Building outline 3D modeling monitoring method and system based on point cloud data

Abstract

The present invention provides a method and system for monitoring three-dimensional modeling of building outlines based on point cloud data, which relates to the technical field of three-dimensional modeling monitoring with point cloud data, and comprises the following steps: performing an offset test on a drone to obtain effective point cloud data; analyzing the offset of the drone under different wind speeds based on the effective point cloud data; scanning the building to obtain initial point cloud data of the building; real-time monitoring of the current wind direction and wind speed, and analyzing the real-time offset of the drone; calibrating the initial point cloud data based on the real-time offset; and performing three-dimensional modeling of the building based on the actual point cloud data. The present invention is used to solve the problem that the existing point cloud data three-dimensional modeling monitoring technology still has the problem that the accuracy of obtaining point cloud data is insufficient, resulting in deviations in some data when constructing a three-dimensional model, and the accuracy of the model cannot be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud data three-dimensional modeling monitoring, and in particular to a method and system for three-dimensional modeling monitoring of building outlines based on point cloud data. Background Art

[0002] Point cloud data 3D modeling and monitoring technology refers to a method of using point cloud data obtained by technologies such as lidar and photogrammetry to perform 3D modeling and monitoring based on these data. It involves converting point cloud data into geometric models to represent objects, scenes or structures in the real world, and monitoring and analyzing by comparing the differences between the model and the actual data.

[0003] The existing point cloud data 3D modeling monitoring technology usually acquires point cloud data by carrying laser radar on a carrier, and the commonly used carriers are cars and drones, but cars are not suitable for scanning buildings. Therefore, drones are usually selected to carry laser radars to acquire point cloud data. When the drone is hovering in the air based on instructions, due to the influence of wind speed and wind direction, the drone will have a certain offset, but the position information of the drone in the flight control system has not changed, so the acquired point cloud data will have a certain offset. The existing point cloud data 3D modeling monitoring technology does not adjust the offset of the point cloud data, resulting in deviations in some data when the 3D model is finally constructed, and the accuracy of the model cannot be guaranteed. For example, in the Chinese patent with the authorization announcement number CN117710603B, a method for 3D building modeling of drone images under the constraint of straight line geometric structure is disclosed. When acquiring point cloud data through the drone, the scheme does not take into account the offset of the drone due to wind speed, resulting in insufficient accuracy of the acquired point cloud data. The existing point cloud data 3D modeling monitoring technology also has the problem of insufficient accuracy of acquiring point cloud data, resulting in deviations in some data when constructing the 3D model, and the accuracy of the model cannot be guaranteed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for monitoring three-dimensional modeling of building contours based on point cloud data, which can build an offset test scene, and based on the offset test scene, perform an offset test on a drone to obtain valid point cloud data, then construct an offset analysis coordinate system and draw an initial reflection line, draw a correct reflection line based on the valid point cloud data, and then analyze the relationship between the offset and wind speed of the drone based on the correct reflection line, and then analyze the offset of the drone at different wind speeds based on the valid point cloud data, and then scan the building to obtain the initial point cloud data of the building. During the scanning process, the current wind direction and wind speed are monitored in real time, and the real-time offset of the drone is analyzed at the same time. The initial point cloud data is calibrated based on the real-time offset to obtain actual point cloud data, and then the building is 3D modeled based on the actual point cloud data, so as to solve the problem that the existing point cloud data three-dimensional modeling monitoring technology still has the problem of insufficient accuracy in obtaining point cloud data, resulting in deviations in some data when building a three-dimensional model, and the accuracy of the model cannot be guaranteed.

[0005] In order to achieve the above object, in a first aspect, the present invention provides a method for monitoring building outline three-dimensional modeling based on point cloud data, comprising the following steps:

[0006] Perform an offset test on the drone to obtain valid point cloud data;

[0007] Analyze the displacement of the UAV under different wind speeds based on the effective point cloud data;

[0008] Scan the building to obtain the initial point cloud data of the building;

[0009] During the scanning process, the current wind direction and wind speed are monitored in real time, and the real-time offset of the drone is analyzed;

[0010] Perform data calibration on the initial point cloud data based on the real-time offset to obtain actual point cloud data;

[0011] 3D modeling of buildings based on actual point cloud data.

[0012] Furthermore, the offset test of the drone to obtain valid point cloud data includes the following sub-steps:

[0013] Build an offset test scenario;

[0014] Based on the offset test scenario, the UAV is tested for offset to obtain valid point cloud data.

[0015] Furthermore, the setting up of the offset test scenario includes the following sub-steps:

[0016] Choose an open area;

[0017] Setting a laser signal reflection device;

[0018] The laser signal reflecting device is equipped with a reflecting plate, and the height of the reflecting plate can be adjusted according to the flying height of the UAV.

[0019] Furthermore, the offset test of the UAV is performed based on the offset test scenario to obtain valid point cloud data, including the following sub-steps:

[0020] The reflector is adjusted to the first flight altitude, and the UAV carrying the laser radar is adjusted to the first flight altitude through the flight control system to ensure that the emission port of the laser radar faces the reflector;

[0021] Monitor the current wind speed and wind direction in real time, marked as monitoring wind speed and monitoring wind direction;

[0022] The laser radar transmits a laser signal to the reflector, and the monitored wind speed and the monitored wind direction at the time of transmission are recorded, which are marked as the transmitting wind speed and the transmitting wind direction respectively; the returned laser point cloud data are received, which are marked as the returned point cloud data, and the monitored wind speed and the monitored wind direction when the returned point cloud data is received are recorded, which are marked as the returned wind speed and the returned wind direction respectively;

[0023] The transmitting wind speed is compared with the return wind speed, and the transmitting wind direction is compared with the return wind direction. If the transmitting wind speed is the same as the return wind speed and the transmitting wind direction is the same as the return wind direction, a valid data signal is output; otherwise, an invalid data signal is output;

[0024] If the output data is a valid signal, the returned point cloud data will be marked as valid point cloud data, and the returned wind speed will be marked as valid wind speed.

[0025] Furthermore, the analysis of the offset of the drone at different wind speeds based on the effective point cloud data includes the following sub-steps:

[0026] Construct the offset analysis coordinate system and draw the initial reflection line;

[0027] Draw the correct reflection line based on the valid point cloud data, and then analyze the relationship between the offset of the drone and the wind speed based on the correct reflection line.

[0028] Furthermore, the step of constructing the offset analysis coordinate system and drawing the initial reflection line includes the following sub-steps:

[0029] The format of the valid point cloud data is (θ, ω, L), where θ is the rotation angle, ω is the vertical angle, and L is the return distance;

[0030] A spatial coordinate system is established with the take-off point of the drone as the origin, the direction to the right of the drone as the X-axis, the direction in front of the drone, that is, the direction of the origin toward the laser signal reflection device as the Y-axis, and the vertically upward direction of the origin as the Z-axis, which is named the offset analysis coordinate system;

[0031] In the offset analysis coordinate system, the coordinate point (0,0, first flight height) is marked as the initial coordinate point;

[0032] Obtain the distance between the laser signal reflection device and the take-off point, mark it as the actual distance, and mark the coordinate point (0, actual distance, first flight altitude) as the reflection coordinate point;

[0033] With the reflection coordinate point as the endpoint, draw a ray along the positive direction of the X axis and parallel to the X axis, marked as the first auxiliary line; with the reflection coordinate point as the endpoint, draw a ray along the positive direction of the Z axis and parallel to the Z axis, marked as the second auxiliary line;

[0034] Draw a straight line with the reflection coordinate point as the endpoint, marked as the initial reflection straight line, and the length of the initial reflection straight line is the return distance;

[0035] Obtain a top view of the offset analysis coordinate system, ignore the influence of the Z-axis direction in the top view, mark the angle between the initial reflection straight line and the first auxiliary line in the top view as the first angle, and mark the angle of the first angle as the first angle;

[0036] A side view of the offset analysis coordinate system is obtained. The influence of the X-axis direction is ignored in the side view. The angle between the initial reflection straight line in the side view and the opposite direction of the Y-axis is marked as the second angle, and the angle of the second angle is marked as the second angle.

[0037] Furthermore, drawing a correct reflection line based on the valid point cloud data and then analyzing the relationship between the offset and wind speed of the drone based on the correct reflection line includes the following sub-steps:

[0038] Obtain valid point cloud data, set the first angle to 180°-θ, set the second angle to 360°-ω, and set the length of the correct reflection line to L;

[0039] Obtain the coordinates of the other end point on the correct reflection line except the reflection coordinate point, and mark it as the receiving coordinate point;

[0040] Calculate the distance between the initial coordinate point and the received coordinate point, mark it as the test offset, and obtain the effective wind speed at the same time;

[0041] Continuously perform analysis in the first test cycle to obtain a number of effective wind speeds and corresponding test offsets;

[0042] A rectangular coordinate system is established with the effective wind speed as the X-axis and the test offset as the Y-axis, named the wind speed offset coordinate system, and the effective wind speed and the corresponding test offset are entered into the wind speed offset coordinate system;

[0043] Perform linear regression on the wind speed offset coordinate system to obtain the wind speed offset function.

[0044] Furthermore, scanning the building to obtain initial point cloud data of the building includes the following sub-steps:

[0045] Use a drone to carry a laser radar to scan the building and obtain initial point cloud data;

[0046] The initial point cloud data includes an initial forward point cloud data set, an initial left point cloud data set, an initial rear point cloud data set, an initial right point cloud data set and an initial top point cloud data set. The initial forward point cloud data set includes a number of initial forward point cloud data, the initial left point cloud data set includes a number of initial left point cloud data, the initial rear point cloud data set includes a number of initial rear point cloud data, the initial right point cloud data set includes a number of initial right point cloud data, and the initial top point cloud data set includes a number of initial top point cloud data.

[0047] Furthermore, during the scanning process, real-time monitoring of the current wind direction and wind speed and analysis of the real-time offset of the drone include the following sub-steps:

[0048] When the laser radar is scanning, the wind direction and wind speed corresponding to the initial point cloud data are recorded synchronously and marked as real-time wind speed and real-time wind direction respectively;

[0049] The real-time wind speed includes the real-time forward wind speed, the real-time left wind speed, the real-time back wind speed, the real-time right wind speed and the real-time top wind speed; the real-time wind direction includes the real-time forward wind direction, the real-time left wind direction, the real-time back wind direction, the real-time right wind direction and the real-time top wind direction;

[0050] The real-time wind speed is substituted into the wind speed offset function, and the calculation result is marked as the real-time offset, which includes a real-time forward offset, a real-time left offset, a real-time back offset, a real-time right offset, and a real-time top offset.

[0051] Furthermore, the data calibration of the initial point cloud data based on the real-time offset to obtain the actual point cloud data includes the following sub-steps:

[0052] A three-dimensional coordinate system is established with the position of the drone as the origin, the right direction of the drone as the horizontal axis, the forward direction of the drone, that is, the direction towards the building as the vertical axis, and the vertical upward direction of the drone as the vertical axis, which is named the offset calibration coordinate system;

[0053] Taking the origin of the offset calibration coordinate system as the starting point, the real-time offset is moved in the opposite direction of the real-time wind direction, and the corresponding coordinate point is marked as the actual coordinate point;

[0054] Obtain any initial point cloud data, draw a straight line with the origin of the offset calibration coordinate system as the endpoint, named the offset straight line, and the length of the offset straight line is L of the initial point cloud data;

[0055] A top view of the offset calibration coordinate system is obtained, which is named as a first image, and a side view of the offset calibration coordinate system is obtained, which is named as a second image; in the first image, an angle between the offset straight line and the positive direction of the horizontal axis is marked as a third angle, and the angle of the third angle is a third angle; in the second image, an angle between the offset straight line and the positive direction of the vertical axis is marked as a fourth angle, and the angle of the fourth angle is a fourth angle;

[0056] The third angle is set to θ, the fourth angle is set to ω, the other end point of the offset straight line is marked as a point cloud coordinate point, the point cloud coordinate point is connected with the actual coordinate point, and the resulting straight line is named the calibration straight line;

[0057] In the first image, the angle between the calibration line and the positive direction of the horizontal axis is marked as the rotation calibration angle, and the angle of the rotation calibration angle is the rotation calibration angle; in the second image, the angle between the calibration line and the positive direction of the vertical axis is marked as the vertical calibration angle, and the angle of the vertical calibration angle is the vertical calibration angle; the length of the calibration line is marked as the calibration distance;

[0058] Calibrate the initial point cloud data currently being analyzed to (rotation calibration angle, vertical calibration angle, calibration distance) to obtain the actual point cloud data;

[0059] All initial point cloud data are calibrated to obtain some actual point cloud data.

[0060] Furthermore, three-dimensional modeling of the building based on the actual point cloud data includes the following sub-steps:

[0061] The actual point cloud data includes an actual forward point cloud data set, an actual left point cloud data set, an actual rearward point cloud data set, an actual right point cloud data set and an actual top point cloud data set, wherein the actual forward point cloud data set includes a number of actual forward point cloud data, the actual left point cloud data set includes a number of actual left point cloud data, the actual rearward point cloud data set includes a number of actual rearward point cloud data, the actual right point cloud data set includes a number of actual right point cloud data, and the actual top point cloud data set includes a number of actual top point cloud data;

[0062] The front model of the building is constructed by the actual forward point cloud data set, the left model of the building is constructed by the actual left point cloud data set, the back model of the building is constructed by the actual rear point cloud data set, the right model of the building is constructed by the actual right point cloud data set, and the top model of the building is constructed by the actual top point cloud data set;

[0063] The front model, left model, back model, right model and top model are spliced ​​together to obtain a three-dimensional model of the building.

[0064] In a second aspect, the present invention provides a building outline three-dimensional modeling monitoring system based on point cloud data, including an offset testing module, an offset analysis module, a building scanning module, an offset calculation module, an offset calibration module and a three-dimensional modeling module; the offset testing module, the offset analysis module, the building scanning module, the offset calculation module and the three-dimensional modeling module are respectively connected to the offset calibration module data;

[0065] The offset test module is used to perform an offset test on the UAV to obtain valid point cloud data;

[0066] The offset analysis module is used to analyze the offset of the UAV at different wind speeds based on the effective point cloud data;

[0067] The building scanning module is used to scan the building to obtain initial point cloud data of the building;

[0068] The offset calculation module is used to monitor the current wind direction and wind speed in real time during the scanning process, and analyze the real-time offset of the drone;

[0069] The offset calibration module is used to calibrate the initial point cloud data based on the real-time offset to obtain actual point cloud data;

[0070] The three-dimensional modeling module is used to perform three-dimensional modeling of the building based on actual point cloud data.

[0071] Beneficial effects of the present invention: The present invention builds an offset test scene, performs an offset test on the drone based on the offset test scene, obtains valid point cloud data, then constructs an offset analysis coordinate system and draws an initial reflection line, draws a correct reflection line based on the valid point cloud data, and then analyzes the relationship between the offset and wind speed of the drone based on the correct reflection line. The advantage is that the drone is light in weight and is easily affected by the wind speed in the air and deviates slightly, but the position corresponding to the hovering command is still saved in the flight control system of the drone, so the received point cloud data will have a certain deviation. By analyzing the relationship between the offset and wind speed of the drone through a large number of experiments, the point cloud data can be calibrated, which improves the accuracy and rationality of the three-dimensional modeling of the point cloud data;

[0072] The present invention analyzes the offset of the drone at different wind speeds based on the effective point cloud data, and then scans the building to obtain the initial point cloud data of the building. During the scanning process, the current wind direction and wind speed are monitored in real time, and the real-time offset of the drone is analyzed. The initial point cloud data is calibrated based on the real-time offset to obtain actual point cloud data. The advantage is that the initial point cloud data is the data obtained after the drone is offset, and the actual point cloud data can be reversely analyzed based on the initial point cloud data and the real-time offset. The actual point cloud data is the point cloud data actually received by the drone at a preset position, which improves the accuracy and effectiveness of three-dimensional modeling of point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0074] Figure 1 is a functional block diagram of the system of the present invention;

[0075] Figure 2 The laser signal reflecting device of the present invention;

[0076] Figure 3 A schematic diagram of the positions of the drone and the laser signal reflecting device of the present invention;

[0077] Figure 4 A schematic diagram of monitoring wind direction of the present invention;

[0078] Figure 5 A schematic diagram of the position of the offset analysis coordinate system and the initial coordinate points of the present invention;

[0079] Figure 6 A top view of the offset analysis coordinate system of the present invention;

[0080] Figure 7 A side view of the offset analysis coordinate system of the present invention;

[0081] Figure 8 A schematic diagram of a method for determining a rotation angle of the present invention;

[0082] Fig. 9 A schematic diagram of a method for determining a vertical angle of the present invention;

[0083] Fig.10 is the wind speed offset coordinate system of the present invention;

[0084] Fig.11 A schematic diagram of the offset calibration coordinate system and the positions of the actual coordinate points of the present invention;

[0085] Fig.12is a schematic diagram of an offset straight line of the present invention;

[0086] Fig.13 is a schematic diagram of a method for determining the third angle of the present invention;

[0087] Fig.14 is a schematic diagram of a method for determining a fourth angle of the present invention;

[0088] Fig.15 is a schematic diagram of a calibration straight line of the present invention;

[0089] Fig.16 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0090] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0091] Example 1, please refer to Figure 1 As shown, the present invention provides a building outline three-dimensional modeling monitoring system based on point cloud data, including an offset testing module, an offset analysis module, a building scanning module, an offset calculation module, an offset calibration module and a three-dimensional modeling module; the offset testing module, the offset analysis module, the building scanning module, the offset calculation module and the three-dimensional modeling module are respectively connected with the offset calibration module data;

[0092] The offset test module is used to test the offset of the drone and obtain valid point cloud data; the offset test module includes a scene building unit and an effective test unit;

[0093] See also Figure 2 As shown, the scene building unit is used to build an offset test scene;

[0094] The scene building unit is configured with a scene building strategy, which includes:

[0095] Choose an open area;

[0096] Setting a laser signal reflection device;

[0097] The laser signal reflecting device is equipped with a reflecting plate, which can adjust its height according to the flying height of the UAV;

[0098] In practical applications, laser signal reflection devices such as Figure 2 As shown, the height of the reflector can be freely adjusted to keep it level with the laser radar carried by the drone. The laser signal reflecting device also has a fixed column and an extension column. The fixed column is used to fix the laser signal reflecting device, and the extension column is used to connect the reflector to the fixed column.

[0099] The effective test unit is used to perform an offset test on the drone based on the offset test scenario to obtain valid point cloud data;

[0100] The effective test unit is configured with an effective test strategy, which includes:

[0101] See also Figure 3 As shown, the reflector is adjusted to a first flight altitude, and the UAV carrying the laser radar is adjusted to the first flight altitude through the flight control system to ensure that the emission port of the laser radar faces the reflector;

[0102] In practical applications, there is no special requirement for the first flight altitude. Generally, the higher the altitude, the greater the wind speed, which is more conducive to the data collection of this experiment. Therefore, the first flight altitude is set to the highest point of the laser signal reflecting device. In this embodiment, the first flight altitude is set to 30m. The positions of the drone and the laser signal reflecting device are as follows: Figure 3 As shown;

[0103] See also Figure 4 As shown, the current wind speed and wind direction are monitored in real time, marked as monitoring wind speed and monitoring wind direction;

[0104] In actual application, the monitored wind speed is 7.2m / s. The monitored wind direction is divided into horizontal wind direction monitoring and vertical wind direction monitoring according to the method of looking down and looking from the side. Figure 4 As shown in the figure, assuming that the launch port is facing north, Figure 4 Take the monitoring horizontal wind direction of 45° and the monitoring vertical wind direction of 45° as an example. At this time, the monitoring wind direction should be 45° east of north, and the elevation angle is 45° upward. If the monitoring horizontal wind direction is 120°, it means 60° west of north.

[0105] The laser radar transmits a laser signal to the reflector, and the monitored wind speed and the monitored wind direction at the time of transmission are recorded, which are marked as the transmitting wind speed and the transmitting wind direction respectively; the returned laser point cloud data are received, which are marked as the returned point cloud data, and the monitored wind speed and the monitored wind direction when the returned point cloud data is received are recorded, which are marked as the returned wind speed and the returned wind direction respectively;

[0106] The transmitting wind speed is compared with the return wind speed, and the transmitting wind direction is compared with the return wind direction. If the transmitting wind speed is the same as the return wind speed and the transmitting wind direction is the same as the return wind direction, a valid data signal is output; otherwise, an invalid data signal is output;

[0107] If the output data is a valid signal, the returned point cloud data will be marked as valid point cloud data, and the returned wind speed will be marked as valid wind speed;

[0108] In practical applications, the recorded transmission wind speed is 7.2m / s, and the transmission wind direction is (45°, 20°), where the first digit represents the monitored horizontal wind direction and the second digit represents the monitored vertical wind direction. When receiving point cloud data, the recorded return wind speed is 7.2m / s, and the return wind direction is (45°, 20°). By comparison, it is found that the transmission wind speed is the same as the return wind speed and the transmission wind direction is the same as the return wind direction. A valid data signal is output, and the returned point cloud data is marked as valid point cloud data, and the returned wind speed is marked as valid wind speed.

[0109] The offset analysis module is used to analyze the offset of the UAV under different wind speeds based on the effective point cloud data; the offset analysis module includes a preprocessing unit and a relationship analysis unit;

[0110] The pre-processing unit is used to construct the offset analysis coordinate system and draw the initial reflection line;

[0111] The preprocessing unit is configured with a preprocessing strategy, which includes:

[0112] The format of valid point cloud data is (θ, ω, L), where θ is the rotation angle, ω is the vertical angle, and L is the return distance;

[0113] See also Figure 5 As shown, a spatial coordinate system is established with the take-off point of the drone as the origin, the direction to the right of the drone as the X-axis, the direction in front of the drone, that is, the direction of the origin toward the laser signal reflection device as the Y-axis, and the vertically upward direction of the origin as the Z-axis, which is named the offset analysis coordinate system;

[0114] In the offset analysis coordinate system, the coordinate point (0,0, first flight height) is marked as the initial coordinate point;

[0115] Obtain the distance between the laser signal reflection device and the take-off point, mark it as the actual distance, and mark the coordinate point (0, actual distance, first flight altitude) as the reflection coordinate point;

[0116] In practical applications, the rotation angle and vertical angle in the point cloud data also use the calculation method of wind direction, and the valid point cloud data obtained is (94°, 358°, 29.6477m). The position of the offset analysis coordinate system and the initial coordinate point are as follows: Figure 5 As shown, the coordinates of the initial coordinate point and the reflector are the same in the X-axis direction and the Z-axis direction on the offset analysis coordinate system, but different in the Y-axis direction; and in this embodiment, the actual distance between the take-off point and the laser signal reflecting device is 30m, so the initial coordinate point is (0,0,30), and the reflected coordinate point is (0,30,30);

[0117] See also Figure 6 to Figure 7 As shown, with the reflection coordinate point as the endpoint, a ray is drawn along the positive direction of the X-axis and parallel to the X-axis, which is marked as the first auxiliary line;

[0118] Draw a straight line with the reflection coordinate point as the endpoint, marked as the initial reflection line, and the length of the initial reflection line is the return distance;

[0119] Obtain a top view of the offset analysis coordinate system, ignore the influence of the Z-axis direction in the top view, mark the angle between the initial reflection straight line and the first auxiliary line in the top view as the first angle, and mark the angle of the first angle as the first angle;

[0120] Obtain a side view of the offset analysis coordinate system. Ignore the influence of the X-axis direction in the side view. Mark the angle between the initial reflection straight line in the side view and the opposite direction of the Y-axis as the second angle. The angle of the second angle is marked as the second angle.

[0121] In practical applications, the top view is as follows Figure 6 As shown, the side view is Figure 7 As shown, the figure shows the positional relationship between the first auxiliary line and the initial reflection straight line, and also shows the determination method of the first angle and the second angle;

[0122] The relationship analysis unit is used to draw a correct reflection line based on the valid point cloud data, and then analyze the relationship between the offset of the drone and the wind speed based on the correct reflection line;

[0123] The relationship analysis unit is configured with a relationship analysis strategy, which includes:

[0124] See also Figures 8 to 9 As shown, valid point cloud data is obtained, the first angle is set to 180°-θ, the second angle is set to 360°-ω, and the length of the correct reflection straight line is set to L;

[0125] In practical applications, Figure 8 is a top view, Fig. 9 is a side view, Figure 8 as well as Fig. 9 The determination methods of the rotation angle and the vertical angle are respectively shown. The rotation angle spans the first and second quadrants, and the first angle is complementary to the rotation angle. Therefore, the first angle can be obtained by subtracting the rotation angle from 180°, and the vertical angle spans the first and fourth quadrants, and the second angle plus the vertical angle is equal to 360°, so the second angle can be obtained by subtracting the vertical angle from 360°. In this embodiment, θ is 94°, ω is 358°, and L is 29.6477m. Therefore, the first angle of the correct reflection line is 86°, the second angle is 2°, and the length is 29.6477.

[0126] Obtain the coordinates of the other end point on the correct reflection line except the reflection coordinate point, and mark it as the receiving coordinate point;

[0127] Calculate the distance between the initial coordinate point and the received coordinate point, mark it as the test offset, and obtain the effective wind speed at the same time;

[0128] Continuously perform analysis in the first test cycle to obtain a number of effective wind speeds and corresponding test offsets;

[0129] See also Fig.10 As shown, a rectangular coordinate system is established with the effective wind speed as the X-axis and the test offset as the Y-axis, named the wind speed offset coordinate system, and the effective wind speed and the corresponding test offset are entered into the wind speed offset coordinate system;

[0130] Perform linear regression on the wind speed offset coordinate system to obtain the wind speed offset function;

[0131] In practical applications, the received coordinate point is Figure 8 as well as Fig. 9 The position of the drone in the image is obtained, and the receiving coordinate point is (0.4358, 0.5733, 30.2674). The test offset is calculated to be 0.7735m. The calculation result retains 4 decimal places, and the corresponding effective wind speed is 7.2m / s. A large number of test offsets and effective wind speeds are collected to provide sufficient data support for subsequent analysis. Therefore, the first test cycle is set. The longer the first test cycle is, the more data there is, and the more accurate the test results are. In this embodiment, the first test cycle is set to 1 week; several test offsets and effective wind speeds are obtained through testing, and the wind speed offset coordinate system is constructed as follows Fig.10 As shown, the wind speed offset function obtained by regression is YW=0.0967×XW+0.0488, where YW is the test offset and XW is the effective wind speed.

[0132] The building scanning module is used to scan the building and obtain the initial point cloud data of the building;

[0133] The building scanning module is configured with a building scanning strategy, which includes:

[0134] Use a drone to carry a laser radar to scan the building and obtain initial point cloud data;

[0135] The initial point cloud data includes an initial forward point cloud data set, an initial left point cloud data set, an initial rearward point cloud data set, an initial right point cloud data set and an initial top point cloud data set, wherein the initial forward point cloud data set includes a number of initial forward point cloud data, the initial left point cloud data set includes a number of initial left point cloud data, the initial rearward point cloud data set includes a number of initial rearward point cloud data, the initial right point cloud data set includes a number of initial right point cloud data, and the initial top point cloud data set includes a number of initial top point cloud data;

[0136] In practical applications, the initial point cloud data is the data obtained after the drone scans the four sides and the top of the building.

[0137] The offset calculation module is used to monitor the current wind direction and wind speed in real time during the scanning process, and analyze the real-time offset of the drone;

[0138] The offset calculation module is configured with an offset calculation strategy, which includes:

[0139] When the laser radar is scanning, the wind direction and wind speed corresponding to the initial point cloud data are recorded synchronously and marked as real-time wind speed and real-time wind direction respectively;

[0140] The real-time wind speed includes the real-time forward wind speed, the real-time left wind speed, the real-time back wind speed, the real-time right wind speed and the real-time top wind speed; the real-time wind direction includes the real-time forward wind direction, the real-time left wind direction, the real-time back wind direction, the real-time right wind direction and the real-time top wind direction;

[0141] Substitute the real-time wind speed into the wind speed offset function, and mark the calculated result as the real-time offset. The real-time offset includes the real-time forward offset, the real-time left offset, the real-time back offset, the real-time right offset, and the real-time top offset.

[0142] In practical applications, the point cloud data carries a timestamp, and the time when different points receive the laser signal is different. Therefore, each initial point cloud data corresponds to a real-time wind speed and a real-time wind direction. This embodiment only takes an initial forward point cloud data as an example to explain the specific analysis process. The calculation process of the remaining initial point cloud data is the same. Taking a random initial forward point cloud data as an example, it is (90°, 45°, 42.7274m), and the corresponding real-time forward wind speed is 8.3m / s. Substitute XW=8.3 into YW=0.0967×XW+0.0488, and the real-time forward offset is calculated to be 0.8514, and the calculation result retains 4 decimal places.

[0143] The offset calibration module is used to calibrate the initial point cloud data based on the real-time offset to obtain the actual point cloud data;

[0144] The offset calibration module is configured with an offset calibration strategy, which includes:

[0145] See also Fig.11 As shown, a three-dimensional coordinate system is established with the position of the drone as the origin, the right direction of the drone as the horizontal axis, the forward direction of the drone, that is, the direction toward the building as the vertical axis, and the vertical upward direction of the drone as the vertical axis, which is named the offset calibration coordinate system;

[0146] Taking the origin of the offset calibration coordinate system as the starting point, the real-time offset is moved in the opposite direction of the real-time wind direction, and the corresponding coordinate point is marked as the actual coordinate point;

[0147] In actual application, the real-time wind direction is (270°, 0°). The drone is facing due east at this time, that is, the real-time wind direction is blowing due west. The opposite direction of the real-time wind direction is (90°, 0°). The origin of the offset calibration coordinate system is actually the launch port of the laser radar carried by the drone. The offset calibration coordinate system and the position of the actual coordinate point are constructed as follows: Fig.11 As shown;

[0148] See also Fig.12 As shown, obtain any initial point cloud data, draw a straight line with the origin of the offset calibration coordinate system as the endpoint, named the offset straight line, and the length of the offset straight line is L of the initial point cloud data;

[0149] See also Figure 13 to Figure 14 As shown, a top view of the offset calibration coordinate system is obtained, which is named as the first image, and a side view of the offset calibration coordinate system is obtained, which is named as the second image; in the first image, the angle between the offset straight line and the positive direction of the horizontal axis is marked as the third angle, and the angle of the third angle is the third angle; in the second image, the angle between the offset straight line and the positive direction of the vertical axis is marked as the fourth angle, and the angle of the fourth angle is the fourth angle;

[0150] The third angle is set to θ, the fourth angle is set to ω, the other end point of the offset straight line is marked as a point cloud coordinate point, the point cloud coordinate point is connected with the actual coordinate point, and the resulting straight line is named the calibration straight line;

[0151] In practical applications, the offset straight line constructed from the initial point cloud data (90°, 45°, 42.7274m) is as follows: Fig.12 As shown, the determination method of the third angle and the fourth angle is as follows Fig.13 as well as Fig.14 As shown, based on Fig.12 , connect the point cloud coordinate points with the actual coordinate points to get the calibration line as Fig.15 As shown;

[0152] In the first image, the angle between the calibration line and the positive direction of the horizontal axis is marked as the rotation calibration angle, and the angle of the rotation calibration angle is the rotation calibration angle; in the second image, the angle between the calibration line and the positive direction of the vertical axis is marked as the vertical calibration angle, and the angle of the vertical calibration angle is the vertical calibration angle; the length of the calibration line is marked as the calibration distance;

[0153] Calibrate the initial point cloud data currently being analyzed to (rotation calibration angle, vertical calibration angle, calibration distance) to obtain the actual point cloud data;

[0154] Calibrate all initial point cloud data to obtain some actual point cloud data;

[0155] In actual applications, the rotation calibration angle and vertical calibration angle of the calibration line are obtained to be 90° and 48° respectively, and the calibration distance is obtained to be 42.0865. The initial point cloud data (90°, 45°, 42.7274m) currently analyzed is changed to the actual point cloud data (90°, 48°, 42.0865m). Due to the large amount of data, this embodiment only lists this actual point cloud data, and the rest of the actual point cloud data are omitted.

[0156] The 3D modeling module is used to perform 3D modeling of buildings based on actual point cloud data;

[0157] The 3D modeling module is configured with 3D modeling strategies, which include:

[0158] The actual point cloud data includes an actual forward point cloud data set, an actual left point cloud data set, an actual rearward point cloud data set, an actual right point cloud data set and an actual top point cloud data set. The actual forward point cloud data set includes a number of actual forward point cloud data, the actual left point cloud data set includes a number of actual left point cloud data, the actual rearward point cloud data set includes a number of actual rearward point cloud data, the actual right point cloud data set includes a number of actual right point cloud data, and the actual top point cloud data set includes a number of actual top point cloud data.

[0159] The front model of the building is constructed by the actual forward point cloud data set, the left model of the building is constructed by the actual left point cloud data set, the back model of the building is constructed by the actual rear point cloud data set, the right model of the building is constructed by the actual right point cloud data set, and the top model of the building is constructed by the actual top point cloud data set;

[0160] The front model, the left model, the back model, the right model and the top model are spliced ​​together to obtain a three-dimensional model of the building;

[0161] In practical applications, models of different surfaces of a building are first constructed using actual point cloud data, and then spliced ​​together to obtain a three-dimensional model of the building.

[0162] Example 2, please refer to Fig.16 As shown, the present invention also provides a building outline three-dimensional modeling monitoring method based on point cloud data, comprising the following steps:

[0163] Step S1, performing an offset test on the drone to obtain valid point cloud data; Step S1 includes the following sub-steps:

[0164] Step S101, building an offset test scene;

[0165] Step S101 includes the following sub-steps:

[0166] Step S101.1, selecting an open area;

[0167] Step S101.2, setting a laser signal reflection device;

[0168] Step S101.3, the laser signal reflecting device is equipped with a reflecting plate, and the height of the reflecting plate can be adjusted according to the flying height of the UAV;

[0169] Step S102, based on the offset test scenario, performing an offset test on the UAV to obtain valid point cloud data;

[0170] Step S102 includes the following sub-steps:

[0171] Step S102.1, adjusting the reflector to a first flight altitude, and adjusting the UAV carrying the laser radar to the first flight altitude through the flight control system, ensuring that the emission port of the laser radar faces the reflector;

[0172] Step S102.2, real-time monitoring of the current wind speed and wind direction, marked as monitoring wind speed and monitoring wind direction;

[0173] Step S102.3, transmitting a laser signal to the reflector through the laser radar, recording the monitored wind speed and the monitored wind direction during the transmission, and marking them as the transmitting wind speed and the transmitting wind direction respectively; receiving the returned laser point cloud data, marking them as the returned point cloud data, and recording the monitored wind speed and the monitored wind direction when the returned point cloud data is received, and marking them as the returned wind speed and the returned wind direction respectively;

[0174] Step S102.4, compare the transmitting wind speed with the return wind speed, and compare the transmitting wind direction with the return wind direction. If the transmitting wind speed is the same as the return wind speed and the transmitting wind direction is the same as the return wind direction, then output a data valid signal; otherwise, output a data invalid signal;

[0175] Step S102.5, if the output data is a valid signal, the returned point cloud data is marked as valid point cloud data, and the returned wind speed is marked as a valid wind speed;

[0176] Step S2, analyzing the offset of the UAV at different wind speeds based on the effective point cloud data; Step S2 includes the following sub-steps:

[0177] Step S201, constructing an offset analysis coordinate system and drawing an initial reflection line;

[0178] Step S201 includes the following sub-steps:

[0179] Step S201.1, the format of the valid point cloud data is (θ, ω, L), where θ is the rotation angle, ω is the vertical angle, and L is the return distance;

[0180] Step S201.2, establish a spatial coordinate system with the take-off point of the drone as the origin, the direction to the right of the drone as the X-axis, the direction in front of the drone, that is, the direction from the origin to the laser signal reflection device as the Y-axis, and the direction vertically upward from the origin as the Z-axis, named as the offset analysis coordinate system;

[0181] Step S201.3, in the offset analysis coordinate system, mark the coordinate point (0, 0, first flight height) as the initial coordinate point;

[0182] Step S201.4, obtaining the distance between the laser signal reflection device and the take-off point, marking it as the actual distance, and marking the coordinate point (0, actual distance, first flight altitude) as the reflection coordinate point;

[0183] Step S201.5, with the reflection coordinate point as the endpoint, draw a ray along the positive direction of the X axis and parallel to the X axis, marked as the first auxiliary line, with the reflection coordinate point as the endpoint, draw a ray along the positive direction of the Z axis and parallel to the Z axis, marked as the second auxiliary line;

[0184] Step S201.6, draw a straight line with the reflection coordinate point as the endpoint, mark it as the initial reflection straight line, and the length of the initial reflection straight line is the return distance;

[0185] Step S201.7, obtaining a top view of the offset analysis coordinate system, ignoring the influence of the Z-axis direction in the top view, marking the angle between the initial reflection straight line and the first auxiliary line in the top view as a first angle, and marking the angle of the first angle as a first angle;

[0186] Step S201.8, obtaining a side view of the offset analysis coordinate system, ignoring the influence of the X-axis direction in the side view, marking the angle between the initial reflection straight line in the side view and the opposite direction of the Y-axis as the second angle, and marking the angle of the second angle as the second angle;

[0187] Step S202, drawing a correct reflection line based on the valid point cloud data, and then analyzing the relationship between the offset of the drone and the wind speed based on the correct reflection line;

[0188] Step S202 includes the following sub-steps:

[0189] Step S202.1, obtaining valid point cloud data, setting the first angle to 180°-θ, the second angle to 360°-ω, and setting the length of the correct reflection line to L;

[0190] Step S202.2, obtaining the coordinates of the other end point on the correct reflection line except the reflection coordinate point, and marking it as the receiving coordinate point;

[0191] Step S202.3, calculate the distance between the initial coordinate point and the received coordinate point, mark it as the test offset, and obtain the effective wind speed;

[0192] Step S202.4, continuously performing analysis in the first test cycle to obtain a number of effective wind speeds and corresponding test offsets;

[0193] Step S202.5, establish a rectangular coordinate system with the effective wind speed as the X-axis and the test offset as the Y-axis, named the wind speed offset coordinate system, and enter the effective wind speed and the corresponding test offset into the wind speed offset coordinate system;

[0194] Step S202.6, performing linear regression on the wind speed offset coordinate system to obtain a wind speed offset function;

[0195] Step S3, scanning the building to obtain initial point cloud data of the building; Step S3 includes the following sub-steps:

[0196] Step S301, scanning the building with a laser radar carried by a drone to obtain initial point cloud data;

[0197] Step S302, the initial point cloud data includes an initial forward point cloud data set, an initial left point cloud data set, an initial rearward point cloud data set, an initial right point cloud data set and an initial top point cloud data set, the initial forward point cloud data set includes a number of initial forward point cloud data, the initial left point cloud data set includes a number of initial left point cloud data, the initial rearward point cloud data set includes a number of initial rearward point cloud data, the initial right point cloud data set includes a number of initial right point cloud data, and the initial top point cloud data set includes a number of initial top point cloud data;

[0198] Step S4, during the scanning process, real-time monitoring of the current wind direction and wind speed, and analysis of the real-time offset of the drone; Step S4 includes the following sub-steps:

[0199] Step S401, when the laser radar is scanning, the wind direction and wind speed corresponding to the initial point cloud data are synchronously recorded and marked as real-time wind speed and real-time wind direction respectively;

[0200] Step S402, the real-time wind speed includes the real-time forward wind speed, the real-time left wind speed, the real-time back wind speed, the real-time right wind speed and the real-time top wind speed, and the real-time wind direction includes the real-time forward wind direction, the real-time left wind direction, the real-time back wind direction, the real-time right wind direction and the real-time top wind direction;

[0201] Step S403, substituting the real-time wind speed into the wind speed offset function, marking the calculation result as the real-time offset, the real-time offset includes the real-time forward offset, the real-time left offset, the real-time back offset, the real-time right offset and the real-time top offset;

[0202] Step S5, calibrating the initial point cloud data based on the real-time offset to obtain actual point cloud data; Step S5 includes the following sub-steps:

[0203] Step S501, establish a three-dimensional coordinate system with the position of the drone as the origin, the right direction of the drone as the horizontal axis, the forward direction of the drone, that is, the direction toward the building as the vertical axis, and the vertical upward direction of the drone as the vertical axis, named as the offset calibration coordinate system;

[0204] Step S502, taking the origin of the offset calibration coordinate system as the starting point, moving the real-time offset in the opposite direction of the real-time wind direction, and marking the corresponding coordinate point as the actual coordinate point;

[0205] Step S503, obtaining any initial point cloud data, drawing a straight line with the origin of the offset calibration coordinate system as the endpoint, named the offset straight line, and the length of the offset straight line is L of the initial point cloud data;

[0206] Step S504, obtaining a top view of the offset calibration coordinate system, named as a first image, and obtaining a side view of the offset calibration coordinate system, named as a second image; in the first image, the angle between the offset straight line and the positive direction of the horizontal axis is marked as a third angle, and the angle of the third angle is a third angle; in the second image, the angle between the offset straight line and the positive direction of the vertical axis is marked as a fourth angle, and the angle of the fourth angle is a fourth angle;

[0207] Step S505, the third angle is set to θ, the fourth angle is set to ω, the other end point of the offset straight line is marked as a point cloud coordinate point, the point cloud coordinate point is connected with the actual coordinate point, and the obtained straight line is named a calibration straight line;

[0208] Step S506: in the first image, the angle between the calibration line and the positive direction of the horizontal axis is marked as the rotation calibration angle, and the angle of the rotation calibration angle is the rotation calibration angle; in the second image, the angle between the calibration line and the positive direction of the vertical axis is marked as the vertical calibration angle, and the angle of the vertical calibration angle is the vertical calibration angle; the length of the calibration line is marked as the calibration distance;

[0209] Step S507, calibrating the initial point cloud data currently being analyzed to (rotation calibration angle, vertical calibration angle, calibration distance) to obtain actual point cloud data;

[0210] Step S508, calibrating all the initial point cloud data to obtain a number of actual point cloud data;

[0211] Step S6, performing three-dimensional modeling of the building based on the actual point cloud data; Step S6 includes the following sub-steps:

[0212] Step S601, the actual point cloud data includes an actual forward point cloud data set, an actual left point cloud data set, an actual rearward point cloud data set, an actual right point cloud data set and an actual top point cloud data set, wherein the actual forward point cloud data set includes a number of actual forward point cloud data, the actual left point cloud data set includes a number of actual left point cloud data, the actual rearward point cloud data set includes a number of actual rearward point cloud data, the actual right point cloud data set includes a number of actual right point cloud data, and the actual top point cloud data set includes a number of actual top point cloud data;

[0213] Step S602, constructing a front model of the building through the actual forward point cloud data set, constructing a left model of the building through the actual left point cloud data set, constructing a back model of the building through the actual rear point cloud data set, constructing a right model of the building through the actual right point cloud data set, and constructing a top model of the building through the actual top point cloud data set;

[0214] Step S603, splicing the front model, the left model, the back model, the right model and the top model to obtain a three-dimensional model of the building.

[0215] Embodiment 3, the present application provides an electronic device, including a processor and a memory, the memory storing computer-readable instructions, when the computer-readable instructions are executed by the processor, the steps in the above method are executed. Through the above technical solution, the processor and the memory are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanisms, the memory stores a computer program executable by the processor, when the electronic device is running, the processor executes the computer program to execute the method in any optional implementation of the above embodiment, to achieve the following functions: perform an offset test on the drone to obtain valid point cloud data; analyze the offset of the drone at different wind speeds based on the valid point cloud data; scan the building to obtain the initial point cloud data of the building; monitor the current wind direction and wind speed in real time, and analyze the real-time offset of the drone at the same time; calibrate the initial point cloud data based on the real-time offset; and perform three-dimensional modeling of the building based on the actual point cloud data.

[0216] Embodiment 4, the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method are executed. Through the above technical solution, when the computer program is executed by the processor, the method in any optional implementation of the above embodiment is executed to achieve the following functions: perform an offset test on the drone to obtain valid point cloud data; analyze the offset of the drone at different wind speeds based on the valid point cloud data; scan the building to obtain the initial point cloud data of the building; monitor the current wind direction and wind speed in real time, and analyze the real-time offset of the drone; calibrate the initial point cloud data based on the real-time offset; and perform three-dimensional modeling of the building based on the actual point cloud data.

[0217] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Red Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0218] The above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A three-dimensional modeling and monitoring method for building contours based on point cloud data, characterized in that: The steps include: Perform an offset test on the drone to obtain valid point cloud data; Analyze the displacement of the UAV under different wind speeds based on the effective point cloud data; Scan the building to obtain the initial point cloud data of the building; During the scanning process, the current wind direction and wind speed are monitored in real time, and the real-time offset of the drone is analyzed; Perform data calibration on the initial point cloud data based on the real-time offset to obtain actual point cloud data; 3D modeling of buildings based on actual point cloud data; The offset test of the UAV to obtain valid point cloud data includes the following sub-steps: Build an offset test scenario; Based on the offset test scenario, the UAV is tested for offset to obtain valid point cloud data; The construction of the offset test scenario includes the following sub-steps: Choose an open area; Setting a laser signal reflection device; The laser signal reflecting device is equipped with a reflecting plate, and the height of the reflecting plate can be adjusted according to the flying height of the UAV; The method of performing an offset test on a drone based on an offset test scenario to obtain valid point cloud data includes the following sub-steps: The reflector is adjusted to the first flight altitude, and the UAV carrying the laser radar is adjusted to the first flight altitude through the flight control system to ensure that the emission port of the laser radar faces the reflector; Monitor the current wind speed and wind direction in real time, marked as monitoring wind speed and monitoring wind direction; The laser radar transmits a laser signal to the reflector, and the monitored wind speed and the monitored wind direction at the time of transmission are recorded, which are marked as the transmitting wind speed and the transmitting wind direction respectively; the returned laser point cloud data are received, which are marked as the returned point cloud data, and the monitored wind speed and the monitored wind direction when the returned point cloud data is received are recorded, which are marked as the returned wind speed and the returned wind direction respectively; The transmitting wind speed is compared with the return wind speed, and the transmitting wind direction is compared with the return wind direction. If the transmitting wind speed is the same as the return wind speed and the transmitting wind direction is the same as the return wind direction, a valid data signal is output; otherwise, an invalid data signal is output; If the output data is a valid signal, the returned point cloud data will be marked as valid point cloud data, and the returned wind speed will be marked as valid wind speed; The method of analyzing the offset of the drone at different wind speeds based on the effective point cloud data includes the following sub-steps: Construct the offset analysis coordinate system and draw the initial reflection line; Draw the correct reflection line based on the valid point cloud data, and then analyze the relationship between the offset and wind speed of the drone based on the correct reflection line; The process of constructing the offset analysis coordinate system and drawing the initial reflection line includes the following sub-steps: The format of the valid point cloud data is (θ, ω, L), where θ is the rotation angle, ω is the vertical angle, and L is the return distance; A spatial coordinate system is established with the take-off point of the drone as the origin, the direction to the right of the drone as the X-axis, the direction in front of the drone, that is, the direction of the origin toward the laser signal reflection device as the Y-axis, and the vertically upward direction of the origin as the Z-axis, which is named the offset analysis coordinate system; In the offset analysis coordinate system, the coordinate point (0,0, first flight height) is marked as the initial coordinate point; Obtain the distance between the laser signal reflection device and the take-off point, mark it as the actual distance, and mark the coordinate point (0, actual distance, first flight altitude) as the reflection coordinate point; With the reflection coordinate point as the endpoint, draw a ray along the positive direction of the X axis and parallel to the X axis, marked as the first auxiliary line; with the reflection coordinate point as the endpoint, draw a ray along the positive direction of the Z axis and parallel to the Z axis, marked as the second auxiliary line; Draw a straight line with the reflection coordinate point as the endpoint, marked as the initial reflection straight line, and the length of the initial reflection straight line is the return distance; Obtain a top view of the offset analysis coordinate system, ignore the influence of the Z-axis direction in the top view, mark the angle between the initial reflection straight line and the first auxiliary line in the top view as the first angle, and mark the angle of the first angle as the first angle; A side view of the offset analysis coordinate system is obtained. The influence of the X-axis direction is ignored in the side view. The angle between the initial reflection straight line in the side view and the opposite direction of the Y-axis is marked as the second angle, and the angle of the second angle is marked as the second angle.

2. The method for three-dimensional modeling and monitoring of building outlines based on point cloud data according to claim 1, characterized in that: Drawing a correct reflection line based on the valid point cloud data and then analyzing the relationship between the offset and wind speed of the drone based on the correct reflection line includes the following sub-steps: Obtain valid point cloud data, set the first angle to 180°-θ, set the second angle to 360°-ω, and set the length of the correct reflection line to L; Obtain the coordinates of the other end point on the correct reflection line except the reflection coordinate point, and mark it as the receiving coordinate point; Calculate the distance between the initial coordinate point and the received coordinate point, mark it as the test offset, and obtain the effective wind speed at the same time; Continuously perform analysis in the first test cycle to obtain a number of effective wind speeds and corresponding test offsets; A rectangular coordinate system is established with the effective wind speed as the X-axis and the test offset as the Y-axis, named the wind speed offset coordinate system, and the effective wind speed and the corresponding test offset are entered into the wind speed offset coordinate system; Perform linear regression on the wind speed offset coordinate system to obtain the wind speed offset function.

3. The method for three-dimensional modeling and monitoring of building outlines based on point cloud data according to claim 2, characterized in that: The step of scanning the building to obtain the initial point cloud data of the building includes the following sub-steps: Use a drone to carry a laser radar to scan the building and obtain initial point cloud data; The initial point cloud data includes an initial forward point cloud data set, an initial left point cloud data set, an initial rear point cloud data set, an initial right point cloud data set and an initial top point cloud data set. The initial forward point cloud data set includes a number of initial forward point cloud data, the initial left point cloud data set includes a number of initial left point cloud data, the initial rear point cloud data set includes a number of initial rear point cloud data, the initial right point cloud data set includes a number of initial right point cloud data, and the initial top point cloud data set includes a number of initial top point cloud data.

4. The method for three-dimensional modeling and monitoring of building outlines based on point cloud data according to claim 3, characterized in that: During the scanning process, real-time monitoring of the current wind direction and wind speed and analysis of the real-time offset of the drone include the following sub-steps: When the laser radar is scanning, the wind direction and wind speed corresponding to the initial point cloud data are recorded synchronously and marked as real-time wind speed and real-time wind direction respectively; The real-time wind speed includes the real-time forward wind speed, the real-time left wind speed, the real-time back wind speed, the real-time right wind speed and the real-time top wind speed; the real-time wind direction includes the real-time forward wind direction, the real-time left wind direction, the real-time back wind direction, the real-time right wind direction and the real-time top wind direction; The real-time wind speed is substituted into the wind speed offset function, and the calculation result is marked as the real-time offset, which includes a real-time forward offset, a real-time left offset, a real-time back offset, a real-time right offset, and a real-time top offset.

5. The method for three-dimensional building outline modeling and monitoring based on point cloud data according to claim 4, characterized in that: The data calibration of the initial point cloud data based on the real-time offset to obtain the actual point cloud data includes the following sub-steps: A three-dimensional coordinate system is established with the position of the drone as the origin, the right direction of the drone as the horizontal axis, the forward direction of the drone, that is, the direction towards the building as the vertical axis, and the vertical upward direction of the drone as the vertical axis, which is named the offset calibration coordinate system; Taking the origin of the offset calibration coordinate system as the starting point, the real-time offset is moved in the opposite direction of the real-time wind direction, and the corresponding coordinate point is marked as the actual coordinate point; Obtain any initial point cloud data, draw a straight line with the origin of the offset calibration coordinate system as the endpoint, named the offset straight line, and the length of the offset straight line is L of the initial point cloud data; A top view of the offset calibration coordinate system is obtained, which is named as a first image, and a side view of the offset calibration coordinate system is obtained, which is named as a second image; in the first image, an angle between the offset straight line and the positive direction of the horizontal axis is marked as a third angle, and the angle of the third angle is a third angle; in the second image, an angle between the offset straight line and the positive direction of the vertical axis is marked as a fourth angle, and the angle of the fourth angle is a fourth angle; The third angle is set to θ, the fourth angle is set to ω, the other end point of the offset straight line is marked as a point cloud coordinate point, the point cloud coordinate point is connected with the actual coordinate point, and the resulting straight line is named the calibration straight line; In the first image, the angle between the calibration line and the positive direction of the horizontal axis is marked as the rotation calibration angle, and the angle of the rotation calibration angle is the rotation calibration angle; in the second image, the angle between the calibration line and the positive direction of the vertical axis is marked as the vertical calibration angle, and the angle of the vertical calibration angle is the vertical calibration angle; the length of the calibration line is marked as the calibration distance; Calibrate the initial point cloud data currently being analyzed to (rotation calibration angle, vertical calibration angle, calibration distance) to obtain the actual point cloud data; All initial point cloud data are calibrated to obtain some actual point cloud data.

6. The method for monitoring building outline three-dimensional modeling based on point cloud data according to claim 5, characterized in that: The 3D modeling of buildings based on actual point cloud data includes the following sub-steps: The actual point cloud data includes an actual forward point cloud data set, an actual left point cloud data set, an actual rearward point cloud data set, an actual right point cloud data set and an actual top point cloud data set, wherein the actual forward point cloud data set includes a number of actual forward point cloud data, the actual left point cloud data set includes a number of actual left point cloud data, the actual rearward point cloud data set includes a number of actual rearward point cloud data, the actual right point cloud data set includes a number of actual right point cloud data, and the actual top point cloud data set includes a number of actual top point cloud data; The front model of the building is constructed by the actual forward point cloud data set, the left model of the building is constructed by the actual left point cloud data set, the back model of the building is constructed by the actual rear point cloud data set, the right model of the building is constructed by the actual right point cloud data set, and the top model of the building is constructed by the actual top point cloud data set; The front model, left model, back model, right model and top model are spliced ​​together to obtain a three-dimensional model of the building.

7. A three-dimensional modeling monitoring system for building outlines based on point cloud data, used to implement the three-dimensional modeling monitoring method for building outlines based on point cloud data according to any one of claims 1 to 6, characterized in that: It includes an offset test module, an offset analysis module, a building scanning module, an offset calculation module, an offset calibration module and a three-dimensional modeling module; the offset test module, the offset analysis module, the building scanning module, the offset calculation module and the three-dimensional modeling module are respectively connected with the offset calibration module data; The offset test module is used to perform an offset test on the UAV to obtain valid point cloud data; The offset analysis module is used to analyze the offset of the UAV at different wind speeds based on the effective point cloud data; The building scanning module is used to scan the building to obtain initial point cloud data of the building; The offset calculation module is used to monitor the current wind direction and wind speed in real time during the scanning process, and analyze the real-time offset of the drone; The offset calibration module is used to calibrate the initial point cloud data based on the real-time offset to obtain actual point cloud data; The three-dimensional modeling module is used to perform three-dimensional modeling of the building based on actual point cloud data.

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