Moving target monitoring method based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle monitoring, and discloses an unmanned aerial vehicle-based moving target monitoring method, which comprises the following steps of: acquiring positioning data of all resident points, monitoring data of all moving targets and sensing data of all time points, classifying to form a data set, constructing a three-dimensional coordinate system, analyzing the three-dimensional coordinate of each resident point, and determining the three-dimensional coordinate of each resident point; the method comprises the following steps: generating a corresponding coordinate data set, analyzing the change trend of each moving target, generating a corresponding difference data set and a monitoring report, performing multi-dimensional analysis on high sensitivity of the target, calculating a resistance index of the unmanned aerial vehicle when the unmanned aerial vehicle flies at each time point, analyzing the power consumption required by the unmanned aerial vehicle to return to a take-off point, and generating a corresponding endurance coefficient; whether the unmanned aerial vehicle continues to track and monitor the moving target or not is judged, the corresponding alarm signal is output, and the intelligent tracking safety is high.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) monitoring, and in particular to a mobile target monitoring method based on an UAV. Background Art

[0002] Drone mobile target monitoring refers to the process of using drone platforms carrying various sensor devices to detect, identify, track, and collect data on moving targets on the ground or in the air. This technology enables efficient real-time monitoring and is particularly suitable for target monitoring tasks in complex environments. Drone mobile target monitoring technology is widely used in fields such as traffic monitoring and disaster inspections, and is characterized by its efficiency, flexibility, and strong real-time performance. Drones are typically equipped with visual sensors such as high-resolution cameras and infrared cameras, and use image recognition algorithms to detect and track targets. For example, a fully automatic embedded system based on YOLOv4-tiny combines a binocular camera with a deep learning algorithm to achieve autonomous and stable tracking in complex environments.

[0003] Currently, traditional drone-based mobile target monitoring lacks in-depth analysis capabilities and cannot analyze the changing trends of mobile targets in multiple dimensions in real time. When controlling a drone to perform a tracking mission, it is necessary to manually judge whether the battery life is sufficient to support the drone to return to the take-off point, which may cause the drone to crash due to insufficient power, resulting in tracking failure. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a mobile target monitoring method based on drones, which has the advantages of high sensitivity in multi-dimensional target analysis and strong security in intelligent tracking. It solves the problem that traditional mobile target monitoring methods based on drones cannot analyze the changing trends of mobile targets in real time and have a high tracking failure rate.

[0005] According to a first aspect of the present invention, a method for monitoring a moving target based on a drone is provided.

[0006] The method includes:

[0007] The positioning data set is collected by connecting the drone to the network. The positioning data set includes the positioning data of all the station points of the drone. The expression of the positioning data set is {D1 s 、D2 s 、D3 s 、...、Dn s}, D1 s To Dn sRepresents the positioning data of the UAV from the first to the nth station point. The first station point is the take-off point of the UAV. The positioning data includes GPS coordinates, altitude above the ground and battery value. s represents the specific time of obtaining a single set of positioning data. The target data set is collected by connecting the UAV to the network. The target data set includes the monitoring data of all mobile targets. The expression of the target data set is {M1 t 、M2 t 、M3 t ,...,Mb t}, M1 t to Mb t represents the monitoring data of the first to bth moving targets, including target distance, target characteristics, target size, moving speed and moving direction, and t represents the specific time of obtaining the monitoring data of a single moving target; the environmental data set is collected by connecting the drone to the network, and the environmental data set includes the sensor data of the drone at all time points. The expression of the environmental data set is {H1 d 、H2 d 、H3 d 、...、Hj d}, H1 d To Hj d It represents the sensing data of the UAV from the first to the ,th time point. The sensing data includes temperature and wind speed. d represents the specific time of obtaining a single set of sensor data.

[0008] Based on the positioning data set, a three-dimensional coordinate system ZB is constructed, the three-dimensional coordinates of each dwelling point are analyzed, and the corresponding coordinate data group Zbsj is generated. According to the pre-set fixed-duration monitoring period Q, combined with the target data set, the change trend of each moving target is analyzed, and the corresponding difference data group Cysj and monitoring report are generated; based on the positioning data set and the environmental data set, the drag index Zlzs of the drone at each time point in flight is calculated, the power consumption required for the drone to return to the take-off point is analyzed, the corresponding endurance coefficient Xhxs is generated, and it is determined whether the drone continues to track and monitor the moving target, and the corresponding alarm signal is output.

[0009] Furthermore, the calculation process of the coordinate data set Zbsj is as follows:

[0010] Extract the positioning data D1 of the first dwell point in the positioning data set s The GPS coordinates of the first station point are marked as (LAT1, LON1), and the height above the ground of the first station point is marked as GD1;

[0011] The GPS coordinates of the first station point are used as the origin O, the east-west direction is the x-axis, the north-south direction is the y-axis, and the vertical direction is the z-axis to construct a three-dimensional coordinate system ZB;

[0012] Extract the positioning data Di of the i-th residence point in the positioning data set s and mark the GPS coordinates of the i-th station as (LAT i ,LON i ), the height above the ground of the i-th station is marked as GD i In the three-dimensional coordinate system ZB, the three-dimensional coordinates of the i-th station point are (x i ,y i , z i );

[0013]

[0014] In the formula, Zbsj represents the coordinate data set, Indicates that the positioning data of the second to nth dwell points are substituted into the three-dimensional coordinate system ZB to generate the corresponding three-dimensional coordinates, LAT i -LAT1 represents the latitude difference between the ith station and the first station, that is, the distance x between the ith station and the first station on the x-axis. i ,LON i -LON1 represents the longitude difference between the i-th station point and the first station point, that is, the distance y between the i-th station point and the first station point on the y-axis i , GD i -GD1 represents the difference between the height of the ith dwelling point from the ground and the height of the first dwelling point from the ground, that is, the distance z between the ith dwelling point and the first dwelling point on the z-axis. i .

[0015] Furthermore, the calculation process of the difference data set Cysj is as follows:

[0016] Extract the monitoring data of the kth mobile target in the target data set and classify and mark it according to the monitoring period Q;

[0017] The target distance of the kth moving target in the first monitoring period Q is marked as The target feature of the kth moving target is marked as The target size of the kth moving target is The moving speed of the kth moving target is marked as The moving direction of the kth moving target is marked as

[0018] The target distance of the kth moving target in the second monitoring period Q is marked as The target feature of the kth moving target is marked as The target size of the kth moving target is The moving speed of the kth moving target is marked as The moving direction of the kth moving target is marked as

[0019]

[0020] In the formula, Cysj represents the difference data group, Indicates the difference between the Q target distance in the second monitoring cycle and the Q target distance in the first monitoring cycle. It represents the intersection of the target feature Q of the second monitoring period and the target feature Q of the first monitoring period. Indicates the difference between the target size Q in the second monitoring cycle and the target size Q in the first monitoring cycle. It represents the difference between the moving speed of Q in the second monitoring period and the moving speed of Q in the first monitoring period. It represents the intersection of the moving direction of the second monitoring period Q and the moving direction of the first monitoring period Q.

[0021] Further: when the difference in target distance in the difference data set Cysj is greater than 15 meters, a distance abnormality report is generated;

[0022] The difference data set Cysj and When there is inconsistency, a feature change report is generated;

[0023] When the target size in the difference data set Cysj is greater than 1 meter, a size change report is generated;

[0024] When the moving speed in the difference data group Cysj is greater than 10 m / s, a moving abnormality report is generated;

[0025] The difference data set Cysj and When there is inconsistency, a target steering report is generated.

[0026] Furthermore, the calculation process of the resistance index Zlzs is as follows:

[0027] Extract the sensor data at the e-th time point in the environmental data set and mark the temperature at the e-th time point as wd e , mark the wind speed at the e-th time point as fs e ;

[0028] FZ e =0.5×KM×C×WM×(fs e ) 2

[0029] In the formula, KM represents the air density, C represents the reference coefficient for the shape and aerodynamic resistance of the drone, 0.5 and C are constants, WM represents the windward area of ​​the drone, 0.5×KM×C×WM×(fs e ) 2 Indicates that according to the laws of aerodynamics, the wind resistance value FZ at the e-th time point is obtained e ;

[0030] Zlzs=α(wd e -cw)+βFZ e

[0031] In the formula, Zlzs represents the drag index Zlzs, cw represents the reference temperature for safe flight of the drone, and wd e -cw represents the difference between the temperature at the e-th time point and the reference temperature, α represents the weight for the temperature difference, β represents the weight for the wind resistance value, α and β are both constants, and α+β=1, α(wd e -cw)+βFZ e It means that according to the weights of α and β, the temperature and wind resistance value are combined to obtain the drag index of the drone when it is flying at the e-th time point.

[0032] Furthermore, the calculation process of the endurance coefficient Xhxs is as follows:

[0033] Extract the battery value of the drone at the e-th time point in the positioning dataset and mark it as dl e ;

[0034]

[0035] In the formula, Xhxs represents the endurance coefficient, LZ represents the conversion coefficient for the resistance index, LZ×Zlzs represents the conversion of the resistance index into the power consumption rate, SLC represents the straight-line distance between the drone and the take-off point, and v represents the maximum flight speed of the drone. Indicates the time it takes for the drone to return to the take-off point. The power consumption rate multiplied by the duration is the power consumption required for the drone to return to the take-off point. It represents the ratio of the power consumption required for the drone to return to the take-off point to the power value at the e-th time point.

[0036] Furthermore: When the endurance coefficient Xhxs ≥ 1, it means that the remaining battery value of the drone is insufficient to continue tracking and monitoring the moving target, and the corresponding battery alarm signal is output.

[0037] According to a second aspect of the present invention, a mobile target monitoring system based on a drone is provided.

[0038] The system includes:

[0039] Data acquisition module and intelligent monitoring module;

[0040] The data acquisition module is composed of a positioning data unit, a target data unit and an environment data unit. The positioning data unit is connected to the drone via a network to collect a positioning data set. The positioning data set includes the positioning data of all the drone's stationary points. The target data unit is connected to the drone via a network to collect a target data set. The target data set includes the monitoring data of all mobile targets. The environment data unit is connected to the drone via a network to collect an environment data set. The environment data set includes the sensor data of the drone at all time points. The data acquisition module transmits the positioning data set, the target data set and the environment data set to the intelligent monitoring module via the network.

[0041] The intelligent monitoring module consists of a positioning analysis unit, a mobile monitoring unit and a tracking management unit. The positioning analysis unit constructs a three-dimensional coordinate system ZB based on the positioning data set, analyzes the three-dimensional coordinates of each stationary point, and generates a corresponding coordinate data set Zbsj. The mobile monitoring unit is set with a fixed-length monitoring period Q, and then combines the target data set to analyze the change trend of each mobile target and generate a corresponding difference data set Cysj and a monitoring report. The tracking management unit calculates the resistance index Zlzs of the drone at each time point of flight based on the positioning data set and the environmental data set, analyzes the power consumption required for the drone to return to the take-off point, generates the corresponding endurance coefficient Xhxs, determines whether the drone continues to track and monitor the mobile target, and outputs the corresponding alarm signal

[0042] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method when executing the program.

[0043] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method when executed by a processor.

[0044] Compared with the existing technology, the present invention provides a method for monitoring mobile targets based on drones, which has the following beneficial effects:

[0045] 1. The present invention connects drones via a network to obtain positioning data of all stationary points, monitoring data of all moving targets, and sensor data of all time points, and classifies them into positioning data sets, target data sets, and environmental data sets; constructs a three-dimensional coordinate system ZB based on the positioning data sets, enhances the drone's environmental adaptability, and enables continuous tracking in unfamiliar environments to avoid getting lost due to environmental influences; analyzes the three-dimensional coordinates of each stationary point and generates a corresponding coordinate data set Zbsj, a fixed-time monitoring period Q, and then combines the target data sets to analyze the changing trends of each moving target, and generates a corresponding difference data set Cysj and a monitoring report; analyzes the changing trends of target distance, target characteristics, target size, moving speed, and moving direction in real time during movement, and has high sensitivity in multi-dimensional target analysis.

[0046] 2. The present invention calculates the drag index Zlzs of the drone at each time point in flight based on the positioning data set and the environmental data set. Ambient temperature and wind speed are key factors affecting the drone's endurance. The power consumption required for the drone to return to the take-off point is analyzed, and a corresponding endurance coefficient Xhxs is generated to determine whether the drone continues to track and monitor the moving target. When the endurance coefficient Xhxs ≥ 1, it indicates that the remaining power value of the drone is insufficient to continue tracking and monitoring the moving target, and a corresponding power alarm signal is output, which ensures strong intelligent tracking security. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0048] Figure 1 A flowchart of a method for monitoring a moving target based on a drone according to an embodiment of the present disclosure is shown;

[0049] Figure 2 A block diagram of a mobile target monitoring system based on a drone according to an embodiment of the present disclosure is shown;

[0050] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] Because traditional drone-based mobile target monitoring lacks in-depth analysis capabilities and cannot analyze the changing trends of mobile targets in multiple dimensions in real time, when controlling the drone to perform tracking tasks, it is necessary to manually judge whether the battery life is sufficient to support the drone to return to the take-off point. This may cause the drone to crash due to insufficient battery, resulting in tracking failure. Therefore, please refer to Figure 1 , provides a mobile target monitoring method based on UAV, including:

[0053] S1, collect positioning data sets through network connection of drones, the positioning data sets include positioning data of all the station points of drones, the expression of the positioning data sets is {D1 s 、D2 s 、D3 s 、...、Dn s}, D1 s To Dn s Represents the positioning data of the UAV from the first to the nth station point. The first station point is the take-off point of the UAV. The positioning data includes GPS coordinates, altitude above the ground and battery value. s represents the specific time of obtaining a single set of positioning data. The target data set is collected by connecting the UAV to the network. The target data set includes the monitoring data of all mobile targets. The expression of the target data set is {M1 t 、M2 t 、M3 t ,...,Mb t}, M1 t to Mb t represents the monitoring data of the first to bth moving targets, including target distance, target characteristics, target size, moving speed and moving direction, and t represents the specific time of obtaining the monitoring data of a single moving target; the environmental data set is collected by connecting the drone to the network, and the environmental data set includes the sensor data of the drone at all time points. The expression of the environmental data set is {H1 d 、H2 d 、H3 d 、...、Hj d}, H1 d To Hj d It represents the sensing data of the UAV from the first to the ,th time point. The sensing data includes temperature and wind speed. d represents the specific time of obtaining a single set of sensor data.

[0054] S2, based on the positioning data set, constructs a three-dimensional coordinate system ZB, analyzes the three-dimensional coordinates of each dwelling point, and generates the corresponding coordinate data group Zbsj. According to the preset fixed-duration monitoring period Q, combined with the target data set, analyzes the change trend of each moving target, and generates the corresponding difference data group Cysj and monitoring report; based on the positioning data set and the environmental data set, calculates the drag index Zlzs of the drone at each time point of flight, analyzes the power consumption required for the drone to return to the take-off point, generates the corresponding endurance coefficient Xhxs, determines whether the drone continues to track and monitor the moving target, and outputs the corresponding alarm signal.

[0055] See also Figure 2 ,A mobile target monitoring system based on UAV, the system includes a data acquisition module and an intelligent monitoring module;

[0056] The data acquisition module consists of a positioning data unit, a target data unit, and an environment data unit. The positioning data unit connects to the UAV through the network to collect the positioning data set. The positioning data set includes the positioning data of all the UAV's station points. The expression of the positioning data set is {D1 s 、D2 s 、D3 s 、...、Dn s}, D1 s To Dn s Indicates the positioning data of the drone from the first to the nth dwelling point. The first dwelling point is the take-off point of the drone. The positioning data includes GPS coordinates, altitude above the ground, and battery value. s represents the specific time of obtaining a single set of positioning data. The coordinates of the dwelling points are stored in a timely manner to effectively prevent the drone from getting lost.

[0057] The target data unit connects to the UAV through the network to collect the target data set. The target data set includes the monitoring data of all mobile targets. The expression of the target data set is {M1 t 、M2 t 、M3 t ,...,Mb t}, M1 t to Mb t represents the monitoring data of the first to bth moving targets. The monitoring data includes target distance, target characteristics, target size, moving speed and moving direction. Specifically, target distance refers to the tracking distance between the UAV and the moving target. Target characteristics include geometric shape, color and texture. Target size refers to the target volume or area. t represents the specific time of obtaining the monitoring data of a single moving target.

[0058] The environmental data unit collects environmental data sets through the network connection of the UAV. The environmental data sets include the sensor data of the UAV at all time points. The expression of the environmental data set is {H1 d、H2 d 、H3 d 、...、Hj d}, H1 d To Hj d represents the sensing data of the UAV from the first to the jth time point. The sensing data includes temperature and wind speed. d represents the specific time when a single set of sensor data is obtained. Ambient temperature and wind speed are key factors affecting the endurance of the UAV.

[0059] The data acquisition module transmits the positioning data set, target data set and environmental data set to the intelligent monitoring module through the network;

[0060] The intelligent monitoring module consists of a positioning analysis unit, a mobile monitoring unit, and a tracking management unit. The positioning analysis unit constructs a three-dimensional coordinate system ZB based on the positioning data set, analyzes the three-dimensional coordinates of each station point, and generates the corresponding coordinate data set Zbsj. The calculation process is as follows:

[0061] Extract the positioning data D1 of the first dwell point in the positioning data set s The GPS coordinates of the first station point are marked as (LAT1, LON1), and the height above the ground of the first station point is marked as GD1;

[0062] The GPS coordinates of the first station point are used as the origin O, the east-west direction is the x-axis, the north-south direction is the y-axis, and the vertical direction is the z-axis to construct a three-dimensional coordinate system ZB;

[0063] Extract the positioning data Di of the i-th residence point in the positioning data set s and mark the GPS coordinates of the i-th station as (LAT i ,LON i ), the height above the ground of the i-th station is marked as GD i In the three-dimensional coordinate system ZB, the three-dimensional coordinates of the i-th station point are (x i ,y i , z i );

[0064]

[0065] In the formula, Zbsj represents the coordinate data set, Indicates that the positioning data of the second to nth dwell points are substituted into the three-dimensional coordinate system ZB to generate the corresponding three-dimensional coordinates, LAT i -LAT1 represents the latitude difference between the ith station and the first station, that is, the distance x between the ith station and the first station on the x-axis. i ,LON i-LON1 represents the longitude difference between the i-th station point and the first station point, that is, the distance y between the i-th station point and the first station point on the y-axis i , GD i -GD1 represents the difference between the height of the ith dwelling point from the ground and the height of the first dwelling point from the ground, that is, the distance z between the ith dwelling point and the first dwelling point on the z-axis. i , which enhances the drone's ability to adapt to the environment. It can still continue tracking in unfamiliar environments and avoid getting lost due to environmental influences;

[0066] The mobile monitoring unit is set with a fixed monitoring period Q. It then analyzes the changing trend of each mobile target in combination with the target data set and generates the corresponding difference data set Cysj and monitoring report. The calculation process is as follows:

[0067] Extract the monitoring data of the kth mobile target in the target data set and classify and mark it according to the monitoring period Q;

[0068] The target distance of the kth moving target in the first monitoring period Q is marked as The target feature of the kth moving target is marked as The target size of the kth moving target is The moving speed of the kth moving target is marked as The moving direction of the kth moving target is marked as

[0069] The target distance of the kth moving target in the second monitoring period Q is marked as The target feature of the kth moving target is marked as The target size of the kth moving target is The moving speed of the kth moving target is marked as The moving direction of the kth moving target is marked as

[0070]

[0071] In the formula, Cysj represents the difference data group, Indicates the difference between the Q target distance in the second monitoring cycle and the Q target distance in the first monitoring cycle. It represents the intersection of the target feature Q of the second monitoring period and the target feature Q of the first monitoring period. Indicates the difference between the target size Q in the second monitoring cycle and the target size Q in the first monitoring cycle. It represents the difference between the moving speed of Q in the second monitoring period and the moving speed of Q in the first monitoring period. It represents the intersection of the moving direction of the second monitoring cycle Q and the moving direction of the first monitoring cycle Q, and analyzes the changing trends of the target distance, target characteristics, target size, moving speed and moving direction in real time during the movement process;

[0072] When the target distance difference in the difference data set Cysj is greater than 15 meters, a distance abnormality report is generated. and When the difference is not consistent, a feature change report is generated. When the target size in the difference data group Cysj is greater than 1 meter, a size change report is generated. When the moving speed in the difference data group Cysj is greater than 10m / s, a movement abnormality report is generated. and When there is inconsistency, a target steering report is generated, and the multi-dimensional analysis target has high sensitivity;

[0073] The tracking management unit calculates the drag index Zlzs of the drone at each time point based on the positioning data set and the environmental data set, analyzes the power consumption required for the drone to return to the take-off point, and generates the corresponding endurance coefficient Xhxs. The calculation process is as follows:

[0074] Extract the sensor data at the e-th time point in the environmental data set and mark the temperature at the e-th time point as wd e , mark the wind speed at the e-th time point as fs e ;

[0075] FZ e =0.5×KM×C×WM×(fs e ) 2

[0076] In the formula, KM represents the air density, C represents the reference coefficient for the shape and aerodynamic resistance of the drone, 0.5 and C are constants, WM represents the windward area of ​​the drone, 0.5×KM×C×WM×(fs e ) 2 Indicates that according to the laws of aerodynamics, the wind resistance value FZ at the e-th time point is obtained e ;

[0077] Zlzs=α(wd e -cw)+βFZ e

[0078] In the formula, Zlzs represents the drag index Zlzs, cw represents the reference temperature for safe flight of the drone, and wd e -cw represents the difference between the temperature at the e-th time point and the reference temperature, α represents the weight for the temperature difference, β represents the weight for the wind resistance value, α and β are both constants, and α+β=1, α(wd e-cw)+βFZ e It indicates that the drag index of the UAV at the e-th time point is obtained according to the α and β weights, the temperature and the wind resistance value;

[0079] Extract the battery value of the drone at the e-th time point in the positioning dataset and mark it as dl e ;

[0080]

[0081] In the formula, Xhxs represents the endurance coefficient, LZ represents the conversion coefficient for the resistance index, LZ×Zlzs represents the conversion of the resistance index into the power consumption rate, SLC represents the straight-line distance between the drone and the take-off point, and v represents the maximum flight speed of the drone. Indicates the time it takes for the drone to return to the take-off point. The power consumption rate multiplied by the duration is the power consumption required for the drone to return to the take-off point. It represents the ratio of the power consumption required for the drone to return to the take-off point to the power value at the e-th time point;

[0082] The tracking management unit determines whether the drone continues to track and monitor the moving target based on the endurance coefficient Xhxs. When the endurance coefficient Xhxs ≥ 1, it means that the remaining battery value of the drone is insufficient to continue tracking and monitoring the moving target, and the corresponding battery alarm signal is output. The intelligent tracking is highly secure.

[0083] Example 1: In this experiment, the station point at 30° north latitude and 117° east longitude was selected as the experimental object. This station point is the take-off point of the drone. At this time, the drone's altitude is 0 meters above the ground. After the drone performs a two-hour tracking mission, the GPS coordinates of the station point are 30.1° north latitude and 117.2° east longitude, and the altitude is 0 meters above the ground. After the mission is completed, the three-dimensional coordinates of the drone's station point are calculated as follows:

[0084]

[0085] In the three-dimensional coordinate system ZB, after the UAV completes the mission, the three-dimensional coordinates of the station point are (0.1, 0.2, 0).

[0086] Example 2: In this experiment, a drone with 70% remaining battery power was selected as the experimental object. After testing, the resistance index at the current time point was 12, the conversion coefficient for the resistance index was 0.03, the straight-line distance between the drone and the take-off point was 15,000 meters, and the maximum flight speed of the drone was 500 meters per minute. The calculation formula for the drone's endurance coefficient Xhxs is as follows:

[0087]

[0088] In the formula, Xhxs represents the endurance coefficient, and 0.03×12 represents the conversion of the resistance index into a power consumption rate, which is 0.36% per minute. Indicates the time it takes for the drone to return to its take-off point, which is 30 minutes. It represents the power consumption rate multiplied by the duration, and the power consumption required for the drone to return to the take-off point is 32.4%. 0.5 represents the ratio of the power consumption required for the drone to return to the take-off point to the remaining power.

[0089] According to an embodiment of the present disclosure, the present invention also provides an electronic device and a readable storage medium.

[0090] Figure 3 A schematic block diagram of an electronic device that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] The electronic device includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in ROM 302 or a computer program loaded from storage unit 308 into RAM 303. RAM 303 can also store various programs and data required for the operation of the electronic device. Computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to bus 304.

[0092] Multiple components in the electronic device are connected to the I / O interface 305, including an input unit 306, such as a keyboard, mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, optical disk, etc.; and a communication unit 309, such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 301 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the drone-based mobile target monitoring method. For example, in some embodiments, the drone-based mobile target monitoring method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the drone-based mobile target monitoring method described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the drone-based mobile target monitoring method by any other suitable means (e.g., via firmware).

[0094] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of the present disclosure, a readable storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of readable storage media can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0098] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0099] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0100] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A mobile target monitoring method based on an unmanned aerial vehicle, characterized in that: include: The positioning data set is collected by connecting the drone to the network. The positioning data set includes the positioning data of all the stationing points of the drone. The expression of the positioning data set is {D1 s 、D2 s 、D3 s 、...、Dn s }, D1 s To Dn s represents the positioning data of the first to nth station points of the UAV. The first station point is the take-off point of the UAV. The positioning data includes GPS coordinates, altitude from the ground and battery value. s represents the specific time of obtaining a single set of positioning data. The target data set is collected by connecting the UAV to the network. The target data set includes the monitoring data of all mobile targets. The expression of the target data set is {M1 t 、M2 t 、M3 t , ..., Mb t }, M1 t to Mb t represents the monitoring data of the first to bth moving targets, including target distance, target characteristics, target size, moving speed and moving direction, and t represents the specific time for obtaining the monitoring data of a single moving target; The environmental data set is collected by connecting the drone to the network. The environmental data set includes the sensor data of the drone at all time points. The expression of the environmental data set is {H1 d 、H2 d 、H3 d 、...、Hj d }, H1 d To Hj d It represents the sensing data of the UAV from the first to the jth time point. The sensing data includes temperature and wind speed. d represents the specific time of obtaining a single set of sensing data. According to the positioning data set, a three-dimensional coordinate system ZB is constructed, the three-dimensional coordinates of each dwelling point are analyzed, and the corresponding coordinate data group Zlsj is generated. According to the preset fixed-duration monitoring period Q, combined with the target data set, the change trend of each moving target is analyzed, and the corresponding difference data group Cysj and monitoring report are generated; according to the positioning data set and the environmental data set, the drag index Zlzs of the drone at each time point is calculated, the power consumption required for the drone to return to the take-off point is analyzed, the corresponding endurance coefficient Xhxs is generated, it is determined whether the drone continues to track and monitor the moving target, and the corresponding alarm signal is output.

2. The method for monitoring a moving target based on an unmanned aerial vehicle according to claim 1, characterized in that: The calculation process of the coordinate data set Zbsj is as follows: Extract the positioning data D1 of the first dwell point in the positioning data set s , and mark the GPS coordinates of the first station point as (LAT1, LON1), and the height above the ground of the first station point as GD1; The GPS coordinates of the first station point are used as the origin O, the east-west direction is the x-axis, the north-south direction is the y-axis, and the vertical direction is the z-axis to construct a three-dimensional coordinate system ZB; Extract the location data Di of the i-th residence point in the location data set s , and mark the GPS coordinates of the i-th station as (LAT i ,LON i ), the height above the ground of the i-th station is marked as GD i In the three-dimensional coordinate system ZB, the three-dimensional coordinates of the i-th station point are (x i ,y i , z i ); In the formula, Zbsj represents the coordinate data set, Indicates that the positioning data of the second to nth dwelling points are substituted into the three-dimensional coordinate system ZB to generate the corresponding three-dimensional coordinates, LAT i -LAT1 represents the latitude difference between the ith station and the first station, that is, the distance x between the ith station and the first station on the x-axis. i ,LON i -LON1 represents the longitude difference between the ith dwelling point and the first dwelling point, that is, the distance y between the ith dwelling point and the first dwelling point on the y-axis i , GD i -GD1 represents the difference between the height of the ith dwelling point from the ground and the height of the first dwelling point from the ground, that is, the distance z between the ith dwelling point and the first dwelling point on the z-axis i .

3. A method for monitoring a moving target based on an unmanned aerial vehicle according to claim 2, characterized in that: The calculation process of the difference data set Cysj is as follows: Extract the monitoring data of the kth mobile target in the target data set and classify and mark it according to the monitoring period Q; The target distance of the kth moving target in the first monitoring period Q is marked as The target feature of the kth moving target is marked as The target size of the kth moving target is The moving speed of the kth moving target is marked as The moving direction of the kth moving target is marked as The target distance of the kth moving target in the second monitoring period Q is marked as The target feature of the kth moving target is marked as The target size of the kth moving target is The moving speed of the kth moving target is marked as The moving direction of the kth moving target is marked as In the formula, Cysj represents the difference data group, It indicates the difference between the Q target distance in the second monitoring cycle and the Q target distance in the first monitoring cycle. represents the intersection of the target feature Q in the second monitoring period and the target feature Q in the first monitoring period, It indicates the difference between the target size of Q in the second monitoring cycle and the target size of Q in the first monitoring cycle. It represents the difference between the moving speed of Q in the second monitoring period and the moving speed of Q in the first monitoring period. It represents the intersection of the moving direction of the second monitoring cycle Q and the moving direction of the first monitoring cycle Q.

4. The method for monitoring a moving target based on an unmanned aerial vehicle according to claim 3, characterized in that: When the difference in target distance in the difference data set Cysj is greater than 15 meters, a distance abnormality report is generated; The difference data set Cysj and When there is inconsistency, a feature change report is generated; When the target size in the difference data set Cysj is greater than 1 meter, a size change report is generated; When the moving speed in the difference data group Cysj is greater than 10 m / s, a moving abnormality report is generated; The difference data set Cysj and When there is inconsistency, a target steering report is generated.

5. The method for monitoring a moving target based on an unmanned aerial vehicle according to claim 4, characterized in that: The calculation process of the resistance index Zlzs is as follows: Extract the sensor data at the e-th time point in the environmental data set and mark the temperature at the e-th time point as wd e , mark the wind speed at the e-th time point as fs e ; FZ e =0.5×KM×C×WM×(fs e ) 2 In the formula, KM represents air density, C represents the reference coefficient for the shape and aerodynamic resistance of the drone, 0.5 and C are constants, WM represents the windward area of ​​the drone, 0.5×KM×C×WM×(fs e ) 2 Indicates that according to the laws of aerodynamics, the wind resistance value FZ at the e-th time point is obtained e ; Zlzs=α(wd e -cw)+βFZ e In the formula, Zlzs represents the drag index Zlzs, cw represents the reference temperature for safe flight of the drone, and wd e -cw represents the difference between the temperature at the e-th time point and the reference temperature, α represents the weight for the temperature difference, β represents the weight for the wind resistance value, α and β are both constants, and α+β=1, α(wd e -cw)+βFZ e It means that according to the α and β weights, the temperature and wind resistance value are combined to obtain the drag index of the drone when it is flying at the e-th time point.

6. The method for monitoring a moving target based on an unmanned aerial vehicle according to claim 5, characterized in that: The calculation process of the endurance coefficient Xhxs is as follows: Extract the battery value of the drone at the e-th time point in the positioning dataset and mark it as dl e ; In the formula, Xhxs represents the endurance coefficient, LZ represents the conversion coefficient for the drag index, LZ×Zlzs represents the conversion of the drag index into the power consumption rate, SLC represents the straight-line distance between the drone and the take-off point, and v represents the maximum flight speed of the drone. Indicates the time it takes for the drone to return to the take-off point. It means the power consumption rate multiplied by the duration, and the power consumption required for the drone to return to the take-off point is obtained. It represents the ratio of the power consumption required for the drone to return to the take-off point to the power value at the e-th time point.

7. The method for monitoring a moving target based on an unmanned aerial vehicle according to claim 6, characterized in that: When the endurance coefficient Xhxs≥1, it means that the remaining power value of the drone is insufficient to continue tracking and monitoring the moving target, and a corresponding power alarm signal is output.

8. A mobile target monitoring system based on drone, characterized in that: include: Data acquisition module and intelligent monitoring module; The data acquisition module is composed of a positioning data unit, a target data unit and an environment data unit. The positioning data unit is connected to the drone through a network to collect a positioning data set, and the positioning data set includes the positioning data of all the stationary points of the drone. The target data unit is connected to the drone through a network to collect a target data set, and the target data set includes the monitoring data of all mobile targets. The environment data unit is connected to the drone through a network to collect an environment data set, and the environment data set includes the sensor data of the drone at all time points. The data acquisition module transmits the positioning data set, the target data set and the environment data set to the intelligent monitoring module through the network; The intelligent monitoring module consists of a positioning analysis unit, a mobile monitoring unit and a tracking management unit. The positioning analysis unit constructs a three-dimensional coordinate system ZB according to the positioning data set, analyzes the three-dimensional coordinates of each residence point, and generates a corresponding coordinate data group Zbsj. The mobile monitoring unit is provided with a monitoring period Q of a fixed duration, and then analyzes the change trend of each mobile target in combination with the target data set, and generates a corresponding difference data group Cysj and a monitoring report. The tracking management unit calculates the resistance index Zlzs of the drone at each time point of flight according to the positioning data set and the environmental data set, analyzes the power consumption required for the drone to return to the take-off point, generates a corresponding endurance coefficient Xhxs, determines whether the drone continues to track and monitor the mobile target, and outputs a corresponding alarm signal.

9. An electronic device, characterized in that: include: at least one processor; A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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