Signal Relay Control Method, System and Medium for Intelligent Low-Altitude Landing and Takeoff Field Beacon

Abstract

The present application relates to the technical field of UAV communication relay, and particularly to a signal relay control method, system and medium for intelligent low-altitude landing field beacon lights. The present application first collects the communication signal strength and light intensity data between the UAV and the beacon in real time, and constructs a signal quality parameter in combination with the positioning information; then combines frequency domain analysis and time domain analysis to establish a communication link quality model and generate an evaluation result; then dynamically calculates the optimal luminous intensity and signal relay power based on the evaluation result; finally, applies these parameters to the system to achieve collaborative optimization; the idea of optimizing the optical signal and the communication signal as a whole realizes the intelligent collaboration of the two signals through the establishment of a signal quality evaluation model, which not only ensures the system performance but also improves the energy utilization efficiency.

Description

Technical Field

[0001] This application relates to the technical field of UAV communication relays, and in particular to a signal relay control method, system and medium for intelligent low-altitude landing field beacons. Background Art

[0002] With the rapid development of UAVs in military and civilian fields, as a new type of UAV infrastructure, the intelligent low-altitude landing field beacon system not only needs to provide precise visual guidance for UAVs, but also needs to have signal relay and position indication functions. Especially in emergency rescue and military operations, the intelligent low-altitude landing field beacon system needs to provide all-weather reliable landing and takeoff guarantee services for various UAVs.

[0003] Existing intelligent low-altitude landing field beacons mainly use LED light sources with fixed brightness and are equipped with simple radio beacons transmitters. The system indicates the position of the landing and takeoff point by emitting optical signals with fixed intensity, and at the same time uses radio beacons for basic communication relay to provide navigation reference for UAVs.

[0004] However, the optical signals and radio beacons of existing intelligent low-altitude landing field beacons work independently of each other, cannot be coordinated and adjusted according to the actual needs of UAVs, and at the same time the power distribution is unreasonable, resulting in energy waste and affecting the continuous working ability of the system. This situation needs to be further improved. Summary of the Invention

[0005] In order to solve the problem that the optical signals and radio beacons of existing low-altitude landing field beacons work independently of each other and cannot be coordinated and adjusted according to the actual needs of UAVs, this application provides a signal relay control method for intelligent low-altitude landing field beacons, and adopts the following technical solutions:

[0006] In a first aspect, this application provides a signal relay control method for intelligent low-altitude landing field beacons, including the following steps:

[0007] Real-time collect the communication signal strength and light intensity data between the UAV and the beacon, and obtain real-time signal quality parameters in combination with positioning information;

[0008] Perform frequency-domain analysis and time-domain analysis on the signal quality parameters, establish a communication link quality model, and generate a signal quality evaluation result;

[0009] Calculate the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters;

[0010] Apply the optimal working parameters to the signal relay system to achieve the collaborative optimization of optical signals and communication signals.

[0011] By adopting the above technical solutions, to solve the problems existing when the low-altitude landing field beacon provides signal relay services for unmanned aerial vehicles; for example, under foggy conditions, existing low-altitude landing field beacons often blindly increase the luminous intensity to improve visibility, but this approach not only causes energy waste but may also affect the working performance of radio beacons; this application first collects the communication signal strength and light intensity data between the unmanned aerial vehicle and the beacon in real time, constructs a signal quality parameter by combining the positioning information; then combines frequency-domain analysis and time-domain analysis to establish a communication link quality model and generate an evaluation result; then dynamically calculates the optimal luminous intensity and signal relay power based on the evaluation result; finally, applies these parameters to the system to achieve collaborative optimization; the idea of optimizing the optical signal and communication signal as a whole realizes the intelligent collaboration of the two signals through establishing a signal quality evaluation model, which not only ensures the system performance but also improves the energy utilization efficiency.

[0012] Optionally, collecting the communication signal strength and light intensity data between the unmanned aerial vehicle and the beacon in real time, and obtaining the real-time signal quality parameter by combining the positioning information, specifically includes the following steps:

[0013] Dynamically calculate the theoretical attenuation values of the communication signal and the optical signal according to the distance between the unmanned aerial vehicle and the beacon;

[0014] Collect the actual communication signal strength and light intensity data, and calculate the deviation degree from the theoretical attenuation value;

[0015] Calculate the corresponding weight coefficient according to the deviation degree, and perform weighted fusion on the communication signal strength and light intensity data according to the weight coefficient;

[0016] Predict the deviation trend of the next position point according to the flight speed of the unmanned aerial vehicle, and compensate the signal quality parameter of the next position point according to the deviation trend.

[0017] By adopting the above technical solutions, when the unmanned aerial vehicle is flying fast, relying only on the signal strength data collected in real time often shows hysteresis. Especially under complex weather conditions, the attenuation characteristics of the optical signal and the communication signal are quite different, which easily causes parameter distortion; this application first establishes a theoretical attenuation model based on the distance between the unmanned aerial vehicle and the beacon; then compares the measured data with the theoretical value to obtain the deviation degree; then introduces a weight coefficient mechanism to perform adaptive fusion on the optical signal and the communication signal; considering the motion characteristics of the unmanned aerial vehicle, adjusts the parameters in advance through a prediction compensation mechanism; organically combines the theoretical model, real-time measurement and prediction compensation, which not only improves the accuracy of parameter acquisition but also solves the delay problem in the fast-moving scenario, providing a reliable data basis for subsequent signal optimization.

[0018] Optionally, calculate a corresponding weight coefficient according to the deviation degree, and perform weighted fusion on the communication signal strength and light intensity data, which specifically includes the following steps:

[0019] Calculate the ratio of the deviation degree between the communication signal and the optical signal to the preset threshold to obtain an initial weight coefficient;

[0020] Obtain the attitude angle, flight speed and meteorological visibility data of the unmanned aerial vehicle, calculate the influence coefficients of each parameter according to the preset rules, and obtain a weight adjustment factor;

[0021] Use the weight adjustment factor to correct the initial weight coefficient to obtain a fused signal quality parameter.

[0022] By adopting the above technical solution, when the unmanned aerial vehicle performs hovering or rapid turning actions, due to the drastic change of the signal reception characteristics caused by the attitude change, the traditional fixed-weight fusion method is difficult to adapt to such complex scenarios, and it is easy to cause inaccurate signal quality evaluation; this application first establishes an initial weight coefficient by calculating the ratio of the signal deviation to the threshold; then innovatively introduces multi-dimensional parameters such as attitude angle, flight speed and meteorological visibility to construct an environmental impact assessment model; finally, dynamically corrects the initial weight through the weight adjustment factor; incorporates the flight dynamic characteristics and environmental factors into the weight calculation system, realizes the adaptive adjustment of the weight, not only improves the accuracy of signal fusion, but also enhances the adaptability of the system to complex flight scenarios.

[0023] Optionally, perform frequency-domain analysis and time-domain analysis on the signal quality parameter, establish a communication link quality model, and generate a signal quality evaluation result, which specifically includes the following steps:

[0024] Divide the signal quality parameter into multiple data segments according to the time window, and perform Fourier transform on each data segment to obtain the signal amplitude spectrum characteristics;

[0025] Calculate the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum characteristics to obtain the signal intensity fluctuation characteristics;

[0026] Identify signal fading events according to the signal intensity fluctuation characteristics, and count the duration and depth of the fading;

[0027] Based on the duration and depth of the fading event, establish a communication link quality scoring standard and generate a signal quality evaluation result.

[0028] By adopting the above technical solution, when the drone flies in an environment blocked by buildings or terrain, the signal will experience short-term fading or periodic attenuation. The traditional evaluation method based on the average signal strength is difficult to accurately reflect this dynamic change characteristic, and it is easy to cause misjudgment of the link quality. This application first introduces time window segmentation and Fourier transform to obtain the frequency domain characteristics of the signal; then, by calculating the amplitude spectrum difference between adjacent data segments, the fluctuation characteristics of the signal strength are accurately captured; then, based on the fluctuation characteristics, fading events are identified and their duration and depth are quantified; finally, a link quality scoring standard considering fading characteristics is established. Applying the time-frequency domain analysis method to signal quality evaluation can not only accurately identify various signal fading events, but also achieve accurate quantification of the link quality, providing a reliable basis for subsequent parameter optimization.

[0029] Optionally, calculate the luminous intensity of the beacon and the signal relay power according to the signal quality evaluation result to obtain the optimal working parameters, which specifically include the following steps:

[0030] Collect the remaining battery power and the current working power, and calculate the sustainable working time;

[0031] When the sustainable working time is less than the preset threshold, determine the minimum guarantee value of the signal relay power according to the signal quality evaluation result;

[0032] Allocate the remaining available power to the luminous intensity according to a preset ratio to obtain the dynamic adjustment value of the luminous intensity;

[0033] Output the optimal working parameters of the beacon according to the minimum guarantee value of the signal relay power and the dynamic adjustment value of the luminous intensity.

[0034] By adopting the above technical solution, when operating continuously in bad weather, the traditional beacon system often consumes power too quickly due to blindly increasing the power, and even fails such as communication interruption or weakening of the optical signal, seriously affecting flight safety. This application first monitors the battery power and power consumption in real time to predict the sustainable working time of the system; then, when the remaining working time is insufficient, it first ensures the minimum guarantee power of the communication signal; then, according to the signal quality evaluation result, the remaining power is allocated to the lighting system according to an intelligent ratio; finally, the optimal working parameters that take into account both performance and battery life are output. Combining the power constraint and signal quality evaluation not only realizes the dynamic balance of communication and lighting functions, but also significantly extends the effective working time of the system, providing a reliable guarantee for long-term continuous operation.

[0035] In a second aspect, this application provides a signal relay control system for an intelligent low-altitude takeoff and landing field beacon, including:

[0036] A signal quality parameter acquisition module, which is used to collect the communication signal strength and light intensity data between the drone and the beacon in real time, and obtain the real-time signal quality parameters in combination with the positioning information;

[0037] A signal quality evaluation module, which is used to perform frequency-domain analysis and time-domain analysis on the signal quality parameters, establish a communication link quality model, and generate a signal quality evaluation result;

[0038] An optimal working parameter acquisition module, which is used to calculate the luminous intensity of the beacon and the signal relay power according to the signal quality evaluation result, and obtain the optimal working parameters;

[0039] A collaborative optimization module, which is used to apply the optimal working parameters to the signal relay system to achieve the collaborative optimization of optical signals and communication signals.

[0040] Optionally, the signal quality parameter acquisition module includes:

[0041] A theoretical attenuation calculation unit, which is used to dynamically calculate the theoretical attenuation values of communication signals and optical signals according to the distance between the drone and the beacon;

[0042] A deviation calculation unit, which is used to collect the actual communication signal strength and light intensity data and calculate the degree of deviation from the theoretical attenuation value;

[0043] A weighted fusion unit, which is used to calculate the corresponding weight coefficients according to the degree of deviation, and perform weighted fusion on the communication signal strength and light intensity data according to the weight coefficients;

[0044] A trend compensation unit, which is used to predict the deviation trend of the next position point according to the drone flight speed, and compensate the signal quality parameters of the next position point according to the deviation trend.

[0045] Optionally, the weighted fusion unit includes:

[0046] An initial weight calculation sub-unit, which is used to calculate the ratio of the deviation degrees of communication signals and optical signals to a preset threshold to obtain an initial weight coefficient;

[0047] An influence factor calculation sub-unit, which is used to obtain the attitude angle, flight speed and meteorological visibility data of the drone, and calculate the influence coefficients of each parameter according to preset rules to obtain a weight adjustment factor;

[0048] A weight correction sub-unit, which is used to correct the initial weight coefficient by using the weight adjustment factor to obtain the fused signal quality parameters.

[0049] Optionally, the signal quality evaluation module includes:

[0050] A frequency domain analysis unit, configured to divide the signal quality parameters into multiple data segments according to a time window, and perform Fourier transform on each data segment to obtain signal amplitude spectrum features;

[0051] A fluctuation feature extraction unit, configured to calculate the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum features to obtain signal intensity fluctuation features;

[0052] A fading event recognition unit, configured to identify signal fading events according to the signal intensity fluctuation features, and count the duration and depth of the fading;

[0053] A quality scoring unit, configured to establish a communication link quality scoring criterion based on the duration and depth of the fading event, and generate a signal quality assessment result.

[0054] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the signal relay control method of the intelligent low-altitude landing field beacon described above are implemented.

[0055] In summary, the present application includes at least one of the following beneficial technical effects:

[0056] The present application first collects the communication signal strength and light intensity data between the drone and the beacon in real time, and constructs signal quality parameters in combination with the positioning information; then combines frequency domain analysis and time domain analysis to establish a communication link quality model and generate an evaluation result; then dynamically calculates the optimal light intensity and signal relay power based on the evaluation result; finally applies these parameters to the system to achieve collaborative optimization; the idea of optimizing the optical signal and the communication signal as a whole realizes the intelligent collaboration of the two signals through establishing a signal quality evaluation model, which not only ensures the system performance, but also improves the energy utilization efficiency;

[0057] When the drone is flying fast, relying only on the signal strength data collected in real time often results in lag. Especially in complex weather conditions, the attenuation characteristics of the optical signal and the communication signal are quite different, which easily causes parameter distortion; the present application first establishes a theoretical attenuation model based on the distance between the drone and the beacon; then compares the measured data with the theoretical value to obtain the deviation degree; then introduces a weight coefficient mechanism to adaptively fuse the optical signal and the communication signal; considering the motion characteristics of the drone, adjusts the parameters in advance through a prediction compensation mechanism; organically combines the theoretical model, real-time measurement and prediction compensation, which not only improves the accuracy of parameter acquisition, but also solves the delay problem in the fast motion scenario, providing a reliable data basis for subsequent signal optimization;

[0058] When the drone performs hovering or rapid turning maneuvers, due to the drastic changes in signal reception characteristics caused by attitude changes, it is difficult for traditional fixed-weight fusion methods to adapt to such complex scenarios, and it is easy to cause inaccurate signal quality assessment. In this application, an initial weight coefficient is first established by calculating the ratio of the signal deviation to the threshold. Then, multi-dimensional parameters such as attitude angle, flight speed, and meteorological visibility are innovatively introduced to construct an environmental impact assessment model. Finally, the initial weight is dynamically corrected by a weight adjustment factor. By incorporating flight dynamic characteristics and environmental factors into the weight calculation system, adaptive adjustment of the weight is achieved, which not only improves the accuracy of signal fusion but also enhances the system's adaptability to complex flight scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic flowchart of a signal relay control method for an intelligent low-altitude landing and takeoff field beacon according to an embodiment of the present application;

[0060] Figure 2 is a schematic flowchart of step S100 in a signal relay control method for an intelligent low-altitude landing and takeoff field beacon according to an embodiment of the present application;

[0061] Figure 3 is a schematic flowchart of step S130 in a signal relay control method for an intelligent low-altitude landing and takeoff field beacon according to an embodiment of the present application;

[0062] Figure 4 is a schematic flowchart of step S200 in a signal relay control method for an intelligent low-altitude landing and takeoff field beacon according to an embodiment of the present application;

[0063] Figure 5 is a schematic flowchart of step S300 in a signal relay control method for an intelligent low-altitude landing and takeoff field beacon according to an embodiment of the present application;

[0064] Figure 6 is a schematic diagram of the modules of a signal relay control system for an intelligent low-altitude landing and takeoff field beacon according to an embodiment of the present application;

[0065] Figure 7 is an internal structure diagram of a low-altitude landing and takeoff field beacon according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any and all possible combinations including one or more of the listed items.

[0067] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more than two.

[0068] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0069] In a first aspect, the present application provides a signal relay control method for an intelligent low-altitude landing and take-off field beacon. Referring to Figure 1 , the method includes the following steps:

[0070] S100. Real-time collect the communication signal strength and light intensity data between the unmanned aerial vehicle (UAV) and the beacon, and obtain the real-time signal quality parameter in combination with the positioning information.

[0071] Among them, the communication signal strength refers to the received signal strength indication (RSSI) of the radio signal emitted by the beacon received by the UAV. The beacon uses a wireless communication module to emit signals, and the UAV is equipped with a signal strength detector to collect RSSI data in real time. The light intensity data refers to the visible light intensity emitted by the beacon measured by the optoelectronic sensor carried on the UAV. The positioning information includes the three-dimensional coordinates of the UAV, which are obtained through the on-board GPS module.

[0072] Specifically, the system has pre-established a signal attenuation mapping table, which contains the theoretical signal strength values at different distances. During the real-time collection process, first obtain the relative distance between the UAV and the beacon according to the GPS positioning, and look up the table to obtain the theoretical attenuation value. Then collect the RSSI value and the light intensity value every 100 ms, calculate the deviation compared with the theoretical value, and obtain the real-time signal quality parameter. To reduce the influence of random fluctuations, the original data is smoothed by using the moving average method.

[0073] S200. Perform frequency-domain analysis and time-domain analysis on the signal quality parameter, establish a communication link quality model, and generate a signal quality evaluation result.

[0074] Among them, the frequency-domain analysis uses the fast Fourier transform method to convert the time-domain signal to the frequency domain for feature extraction. The time-domain analysis mainly statistics the statistical features such as the mean and variance of the signal. The communication link quality model uses a rule-based scoring system to map the frequency-domain and time-domain features to a quality score of 0-100.

[0075] Specifically, the system has a pre-set scoring rule library that contains scoring criteria corresponding to different combinations of features. In actual applications, first, the signal quality parameters are segmented by a 2-second time window, and the FFT is performed on each segment of data to obtain the spectrum. Then, the main features of the spectrum are extracted, such as the intensity of the main frequency component and the harmonic distribution, and the time-domain statistics are calculated simultaneously. Finally, scores are assigned to each feature according to the rule library and weighted and summed to obtain the final quality evaluation score.

[0076] S300. Calculate the luminous intensity of the beacon and the signal relay power according to the signal quality evaluation result to obtain the optimal operating parameters.

[0077] Among them, the luminous intensity and the signal relay power are two key operating parameters of the beacon, which directly affect the system performance and energy consumption. The optimal operating parameters refer to the parameter combination that minimizes power consumption on the premise of meeting the communication quality requirements. The system has established a simple parameter lookup table that contains recommended parameter values corresponding to different quality evaluation scores.

[0078] Specifically, first, obtain the current remaining battery power according to the battery management module and calculate the sustainable working time. When the remaining working time is sufficient, directly look up the recommended power parameters according to the quality evaluation score in the table; when the remaining time is insufficient, prioritize ensuring the communication power, and allocate the remaining power to the lighting system.

[0079] S400. Apply the optimal operating parameters to the signal relay system to achieve the collaborative optimization of optical signals and communication signals.

[0080] Among them, the signal relay system includes a light-emitting control unit and a communication control unit. The light-emitting control unit controls the luminous intensity of the LED lamp group by using the PWM dimming method, and the communication control unit realizes the signal relay function by modulating the transmission power of the radio frequency module.

[0081] Specifically, the system adopts a simple closed-loop control strategy and updates the operating parameters once every 1 second. The controller first checks the current power status, and then sends the calculated optimal power parameters to the light-emitting control unit and the communication control unit respectively. After receiving the new parameters, the two control units smoothly transition to the new operating state through a soft start method to avoid system instability caused by parameter mutations. At the same time, record the power adjustment records for subsequent optimization analysis.

[0082] In one embodiment, referring to Figure 2 , in step S100, the communication signal strength and light intensity data between the UAV and the beacon are collected in real time, and the real-time signal quality parameters are obtained in combination with the positioning information, which specifically includes the following steps:

[0083] S110. Dynamically calculate the theoretical attenuation values of the communication signal and the optical signal according to the distance between the UAV and the beacon.

[0084] In this embodiment, the theoretical attenuation of the communication signal is calculated using the free space propagation model, mainly considering path loss. The system has pre-established a distance-attenuation look-up table, which contains the standard attenuation values every 10 meters within the range of 0 - 1000 meters. The attenuation of the optical signal follows the inverse square law of light intensity, and the theoretical value is also obtained by looking up the table. The distance between the UAV and the beacon is obtained by calculating the relative position through GPS positioning.

[0085] Specifically, the system obtains the UAV position data every 100 ms and calculates the straight-line distance to the beacon. For the communication signal, first, look up the distance-attenuation table to obtain the standard attenuation values of two adjacent distance points, and then obtain the theoretical attenuation at the exact distance through linear interpolation. For the optical signal, directly substitute it into the inverse square formula to calculate the theoretical light intensity. For example, at a distance of 500 meters, the theoretical attenuation of the communication signal is about -75 dBm, and the optical signal attenuates to 1 / 25 of the initial intensity.

[0086] S120. Collect the actual communication signal strength and optical intensity data, and calculate the deviation degree from the theoretical attenuation value.

[0087] In this embodiment, the signal strength data is collected by the receiving module on the UAV, and the sampling frequency is 100 Hz. The optical intensity data is collected by the photoelectric sensor, and the sampling frequency is synchronized with the signal strength. To reduce the influence of random fluctuations, the system uses a 10-point moving average for data smoothing. The deviation degree is defined as the relative deviation percentage of the measured value from the theoretical value.

[0088] Specifically, the system has pre-set a deviation evaluation matrix, which maps different degrees of deviation to 5 levels. After each sampling, calculate the difference between the measured value and the theoretical value of the communication signal and the optical signal respectively, and divide it by the theoretical value to obtain the relative deviation. Then query the evaluation matrix to determine the deviation level, denoted as level 1 - 5. For example, when the measured signal strength is 30% lower than the theoretical value, it is determined as a level 4 deviation; when the optical intensity deviation is within ±10%, it is determined as a level 1 deviation.

[0089] S130. Calculate the corresponding weight coefficient according to the deviation degree, and perform weighted fusion on the communication signal strength and optical intensity data according to the weight coefficient.

[0090] In this embodiment, the weight coefficient adopts the inverse proportion distribution method, and the greater the deviation of the signal, the smaller the weight. The system has established a weight distribution table, which maps 5 deviation levels to the weight range of 0.1 - 0.9. The weighted fusion adopts the arithmetic average method to obtain a comprehensive quality score of 0 - 100.

[0091] Specifically, first, the initial weights of the two signals are obtained by looking up a table according to their deviation levels. Then, the two weights are normalized so that their sum is 1. Finally, the normalized weights are multiplied by the corresponding signal quality values and summed to obtain the fused quality parameter. For example, when the communication signal has a deviation level of 2 (weight 0.7) and the optical signal has a deviation level of 4 (weight 0.3), the final weighted score is the weighted average of the two.

[0092] Furthermore, the system integrates a fluxgate sensor array in the beacon base to obtain auxiliary positioning information by utilizing the local magnetic field changes generated by the operation of the drone motor. Since the motor layouts and power characteristics of different models of drones are different, the magnetic field disturbance patterns they generate are unique. By analyzing the magnetic field gradient distribution and time-domain characteristics, the system can accurately identify the approaching direction and attitude changes of the drone even when the optoelectronic signals are limited. Especially in the low-altitude strong electromagnetic interference environment, a correction mechanism that combines the magnetic field disturbance characteristics generated by the operation of the drone motor provides a reliable reference basis for weight allocation. For example, when the detected magnetic field disturbance characteristics indicate that the drone is making a low-altitude hover adjustment, the system correspondingly increases the weight of the communication signal to ensure the stable transmission of control commands. In particular, there is a significant correlation between the harmonic components of the magnetic field disturbance generated by the operation of the drone motor and its flight state. The system performs wavelet transform on the magnetic field signal to extract the harmonic energy distribution in the characteristic frequency band. When the drone is in a stable hover state, the proportion of the fundamental frequency harmonic energy is the highest; when making attitude adjustments, the proportion of the energy of the higher-order harmonics will increase significantly; during fast flight, due to the influence of the Doppler effect, the harmonic frequency will have a predictable shift. Further, in the strong electromagnetic interference environment, the magnetic field harmonic characteristics of the drone motor still remain relatively stable because the interference source usually does not generate significant interference at the motor characteristic frequencies. The system utilizes this characteristic and introduces a credibility evaluation mechanism based on harmonic stability when calculating the weights. When the environmental electromagnetic interference is strong, the system preferentially refers to the signal channels with stable harmonic characteristics, thus ensuring the reliability of signal fusion.

[0093] S140. Predict the deviation trend of the next position point according to the flight speed of the drone, and compensate the signal quality parameter of the next position point according to the deviation trend.

[0094] Among them, the deviation trend prediction adopts the linear extrapolation method, and the predicted position of the next sampling point is calculated based on the velocity vector of the current position. The system maintains the deviation historical data of the nearest 100 sampling points for analyzing the local change trend. The calculation of the compensation amount adopts the proportional-integral method to correct the predicted deviation.

[0095] Specifically, first calculate the instantaneous speed and movement direction of the drone based on GPS data, and predict the position coordinates 100 ms later. Then analyze the deviation change pattern of similar position points in the historical data to obtain the trend prediction value. Finally, combine the trend prediction value with the current deviation value to generate a compensation coefficient, and pre-compensate the signal quality parameters of the next position point. For example, when it is detected that the drone is moving away from the beacon and at a relatively high speed, the system will correspondingly increase the compensation coefficient and pre-adjust the signal quality parameters downward.

[0096] In one embodiment, referring to Figure 3 , in step S130, calculate the corresponding weight coefficient according to the deviation degree, and perform weighted fusion on the communication signal strength and light intensity data according to the weight coefficient, which specifically includes the following steps:

[0097] S131. Calculate the ratio of the deviation degree of the communication signal and the light signal to the preset threshold to obtain the initial weight coefficient.

[0098] In this embodiment, the system pre-sets a piecewise linear threshold table, which includes three levels of deviation thresholds: slight deviation, medium deviation, and severe deviation. The initial weight coefficient is normalized so that the sum of the weights of the two signals is 1. The deviation degree is calculated by the relative error between the measured value and the theoretical value, and is recorded in percentage form.

[0099] Specifically, first calculate the ratio of the relative deviation of each signal to the corresponding threshold. When the deviation is less than the slight threshold, the weight coefficient is set to 0.8 - 1.0; when it is between the slight and medium thresholds, the weight coefficient is 0.5 - 0.8; when it is between the medium and severe thresholds, the weight coefficient is 0.2 - 0.5; when it exceeds the severe threshold, the weight coefficient is less than 0.2. For example, when the communication signal deviation is 15%, which is between the slight and medium thresholds, the weight coefficient is calculated to be 0.75 through linear interpolation.

[0100] S132. Obtain the attitude angle, flight speed, and meteorological visibility data of the drone, and calculate the influence coefficient of each parameter according to the preset rules to obtain the weight adjustment factor.

[0101] Among them, the attitude angle data is provided by the IMU sensor of the drone, including the pitch angle, roll angle, and yaw angle. The flight speed is calculated by GPS position differential. The meteorological visibility data is obtained in real time from the nearest meteorological station. The system establishes an influence factor database to store the influence coefficients corresponding to different parameter intervals.

[0102] Specifically, the system presets a set of reference conditions, such as level flight attitude (pitch angle and roll angle within ±5°), low-speed flight (speed less than 10 m / s), and good visibility (greater than 10 km). When the actual parameters deviate from the reference conditions, the corresponding influence coefficients are obtained by querying the database. The final adjustment factor is the product of all influence coefficients. For example, when the drone climbs at a high speed with a 20° pitch angle, the attitude influence coefficient is 0.8 and the speed influence coefficient is 0.9. Multiplying the two gives an adjustment factor of 0.72.

[0103] S133. Use the weight adjustment factor to correct the initial weight coefficient to obtain the fused signal quality parameter.

[0104] Among them, the weight correction adopts a multiplicative adjustment method, multiplying the initial weight by the adjustment factor to obtain the final weight. The system sets weight upper and lower limit constraints to ensure that the corrected weight is within the valid range.

[0105] Specifically, the system maintains a weight correction record table to record the results of the last 100 corrections. Each time of calculation, first multiply the initial weight by the current adjustment factor. If the result exceeds the limit range, truncation processing is performed. Then, the weighted average of the quality values of the two signals is calculated using the corrected weight to obtain the final fusion parameter.

[0106] In one embodiment, referring to Figure 4 , in step S200, perform frequency-domain analysis and time-domain analysis on the signal quality parameter, establish a communication link quality model, and generate a signal quality evaluation result, which specifically includes the following steps:

[0107] S210. Divide the signal quality parameter into multiple data segments according to a time window, and perform Fourier transform on each data segment to obtain the signal amplitude spectrum characteristics.

[0108] Specifically, the system has pre-established a spectrum feature database containing standard spectral patterns under typical working conditions. For each 2-second data segment, first perform a 1024-point FFT operation to obtain the spectrum in the range of 0 - 50 Hz. Then extract the first 3 frequency components with the largest amplitudes and their amplitudes, calculate the ratio of each harmonic to the fundamental frequency, and estimate the noise floor level. For example, the main frequency component of a certain data segment is at 5 Hz, with an amplitude of 0.8, a harmonic ratio of 0.3, and a noise floor of -40 dB.

[0109] S220. Calculate the amplitude spectrum difference between adjacent data segments based on the signal amplitude spectrum characteristics to obtain the signal intensity fluctuation characteristics.

[0110] In this embodiment, the amplitude spectrum difference is obtained by calculating the Euclidean distance of the spectral vectors of adjacent time windows. The system sets up a fluctuation feature extraction table to map the spectral difference into three states: stable, fluctuating, and drastic change. To improve reliability, the system performs a moving average process on the differences of three consecutive windows.

[0111] Specifically, first calculate the differences of each pair of adjacent windows in three dimensions: the main frequency component, the harmonic ratio, and the noise floor. Then perform state judgment according to a preset threshold: a difference less than 20% is in a stable state, 20% - 50% is in a fluctuating state, and greater than 50% is in a drastic change state. At the same time, record the duration and occurrence frequency of each state for subsequent analysis. For example, when it is detected that the spectral differences of three consecutive windows are all greater than 50%, it is determined that the signal intensity fluctuates violently.

[0112] S230. Identify the signal fading event according to the signal intensity fluctuation feature, and count the duration and depth of the fading.

[0113] In this embodiment, the system pre - establishes a fading event feature library and defines three typical modes: fast fading, slow fading, and periodic fading. The fading depth is defined as the decrease amplitude of the signal intensity relative to the normal level, expressed in dB. The duration records the time interval from the start of the fading to the signal recovery.

[0114] Specifically, the system uses a state machine to track the change process of the signal intensity. When it is detected that the signal intensity is more than 6 dB below the normal level, the fading event detection is triggered. Classify the fading according to the intensity decrease rate: fast fading is manifested as a decrease of more than 10 dB within 0.1 second, slow fading is a slow decrease within 1 second, and periodic fading is manifested as regular intensity fluctuations. The system records the type, depth, and duration of each fading and stores them in the event log.

[0115] S240. Establish a communication link quality scoring standard based on the duration and depth of the fading event, and generate a signal quality assessment result.

[0116] In this embodiment, the scoring standard uses a 100 - point system and is implemented based on a weighted scoring table. The scoring table maps the three features of the fading (type, depth, duration) into different deduction items. The system maintains a 60 - second moving scoring window, and the fading events occurring during this period are cumulatively deducted points to obtain the final link quality score.

[0117] Specifically, the system scores each fading event: 20 points are deducted for each fast fade, 10 points for each slow fade, and 5 - 15 points are deducted for periodic fading according to the frequency. An additional 5 points are deducted for every 3 dB increase in fade depth, and an additional 3 points are deducted for every second the duration exceeds 1 second. The deductions for each item are weighted and then added together to obtain the overall score for this time window. For example, if there are 2 fast fades (depth 12 dB, duration 0.5 seconds) and 1 slow fade (depth 6 dB, duration 2 seconds) within 1 minute, the total score is 65 points.

[0118] In one embodiment, referring to Figure 5 , in step S300, according to the signal quality assessment result, calculate the luminous intensity of the beacon and the signal relay power to obtain the optimal operating parameters, which specifically include the following steps:

[0119] S310. Collect the remaining battery power and the current operating power, and calculate the sustainable operating time.

[0120] In this embodiment, the system collects the real - time voltage, current, and temperature data of the battery pack through the Battery Management Unit (BMS). The BMS is built - in with a power estimation table, which maps the voltage value to the remaining power percentage according to the discharge curve characteristics. The current operating power includes the total power consumption of the signal relay module and the LED lighting module, and is monitored in real - time through a power detection circuit.

[0121] Specifically, the system updates the estimated value of the sustainable operating time every 1 minute. First, read the remaining power data provided by the BMS and look up the corresponding available energy value in the table. Then divide the available energy by the current total power consumption to obtain the theoretical operating time. Considering the influence of the battery efficiency change with temperature, the system looks up the correction factor table according to the battery temperature for compensation. For example, when the remaining power is 60%, the current power consumption is 10 W, and the battery temperature is 25 °C, the estimated sustainable operating time is about 6 hours.

[0122] S320. When the sustainable operating time is less than the preset threshold, determine the minimum guaranteed value of the signal relay power according to the signal quality assessment result.

[0123] Among them, the system sets a reminder threshold and a guarantee threshold. The minimum guaranteed value refers to the minimum power required to maintain the basic communication function, which is determined by a stepped power configuration table. This configuration table divides the signal quality assessment scores into 5 levels, and each level corresponds to different minimum power requirements.

[0124] Specifically, when the sustainable time is lower than the reminder threshold, the system enters the energy - saving mode. First, query the power configuration table to obtain the reference power value corresponding to the current signal quality level. Then calculate the dynamic adjustment coefficient according to the difference between the remaining time and the guarantee threshold. The final minimum guaranteed value is equal to the reference power multiplied by the adjustment coefficient.

[0125] S330. Allocate the remaining available power to the luminous intensity according to a preset ratio to obtain a dynamic adjustment value of the luminous intensity.

[0126] In this embodiment, the system establishes a power allocation priority table, which defines the allocation ratios of communication power and lighting power under different working states. The remaining available power is equal to the maximum output power of the battery minus the minimum guarantee value of signal relay. The luminous intensity adopts the PWM dimming method, and the duty cycle has an approximate linear relationship with the power.

[0127] Specifically, the system pre-sets three power allocation schemes: standard mode (communication: lighting = 4:6), energy-saving mode (6:4), and emergency mode (8:2). Select the allocation scheme according to the current power state and calculate the available power of the lighting system. Then, convert the power value into the corresponding PWM duty cycle by looking up a table to obtain the LED drive signal.

[0128] S340. Output the optimal working parameters of the beacon according to the minimum guarantee value of the signal relay power and the dynamic adjustment value of the luminous intensity.

[0129] In this embodiment, the optimal working parameters include two control variables: the transmitter power level and the LED drive duty cycle. The system maintains a parameter configuration log to record the historical data of power adjustment.

[0130] Specifically, the system adopts a soft start strategy to achieve smooth parameter transition. First, check the time interval since the last adjustment to determine whether parameter update is allowed. When the update condition is met, calculate the difference between the new power parameter and the current value, and gradually adjust it to the target value in steps according to the maximum allowable step size. At the same time, record the adjustment time and parameter value in the configuration log.

[0131] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0132] In a second aspect, the present application provides a signal relay control system for an intelligent low-altitude takeoff and landing field beacon. The signal relay control system for the intelligent low-altitude takeoff and landing field beacon of the present application will be described below in combination with the above signal relay control method for the intelligent low-altitude takeoff and landing field beacon.

[0133] Refer to Figure 6 , a signal relay control system for an intelligent low-altitude takeoff and landing field beacon, comprising:

[0134] A signal quality parameter acquisition module, configured to collect the communication signal strength and light intensity data between the unmanned aerial vehicle and the beacon in real time, and obtain real-time signal quality parameters in combination with the positioning information;

[0135] A signal quality assessment module, which is used to perform frequency-domain analysis and time-domain analysis on signal quality parameters, establish a communication link quality model, and generate signal quality assessment results;

[0136] An optimal working parameter acquisition module, which is used to calculate the luminous intensity of the beacon and the signal relay power according to the signal quality assessment results to obtain the optimal working parameters;

[0137] A collaborative optimization module, which is used to apply the optimal working parameters to the signal relay system to achieve the collaborative optimization of optical signals and communication signals.

[0138] In one embodiment, the signal quality parameter acquisition module includes:

[0139] A theoretical attenuation calculation unit, which is used to dynamically calculate the theoretical attenuation values of communication signals and optical signals according to the distance between the UAV and the beacon;

[0140] A deviation calculation unit, which is used to collect the actual communication signal strength and light intensity data and calculate the degree of deviation from the theoretical attenuation value;

[0141] A weighted fusion unit, which is used to calculate the corresponding weight coefficients according to the degree of deviation and perform weighted fusion on the communication signal strength and light intensity data according to the weight coefficients;

[0142] A trend compensation unit, which is used to predict the deviation trend of the next position point according to the UAV flight speed and compensate the signal quality parameters of the next position point according to the deviation trend.

[0143] In one embodiment, the weighted fusion unit includes:

[0144] An initial weight calculation sub-unit, which is used to calculate the ratio of the deviation degrees of communication signals and optical signals to a preset threshold to obtain an initial weight coefficient;

[0145] An influence factor calculation sub-unit, which is used to obtain the attitude angle, flight speed and meteorological visibility data of the UAV, calculate the influence coefficients of each parameter according to preset rules, and obtain a weight adjustment factor;

[0146] A weight correction sub-unit, which is used to correct the initial weight coefficient by using the weight adjustment factor to obtain the fused signal quality parameters.

[0147] In one embodiment, the signal quality assessment module includes:

[0148] A frequency-domain analysis unit, which is used to divide the signal quality parameters into multiple data segments according to a time window and perform Fourier transform on each data segment to obtain signal amplitude spectrum characteristics;

[0149] A fluctuation feature extraction unit, configured to calculate the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum feature, and obtain the signal intensity fluctuation feature;

[0150] A fading event recognition unit, configured to recognize the signal fading event according to the signal intensity fluctuation feature, and count the duration and depth of the fading;

[0151] A quality scoring unit, configured to establish a communication link quality scoring criterion based on the duration and depth of the fading event, and generate a signal quality assessment result.

[0152] In one embodiment, the optimal working parameter acquisition module includes:

[0153] A working time calculation unit, configured to collect the remaining battery power and the current working power, and calculate the sustainable working time;

[0154] A power guarantee unit, configured to determine the minimum guarantee value of the signal relay power according to the signal quality assessment result when the sustainable working time is less than a preset threshold;

[0155] A light intensity adjustment unit, configured to allocate the remaining available power to the light emission intensity according to a preset ratio, and obtain a dynamic adjustment value of the light emission intensity;

[0156] A parameter output unit, configured to output the optimal working parameters of the beacon according to the minimum guarantee value of the signal relay power and the dynamic adjustment value of the light emission intensity.

[0157] In one embodiment, the present application provides a low-altitude takeoff and landing field beacon, and its internal structure diagram can be as Figure 7 shown. The low-altitude takeoff and landing field beacon includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the low-altitude takeoff and landing field beacon is used to provide computing and control capabilities. The memory of the low-altitude takeoff and landing field beacon includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the low-altitude takeoff and landing field beacon is used to store data. The network interface of the low-altitude takeoff and landing field beacon is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a signal relay control method for an intelligent low-altitude takeoff and landing field beacon.

[0158] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the low-altitude takeoff and landing field beacon to which the solution of the present application is applied. The specific low-altitude takeoff and landing field beacon may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0159] In one embodiment, a low-altitude takeoff and landing field beacon is further provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0160] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The above computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0161] The above are all the preferred embodiments of the present application. The protection scope of the present application is not limited thereby. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A signal relay control method for an intelligent low-altitude takeoff and landing field beacon, characterized in that It includes the following steps: Dynamically calculate the theoretical attenuation values of communication signals and optical signals according to the distance between the drone and the beacon; Real-time collect the actual communication signal strength and optical intensity data between the drone and the beacon, and calculate the deviation degree from the theoretical attenuation value; Obtain the initial weight coefficient according to the ratio of the deviation degrees of communication signals and optical signals to the preset threshold; Obtain the attitude angle, flight speed and meteorological visibility data of the drone, calculate the influence coefficients of each parameter according to the preset rules, and obtain the weight adjustment factor; Use the weight adjustment factor to correct the initial weight coefficient to obtain the fused signal quality parameter; Predict the deviation trend of the next position point according to the drone flight speed, and compensate the signal quality parameter of the next position point according to the deviation trend, so as to obtain the real-time signal quality parameter in combination with the positioning information; Perform frequency-domain analysis and time-domain analysis on the signal quality parameter. Specifically, divide the signal quality parameter into multiple data segments according to the time window, and perform Fourier transform on each data segment to obtain the signal amplitude spectrum characteristics; Calculate the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum characteristics to obtain the signal intensity fluctuation characteristics; Identify signal fading events according to the signal intensity fluctuation characteristics, and count the duration and depth of fading; Establish a communication link quality scoring standard based on the duration and depth of the fading event, and generate a signal quality evaluation result; Collect the remaining battery power and the current working power, and calculate the sustainable working time; When the sustainable working time is less than the preset threshold, determine the minimum guaranteed value of the signal relay power according to the signal quality evaluation result; Allocate the remaining available power to the luminous intensity according to the preset ratio to obtain the dynamic adjustment value of the luminous intensity; Output the optimal working parameters of the beacon according to the minimum guaranteed value of the signal relay power and the dynamic adjustment value of the luminous intensity; Apply the optimal working parameters to the signal relay system to realize the collaborative optimization of optical signals and communication signals.

2. A signal relay control system for an intelligent low-altitude takeoff and landing field beacon, characterized in that, It includes: A signal quality parameter acquisition module, including: A theoretical attenuation calculation unit for dynamically calculating the theoretical attenuation values of communication signals and optical signals according to the distance between the drone and the beacon; A deviation calculation unit for real-time collecting the actual communication signal strength and optical intensity data between the drone and the beacon, and calculating the deviation degree from the theoretical attenuation value; A weighted fusion unit, where the initial weight calculation sub-unit is used to calculate the ratio of the deviation degrees of communication signals and optical signals to the preset threshold to obtain the initial weight coefficient; the influence factor calculation sub-unit is used to obtain the attitude angle, flight speed and meteorological visibility data of the drone, calculate the influence coefficients of each parameter according to the preset rules, and obtain the weight adjustment factor; the weight correction sub-unit is used to correct the initial weight coefficient with the weight adjustment factor to obtain the fused signal quality parameter; A trend compensation unit for predicting the deviation trend of the next position point according to the drone flight speed, and compensating the signal quality parameter of the next position point according to the deviation trend, so as to obtain the real-time signal quality parameter in combination with the positioning information; A signal quality assessment module for performing frequency-domain analysis and time-domain analysis on the signal quality parameters, including: A frequency-domain analysis unit for dividing the signal quality parameters into multiple data segments according to a time window and performing Fourier transform on each data segment to obtain signal amplitude spectrum characteristics; A fluctuation feature extraction unit for calculating the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum characteristics to obtain signal intensity fluctuation characteristics; A fading event recognition unit for recognizing signal fading events according to the signal intensity fluctuation characteristics and counting the duration and depth of the fading; A quality scoring unit for establishing a communication link quality scoring criterion based on the duration and depth of the fading event and generating a signal quality assessment result; An optimal working parameter acquisition module, including: A working time calculation unit for collecting the remaining battery power and the current working power and calculating the sustainable working time; A power guarantee unit for determining the minimum guarantee value of the signal relay power according to the signal quality assessment result when the sustainable working time is less than a preset threshold; An optical intensity adjustment unit for allocating the remaining available power to the luminous intensity according to a preset ratio to obtain a dynamic adjustment value of the luminous intensity; A parameter output unit for outputting the optimal working parameters of the beacon according to the minimum guarantee value of the signal relay power and the dynamic adjustment value of the luminous intensity; A cooperative optimization module for applying the optimal working parameters to the signal relay system to achieve cooperative optimization of optical signals and communication signals.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the signal relay control method of the intelligent low-altitude takeoff and landing field beacon described in claim 1 are implemented.

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