Unmanned aerial vehicle low-altitude monitoring and ground GNSS sensing cooperative work system and method

Abstract

The invention discloses an unmanned aerial vehicle low-altitude monitoring and ground GNSS sensing cooperative work system and method. The unmanned aerial vehicle low-altitude monitoring and ground GNSS sensing cooperative work system comprises a data acquisition module, an equipment management module, a data transmission module, a data storage module, a data processing module and a data application module. The method comprises the following steps: step 1, initializing a system; step 2, data acquisition; step 3, data processing; step 4, data application; according to the data acquisition module, low-altitude aerial survey and ground GNSS monitoring are combined, global monitoring is achieved through low-altitude aerial survey, and a geological high-risk area is monitored through a ground GNSS, so that rich and comprehensive monitoring data are obtained, the blind area of single GNSS monitoring is made up, and the limitation of manual monitoring is overcome; the data processing module corrects data errors by adopting various methods, so that the accuracy of monitoring data is improved; and the data application module performs fitting and registration on the multi-period three-dimensional data and extracts deformation features and trends, thereby providing a reliable basis for disaster monitoring and early warning.

Description

Technical Field

[0001] The present invention relates to the field of geological monitoring technology, and in particular to a system and method for collaborative working of unmanned aerial vehicle (UAV) low-altitude monitoring and ground GNSS sensing. Background Art

[0002] In the field of geological monitoring, existing manual geological monitoring and contact GNSS monitoring methods have many drawbacks and are unable to meet the current complex and changeable geological environment monitoring needs, especially in dealing with frequent geological landslide and collapse disasters.

[0003] Artificial geological monitoring mainly relies on technical means such as manual inspections, fixed-point observations and regular measurements. This monitoring method not only consumes a lot of manpower, material resources and time costs, but also the amount of data that can be obtained is extremely limited, and only information from a few points can be obtained; in addition, since different personnel may collect data at different times, their operating methods and observation standards are difficult to completely unify, resulting in difficulty in ensuring data consistency; what is more serious is that in some mining areas, the mountains and collapse areas, monitoring points are often difficult to reach, and workers face great personal safety risks when conducting monitoring operations, which further limits the feasibility and effectiveness of manual monitoring. Although contact GNSS monitoring can provide relatively accurate point data, it can only monitor limited fixed discrete points. Therefore, there are a large number of spatial blind spots, and it is impossible to fully cover complex areas such as distributed slopes in mining areas and large-scale goaf areas. It is difficult to meet multi-dimensional monitoring needs, resulting in incomplete monitoring data and insufficient information for overall geological condition assessment. In addition, existing geological monitoring methods lack effective error correction methods, and the accuracy of the data cannot be guaranteed. At the same time, in terms of data application, existing technologies lack in-depth analysis of data, and cannot accurately extract the deformation characteristics and trends of geological bodies from a large amount of complex monitoring data. It is difficult to achieve a comprehensive and accurate assessment and prediction of potential geological disasters, and it is difficult to capture key change information before a disaster occurs, resulting in delayed or inaccurate disaster warnings, and unable to provide timely and effective decision-making basis for disaster prevention and control, which makes it passive when responding to geological disasters and difficult to ensure the safety of people’s lives and property and the stability of the ecological environment. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for cooperating between UAV low-altitude monitoring and ground GNSS sensing to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a collaborative working system of unmanned aerial vehicle low-altitude monitoring and ground GNSS sensing, including a data acquisition module, an equipment management module, a data transmission module, a data storage module, a data processing module and a data application module, wherein the data transmission module establishes data connections with the data acquisition module, the equipment management module and the data storage module respectively, the data storage module establishes a data connection with the data processing module, and the data processing module establishes a data connection with the data application module.

[0006] Preferably, the data acquisition module includes a low-altitude aerial survey submodule and a ground GNSS monitoring station submodule.

[0007] Preferably, the low-altitude aerial survey submodule includes a drone unit, a lidar scanning unit, a flight attitude sensor unit and an oblique photography acquisition unit, and the ground GNSS monitoring station submodule includes a GNSS receiver unit and a meteorological sensor unit.

[0008] Preferably, the equipment management module includes a drone management submodule and a monitoring station management submodule.

[0009] Preferably, the data processing module includes a data format conversion submodule, a data error correction submodule and a space reference unification submodule.

[0010] Preferably, the data error correction submodule includes a point cloud data error correction unit, a GNSS data error correction unit and an oblique photography data correction unit; the point cloud data error correction unit includes a geometric correction subunit, a deviation compensation subunit and a point cloud noise filtering subunit; the GNSS data error correction unit includes an ionospheric delay correction subunit, a tropospheric delay correction subunit and a multipath effect reduction subunit.

[0011] Preferably, the data application module includes a multi-period three-dimensional data fitting and registration submodule, a result analysis submodule and a visualization processing submodule, the result analysis submodule includes a change analysis unit and a trend prediction unit, and the visualization processing submodule includes a three-dimensional model display unit and a chart display unit.

[0012] The method for cooperating between UAV low-altitude monitoring and ground GNSS sensing includes step one, system initialization; step two, data acquisition; step three, data processing; step four, data application;

[0013] In the above step 1, a ground GNSS monitoring station submodule is arranged in the area to be monitored to monitor the geological high-risk area, and parameters of the low-altitude aerial survey submodule and the ground GNSS monitoring station submodule are set through the equipment management module;

[0014] In the above step 2, the real-time monitoring data of the area to be monitored is obtained by using the data acquisition module, and transmitted to the data storage module through the data transmission module;

[0015] In the above step 3, the data processing module obtains real-time monitoring data from the data storage module, firstly unifies the data format of the three-dimensional point cloud data, oblique photography data and GNSS coordinate monitoring data of the geological high-risk area in the monitored area through the data format conversion submodule, then performs error correction on the three-dimensional point cloud data, oblique photography data and GNSS coordinate monitoring data of the geological high-risk area in the monitored area through the data error correction submodule, and finally unifies the data of the monitored area into the same spatial reference through the spatial reference unification submodule;

[0016] In the above step four, the multi-period three-dimensional data fitting and registration submodule of the data application module fits and aligns the data processed in step three in combination with historical data, and the result analysis submodule performs difference calculation, change analysis and trend prediction on the fitted and registered data, and the visualization processing submodule visualizes the analysis results.

[0017] Preferably, in step 2, the real-time monitoring data includes UAV flight attitude data, three-dimensional point cloud data of the monitored area, oblique photography data, meteorological data and GNSS coordinate monitoring data of high-risk areas.

[0018] Preferably, in step four, the method adopted by the multi-period three-dimensional data fitting and registration submodule is one or more combinations of ICP registration algorithm, iterative closest point algorithm, Gaussian mixture model, feature-based registration algorithm, multi-scale transformation algorithm, and deep learning algorithm.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: the data acquisition module of the present invention combines low-altitude aerial survey with ground-based GNSS monitoring, uses low-altitude aerial survey to achieve global monitoring, and uses ground-based GNSS to monitor geological high-risk areas, thereby obtaining rich and comprehensive monitoring data, making up for the blind spots of single GNSS monitoring, and overcoming the limitations of manual monitoring; the data processing module corrects data errors by adopting multiple methods to improve the accuracy of monitoring data; the data application module extracts deformation characteristics and trends by fitting and aligning multi-period three-dimensional data, providing a reliable basis for disaster monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a system structure block diagram of the present invention;

[0021] Figure 2 It is a structural block diagram of the data acquisition module of the present invention;

[0022] Figure 3It is a structural block diagram of the data processing module of the present invention;

[0023] Figure 4 It is a structural block diagram of the data error correction submodule of the present invention;

[0024] Figure 5 It is a structural block diagram of the data application module of the present invention;

[0025] Figure 6 The figure is a flow chart of the method of the present invention.

[0026] In the figure: 1. Data acquisition module; 11. Low-altitude aerial survey submodule; 111. UAV unit; 112. LiDAR scanning unit; 113. Flight attitude sensor unit; 114. Oblique photography acquisition unit; 12. Ground GNSS monitoring station submodule; 121. GNSS receiver unit; 122. Meteorological sensor unit; 2. Equipment management module; 21. UAV management submodule; 22. Monitoring station management submodule; 3. Data transmission module; 4. Data storage module; 5. Data processing module; 51. Data format conversion submodule; 52. Data error correction submodule; 521. Point cloud data error correction unit; 5211. He correction subunit; 5212, deviation compensation subunit; 5213, point cloud noise filtering subunit; 522, GNSS data error correction unit; 5221, ionospheric delay correction subunit; 5222, tropospheric delay correction subunit; 5223, multipath effect reduction subunit; 523, oblique photography data correction unit; 53, spatial reference unification submodule; 6, data application module; 61, multi-period three-dimensional data fitting and registration submodule; 62, result analysis submodule; 621, change analysis unit; 622, trend prediction unit; 63, visualization processing submodule; 631, three-dimensional model display unit; 632, chart display unit. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Please see attached Figure 1 -Attached Figure 5An embodiment of the present invention provides a collaborative working system of UAV low-altitude monitoring and ground GNSS sensing, including a data acquisition module 1, a device management module 2, a data transmission module 3, a data storage module 4, a data processing module 5 and a data application module 6. The data transmission module 3 establishes data connections with the data acquisition module 1, the device management module 2 and the data storage module 4 respectively, the data storage module 4 establishes a data connection with the data processing module 5, the data processing module 5 establishes a data connection with the data application module 6, the device management module 2 is used to manage the data acquisition module 1, the data acquisition module 1 is used to acquire data and the data is transmitted to the data application module 6 through the data transmission module 3. The data storage module 4 and the data processing module 5 obtain data from the data storage module 4 and process the data. The data application module 6 analyzes the processed data and outputs the analysis results. The data acquisition module 1 includes a low-altitude aerial survey submodule 11 and a ground GNSS monitoring station submodule 12. The low-altitude aerial survey submodule 11 is used to perform low-altitude aerial survey and obtain three-dimensional point cloud data of the area to be monitored. The ground GNSS monitoring station submodule 12 is used to monitor geological high-risk areas. The low-altitude aerial survey submodule 11 includes a drone unit 111, a laser radar scanning unit 112, a flight attitude sensor unit 113 and an oblique photography acquisition unit 114. The ground GNSS monitoring station submodule 12 is used to monitor geological high-risk areas. The SS monitoring station submodule 12 includes a GNSS receiver unit 121 and a meteorological sensor unit 122. The laser radar scanning unit 112 is used to obtain the three-dimensional point cloud data of the area to be monitored. The flight attitude sensor unit 113 is used to obtain the flight attitude data of the drone unit 111. The oblique photography acquisition unit 114 is used to collect oblique photography data. The GNSS receiver unit 121 is used to obtain the coordinate data of the geological high-risk area. The meteorological sensor unit 122 is used to obtain the meteorological data of the area where the GNSS receiver unit 121 is located; the equipment management module 2 includes a drone management submodule 21 and a monitoring station management submodule 22. The aircraft management submodule 21 is used to manage the low-altitude aerial survey submodule 11, and the monitoring station management submodule 22 is used to manage the ground GNSS monitoring station submodule 12; the data processing module 5 includes a data format conversion submodule 51, a data error correction submodule 52 and a spatial reference unification submodule 53, the data format conversion submodule 51 converts the three-dimensional point cloud data, oblique photography data and GNSS coordinate monitoring data of the high-risk area to a universal three-dimensional data format, the data error correction submodule 52 is used to correct data errors, and the spatial reference unification submodule 53 is used to unify the data obtained in the monitored area into the same spatial reference;The data error correction submodule 52 includes a point cloud data error correction unit 521, a GNSS data error correction unit 522 and a tilt photography data correction unit 523. The point cloud data error correction unit 521 includes a geometric correction subunit 5211, a deviation compensation subunit 5212 and a point cloud noise filtering subunit 5213. The GNSS data error correction unit 522 includes an ionospheric delay correction subunit 5221, a tropospheric delay correction subunit 5222 and a multipath effect reduction subunit 5223. The geometric correction subunit 52 11 performs geometric correction on the measurement error of the laser radar through the ground control point. The deviation compensation subunit 5212 compensates the point cloud deviation caused by the flight attitude based on the data of the flight attitude sensor unit 113. The point cloud noise filtering subunit 5213 uses a filtering algorithm to remove the point cloud noise caused by atmospheric scattering, noise and other factors. The ionospheric delay correction subunit 5221 eliminates or weakens the ionospheric delay error through the dual-frequency combination observation value according to the relationship between the ionospheric delay and the frequency. The tropospheric delay correction subunit 5222 corrects the ionospheric delay error based on the meteorological data. The observation values ​​are corrected, the multipath effect reduction subunit 5223 analyzes and processes the observation data of multiple epochs to identify and eliminate the data greatly affected by the multipath effect, and the oblique photography data correction unit 523 performs geometric correction, position correction and radiation correction on the oblique photography data to eliminate the errors caused by factors such as lens distortion, flight attitude change, atmospheric refraction, and lighting conditions; the data application module 6 includes a multi-period three-dimensional data fitting and registration submodule 61, a result analysis submodule 62 and a visualization processing submodule 63, the result analysis submodule 62 includes a change analysis unit 621 and a trend prediction unit 622, and the visualization processing submodule 63 includes a three-dimensional model display unit 631 and a chart display unit 632, and the multi-period three-dimensional data fitting and registration submodule 61 is used to fit and register the multi-period data, the result analysis submodule 62 uses the change analysis unit 621 and the trend prediction unit 622 to analyze and predict the trend of the fitting and registration results, and the visualization processing submodule 63 uses the three-dimensional model display unit 631 and the chart display unit 632 to intuitively present the results. ;

[0029] The method for cooperating between UAV low-altitude monitoring and ground GNSS sensing includes step one, system initialization; step two, data acquisition; step three, data processing; step four, data application;

[0030] In the above step 1, a ground GNSS monitoring station submodule 12 is arranged in the area to be monitored to monitor the geological high-risk area, and parameters of the low-altitude aerial survey submodule 11 and the ground GNSS monitoring station submodule 12 are set through the equipment management module 2;

[0031] In the above step 2, the real-time monitoring data of the area to be monitored is obtained by using the data acquisition module 1, and transmitted to the data storage module 4 through the data transmission module 3; wherein the real-time monitoring data includes the flight attitude data of the drone, the three-dimensional point cloud data of the area to be monitored, the oblique photography data, the meteorological data and the GNSS coordinate monitoring data of the high-risk area;

[0032] In the above step 3, the data processing module 5 obtains the real-time monitoring data from the data storage module 4, firstly unifies the data format of the three-dimensional point cloud data, the oblique photography data and the GNSS coordinate monitoring data of the geological high-risk area in the monitored area through the data format conversion submodule 51, then performs error correction on the three-dimensional point cloud data, the oblique photography data and the GNSS coordinate monitoring data of the geological high-risk area in the monitored area through the data error correction submodule 52, and finally unifies the data of the monitored area into the same spatial reference through the spatial reference unification submodule 53;

[0033] In the above step four, the multi-period three-dimensional data fitting and registration submodule 61 of the data application module 6 fits and aligns the data processed in step three in combination with historical data, and the result analysis submodule 62 performs difference calculation, change analysis and trend prediction on the fitted and registered data, and the visualization processing submodule 63 visualizes the analysis results; wherein, the method adopted by the multi-period three-dimensional data fitting and registration submodule 61 is one or more combinations of ICP registration algorithm, iterative closest point algorithm, Gaussian mixture model, feature-based registration algorithm, multi-scale transformation algorithm, and deep learning algorithm.

[0034] Based on the above, the advantage of the present invention is that when the present invention is used, the equipment management module 2 is used to manage the data acquisition module 1, the data acquisition module 1 is used to acquire data and transmit it to the data storage module 4 through the data transmission module 3, the data processing module 5 acquires data from the data storage module 4 and processes it, and the data application module 6 analyzes the processed data and outputs the analysis results; specifically: the equipment management module 2 performs unified management of the low-altitude aerial survey submodule 11 through the drone management submodule 21, and performs unified management of the ground GNSS monitoring station submodule 12 through the monitoring station management submodule 22; in the data acquisition stage, the drone unit 111 of the low-altitude aerial survey submodule 11 takes off after planning the flight parameters according to the preset task. , carrying the laser radar scanning unit 112 and the flight attitude sensor unit 113 to arrive above the target area, and fly stably according to the predetermined route. The laser radar scanning unit 112 continuously emits and receives laser pulses to obtain high-precision three-dimensional point cloud data. The flight attitude sensor unit 113 monitors the attitude information of the drone in real time to provide a basis for the spatial position correction of the point cloud data. At the same time, the ground GNSS monitoring station submodule 12 performs real-time monitoring of geological high-risk areas, uses the GNSS receiver unit 121 to receive satellite signals to calculate the three-dimensional coordinates of the monitoring point, and the meteorological sensor unit 122 collects meteorological data for correcting the GNSS measurement results. The oblique photography acquisition unit 114 collects oblique photography data. The acquired data of the area to be monitored is obtained through the data The transmission module 3 transmits the data to the data storage module 4, and the data processing module 5 obtains the data from the data storage module 4 for processing; in the data processing stage, the data format conversion submodule 51 first converts the data of the monitored area into a universal three-dimensional data format, and the point cloud data error correction unit 521 in the data error correction submodule 52 corrects the point cloud data error through the geometric correction subunit 5211, the deviation compensation subunit 5212 and the point cloud noise filtering subunit 5213, specifically: the geometric correction subunit 5211 performs geometric correction on the measurement error of the laser radar through the ground control point, the deviation compensation subunit 5212 compensates for the point cloud deviation caused by the flight attitude based on the data of the flight attitude sensor unit 113, and the point cloud noise filtering subunit 5213 is used to correct the point cloud data error. The cloud noise filtering subunit 5213 uses a filtering algorithm to remove point cloud noise caused by atmospheric scattering, noise and other factors; the GNSS data error correction unit 522 uses the ionospheric delay correction subunit 5221, the tropospheric delay correction subunit 5222 and the multipath effect reduction subunit 5223 to improve the GNSS data accuracy, specifically: the ionospheric delay correction subunit 5221 eliminates or reduces the ionospheric delay error through the dual-frequency combination observation value according to the relationship between the ionospheric delay and the frequency, the tropospheric delay correction subunit 5222 corrects the observation value based on the meteorological data, and the multipath effect reduction subunit 5223 identifies and eliminates the data greatly affected by the multipath effect by analyzing and processing the observation data of multiple epochs;The oblique photography data correction unit 523 performs geometric correction, position correction and radiation correction on the oblique photography data, and finally the spatial reference unification submodule 53 unifies the data of the monitored area into the same spatial reference; in the data application stage, the data application module 6 uses the multi-period three-dimensional data fitting and registration submodule 61 to fit and register the multi-period data, the result analysis submodule 62 uses the change analysis unit 621 and the trend prediction unit 622 to analyze and predict the trend of the fitting results, and the visualization submodule 63 uses the three-dimensional model display unit 631 and the chart display unit 632 to present the results intuitively, specifically: the multi-period three-dimensional data fitting and registration submodule 61 fits the processed three-dimensional data in combination with the historical data, and the change analysis unit 621 accurately calculates the three-dimensional data according to the fitting results. Calculate the three-dimensional deformation of the monitoring point, including displacement, settlement, tilt and other parameters, and perform statistical analysis. The trend prediction unit 622 uses the ARIMA model and the neural network model to make short-term or long-term predictions on the deformation of the monitoring point based on the fitted deformation model and historical data. According to the prediction results, the stability of the target area is evaluated to determine whether there are potential safety risks, such as landslides, collapses and other geological disaster risks. The three-dimensional model display unit 631 integrates point cloud data, monitoring point coordinate data, fitting surfaces and other information into the three-dimensional model, and enhances the realism and visualization of the model by adding materials and lighting effects. The chart display unit 632 draws visualization charts such as deformation histograms and contour maps to intuitively display the deformation distribution of the target area. ;

[0035] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A UAV low-altitude monitoring and ground GNSS sensing collaborative working system, comprising a data acquisition module (1), an equipment management module (2), a data transmission module (3), a data storage module (4), a data processing module (5) and a data application module (6), characterized in that: The data transmission module (3) establishes data connections with the data acquisition module (1), the device management module (2) and the data storage module (4) respectively; the data storage module (4) establishes data connection with the data processing module (5); and the data processing module (5) establishes data connection with the data application module (6).

2. The UAV low-altitude monitoring and ground GNSS sensing collaborative working system according to claim 1 is characterized by: The data acquisition module (1) comprises a low-altitude aerial survey submodule (11) and a ground GNSS monitoring station submodule (12).

3. The UAV low-altitude monitoring and ground GNSS sensing collaborative working system according to claim 2 is characterized by: The low-altitude aerial survey submodule (11) comprises a drone unit (111), a laser radar scanning unit (112), a flight attitude sensor unit (113) and an oblique photography acquisition unit (114), and the ground GNSS monitoring station submodule (12) comprises a GNSS receiver unit (121) and a meteorological sensor unit (122).

4. The UAV low-altitude monitoring and ground GNSS sensing collaborative working system according to claim 1 is characterized by: The equipment management module (2) comprises a drone management submodule (21) and a monitoring station management submodule (22).

5. The UAV low-altitude monitoring and ground GNSS sensing collaborative working system according to claim 1 is characterized by: The data processing module (5) comprises a data format conversion submodule (51), a data error correction submodule (52) and a space reference unification submodule (53).

6. The UAV low-altitude monitoring and ground GNSS sensing collaborative working system according to claim 5 is characterized by: The data error correction submodule (52) comprises a point cloud data error correction unit (521), a GNSS data error correction unit (522) and an oblique photography data correction unit (523); the point cloud data error correction unit (521) comprises a geometric correction subunit (5211), a deviation compensation subunit (5212) and a point cloud noise filtering subunit (5213); the GNSS data error correction unit (522) comprises an ionospheric delay correction subunit (5221), a tropospheric delay correction subunit (5222) and a multipath effect reduction subunit (5223).

7. The UAV low-altitude monitoring and ground GNSS sensing collaborative working system according to claim 1 is characterized by: The data application module (6) includes a multi-period three-dimensional data fitting and registration submodule (61), a result analysis submodule (62) and a visualization processing submodule (63), wherein the result analysis submodule (62) includes a change analysis unit (621) and a trend prediction unit (622), and the visualization processing submodule (63) includes a three-dimensional model display unit (631) and a chart display unit (632).

8. A method for cooperating between UAV low-altitude monitoring and ground GNSS sensing, comprising step 1, system initialization; step 2, data acquisition; step 3, data processing; step 4, data application; characterized in that: In the above step 1, a ground GNSS monitoring station submodule (12) is arranged in the area to be monitored to monitor the geological high-risk area, and parameters of the low-altitude aerial survey submodule (11) and the ground GNSS monitoring station submodule (12) are set through the equipment management module (2); In the above step 2, the real-time monitoring data of the area to be monitored is obtained by using the data acquisition module (1), and is transmitted to the data storage module (4) through the data transmission module (3); In the above step 3, the data processing module (5) obtains the real-time monitoring data from the data storage module (4), firstly unifies the data format of the three-dimensional point cloud data, the oblique photography data and the GNSS coordinate monitoring data of the geological high-risk area in the monitored area through the data format conversion submodule (51), then performs error correction on the three-dimensional point cloud data, the oblique photography data and the GNSS coordinate monitoring data of the geological high-risk area in the monitored area through the data error correction submodule (52), and finally unifies the data of the monitored area into the same spatial reference through the spatial reference unification submodule (53); In the above step 4, the multi-period three-dimensional data fitting and registration submodule (61) of the data application module (6) fits and registers the data processed in step 3 in combination with historical data, and the result analysis submodule (62) performs difference calculation, variation analysis and trend prediction on the fitted and registered data, and the visualization processing submodule (63) visualizes the analysis results.

9. The method for cooperating between UAV low-altitude monitoring and ground GNSS sensing according to claim 8, characterized in that: In step 2, the real-time monitoring data includes the flight attitude data of the UAV, the three-dimensional point cloud data of the monitored area, the oblique photography data, the meteorological data and the GNSS coordinate monitoring data of the high-risk area.

10. The method for cooperating between UAV low-altitude monitoring and ground GNSS sensing according to claim 8, characterized in that: In the step 4, the method adopted by the multi-period three-dimensional data fitting and registration submodule (61) is one or more combinations of an ICP registration algorithm, an iterative closest point algorithm, a Gaussian mixture model, a feature-based registration algorithm, a multi-scale transformation algorithm, and a deep learning algorithm.

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