Determine the length of the key according to the symmetric encryption algorithm;

[0036] A set of initial keys is randomly generated as the initial population, each key represents an individual;

[0037] Define a fitness function for evaluating the quality of each key;

[0038] According to the fitness function, the fitness value of each initial key is calculated;

[0039] According to the fitness function value, determine the individuals in the population to enter the crossover operation to generate a new key;

[0040] The new key is mutated and optimized iteratively until the preset number of iterations is reached to obtain the optimized key.

[0041] Furthermore, based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, a three-dimensional spatial positioning solution is performed to generate three-dimensional coordinate point cloud data of the target area, including:

[0042] Convert the flight trajectory and attitude information of the drone from the drone's own coordinate system to the global coordinate system;

[0043] According to the attitude and position information of the UAV, the radio wave ranging data is converted from the UAV coordinate system to the global coordinate system;

[0044] For each ranging data point, a ranging equation is established between it and the target point according to its position and ranging value in the global coordinate system;

[0045] Solve the ranging equation to determine the three-dimensional coordinates of the target point in the global coordinate system;

[0046] All the calculated target point positions are combined to form the three-dimensional coordinate point cloud data of the target area.

[0047] Furthermore, a grid model is constructed based on the three-dimensional coordinate point cloud data of the target area, and smoothing and texture mapping are performed to generate a three-dimensional terrain model, including:

[0048] Read the three-dimensional coordinate point cloud data of the target area, calculate the distance between each point and its neighboring points for each point in the three-dimensional coordinate point cloud data, and perform screening to obtain a filtered three-dimensional coordinate point cloud data set;

[0049] By triangulation, the filtered three-dimensional coordinate point cloud data set is converted into a triangular mesh determined by three vertices;

[0050] For each vertex in the triangle mesh, find the adjacent vertices directly connected to it, and for each vertex, calculate the average position of all its adjacent vertices;

[0051] Move the vertex to the average position of its adjacent vertices by a certain distance, and repeat the vertex position adjustment process until the preset number of iterations is reached to obtain an optimized triangular mesh;

[0052] Acquire a two-dimensional texture image of a target area, and preprocess the two-dimensional texture image to obtain a preprocessed texture image;

[0053] For each triangle in the triangular mesh, the texture coordinates of the triangular mesh are calculated according to the corresponding positions of its vertices on the texture image;

[0054] Using texture coordinates, the preprocessed texture image is mapped onto the triangular mesh, and corresponding texture information is assigned to each triangular mesh to obtain mapped texture information;

[0055] The final 3D terrain model is generated based on the optimized triangular mesh and mapped texture information.

[0056] In a second aspect, a terrain surveying and mapping system based on aerial surveying and mapping technology comprises:

[0057] The transmitting module is used to transmit signals to the target area by the radio wave ranging device according to the flight route of the UAV;

[0058] A receiving module, used to receive a reflected signal reflected from a target terrain area and calculate a time difference between the reflected signal and the transmitted signal;

[0059] A processing module is used to convert the time difference into an observed value of the distance, and update the state estimation value at the current moment according to the observed value, where the state estimation value is the estimated distance between the UAV and the target area, and the steps are repeated continuously to capture the radio wave ranging data of the entire target area;

[0060] A transmission module is used to transmit the radio wave ranging data to the ground processing system in real time through an encryption algorithm;

[0061] A preprocessing module, used for the ground processing system to preprocess the radio wave ranging data to obtain preprocessed radio wave ranging data;

[0062] The calculation module is used to perform three-dimensional spatial positioning calculation based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, and generate three-dimensional coordinate point cloud data of the target area;

[0063] The construction module is used to construct a grid model based on the three-dimensional coordinate point cloud data of the target area, and perform smoothing and texture mapping to generate a three-dimensional terrain model.

[0064] According to a third aspect, a computing device includes:

[0065] one or more processors;

[0066] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0067] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0068] The above scheme of the present invention includes at least the following beneficial effects.

[0069] By precisely controlling the flight path of the drone, comprehensive and efficient mapping of the target area can be ensured. By receiving the reflected signal and calculating the time difference, the distance between the drone and the target terrain area can be accurately measured. This method has the characteristics of high precision and fast response, which helps to improve the accuracy and efficiency of terrain mapping. Converting the time difference into a distance observation value can intuitively reflect the actual distance between the drone and the target area. By continuously updating the state estimate, the position of the drone and the changes in the target area can be tracked in real time, ensuring the real-time and accuracy of the mapping data.

[0070] By repeating the measurement process, the target area can be fully covered to ensure that complete and continuous terrain data is obtained. The use of encryption algorithms for data transmission can ensure the security and integrity of data during transmission. The real-time transmission function ensures that the ground processing system can receive the latest surveying and mapping data in a timely manner, improving processing efficiency.

[0071] The preprocessing step can remove noise and outliers in the raw data, improving the reliability and accuracy of the data. Combined with the flight trajectory and attitude information of the drone, the preprocessed data can be accurately positioned in three dimensions.

[0072] By constructing a mesh model and performing smoothing and texture mapping, a realistic 3D terrain model can be generated. This model not only has a high degree of realism and visualization, but also helps to better understand and analyze the terrain characteristics of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flowchart of a terrain surveying and mapping method based on aerial surveying and mapping technology provided by an embodiment of the present invention.

[0074] Figure 2 It is a schematic diagram of a terrain surveying and mapping system based on aerial surveying and mapping technology provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0076] like Figure 1 As shown, an embodiment of the present invention provides a terrain surveying and mapping method based on aerial surveying and mapping technology, and the method comprises the following steps:

[0077] Step 11, according to the flight route of the drone, the radio wave ranging device transmits a signal to the target area;

[0078] Step 12, receiving a reflected signal reflected from the target terrain area, and calculating a time difference between the reflected signal and the transmitted signal;

[0079] Step 13, convert the time difference into an observed value of the distance, and update the state estimation value at the current moment according to the observed value. The state estimation value is the estimated distance between the UAV and the target area. Repeat the steps to capture the radio wave ranging data of the entire target area.

[0080] Step 14, transmitting the radio wave ranging data to the ground processing system in real time through an encryption algorithm;

[0081] Step 15, the ground processing system pre-processes the radio wave ranging data to obtain pre-processed radio wave ranging data;

[0082] Step 16, performing three-dimensional spatial positioning calculation based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, to generate three-dimensional coordinate point cloud data of the target area;

[0083] Step 17, constructing a grid model based on the three-dimensional coordinate point cloud data of the target area, and performing smoothing and texture mapping to generate a three-dimensional terrain model.

[0084] In an embodiment of the present invention, step 11, by transmitting signals according to a predetermined flight route, the entire target area can be systematically covered to avoid missing or repeated measurements. Step 12, provides key time data for converting signal transmission time into physical distance. Step 13, by continuously updating the state estimate, the distance change between the drone and the target area can be tracked in real time, thereby ensuring the real-time and accuracy of the surveying and mapping data. Step 14, encrypted transmission ensures the security of the data to prevent interception or tampering during transmission. Real-time transmission ensures that the ground processing system can process the latest data in a timely manner and improve surveying and mapping efficiency. Step 15, the preprocessing step can remove noise and outliers in the original data and improve the accuracy of subsequent three-dimensional modeling. Step 16, by combining the ranging data and the dynamic information of the drone, the three-dimensional coordinates of the target area can be accurately calculated. Step 17, by constructing a mesh model and performing smoothing and texture mapping, a realistic three-dimensional terrain model can be generated.

[0085] In a preferred embodiment of the present invention, the above step 11, according to the flight route of the drone, the radio wave ranging device transmits a signal to the target area, may include:

[0086] Step 111, defining ant individuals according to the flight route of the drone, wherein each ant represents a signal transmission strategy of a radio wave ranging device, and the signal transmission strategy includes a transmission direction, a transmission intensity, and a transmission time;

[0087] Step 112, determining the pheromone concentration used to indicate the pros and cons of different emission strategies based on the individual ants;

[0088] Step 113, constructing a solution space based on the pheromone concentration, the solution space consists of all possible emission points on the flight path of the drone, and each point may be the current position where the ant chooses to emit a signal;

[0089] Step 114, each ant selects the next emission point according to a certain probability based on the current position and the pheromone concentration in the solution space; each time the ant selects an emission point, the pheromone concentration at the emission point is updated according to the quality of the emission point;

[0090] Step 115, when all ants complete a round of launch point selection, evaluate the performance of the launch strategy represented by each ant to obtain an evaluation result;

[0091] Step 116, according to the evaluation result, the pheromone update rule is adjusted, and the ant path selection and evaluation optimization phases are repeated until a preset number of iterations is reached to obtain a final radio wave ranging device emission strategy, which includes a final emission point sequence, emission intensity, and emission time;

[0092] Step 117: According to the final transmission strategy of the radio wave ranging device, the radio wave ranging device transmits a signal to the target area.

[0093] In the embodiment of the present invention, step 111, by introducing individual ants as representatives of signal emission strategies, the complex emission decision problem can be converted into a computable optimization problem, which is convenient for subsequent processing and solution. Step 112, the introduction of pheromone concentration provides a quantitative indicator for evaluating the advantages and disadvantages of different emission strategies, making the selection and adjustment of strategies more reliable. Step 113, the construction of the solution space provides the range of emission points for individual ants to select, ensuring the comprehensiveness and diversity of the search, and helping to find a better emission strategy. Step 114, through the probabilistic selection mechanism, ants can explore different emission point combinations in the solution space, and update the pheromone concentration according to the advantages and disadvantages of the emission points, thereby realizing the dynamic adjustment and optimization of the strategy. Step 115, regularly evaluating the performance of the strategy represented by the ants, helps to timely discover and retain excellent emission strategies. Step 116, by continuously adjusting and optimizing the pheromone update rules, it is possible to guide individual ants to converge to a better emission strategy, thereby improving the optimization ability of the entire system. Step 117, after multiple iterations of optimization, the signal is emitted according to the final emission strategy, which can ensure that the coverage range and measurement accuracy of the signal are optimal.

[0094] Step 111, in this step, the signal transmission strategy of the radio wave ranging device is defined and optimized. Each defined "ant individual" is actually a virtual agent, which represents a possible signal transmission strategy.

[0095] Specifically, first, a search space is determined based on the drone's scheduled flight route. This space contains all the positions and directions where the drone may transmit signals during flight. Then, a series of ant individuals are defined in this search space, and each ant is assigned a unique signal transmission strategy. This signal transmission strategy is a composite parameter that includes three key elements: transmission direction, transmission intensity, and transmission time:

[0096] The transmission direction refers to the direction in which the radio wave ranging device transmits the signal. In three-dimensional space, this can be defined by the azimuth and elevation angles. The choice of transmission direction is crucial to ensure that the signal can accurately cover the target area.

[0097] Transmission strength represents the power or amplitude of the signal. The setting of transmission strength needs to balance the signal coverage and the energy consumption of the device. A signal that is too strong may cause unnecessary energy waste, while a signal that is too weak may cause some areas of the target area to not receive the signal.

[0098] The transmission time refers to the specific time point or time period during the flight of the drone to transmit the signal. Reasonable transmission time arrangement can avoid interference between signals and ensure that data of the target area can be continuously and effectively collected during the flight of the drone.

[0099] The transmission strategy represented by each ant individual is a specific combination of these three parameters. In the initial stage of the algorithm, these parameters may be randomly generated or initialized according to some preset rules. As the algorithm runs and iterates, these parameters will be dynamically adjusted and optimized according to the optimization objectives (such as signal coverage quality, energy efficiency, etc.).

[0100] Step 112, in this step, the concept of "pheromone concentration" is introduced to quantitatively evaluate the advantages and disadvantages of different emission strategies. Pheromones are a core element in the ant colony algorithm. They simulate the chemical substances released by real ants during foraging and are used to guide the behavior of other ants.

[0101] Specifically, the launch strategy represented by each ant individual will be assigned an initial pheromone concentration value. This value can be dynamically allocated according to certain rules (such as the complexity of the strategy, the initial evaluation score, etc.). The level of pheromone concentration will directly reflect the quality of the launch strategy: the higher the concentration, the more likely the strategy is to be selected as the optimal solution; conversely, the lower the concentration, the relatively poorer the strategy. The calculation formula for the initial pheromone concentration value is:

[0102] ;

[0103] in, Indicates The ant individuals are in The optional number of emission directions at each emission point; Indicates The ant individuals are in The number of adjustable levels of emission intensity at each emission point; Indicates The ant individuals are in The number of time slots into which the transmission time can be divided at each transmission point; , and are the weight coefficients corresponding to the emission direction, emission intensity and emission time respectively; Indicates The ant individuals are in The pheromone concentration at each emission point, is the initial pheromone concentration base, It is The ant individuals are in The initial evaluation score of the strategy represented by the emission point, is the pheromone evaporation rate.

[0104] As the algorithm progresses, the pheromone concentration will be updated according to the performance of individual ants. For example, if the transmission strategy represented by an individual ant performs well in actual applications (such as wide signal coverage and high data accuracy), then its pheromone concentration will be increased, thereby attracting more ants to choose this strategy. Conversely, if the performance is poor, the pheromone concentration will decrease, reducing the chance of being selected.

[0105] The pheromone concentration is updated based on the performance of individual ants and the iterative process of the algorithm. The pheromone concentration is updated after each iteration step, based on the quality of the solution represented by the individual ants. The following is a basic pheromone concentration update formula:

[0106] ;

[0107] in, is the updated pheromone concentration; is the pheromone evaporation rate, a constant between 0 and 1; is the number of ant individuals; It is Ant individuals at the launch point The pheromone increment left on the surface. , is the signal coverage ratio; Expressed as a function of transmit power and path loss, for example, ,in, is the transmit power, is the path loss.

[0108] Step 113, after determining the pheromone concentration of each ant individual, the next step is to construct a solution space. This solution space is composed of all possible emission points on the flight path of the drone, and each point may be the current location where the ant chooses to emit a signal.

[0109] The construction of the solution space is one of the key steps in the ant colony algorithm, which determines the scope and diversity of the solutions that the algorithm can search. In this step, a set of all possible launch points is generated based on the flight path of the drone and the predetermined mapping area. These launch points can be discrete point sets or position coordinates in continuous space.

[0110] Each emission point is assigned an initial pheromone concentration value, which is usually associated with the pheromone concentration of the individual ant in step 112. For example, if an individual ant selects a certain emission point as the location for its signal emission, the pheromone concentration of the emission point will be updated according to the pheromone concentration of the individual ant.

[0111] Step 114, for each ant, according to its current position and the pheromone concentration in the solution space, combined with heuristic information (such as distance), a probabilistic selection mechanism (such as a roulette algorithm) is used to select the next launch point. The probabilistic selection mechanism balances the influence of pheromone concentration (representing historical experience) and heuristic information (representing immediate visibility), so that ants are likely to choose paths with high pheromone concentrations or explore new paths. Whenever an ant selects a launch point, the pheromone concentration at the launch point is updated according to the quality of the launch point (such as signal coverage, signal strength, etc.). The quality of the launch point can be calculated by an evaluation function that comprehensively considers factors such as signal coverage and signal strength. The following is an example of a possible evaluation function:

[0112] ;

[0113] in, Indicates the emission point The evaluation function value of Indicates the emission point The signal coverage area can be a ratio between 0 and 1, which represents the ratio of the actual coverage area to the expected coverage area; Indicates the emission point The signal strength can be a quantitative value, such as dBm (decibel milliwatt); Representation and emission point The related transmit power consumption is a quantitative value that indicates the cost. , and is the weight coefficient.

[0114] Step 115, when all ants have completed a round of emission point selection, evaluate the performance of the emission strategy represented by each ant; record the evaluation results of each ant for subsequent pheromone update rules and strategy adjustments. The performance of the emission strategy represented by each ant can be evaluated through a comprehensive evaluation function, which should consider all relevant performance indicators, such as signal coverage, signal strength, and transfer costs between emission points. The following is an example of a possible comprehensive evaluation function:

[0115] ;

[0116] in, Indicates Comprehensive performance evaluation of the launch strategies represented by ants; Indicates The sequence of emission points chosen by the ants.

[0117] Step 116, according to the evaluation results in step 115, adjust the pheromone update rules. For example, the amount of pheromone delivered to the paths with good performance can be increased, and the amount of pheromone delivered to the paths with poor performance can be reduced. Repeat the ant path selection and evaluation optimization phase until the preset number of iterations is reached. In each iteration, the ants reselect the path based on the updated pheromone concentration and heuristic information, and then evaluate the performance again and update the pheromone. When the iteration stops, the final radio wave ranging device launch strategy is generated based on the historical paths and evaluation results of all ants. This strategy includes key parameters such as the sequence of launch points, launch intensity, and launch time.

[0118] Step 117, the final radio wave ranging device transmission strategy generated in step 116 is applied to the actual radio wave ranging device. According to the transmission point sequence, transmission intensity and transmission time specified in the strategy, the radio wave ranging device transmits a signal to the target area. During the transmission process, the state and performance of the signal can be monitored in real time to ensure the effective execution of the strategy.

[0119] In a preferred embodiment of the present invention, the above step 12, receiving the reflected signal reflected from the target terrain area, and calculating the time difference between the reflected signal and the transmitted signal, may include:

[0120] Step 121, after transmitting the signal, the radio wave ranging device captures the reflected signal, specifically including: the radio wave ranging device transmits a signal to the target terrain area; the device turns on the receiving mode and waits for the signal reflected from the target terrain area; when the reflected signal is received, the device captures and stores it for subsequent processing;

[0121] Step 122, initialize the gradient descent parameters, calculate the estimated difference between the received reflected signal and the transmitted signal, specifically including: setting the learning rate (Learning Rate), the learning rate determines the step size of the gradient descent algorithm to update the estimated difference in each iteration. The learning rate is a positive decimal, such as 0.01, 0.001, etc., and the specific value needs to be adjusted according to the actual situation; determine the number of iterations (Number of Iterations), which is the maximum number of times the gradient descent algorithm is executed. It determines the degree of effort of the algorithm to try to find the optimal solution. Too few iterations may cause the algorithm to fail to converge to the optimal solution, while too many iterations may waste computing resources. Usually, the number of iterations is set according to the complexity of the problem and the required accuracy; calculate the estimated difference between the received reflected signal and the transmitted signal.

[0122] The best matching point between the two signals is found by using signal processing technology (such as Pearson correlation) to ensure that the transmitted signal and the received reflected signal are aligned in time. After the signals are aligned, an initial time difference estimate can be obtained by measuring the time interval between the transmitted signal and the reflected signal. This time difference estimate will be used as the starting point of the gradient descent algorithm. Among them, the best matching point between the two signals is found by using signal processing technology (such as Pearson correlation), which specifically includes:

[0123] The Pearson correlation coefficient is a measure of the linear relationship between two data sets. Its value range is between -1 and 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. In signal processing, the transmitted signal and the reflected signal can be treated as two data sets and the Pearson correlation coefficient between them can be calculated. This involves sampling each signal and then calculating the correlation between the samples.

[0124] To find the best match, a sliding window is applied to the reflected signal and the signal within the window is correlated with the transmitted signal. By moving the sliding window and calculating the correlation coefficient at each position, one or more positions can be found where the correlation between the transmitted and reflected signals is the highest.

[0125] The location of the highest correlation coefficient indicates the best match point between the transmitted signal and the reflected signal. At this point, the two signals are best aligned in time. The time offset corresponding to this best match point is the initial estimate of the time difference estimate to be found. Once the best match point is determined, an initial time difference estimate can be obtained by measuring the time interval between the transmitted signal and the reflected signal at this point. This time difference estimate will serve as the starting point for the subsequent gradient descent optimization process.

[0126] Step 123, define a loss function for measuring the accuracy of the estimation error, wherein the calculation formula of the loss function is:

[0127] ;

[0128] in, is the sample size (if there are multiple measurements), It is Real time difference , It is Estimated time difference ;

[0129] Step 124, adjusting the estimated difference according to the gradient information of the loss function, repeating this process until a preset number of iterations is reached to obtain the final time difference, specifically includes:

[0130] For the mean square error loss function, its gradient is ; Update the estimated time difference using the gradient descent formula: , substitute the calculated gradient into the above formula to get the new estimated time difference ,in, is the updated parameter value; is the parameter value before updating; is the learning rate, which is a positive number used to control the size of the gradient descent step. A larger learning rate may cause the update step to be too large, making the optimization process unstable; while a smaller learning rate may cause the update step to be too small, making the optimization process slow; is the mean square error loss function exist The gradient at , the gradient is a vector indicating the direction in which the function value increases fastest. In gradient descent, the parameters are updated in the opposite direction of the gradient in the hope of finding a parameter value that makes the loss function value smaller. It is the gradient operator, which means to find the gradient of the function.

[0131] If the current number of iterations does not reach the preset maximum number of iterations, the iteration will be returned to the step to continue. If the maximum number of iterations is reached, the iteration will be stopped. After the iteration is completed, the estimated time difference after the final optimization is output. This value is an estimate of the time difference obtained after optimization by the gradient descent algorithm and should be more accurate than the initial estimate.

[0132] In an embodiment of the present invention, step 121, capturing the reflected signal, is the first step of the measurement process, ensuring that the device can receive the signal reflected from the target terrain area. Step 122, by initializing the parameters of the gradient descent, prepare for the subsequent iterative optimization process. The calculation of the estimated difference provides a starting point for the iteration, which helps the algorithm converge to the real time difference faster. Step 123, the loss function is the key to measuring the accuracy of the estimated time difference. It can help the algorithm judge the accuracy of the current estimated difference during the iteration process, thereby guiding the subsequent adjustment direction. A suitable loss function can significantly improve the convergence speed and accuracy of the algorithm. Step 124, by using the gradient information of the loss function to adjust the estimated difference, each iteration can be made in a more accurate direction. This method can gradually reduce the error between the estimated difference and the real time difference until a satisfactory accuracy is achieved. By repeating this process until the preset number of iterations is reached, it can be ensured that the final time difference has a high accuracy. This iterative optimization method not only improves the accuracy of the measurement, but also enhances the stability and reliability of the measurement.

[0133] In another preferred embodiment of the present invention, the above step 13 may include:

[0134] Use the time difference estimate obtained previously by gradient descent or other methods; convert the time difference t into a distance d, specifically: use the speed of radio waves in air (close to the speed of light c) for calculation: , where the division by 2 is because the signal needs to go back and forth, so the time needs to be divided into two parts, and this distance is recorded as the observation value at the current moment. The specific update of the state estimate includes:

[0135] Get the current location of the drone, which can be obtained through the drone's navigation system (such as GPS, IMU, etc.). Combine the drone's current location and the observed distance value, and use a state estimation algorithm (such as a Kalman filter) to update the estimated distance between the drone and the target area. The updated state estimate will be used as the estimated distance between the drone and the target area at the current moment.

[0136] As the drone moves, keep repeating the above steps of time difference measurement and state estimation update; ensure that the drone flies in the target area according to the preset route or mode to collect comprehensive radio wave ranging data. Record the observations and estimates at each moment for subsequent data processing and analysis. After collecting the radio wave ranging data for the entire target area, post-process the data, such as filtering, smoothing, or interpolation, to improve the data quality. Analyze the processed data to extract terrain, obstacles, or other relevant information about the target area. When the drone completes scanning the entire target area and has collected enough data, stop repeating the above steps. Ensure the drone returns safely and download the collected data for further analysis.

[0137] In a preferred embodiment of the present invention, the above step 14, transmitting the radio wave ranging data to the ground processing system in real time through an encryption algorithm, includes:

[0138] Step 141, based on the symmetric encryption algorithm, generates an initial encryption key, specifically including: selecting a symmetric encryption algorithm, such as AES (Advanced Encryption Standard); setting the key length, such as 128 bits, 192 bits or 256 bits; using a secure random number generator to generate a random binary string that meets the set length as the initial encryption key. For example, select AES-128 as the symmetric encryption algorithm, use a secure random number generator to generate a 128-bit random binary string: 0x1234567890ABCDEF1234567890ABCDEF, this binary string is the initial encryption key;

[0139] Step 142, optimizing the initial encryption key to obtain an optimized key;

[0140] Step 143, encrypting the radio wave ranging data using the optimized key to obtain the encrypted ranging data, specifically includes: converting the radio wave ranging data into a binary format; encrypting the binary data using the optimized key and a symmetric encryption algorithm (such as AES); and outputting the encrypted binary data as the encrypted ranging data. For example, assuming that the radio wave ranging data is: 10.5 meters, it is converted into a binary representation; encrypted using the optimized key: 0xAABBCCDDEEFF00112233445566778899 and the AES-128 algorithm; the encrypted ranging data may be similar to: 0x96385BFD27A835C1B76EFDF1239A8B6D (the actual value will vary according to the specific algorithm and key);

[0141] Step 144, encapsulate the encrypted ranging data into a transmission data packet format to obtain an encapsulated data packet, specifically including: defining a data packet format, including fields such as a header, data length, data type, timestamp, and encrypted ranging data; assembling the encrypted ranging data and other necessary information according to the data packet format, and outputting the assembled data packet. For example, the data packet format is defined as: [header (2 bytes) | data length (2 bytes) | data type (1 byte) | timestamp (8 bytes) | encrypted ranging data (N bytes)];

[0142] Assembling the data packet:

[0143] [0xAA55|0x0010|0x01|0x1234567890ABCDEF|0x96385BFD27A835C1B76EFDF1239A8B6D], where the packet header is 0xAA55, the data length is 16 bytes (0x0010), the data type is ranging data (0x01), the timestamp is the current time (0x1234567890ABCDEF), and the encrypted ranging data is the result obtained by the previous encryption;

[0144] Step 145, sending the encapsulated data packet to the ground processing system, specifically includes: selecting a data transmission protocol, such as UDP (User Datagram Protocol) or TCP (Transmission Control Protocol); establishing a network connection with the ground processing system; sending the encapsulated data packet to the ground processing system through the network connection. For example, UDP is selected as the data transmission protocol, a UDP connection is established with the ground processing system, and a port number (such as 12345) is specified;

[0145] The encapsulated data packet:

[0146] [0xAA55|0x0010|0x01|0x1234567890ABCDEF|0x96385BFD27A835C1B76EFDF1239A8B6D] is sent to the ground processing system via a UDP connection.

[0147] In an embodiment of the present invention, the initial encryption key is generated by a symmetric encryption algorithm, which can ensure the basic security of the encryption process. The encryption and decryption use the same key, thereby ensuring the encryption efficiency. Secondly, the initial encryption key is optimized to obtain an optimized key. The process of optimizing the key can further improve the complexity and security of encryption, making the key more difficult to crack, thereby enhancing the security of the transmitted data. Then, the radio wave ranging data is encrypted using the optimized key to ensure the confidentiality and integrity of the ranging data during transmission. Even if the data is intercepted, the original data cannot be decrypted without the correct key, thereby protecting the security of sensitive information. In addition, the encrypted ranging data is encapsulated into a transmitted data packet format, which can facilitate data transmission and processing. The encapsulated data packet contains necessary information, such as data length, type, timestamp, etc., so that the ground processing system can correctly parse and process the received data. Finally, the encapsulated data packet is sent to the ground processing system, which can realize the real-time transmission and processing of the radio wave ranging data. The ground processing system can further analyze and process the received data, thereby realizing real-time monitoring and management of the position and status of the drone. In summary, this process ensures the security, confidentiality and integrity of radio wave ranging data during transmission, while improving the efficiency and reliability of data transmission.

[0148] In a preferred embodiment of the present invention, the above step 142, optimizing the initial encryption key to obtain an optimized key, includes:

[0149] Step 1421, determining the length of the key according to the symmetric encryption algorithm, the implementation process: selecting the symmetric encryption algorithm to be used, such as AES; determining the length of the key according to the standard of the selected algorithm; for AES, the optional key length is usually 128 bits, 192 bits or 256 bits; setting the key length to a variable keyLength for use in subsequent steps;

[0150] Step 1422, randomly generating a set of initial keys as an initial population, each key representing an individual, specifically including: determining a population size populationSize, that is, the number of initial keys to be generated; using a secure random number generator to generate populationSize random binary strings of length keyLength; storing these random binary strings in an array or list as the initial population;

[0151] Step 1423, define a fitness function for evaluating the quality of each key. The fitness function The calculation formula is: ,in, is a sum index that represents the possible values ​​for each bit in the key. The value range of is {0, 1}, because the key is composed of binary bits, each of which is either 0 or 1; Indicates that the bit value in the key is For an ideal random binary sequence, the probability of each bit being 0 or 1 should be equal, that is, P(0)=P(1)=0.5. Entropy is an indicator of the uncertainty of a random variable. For a binary key, entropy can be used to measure the amount of information per bit in the key. If the key is completely random, then each bit contains the same amount of information and the entropy value will reach the maximum.

[0152] In the formula, The entropy of the key is calculated. The summation symbol Express Sum all possible values ​​of (0 and 1). For each Value, calculation , then add these values ​​and take the negative sign to get the entropy value. The logarithm here is base 2 because the key is binary;

[0153] Step 1424, calculating the fitness value of each initial key according to the fitness function, specifically including: traversing each key in the initial population; for each key, calling the fitness function to calculate its fitness value; storing each key and its corresponding fitness value in a data structure, such as a dictionary or an object array;

[0154] Step 1425, according to the fitness function value, determine the individuals in the population to enter the crossover operation to generate a new key, specifically including: selecting a crossover strategy, such as roulette selection; selecting two parent keys from the population for crossover operation according to the selected strategy and the fitness value; performing a crossover operation, such as a single-point crossover, to generate a new child key; repeating this process until a sufficient number of new keys are generated to form the next generation population;

[0155] Step 1426, mutating the new key and iteratively optimizing until a preset number of iterations is reached to obtain an optimized key, specifically including: setting a mutation rate mutationRate, which represents the proportion of keys that mutate in each iteration; for each key in each generation of the population, performing a mutation operation with a probability of mutationRate, where the mutation can simply flip one or more bits in the key, repeating the selection, crossover and mutation operations to form a new population, and iteratively optimizing; when the preset number of iterations is reached, stopping the iteration and selecting the key with the highest fitness value as the optimized key. Through this process, an encryption key optimized by a genetic algorithm can be obtained, which may be cryptographically stronger than the initial randomly generated key.

[0156] In an embodiment of the present invention, by determining the length of the key, it can be ensured that the generated key meets the requirements of the symmetric encryption algorithm, thereby ensuring the security and effectiveness of the encryption. Secondly, a group of initial keys are randomly generated as the initial population, which can ensure the diversity of the search space and avoid falling into a local optimal solution. Then, by defining a fitness function, the quality of each key can be quantified. Next, a selection operation is performed according to the fitness function value, so that excellent keys can be retained and inferior keys can be eliminated, thereby improving the overall quality of the population. In addition, by generating new keys through crossover operations, the excellent features of the parent key can be inherited, while new changes are introduced to increase the diversity of the population. Finally, mutation operations are performed on the new keys to further explore the search space and avoid the algorithm from stagnating. By iterative optimization until a preset number of iterations is reached, an optimized key can be obtained.

[0157] In a preferred embodiment of the present invention, the above step 15 may include:

[0158] The specific implementation process of step 15 can be divided into the following steps:

[0159] Step 151, the ground processing system receives the original radio wave ranging data from the ranging device through a dedicated data receiving device or interface. These data include information such as the timestamp of signal transmission and reception, signal strength, frequency, etc. If the received data format is incompatible with the ground processing system, format conversion is required to ensure that the data can be correctly processed by the system. The received and format-converted data is stored in the system's database for subsequent processing and analysis.

[0160] Step 152, identify and remove outliers in the ranging data through statistical methods (such as Z-score, IQR rule, etc.), which may be caused by equipment failure, interference or transmission error. Check whether the received data is complete, such as whether there are missing timestamps, signal strength values, etc. For missing data, interpolate or remove them according to the specific situation. Verify whether the logical relationship between the data is reasonable, such as checking the order of sending and receiving timestamps, the reasonable range of signal strength, etc.

[0161] Step 153, in order to reduce the volatility of the ranging data, the data can be smoothed by using methods such as sliding average and exponential smoothing to improve the stability and reliability of the ranging data. The noise and interference signals in the ranging data can be removed by using a low-pass filter method to extract more accurate ranging information.

[0162] Step 154, find the corresponding signal sending timestamp and receiving timestamp from the preprocessed ranging data; calculate the difference between the two timestamps, which represents the propagation time of the signal from sending to receiving; because the accuracy and unit of the timestamp may be different for different devices or different measurement conditions, it is necessary to standardize the time propagation characteristics, and the time difference can be converted into a unified unit (such as microseconds, milliseconds, etc.), and necessary mathematical transformations (such as logarithmic transformation, normalization, etc.) can be performed to facilitate subsequent algorithm processing; extract the strength values ​​of the sending and receiving signals from the preprocessed data, if the data contains the signal strengths of multiple receiving points, the average signal strength can be calculated; by comparing the difference between the sending signal strength and the receiving signal strength, the attenuation degree of the signal can be estimated. Similar to the time feature, the signal attenuation degree also needs to be standardized, and normalization can be used to make the attenuation characteristics comparable under different measurement conditions.

[0163] Step 155, outputting the radio wave ranging data processed by the above steps.

[0164] In a preferred embodiment of the present invention, the above step 16 performs three-dimensional spatial positioning calculation based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the drone to generate three-dimensional coordinate point cloud data of the target area, including:

[0165] Step 161, converting the flight trajectory and attitude information of the UAV from the UAV's own coordinate system to the global coordinate system, specifically including: obtaining the UAV's real-time position (longitude, latitude, altitude) and attitude information (pitch angle, yaw angle, roll angle) from the UAV's navigation system (such as GPS, IMU, etc.); determining the UAV's own coordinate system (usually with the UAV's center of mass as the origin, the nose direction as the X-axis, the wing direction as the Y-axis, and the vertical downward as the Z-axis), defining a global coordinate system (such as the WGS-84 coordinate system, or other local coordinate systems suitable for a specific area); using attitude information (Euler angles or quaternions) to construct a rotation matrix, which describes the rotation relationship from the UAV coordinate system to the global coordinate system; applying the rotation matrix and position offset (the position of the UAV in the global coordinate system) to convert each point on the UAV's flight trajectory from the UAV coordinate system to the global coordinate system;

[0166] Step 162, based on the attitude and position information of the drone, convert the radio wave ranging data from the drone coordinate system to the global coordinate system, specifically including: obtaining ranging data from the radio wave ranging device, including the ranging value (distance) and the corresponding measurement timestamp; ensuring that the radio wave ranging data is synchronized with the attitude and position data of the drone in time so as to accurately correspond to the attitude and position of the drone at each ranging data point; pairing each ranging data point with the attitude and position information of the drone at that time point; using the same rotation matrix and position offset as in step 161, converting the radio wave ranging data (usually expressed as a vector from the drone to the target point) from the drone coordinate system to the global coordinate system; in the global coordinate system, this vector will be added to the drone position to obtain the estimated position of the target point in the global coordinate system;

[0167] Step 163, for each ranging data point, according to its position and ranging value in the global coordinate system, establish a ranging equation between the data point and the target point, specifically including:

[0168] From the set of distance measurement data points that have been converted to the global coordinate system, select a distance measurement data point as the current processing point; assuming that the coordinates of the target point in the global coordinate system are (X t , Y t , Z t ), the coordinates of the current distance measurement data point in the global coordinate system are (X d , Y d , Z d ), the distance value is d. According to the distance formula between two points in three-dimensional space, the distance equation can be established. The distance formula is:

[0169] ;

[0170] This equation indicates that the distance between the target point and the ranging data point should be equal to the ranging value d.

[0171] Step 164, solving the distance measurement equation to determine the three-dimensional coordinates of the target point in the global coordinate system, repeating the above steps to establish a distance measurement equation for each distance measurement data point; combining all the established distance measurement equations into an equation system, after the equation system is established, it is possible to prepare for simultaneous solution to determine the three-dimensional coordinates of the target point in the global coordinate system;

[0172] Step 165, combining all the calculated target point positions to form the three-dimensional coordinate point cloud data of the target area, specifically including: obtaining the three-dimensional coordinates of each target point in the global coordinate system by solving the ranging equations; collecting and sorting these scattered coordinate point data to ensure that the coordinate information (X, Y, Z) of each point is complete and accurate. Before combining, the data format of all coordinate points needs to be unified into the same standard format. Use three-dimensional modeling software or point cloud processing software to import all sorted three-dimensional coordinate points into the same project. These software usually have powerful point cloud processing capabilities and can quickly generate three-dimensional point cloud models based on coordinate points. After generating the point cloud data, it may be found that there are some obvious noise points or abnormal points, which may be caused by measurement errors, signal interference, etc. Therefore, it is necessary to optimize the point cloud data, such as removing noise points, smoothing the surface, etc., to improve the quality and accuracy of the point cloud data.

[0173] The spatial resolution of point cloud data can be adjusted according to actual needs and application scenarios. For example, when the characteristics of the target area need to be finely represented, the density of the point cloud can be increased; when only the rough outline is needed, the density of the point cloud can be appropriately reduced.

[0174] In an embodiment of the present invention, by integrating the flight trajectory, attitude information and radio wave ranging data of the UAV, the position of the target point in the global coordinate system can be determined more accurately. This multi-dimensional data fusion method can effectively reduce the error caused by a single data source, thereby improving the overall positioning accuracy. Radio wave ranging technology is usually highly adaptable to the environment and can perform stable ranging under different weather and lighting conditions. Combined with the flexibility of the UAV, this method can achieve efficient three-dimensional spatial positioning in a complex and changeable environment. Through automated data preprocessing and coordinate conversion processes, manual intervention and data processing time can be greatly reduced. In addition, by using computer algorithms for joint solution and point cloud data generation, the three-dimensional coordinate information of the target area can be quickly obtained, thereby improving the efficiency of the entire positioning solution process. The generated three-dimensional coordinate point cloud data can intuitively show the spatial structure and detailed features of the target area.

[0175] In a preferred embodiment of the present invention, the above step 17, constructing a grid model based on the three-dimensional coordinate point cloud data of the target area, and performing smoothing and texture mapping to generate a three-dimensional terrain model, includes:

[0176] Step 171, read the three-dimensional coordinate point cloud data of the target area, for each point in the three-dimensional coordinate point cloud data, calculate the distance between each point and its neighboring points, and filter to obtain a filtered three-dimensional coordinate point cloud data set, specifically including: using software or programming libraries (such as PCL, MeshLab or custom scripts) to open and read the three-dimensional coordinate point cloud data file of the target area. These files are usually stored in a specific format, such as PLY, OBJ or LAS. For each point in the point cloud data, its neighboring points need to be determined. This can be achieved by a variety of methods, such as using a kd tree (k-dimensional tree) or a space partitioning technique (such as an octree) to efficiently query neighboring points. Set a distance threshold or number of neighboring points (k value) to determine the neighborhood range of each point. This threshold can be adjusted according to the density of the point cloud and the desired filtering effect. For each point, calculate the Euclidean distance between it and the neighboring point, that is, the straight-line distance between the two points in three-dimensional space. Vectorized operations or parallel computing can be used to accelerate this process, especially when processing large-scale point cloud data.

[0177] Filter based on the calculated distance. For example, you can set a minimum distance threshold to remove points that are too close to neighboring points, which helps reduce redundancy and noise in the data. Similarly, you can set a maximum distance threshold to remove isolated points or points that are too far away from the main point cloud, which helps remove outliers. After filtering, the retained points are combined into a new, filtered 3D coordinate point cloud dataset, which will serve as input for subsequent steps (such as triangulation, mesh optimization, etc.). If necessary, the filtered point cloud dataset can be saved as a new file for subsequent use or analysis;

[0178] Step 172, through triangulation, converts the filtered three-dimensional coordinate point cloud data set into a triangular mesh determined by three vertices, specifically including: selecting a suitable triangulation algorithm (such as Delaunay triangulation) according to the characteristics of the filtered three-dimensional coordinate point cloud data set (such as point distribution, density, etc.); applying the selected triangulation algorithm to connect the points in the filtered point cloud data set into triangles to form a continuous triangular mesh. In this process, it is necessary to ensure that there is no overlap between the generated triangles and to keep the uniformity and regularity of the mesh as much as possible. The generated triangular mesh data is stored in a suitable data structure to facilitate processing and access in subsequent steps. Usually, the triangular mesh data will include information such as the vertex index and vertex coordinates of each triangle.

[0179] Step 173, for each vertex in the triangular mesh, find the adjacent vertices directly connected to it, and for each vertex, calculate the average position of all its adjacent vertices, specifically including: for each vertex in the triangular mesh, start a traversal process, which can be achieved by accessing the data structure storing the triangular mesh data. For the vertex currently traversed, find the adjacent vertices directly connected to it, which can be achieved by checking other triangles that share edges with the vertex; add the found adjacent vertices to the adjacency list of the current vertex. For each adjacent vertex of the current vertex, obtain its three-dimensional coordinates, calculate the average value of the coordinates of all adjacent vertices, and obtain an average position point. The calculated average position point is associated with the current vertex, which can be stored in the additional information of the vertex, or saved as a new data structure. Continue to traverse the next vertex in the triangular mesh and repeat the above process until all vertices have been processed;

[0180] Step 174, move the vertex to the average position of its adjacent vertices for a certain distance, repeat the vertex position adjustment process until the preset number of iterations is reached to obtain the optimized triangular mesh, specifically including: setting a preset number of iterations, which is to control the termination condition of the optimization process and prevent infinite loops. A moving factor or step size can be set to control the distance the vertex moves in each iteration to avoid mesh deformation caused by excessive movement; counting from 0, entering the iteration loop, adjusting the position of each vertex in the triangular mesh, for the current vertex, according to the average position of the adjacent vertices calculated in step 173, determine a vector from the current vertex to the average position, add the calculated moving vector to the original position of the current vertex, thereby obtaining a new vertex position, and updating the coordinate information of the vertex in the triangular mesh data structure. After completing the position adjustment of all vertices each time, check whether the current number of iterations has reached the preset number of iterations. If not, return to step 3 to continue the next iteration. If it has been reached, terminate the iteration process, and save the optimized triangular mesh data after completing all iterations;

[0181] Step 175, obtaining a two-dimensional texture image of the target area, and preprocessing the two-dimensional texture image to obtain a preprocessed texture image, specifically includes:

[0182] According to the target area, the corresponding two-dimensional texture images are obtained through aerial photography, satellite remote sensing, ground photography or other methods; the obtained two-dimensional texture images are preprocessed to improve their quality and applicability. The preprocessing steps include:

[0183] Color correction adjusts the brightness, contrast, and color balance of an image to ensure that the color representation of the image is consistent and true.

[0184] Noise removal, applying image filtering techniques (such as Gaussian filtering) to reduce or eliminate noise and clutter in images.

[0185] Resizing: resize the image as needed to match the resolution and detail requirements of the 3D model.

[0186] Distortion correction: if the image is distorted (such as lens distortion), the corresponding algorithm is applied to correct it.

[0187] Cropping and stitching: crop specific areas in an image as needed, or stitch multiple images into a continuous texture map;

[0188] Step 176, for each triangle in the triangular mesh, the texture coordinates of the triangular mesh are calculated according to the corresponding positions of its vertices on the texture image, which specifically includes:

[0189] The texture coordinate system uses UV coordinates, where the value range of U and V is generally [0, 1], indicating the horizontal and vertical positions of the texture image. According to the size of the texture image, set the origin of the UV coordinates (the lower left corner of the image) and the unit length (that is, the width and height of the texture image correspond to 1 unit length of the UV coordinates). Start traversing each triangle in the triangular mesh, which can be achieved by accessing the data structure of the triangular mesh, which should contain the vertex information of each triangle. For each vertex of the currently traversed triangle, determine its corresponding position on the two-dimensional texture image; if the direct correspondence between the vertex and the texture image pixel has been saved during the construction of the triangular mesh, directly obtain these corresponding positions. If there is no direct correspondence, it may be necessary to use projection technology or other algorithms to calculate the projection position of the vertex on the texture image.

[0190] According to the corresponding position of the vertex on the texture image, calculate the UV texture coordinates of each vertex. For the U coordinate, calculate the relative position of the corresponding position of the vertex in the horizontal direction of the texture image, that is, (x coordinate of the corresponding position of the vertex / texture image width); for the V coordinate, calculate the relative position of the corresponding position of the vertex in the vertical direction of the texture image, that is, (y coordinate of the corresponding position of the vertex / texture image height); ensure that the calculated UV coordinate value is within the range of [0, 1], or make appropriate adjustments as needed.

[0191] Associating the calculated UV texture coordinates with the corresponding vertices of the current triangle in the triangle mesh can be achieved by adding an additional texture coordinate field for each vertex in the triangle mesh data structure. Continue traversing the next triangle in the triangle mesh and repeat steps 4 to 6 until the texture coordinates of all triangle vertices are calculated and stored;

[0192] Step 177, using texture coordinates, maps the preprocessed texture image to the triangular mesh, assigns corresponding texture information to each triangular mesh, so as to obtain mapped texture information, specifically including: initializing a graphics processing library (such as OpenGL, DirectX, etc.), which provides a texture mapping function; using the graphics processing library function to load the preprocessed texture image into memory, preparing to apply it to the triangular mesh; traversing each triangle in the triangular mesh, preparing to assign a texture to each triangle; for each triangle, sampling a corresponding color value from the texture image according to the texture coordinates of its vertices; the graphics processing library provides an interpolation function, which can automatically calculate the texture coordinates for each point inside the triangle according to the texture coordinates of the three vertices of the triangle, and sample the color from the texture image; applying the sampled color value to each triangle of the triangular mesh, so that each triangle displays a part of the texture image, and this process is repeated on each face of the triangular mesh until the entire triangular mesh is covered with the texture;

[0193] Step 178, generating a final three-dimensional terrain model based on the optimized triangular mesh and the mapped texture information, specifically includes:

[0194] Use 3D modeling software to export the optimized triangular mesh and texture information into a complete 3D model file, which contains the geometry and texture information of the mesh; select a 3D model file format for export, such as OBJ, FBX, DAE, etc. These formats usually support texture mapping well. During the export process, set the necessary parameters to ensure the quality and compatibility of the model, including the compression method of the texture, the accuracy of the model, etc.; perform the export operation to generate a 3D model file containing the optimized triangular mesh and mapped texture information.

[0195] In an embodiment of the present invention, by calculating the distance between each point and its neighboring points and screening them, noise points and outliers can be effectively removed, thereby improving the accuracy of the three-dimensional coordinate point cloud data. The triangulation process converts the point cloud data into a triangular mesh, and the subsequent mesh optimization steps (such as vertex position adjustment) further improve the quality of the mesh. These optimization measures help to reduce irregular triangles and sharp features in the mesh, making the mesh smoother and more uniform, thereby improving the visual effect and geometric accuracy of the final three-dimensional terrain model. By obtaining a two-dimensional texture image of the target area and mapping it to a triangular mesh, the realism and detail expression of the three-dimensional terrain model can be significantly increased. Texture mapping not only provides surface color information of the model, but also simulates natural effects such as lighting and shadows to make the model more realistic.

[0196] like Figure 2 As shown, an embodiment of the present invention further provides a terrain surveying and mapping system 20 based on aerial surveying and mapping technology, comprising:

[0197] The transmitting module 21 is used for transmitting a signal to a target area by a radio wave ranging device according to the flight route of the UAV;

[0198] The receiving module 22 is used to receive the reflected signal reflected from the target terrain area and calculate the time difference between the reflected signal and the transmitted signal;

[0199] The processing module 23 is used to convert the time difference into an observed value of the distance, and update the state estimation value at the current moment according to the observed value, where the state estimation value is the estimated distance between the UAV and the target area, and the steps are repeated continuously to capture the radio wave ranging data of the entire target area;

[0200] The transmission module 24 is used to transmit the radio wave ranging data to the ground processing system in real time through an encryption algorithm;

[0201] A preprocessing module 25 is used for the ground processing system to preprocess the radio wave ranging data to obtain preprocessed radio wave ranging data;

[0202] The calculation module 26 is used to perform three-dimensional spatial positioning calculation based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, and generate three-dimensional coordinate point cloud data of the target area;

[0203] The construction module 27 is used to construct a grid model according to the three-dimensional coordinate point cloud data of the target area, and perform smoothing and texture mapping to generate a three-dimensional terrain model.

[0204] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0205] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0206] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0207] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A terrain surveying and mapping method based on aerial surveying and mapping technology, characterized in that: The method comprises: Based on the drone’s flight path, the radio wave ranging device transmits a signal to the target area; Receive the reflected signal reflected from the target terrain area and calculate the time difference between the reflected signal and the transmitted signal; Convert the time difference into an observed value of the distance. Based on the observed value, update the state estimate at the current moment. The state estimate is the estimated distance between the drone and the target area. Repeat the steps to capture the radio wave ranging data of the entire target area. The radio wave ranging data is transmitted to the ground processing system in real time through an encryption algorithm; The ground processing system pre-processes the radio wave ranging data to obtain pre-processed radio wave ranging data; Based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, three-dimensional spatial positioning is solved to generate three-dimensional coordinate point cloud data of the target area; According to the three-dimensional coordinate point cloud data of the target area, a mesh model is constructed, and smoothing and texture mapping are performed to generate a three-dimensional terrain model; Based on the drone's flight path, the radio wave ranging device transmits signals to the target area, including: According to the flight route of the drone, ant individuals are defined, where each ant represents a signal transmission strategy of a radio wave ranging device. The signal transmission strategy includes transmission direction, transmission intensity and transmission time. According to the individual ants, the pheromone concentration used to indicate the advantages and disadvantages of different emission strategies is determined; According to the pheromone concentration, the solution space is constructed. The solution space consists of all possible emission points on the flight path of the drone. Each point may be the current location where the ant chooses to emit a signal. Each ant selects the next launch point according to a certain probability based on the current position and the pheromone concentration in the solution space; each time the ant selects a launch point, the pheromone concentration at the launch point is updated according to the quality of the launch point; When all ants have completed a round of launch point selection, the performance of the launch strategy represented by each ant is evaluated to obtain the evaluation results; According to the evaluation results, the pheromone update rule is adjusted, and the ant path selection and evaluation optimization stages are repeated until the preset number of iterations is reached to obtain the final radio wave ranging device emission strategy, which includes the final emission point sequence, emission intensity and emission time; According to the final radio wave ranging device transmission strategy, the radio wave ranging device transmits a signal to the target area; Receive the reflected signal from the target terrain area and calculate the time difference between the reflected signal and the transmitted signal, including: After transmitting the signal, the radio wave ranging device captures the reflected signal; Initialize the gradient descent parameters and calculate the estimated difference between the received reflected signal and the transmitted signal; Define a loss function to measure the accuracy of the estimation error; According to the gradient information of the loss function, adjust the estimated difference and repeat this process until the preset number of iterations is reached to obtain the final time difference; The radio wave ranging data is transmitted to the ground processing system in real time through an encryption algorithm, including: Generate an initial encryption key according to the symmetric encryption algorithm; Optimizing the initial encryption key to obtain an optimized key; Encrypting the radio wave ranging data using the optimized key to obtain encrypted ranging data; Encapsulating the encrypted ranging data into a data packet format for transmission to obtain an encapsulated data packet; Send the encapsulated data packets to the ground processing system; Optimizing the initial encryption key to obtain an optimized key includes: Determine the length of the key according to the symmetric encryption algorithm; A set of initial keys is randomly generated as the initial population, each key represents an individual; Define a fitness function for evaluating the quality of each key; According to the fitness function, the fitness value of each initial key is calculated; According to the fitness function value, determine the individuals in the population to enter the crossover operation to generate a new key; Perform mutation operation on the new key and iteratively optimize it until the preset number of iterations is reached to obtain the optimized key; Based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, three-dimensional spatial positioning is solved to generate three-dimensional coordinate point cloud data of the target area, including: Convert the flight trajectory and attitude information of the drone from the drone's own coordinate system to the global coordinate system; According to the attitude and position information of the UAV, the radio wave ranging data is converted from the UAV coordinate system to the global coordinate system; For each ranging data point, a ranging equation is established between it and the target point according to its position and ranging value in the global coordinate system; Solve the ranging equation to determine the three-dimensional coordinates of the target point in the global coordinate system; Combine all the calculated target point positions to form the three-dimensional coordinate point cloud data of the target area; According to the 3D coordinate point cloud data of the target area, a mesh model is constructed, and smoothing and texture mapping are performed to generate a 3D terrain model, including: Read the three-dimensional coordinate point cloud data of the target area, calculate the distance between each point and its neighboring points for each point in the three-dimensional coordinate point cloud data, and perform screening to obtain a filtered three-dimensional coordinate point cloud data set; By triangulation, the filtered three-dimensional coordinate point cloud data set is converted into a triangular mesh determined by three vertices; For each vertex in the triangle mesh, find the adjacent vertices directly connected to it, and for each vertex, calculate the average position of all its adjacent vertices; Move the vertex to the average position of its adjacent vertices by a certain distance, and repeat the vertex position adjustment process until the preset number of iterations is reached to obtain an optimized triangular mesh; Acquire a two-dimensional texture image of a target area, and preprocess the two-dimensional texture image to obtain a preprocessed texture image; For each triangle in the triangular mesh, the texture coordinates of the triangular mesh are calculated according to the corresponding positions of its vertices on the texture image; Using texture coordinates, the preprocessed texture image is mapped onto the triangular mesh, and corresponding texture information is assigned to each triangular mesh to obtain mapped texture information; The final 3D terrain model is generated based on the optimized triangular mesh and mapped texture information.

2. A terrain surveying and mapping system based on aerial surveying and mapping technology, characterized in that: Applied in the method as claimed in claim 1, comprising: The transmitting module is used to transmit signals to the target area by the radio wave ranging device according to the flight route of the UAV; A receiving module, used to receive a reflected signal reflected from a target terrain area and calculate a time difference between the reflected signal and the transmitted signal; A processing module is used to convert the time difference into an observed value of the distance, and update the state estimation value at the current moment according to the observed value, where the state estimation value is the estimated distance between the UAV and the target area, and the steps are repeated continuously to capture the radio wave ranging data of the entire target area; A transmission module is used to transmit the radio wave ranging data to the ground processing system in real time through an encryption algorithm; A preprocessing module, used for the ground processing system to preprocess the radio wave ranging data to obtain preprocessed radio wave ranging data; The calculation module is used to perform three-dimensional spatial positioning calculation based on the pre-processed radio wave ranging data, as well as the flight trajectory and attitude information of the UAV, and generate three-dimensional coordinate point cloud data of the target area; The construction module is used to construct a grid model based on the three-dimensional coordinate point cloud data of the target area, and perform smoothing and texture mapping to generate a three-dimensional terrain model.

3. A computing device, characterized in that: include: one or more processors; A storage device, used for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to claim 1 when executed by a processor.

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