Signal strength measurement method and apparatus, system, storage medium and product

Abstract

Disclosed in embodiments of the present application are a signal strength measurement method and apparatus, a system, a storage medium and a product. The method comprises: receiving unmanned aerial vehicle pose data of an unmanned aerial vehicle in an area to be measured, and terminal position data collected by a ground test terminal interacting with the unmanned aerial vehicle; on the basis of the unmanned aerial vehicle pose data and the terminal position data, establishing a plurality of position relationship features between the unmanned aerial vehicle and the ground test terminal; and inputting the plurality of position relationship features into a target signal prediction model to obtain the signal strength of said area.

Description

Signal strength detection method, device, system, storage medium, and product

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on January 19, 2024, with application number 202410081498.2 and application name “A signal strength detection method, device and storage medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of wireless technology, and is related to, but not limited to, a signal strength detection method and device, system, storage medium, and product. Background Art

[0004] With the gradual expansion of the drone market, drones have great application prospects in multiple scenarios such as emergency rescue, transportation and logistics, and agricultural plant protection. Good aerial network coverage planning is an important prerequisite for aerial operations such as drone emergency communications. It is necessary to predict the coverage of drone aerial wireless networks and adjust the wireless network base station propagation model in a timely manner based on the prediction results.

[0005] In the related art, a continuous wave (CW) method is set up on the ground and data is collected in a specified scenario. The transmitted signal is received by fixing a CW transmitter and receiver on the ground end. The received transmitted signal is manually exported and then the parameters of the wireless network base station propagation model are calibrated based on the existing ground propagation model (i.e., mathematical formula model, such as OKumra-Hata, Uma, etc.), and network signal prediction is performed based on the calibrated model.

[0006] Because the air-to-ground wireless channel is different from the wireless propagation channel of the stationary transmitting base station on the ground, in the case of emergency rescue, the drone carries the aerial base station and continuously hovers and moves at high speed. In addition, the air-to-ground wireless channel of the fixed-wing drone in the emergency state is a time-varying nonlinear system, which leads to inaccurate model calibration and reduces the accuracy of network signal prediction.

[0007] Summary of the Invention

[0008] To solve the above technical problems, the embodiments of the present application hope to provide a signal strength detection method and device, system, storage medium, and product that can improve the accuracy of network signal prediction.

[0009] The technical solution of this application is achieved as follows:

[0010] This embodiment of the present application provides a signal strength detection method, which includes:

[0011] Receiving drone posture data of the drone in the area to be detected, and terminal position data collected by a ground test terminal interacting with the drone;

[0012] Establishing a plurality of position relationship features between the drone and the ground test terminal according to the drone posture data and the terminal position data;

[0013] The multiple position relationship features are input into a target signal prediction model to obtain the signal strength of the area to be detected.

[0014] An embodiment of the present application provides a signal strength detection device, the device comprising:

[0015] a receiving part configured to receive drone posture data of the drone in the area to be detected, and terminal position data collected by a ground test terminal interacting with the drone;

[0016] An establishing part, configured to establish a plurality of position relationship features between the drone and the ground test terminal according to the drone posture data and the terminal position data;

[0017] The input part is configured to input the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected.

[0018] The present application provides a signal strength detection system, which includes a drone, a ground test terminal, and a signal strength detection device.

[0019] The drone is configured to collect drone posture data;

[0020] The ground test terminal is configured to collect terminal location data;

[0021] The signal strength detection device is configured to establish multiple sample position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data; use the multiple sample position relationship features to train an initial signal prediction model to obtain a target signal prediction model; and use the target signal prediction model to predict the signal strength of the area to be detected.

[0022] An embodiment of the present application provides a signal strength detection device, the device comprising:

[0023] A memory, a processor and a communication bus, wherein the memory communicates with the processor via the communication bus, the memory stores a signal strength detection program executable by the processor, and when the signal strength detection program is executed, the signal strength detection method described above is executed by the processor.

[0024] An embodiment of the present application provides a storage medium storing a computer program for use in a signal strength detection device. When the computer program is executed by a processor, the above-mentioned signal strength detection method is implemented.

[0025] An embodiment of the present application further provides a computer program product, including a computer program, which can be executed by a processor of a signal strength detection device to complete the steps of any of the aforementioned methods.

[0026] The embodiment of the present application provides a signal strength detection method and device, system, storage medium, and product. The signal strength detection method includes: receiving the drone posture data of the drone in the area to be detected, and the terminal position data collected by the ground test terminal that interacts with the drone; establishing multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data; inputting the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected. Using the above method to implement the solution, the signal strength detection device establishes multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data. The multiple position relationship features include multiple types of relationship features such as distance, angle, and channel; so that the multiple types of relationship features can be used to accurately describe the signal parameters of the air-to-ground wireless channel of the fixed-wing drone in the emergency state when the drone is continuously circling and moving at high speed with the aerial base station, which improves the accuracy of network signal prediction.

[0027] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to more clearly understand the technical means of the embodiments of the present application, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Various other advantages and benefits will become apparent to those skilled in the art by reading the detailed description of the preferred embodiment below. The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present application. In the accompanying drawings:

[0029] FIG1 is a flow chart of a signal strength detection method provided in an embodiment of the present application;

[0030] FIG2 is a diagram illustrating an exemplary aerospace propagation model system architecture according to an embodiment of the present application;

[0031] FIG3 is a schematic diagram showing the connection of various modules of an exemplary airborne part provided in an embodiment of the present application;

[0032] FIG4 is a schematic diagram of an exemplary distance calculation principle provided in an embodiment of the present application;

[0033] FIG5 is a schematic diagram of an exemplary angle calculation principle provided in an embodiment of the present application;

[0034] FIG6 is a flowchart of an exemplary propagation model correction method provided in an embodiment of the present application;

[0035] FIG7 is a first schematic diagram of the structure of a signal strength detection device provided in an embodiment of the present application;

[0036] FIG8 is a second schematic diagram of the structure of a signal strength detection device provided in an embodiment of the present application;

[0037] FIG9 is a schematic diagram of the structure of a signal strength detection system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. It should be understood that the specific embodiments described herein are merely intended to explain the present application and are not intended to limit the present application. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0039] The present application provides a signal strength detection method, which is applied to a signal strength detection device. FIG1 is a flow chart of a signal strength detection method provided in an embodiment of the present application. As shown in FIG1 , the signal strength detection method may include:

[0040] S101: Receive drone posture data of the drone in the area to be detected, and terminal position data collected by a ground test terminal interacting with the drone.

[0041] A signal strength detection method provided in an embodiment of the present application is suitable for scenarios where a target signal prediction model is used to predict the signal strength of an area to be detected based on drone posture data and terminal position data.

[0042] In the embodiments of the present application, the signal strength detection device can be implemented in various forms. For example, the signal strength detection device described in the present application can include devices such as mobile phones, cameras, tablet computers, laptop computers, PDAs, portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, and other devices, as well as devices such as digital TVs, desktop computers, and servers.

[0043] It should be noted that the signal strength detection device may also be a cloud platform.

[0044] It should be noted that drone pose data refers to the drone's pose data, while terminal location data refers to the location data of the ground test terminal. Specifically, terminal location data includes information such as the Reference Signal Received Power (RSRP) measured by the ground test terminal and the ground test terminal's location; drone pose data includes information such as the drone's flight attitude and location.

[0045] In an embodiment of the present application, the signal strength detection device can receive drone posture data of the drone in the area to be detected transmitted by a satellite device.

[0046] In this embodiment of the present application, a transmitting base station is provided on the drone. The drone uses the transmitting base station to transmit the drone's position data to a satellite device. The satellite device then transmits the drone's position data to a signal strength detection device via a ground wireless relay. A ground test terminal is connected to the signal strength detection device, and the signal strength detection device can directly obtain the terminal position data from the ground test terminal interacting with the drone.

[0047] For example, the location relationship between drones, satellite equipment, ground wireless relays, ground test terminals, and a cloud platform is shown in Figure 2: the satellite equipment (airborne wireless relays) is located in the aerospace segment, the drone is located in the airborne segment, and the ground wireless relays, ground test terminals, core network, and cloud platform (signal strength detection device) are located in the ground segment. Specifically, the aerospace segment consists of relay equipment such as satellite communications, providing communication traffic for remote beyond-line-of-sight control of drones and backhaul of communication data such as propagation model levels. Leveraging the wireless coverage of satellite communications, drones can maintain uninterrupted cellular communication links at any time and from any location during flight, meeting the propagation model environment acquisition requirements and air-to-ground altitude requirements in various scenarios. The ground segment consists of ground wireless relay stations, a core network, ground test terminals, and a cloud platform. Ground wireless relay stations receive aircraft control information, communication service data, and drone attitude-related data transmitted by aerospace satellites and other relay equipment. Ground wireless relay stations connect to the mobile core network and cloud platform via wireless or wired connections. The ground test terminal collects network data such as RSRP and signal-to-interference-plus-noise ratio (SINR) for propagation model calibration. This data is then transmitted to the cloud platform for data fusion and model calibration along with the drone attitude data received by the cloud platform. The airborne component primarily comprises the drone platform, airborne relay equipment, and airborne base station. Figure 3 shows the connections between these modules: the drone platform, airborne relay equipment module, airborne base station module, aircraft interface port, and Global Positioning System (GPS). The airborne base station primarily transmits propagation signals and relays control signal data. It consists of a baseband processing unit, a radio frequency unit, and an airborne antenna. The airborne antenna primarily receives signals from the base station. The drone platform primarily provides flight testing and carries the terminal and test antenna. The airborne relay equipment primarily transmits and relays base station communication resources. The aircraft interface port, which can be a serial port or Ethernet port, connects to GPS information through the airborne base station.

[0048] In an embodiment of the present application, the signal strength detection device can transmit data acquisition instructions to the ground test terminal and the drone respectively. The ground test terminal sends terminal position data to the signal strength detection device according to the data acquisition instruction; the drone sends drone posture data to the data transmission device through the air wireless relay and the ground wireless relay according to the data acquisition instruction.

[0049] In an embodiment of the present application, the ground wireless relay can send all the posture data of the received drone to the signal strength detection device, and the ground wireless relay can also send the posture data of the drone within a preset time period to the signal strength detection device; the specific details can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0050] It should be noted that the preset time period can be a time period configured in the ground wireless relay, or a time period carried in the data acquisition instruction transmitted by the signal strength detection device, or a time period obtained by the ground wireless relay in other ways. The specific way in which the ground wireless relay obtains the preset time period can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0051] It should be noted that the preset time period can be a data acquisition cycle, that is, the time period between the data acquisition instruction sent by the signal strength detection device and the data acquisition instruction sent last time, or it can be other time periods. The specific preset time period can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0052] S102: Establish multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data.

[0053] In an embodiment of the present application, after the signal strength detection device receives the drone posture data of the drone in the area to be detected and the terminal position data collected by the ground test terminal that interacts with the drone, it establishes multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data.

[0054] In the embodiment of the present application, the multiple position relationship features include multiple types of relationship features such as distance, angle, and channel.

[0055] It should be noted that the multiple position relationship features established are shown in Table 1:

[0056] Table 1

[0057] In an embodiment of the present application, the signal strength detection device establishes a process of multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data, including: preprocessing the drone posture data to obtain preprocessed drone posture data; preprocessing the terminal position data to obtain preprocessed terminal position data; and establishing multiple position relationship features based on the preprocessed drone posture data and the preprocessed terminal position data.

[0058] In an embodiment of the present application, the signal strength detection device preprocesses the drone posture data in the same manner as the terminal position data.

[0059] In the embodiment of the present application, the preprocessing method includes eliminating points with excessively large or small signals in the drone posture data or the terminal position data (such as points with RSRP exceeding -140 and less than -40), eliminating abnormal position points in the drone posture data or the terminal position data (such as data points with no longitude and latitude positions recorded by GPS, or data points with longitude and latitude drifting out of a preset area, etc.), deleting duplicate points, etc. The preprocessing method can also be other methods. The specific preprocessing method can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0060] In an embodiment of the present application, multiple positional relationship characteristics of the signal strength detection device include: the horizontal distance between the UAV and the ground test terminal, the vertical distance between the UAV and the ground test terminal, the straight-line distance between the UAV and the ground test terminal, the first azimuth angle of the ground test terminal relative to the UAV, the first elevation angle of the ground test terminal relative to the UAV, the second azimuth angle of the ground test terminal relative to the antenna, the second elevation angle of the ground test terminal relative to the antenna and the multi-dimensional dynamic antenna gain.

[0061] In an embodiment of the present application, the signal strength detection device establishes a plurality of position relationship features based on the preprocessed UAV posture data and the preprocessed terminal position data, including: obtaining the first longitude and latitude of the UAV and the first altitude of the UAV from the preprocessed UAV posture data; obtaining the second longitude and latitude of the ground test terminal, the second altitude of the ground test terminal, and the roll angle and heading angle of the UAV from the preprocessed terminal position data; determining the horizontal distance between the UAV and the ground test terminal based on the first longitude and latitude, the second longitude and latitude and the radius of the earth; determining the vertical distance between the UAV and the ground test terminal based on the first altitude and the second altitude; determining the vertical distance between the UAV and the ground test terminal based on the horizontal distance and the vertical distance. Determine the straight-line distance between the UAV and the ground test terminal; determine the first azimuth angle of the ground test terminal relative to the UAV based on the first longitude and latitude and the second longitude and latitude; determine the first elevation angle of the ground test terminal relative to the UAV based on the horizontal distance and the vertical distance; determine the second azimuth angle of the ground test terminal relative to the antenna based on the first azimuth angle and the roll angle; determine the second elevation angle of the ground test terminal relative to the antenna based on the first elevation angle and the heading angle; determine the multi-dimensional dynamic antenna gain that matches the second azimuth angle and the second elevation angle; determine the horizontal distance, vertical distance, straight-line distance, first azimuth angle, first elevation angle, second azimuth angle, second elevation angle and multi-dimensional dynamic antenna gain as multiple position relationship features.

[0062] It should be noted that the drone is provided with an antenna, also referred to as a drone antenna. Based on the first azimuth angle and the roll angle, a second azimuth angle of the ground test terminal relative to the drone antenna is determined.

[0063] It should be noted that the first longitude and longitude of the UAV include the first longitude and the first latitude; the second longitude and longitude of the ground test terminal include the second longitude and the second latitude.

[0064] In this embodiment of the present application, if the horizontal distance between the drone base station and the ground receiving terminal is d 2D As shown in Figure 4, point A represents the location of the ground test terminal, point B represents the location of the drone, and R represents the average radius of the Earth. Aj and Bj represent the longitudes of points A and B, respectively. Aw and Bw represent the latitudes of points A and B, respectively. c represents the angle between the two endpoints of the "arc" at point C and the center of the Earth.

[0065] Derived from the spherical cosine formula, the angle c calculation formula is shown in formula (1):

[0066] c=arccos(cos(90-Bw)cos(90-Aw)+sin(90-Bw)sin(90-Aw)cos(Bj-Aj)) (1)

[0067] According to formula (2), the horizontal distance d between the UAV and the ground test terminal can be determined: 2D (i.e. Dis_2d in Figure 4). As shown in Figure 5, the three-dimensional distance formula (3) can be used to determine the straight-line distance d between the UAV and the ground test terminal. 3D (i.e. Dis_3d in Figure 5):

[0068] d 2D =R*c (2)

[0069] It should be noted that in formula (3), H UT is the height of the ground test terminal from the ground (i.e. the second height), and H_drone is the height of the drone from the ground (i.e. the first height).

[0070] The vertical distance between the UAV and the ground test terminal, that is, the signal propagation height difference H d As shown in formula (4):

[0071] H d =H_drone-H UT (4)

[0072] Furthermore, formula (3) can be expressed as shown in formula (5):

[0073] The first azimuth between the base station and the drone It can be obtained from formula (6):

[0074] Formula (7) can be used to determine the first elevation angle θ between the UAV and the test terminal relative to the antenna: ue :

[0075] θ ue =arctan(d 2d / H d ) (7)

[0076] In the embodiment of the present application, the second azimuth angle of the ground test terminal relative to the 3D antenna gain (ie, the multi-dimensional dynamic antenna gain) The second elevation angle θ is affected by the roll angle of the UAV's flight attitude (roll angle ) and heading angle (θ uav The second azimuth angle of the terminal relative to the antenna and the second elevation angle θ is the first azimuth angle of the terminal relative to the UAV The first elevation angle θ ue Roll angle of the drone Heading angle θ uav The difference is then divided by the modulus of 2π. The specific calculation method is shown in formulas (8)-(9):

[0077] θ=(θ ue -θ uav )mod(2π) (9)

[0078] In the embodiment of the present application, the angle obtained by formula (8)-(9) θ, searching for the constructed system 3D antenna simulation value (the corresponding relationship between the preset second elevation angle, the preset second azimuth angle and the preset gain) can obtain the dynamic 3D antenna gain value at the corresponding position, that is, the multi-dimensional dynamic antenna gain G Tx3D .

[0079] S103: Input multiple position relationship features into a target signal prediction model to obtain the signal strength of the area to be detected.

[0080] In an embodiment of the present application, the signal strength detection device establishes multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data, and then inputs the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected.

[0081] In an embodiment of the present application, the signal strength detection device inputs multiple position relationship features into a target signal prediction model to obtain the signal strength of the area to be detected, including: screening out at least one target feature from multiple position relationship features; inputting at least one target feature into the target signal prediction model to obtain the target path loss of the area to be detected; and determining the signal strength based on the target path loss.

[0082] In an embodiment of the present application, the process of determining the signal strength based on the target path loss includes: obtaining the signal transmission power of the drone corresponding to the area to be detected, the receiving antenna gain of the ground test terminal corresponding to the area to be detected, and the receiving power when the ground test terminal receives the signal transmitted by the drone; determining the signal strength based on the receiving power, signal transmission power, receiving antenna gain and target path loss.

[0083] In the embodiment of the present application, at least one target feature corresponding to the area to be detected obtained by the signal strength detection device {such as target straight-line distance, target horizontal distance, target first elevation angle, target heading angle, target first azimuth angle}, that is, By feeding the trained XGboost model (i.e., the target signal prediction model), the target path loss Loss_test can be predicted. The signal strength of the area to be detected can be further determined using formula (10), i.e., the level of the network signal P test :

[0084] P test =P Tx_test +G Tx3D_test +G rx_test -Loss_test (10)

[0085] Among them, {G Tx3D_test , P Tx_test , G rx_test} are all known parameters of the test system, G Tx3D_test is the dynamic transmission 3D gain of the antenna in the test scene, and the corresponding gain value can be queried by the constructed 3D gain (that is, it can be determined using Formula 13); P Tx_test is the base station cell transmission power of the test scenario, which is the value that can be tested by the ground test terminal; G rx_test is the receiving antenna gain of the test scenario, and is the parameter configuration value of the device itself; P testThe power value collected by the ground test terminal for the test scenario.

[0086] In the embodiment of the present application, P test That is, the predicted value of the signal strength of the test area given by the target signal prediction model. The signal strength of the test area can be predicted in combination with the posture of the drone, and the optimal flight plan can be given to save flight costs.

[0087] In an embodiment of the present application, the process of a signal strength detection device screening out at least one target feature from multiple position relationship features includes: determining a first path loss; respectively determining the correlation coefficients between multiple position relationship features and the first path loss to obtain multiple correlation coefficients; sorting the multiple correlation coefficients in descending order of numerical values ​​to obtain a sorted sequence; starting from the first sorting position of the sorted sequence, screening a preset number of correlation coefficients to obtain at least one target correlation coefficient; and obtaining at least one target feature corresponding to the at least one target correlation coefficient from the multiple position relationship features.

[0088] In an embodiment of the present application, the first path loss is the path loss during signal transmission between the drones corresponding to the multiple position relationship features and the ground test terminal.

[0089] In an embodiment of the present application, the Pearson correlation coefficient calculation method can be used to determine the correlation coefficients between multiple position relationship characteristics and the first path loss respectively, and obtain multiple correlation coefficients; the Spearman rank correlation coefficient calculation method can also be used to determine the correlation coefficients between multiple position relationship characteristics and the first path loss respectively, and obtain multiple correlation coefficients; other correlation coefficient calculation methods can also be used to determine the correlation coefficients between multiple position relationship characteristics and the first path loss, and obtain multiple correlation coefficients; the specific implementation method can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0090] Exemplarily, the correlation coefficient determination method in formula (11) can be used to determine the correlation coefficients between the multiple position relationship features and the first path loss, respectively, to obtain multiple correlation coefficients:

[0091] It should be noted that X represents the feature vector corresponding to multiple position relationship features, Y represents the first path loss, cov(X, Y) represents the covariance between each position relationship feature and the first path loss, μ X Represents the mean of the position relationship feature, μ Y represents the mean of the first path loss value, σ X Represents the standard deviation of positional relationship characteristics, σ Y Indicates the standard deviation of the first path loss value.

[0092] In an embodiment of the present application, multiple correlation coefficients can be sorted in descending order of numerical values ​​to obtain a sorted sequence; or multiple correlation coefficients can be sorted in ascending order of numerical values ​​to obtain a sorted sequence; the specific sorting method can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0093] In an embodiment of the present application, if multiple correlation coefficients are sorted in descending numerical order to obtain a sorted sequence, then starting from the first sorting position of the sorted sequence, a preset number of correlation coefficients are screened to obtain at least one target correlation coefficient; if multiple correlation coefficients are sorted in ascending numerical order to obtain a sorted sequence, then starting from the last sorting position of the sorted sequence, a preset number of correlation coefficients are screened to obtain at least one target correlation coefficient.

[0094] It should be noted that the preset number can be the number configured in the signal strength detection device, the number transmitted to the signal strength detection device by other devices, or the number obtained by the signal strength detection device in other ways; the specific way in which the signal strength detection device obtains the preset number can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0095] For example, m (preset number) characteristic variables with the largest correlation coefficient with path loss can be selected by sorting in descending order of correlation values, for example: {straight-line distance, horizontal distance, first elevation angle, heading angle, first azimuth angle} as characteristic vectors (at least one target feature) to construct the propagation model.

[0096] In an embodiment of the present application, the number of the at least one target feature is less than or equal to the number of the plurality of positional relationship features.

[0097] In an embodiment of the present application, the process of the signal strength detection device determining the first path loss includes: determining the multi-dimensional dynamic antenna gain; obtaining the cell transmission power of the drone to transmit the signal to the ground test terminal, the receiving antenna gain of the ground test terminal and the receiving power of the ground test terminal receiving the drone transmitted signal; determining the first path loss based on the multi-dimensional dynamic antenna gain, the cell transmission power, the receiving antenna gain and the receiving power.

[0098] In an embodiment of the present application, the cell transmission power of the signal transmitted by the drone to the ground test terminal, the receiving antenna gain of the ground test terminal, and the receiving power of the ground test terminal receiving the signal transmitted by the drone can be obtained from the database, or the cell transmission power can be obtained from the drone, and the receiving antenna gain and receiving power can be obtained from the ground test terminal; the specific method of obtaining the cell transmission power, receiving antenna gain, and receiving power can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0099] In an embodiment of the present application, the process of determining the first path loss based on the multi-dimensional dynamic antenna gain, cell transmit power, receive antenna gain and receive power can be to determine the sum of the multi-dimensional dynamic antenna gain, cell transmit power and receive antenna gain, and then determine the difference from the receive power to obtain the first path loss.

[0100] For example, as shown in formula (12):

[0101] P L =P Tx +P Tx3D +G rx -P Rx (12)

[0102] It should be noted that P L is the first path loss; P Tx is the cell transmission power; P Tx3D is the multi-dimensional dynamic antenna gain; G rx is the receiving antenna gain; P Rx is the received power.

[0103] In an embodiment of the present application, the process of the signal strength detection device determining the multi-dimensional dynamic antenna gain includes: obtaining the correspondence between the preset second elevation angle, the preset second azimuth angle and the preset gain; and determining the multi-dimensional dynamic antenna gain based on the second elevation angle, the second azimuth angle and the correspondence.

[0104] In an embodiment of the present application, the correspondence between the preset second elevation angle, the preset second azimuth angle and the preset gain can be configured in the signal strength detection device, or can be transmitted to the signal strength detection device by other devices, or can be obtained by the signal strength detection device through other methods. The specific method in which the signal strength detection device obtains the correspondence between the preset second elevation angle, the preset second azimuth angle and the preset gain can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0105] Exemplarily, the corresponding relationship between the preset second elevation angle, the preset second azimuth angle and the preset gain is shown in formula (13):

[0106] It should be noted that Φ s is the horizontal angle of the ground test terminal in the base station coordinates (preset second azimuth angle), θ s is the elevation angle of the terminal in the base station coordinates (preset second elevation angle), the horizontal gain and vertical gain of the terminal position are H(Φ s ) and V(θ s). H(0) is the horizontal gain value of the antenna when the horizontal angle is 0 degrees, and H(π) refers to the horizontal gain value of the antenna when the horizontal angle is 180 degrees. Usually, antennas only have two-dimensional gain charts (horizontal angle is 0, vertical angle is 0-360, that is, H(0); and vertical angle is 0, horizontal angle is 0-360, that is, V(0), the corresponding gain table), which can be read from the given two-dimensional gain table. V(θ s ) is the elevation angle of the terminal in the base station coordinates, (π-θ s ) is the elevation angle of the 180° terminal in the base station coordinates. These can be obtained by reading the original two-dimensional table of the antenna. s ,θ s ) is the interpolation value corresponding to Φ s and θ s The three-dimensional antenna gain value at the angle is also called the preset gain. The value range of each angle in Formula 13 is 0-360.

[0107] In the embodiment of the present application, the process of determining the multi-dimensional dynamic antenna gain according to the corresponding relationship between the second elevation angle, the second azimuth angle and the second elevation angle can be as follows: s and θ s , the multi-dimensional dynamic antenna gain is determined when the other parameter values ​​in Formula 13 remain unchanged.

[0108] In an embodiment of the present application, the signal strength detection inputs multiple position relationship features into the target signal prediction model. Before obtaining the signal strength of the area to be detected, the sample drone posture data transmitted by the drone and the sample terminal position data collected by the ground test terminal that interacts with the drone are also obtained; based on the sample drone posture data and the sample terminal position data, multiple sample position relationship features are established between the drone and the ground test terminal; and the multiple sample position relationship features are used to train the initial signal prediction model to obtain the target signal prediction model.

[0109] In an embodiment of the present application, an information processing device may obtain sample drone pose data transmitted by a satellite device. The sample drone pose data transmitted by the satellite device may be historical drone pose data, including historical drone flight attitude information and historical drone position information. The sample terminal position data may be historical terminal position data collected by a ground test terminal, including information such as historical reference signal received power (RSRP) measured by the ground test terminal and the historical position of the ground test terminal.

[0110] In an embodiment of the present application, signal strength detection can obtain in the database sample drone posture data transmitted by the drone based on satellite equipment, and sample terminal position data collected by the ground test terminal that interacts with the drone; sample drone posture data transmitted by the drone based on satellite equipment, and sample terminal position data collected by the ground test terminal that interacts with the drone can also be obtained through other methods; the specific method of obtaining sample drone posture data transmitted by the drone based on satellite equipment, and sample terminal position data collected by the ground test terminal that interacts with the drone can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0111] In this embodiment of the present application, since the drone is equipped with a transmitting base station, the drone can use the transmitting base station to transmit sample drone position data to the satellite device. The satellite device then transmits the sample drone position data to the signal strength detection device via a ground wireless relay. Because the ground test terminal is connected to the signal strength detection device, the signal strength detection device can directly obtain the sample terminal position data from the ground test terminal interacting with the drone.

[0112] In an embodiment of the present application, the implementation method of establishing multiple sample position relationship features between a drone and a ground test terminal based on sample drone posture data and sample terminal position data is the same as the implementation method of establishing multiple position relationship features between a drone and a ground test terminal based on drone posture data and terminal position data. The specific implementation process can refer to the implementation process of establishing multiple position relationship features between a drone and a ground test terminal based on drone posture data and terminal position data.

[0113] In an embodiment of the present application, the process of signal strength detection using multiple sample position relationship features to train an initial signal prediction model to obtain a target signal prediction model includes: screening out at least one sample target feature from multiple sample position relationship features; and using at least one sample target feature to train the initial signal prediction model to obtain a target signal prediction model.

[0114] In an embodiment of the present application, the method of signal strength detection to screen out at least one sample target feature from multiple sample position relationship features is the same as the method of screening out at least one target feature from multiple position relationship features. For details, please refer to the implementation process of screening out at least one target feature from multiple position relationship features.

[0115] In an embodiment of the present application, the signal strength detection device uses at least one sample target feature to train an initial signal prediction model to obtain a target signal prediction model, including: inputting at least one sample target feature into the initial signal prediction model to obtain an output path loss; determining the model loss of the initial signal prediction model based on the output path loss and the sample path loss; when the model loss is greater than or equal to a preset loss threshold, continuing to train the initial signal prediction model using at least one sample target feature and the sample path loss to obtain a training model; when the training model loss of the training model is less than the preset loss threshold, determining the training model as the target signal prediction model.

[0116] It should be noted that the sample path loss is the path loss for signal transmission between the drone and the ground test terminal corresponding to the multiple sample position relationship characteristics. The sample path loss is determined in the same manner as the first path loss. For specific methods for determining the sample path loss, refer to the implementation process for determining the first path loss.

[0117] In an embodiment of the present application, the preset loss threshold can be a loss value configured in the signal strength detection device, or a loss value transmitted to the signal strength detection device by other devices, or a loss value obtained by the signal strength detection device through other methods. The specific method in which the signal strength detection device obtains the preset loss threshold can be determined according to actual conditions, and the embodiment of the present application does not limit this.

[0118] In an embodiment of the present application, the initial signal prediction model can be a model established using the XGBoost algorithm, or a model established in other ways. The specific initial signal prediction model can be determined based on actual conditions, and the embodiment of the present application does not limit this.

[0119] In an embodiment of the present application, the process of the signal strength detection device determining the model loss of the initial signal prediction model based on the output path loss and the sample path loss includes: determining the training loss based on the output path loss and the sample path loss; obtaining the gradient update parameter of the initial signal prediction model; determining the regularization loss of the initial signal prediction model based on the gradient update parameter; and determining the model loss of the initial signal prediction model based on the training loss and the regularization loss.

[0120] In an embodiment of the present application, the process of determining the model loss of the initial signal prediction model based on the training loss and the regularization loss can be to determine the sum of the training loss and the regularization loss to obtain the model loss.

[0121] In the embodiment of the present application, the XGBoost algorithm is used as the core algorithm for air-to-ground path loss inversion in a high-dynamic UAV emergency scenario. The loss function of the algorithm is shown in formula (14), which represents the model's fit to the data. i and Represent the actual and predicted path loss values ​​respectively. i The path loss value P is calculated by combining the measured data with formula (12) L , is the path loss value predicted by the XGBoost algorithm. x represents the input features {straight-line distance, horizontal distance, first elevation angle, heading angle, first azimuth angle}, that is,

[0122] Among them, the loss function (i.e., model loss) consists of two parts: training error and regularization term. The regularization term is expressed as formula (15), which is formed by adding the regularization terms of i trees and represents the penalty parameter of the model to solve the overfitting problem. Parameters γ, T, λ, ω j Represents the gradient update parameters of the model. i represents the i-th tree, Represents the path loss sample training error.

[0123] Calling the XGboost data package interface to solve the optimal parameter value requires steps (1)-(4):

[0124] (1) First, the test data set needs to be cross-validated and divided into a training set and a test set: {x_train, y_trian; x_test, y_test}

[0125] (2) The underlying layer of Xgboost is based on a decision tree and is optimized through the optimal split point. The adjustable parameters of the model are: Params = {objective, lamda, gamma, max_depth}, where:

[0126] Objective: Solve the optimal regression tree based on this function.

[0127] lamda: Regularization weight term coefficient. Increasing this value makes the model more conservative.

[0128] gamma: Penalty coefficient, which specifies the minimum loss function drop required for node splitting.

[0129] max_depth: specifies the maximum depth of the tree and finds the optimal value to prevent overfitting.

[0130] (3) Use the test data to fit the model using XGBRgressor(params).fit(X_train,y_train). The quality of the model and whether it meets the requirements are determined based on the MSE (mean square error) or MAE (mean absolute error) of the fitting results. If the error does not meet the requirements, the parameters are repeatedly modified to find the optimal value.

[0131] (4) After training, the required model optimal parameters Params are obtained and the model is saved.

[0132] In the embodiment of the present application, the signal strength detection device is a fixed-wing UAV-based air-to-ground propagation model correction system and algorithm. The system sets up a transmitting base station on a high-altitude UAV and uses a large fixed-wing aerospace base station to collect propagation model data for a specified altitude and scene. The collected data is transmitted to a cloud platform center. The cloud platform center combines the received flight attitude data to construct 3D dynamic antenna gain and other feature vectors in real time. The cloud platform then uses a machine learning model based on the XGboost algorithm to construct a propagation model and calibrate parameters to obtain a corrected system, which can then estimate air-to-ground wireless coverage signals. The specific processing flow chart of the signal strength detection device is shown in Figure 6: First, the system is built using drones, satellite equipment, ground test terminals, ground wireless relays, cloud platforms, etc., and then the cloud platform center issues instructions to collect database information, obtaining the drone posture data (drone posture data) transmitted by the satellite equipment and the terminal position data (ground test terminal data) collected by the ground test terminal that interacts with the drone. The drone posture data and terminal position data are preprocessed to obtain preprocessed drone posture data and preprocessed terminal position data. Based on the preprocessed drone posture data and preprocessed terminal position data, multiple position relationship features are established: including multi-dimensional dynamic antenna gain (dynamic 3D gain), horizontal distance (2D distance), straight-line distance (3D distance), elevation angle (first elevation angle, second elevation angle), azimuth angle (first azimuth angle, second azimuth angle), etc. Then, at least one sample target feature is selected from multiple position relationship features; the initial signal prediction model is trained using the at least one sample target feature to obtain a target signal prediction model (Xgboost algorithm prediction); and the target signal prediction model is used to predict the signal strength of the area to be detected (network signal prediction).

[0133] It is understandable that the ground propagation model system and method in the prior art are only applicable to the ground and low-altitude scenes below 300m. The method in the embodiment of the present application fills the gap in the air-to-ground high-altitude propagation model correction system. The transmitting source of the traditional propagation model correction system is in a fixed and static state, which does not meet the high-dynamic operation scenario of the drone hovering during emergency rescue. The influence of other multiple parameters such as antenna dynamic gain, drone flight attitude, elevation angle, etc. on the construction of the propagation model in high-dynamic scenarios is not considered. The correction system in the embodiment of the present application, by setting up a transmitting base station on the high-altitude drone, the ground test end transmits the received data to the cloud platform center. The cloud platform center combines the received feature vectors in high-dynamic scenes such as 3D dynamic antenna gain during flight, and uses the XGboost machine learning algorithm to comprehensively consider the influencing factors of the propagation model under various feature environments, and the constructed model prediction accuracy is higher.

[0134] It can be understood that the signal strength detection device establishes multiple position relationship features between the UAV and the ground test terminal based on the UAV posture data and terminal position data. The multiple position relationship features include multiple types of relationship features such as distance, angle and channel; so that multiple types of relationship features can be used to accurately describe the signal parameters of the air-to-ground wireless channel of the fixed-wing UAV in an emergency state when the UAV is continuously circling and moving at high speed with an aerial base station, thereby improving the accuracy of network signal prediction.

[0135] Based on the same inventive concept as the above-mentioned signal strength detection method, an embodiment of the present application provides a signal strength detection device 10, corresponding to a signal strength detection method. FIG7 is a first schematic diagram of the structure of a signal strength detection device provided in an embodiment of the present application. The signal strength detection device 10 may include:

[0136] The receiving part 101 is configured to receive the drone posture data of the drone in the area to be detected, and the terminal position data collected by the ground test terminal interacting with the drone;

[0137] An establishing section 102 is configured to establish a plurality of position relationship features between the drone and the ground test terminal based on the drone pose data and the terminal position data;

[0138] The input part 103 is configured to input the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected.

[0139] In some embodiments of the present application, the plurality of positional relationship features include:

[0140] The horizontal distance between the UAV and the ground test terminal, the vertical distance between the UAV and the ground test terminal, the straight-line distance between the UAV and the ground test terminal, the first azimuth angle of the ground test terminal relative to the UAV, the first elevation angle of the ground test terminal relative to the UAV, the second azimuth angle of the ground test terminal relative to the antenna, the second elevation angle of the ground test terminal relative to the antenna and the multi-dimensional dynamic antenna gain.

[0141] In some embodiments of the present application, the device further comprises a screening portion and a determining portion;

[0142] The screening part is configured to screen out at least one target feature from the plurality of positional relationship features;

[0143] The input part 103 is configured to input the at least one target feature into the target signal prediction model to obtain the target path loss of the area to be detected;

[0144] The determining part is configured to determine the signal strength according to the target path loss.

[0145] In some embodiments of the present application, the apparatus further comprises a sorting portion and an acquiring portion;

[0146] The determining part is configured to determine a first path loss; respectively determine correlation coefficients between the plurality of position relationship features and the first path loss to obtain a plurality of correlation coefficients;

[0147] The sorting part is configured to sort the multiple correlation coefficients in descending order of numerical values ​​to obtain a sorted sequence;

[0148] The screening part is configured to screen a preset number of correlation coefficients starting from the first sorting position of the sorting sequence to obtain at least one target correlation coefficient;

[0149] The acquisition part is configured to acquire at least one target feature corresponding to the at least one target correlation coefficient from the multiple positional relationship features.

[0150] In some embodiments of the present application, the determining part is configured to determine a multi-dimensional dynamic antenna gain; determine the first path loss according to the multi-dimensional dynamic antenna gain, the cell transmit power, the receive antenna gain, and the receive power;

[0151] The acquisition part is configured to obtain the cell transmission power of the signal transmitted by the drone to the ground test terminal, the receiving antenna gain of the ground test terminal and the receiving power of the ground test terminal receiving the signal transmitted by the drone.

[0152] In some embodiments of the present application, the acquiring part is configured to acquire a correspondence between a preset second elevation angle, a preset second azimuth angle, and a preset gain;

[0153] The determining part is configured to determine the multi-dimensional dynamic antenna gain according to the second elevation angle, the second azimuth angle and the corresponding relationship.

[0154] In some embodiments of the present application, the apparatus further comprises a processing portion;

[0155] The processing part is configured to pre-process the drone posture data to obtain pre-processed drone posture data; pre-process the terminal position data to obtain pre-processed terminal position data;

[0156] The establishing part 102 is configured to establish the multiple position relationship features based on the preprocessed drone posture data and the preprocessed terminal position data.

[0157] In some embodiments of the present application, the apparatus further comprises a training portion;

[0158] The acquisition part is configured to acquire sample drone pose data transmitted by the drone and sample terminal position data collected by the ground test terminal interacting with the drone;

[0159] The establishing part 102 is configured to establish a plurality of sample position relationship features between the drone and the ground test terminal based on the sample drone pose data and the sample terminal position data;

[0160] The training part is configured to train an initial signal prediction model using the multiple sample position relationship features to obtain a target signal prediction model.

[0161] In some embodiments of the present application, the screening portion is configured to screen out at least one sample target feature from the plurality of sample position relationship features;

[0162] The training part is configured to train an initial signal prediction model using the at least one sample target feature to obtain a target signal prediction model.

[0163] In some embodiments of the present application, the input part 103 is configured to input the at least one sample target feature into the initial signal prediction model to obtain an output path loss;

[0164] The determining part is configured to determine the model loss of the initial signal prediction model according to the output path loss and the sample path loss; if the training model loss of the training model is less than the preset loss threshold, determine the training model as the target signal prediction model;

[0165] The training part is configured to continue training the initial signal prediction model using the at least one sample target feature and the sample path loss to obtain a training model when the model loss is greater than or equal to a preset loss threshold.

[0166] In some embodiments of the present application, the determining part is configured to determine the training loss according to the output path loss and the sample path loss; determine the regularization loss of the initial signal prediction model according to the gradient update parameter; determine the model loss of the initial signal prediction model according to the training loss and the regularization loss;

[0167] The acquisition part is configured to acquire the gradient update parameters of the initial signal prediction model.

[0168] It should be noted that, in actual applications, the above-mentioned receiving part 101, establishing part 102 and input part 103 can be implemented by a processor 104 on the signal strength detection device 10, specifically a CPU (Central Processing Unit), MPU (Microprocessor Unit), DSP (Digital Signal Processing) or Field Programmable Gate Array (FPGA); the above-mentioned data storage can be implemented by the memory 105 on the signal strength detection device 10.

[0169] An embodiment of the present application further provides a signal strength detection device 10. As shown in FIG8 , the signal strength detection device 10 includes: a processor 104, a memory 105, and a communication bus 106. The memory 105 communicates with the processor 104 via the communication bus 106. The memory 105 stores a program executable by the processor 104. When the program is executed, the signal strength detection method described above is executed by the processor 104.

[0170] In practical applications, the memory 105 may be a volatile memory, such as a random-access memory (RAM); or a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or a combination of the above types of memory, and provide instructions and data to the processor 104.

[0171] An embodiment of the present application provides a computer-readable storage medium having a computer program thereon, which implements the signal strength detection method described above when the program is executed by the processor 104 .

[0172] Illustratively, an embodiment of the present application further provides a computer program product, including a computer program, which can be executed by the processor 104 of the signal strength detection device 10 to complete the steps of any of the aforementioned methods.

[0173] It can be understood that the signal strength detection device establishes multiple position relationship features between the UAV and the ground test terminal based on the UAV posture data and terminal position data. The multiple position relationship features include multiple types of relationship features such as distance, angle and channel; so that multiple types of relationship features can be used to accurately describe the signal parameters of the air-to-ground wireless channel of the fixed-wing UAV in an emergency state when the UAV is continuously circling and moving at high speed with an aerial base station, thereby improving the accuracy of network signal prediction.

[0174] In another embodiment of the present application, see FIG9 , which shows a structural diagram of a signal strength detection system 1 provided in an embodiment of the present application. As shown in FIG9 , the signal strength detection system includes the aforementioned signal strength detection device 10, a drone 11, and a ground test terminal 12; wherein,

[0175] The drone 11 is configured to collect drone posture data;

[0176] The ground test terminal 12 is configured to collect terminal location data;

[0177] The signal strength detection device 10 is configured to establish multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data; input the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected.

[0178] In some embodiments of the present application, the signal strength detection system further includes a satellite device;

[0179] The satellite device is configured to obtain the drone posture data and forward the drone posture data to the signal strength detection device.

[0180] It can be understood that the signal strength detection system sets up a transmitting base station on the UAV, uses satellite equipment to forward the UAV posture data to the signal strength detection device, and uses the ground test terminal to collect the terminal position data to the signal strength detection device, thereby establishing multiple position relationship features between the UAV and the ground test terminal based on the UAV posture data and terminal position data. The multiple position relationship features include multiple types of relationship features such as distance, angle, and channel; multiple types of relationship features can be used to accurately describe the signal parameters of the air-to-ground wireless channel of the fixed-wing UAV in an emergency state when the UAV is equipped with an aerial base station and continuously hovers and moves at high speed, thereby improving the accuracy of network signal prediction.

[0181] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0182] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0183] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0185] The above description is merely a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Industrial Applicability

[0186] The embodiment of the present application provides a signal strength detection method and device, system, storage medium, and product. Among them, the signal strength detection method includes: receiving the drone posture data of the drone in the area to be detected, and the terminal position data collected by the ground test terminal that interacts with the drone; establishing multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data; inputting the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected. Using the above method to implement the solution, the signal strength detection device establishes multiple position relationship features between the drone and the ground test terminal based on the drone posture data and the terminal position data. The multiple position relationship features include multiple types of relationship features such as distance, angle, and channel; so that the multiple types of relationship features can be used to accurately describe the signal parameters of the air-to-ground wireless channel of the fixed-wing drone in the emergency state when the drone is continuously circling and moving at high speed with the aerial base station, which improves the accuracy of network signal prediction.

Claims

1. A signal strength detection method, the method comprising: Receiving the UAV pose data of the UAV in the area to be detected, and the terminal position data collected by a ground test terminal interacting with the UAV; Establishing a plurality of position relationship features between the UAV and the ground test terminal according to the UAV pose data and the terminal position data; Inputting the plurality of position relationship features into a target signal prediction model to obtain the signal strength of the area to be detected.

2. The method according to claim 1, wherein, The plurality of position relationship features include: The horizontal distance between the UAV and the ground test terminal, the vertical distance between the UAV and the ground test terminal, the straight-line distance between the UAV and the ground test terminal, the first azimuth angle of the ground test terminal relative to the UAV, the first elevation angle of the ground test terminal relative to the UAV, the second azimuth angle of the ground test terminal relative to the antenna, the second elevation angle of the ground test terminal relative to the antenna, and the multi-dimensional dynamic antenna gain.

3. The method according to claim 1, wherein The inputting the plurality of position relationship features into a target signal prediction model to obtain the signal strength of the area to be detected includes: Screening at least one target feature from the plurality of position relationship features; Inputting the at least one target feature into the target signal prediction model to obtain the target path loss of the area to be detected; Determining the signal strength according to the target path loss.

4. The method according to claim 3, wherein, The screening at least one target feature from the plurality of position relationship features includes: Determining a first path loss; Respectively determining the correlation coefficients between the plurality of position relationship features and the first path loss to obtain a plurality of correlation coefficients; Sorting the plurality of correlation coefficients in a numerical descending order to obtain a sorted sequence; Starting from the first sorting position of the sorted sequence, screening a preset number of correlation coefficients to obtain at least one target correlation coefficient; Obtaining at least one target feature corresponding to the at least one target correlation coefficient among the plurality of position relationship features.

5. The method according to claim 4, wherein The determining a first path loss includes: Determining the multi-dimensional dynamic antenna gain; Obtaining the cell transmission power of the UAV transmitting a signal to the ground test terminal, the receiving antenna gain of the ground test terminal, and the received power of the ground test terminal receiving the signal transmitted by the UAV; Determining the first path loss according to the multi-dimensional dynamic antenna gain, the cell transmission power, the receiving antenna gain, and the received power.

6. The method according to claim 5, wherein, The determining the multi-dimensional dynamic antenna gain includes: Obtaining the corresponding relationship between a preset second elevation angle, a preset second azimuth angle, and a preset gain; Determining the multi-dimensional dynamic antenna gain according to the second elevation angle, the second azimuth angle, and the corresponding relationship.

7. The method according to claim 1, wherein The establishing a plurality of position relationship features between the UAV and the ground test terminal according to the UAV pose data and the terminal position data includes: Performing preprocessing on the UAV pose data to obtain preprocessed UAV pose data; Performing preprocessing on the terminal position data to obtain preprocessed terminal position data; Based on the preprocessed UAV pose data and the preprocessed terminal position data, the multiple position relationship features are established.

8. The method according to claim 1, wherein Before inputting the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected, the method further includes: Obtaining the sample UAV pose data transmitted by the UAV and the sample terminal position data collected by the ground test terminal interacting with the UAV; Based on the sample UAV pose data and the sample terminal position data, multiple sample position relationship features between the UAV and the ground test terminal are established; Using the multiple sample position relationship features to train the initial signal prediction model to obtain the target signal prediction model.

9. The method according to claim 8, wherein, The using the multiple sample position relationship features to train the initial signal prediction model to obtain the target signal prediction model includes: Selecting at least one sample target feature from the multiple sample position relationship features; Using the at least one sample target feature to train the initial signal prediction model to obtain the target signal prediction model.

10. The method according to claim 9, wherein, The using the at least one sample target feature to train the initial signal prediction model to obtain the target signal prediction model includes: Inputting the at least one sample target feature into the initial signal prediction model to obtain the output path loss; Determining the model loss of the initial signal prediction model according to the output path loss and the sample path loss; When the model loss is greater than or equal to the preset loss threshold, using the at least one sample target feature and the sample path loss to continue training the initial signal prediction model to obtain a training model; When the training model loss of the training model is less than the preset loss threshold, determining the training model as the target signal prediction model.

11. The method according to claim 10, wherein, The determining the model loss of the initial signal prediction model according to the output path loss and the sample path loss includes: Determining the training loss according to the output path loss and the sample path loss; Obtaining the gradient update parameter of the initial signal prediction model; Determining the regularization loss of the initial signal prediction model according to the gradient update parameter; Determining the model loss of the initial signal prediction model according to the training loss and the regularization loss.

12. A signal strength detection system, the system includes a UAV, a ground test terminal and a signal strength detection device: The UAV is configured to collect UAV pose data; The ground test terminal is configured to collect terminal position data; The signal strength detection device is configured to establish multiple position relationship features between the UAV and the ground test terminal according to the UAV pose data and the terminal position data; input the multiple position relationship features into the target signal prediction model to obtain the signal strength of the area to be detected.

13. A signal strength detection device, the device includes: A receiving part configured to receive the UAV pose data of the UAV in the area to be detected and the terminal position data collected by the ground test terminal interacting with the UAV; A establishing part, configured to establish a plurality of position relationship features between the drone and the ground test terminal according to the drone pose data and the terminal position data; An input part, configured to input the plurality of position relationship features into a target signal prediction model to obtain the signal strength of a to-be-detected area.

14. A signal strength detection device, the device comprising: A memory, a processor and a communication bus, the memory communicates with the processor through the communication bus, the memory stores a program for signal strength detection executable by the processor, and when the program for signal strength detection is executed, the method according to any one of claims 1 to 11 is executed by the processor.

15. A storage medium, on which a computer program is stored, applied to a signal strength detection device, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

16. A computer program product, comprising a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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