Rapid optimization method and system for unmanned aerial vehicle to get close to photography viewpoint

Abstract

The invention discloses a rapid optimization method and system for an unmanned aerial vehicle to get close to a photographing viewpoint, and relates to the technical field of photographing viewpoint optimization. A plurality of photographing targets of the unmanned aerial vehicle on a flight path are set, and the unmanned aerial vehicle is started to fly on the flight path; loading a regional three-dimensional map of a corresponding photographing target on a flight path through which the unmanned aerial vehicle passes, establishing a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photographing target, and analyzing a viewpoint imaging index of the unmanned aerial vehicle to the corresponding photographing target by the viewpoint evaluation model; the state information of the unmanned aerial vehicle in the flight process is obtained in real time, the state information of the unmanned aerial vehicle is transmitted back to the unmanned aerial vehicle operation end, and whether viewpoint optimization at the corresponding photographing target is carried out or not is determined according to the state information of the unmanned aerial vehicle obtained in real time and the viewpoint imaging index of the viewpoint evaluation model for the photographing target. According to the invention, on-demand optimization of shooting viewpoints is realized, and the optimization efficiency, the optimization precision and the imaging quality during shooting of the unmanned aerial vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photography viewpoint optimization, and in particular to a method and system for quickly optimizing a photography viewpoint for an unmanned aerial vehicle (UAV). Background Art

[0002] In existing drone photography technology, operators are usually required to manually adjust the drone's flight path and shooting angle based on experience to achieve the desired photography effect. However, this method is often time-consuming and inefficient, especially in complex environments or when multiple photography targets need to be photographed.

[0003] In addition, due to the lack of three-dimensional information about the surrounding environment of the photographic target, the drone may not be able to obtain the best viewpoint when taking close-up photos, which affects the image quality. Therefore, how to achieve rapid viewpoint optimization when taking close-up photos, and improve optimization efficiency, optimization accuracy and image quality has always been a key and difficult problem in the field of drone photography technology. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a method and system for quickly optimizing the photography viewpoint of a drone.

[0005] The purpose of the present invention can be achieved by the following technical solution: A method for quickly optimizing the viewpoint of a drone close to photography, comprising the following steps: Step S1: setting a number of photographic targets on the flight path of the UAV, starting the UAV to fly on the flight path, and loading a three-dimensional map of the area where the UAV passes the corresponding photographic targets on the flight path; Step S2: establishing a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photographic target, and using the viewpoint evaluation model to analyze the viewpoint imaging index of the drone for the corresponding photographic target; Step S3: Acquire the drone status information of the drone in real time during flight, and transmit the drone status information back to the drone operation terminal; Step S4: Determine whether to optimize the viewpoint at the corresponding photography target based on the drone status information obtained in real time and the viewpoint imaging index of the photography target by the viewpoint evaluation model.

[0006] Furthermore, the process of setting a number of photographic targets on the flight path of the drone includes: Construct the flight path corresponding to the UAV, select several photography targets on the flight path, and label them as 1, 2, 3, ..., k, where k is a natural number greater than 0, and obtain the longitude and latitude coordinates of the photography viewpoint corresponding to each photography target; The longitude and latitude coordinates of the photographic viewpoint corresponding to each photographic target are marked on the flight path, and the path segment corresponding to each two photographic viewpoints on the flight path is taken as a path segment to be monitored.

[0007] Furthermore, the process of starting the UAV to fly on the flight path and loading the three-dimensional map of the area where the UAV passes the corresponding photography target on the flight path includes: Set the initial working parameters of the drone, start the drone and fly it along the flight path, capture the control signal strength received by the drone in real time in each path segment to be monitored included in the flight path, create a coordinate system, and construct a line chart showing the intensity change trend of the control signal strength of the drone over time in each path segment to be monitored; Set a signal strength threshold, and draw a horizontal determination line on the coordinate system according to the signal strength threshold. The number of coordinate points below the determination line in the strength change trend line graph is recorded as N, where N is a natural number greater than 0. The control signal strength corresponding to the coordinate points on or above the determination line meets the requirement, otherwise, it does not meet the requirement. When N≤3, the signal enhancement module built into the drone performs the control signal enhancement operation; When 4<N≤8, the UAV operator enhances the control signal, builds an encrypted channel between the UAV operator and the UAV, and transmits the control signal to the UAV through the encrypted channel; When N>8, stop the flight of the current drone; When the UAV passes each photographic viewpoint on the flight path, it is determined whether the real-time flight coordinates of the UAV are in the loading area set for the corresponding photographic viewpoint. If so, the three-dimensional map of the area at the corresponding photographic target is automatically loaded. If not, no operation is performed.

[0008] Furthermore, a corresponding viewpoint evaluation model is established according to the regional three-dimensional map loaded at the photographic target, and the process of analyzing the viewpoint imaging index of the drone to the corresponding photographic target by the viewpoint evaluation model includes: Parse the regional three-dimensional map loaded at each photographic target, and then obtain the building information of the target area corresponding to each photographic target, the building information is composed of several buildings in the target area, and construct a viewpoint evaluation model of the regional three-dimensional map corresponding to each photographic target, the viewpoint evaluation model is composed of several model input nodes and several corresponding model output nodes; The AR rendering information of any building in the three-dimensional map of the corresponding area is input into the model input node, and then any model output node calculates the viewpoint imaging index corresponding to the ideal situation according to the AR rendering information of the currently input building, and inputs the building information corresponding to each photographic target currently acquired by the drone into the viewpoint evaluation model, thereby obtaining the viewpoint imaging index under the actual situation in each target area; The viewpoint imaging indicators under ideal and actual conditions are analyzed, and then the viewpoint imaging score of the drone at each photographic target is obtained. According to the results of the viewpoint imaging score, it is analyzed whether there is a viewpoint optimization requirement for the corresponding photographic target.

[0009] Furthermore, the process of obtaining the drone status information of the drone in real time during flight and transmitting the drone status information back to the drone operation end includes: Build a drone monitoring network, which consists of several distributed monitoring nodes. Perform code injection test on the drone monitoring network. If the code injection test passes, no operation is performed. If it fails, reconfigure the network firewall associated with the drone monitoring network. Each distributed monitoring node corresponding to the drone monitoring network is used to monitor a monitored path segment of the drone in real time during flight, and then obtain the drone status information under the corresponding monitored path segment; A transmission link is constructed between the UAV and the UAV operator, and the UAV status information of the UAV in each path segment to be monitored is transmitted back to the UAV operator through the transmission link and stored by the UAV operator.

[0010] Furthermore, according to the drone status information obtained in real time and the viewpoint imaging index of the photographic target by the viewpoint evaluation model, the process of determining whether to optimize the viewpoint at the corresponding photographic target includes: When the result obtained after analyzing the viewpoint imaging index is that there is a viewpoint optimization demand for the corresponding photographic target, and the drone status information of the monitored path segment corresponding to the photographic target meets the preset standard status information, it is decided to optimize the viewpoint at the current photographic target; When the result of analyzing the viewpoint imaging indicators is that there is no viewpoint optimization demand for the corresponding photographic target, or the result of analyzing the viewpoint imaging indicators is that there is a viewpoint optimization demand for the corresponding photographic target, but the drone status information of the monitored path segment corresponding to the photographic target does not meet the standard status information, it is decided not to perform viewpoint optimization at the current photographic target.

[0011] Furthermore, the viewpoint optimization process includes: For the photography target that is determined to be optimized for viewpoint, the photography viewpoint corresponding to the current photography target is taken as the central viewpoint, and the adjacent viewing angle areas of the central viewpoint are set. The number of adjacent viewing angle areas is 8, which are distributed in the north, south, east, west, northeast, northwest, southeast and southwest of the central viewpoint; The viewpoint imaging score is performed on each adjacent viewpoint area, and then the adjacent viewpoint area with the highest viewpoint imaging score is selected as the best viewpoint area, and the UAV is controlled to conduct further best viewpoint search in the best viewpoint area, and then the UAV is controlled to get close to the photographic target at the best viewpoint and shoot the photographic target.

[0012] Furthermore, a system for quickly optimizing the viewpoint of a drone close to a photography point of view is provided, the system comprising: The regional map loading module sets a number of photographic targets on the flight path of the UAV, starts the UAV to fly on the flight path, and loads a three-dimensional map of the area where the UAV passes the corresponding photographic targets on the flight path; The viewpoint model building module is used to establish a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photography target, and the viewpoint evaluation model analyzes the viewpoint imaging index of the drone for the corresponding photography target; The drone real-time information module is used to obtain the drone status information of the drone in real time during the flight and transmit the drone status information back to the drone operation end; The viewpoint optimization module is used to decide whether to optimize the viewpoint at the corresponding photographic target based on the real-time acquired drone status information and the viewpoint imaging index at the photographic target by the viewpoint evaluation model.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. By acquiring the drone status information in real time and combining the constructed viewpoint evaluation model with the viewpoint imaging index analysis results of each photographic target, it is possible to quickly determine whether it is necessary to perform viewpoint optimization at the corresponding photographic target, thus reducing the time required for manual adjustment, achieving on-demand optimization, and effectively improving optimization efficiency and accuracy; 2. For the photography target that is determined to perform viewpoint optimization, set its corresponding adjacent viewing angle area. After scoring the viewpoint imaging of each adjacent viewing area, the adjacent viewing area with the highest viewpoint imaging score is selected as the best viewing area, and the UAV is controlled to conduct further best viewpoint search in the best viewing area. The UAV is controlled to get close to the photographic target at the best viewpoint and shoot the photographic target, which ensures the imaging quality of the UAV when shooting to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0015] like Figure 1 As shown, a method for quickly optimizing the viewpoint of a drone close to a photography point includes the following steps: Step S1: setting a number of photographic targets on the flight path of the UAV, starting the UAV to fly on the flight path, and loading a three-dimensional map of the area where the UAV passes the corresponding photographic targets on the flight path; Step S2: establishing a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photographic target, and using the viewpoint evaluation model to analyze the viewpoint imaging index of the drone for the corresponding photographic target; Step S3: Acquire the drone status information of the drone in real time during flight, and transmit the drone status information back to the drone operation terminal; Step S4: Determine whether to optimize the viewpoint at the corresponding photography target based on the drone status information obtained in real time and the viewpoint imaging index of the photography target by the viewpoint evaluation model.

[0016] It should be further explained that, in the specific implementation process, the process of setting several photographic targets on the flight path of the drone includes: Construct the flight path corresponding to the UAV; Selecting a number of photographic targets on the flight path, the photographic targets are key areas and targets that need to be photographed, and labeling the several photographic targets as 1, 2, 3, ..., k, where k is a natural number greater than 0; Obtain the longitude and latitude coordinates of the photography viewpoint corresponding to each photography target, and denote the longitude and latitude coordinates of the photography viewpoint corresponding to the photography target labeled k as JW[k], where JW[k] = [px, py], where px is the longitude coordinate of the photography viewpoint, and py is the latitude coordinate of the photography viewpoint; Mark the latitude and longitude coordinates of each photography target corresponding to the photography viewpoint on the flight path; The path segment corresponding to each two photographic viewpoints on the flight path is regarded as a path segment to be monitored.

[0017] It should be further explained that, in the specific implementation process, the process of starting the UAV to fly on the flight path and loading the three-dimensional map of the area where the UAV passes the corresponding photography target on the flight path includes: Set the initial working parameters of the drone, including the climbing height, flight speed and flight direction, start the drone, and let it fly along the flight path, capturing the control signal strength received by the drone in each monitored path segment in real time; When the UAV is flying on the flight path, the UAV is controlled by a ground operator at the UAV operation terminal, and the UAV operation terminal sends a real-time control signal to the UAV flying on the flight path; Create a coordinate system, and construct a line graph of the intensity change trend of the control signal strength over time for each path segment to be monitored. The horizontal axis of the intensity change trend line graph is a number of time nodes, and the vertical axis is the value of the control signal strength at each time node; Set the signal strength threshold, and draw a horizontal determination line on the coordinate system according to the signal strength threshold. The number of coordinate points below the determination line in the strength change trend line graph is recorded as N, where N is a natural number greater than 0. The control signal strength corresponding to the coordinate point on the strength change trend line graph that is on the determination line or higher than the determination line meets the requirement, otherwise, it does not meet the requirement; When N≤3, the signal enhancement module built into the drone will enhance the received control signal; When 4<N≤8, the UAV operator enhances the control signal, builds an encrypted channel between the UAV operator and the UAV, and transmits the control signal to the UAV through the encrypted channel; When N>8, stop the flight of the current drone; When the UAV passes each photographic viewpoint on the flight path, it is determined whether the real-time flight coordinates of the UAV are in the loading area of ​​the corresponding photographic viewpoint. If so, the three-dimensional map of the area at the corresponding photographic target is automatically loaded. If not, no operation is performed.

[0018] It should be noted that the loading area is set according to the latitude and longitude coordinates of the photographic viewpoint, specifically: a spherical area is constructed with the photographic viewpoint as the center and a radius R set, where R is a natural number greater than 0, and the value of R is set by professionals according to actual loading requirements.

[0019] It should be further explained that, in the specific implementation process, a corresponding viewpoint evaluation model is established according to the regional three-dimensional map loaded at the photographic target, and the process of analyzing the viewpoint imaging index of the drone to the corresponding photographic target by the viewpoint evaluation model includes: Parsing the regional three-dimensional map loaded at each photographic target, thereby obtaining building information of the target area corresponding to each photographic target, wherein the building information is composed of a number of buildings in the target area; Construct a viewpoint evaluation model for the three-dimensional map of the area corresponding to each photographic target; The viewpoint evaluation model is composed of a plurality of model input nodes and a plurality of corresponding model output nodes. The AR rendering information of any building in the three-dimensional map of the corresponding area is input into the model input node, and then any model output node calculates the corresponding viewpoint imaging index under ideal conditions according to the AR rendering information of the currently input building. It should be noted that the AR drawing information is used to reflect the overall situation of the building, including the building appearance outline, building size ratio, building surrounding environment information and building key identification points. The building key identification points are the intersection points formed between the building planes. The above operations are performed in several model input nodes in the viewpoint evaluation model, thereby obtaining the viewpoint imaging index of the corresponding target area at each photographic target under ideal conditions; The ideal viewpoint imaging indicators include ideal viewing angle coverage, ideal imaging resolution, ideal building occlusion rate and safe distance. The building information corresponding to each photographic target currently acquired by the drone is input into the viewpoint evaluation model to obtain the viewpoint imaging indicators under the actual situation in each target area. The viewpoint imaging indicators in actual situations include actual viewing angle coverage, actual imaging resolution, actual building occlusion rate, and actual distance; Calculate the viewpoint imaging score of the drone at each photographic target; The viewpoint imaging score is recorded as Score. In the initial case, Score = 0; When the actual viewing angle coverage range and the ideal viewing angle coverage range are within the preset coverage range allowable deviation, the Score is assigned a value of 1, otherwise, no value is assigned; When the resolution difference between the actual imaging resolution and the ideal imaging resolution is within the preset error tolerance range, the Score is assigned a value of 1, otherwise, no value is assigned; When the occlusion rate difference between the actual building occlusion rate and the ideal building occlusion rate is within the preset difference allowable range, the Score is assigned a value of 1, otherwise, no value is assigned; When the actual distance is greater than or equal to the safety distance, the score is assigned 1, otherwise, no score is assigned; Therefore, the value of Score is an integer from 0 to 4; When Score≤2, the analysis shows that the corresponding photography target has a need for viewpoint optimization; When 2<Score≤4, the analysis shows that there is no need for viewpoint optimization for the corresponding photography target.

[0020] It should be further explained that, in the specific implementation process, the process of obtaining the drone status information of the drone in real time during flight and transmitting the drone status information back to the drone operation end includes: Constructing a drone monitoring network, wherein the drone monitoring network is composed of a plurality of distributed monitoring nodes, the plurality of distributed monitoring nodes are numbered, and the number is recorded as i, then i=1, 2, 3, ..., m, wherein m is a natural number greater than 0; The code injection test was conducted on the drone monitoring network, and the remaining code fragments in the drone monitoring network were counted after the code injection test. The content of the code injection test was as follows: Create several attack codes and inject each attack code into the drone monitoring network in turn. The network firewall associated with the drone monitoring network starts intercepting the attack code and marks the part of the attack code that is not successfully intercepted by the network firewall as a residual code fragment. Get the code interception rate of the current network firewall and record the code interception rate as , then we have the following: ; in, It represents the total amount of data of several attack codes injected into the drone monitoring network. It indicates the amount of data corresponding to the remaining code fragments in the drone monitoring network after the network firewall of the drone monitoring network intercepts the attack code; Set a network interception success threshold, and record the network interception success threshold as μ; when When , it marks that the current network firewall interception is successful; when When the network firewall interception fails, it marks the current one; Set an interception cycle and divide the interception cycle into several sub-test cycles. In each sub-test cycle, create several corresponding attack codes and inject them into the drone monitoring network, and then obtain the code interception rate corresponding to each sub-test cycle to count the number of sub-test cycles successfully intercepted by the network firewall. If the number of sub-test cycles successfully intercepted by the network firewall exceeds 2 / 3 of the total number of all sub-test cycles in the interception cycle, the drone monitoring network is in a safe monitoring environment and no operation is performed; If the number of sub-test cycles successfully intercepted by the network firewall is less than 2 / 3 or less of the total number of all sub-test cycles under the interception cycle, the drone monitoring network is in a dangerous monitoring environment, and the network firewall associated with the drone monitoring network needs to be reconfigured.

[0021] The drone monitoring network in a safety monitoring environment, each corresponding distributed monitoring node is used to monitor a monitored path segment of the drone in real time during flight, and then obtain the real-time drone status information under the corresponding monitored path segment; The drone status information includes real-time location information, attitude information, power module status, and sensor status; specifically as follows: Position information: including GPS coordinates, relative ground height, relative starting point position, ground speed and vertical speed; Attitude information: roll angle, pitch angle, yaw angle and angular velocity at each time point during the flight of the drone; Power module status: battery voltage, battery current, remaining battery power, and motor speed of the built-in power unit used to power the drone; Sensor status: The drone is equipped with inertial sensors, air pressure sensors and ultrasonic sensors to monitor their working status in real time; Among them, the inertial sensor is used to measure the acceleration and angular velocity of the drone during flight, the air pressure sensor is used to measure the flight altitude, and the ultrasonic sensor is used to send ultrasonic waves during the flight of the drone and receive the ultrasonic waves reflected by obstacles in real time to locate obstacles and avoid them. A transmission link is constructed between the UAV and the UAV operator, and the UAV status information of the UAV in each path segment to be monitored is transmitted back to the UAV operator through the transmission link and stored by the UAV operator.

[0022] It should be further explained that, in the specific implementation process, the process of determining whether to optimize the viewpoint at the corresponding photography target based on the real-time acquired drone status information and the viewpoint imaging index at the photography target by the viewpoint evaluation model includes: When the result obtained after analyzing the viewpoint imaging index is that there is a viewpoint optimization demand for the corresponding photographic target, and the drone status information of the monitored path segment corresponding to the photographic target meets the preset standard status information, it is decided to optimize the viewpoint at the current photographic target; When the result of analyzing the viewpoint imaging indicators is that there is no viewpoint optimization demand for the corresponding photographic target, or the result of analyzing the viewpoint imaging indicators is that there is a viewpoint optimization demand for the corresponding photographic target, but the drone status information of the monitored path segment corresponding to the photographic target does not meet the standard status information, it is decided not to perform viewpoint optimization at the current photographic target.

[0023] It should be further explained that the preset standard status information is defined by the ground operator corresponding to the drone operation end according to actual conditions, and specifically includes the standard position information of the drone in normal flight state, the standard position information includes the range of the drone's height relative to the ground, the ground speed and the vertical speed corresponding to each other's safe speed range; includes standard attitude information, the standard attitude information includes the parameter ranges corresponding to the roll angle, pitch angle, yaw angle and angular velocity during the flight of the drone; includes standard power module status, the standard power module status includes the normal parameter ranges corresponding to the battery voltage, battery current, remaining battery power and motor speed of the built-in power unit that powers the drone; also includes standard sensor status, the standard sensor status includes the operating parameters of the inertial sensors, air pressure sensors and ultrasonic sensors arranged on the drone when they are in normal working conditions.

[0024] When at least one of the following conditions is met, it is judged that the drone status information does not meet the standard status information. The conditions are as follows: The location information does not conform to standard location information; The posture information does not conform to the standard posture information; The power module status does not meet the standard power module status; The sensor status does not conform to the standard sensor status; Otherwise, it is determined that the drone status information meets the standard status information; The content of the viewpoint optimization is: For the photography target that is determined to be optimized for viewpoint, the photography viewpoint corresponding to the current photography target is taken as the central viewpoint, and the adjacent viewing angle areas of the central viewpoint are set. The number of the adjacent viewing angle areas is 8, which are respectively distributed in the north, south, east, west, northeast, northwest, southeast and southwest of the central viewpoint; The viewpoint imaging score is performed on each adjacent viewpoint area, and then the adjacent viewpoint area with the highest viewpoint imaging score is selected as the best viewpoint area, and the drone is controlled to further search for the best viewpoint in the best viewpoint area, and then the drone is controlled to get close to the photography target at the best viewpoint and shoot the photography target; The content of the optimal viewpoint search is: In the optimal viewing angle area, several viewpoint coordinates are set. At each viewpoint coordinate, a corresponding viewing angle image is taken by the drone, and the viewpoint coordinates are marked on the corresponding viewing angle image. All viewing angle images are obtained for comparison, and a viewing angle image with the best imaging effect is selected, and the viewpoint corresponding to the viewpoint coordinates marked on the viewing angle image is taken as the optimal viewpoint; The viewpoint coordinates of the best viewpoint are sent to the drone, and the drone moves to the best viewpoint.

[0025] The present invention also provides a system for quickly optimizing the viewpoint of a drone's close-up photography, the system comprising: The regional map loading module sets a number of photographic targets on the flight path of the UAV, starts the UAV to fly on the flight path, and loads a three-dimensional map of the area where the UAV passes the corresponding photographic targets on the flight path; The viewpoint model building module is used to establish a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photography target, and the viewpoint evaluation model analyzes the viewpoint imaging index of the drone for the corresponding photography target; The drone real-time information module is used to obtain the drone status information of the drone in real time during the flight and transmit the drone status information back to the drone operation end; The viewpoint optimization module is used to decide whether to optimize the viewpoint at the corresponding photographic target based on the real-time acquired drone status information and the viewpoint imaging index at the photographic target by the viewpoint evaluation model.

[0026] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for quickly optimizing the viewpoint of a drone's close-up photography, characterized in that: The following steps are involved: Step S1: setting a number of photographic targets on the flight path of the UAV, starting the UAV to fly on the flight path, and loading a three-dimensional map of the area where the UAV passes the corresponding photographic targets on the flight path; Step S2: establishing a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photographic target, and using the viewpoint evaluation model to analyze the viewpoint imaging index of the drone for the corresponding photographic target; Step S3: Acquire the drone status information of the drone in real time during the flight, and transmit the drone status information back to the drone operation terminal; Step S4: Determine whether to optimize the viewpoint at the corresponding photography target based on the drone status information obtained in real time and the viewpoint imaging index of the photography target by the viewpoint evaluation model.

2. The method for rapid optimization of the viewpoint of close-up photography of a drone according to claim 1, characterized in that: The process of setting up several photography targets on the drone's flight path includes: Construct the flight path corresponding to the UAV, select several photography targets on the flight path, and label them as 1, 2, 3, ..., k, where k is a natural number greater than 0, and obtain the longitude and latitude coordinates of the photography viewpoint corresponding to each photography target; The longitude and latitude coordinates of the photographic viewpoint corresponding to each photographic target are marked on the flight path, and the path segment corresponding to each two photographic viewpoints on the flight path is taken as a path segment to be monitored.

3. The method for rapid optimization of the viewpoint of close-up photography of a drone according to claim 2, characterized in that: The process of starting the UAV to fly on the flight path and loading the three-dimensional map of the area where the UAV passes the corresponding photography target on the flight path includes: Set the initial working parameters of the drone, start the drone and fly it along the flight path, capture the control signal strength received by the drone in real time in each path segment to be monitored included in the flight path, create a coordinate system, and construct a line chart showing the intensity change trend of the control signal strength of the drone over time in each path segment to be monitored; Set a signal strength threshold, and draw a horizontal determination line on the coordinate system according to the signal strength threshold. The number of coordinate points below the determination line in the strength change trend line graph is recorded as N, where N is a natural number greater than 0. The control signal strength corresponding to the coordinate points on or above the determination line meets the requirement, otherwise, it does not meet the requirement. When N≤3, the signal enhancement module built into the drone performs the control signal enhancement operation; When 4<N≤8, the UAV operator enhances the control signal, builds an encrypted channel between the UAV operator and the UAV, and transmits the control signal to the UAV through the encrypted channel; When N>8, stop the flight of the current drone; When the UAV passes each photographic viewpoint on the flight path, it is determined whether the real-time flight coordinates of the UAV are in the loading area set for the corresponding photographic viewpoint. If so, the three-dimensional map of the area at the corresponding photographic target is automatically loaded. If not, no operation is performed.

4. The method for rapid optimization of the viewpoint of close-up photography of a drone according to claim 3 is characterized in that: The corresponding viewpoint evaluation model is established according to the regional three-dimensional map loaded at the photographic target. The process of analyzing the viewpoint imaging index of the drone to the corresponding photographic target by the viewpoint evaluation model includes: Parse the regional three-dimensional map loaded at each photographic target, and then obtain the building information of the target area corresponding to each photographic target, the building information is composed of several buildings in the target area, and construct a viewpoint evaluation model of the regional three-dimensional map corresponding to each photographic target, the viewpoint evaluation model is composed of several model input nodes and several corresponding model output nodes; The AR rendering information of any building in the three-dimensional map of the corresponding area is input into the model input node, and then any model output node calculates the viewpoint imaging index corresponding to the ideal situation according to the AR rendering information of the currently input building, and inputs the building information corresponding to each photographic target currently acquired by the drone into the viewpoint evaluation model, thereby obtaining the viewpoint imaging index under the actual situation in each target area; The viewpoint imaging indicators under ideal and actual conditions are analyzed, and then the viewpoint imaging score of the drone at each photographic target is obtained. According to the results of the viewpoint imaging score, it is analyzed whether there is a viewpoint optimization requirement for the corresponding photographic target.

5. The method for rapid optimization of the viewpoint of close-up photography of a drone according to claim 4, characterized in that: The process of obtaining the drone status information of the drone in real time during flight and transmitting the drone status information back to the drone operation end includes: Build a drone monitoring network, which consists of several distributed monitoring nodes. Perform code injection test on the drone monitoring network. If the code injection test passes, no operation is performed. If it fails, reconfigure the network firewall associated with the drone monitoring network. Each distributed monitoring node corresponding to the drone monitoring network is used to monitor a monitored path segment of the drone in real time during flight, and then obtain the drone status information under the corresponding monitored path segment; A transmission link is constructed between the UAV and the UAV operator, and the UAV status information of the UAV in each path segment to be monitored is transmitted back to the UAV operator through the transmission link and stored by the UAV operator.

6. The method for rapid optimization of the viewpoint of close-up photography of a drone according to claim 5, characterized in that: The process of deciding whether to optimize the viewpoint at the corresponding photography target based on the real-time acquired drone status information and the viewpoint imaging index of the photography target by the viewpoint evaluation model includes: When the result obtained after analyzing the viewpoint imaging index is that there is a viewpoint optimization demand for the corresponding photographic target, and the drone status information of the monitored path segment corresponding to the photographic target meets the preset standard status information, it is decided to optimize the viewpoint at the current photographic target; When the result of analyzing the viewpoint imaging indicators is that there is no viewpoint optimization demand for the corresponding photographic target, or the result of analyzing the viewpoint imaging indicators is that there is a viewpoint optimization demand for the corresponding photographic target, but the drone status information of the monitored path segment corresponding to the photographic target does not meet the standard status information, it is decided not to perform viewpoint optimization at the current photographic target.

7. The method for rapid optimization of the viewpoint of close-up photography of a drone according to claim 6, characterized in that: The process of viewpoint optimization includes: For the photography target that is determined to be optimized for viewpoint, the photography viewpoint corresponding to the current photography target is taken as the central viewpoint, and the adjacent viewing angle areas of the central viewpoint are set. The number of adjacent viewing angle areas is 8, which are distributed in the north, south, east, west, northeast, northwest, southeast and southwest of the central viewpoint; The viewpoint imaging score is performed on each adjacent viewpoint area, and then the adjacent viewpoint area with the highest viewpoint imaging score is selected as the best viewpoint area, and the UAV is controlled to conduct further best viewpoint search in the best viewpoint area, and then the UAV is controlled to get close to the photographic target at the best viewpoint and shoot the photographic target.

8. A system for quickly optimizing the viewpoint of a drone when it is close to photography, used to implement the method for quickly optimizing the viewpoint of a drone when it is close to photography as described in any one of claims 1 to 7, characterized in that: The system includes: The regional map loading module sets a number of photographic targets on the flight path of the UAV, starts the UAV to fly on the flight path, and loads a three-dimensional map of the area where the UAV passes the corresponding photographic targets on the flight path; The viewpoint model building module is used to establish a corresponding viewpoint evaluation model according to the regional three-dimensional map loaded at the photographic target, and the viewpoint evaluation model analyzes the viewpoint imaging index of the drone for the corresponding photographic target; The drone real-time information module is used to obtain the drone status information of the drone in real time during the flight and transmit the drone status information back to the drone operation end; The viewpoint optimization module is used to decide whether to optimize the viewpoint at the corresponding photographic target based on the real-time acquired drone status information and the viewpoint imaging index at the photographic target by the viewpoint evaluation model.

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