A method and system for aerial surveying using unmanned aerial vehicles (UAVs).

Abstract

This invention provides an aerial photography measurement method and system for unmanned aerial vehicles (UAVs), relating to the fields of photogrammetry and navigation technology. The invention utilizes an inertial measurement unit (IMU) and a visual odometry (VAO) to sense static obstruction and dynamic interference sources. It generates a trigger offset compensation amount based on the difference between forward and delayed compensation distances, adaptively selecting either an advance or delayed exposure strategy. Furthermore, it monitors the onboard noise ratio difference between single-frequency and dual-frequency receivers in real time, establishing a time delay jitter prediction model. It then jointly compares the spatial displacement corresponding to the predicted time delay value from the pulse circuit with the predicted value from the dual-frequency position calculation to estimate whether the timing error exceeds the limit. When the positioning reliability coefficient is below a threshold, it replaces the single-frequency GNSS trigger judgment with a visual and inertial fusion calculation, dynamically updating the pulse compensation parameters to ensure timing alignment between the exposure time and the positioning time, guaranteeing the coverage integrity and coordinate accuracy of aerial photographs in complex environments.

Description

Technical Field

[0001] This invention relates to the field of photogrammetry and navigation technology, specifically to an unmanned aerial vehicle (UAV) aerial photogrammetry method and system. Background Technology

[0002] UAV aerial photogrammetry originated in the mid-19th century. Since the advent of aerial photography using balloons equipped with cameras, it has undergone a leapfrog development from manned airborne platforms to UAV systems. In the 21st century, with breakthroughs in microelectronics, navigation and positioning, and small remote sensing sensor technologies, UAV aerial surveying systems emerged and quickly became an important supplement to traditional aerial photogrammetry and satellite remote sensing. With its mobility, ease of operation, low cost, and high spatiotemporal resolution data acquisition capabilities, it has been widely applied in basic surveying and mapping, geological disaster monitoring, smart city construction, and agricultural and forestry resource surveys. In recent years, with the deep integration of computer vision and artificial intelligence algorithms, UAV aerial surveying is gradually evolving towards full automation, real-time operation, and intelligence, greatly promoting the transformation and upgrading of the surveying and mapping geographic information industry.

[0003] In the prior art, publication number CN102607527A, entitled "A Method and System for Aerial Photography Measurement of a UAV," can obtain precise exposure point coordinates for aerial photography and simultaneously record the exposure time signal of the aerial camera. One method for aerial photography of a UAV includes acquiring single-frequency and dual-frequency signals related to the UAV's position from a global navigation satellite system; when the single-frequency signal matches the predetermined flight path parameters of the UAV, a trigger relay is activated to perform photography; simultaneously, the exposure time during photography and the corresponding dual-frequency signal obtained using a pulse circuit are recorded, and the UAV's position is obtained based on the dual-frequency signal.

[0004] Current UAV aerial surveying solutions rely on single-frequency GNSS signals for determining photographic triggers and dual-frequency GNSS signals for precise exposure timing. The reliability of the entire measurement process depends on the continuous and stable availability of GNSS signals. However, in densely populated urban areas, mountainous valleys, or environments with electromagnetic interference, single-frequency receivers are highly susceptible to signal blockage and multipath effects, causing position calculations to deviate from actual flight path parameters. This leads to inaccurate photographic trigger timing—problems such as missed shots, repeated triggers, or trigger position shifts arise, directly compromising the coverage integrity and geometric consistency of aerial images.

[0005] The aforementioned issue of degraded signal quality in the Global Navigation Satellite System (GNSS) further exacerbates a more subtle structural flaw within the system: an inherent sampling rate asymmetry exists between the single-frequency receiver (4Hz) and the dual-frequency receiver (20Hz). The system relies on a time delay compensation mechanism using pulse circuits to bridge this timing difference. However, this compensation model is essentially calibrated based on static empirical values. When the GNSS signal quality dynamically deteriorates due to environmental factors, the trigger delay jitter of the pulse circuits increases and changes non-linearly. Static compensation parameters cannot track this dynamic error in real time, resulting in a residual deviation between the recorded exposure time and the actual UAV position. This deviation is neither perceptible during flight nor easily quantified and eliminated in post-processing, ultimately seeping into the measurement results as a latent error and jeopardizing the accuracy of the mapping. Summary of the Invention

[0006] The purpose of this invention is to provide an unmanned aerial vehicle (UAV) aerial surveying method and system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for aerial surveying using unmanned aerial vehicles (UAVs), comprising the following steps:

[0009] S1. First, construct a three-dimensional flight path model of the geographical environment characteristics of the target survey area in advance, and transmit the three-dimensional flight path model to the ground simulation platform for simulated flight verification. Determine the photography reference points and several sets of candidate flight paths within the survey area, and generate the signal environment prediction quality score of each flight path according to the several sets of candidate flight paths. Select the optimal flight path for the signal environment for the first time.

[0010] S2. Based on the initially selected optimal route for the signal environment, identify and classify the sources of interference with the global navigation satellite system signals within the route coverage area. The interference sources include static interference sources and dynamic interference sources. Continuously monitor the static interference source areas and dynamic interference source areas respectively to obtain static occlusion geometric information and dynamic interference intensity information. Based on the spatial relationship between the static occlusion geometric information and the photography reference point, generate the trigger offset compensation amount and select the photography trigger correction rule.

[0011] S3. Based on dynamic interference intensity information, obtain the signal-to-carrier-to-noise ratio of the single-frequency receiver in real time, determine the current signal quality level of the global navigation satellite system, identify the trend of signal quality degradation, monitor and mark dangerous low-quality signal periods, establish a time delay jitter prediction model based on dangerous low-quality signal periods, and obtain the time intervals after training. The predicted delay value and timing of the subsequent pulse circuit The predicted position of the dual-frequency receiver is calculated and then compared with the predicted position to determine whether the current timing error exceeds the safety threshold.

[0012] S4. Based on the estimation in S3, if the timing error exceeds the safety threshold, the trained time delay jitter prediction model will be activated to dynamically correct the exposure time recorded value to test the current positioning reliability coefficient of the UAV, and the positioning reliability coefficient will be used as the basis for safety decision-making for adaptive adjustment of the trigger timing.

[0013] Furthermore, multi-source geographic data is collected in advance for the target survey area. The multi-source geographic data includes topographic elevation data, building outline data, and historical global navigation satellite system signal obstruction distribution data of the survey area. The preprocessed multi-source geographic data is then fused and summarized to obtain the survey area environmental dataset.

[0014] A three-dimensional flight path model covering the target survey area was constructed using three-dimensional simulation software. The three-dimensional flight path model was then transmitted to a ground simulation platform. Based on the three-dimensional flight path model, the photography reference points for UAV aerial photography operations and several sets of candidate flight paths were determined. The photography reference points were theoretical exposure position sequences calculated based on predetermined overlap and ground resolution requirements.

[0015] Furthermore, several candidate routes are numbered and labeled, and the flight process of each candidate route is simulated in the three-dimensional route model. The average number of satellite visible points, the average signal-to-noise ratio, and the intensity of predicted multipath error of the UAV during the flight of each candidate route are monitored and recorded respectively, so as to comprehensively generate the signal environment prediction quality score of each candidate route.

[0016] The calculation logic for the signal environment prediction quality score is as follows: the ratio of the average number of visible satellites to the preset visible number benchmark value and the ratio of the average signal-to-noise ratio to the preset signal-to-noise ratio benchmark value are weighted and summed, and then the penalty term corresponding to the prediction multipath error intensity is subtracted. The higher the final score, the more stable the global navigation satellite system signal environment of the route. At the same time, the signal environment prediction quality scores of each candidate route are recorded and compared, and the candidate route with the highest score is selected as the initially selected optimal signal environment route.

[0017] Furthermore, the initially selected optimal route in the signal environment is used as the current flight path option for the UAV. Based on the three-dimensional route model and combined with the initially selected optimal route in the signal environment, the flight environment is assisted in perception using the airborne inertial measurement unit and visual odometry to obtain spatial occlusion distribution data of the route coverage area.

[0018] The spatial shielding distribution data includes the location and geometric outline of high-rise building clusters, mountain shielding surfaces, and artificial electromagnetic transmission facilities within the flight route area, and based on this, the static interference source areas and dynamic interference source areas are located and identified; the static interference sources include, but are not limited to, high-rise buildings, mountains, power transmission towers and tunnel structures, and the dynamic interference sources include, but are not limited to, low-orbit satellite signal scintillation bands, mobile communication base station scheduling interference and temporary electromagnetic transmission equipment.

[0019] Furthermore, based on the locked static interference source area and dynamic interference source area, signal quality monitoring nodes are deployed in the static interference source area and dynamic interference source area respectively, and static masking geometric information is continuously monitored and recorded in the static interference source area.

[0020] The static shielding geometry information includes the basic spatial shape of the shielding body, the elevation angle shielding range, the flight length of the UAV traversing the shielding area, and the width of the signal transition band at the shielding boundary.

[0021] Simultaneously, dynamic interference intensity information is continuously monitored and recorded within the dynamic interference source area. The dynamic interference intensity information includes the single-frequency receiver signal-to-noise ratio and signal interruption frequency at each time node within the monitoring period, and the average intensity of dynamic interference within the monitoring period is generated. At the same time, the development trend of dynamic interference is identified, including both strengthening and weakening trends.

[0022] Furthermore, based on the static shielding geometry information within the static interference source area, and combined with the spatial coordinates of the photography reference point determined in S1, the critical points for signal stability before the UAV enters the static shielding area and the critical points for signal recovery after leaving the static shielding area are locked. The trigger forward compensation distance dominated by visual odometry and the trigger delay compensation distance dominated by inertial measurement unit integration are obtained respectively, thereby generating the trigger offset compensation amount.

[0023] The calculation logic for the trigger offset compensation amount is as follows: the absolute value of the difference between the trigger forward compensation distance and the trigger delay compensation distance represents the degree of offset of the trigger position; if the offset direction corresponding to the trigger offset compensation amount points forward of the flight path, it means that the current UAV should perform photography according to the advance exposure strategy corresponding to the trigger forward compensation distance.

[0024] If the triggered offset compensation amount is zero, it means that the two compensation strategies are equivalent and can be selected arbitrarily; if the offset direction corresponding to the triggered offset compensation amount points behind the flight path, it means that the current UAV should perform photography according to the delayed exposure strategy corresponding to the triggered delay compensation distance.

[0025] Furthermore, the increasing trend refers to the trend of dynamic interference intensity continuously increasing over time, causing the single-frequency receiver signal carrier-to-noise ratio to fall below a preset safety threshold; the decreasing trend refers to the trend of dynamic interference intensity continuously decreasing over time, causing the single-frequency receiver signal carrier-to-noise ratio to recover to above the preset safety threshold.

[0026] Based on the increasing dynamic interference, the difference between the current carrier-to-noise ratio (CNR) of the single-frequency receiver and the current CNR of the dual-frequency receiver is obtained in real time, i.e., the signal quality difference. Then, based on the trained time delay jitter prediction model, the time-series jitter is obtained separately. The predicted delay value and timing of the subsequent pulse circuit The predicted value of the dual-frequency receiver position is calculated; the calculation logic for the predicted delay value of the pulse circuit is as follows:

[0027] The product of the measured time delay of the pulse circuit at time t and the average value of the dynamic interference intensity is then superimposed with the time step. The estimated rate of change of time delay within the time frame is used to synthesize the predicted time delay; the calculation logic for the predicted value of the dual-frequency receiver position solution is as follows: the position coordinates of the dual-frequency receiver at time t, the flight velocity vector of the UAV at time t, and the time step are used. The sum of the products of these factors is used to derive the predicted location.

[0028] Furthermore, at any time The predicted delay value and timing of the subsequent pulse circuit The predicted positions of the dual-frequency receivers are then jointly compared to determine whether the current timing error exceeds the safety threshold.

[0029] If the spatial displacement between the pulse circuit prediction delay value and the position calculation prediction value exceeds the preset maximum allowable position deviation, it indicates that the current timing error exceeds the safety threshold. At this time, the system will automatically suspend the current static compensation parameters and start the dynamic adaptive compensation scheme.

[0030] If the spatial displacement between the pulse circuit prediction delay value and the position calculation prediction value does not exceed the preset maximum allowable position deviation, it means that the current timing error is within the safety threshold. At this time, the system's exposure time recording status does not change, and the current compensation parameters are used to continue to execute the photography trigger.

[0031] Furthermore, for situations where the current timing error exceeds the safety threshold, and considering the current signal quality differences, a positioning reliability coefficient is generated, and a dynamic adaptive compensation scheme is initiated. The specific scheme details are as follows:

[0032] The calculation logic of the positioning reliability coefficient is as follows: the ratio of the preset reference carrier-to-noise ratio to the current measured carrier-to-noise ratio is used as the signal attenuation factor, and the product of the signal attenuation factor and the current signal quality difference is used as the penalty weight. The result obtained by subtracting the penalty weight from the value is the positioning reliability coefficient. The closer the positioning reliability coefficient is to one, the higher the reliability of the current positioning result. The closer it is to zero, the more the current positioning result needs to trigger compensation intervention.

[0033] When the positioning confidence coefficient is lower than the preset confidence threshold, the system uses the fusion calculation result of visual odometry and inertial measurement unit to replace the single-frequency global navigation satellite system signal as the basis for judging the photography trigger. At the same time, the compensation parameters of the pulse circuit are updated in real time with dynamic time delay correction to ensure that the timing alignment error between the exposure time and the positioning time of the dual-frequency receiver is always controlled within the preset accuracy range, thereby ensuring the coverage integrity and coordinate positioning accuracy of aerial photography in complex signal environments.

[0034] An unmanned aerial surveying system includes:

[0035] The route pre-evaluation module is used to construct a three-dimensional route model in advance based on the geographical environmental characteristics of the target survey area, and transmit the three-dimensional route model to the ground simulation platform for simulated flight verification. It determines the photography reference points and several sets of candidate routes within the survey area, and generates a signal environment prediction quality score for each route based on the several sets of candidate routes, and initially selects the route with the best signal environment.

[0036] The interference identification module is used to identify and classify the global navigation satellite system signal interference sources within the coverage area of ​​the initially selected optimal route in the signal environment. The interference source categories include static interference sources and dynamic interference sources. The module continuously monitors the static interference source areas and the dynamic interference source areas to obtain static occlusion geometric information and dynamic interference intensity information, respectively. Based on the spatial relationship between the static occlusion geometric information and the photography reference point, the module generates a trigger offset compensation amount and selects photography trigger correction rules.

[0037] The delay warning module is used to acquire the signal-to-carrier-to-noise ratio of a single-frequency receiver in real time based on dynamic interference intensity information, determine the current signal quality level of the global navigation satellite system, identify the trend of signal quality degradation, monitor and mark dangerous low-quality signal periods, establish a delay jitter prediction model based on dangerous low-quality signal periods, and acquire the time intervals after training. The predicted delay value and timing of the subsequent pulse circuit The predicted position of the dual-frequency receiver is calculated and then compared with the predicted position to determine whether the current timing error exceeds the safety threshold.

[0038] The reliability correction module is used to dynamically correct the exposure time recorded value if the timing error exceeds the safety threshold, based on the estimation. This is done by activating the trained latency jitter prediction model to test the current positioning reliability coefficient of the UAV and using the positioning reliability coefficient as the basis for safety decision-making to trigger adaptive adjustments.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] This invention constructs a three-dimensional flight path model covering the target survey area by pre-fusing multi-source data such as topographic elevation data, building outline data, and historical global navigation satellite system signal obstruction distribution data of the target survey area. The flight process of each candidate flight path is simulated on a ground simulation platform. A signal environment prediction quality score is generated for each candidate flight path by subtracting a penalty term corresponding to the predicted multipath error intensity from the weighted sum of the ratio of the average satellite visibility number to a preset visibility benchmark and the ratio of the average signal-to-noise ratio to a preset signal-to-noise ratio benchmark. This allows for the selection of the flight path with the optimal signal environment from the source. Furthermore, based on an airborne inertial measurement unit and visual odometry, the model analyzes high-rise building clusters, mountain obstruction surfaces, and artificial electromagnetic emission facilities within the flight path coverage area. The system employs spatial perception to identify and classify static and dynamic interference source areas, pinpointing the critical points for signal stability before the UAV enters the static shielded area and the critical points for signal recovery after leaving the static shielded area. It generates the trigger offset compensation amount by subtracting the trigger delay compensation distance from the trigger advance compensation distance, and selects either an early exposure strategy or a delayed exposure strategy to perform photographic trigger correction. This effectively overcomes the problem of inaccurate photographic trigger timing caused by signal shielding and multipath effects in urban high-rise dense areas, mountain valleys, and electromagnetic interference environments where single-frequency receivers are affected. It eliminates defects such as missed shots, repeated triggers, and trigger position offsets, ensuring the coverage integrity and geometric consistency of aerial photographs.

[0041] This invention generates the average dynamic interference intensity within a monitoring period by real-time monitoring of the carrier-to-noise ratio (CNR) and signal interruption frequency of a single-frequency receiver, identifies the increasing trend of dynamic interference, and obtains the difference between the current CNR of the single-frequency receiver and the current CNR of the dual-frequency receiver, i.e., the signal quality difference. The time step is calculated by superimposing the product of the measured delay of the pulse circuit at time t and the average dynamic interference intensity. The predicted time delay value of the pulse circuit is obtained by estimating the rate of change of time delay within the time interval, using the position coordinates of the dual-frequency receiver at time t, the flight velocity vector of the UAV at time t, and the time step. The sum of the products yields the predicted position of the dual-frequency receiver. The timing error is estimated by jointly comparing the corresponding spatial displacements of the two measurements with the preset maximum allowable position deviation. When the timing error exceeds the safety threshold, the ratio of the preset reference carrier-to-noise ratio to the current measured carrier-to-noise ratio is used as the signal attenuation factor, and the product of the signal attenuation factor and the current signal quality difference is used as the penalty weight. The positioning reliability coefficient is generated by subtracting the penalty weight from the value. When the positioning reliability coefficient is lower than the preset reliability threshold, the system automatically replaces the single-frequency global navigation satellite system with the fusion calculation result of visual odometry and inertial measurement unit. The system uses the signal as the basis for determining the trigger for photography, and updates the pulse circuit compensation parameters in real time with dynamic time delay correction. This effectively solves the structural defects in the existing scheme, such as the nonlinear increase of pulse circuit trigger delay jitter caused by the asymmetry of sampling rates between the single-frequency receiver (4Hz) and the dual-frequency receiver (20Hz) under signal degradation environment, and the inability of the static empirical value compensation model to track dynamic errors in real time. It also eliminates the hidden error problem that is difficult to quantify and remove in the post-processing stage due to the residual deviation between the exposure time and the actual UAV position, and ensures the coordinate positioning accuracy and image quality of aerial photography under complex signal environments. Attached Figure Description

[0042] Figure 1 This is a schematic diagram illustrating the application of the method of the present invention;

[0043] Figure 2 This is a schematic diagram of the overall method flow of the present invention;

[0044] Figure 3 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0045] To make the above-mentioned objectives, features and advantages of the present invention more readily understood, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0047] Please see Figure 1 and Figure 2 This invention provides a technical solution: a method for aerial surveying using unmanned aerial vehicles (UAVs), comprising the following steps:

[0048] S1. First, construct a three-dimensional flight path model of the geographical environment characteristics of the target survey area in advance, and transmit the three-dimensional flight path model to the ground simulation platform for simulated flight verification. Determine the photography reference points and several sets of candidate flight paths within the survey area, and generate the signal environment prediction quality score of each flight path according to the several sets of candidate flight paths. Select the optimal flight path for the signal environment for the first time.

[0049] S2. Based on the initially selected optimal route for the signal environment, identify and classify the sources of interference with the global navigation satellite system signals within the route coverage area. The interference sources include static interference sources and dynamic interference sources. Continuously monitor the static interference source areas and dynamic interference source areas respectively to obtain static occlusion geometric information and dynamic interference intensity information. Based on the spatial relationship between the static occlusion geometric information and the photography reference point, generate the trigger offset compensation amount and select the photography trigger correction rule.

[0050] S3. Based on dynamic interference intensity information, obtain the signal-to-carrier-to-noise ratio of the single-frequency receiver in real time, determine the current signal quality level of the global navigation satellite system, identify the trend of signal quality degradation, monitor and mark dangerous low-quality signal periods, establish a time delay jitter prediction model based on dangerous low-quality signal periods, and obtain the time intervals after training. The predicted delay value and timing of the subsequent pulse circuit The predicted position of the dual-frequency receiver is calculated and then compared with the predicted position to determine whether the current timing error exceeds the safety threshold.

[0051] S4. Based on the estimation in S3, if the timing error exceeds the safety threshold, the trained time delay jitter prediction model will be activated to dynamically correct the exposure time recorded value to test the current positioning reliability coefficient of the UAV, and the positioning reliability coefficient will be used as the basis for safety decision-making for adaptive adjustment of the trigger timing.

[0052] Multi-source geographic data is collected in advance for the target survey area. The multi-source geographic data includes topographic elevation data, building outline data and historical global navigation satellite system signal obstruction distribution data of the survey area. The pre-processed multi-source geographic data is then fused and summarized to obtain the environmental dataset of the survey area.

[0053] A three-dimensional flight path model covering the target survey area was constructed using three-dimensional simulation software. The three-dimensional flight path model was then transmitted to a ground simulation platform. Based on the three-dimensional flight path model, the photography reference points for UAV aerial photography operations and several sets of candidate flight paths were determined. The photography reference points were theoretical exposure position sequences calculated based on predetermined overlap and ground resolution requirements.

[0054] Several candidate routes are numbered and labeled. The flight process of each candidate route is simulated in a three-dimensional route model. The average number of satellite visible points, the average signal-to-noise ratio, and the intensity of predicted multipath error of the UAV during the flight of each candidate route are monitored and recorded respectively. In order to comprehensively generate the signal environment prediction quality score of each candidate route.

[0055] The calculation logic for the signal environment prediction quality score is as follows: the ratio of the average number of visible satellites to the preset visible number benchmark value and the ratio of the average signal-to-noise ratio to the preset signal-to-noise ratio benchmark value are weighted and summed, and then the penalty term corresponding to the prediction multipath error intensity is subtracted. The higher the final score, the more stable the global navigation satellite system signal environment of the route. At the same time, the signal environment prediction quality scores of each candidate route are recorded and compared, and the candidate route with the highest score is selected as the initially selected optimal signal environment route.

[0056] Furthermore, the specific calculation method for the signal environment prediction quality score includes:

[0057] Each candidate route is assigned a number, and the signal environment prediction quality score for the i-th candidate route is denoted as Qi, calculated using the following formula:

[0058]

[0059] In the formula, The number of visible satellites during the simulated flight of the i-th candidate flight path is represented by the number of satellites, and the statistical range includes all visible satellites with an elevation angle greater than 10°. The preset visible number baseline value is preferably 8, which represents the typical number of visible satellites under open sky conditions; The carrier-to-noise ratio of each visible satellite signal during the simulated flight of the i-th candidate route is the average value, in dB-Hz. The preferred value for the preset carrier-to-noise ratio is 45dB-Hz, which represents the normal carrier-to-noise ratio level of a single-frequency receiver in a typical open environment. The normalized prediction multipath error intensity corresponding to the i-th candidate route is calculated as follows:

[0060]

[0061] Where M i M represents the original value of the multipath error intensity estimated in the 3D flight path model for the i-th candidate flight path based on the geometric relationship of the shielding body and the difference in signal reflection path length, in mm; max The maximum value of the multipath error intensity among all candidate routes is used to normalize the penalty term to the interval [0, 1] to ensure that its dimensions are consistent with the first two terms;

[0062] α, β, and γ are the satellite visibility number weight, carrier-to-noise ratio weight, and multipath penalty weight, respectively, and they satisfy the following:

[0063]

[0064] The preferred values ​​are α=0.30, β=0.45, and γ=0.25. The basis for these values ​​is as follows: the carrier-to-noise ratio directly reflects the signal tracking quality of the receiver and has the greatest impact on the trigger timing judgment, so it is given the highest weight; the number of visible satellites determines the availability of the positioning solution, so it is given a secondary weight; and the multipath error, as a penalty term, reflects the indirect impact of the geometric environment on the signal quality, so it is given the lowest weight.

[0065] Q i The theoretical range is [−γ, α+β], i.e., (−0.25, 0.75]; after all candidate routes have been calculated, the system selects the route that satisfies... Candidate routes i ∗ The optimal route for the signal environment is initially selected, where n is the total number of candidate routes.

[0066] Furthermore, the signal quality level is based on the real-time carrier-to-noise ratio (C / N0) of the single-frequency receiver. (1f) They are divided into three levels, and the specific criteria for division are shown in the table below:

[0067] Level Number Level Name Carrier-to-noise ratio range System response strategy L1 Normal signal <![CDATA[C / N0 (1f) ≥40dB-Hz]]> Maintain current compensation parameters and execute photography triggering normally. L2 degraded signal <![CDATA[30dB-Hz≤C / N0 (1f) <40dB-Hz]]> Initiate latency jitter monitoring and enter early warning mode. L3 Dangerous low-quality signals <![CDATA[C / N0 (1f) <30dB-Hz]]> The delay jitter prediction model is activated during periods marked as dangerous low-quality signal periods.

[0068] The entry condition for a dangerous low-quality signal period is: the carrier-to-noise ratio of the single-frequency receiver signal is below 30 dB-Hz for a continuous duration of Tentry = 2 seconds. The system will then set the current time t... start Mark the start point of the dangerous period; the exit condition is: the carrier-to-noise ratio of the single-frequency receiver signal recovers to above 40dB-Hz within a continuous duration Texit=3s, and the system sets the current time t end The end point of the dangerous period is marked; the design purpose of time hysteresis Texit > Tentry is to prevent frequent switching in and out of the dangerous period when the signal jitters near the threshold, thereby ensuring the stability of the compensation strategy.

[0069] The optimal flight path for the signal environment initially selected is used as the current flight path option for the UAV. Based on the three-dimensional flight path model and the optimal flight path for the signal environment initially selected, the airborne inertial measurement unit and visual odometry are used to assist in the perception of the flight environment and obtain the spatial occlusion distribution data of the flight path coverage area.

[0070] The spatial shielding distribution data includes the location and geometric outline of high-rise building clusters, mountain shielding surfaces, and artificial electromagnetic transmission facilities within the flight route area, and based on this, the static interference source areas and dynamic interference source areas are located and identified; the static interference sources include, but are not limited to, high-rise buildings, mountains, power transmission towers and tunnel structures, and the dynamic interference sources include, but are not limited to, low-orbit satellite signal scintillation bands, mobile communication base station scheduling interference and temporary electromagnetic transmission equipment.

[0071] Based on the locked static interference source area and dynamic interference source area, signal quality monitoring nodes are deployed in the static interference source area and dynamic interference source area respectively, and static masking geometric information is continuously monitored and recorded in the static interference source area.

[0072] The static shielding geometry information includes the basic spatial shape of the shielding body, the elevation angle shielding range, the flight length of the UAV traversing the shielding area, and the width of the signal transition band at the shielding boundary.

[0073] Simultaneously, dynamic interference intensity information is continuously monitored and recorded within the dynamic interference source area. The dynamic interference intensity information includes the single-frequency receiver signal-to-noise ratio and signal interruption frequency at each time node within the monitoring period, and the average intensity of dynamic interference within the monitoring period is generated. At the same time, the development trend of dynamic interference is identified, including both strengthening and weakening trends.

[0074] Based on the static shielding geometry information within the static interference source area, and combined with the spatial coordinates of the photography reference point determined in S1, the critical points for signal stability before the UAV enters the static shielding area and the critical points for signal recovery after leaving the static shielding area are locked. The trigger forward compensation distance dominated by visual odometry and the trigger delay compensation distance dominated by inertial measurement unit integration are obtained respectively, thereby generating the trigger offset compensation amount.

[0075] The calculation logic for the trigger offset compensation amount is as follows: the absolute value of the difference between the trigger forward compensation distance and the trigger delay compensation distance represents the degree of offset of the trigger position; if the offset direction corresponding to the trigger offset compensation amount points forward of the flight path, it means that the current UAV should perform photography according to the advance exposure strategy corresponding to the trigger forward compensation distance.

[0076] If the triggered offset compensation amount is zero, it means that the two compensation strategies are equivalent and can be selected arbitrarily; if the offset direction corresponding to the triggered offset compensation amount points behind the flight path, it means that the current UAV should perform photography according to the delayed exposure strategy corresponding to the triggered delay compensation distance.

[0077] The increasing trend refers to the trend that the dynamic interference intensity continuously increases over time, causing the single-frequency receiver signal carrier-to-noise ratio to fall below a preset safety threshold; the decreasing trend refers to the trend that the dynamic interference intensity continuously decreases over time, causing the single-frequency receiver signal carrier-to-noise ratio to recover to above the preset safety threshold.

[0078] Based on the increasing dynamic interference, the difference between the current carrier-to-noise ratio (CNR) of the single-frequency receiver and the current CNR of the dual-frequency receiver is obtained in real time, i.e., the signal quality difference. Then, based on the trained time delay jitter prediction model, the time-series jitter is obtained separately. The predicted delay value and timing of the subsequent pulse circuit The predicted value of the dual-frequency receiver position is calculated; the calculation logic for the predicted delay value of the pulse circuit is as follows:

[0079] The product of the measured time delay of the pulse circuit at time t and the average value of the dynamic interference intensity is then superimposed with the time step. The estimated rate of change of time delay within the time frame is used to synthesize the predicted time delay; the calculation logic for the predicted value of the dual-frequency receiver position solution is as follows: the position coordinates of the dual-frequency receiver at time t, the flight velocity vector of the UAV at time t, and the time step are used. The sum of the products of these factors is used to derive the predicted location.

[0080] Time The predicted delay value and timing of the subsequent pulse circuit The predicted positions of the dual-frequency receivers are then jointly compared to determine whether the current timing error exceeds the safety threshold.

[0081] If the spatial displacement between the pulse circuit prediction delay value and the position calculation prediction value exceeds the preset maximum allowable position deviation, it indicates that the current timing error exceeds the safety threshold. At this time, the system will automatically suspend the current static compensation parameters and start the dynamic adaptive compensation scheme.

[0082] If the spatial displacement between the pulse circuit prediction delay value and the position calculation prediction value does not exceed the preset maximum allowable position deviation, it means that the current timing error is within the safety threshold. At this time, the system's exposure time recording status does not change, and the current compensation parameters are used to continue to execute the photography trigger.

[0083] In response to the situation where the current timing error exceeds the safety threshold, and considering the current signal quality difference, a positioning reliability coefficient is generated, and a dynamic adaptive compensation scheme is initiated. The specific scheme details are as follows:

[0084] The calculation logic of the positioning reliability coefficient is as follows: the ratio of the preset reference carrier-to-noise ratio to the current measured carrier-to-noise ratio is used as the signal attenuation factor, and the product of the signal attenuation factor and the current signal quality difference is used as the penalty weight. The result obtained by subtracting the penalty weight from the value is the positioning reliability coefficient. The closer the positioning reliability coefficient is to one, the higher the reliability of the current positioning result. The closer it is to zero, the more the current positioning result needs to trigger compensation intervention.

[0085] When the positioning confidence coefficient is lower than the preset confidence threshold, the system uses the fusion calculation result of visual odometry and inertial measurement unit to replace the single-frequency global navigation satellite system signal as the basis for judging the photography trigger. At the same time, the compensation parameters of the pulse circuit are updated in real time with dynamic time delay correction to ensure that the timing alignment error between the exposure time and the positioning time of the dual-frequency receiver is always controlled within the preset accuracy range, thereby ensuring the coverage integrity and coordinate positioning accuracy of aerial photography in complex signal environments.

[0086] Furthermore, the methods for obtaining the trigger forward compensation distance Df and the trigger delay compensation distance Db, as well as the logic for determining the direction of the trigger offset compensation amount Dc, are as follows.

[0087] The trigger advance compensation distance Df is mainly based on the pose increment output by the visual odometry. Its physical meaning is: when the global navigation satellite system signal is still in a stable state, the distance that the UAV travels from the signal stability critical point to the theoretical exposure position represents the displacement that should be triggered for exposure in advance. This distance is obtained by time integration of the heading velocity component output by the visual odometry within the interval from the signal stability critical moment to the theoretical exposure moment.

[0088] The trigger delay compensation distance Db is mainly based on the integration of the airborne inertial measurement unit. Its physical meaning is: during the interruption of the global navigation satellite system signal, the distance that the UAV continues to travel from the theoretical exposure position to the critical point of signal recovery represents the displacement that should be delayed in triggering the exposure. This distance is obtained by time integration of the heading velocity component obtained by the inertial measurement unit integration solution within the interval from the theoretical exposure time to the critical time of signal recovery.

[0089] The trigger offset compensation amount Dc is defined as the absolute value of the difference between the trigger advance compensation distance and the trigger delay compensation distance, that is:

[0090]

[0091] The logic for determining the trigger offset direction is as follows: When Df > Db, the offset direction points forward of the flight path, and the system executes the early exposure strategy with an early trigger distance of Df; when Df < Db, the offset direction points backward of the flight path, and the system executes the delayed exposure strategy with a delayed trigger distance of Db; when Df = Db, the two compensation strategies are theoretically equivalent, and the system executes the early exposure strategy according to the default priority. The basis for this is that when the exposure is triggered at the forefront of the signal obscuration area, the global navigation satellite system signal is still in a stable state, and the reliability of the obtained position calculation result is higher than that obtained by integrating based on the inertial measurement unit near the signal recovery critical point.

[0092] Furthermore, the construction, training process, and inference output calculation method of the latency jitter prediction model are as follows:

[0093] The training samples are derived from historical flight data during periods of dangerous low-quality signals. The input features of each sample include: the measured carrier-to-noise ratio of the single-frequency receiver at the current time, the mean dynamic interference intensity within a 10-second sliding window ending at the current time, the first-order differential estimate of the pulse circuit delay within the previous time step (i.e., the rate of change of delay), the measured pulse circuit delay at the current time, and the signal interruption frequency at the current time. The corresponding output label is the true value of the measured pulse circuit delay at the next prediction time.

[0094] The latency jitter prediction model preferably employs a long short-term memory network with temporal memory capabilities, with an optimal number of hidden layer nodes of 64 and a preferred number of time unfolding steps of 10. The mean squared error is used as the loss function, and the Adam optimizer is used for training. The initial learning rate is set to 10⁻³, the maximum number of training epochs is 200, and an early stopping strategy is adopted to prevent overfitting. The model completes initial training on a ground simulation platform, and during the flight execution phase, it is fine-tuned online using new samples accumulated after each period of dangerous low-quality signal as the increment to continuously track the changing pattern of dynamic error.

[0095] After the trained model is input with the above feature vector at the current time, it directly outputs the pulse circuit prediction delay value for the next prediction time. At the same time, the system uses the product of the current measured delay value, the delay change rate and the prediction step size as the linear extrapolation reference value. When the difference between the model prediction value and the linear extrapolation reference value exceeds 0.5μs, the system records the difference for post-processing quality evaluation. In engineering practice, the model prediction value is preferred as the formal output.

[0096] The predicted position value of the dual-frequency receiver is obtained by superimposing the product of the flight velocity vector and the prediction step size on the current measured position coordinates of the dual-frequency receiver, and then superimposing half of the product of the acceleration vector output by the inertial measurement unit and the square of the prediction step size. The acceleration correction term is introduced based on the fact that in aerial photography operations with an accuracy requirement of better than 5cm, even under typical maneuvering acceleration conditions, the position correction corresponding to this term is about several millimeters. Retaining this term can effectively eliminate the systematic deviation introduced by the uniform velocity assumption. The preferred prediction step size is 0.05s, which corresponds to the 20Hz sampling rate of the dual-frequency receiver.

[0097] Furthermore, the joint comparison method between the pulse circuit prediction delay value and the dual-frequency receiver position calculation prediction value, and the spatial mapping calculation method for the timing error are as follows:

[0098] The pulse circuit prediction delay is a time quantity in microseconds. To achieve joint comparison with the position calculation prediction value, it needs to be mapped to the corresponding spatial displacement. The mapping method is to multiply the current flight speed of the UAV by the pulse circuit prediction delay value. The result is the spatial displacement corresponding to the pulse circuit delay in the UAV's flight direction. Then, the absolute value of the difference between this spatial displacement and the displacement component of the dual-frequency receiver position calculation prediction value in the heading direction is taken. The resulting comprehensive spatial deviation is the quantitative representation of the timing error.

[0099] When the overall spatial deviation exceeds the preset maximum allowable positional deviation e max (The preferred value is 0.05m, corresponding to the planar accuracy requirement of feature points on a 1:500 scale topographic map.) When the current timing error exceeds the safety threshold, the system writes the static compensation parameters of the current pulse circuit into the suspend register for temporary storage, and then the dynamic adaptive compensation scheme takes over the output of the pulse circuit's compensation parameters; when the overall spatial deviation does not exceed e max If the timing error is determined to be within the safety threshold, the system maintains the current compensation parameters unchanged and continues to use the static compensation strategy to trigger photography.

[0100] The recovery conditions for static compensation parameters are as follows: after the single-frequency receiver signal carrier-to-noise ratio recovers to above 40dB-Hz and lasts for more than 3 seconds, the system exits the dynamic adaptive compensation scheme, reads the static compensation parameters from the pending register and rewrites them into the pulse circuit compensation register, and restores the normal static compensation state.

[0101] Furthermore, the complete calculation method for the location reliability coefficient, the outlier truncation protection rules, and the method for generating the dynamic delay correction amount are detailed below:

[0102] The signal quality difference is defined as the difference between the measured carrier-to-noise ratio (CNR) of the dual-frequency receiver and the measured CNR of the single-frequency receiver at the same time. Under normal operating conditions, this difference is non-negative. When a negative value appears, it indicates that the signal quality of the single-frequency receiver is abnormally better than that of the dual-frequency receiver. In this case, the system truncates the difference to zero to prevent directional errors in subsequent calculations.

[0103] The signal attenuation factor is defined as the ratio of a preset reference carrier-to-noise ratio (preferably 45dB-Hz) to the measured carrier-to-noise ratio of the current single-frequency receiver, reflecting the degree of attenuation of the current signal quality relative to normal operating conditions. To prevent the attenuation factor from becoming infinitely large when the carrier-to-noise ratio of the single-frequency receiver approaches zero, the system sets an upper limit truncation for the signal attenuation factor, preferably 3.0.

[0104] The penalty weight is calculated by multiplying the signal attenuation factor and the normalized signal quality difference. The signal quality difference is normalized using a preset normalized reference value (preferably 15dB-Hz) as the denominator to constrain it to a reasonable range. To ensure that the positioning reliability coefficient is always non-negative, the system sets an upper limit truncation for the penalty weight, with a preferred upper limit value of 0.95.

[0105] The positioning reliability coefficient is obtained by subtracting the penalty weight from the value 1, and its value range is [0.05, 1]. The closer the positioning reliability coefficient is to 1, the higher the reliability of the current positioning result. The closer it is to 0, the more it indicates that the current positioning result needs to trigger compensation intervention. The preset reliability threshold is preferably 0.6. When the positioning reliability coefficient is lower than this threshold, the system uses the fusion calculation result of visual odometry and inertial measurement unit to replace the single-frequency global navigation satellite system signal as the basis for judging the photography trigger.

[0106] The dynamic delay correction is calculated from the difference between the current predicted output value of the delay jitter prediction model and the current measured delay value of the pulse circuit, reflecting the expected change in delay within the prediction step. The system updates the compensation parameters of the pulse circuit in real time with this correction to ensure that the timing alignment error between the exposure time and the positioning time of the dual-frequency receiver is always controlled within the preset accuracy range. The preset accuracy range of the timing alignment error is preferably set to an equivalent spatial displacement of no more than 0.05m, which is consistent with the maximum allowable position deviation in Example 5, thereby ensuring the coverage integrity and coordinate positioning accuracy of aerial photography in complex signal environments.

[0107] An unmanned aerial surveying system includes:

[0108] The route pre-evaluation module is used to construct a three-dimensional route model in advance based on the geographical environmental characteristics of the target survey area, and transmit the three-dimensional route model to the ground simulation platform for simulated flight verification. It determines the photography reference points and several sets of candidate routes within the survey area, and generates a signal environment prediction quality score for each route based on the several sets of candidate routes, and initially selects the route with the best signal environment.

[0109] The interference identification module is used to identify and classify the global navigation satellite system signal interference sources within the coverage area of ​​the initially selected optimal route in the signal environment. The interference source categories include static interference sources and dynamic interference sources. The module continuously monitors the static interference source areas and the dynamic interference source areas to obtain static occlusion geometric information and dynamic interference intensity information, respectively. Based on the spatial relationship between the static occlusion geometric information and the photography reference point, the module generates a trigger offset compensation amount and selects photography trigger correction rules.

[0110] The delay warning module is used to acquire the signal-to-carrier-to-noise ratio of a single-frequency receiver in real time based on dynamic interference intensity information, determine the current signal quality level of the global navigation satellite system, identify the trend of signal quality degradation, monitor and mark dangerous low-quality signal periods, establish a delay jitter prediction model based on dangerous low-quality signal periods, and acquire the time intervals after training. The predicted delay value and timing of the subsequent pulse circuit The predicted position of the dual-frequency receiver is calculated and then compared with the predicted position to determine whether the current timing error exceeds the safety threshold.

[0111] The reliability correction module is used to dynamically correct the exposure time recorded value if the timing error exceeds the safety threshold, based on the estimation. This is done by activating the trained latency jitter prediction model to test the current positioning reliability coefficient of the UAV and using the positioning reliability coefficient as the basis for safety decision-making to trigger adaptive adjustments.

[0112] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization.

[0113] The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random-access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0114] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for aerial surveying using unmanned aerial vehicles (UAVs), characterized in that, The specific steps include: S1. First, construct a three-dimensional flight path model of the geographical environment characteristics of the target survey area in advance, and transmit the three-dimensional flight path model to the ground simulation platform for simulated flight verification. Determine the photography reference points and several sets of candidate flight paths within the survey area, and generate the signal environment prediction quality score of each flight path according to the several sets of candidate flight paths. Select the optimal flight path for the signal environment for the first time. S2. Based on the initially selected optimal route for the signal environment, identify and classify the sources of interference with the global navigation satellite system signals within the route coverage area. The interference sources include static interference sources and dynamic interference sources. Continuously monitor the static interference source areas and dynamic interference source areas respectively to obtain static occlusion geometric information and dynamic interference intensity information. Based on the spatial relationship between the static occlusion geometric information and the photography reference point, generate the trigger offset compensation amount and select the photography trigger correction rule. S3. Based on dynamic interference intensity information, obtain the signal-to-carrier-to-noise ratio of the single-frequency receiver in real time, determine the current signal quality level of the global navigation satellite system, identify the trend of signal quality degradation, monitor and mark dangerous low-quality signal periods, establish a time delay jitter prediction model based on dangerous low-quality signal periods, and obtain the time intervals after training. The predicted delay value and timing of the subsequent pulse circuit The predicted position of the dual-frequency receiver is calculated and then compared with the predicted position to determine whether the current timing error exceeds the safety threshold. S4. Based on the estimation in S3, if the timing error exceeds the safety threshold, the trained time delay jitter prediction model will be activated to dynamically correct the exposure time recorded value to test the current positioning reliability coefficient of the UAV, and the positioning reliability coefficient will be used as the basis for safety decision-making for adaptive adjustment of the trigger timing.

2. The UAV aerial surveying method according to claim 1, characterized in that: Multi-source geographic data is collected in advance for the target survey area. The multi-source geographic data includes topographic elevation data, building outline data and historical global navigation satellite system signal masking distribution data of the survey area. The pre-processed multi-source geographic data is then fused and summarized to obtain the survey area environmental dataset. A three-dimensional flight path model covering the target survey area was constructed using three-dimensional simulation software. The three-dimensional flight path model was then transmitted to a ground simulation platform. Based on the three-dimensional flight path model, the photography reference points for UAV aerial photography operations and several sets of candidate flight paths were determined. The photography reference points were theoretical exposure position sequences calculated based on predetermined overlap and ground resolution requirements.

3. The UAV aerial surveying method according to claim 1, characterized in that: Several candidate routes are numbered and labeled. The flight process of each candidate route is simulated in a three-dimensional route model. The average number of satellite visible points, the average signal-to-noise ratio, and the intensity of predicted multipath error of the UAV during the flight of each candidate route are monitored and recorded respectively. In order to comprehensively generate the signal environment prediction quality score of each candidate route. The calculation logic for the signal environment prediction quality score is as follows: the ratio of the average number of visible satellites to the preset visible number benchmark value and the ratio of the average signal-to-noise ratio to the preset signal-to-noise ratio benchmark value are weighted and summed, and then the penalty term corresponding to the prediction multipath error intensity is subtracted. The higher the final score, the more stable the global navigation satellite system signal environment of the route. At the same time, the signal environment prediction quality scores of each candidate route are recorded and compared, and the candidate route with the highest score is selected as the initially selected optimal signal environment route.

4. The UAV aerial surveying method according to claim 3, characterized in that: The optimal flight path for the signal environment initially selected is used as the current flight path option for the UAV. Based on the three-dimensional flight path model and the optimal flight path for the signal environment initially selected, the airborne inertial measurement unit and visual odometry are used to assist in the perception of the flight environment and obtain the spatial occlusion distribution data of the flight path coverage area. The spatial shielding distribution data includes the location and geometric outline of high-rise building clusters, mountain shielding surfaces, and artificial electromagnetic transmission facilities within the flight route area, and based on this, the static interference source areas and dynamic interference source areas are located and identified; the static interference sources include, but are not limited to, high-rise buildings, mountains, power transmission towers and tunnel structures, and the dynamic interference sources include, but are not limited to, low-orbit satellite signal scintillation bands, mobile communication base station scheduling interference and temporary electromagnetic transmission equipment.

5. The UAV aerial surveying method according to claim 4, characterized in that: Based on the locked static interference source area and dynamic interference source area, signal quality monitoring nodes are deployed in the static interference source area and dynamic interference source area respectively, and static masking geometric information is continuously monitored and recorded in the static interference source area. The static shielding geometry information includes the basic spatial shape of the shielding body, the elevation angle shielding range, the flight length of the UAV traversing the shielding area, and the width of the signal transition band at the shielding boundary. Simultaneously, dynamic interference intensity information is continuously monitored and recorded within the dynamic interference source area. The dynamic interference intensity information includes the single-frequency receiver signal-to-noise ratio and signal interruption frequency at each time node within the monitoring period, and the average intensity of dynamic interference within the monitoring period is generated. At the same time, the development trend of dynamic interference is identified, including both strengthening and weakening trends.

6. The UAV aerial surveying method according to claim 5, characterized in that: Based on the static shielding geometry information within the static interference source area, and combined with the spatial coordinates of the photography reference point determined in S1, the critical points for signal stability before the UAV enters the static shielding area and the critical points for signal recovery after leaving the static shielding area are locked. The trigger forward compensation distance dominated by visual odometry and the trigger delay compensation distance dominated by inertial measurement unit integration are obtained respectively, thereby generating the trigger offset compensation amount. The calculation logic for the trigger offset compensation amount is as follows: the absolute value of the difference between the trigger forward compensation distance and the trigger delay compensation distance represents the degree of offset of the trigger position; If the offset direction corresponding to the triggered offset compensation amount points forward of the flight path, it means that the current UAV should perform photography according to the advance exposure strategy corresponding to the triggered forward compensation distance; If the triggered offset compensation amount is zero, it means that the two compensation strategies are equivalent and can be selected arbitrarily; if the offset direction corresponding to the triggered offset compensation amount points behind the flight path, it means that the current UAV should perform photography according to the delayed exposure strategy corresponding to the triggered delay compensation distance.

7. The UAV aerial surveying method according to claim 5, characterized in that: The enhancement trend refers to the trend that the dynamic interference intensity continues to increase over time, causing the single-frequency receiver signal carrier-to-noise ratio to fall below a preset safety threshold. The weakening trend refers to the trend that the dynamic interference intensity continues to decrease over time, and the signal-to-noise ratio of the single-frequency receiver recovers to above the preset safety threshold. Based on the increasing dynamic interference, the difference between the current carrier-to-noise ratio (CNR) of the single-frequency receiver and the current CNR of the dual-frequency receiver is obtained in real time, i.e., the signal quality difference. Then, based on the trained time delay jitter prediction model, the time-series jitter is obtained separately. The predicted delay value and timing of the subsequent pulse circuit The predicted value of the dual-frequency receiver position is calculated; the calculation logic for the predicted delay value of the pulse circuit is as follows: The product of the measured time delay of the pulse circuit at time t and the average value of the dynamic interference intensity is then superimposed with the time step. The estimated rate of change of time delay within the time frame is used to synthesize the predicted time delay; the calculation logic for the predicted value of the dual-frequency receiver position solution is as follows: the position coordinates of the dual-frequency receiver at time t, the flight velocity vector of the UAV at time t, and the time step are used. The sum of the products of these factors is used to derive the predicted location.

8. The UAV aerial surveying method according to claim 7, characterized in that: Time The predicted delay value and timing of the subsequent pulse circuit The predicted positions of the dual-frequency receivers are then jointly compared to determine whether the current timing error exceeds the safety threshold. If the spatial displacement between the pulse circuit prediction delay value and the position calculation prediction value exceeds the preset maximum allowable position deviation, it indicates that the current timing error exceeds the safety threshold. At this time, the system will automatically suspend the current static compensation parameters and start the dynamic adaptive compensation scheme. If the spatial displacement between the pulse circuit prediction delay value and the position calculation prediction value does not exceed the preset maximum allowable position deviation, it means that the current timing error is within the safety threshold. At this time, the system's exposure time recording status does not change, and the current compensation parameters are used to continue to execute the photography trigger.

9. The UAV aerial surveying method according to claim 8, characterized in that: In response to the situation where the current timing error exceeds the safety threshold, and considering the current signal quality difference, a positioning reliability coefficient is generated, and a dynamic adaptive compensation scheme is initiated. The specific scheme details are as follows: The calculation logic of the positioning reliability coefficient is as follows: the ratio of the preset reference carrier-to-noise ratio to the current measured carrier-to-noise ratio is used as the signal attenuation factor, and the product of the signal attenuation factor and the current signal quality difference is used as the penalty weight. The result obtained by subtracting the penalty weight from the value is the positioning reliability coefficient. The closer the positioning reliability coefficient is to one, the higher the reliability of the current positioning result. The closer it is to zero, the more the current positioning result needs to trigger compensation intervention. When the positioning confidence coefficient is lower than the preset confidence threshold, the system uses the fusion calculation result of visual odometry and inertial measurement unit to replace the single-frequency global navigation satellite system signal as the basis for judging the photography trigger. At the same time, the compensation parameters of the pulse circuit are updated in real time with dynamic time delay correction to ensure that the timing alignment error between the exposure time and the positioning time of the dual-frequency receiver is always controlled within the preset accuracy range, thereby ensuring the coverage integrity and coordinate positioning accuracy of aerial photography in complex signal environments.

10. An unmanned aerial vehicle (UAV) aerial surveying system, characterized in that: The UAV aerial surveying method is used to perform the method described in any one of claims 1-9, including: The route pre-evaluation module is used to construct a three-dimensional route model in advance based on the geographical environmental characteristics of the target survey area, and transmit the three-dimensional route model to the ground simulation platform for simulated flight verification. It determines the photography reference points and several sets of candidate routes within the survey area, and generates a signal environment prediction quality score for each route based on the several sets of candidate routes, and initially selects the route with the best signal environment. The interference identification module is used to identify and classify the global navigation satellite system signal interference sources within the coverage area of ​​the initially selected optimal route in the signal environment. The interference source categories include static interference sources and dynamic interference sources. The module continuously monitors the static interference source areas and the dynamic interference source areas to obtain static occlusion geometric information and dynamic interference intensity information, respectively. Based on the spatial relationship between the static occlusion geometric information and the photography reference point, the module generates a trigger offset compensation amount and selects photography trigger correction rules. The delay warning module is used to acquire the signal-to-carrier-to-noise ratio of a single-frequency receiver in real time based on dynamic interference intensity information, determine the current signal quality level of the global navigation satellite system, identify the trend of signal quality degradation, monitor and mark dangerous low-quality signal periods, establish a delay jitter prediction model based on dangerous low-quality signal periods, and acquire the time intervals after training. The predicted delay value and timing of the subsequent pulse circuit The predicted position of the dual-frequency receiver is calculated and then compared with the predicted position to determine whether the current timing error exceeds the safety threshold. The reliability correction module is used to dynamically correct the exposure time recorded value if the timing error exceeds the safety threshold, based on the estimation. This is done by activating the trained latency jitter prediction model to test the current positioning reliability coefficient of the UAV and using the positioning reliability coefficient as the basis for safety decision-making to trigger adaptive adjustments.

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