Airway determination method and device, electronic equipment and computer program product

Abstract

The invention discloses an air route determination method and device, electronic equipment and a computer program product. The method relates to the field of low-altitude economy, and comprises the following steps: acquiring historical flight data acquired by a historical unmanned aerial vehicle in a historical time period, and acquiring historical base station signal data of a communication base station passed by the historical unmanned aerial vehicle, the historical flight data being acquired by a positioning assembly of the historical unmanned aerial vehicle; the historical base station signal data is collected by an airborne component of the historical unmanned aerial vehicle; acquiring area data of a target area, inputting the historical flight data, the historical base station signal data and the area data into a signal prediction model, and processing to obtain an electromagnetic signal intensity graph; and route data are obtained, a target route is constructed according to the electromagnetic signal intensity graph and the route data, and the target route refers to a flight route for controlling the target unmanned aerial vehicle within the preset time period. According to the invention, the technical problem of low precision of route planning when the route of the target unmanned aerial vehicle is planned in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of low-altitude economy, in particular to a flight path determination method and device, electronic equipment and computer program product. BACKGROUND

[0002] With the development of low-altitude economy, the application of unmanned aerial vehicle technology in the fields of logistics distribution, power inspection, emergency communication, etc. has been unprecedentedly expanded. However, the large-scale and commercial operation of unmanned aerial vehicles in low-altitude environment puts forward strict requirements on the continuity, reliability and low delay of wireless communication link. Although wireless public network occupies a dominant position in beyond line of sight unmanned aerial vehicle control and data backhaul due to its extensive coverage and mature network architecture, the public network mainly serves ground users. When facing three-dimensional low-altitude environment, urban buildings will form a "canyon effect", and mountains will be blocked and reflected, so that low-altitude wireless signal transmission encounters multiple obstacles: signal reflection and scattering between high-rise buildings increase the uncertainty and interference of communication link, and signal attenuation and multipath effect caused by mountains and hills further deteriorate signal quality, resulting in complexity of signal coverage in three-dimensional low-altitude environment. Traditional two-dimensional map cannot accurately represent the low-altitude signal environment, especially cannot capture the characteristics of signal strength change with height and its fluctuation in space-time dimension.

[0003] Most current unmanned aerial vehicle flight path planning systems mainly focus on obstacle avoidance, flight efficiency and airspace compliance. The communication model embedded in the unmanned aerial vehicle is often based on two-dimensional coverage assumption, ignoring the influence of unique signal propagation law in low-altitude environment, network dynamic load and sudden interference. Especially in the case of significant signal difference between urban high-rise buildings and underground space, the two-dimensional coverage model cannot guide the unmanned aerial vehicle to adjust the flight height to obtain the best signal quality. In addition, the static model cannot respond to the changes of real-time network state in time, resulting in inconsistency between the pre-planned flight path and the actual signal environment. The lack of flight path planning mechanism considering communication quality as a continuous and optimized index only meets the primary demand of connection existence, and cannot guarantee high-level communication needs such as high-definition image transmission and low-delay instruction reception.

[0004] In order to solve the above problems, the related technology proposes an unmanned aerial vehicle path planning method, which aims to reduce communication interruption time and improve task execution efficiency in complex dynamic environment, but it does not deeply solve the problems of signal change with height and real-time perception of network load. In addition, the path planning technology of unmanned aerial vehicle cluster is also proposed, which focuses on cluster management and cooperative flight, but lacks the mechanism of optimizing the communication strength of a single unmanned aerial vehicle, and fails to provide an effective solution to complex communication environment.

[0005] In view of the technical problem that the accuracy of the planned flight path is low when planning the flight path of the target unmanned aerial vehicle in the related technology, no effective solution has been proposed so far. SUMMARY

[0006] The main purpose of the present application is to provide a route determination method and device, electronic equipment and computer program product, to solve the technical problem of low accuracy of planned route in related art.

[0007] In order to achieve the above purpose, according to one aspect of the present application, a route determination method is provided. The method comprises: obtaining historical flight data collected by a historical unmanned aerial vehicle in a historical time period, and obtaining historical base station signal data of a communication base station passed through by the historical unmanned aerial vehicle, wherein the historical flight data is collected by a positioning component deployed on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an airborne component deployed on the historical unmanned aerial vehicle; obtaining regional data of a target region, inputting the historical flight data, the historical base station signal data and the regional data into a signal prediction model, and processing to obtain an electromagnetic signal intensity map, wherein the target region refers to a region passed through by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used to indicate the signal intensity of the target region; obtaining route data, and constructing a target route according to the electromagnetic signal intensity map and the route data, wherein the target route refers to a flight route of the target unmanned aerial vehicle in the preset time period.

[0008] Further, before inputting the historical flight data, the historical base station signal data and the regional data into the signal prediction model and processing to obtain the electromagnetic signal intensity map, the method further comprises: obtaining a structure function, and constructing a signal prediction model based on the structure function, wherein the structure function is used to indicate the statistical characteristics of the change of signal intensity with spatial position; inputting the historical flight data, the historical base station signal data and the regional data into the signal prediction model and processing to obtain the electromagnetic signal intensity map comprises: performing grid division on the regional data by the signal prediction model to obtain a grid set, wherein the regional data at least includes one of the following: a regional map of the target region, a building model and communication base station position information; performing interpolation calculation on each grid in the grid set based on the historical base station signal data by the signal prediction model to obtain a signal intensity value of each grid in the grid set; merging the signal intensity value of each grid in the grid set to obtain a signal intensity distribution, and performing visual processing on the signal intensity distribution to obtain the electromagnetic signal intensity map.

[0009] Further, the acquiring the route data comprises: extracting task data from the route generation instruction in a case that the route generation instruction is received, wherein the route generation instruction is used to indicate a task position of the target UAV, the task data is used to indicate a task start point and an end point position of the target UAV; extracting signal data associated with the electromagnetic signal intensity map, taking the signal data as an electromagnetic constraint, taking the task data as a task constraint, and taking the region data as a geometric constraint, and combining the signal data, the task data and the region data of the target region to obtain the route data.

[0010] Further, the constructing the target route according to the electromagnetic signal intensity map and the route data comprises: extracting a region map of the target region and a building model from the route data, fusing the region map, the building model and the electromagnetic signal intensity map to obtain a flight environment model; acquiring an initial route target, and performing path planning on the flight environment model according to the initial route target to obtain an initial route, wherein the route target is used to indicate a flight target of the target UAV, the initial route target at least comprises one of a flight time target and a signal intensity target, and the initial route is associated with a flight time parameter and a signal intensity parameter; determining a route weight proportion value, and performing parameter adjustment on the initial route according to the route weight proportion value to obtain the target route.

[0011] Further, the performing path planning on the flight environment model according to the initial route target to obtain the initial route comprises: acquiring a path search algorithm, determining M candidate paths according to the task data in the route data by the path search algorithm, wherein each candidate path is associated with a flight path, a path smoothness and a signal intensity, M is a positive integer; acquiring a cost function, calculating a function value of each candidate path by the cost function to obtain M path function values, wherein the cost function is constructed by a flight path parameter, a path smoothness parameter and a signal intensity parameter; screening a minimum path function value from the M path function values, and determining a candidate path corresponding to the minimum path function value as the initial route.

[0012] Further, after the target route is constructed according to the electromagnetic signal intensity map and the route data, the method further comprises: receiving a UAV flight instruction, and determining a take-off and landing type according to the UAV flight instruction, wherein the take-off and landing type at least comprises one of a vertical take-off and landing type and a near-vertical take-off and landing type; performing parameter adjustment on the target UAV according to the take-off and landing type, and controlling the adjusted target UAV to fly.

[0013] Further, after controlling the adjusted target UAV to fly, the method further includes: obtaining flight data of the adjusted target UAV flying, and extracting flight signal data from the flight data, wherein the flight signal data is used to indicate base station signal data of a communication base station passed through by the target UAV; extracting path signal data from the target flight path, and calculating a difference between the path signal data and the flight signal data to obtain a signal difference value, wherein the path signal data is used to predict signal data of a communication base station passed through by the target UAV in the target region; in a case where the signal difference value is greater than or equal to a signal threshold, inputting the flight signal data into a path correction model to obtain an updated electromagnetic signal strength map through processing, and adjusting the target flight path based on the updated electromagnetic signal strength map.

[0014] To achieve the above object, according to another aspect of the present application, a flight path determination device is provided. The device comprises: a first obtaining unit configured to obtain historical flight data collected by a historical UAV in a historical time period, and obtain historical base station signal data of a communication base station passed through by the historical UAV, wherein the historical flight data is collected by a positioning component arranged on the historical UAV, and the historical base station signal data is collected by an on-board component arranged on the historical UAV; a second obtaining unit configured to obtain region data of a target region, and input the historical flight data, the historical base station signal data and the region data into a signal prediction model to obtain an electromagnetic signal strength map through processing, wherein the target region refers to a region passed through by a target UAV in a preset time period, and the electromagnetic signal strength map is used to indicate signal strength of the target region; and a third obtaining unit configured to obtain flight path data, and construct a target flight path according to the electromagnetic signal strength map and the flight path data, wherein the target flight path refers to a flight route of the target UAV in the preset time period.

[0015] According to another aspect of the present application, a computer readable storage medium is also provided, which comprises a stored executable program, wherein when the executable program is run, the computer readable storage medium controls the device where the computer readable storage medium is located to execute any of the above flight path determination methods.

[0016] According to another aspect of the present application, an electronic device is also provided, which comprises one or more processors and a memory, the memory stores an executable program, and the processor is configured to run the program, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above flight path determination methods.

[0017] According to another aspect of the present application, a computer program product is also provided, which comprises a computer program, wherein when the computer program is executed by a processor, any of the above flight path determination methods is implemented.

[0018] In this embodiment, a route determination method is adopted. This involves acquiring historical flight data collected by a historical UAV within a historical time period, and acquiring historical base station signal data from communication base stations along the UAV's route. The historical flight data is collected by a positioning component deployed on the historical UAV, and the historical base station signal data is collected by an onboard component deployed on the historical UAV. Regional data of the target area is acquired. The historical flight data, historical base station signal data, and regional data are input into a signal prediction model to obtain an electromagnetic signal strength map. The target area refers to the area traversed by the target UAV within a preset time period, and the electromagnetic signal strength map indicates the signal strength of the target area. Route data is acquired, and a target route is constructed based on the electromagnetic signal strength map and route data. The target route refers to the flight path of the target UAV within a preset time period. This method solves the technical problem of low accuracy in planning the route of a target UAV in related technologies. By inputting historical flight data, historical base station signal data, and regional data into a signal prediction model to obtain an electromagnetic signal strength map, and constructing the target route based on the electromagnetic signal strength map and route data, the technical effect of improving the accuracy of planning the target UAV's route is achieved. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) used to implement a method for determining flight routes;

[0021] Figure 2 This is a flowchart of a route determination method provided according to an embodiment of this application;

[0022] Figure 3 This is a flowchart of an optional route determination method provided according to an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of a route determination device provided according to an embodiment of this application;

[0024] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] In order to enable the person skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties. For example, interfaces are provided between the system and related users or institutions to provide users with corresponding operation entry for users to choose to agree or refuse automatic decision results. Before obtaining the relevant information, the interface needs to send a request to the aforementioned user or institution, and after receiving the consent information fed back by the aforementioned user or institution, the relevant information is obtained; if the user chooses to refuse, the expert decision process is entered. Users can decode real-time data usage purposes through authorization and have the right to withdraw authorization or delete data at any time. After withdrawing authorization, the system will terminate the relevant data processing within 24 hours.

[0028] It should be noted that the information collected in the present application is information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with relevant laws, regulations and standards in the relevant region, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation entry for users to choose to authorize use or refuse to use.

[0029] Embodiment 1

[0030] According to an embodiment of this application, a method embodiment for determining a route is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) used to implement a method for determining flight routes, such as... Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1 The processor 102 (which may include, but is not limited to, a microprocessor MCU (Microcontroller Unit) or a programmable gate array (FPGA)) is shown as 102a, 102b, ..., 102n. It also includes a memory 104 for storing data and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a keyboard, a cursor control device, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0032] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0033] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage devices corresponding to the route determination method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the route determination method described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) and a network interface, which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0035] The display can be, for example, a touch screen type liquid crystal display (Liquid Crystal Display, LCD), which can enable a user to interact with the user interface of the computer terminal 10 (or mobile device).

[0036] Under the above-mentioned operating environment, the present application provides a route determination method as shown in Figure 2 Figure 2 is a flowchart of the route determination method provided according to the embodiments of the present application, as shown in Figure 2

[0037] In step S201, historical flight data collected by a historical unmanned aerial vehicle in a historical time period is obtained, and historical base station signal data of a communication base station passed through by the historical unmanned aerial vehicle is obtained, wherein the historical flight data is collected by a positioning component arranged on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an on-board component arranged on the historical unmanned aerial vehicle.

[0038] ​​Specifically, in order to determine the flight route of the target UAV, first, the historical flight data of a historical UAV that has performed flight operations in previous flight tasks can be acquired, and historical base station signal data collected by the historical UAV can be acquired, and these data can be constituted into perception data flow, wherein the historical flight data can include flight trajectory, flight speed, flight height, on-board sensor state (such as IMU (Inertial Measurement Unit) data), environmental parameters (such as wind speed, temperature) in the flight task, etc. of the historical UAV, which are collected and recorded in real time by the RTK (Real-Time Kinematic) / GPS (Global Positioning System) positioning component deployed on the historical UAV. The historical base station signal data includes the signal reception condition of the historical UAV when passing through each base station, specific indicators such as reference signal received power, signal-to-interference-plus-noise ratio, etc. which are collected and recorded in real time by the on-board component such as the RF (Radio Frequency) signal receiving and measuring module deployed on the historical UAV; the communication base station refers to the ground communication facility that provides public network signal coverage. In addition, the historical UAV can also be deployed with a UAV platform, an inertial measurement unit, a barometer, an RF signal receiving and measuring module (such as a 4G (Fourth Generation Wireless System) / 5G (Fifth Generation Wireless System) module, that is, an on-board component), a data processing and communication unit. During the flight of the UAV, the signal strength parameters (such as RSRP (Reference Signal Received Power), SINR (Signal-to-Interference-plus-Noise Ratio)) of multiple base stations in the area passed through are measured in real time by the on-board component, and these information are bound with the high-precision position information (longitude, latitude, altitude) collected by the positioning component to form real-time perception data flow. The on-board end can also measure the attitude, speed and acceleration information of the UAV itself to provide flight state constraints for subsequent path planning.

[0039] By analyzing the flight route and flight state of the unmanned aerial vehicle in the historical flight task, and analyzing the signal strength change of the unmanned aerial vehicle passing through each base station, the flight characteristics of the unmanned aerial vehicle at different altitude layers under different geographical conditions can be understood, and a signal strength distribution graph in three-dimensional space, i.e. a signal field model, can be constructed. The signal field model not only depicts the signal coverage area, but also reveals the law of signal strength change with height, as well as the influence of terrain and buildings on signal propagation, so as to predict and plan the signal quality of the unmanned aerial vehicle on a specific flight path, and realize the safety of the flight task and the reliability of the communication link.

[0040] In step S202, the region data of the target region is obtained, and the historical flight data, the historical base station signal data and the region data are input into a signal prediction model to obtain an electromagnetic signal strength graph, wherein the target region refers to a region passed through by the target unmanned aerial vehicle in a preset time period, and the electromagnetic signal strength graph is used to indicate the signal strength of the target region.

[0041] It should be noted that the target region refers to the geographical region of the target unmanned aerial vehicle in the near future in a preset time period. The selection of this region is based on the planning of the unmanned aerial vehicle flight task, and can include the starting point, the end point and the key regions possibly passed through on the way. The region data includes the region map of the target region, the three-dimensional building model in the target region, the weather condition, the network infrastructure distribution (such as the position of 4G / 5G base station) and other information. These data are used to accurately simulate the signal propagation path and evaluate the factors such as signal shielding, reflection and attenuation.

[0042] Specifically, after obtaining the related data of the historical unmanned aerial vehicle flight, the detailed geographical and network information of the target region can be obtained, and the historical flight data, the historical base station signal data and the region data are input into a signal prediction model. The signal prediction model is based on the principle of wireless communication and statistical method, and is constructed by combining the historical flight data, the historical base station signal data and the region data. After considering various physical effects of signal propagation, the model can comprehensively analyze the signal strength distribution at different altitudes and different geographical positions in the target region, and then output the predicted signal strength at any position in the target region, so as to obtain the electromagnetic signal strength graph. The generated electromagnetic signal strength graph can indicate the signal strength at different positions and different altitudes in the target region, and is presented in the form of color spectrum, such as red region representing the strongest signal and blue region representing the weakest signal, and the intermediate color band representing the transition region of signal strength. This helps the unmanned aerial vehicle to select the optimal signal path during flight, avoid signal blind area, and improve the communication quality and safety of the flight task.

[0043] In step S203, the route data is obtained, and a target route is constructed according to the electromagnetic signal strength graph and the route data, wherein the target route refers to the flight route of the target unmanned aerial vehicle in a preset time period.

[0044] Specifically, after obtaining the electromagnetic signal intensity map of the target area, the key parameters such as the starting point, the ending point, the flight height, the flight speed, and the flight direction in the unmanned aerial vehicle task planning can be acquired, that is, the route data is acquired, and then the flight route of the target unmanned aerial vehicle in the preset time period is constructed according to the electromagnetic signal intensity map and the route data, that is, the target route is obtained.

[0045] It should be noted that when constructing the target route, the flight path can be generated by using a heuristic search algorithm (such as an improved A algorithm) or an optimization algorithm (such as a genetic algorithm, simulated annealing, etc.). In the path generation process, the signal intensity distribution in the electromagnetic signal intensity map can be used as a constraint condition to select the flight route with the best signal intensity while meeting the task parameters, so as to avoid the weak signal area, reduce the risk of communication interruption, and improve the stability and reliability of the flight task. First, the route data is read, and then the data is combined with the electromagnetic signal intensity map, and the comprehensive cost of each possible path is calculated by using a cost function, including the flight distance, the path smoothness, and the total signal intensity along the path. Then, through iterative calculation and optimization, the path with the lowest cost, that is, the target route with the optimal signal and meeting the flight efficiency, is found. Once the target route planning is completed, the unmanned aerial vehicle can fly according to the planned path.

[0046] The method for determining the route provided by the embodiments of the present application acquires historical flight data collected by a historical unmanned aerial vehicle in a historical time period and historical base station signal data of a communication base station passed through by the historical unmanned aerial vehicle, wherein the historical flight data is collected by a positioning component deployed on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an on-board component deployed on the historical unmanned aerial vehicle; acquires regional data of a target area, inputs the historical flight data, the historical base station signal data, and the regional data into a signal prediction model, and processes to obtain an electromagnetic signal intensity map, wherein the target area refers to an area passed through by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used to indicate the signal intensity of the target area; acquires route data, and constructs a target route according to the electromagnetic signal intensity map and the route data, wherein the target route refers to a flight route of the target unmanned aerial vehicle in the preset time period, thereby solving the technical problem of low accuracy of the planned route of the target unmanned aerial vehicle in the related art, acquiring the electromagnetic signal intensity map by inputting the historical flight data, the historical base station signal data, and the regional data into the signal prediction model, constructing the target route according to the electromagnetic signal intensity map and the route data, and thus achieving the technical effect of improving the accuracy of the planned route of the target unmanned aerial vehicle.

[0047] Optionally, in the method for determining a flight path provided in the embodiments of the present application, before the historical flight data, historical base station signal data and regional data are input into the signal prediction model to process the electromagnetic signal intensity map, the method further comprises: obtaining a structure function, and constructing the signal prediction model based on the structure function, wherein the structure function is used to indicate statistical characteristics of signal intensity changes with spatial position; and inputting the historical flight data, historical base station signal data and regional data into the signal prediction model to process the electromagnetic signal intensity map comprises: performing grid division on the regional data by the signal prediction model to obtain a grid set, wherein the regional data at least includes one of the following: a regional map of the target region, a building model and communication base station position information; performing interpolation calculation on each grid in the grid set based on the historical base station signal data by the signal prediction model to obtain a signal intensity value of each grid in the grid set; and merging the signal intensity value of each grid in the grid set to obtain a signal intensity distribution, and performing visual processing on the signal intensity distribution to obtain the electromagnetic signal intensity map.

[0048] It should be noted that the signal prediction model can be constructed based on the structure function, wherein the structure function is a variation function reflecting signal intensity data, and can reflect statistical characteristics of signal intensity changes with spatial position. The variation function can be spherical, exponential, Gaussian, etc., and can be determined by a semi-variogram analysis method. Then, the signal prediction model is constructed based on the determined variation function, wherein the signal prediction model can be an Ordinary Kriging model, which can consider the global average trend of data, so as to predict the signal intensity distribution with spatial correlation.

[0049] After obtaining the signal prediction model, the signal prediction model can be used to predict the signal intensity at different spatial positions in the target region based on the historical base station signal data and the historical flight data, by analyzing the fluctuation mode of signal intensity in these data, and revealing the distribution rule of signal intensity in three-dimensional space, such as signal fluctuation amplitude, correlation distance and direction dependence, etc. Specifically, first, the signal prediction model can be used to divide the target region into three-dimensional grids (voxels), wherein the size and form of the grid are determined according to the signal environment and accuracy requirement, and each grid represents a small region in space, which is used for subsequent signal intensity calculation and prediction. Then, the signal prediction model is used to perform interpolation calculation on the signal intensity value of each grid (voxel), that is, to predict the signal intensity of each voxel to minimize the prediction variance, so as to fill in the regions not directly measured in the historical data, and to predict the signal intensity of these grids.

[0050] Further, the signal strength value obtained by the interpolation operation is assigned to each three-dimensional grid (voxel), and a three-dimensional spatial distribution of signal strength is constructed to obtain a signal strength distribution, which can show the change of signal strength with spatial position. Finally, the signal strength distribution is presented in the form of a heat map, contour map or three-dimensional cloud chart through a visualization platform, for example, color coding (such as red, yellow, blue) representing the high and low of signal strength, thereby obtaining an electromagnetic signal strength map.

[0051] The embodiment constructs a signal prediction model through a structure function, and outputs an electromagnetic signal strength map using the model, which not only fills the gap in signal optimization planning in the prior art, but also provides reliable communication environment information for the flight path planning of the unmanned aerial vehicle, so that the unmanned aerial vehicle can maintain a high-quality communication link during task execution, and the safety and success rate of the flight task are improved.

[0052] Optionally, in the flight path determination method provided in the embodiments of the present application, the flight path data is obtained by extracting task data from the flight path generation instruction in the case where the flight path generation instruction is received, wherein the flight path generation instruction is used to indicate the task position of the target unmanned aerial vehicle, and the task data is the task start point and end point position data of the target unmanned aerial vehicle; signal data associated with the electromagnetic signal strength map is extracted, and the signal data is used as an electromagnetic constraint, the task data is used as a task constraint, and the region data is used as a geometric constraint, and the flight path data is obtained based on the combination of the signal data, the task data and the region data of the target region.

[0053] After the electromagnetic signal strength map is constructed, in order to construct the target flight path, flight path data needs to be obtained. Specifically, first, task data is extracted from the received flight path generation instruction, wherein the instruction can contain basic information of the task, such as task type (patrol, logistics, emergency response, etc.), task time window, and task start point and end point position data. At the same time, the electromagnetic signal strength map can be parsed to extract signal information related to the task start point and end point position and signal strength information along the possible flight path direction, thereby obtaining signal data associated with the electromagnetic signal strength map, wherein the signal data refers to the signal strength value of each position point in the electromagnetic signal strength map, which can reflect the signal coverage that the unmanned aerial vehicle may encounter during flight, including the distribution of signal strength and potential blind or weak areas. The signal quality constraint is provided for the flight path planning algorithm, so that the planned flight path can make the unmanned aerial vehicle fly in an environment with sufficient signal strength, avoid signal loss or communication interruption, and improve the communication stability and safety of the flight task.

[0054] Then, the signal data, the task data and the region data are combined to obtain the flight path data, wherein the flight path data contains all the necessary elements for the flight path planning of the unmanned aerial vehicle, including the start point and end point of the task, the flight recommendation of the optimal signal strength region, and the obstacle information to be avoided, etc.

[0055] The embodiment effectively generates the flight path data by extracting the task data from the flight path generation instruction, combining the signal strength information provided by the electromagnetic signal strength map, and the detailed geographic information of the target area, and clearly defines the geographic range and basic flight direction of the UAV flight task, greatly improves the flight safety and task success rate of the UAV in the complex low-altitude environment, and provides strong technical support for the large-scale UAV.

[0056] Optionally, in the flight path determination method provided by the embodiment of the application, the target flight path is constructed according to the electromagnetic signal strength map and the flight path data, including: extracting a region map and a building model of the target area from the flight path data, fusing the region map, the building model, and the electromagnetic signal strength map to obtain a flight environment model; obtaining an initial flight path target, and performing path planning on the flight environment model according to the initial flight path target to obtain an initial flight path, wherein the flight path target is used to indicate a flight target of the target UAV, and the initial flight path target at least includes one of a flight time target and a signal strength target, and the initial flight path is associated with a flight time parameter and a signal strength parameter; determining a flight path weight proportion value, and performing parameter adjustment on the initial flight path based on the flight path weight proportion value to obtain the target flight path.

[0057] Specifically, when constructing the target flight path, first, the region map and the building model of the target area can be extracted from the flight path data, wherein the region map refers to a detailed geographic information map in the target area, including natural and artificial geographic features such as terrain, road layout, water body, and vegetation condition; and the building model refers to the building layout and form information in the three-dimensional space of the target area, including the height, material, shape, and the like of the building. Then, the region map, the building model, and the electromagnetic signal strength map are superimposed and integrated to generate a comprehensive three-dimensional model containing geographic obstacles and signal strength distribution, that is, the flight environment model is obtained.

[0058] Further, after obtaining the optimization target of the target UAV, that is, obtaining the initial flight path target, the flight environment model is path planned according to the initial flight path target to obtain the initial flight path, wherein the initial flight path target can be the shortest flight time, the optimal signal strength, the smoothest path, and the like. Then, the initial flight path is parameter adjusted according to the flight path weight proportion value determined by the user, so as to generate the target flight path that is more in line with the task demand and the comprehensive optimization target, wherein the flight path weight proportion value refers to the relative importance of different optimization targets (such as flight time and signal strength) in path planning.

[0059] The embodiment extracts a map and a building model of a target area, combines an electromagnetic signal strength map, and constructs a flight environment model that comprehensively reflects the characteristics of the low-altitude environment. Then, a target flight path is generated based on a specific flight path target, which not only provides a more accurate and safe flight path planning for the unmanned aerial vehicle, but also significantly improves the communication stability and efficiency of task execution, opening up a new way for the automation and intelligent control of unmanned aerial vehicles in complex low-altitude environments.

[0060] Optionally, in the method for determining the flight path provided in the embodiment of the application, the path planning is performed on the flight environment model according to the initial flight path target to obtain the initial flight path, which includes: obtaining a path search algorithm, determining M candidate paths from the task data in the flight path data by the path search algorithm, wherein each candidate path is associated with a flight path, a path smoothness, and a signal strength, and M is a positive integer; obtaining a cost function, calculating a function value of each candidate path by using the cost function to obtain M path function values, wherein the cost function is constructed from a flight path parameter, a path smoothness parameter, and a signal strength parameter; and selecting the minimum path function value from the M path function values, and determining the candidate path corresponding to the minimum path function value as the initial flight path.

[0061] Specifically, after obtaining the initial flight path target, first, the candidate paths can be determined from the task data in the flight path data by the path search algorithm, wherein the path search algorithm can be A algorithm, Dijkstra algorithm, RRT (rapidly-exploring random tree) algorithm, etc., and each path is associated with its own flight path, path smoothness, and signal strength information along the path. Then, a cost function is obtained, and a function value of a quantitative index for evaluating the advantages and disadvantages of the candidate paths is calculated by using the cost function, that is, a corresponding path function value is obtained, wherein the cost function is used to quantitatively evaluate the comprehensive cost of each candidate path, and is constructed from a flight path parameter, a path smoothness parameter, and a signal strength parameter. The flight path parameter can be the length or flight time of the path, the path smoothness parameter evaluates the tortuosity of the path, and the signal strength parameter measures the stability of the signal on the path; the smaller the path function value, the better the path performs in terms of flight efficiency, smoothness, and signal strength. Finally, the minimum path function value is selected from the above path function values, and the candidate path corresponding to the minimum path function value is determined as the initial flight path.

[0062] The embodiment selects the optimal initial flight path from a plurality of possible flight paths by obtaining a path search algorithm and a cost function, which not only improves the flight safety of the unmanned aerial vehicle in the complex low-altitude environment, but also significantly improves the communication stability and efficiency of the flight task.

[0063] Optionally, in the method for determining a flight path provided in the embodiments of the present application, after the target flight path is constructed according to the electromagnetic signal intensity map and the flight path data, the method further comprises: receiving a flight instruction of the UAV, determining a take-off and landing type according to the flight instruction of the UAV, wherein the take-off and landing type at least includes one of the following: a vertical take-off and landing type and a near-vertical take-off and landing type; adjusting parameters of the target UAV according to the take-off and landing type, and controlling the adjusted target UAV to fly.

[0064] Specifically, after obtaining the target flight path, the take-off and landing type can be determined according to the task details of the UAV to be performed in the case of receiving the flight instruction of the UAV, wherein the flight instruction of the UAV can include start and end points, flight height, speed, etc.; the take-off and landing type refers to the operation mode of the UAV taking off and landing, including the vertical take-off and landing type and the near-vertical take-off and landing type, the vertical take-off and landing type refers to the UAV taking off and landing vertically in any plane without the need for a runway; the near-vertical take-off and landing type needs limited horizontal movement to assist attitude adjustment on the basis of vertical take-off and landing. Once the take-off and landing type is determined, the specific flight parameters of the UAV need to be adjusted according to this type, including but not limited to adjusting the acceleration at take-off, the control rules of flight height, and the deceleration strategy at landing. For example, for the vertical take-off and landing type, the vertical speed control strategy of the UAV needs to be adjusted, the thrust distribution of the rotor needs to be optimized, and stable take-off and landing in a limited space needs to be ensured; for the near-vertical take-off and landing type, the horizontal movement of the UAV during the take-off and landing stage needs to be additionally considered to avoid possible obstacles. Through accurate adjustment of the flight parameters of the UAV, the performance of the UAV can be maximized, and the risk of accidents caused by improper operation can be reduced. After all the parameter adjustments and planning are completed, the target UAV can be controlled to start the flight task according to the adjusted parameters, and the flight path with optimal signal strength can be realized.

[0065] The embodiments of the present application receive and analyze the flight instruction of the UAV, determine the take-off and landing type, and finely adjust the flight parameters of the UAV based on the take-off and landing type, thereby realizing safe and stable flight of the UAV in a complex low-altitude environment, improving the adaptability and success rate of the UAV task, and reducing the complexity and risk of the operation of the UAV.

[0066] Optionally, in the method for determining a flight path provided in the embodiments of the present application, after the adjusted target UAV is controlled to fly, the method further comprises: obtaining flight data of the adjusted target UAV flying, and extracting flight signal data from the flight data, wherein the flight signal data is used to indicate base station signal data of a communication base station passed by the target UAV; extracting path signal data from the target flight path, and calculating a difference between the path signal data and the flight signal data to obtain a signal difference, wherein the path signal data is used to predict signal data of a communication base station passed by the target UAV in the target region; in a case where the signal difference is greater than or equal to a signal threshold, inputting the flight signal data into a path correction model to obtain an updated electromagnetic signal strength map, and adjusting the target flight path based on the updated electromagnetic signal strength map.

[0067] Specifically, after the target UAV is controlled to fly, in order to further optimize the flight path, first, all flight data of the adjusted target UAV flying can be obtained, such as position information, speed, acceleration, attitude, battery status, communication status, etc., to provide necessary information support for subsequent real-time signal monitoring and dynamic correction of the flight path. Then, the strength data of the received base station signal, i.e., the flight signal data, is extracted from the flight data.

[0068] Further, the flight signal data is compared with the previously predicted path signal data, and through the feedback of real-time signal strength, the signal quality difference between the actual path and the expected planned path is judged to provide a basis for the correction of the flight path. At this time, the path signal data can be extracted from the target flight path, and the difference between the path signal data and the flight signal data is calculated to evaluate the signal strength difference between the actual flight path of the UAV and the predicted path, thereby obtaining a signal difference. The path signal data refers to the communication base station signal data that should be received by the target UAV during flight, which is predicted according to the target flight path (i.e., the flight path optimized according to the signal strength). When the signal difference is greater than or equal to a signal threshold, the actual signal strength is lower than expected, and the flight path needs to be corrected at this time, i.e., the flight signal data is input into a path correction model to obtain an updated electromagnetic signal strength map, and the target flight path is re-evaluated based on the updated electromagnetic signal strength map to adjust the flight path of the UAV to cope with signal changes, so that the UAV flies in an area with optimal signal strength.

[0069] The embodiment can quickly respond, update the electromagnetic signal strength map, and adjust the flight path to optimize the signal strength when the signal difference exceeds the preset signal threshold by collecting and analyzing the flight signal data of the unmanned aerial vehicle in real time and comparing the signal data with the pre-planned path signal data, greatly improving the flight safety and communication stability of the unmanned aerial vehicle in a complex low-altitude environment, effectively responding to unexpected signal changes encountered during flight, and enabling the unmanned aerial vehicle to always be in the signal optimal region during task execution, thereby improving the efficiency and success rate of task execution.

[0070] The embodiment of the present application also provides an optional flight path determination method, Figure 3 The flowchart of the optional flight path determination method provided by the embodiment of the present application is shown in FIG. 1, and the method comprises the following steps. Figure 3

[0071] In order to generate a signal-optimal flight path, first, the signal strength parameters of multiple communication base stations in the region passed through during the execution of a flight task by a historical unmanned aerial vehicle can be continuously collected by an on-board component, and the collected signal strength data can be bound with high-precision position information (longitude, latitude, and altitude) of the unmanned aerial vehicle to form a real-time perception data stream. A data receiving server on the ground simultaneously receives a pre-stored regional map of a target region, a three-dimensional building model, and base station position information, and constructs a regional data to establish a multi-source information library that comprehensively reflects the signal environment and geographical features of the task region.

[0072] After the above data is received on the ground, data fusion and preprocessing can be performed on the data, and a signal prediction model constituted by a Kriging spatial interpolation technique and a wireless propagation model can be used to process the data to generate a three-dimensional voxelized electromagnetic signal strength map, wherein each voxel represents a small region in space and stores a predicted signal strength value. By dynamically constructing a high-precision three-dimensional signal strength model, the signal coverage condition in the task region can be quantitatively mastered, and reliable signal quality constraints can be provided for flight path planning to avoid planning a path through a signal blind area or an unstable region.

[0073] Further, after a user inputs a flight path generation instruction of a flight task, such as a starting point, an end point, and constraint conditions, through a visual monitoring platform, an optimization algorithm and a heuristic search method can be used by a flight path planner, and a cost function can be used to comprehensively consider the flight distance, path smoothness, and total signal strength along the path to generate a signal-optimal target flight path. The target flight path comprehensively considers the flight distance, path smoothness, and signal strength along the path, can avoid known geographical obstacles and signal blind areas, and meets the path continuity and smoothness. Then, the unmanned aerial vehicle is controlled to execute the flight task according to the target flight path.

[0074] ​In the process of flight of the UAV, signal monitoring and evaluation need to be performed, that is, flight data of the target UAV after adjustment is acquired, flight signal data is extracted from the flight data, path signal data is extracted from the target path, and the path signal data and the flight signal data are compared. If the difference between the two is less than a signal threshold, it indicates that the signal is good or meets the expectation, and the UAV continues to fly. If the difference between the two is greater than or equal to the signal threshold, or it is predicted that the region in front may enter a signal blind area (that is, the problem of signal attenuation or deviation from the expectation occurs), a dynamic path correction mechanism is triggered. At this time, the electromagnetic signal strength map can be incrementally updated by the path correction model according to the latest collected signal data, and the local path can be re-planned and optimized within a millisecond time to generate local avoidance instructions to guide the UAV to fly to a region with stronger signal, thereby realizing the continuity of the flight task and the stability of the communication link.

[0075] The embodiment provides the UAV with a path that takes into account both geographical obstacles and optimized signal strength, significantly enhancing the task reliability and intelligent level of the UAV in a complex low-altitude environment, effectively solving the limitations of traditional path planning techniques in signal coverage depth and dynamic adaptability, and providing strong technical support for the further development and application of UAV technology.

[0076] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0077] Embodiment 2

[0078] The embodiment of the present application also provides a path determination device. It should be noted that the path determination device of the embodiment of the present application can be used to execute the path determination method provided by the embodiment of the present application. The path determination device provided by the embodiment of the present application is introduced as follows.

[0079] According to the embodiment of the present application, a device for implementing the above-mentioned path determination method is also provided, Figure 4 is a schematic diagram of the path determination device provided by the embodiment of the present application, as Figure 4 shown, the device comprises a first acquisition unit 40, a second acquisition unit 41, and a third acquisition unit 42.

[0080] The first obtaining unit 40 is configured to obtain historical flight data collected by a historical unmanned aerial vehicle in a historical time period and obtain historical base station signal data of a communication base station passed through by the historical unmanned aerial vehicle, wherein the historical flight data is collected by a positioning component arranged on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an on-board component arranged on the historical unmanned aerial vehicle;

[0081] The second obtaining unit 41 is configured to obtain region data of a target region, input the historical flight data, the historical base station signal data and the region data into a signal prediction model, and process to obtain an electromagnetic signal intensity map, wherein the target region refers to a region passed through by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used to indicate signal intensity of the target region.

[0082] The third obtaining unit 42 is configured to obtain air route data and construct a target air route according to the electromagnetic signal intensity map and the air route data, wherein the target air route refers to a flight route of the target unmanned aerial vehicle in the preset time period.

[0083] The air route determination apparatus provided by the embodiment of the present application obtains the historical flight data collected by the historical unmanned aerial vehicle in the historical time period through the first obtaining unit 40 and obtains the historical base station signal data of the communication base station passed through by the historical unmanned aerial vehicle, wherein the historical flight data is collected by the positioning component arranged on the historical unmanned aerial vehicle, and the historical base station signal data is collected by the on-board component arranged on the historical unmanned aerial vehicle; the second obtaining unit 41 obtains the region data of the target region, inputs the historical flight data, the historical base station signal data and the region data into the signal prediction model, and processes to obtain the electromagnetic signal intensity map, wherein the target region refers to the region passed through by the target unmanned aerial vehicle in the preset time period, and the electromagnetic signal intensity map is used to indicate the signal intensity of the target region; the third obtaining unit 42 obtains the air route data and constructs the target air route according to the electromagnetic signal intensity map and the air route data, wherein the target air route refers to the flight route of the target unmanned aerial vehicle in the preset time period, thereby solving the technical problem of low accuracy of the planned air route of the target unmanned aerial vehicle in the related art, processing the electromagnetic signal intensity map by inputting the historical flight data, the historical base station signal data and the region data into the signal prediction model, constructing the target air route according to the electromagnetic signal intensity map and the air route data, and further achieving the technical effect of improving the accuracy of the planned air route of the target unmanned aerial vehicle.

[0084] Optionally, in the flight path determination apparatus provided by the embodiments of the present application, the apparatus further comprises a fourth obtaining unit, configured to obtain a structure function before inputting the historical flight data, the historical base station signal data and the region data into the signal prediction model and processing to obtain the electromagnetic signal intensity map, the signal prediction model is constructed based on the structure function, and the structure function is used to indicate statistical characteristics of signal intensity changing with spatial position; the apparatus further comprises a division module, configured to divide the region data by the signal prediction model to obtain a grid set, wherein the region data at least includes one of the following: a region map of the target region, a building model and communication base station position information; a calculation module, configured to perform interpolation calculation on each grid in the grid set based on the historical base station signal data by the signal prediction model to obtain a signal intensity value of each grid in the grid set; and a merging module, configured to merge the signal intensity value of each grid in the grid set to obtain a signal intensity distribution, and perform visual processing on the signal intensity distribution to obtain the electromagnetic signal intensity map.

[0085] Optionally, in the flight path determination apparatus provided by the embodiments of the present application, the third obtaining unit 42 comprises a first extraction module, configured to extract task data from the flight path generation instruction in the case of receiving the flight path generation instruction, wherein the flight path generation instruction is used to indicate a task position of the target UAV, and the task data is task start point and end point position data of the target UAV; and a second extraction module, configured to extract signal data associated with the electromagnetic signal intensity map, take the signal data as electromagnetic constraints, take the task data as task constraints, take region data as geometric constraints, and combine the signal data, the task data and the region data of the target region to obtain the flight path data.

[0086] Optionally, in the flight path determination apparatus provided by the embodiments of the present application, the third obtaining unit 42 comprises a third extraction module, configured to extract a region map of the target region and a building model from the flight path data, and fuse the region map, the building model and the electromagnetic signal intensity map to obtain a flight environment model; a first obtaining module, configured to obtain an initial flight path target, and perform path planning on the flight environment model according to the initial flight path target to obtain an initial flight path, wherein the flight path target is used to indicate a flight target of the target UAV, the initial flight path target at least includes one of the following: a flight time target and a signal intensity target, and the initial flight path is associated with a flight time parameter and a signal intensity parameter; and a determination module, configured to determine a flight path weight proportion value, and perform parameter adjustment on the initial flight path based on the flight path weight proportion value to obtain the target flight path.

[0087] Optionally, in the device for determining a flight path provided in the embodiments of the present application, the third obtaining unit 42 comprises: a second obtaining module, configured to obtain a path search algorithm, and configured to determine M candidate paths according to the task data in the flight path data by using the path search algorithm, wherein each candidate path is associated with a flight path, a path smoothness and a signal strength, and M is a positive integer; a third obtaining module, configured to obtain a cost function, and configured to calculate a function value of each candidate path by using the cost function to obtain M path function values, wherein the cost function is constructed by a flight path parameter, a path smoothness parameter and a signal strength parameter; and a screening module, configured to screen a minimum path function value from the M path function values, and configured to determine a candidate path corresponding to the minimum path function value as an initial flight path.

[0088] Optionally, in the device for determining a flight path provided in the embodiments of the present application, the device further comprises: a receiving unit, configured to receive a UAV flight instruction after constructing the target flight path according to the electromagnetic signal strength map and the flight path data, and configured to determine a take-off and landing type according to the UAV flight instruction, wherein the take-off and landing type at least comprises one of the following: a vertical take-off and landing type and a near-vertical take-off and landing type; and an adjusting unit, configured to perform parameter adjustment on the target UAV according to the take-off and landing type, and configured to control the adjusted target UAV to fly.

[0089] Optionally, in the device for determining a flight path provided in the embodiments of the present application, the device further comprises: a fifth obtaining unit, configured to obtain flight data of the adjusted target UAV after controlling the adjusted target UAV to fly, and configured to extract flight signal data from the flight data, wherein the flight signal data is used to indicate base station signal data of a communication base station passed through by the target UAV; an extracting unit, configured to extract path signal data from the target flight path, and configured to calculate a difference value between the path signal data and the flight signal data to obtain a signal difference value, wherein the path signal data is used to predict signal data of a communication base station passed through by the target UAV in a target area; and an input unit, configured to input the flight signal data into a path correction model in a case where the signal difference value is greater than or equal to a signal threshold value, and configured to process to obtain an updated electromagnetic signal strength map, and configured to adjust the target flight path based on the updated electromagnetic signal strength map.

[0090] It should be noted that the first obtaining unit 40, the second obtaining unit 41 and the third obtaining unit 42 correspond to steps S201 to S203 in Embodiment 1, and the above units have the same instances and application scenarios as the corresponding steps, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware components or software components stored in a memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, …, 102n), and the above units can also be run in the computer terminal 10 provided in Embodiment 1 as a part of the device.

[0091] Embodiment 3

[0092] The embodiment of the present application can provide a computer terminal, which can be any one of computer terminal devices in a computer terminal group. Alternatively, in the embodiment, the computer terminal can be replaced by a mobile terminal or an electronic device or other terminal device.

[0093] Alternatively, in the embodiment, the computer terminal can be located in at least one of a plurality of network devices of a computer network.

[0094] In the embodiment, the computer terminal can execute program codes of the following steps in the route determination method: obtaining historical flight data collected by a historical unmanned aerial vehicle in a historical time period, and obtaining historical base station signal data of communication base stations passed by the historical unmanned aerial vehicle, wherein the historical flight data is collected by a positioning component deployed on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an on-board component deployed on the historical unmanned aerial vehicle; obtaining region data of a target region, inputting the historical flight data, the historical base station signal data and the region data into a signal prediction model, and processing to obtain an electromagnetic signal intensity map, wherein the target region refers to a region passed by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used to indicate signal intensity of the target region; obtaining route data, and constructing a target route according to the electromagnetic signal intensity map and the route data, wherein the target route refers to a flight route of the target unmanned aerial vehicle in the preset time period.

[0095] Alternatively, the computer terminal can execute program codes of the following steps in the route determination method: obtaining a structure function, and constructing a signal prediction model based on the structure function, wherein the structure function is used to indicate statistical characteristics of signal intensity changing with spatial position; performing grid division on the region data by the signal prediction model to obtain a grid set, wherein the region data at least includes one of the following: a region map of the target region, a building model and communication base station position information; performing interpolation calculation on each grid in the grid set based on the historical base station signal data by the signal prediction model to obtain signal intensity values of each grid in the grid set; merging the signal intensity values of each grid in the grid set to obtain a signal intensity distribution, and performing visual processing on the signal intensity distribution to obtain the electromagnetic signal intensity map.

[0096] Optionally, the computer terminal can execute program codes of the following steps in the method for determining a flight path: in the case of receiving a flight path generation instruction, extracting task data from the flight path generation instruction, wherein the flight path generation instruction is used to indicate a task position of a target UAV, and the task data is used to indicate a task start point and an end point position of the target UAV; extracting signal data associated with the electromagnetic signal intensity map, taking the signal data as an electromagnetic constraint, taking the task data as a task constraint, and taking region data as a geometric constraint, and combining the signal data, the task data, and the region data of the target region to obtain flight path data.

[0097] Optionally, the computer terminal can execute program codes of the following steps in the method for determining a flight path: extracting a region map of a target region and a building model from the flight path data, fusing the region map, the building model, and the electromagnetic signal intensity map to obtain a flight environment model; obtaining an initial flight path target, and performing path planning on the flight environment model according to the initial flight path target to obtain an initial flight path, wherein the flight path target is used to indicate a flight target of the target UAV, and the initial flight path target at least includes one of a flight time target and a signal strength target, and the initial flight path is associated with a flight time parameter and a signal strength parameter; determining a flight path weight proportion value, and performing parameter adjustment on the initial flight path based on the flight path weight proportion value to obtain a target flight path.

[0098] Optionally, the computer terminal can execute program codes of the following steps in the method for determining a flight path: obtaining a path search algorithm, and determining M candidate paths from the task data in the flight path data according to the path search algorithm, wherein each candidate path is associated with a flight path, a path smoothness, and a signal strength, M is a positive integer; obtaining a cost function, and calculating a function value of each candidate path by using the cost function to obtain M path function values, wherein the cost function is constructed by a flight path parameter, a path smoothness parameter, and a signal strength parameter; and selecting a minimum path function value from the M path function values, and determining a candidate path corresponding to the minimum path function value as an initial flight path.

[0099] Optionally, the computer terminal can execute program codes of the following steps in the method for determining a flight path: receiving a UAV flight instruction, and determining a take-off and landing type according to the UAV flight instruction, wherein the take-off and landing type at least includes one of a vertical take-off and landing type and a near-vertical take-off and landing type; performing parameter adjustment on the target UAV according to the take-off and landing type, and controlling the adjusted target UAV to fly.

[0100] Optionally, the aforementioned computer terminal can execute program code for the following steps in the route determination method: acquiring adjusted flight data of the target UAV during flight; extracting flight signal data from the flight data, wherein the flight signal data is used to indicate the base station signal data of the communication base stations the target UAV passes through; extracting path signal data from the target route, and calculating the difference between the path signal data and the flight signal data to obtain a signal difference, wherein the path signal data is used to predict the signal data of the communication base stations the target UAV passes through in the target area; if the signal difference is greater than or equal to a signal threshold, inputting the flight signal data into the path correction model, processing it to obtain an updated electromagnetic signal strength map, and adjusting the target route based on the updated electromagnetic signal strength map.

[0101] Optionally, Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (Only one is shown) processor 502, memory 504, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0102] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the route determination method and apparatus in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the aforementioned route determination method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] The processor can access the information and application programs stored in the memory via the transmission device to execute the steps described above in the route determination method.

[0104] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5different configurations are shown.

[0105] Those skilled in the art can understand that all or part of the steps of various methods in the above embodiments can be completed by instructing the terminal device related hardware through a program, and the program can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0106] Embodiment 4

[0107] The embodiments of the present application also provide a storage medium. Optionally, in the embodiment, the storage medium can be used to save the program code executed by the route determination method provided in the embodiment 1.

[0108] Optionally, in the embodiment, the storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0109] Optionally, in the embodiment, the storage medium is configured to store program code for performing the following steps: obtaining historical flight data collected by a historical unmanned aerial vehicle in a historical time period, and obtaining historical base station signal data of a communication base station passed through by the historical unmanned aerial vehicle, wherein the historical flight data is collected by a positioning component deployed on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an on-board component deployed on the historical unmanned aerial vehicle; obtaining region data of a target region, inputting the historical flight data, the historical base station signal data and the region data into a signal prediction model, and processing to obtain an electromagnetic signal intensity map, wherein the target region refers to a region passed through by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used to indicate the signal intensity of the target region; obtaining route data, and constructing a target route according to the electromagnetic signal intensity map and the route data, wherein the target route refers to a flight route of the target unmanned aerial vehicle in the preset time period.

[0110] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute the steps of the route determination method.

[0111] The above embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0112] In the above embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0113] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0114] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0115] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0116] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0117] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method of determining a route, characterized by, The method comprises the following steps: acquiring historical flight data collected by a historical unmanned aerial vehicle in a historical time period, and acquiring historical base station signal data of a communication base station passed through by the historical unmanned aerial vehicle, wherein the historical flight data is collected by a positioning component arranged on the historical unmanned aerial vehicle, and the historical base station signal data is collected by an on-board component arranged on the historical unmanned aerial vehicle; acquiring region data of a target region, inputting the historical flight data, the historical base station signal data and the region data into a signal prediction model, and processing to obtain an electromagnetic signal intensity map, wherein the target region refers to a region passed through by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used for indicating signal intensity of the target region; acquiring route data, and constructing a target route according to the electromagnetic signal intensity map and the route data, wherein the target route refers to a flight route of the target unmanned aerial vehicle in the preset time period.

2. The method of claim 1, wherein, Before the historical flight data, the historical base station signal data and the region data are input into the signal prediction model and processed to obtain the electromagnetic signal intensity map, the method further comprises the following steps: acquiring a structure function, and constructing the signal prediction model based on the structure function, wherein the structure function is used for indicating statistical characteristics of signal intensity changing with spatial position; inputting the historical flight data, the historical base station signal data and the region data into the signal prediction model, and processing to obtain the electromagnetic signal intensity map comprises the following steps: dividing the region data into a grid set by the signal prediction model, wherein the region data at least comprises one of the following: a region map of the target region, a building model and communication base station position information; performing interpolation calculation on each grid in the grid set based on the historical base station signal data by the signal prediction model, to obtain a signal intensity value of each grid in the grid set; merging the signal intensity value of each grid in the grid set to obtain a signal intensity distribution, and performing visual processing on the signal intensity distribution to obtain the electromagnetic signal intensity map.

3. The method of claim 1, wherein, acquiring route data comprises the following steps: extracting task data from a route generation instruction in a case where the route generation instruction is received, wherein the route generation instruction is used for indicating a task position of the target unmanned aerial vehicle, and the task data refers to task start point and end point position data of the target unmanned aerial vehicle; extracting signal data associated with the electromagnetic signal intensity map, taking the signal data as electromagnetic constraints, taking the task data as task constraints, taking the region data as geometric constraints, and combining the signal data, the task data and the region data of the target region to obtain the route data.

4. The method of claim 1, wherein, constructing a target route according to the electromagnetic signal intensity map and the route data comprises the following steps: extracting a region map and a building model of the target region from the route data, and fusing the region map, the building model and the electromagnetic signal intensity map to obtain a flight environment model; obtaining an initial flight target, and performing path planning on the flight environment model according to the initial flight target to obtain an initial route, wherein the flight target is used to indicate a flight target of the target UAV, and the initial flight target comprises at least one of a flight time target and a signal strength target, and the initial route is associated with a flight time parameter and a signal strength parameter; determining a route weight proportion value, and performing parameter adjustment on the initial route based on the route weight proportion value to obtain the target route.

5. The method of claim 4, wherein, The path planning on the flight environment model according to the initial flight target to obtain an initial route comprises: obtaining a path search algorithm, and determining M candidate paths from the path search algorithm according to task data in the route data, wherein each candidate path is associated with a flight path, a path smoothness, and a signal strength, M is a positive integer; obtaining a cost function, and calculating a function value of each candidate path by using the cost function to obtain M path function values, wherein the cost function is constructed by a flight path parameter, a path smoothness parameter, and a signal strength parameter; selecting a minimum path function value from the M path function values, and determining a candidate path corresponding to the minimum path function value as the initial route.

6. The method of claim 1, wherein, After the target route is constructed according to the electromagnetic signal strength map and the route data, the method further comprises: receiving a UAV flight instruction, and determining a take-off and landing type according to the UAV flight instruction, wherein the take-off and landing type comprises at least one of a vertical take-off and landing type and a near-vertical take-off and landing type; performing parameter adjustment on the target UAV according to the take-off and landing type, and controlling the adjusted target UAV to fly.

7. The method of claim 6, wherein, After the adjusted target UAV is controlled to fly, the method further comprises: obtaining flight data of the adjusted target UAV, and extracting flight signal data from the flight data, wherein the flight signal data is used to indicate base station signal data of a communication base station passed through by the target UAV; extracting path signal data from the target route, and calculating a difference value between the path signal data and the flight signal data to obtain a signal difference value, wherein the path signal data is used to predict signal data of a communication base station passed through by the target UAV in the target area; in a case where the signal difference value is greater than or equal to a signal threshold value, inputting the flight signal data into a path correction model to obtain an updated electromagnetic signal strength map, and adjusting the target route based on the updated electromagnetic signal strength map.

8. A route determination device, characterized by comprising: comprises: a first obtaining unit, configured to obtain historical flight data collected by a historical UAV in a historical time period, and obtain historical base station signal data of a communication base station passed through by the historical UAV, wherein the historical flight data is collected by a positioning component arranged on the historical UAV, and the historical base station signal data is collected by an on-board component arranged on the historical UAV; The second acquisition unit is configured to acquire region data of a target region, input the historical flight data, the historical base station signal data and the region data into a signal prediction model, and process to obtain an electromagnetic signal intensity map, wherein the target region refers to a region passed through by a target unmanned aerial vehicle in a preset time period, and the electromagnetic signal intensity map is used to indicate signal intensity of the target region. The third acquisition unit is configured to acquire air route data, and construct a target air route according to the electromagnetic signal intensity map and the air route data, wherein the target air route refers to a flight route of the target unmanned aerial vehicle in the preset time period.

9. An electronic device, comprising: The method comprises the following steps: a memory storing an executable program; a processor configured to run the program, wherein the program performs the method for determining the air route according to any one of claims 1 to 7 when running.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the steps of the method for determining the air route according to any one of claims 1 to 7. The computer instructions are executed by the processor to implement the steps of the method for determining the air route according to any one of claims 1 to 7.

Images