Signal relay control method and system for intelligent low-altitude takeoff and landing field light beacon and medium

Abstract

The invention relates to the technical field of unmanned aerial vehicle communication relay, in particular to a signal relay control method and system for intelligent low-altitude take-off and landing field light beacon and a medium. The method comprises the following steps: firstly, collecting communication signal intensity and light intensity data between an unmanned aerial vehicle and a light beacon in real time, and constructing a signal quality parameter in combination with positioning information; establishing a communication link quality model and generating an evaluation result in a mode of combining frequency domain analysis and time domain analysis; dynamically calculating optimal luminous intensity and signal relay power based on an evaluation result; and finally, applying the parameters to the system to realize collaborative optimization. The optical signal and the communication signal are optimized as a whole, and intelligent cooperation of the two signals is realized by establishing the signal quality evaluation model, so that the system performance is ensured, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicle communication relay, and in particular to a signal relay control method, system and medium for intelligent low-altitude take-off and landing field beacons. Background Art

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

[0003] Existing intelligent low-altitude take-off and landing field beacons mainly use fixed-brightness LED light sources and are equipped with simple radio beacon transmitters. The system indicates the location of the take-off and landing point by emitting a fixed-intensity light signal, while using the radio beacon for basic communication relay to provide navigation references for drones.

[0004] However, the optical signals and radio beacons of existing intelligent low-altitude take-off and landing field lights work independently and cannot be coordinated and adjusted according to the actual needs of drones. At the same time, the power distribution is unreasonable, resulting in energy waste and affecting the system's ability to continue working. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem that the existing low-altitude take-off and landing field beacon light signals and radio beacons work independently and cannot be coordinated and adjusted according to the actual needs of the drone, the present application provides a signal relay control method for an intelligent low-altitude take-off and landing field beacon, which adopts the following technical solutions: In a first aspect, the present application provides a signal relay control method for an intelligent low-altitude take-off and landing field beacon, comprising the following steps: Collect the communication signal strength and light intensity data between the drone and the beacon in real time, and obtain real-time signal quality parameters in combination with positioning information; Performing frequency domain analysis and time domain analysis on the signal quality parameters, establishing a communication link quality model, and generating a signal quality evaluation result; Calculate the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters; The optimal operating parameters are applied to the signal relay system to achieve coordinated optimization of optical signals and communication signals.

[0006] By adopting the above technical scheme, in order to solve the problem that low-altitude landing and take-off field beacons provide signal relay services for UAVs; for example, in foggy conditions, existing low-altitude landing and take-off field beacons often blindly increase the luminous intensity to improve visibility, but this practice not only causes energy waste, but also may affect the working performance of radio beacons; this application first collects the communication signal strength and light intensity data between the UAV and the beacon in real time, and constructs signal quality parameters in combination with positioning information; then combines frequency domain analysis and time domain analysis to establish a communication link quality model and generate evaluation results; then dynamically calculates the optimal luminous intensity and signal relay power based on the evaluation results; finally, these parameters are applied to the system to achieve collaborative optimization; the idea of ​​optimizing optical signals and communication signals as a whole, and realizing intelligent collaboration of the two signals by establishing a signal quality evaluation model, which not only ensures system performance, but also improves energy utilization efficiency.

[0007] Optionally, the communication signal strength and light intensity data between the drone 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: According to the distance between the drone and the beacon, the theoretical attenuation value of the communication signal and the optical signal is dynamically calculated; Collecting actual communication signal strength and light intensity data, and calculating the degree of deviation from the theoretical attenuation value; Calculating a corresponding weight coefficient according to the degree of deviation, and performing weighted fusion of the communication signal strength and the light intensity data according to the weight coefficient; The deviation trend of the next position point is predicted according to the flight speed of the UAV, and the signal quality parameter of the next position point is compensated according to the deviation trend.

[0008] By adopting the above technical solution, when the UAV flies quickly, relying solely on the real-time collected signal strength data will often lead to lag, especially under complex weather conditions, the attenuation characteristics of the optical signal and the communication signal are quite different, which can easily cause parameter distortion; the application first establishes a theoretical attenuation model based on the distance between the UAV and the beacon; then compares the measured data with the theoretical value to obtain the degree of deviation; then introduces a weight coefficient mechanism to adaptively fuse the optical signal and the communication signal; considers the motion characteristics of the UAV, and adjusts the parameters in advance through a predictive compensation mechanism; organically combines theoretical models, real-time measurements and predictive compensation, which not only improves the accuracy of parameter acquisition, but also solves the delay problem in fast motion scenarios, and provides a reliable data basis for subsequent signal optimization.

[0009] Optionally, a corresponding weight coefficient is calculated according to the degree of deviation, and weighted fusion of the communication signal strength and the light intensity data is performed according to the weight coefficient, specifically comprising the following steps: Calculating the ratio of the degree of deviation between the communication signal and the optical signal to a preset threshold value to obtain an initial weight coefficient; Obtain the attitude angle, flight speed and weather visibility data of the UAV, calculate the influence coefficient of each parameter according to the preset rules, and obtain the weight adjustment factor; The initial weight coefficient is corrected using the weight adjustment factor to obtain a fused signal quality parameter.

[0010] By adopting the above technical solution, when the UAV performs hovering or rapid turning actions, the signal reception characteristics change dramatically due to the change in attitude. The traditional fixed weight fusion method is difficult to adapt to such complex scenarios, which easily causes inaccurate signal quality assessment. The present 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, the initial weight is dynamically corrected through the weight adjustment factor; the flight dynamic characteristics and environmental factors are incorporated into the weight calculation system to achieve adaptive adjustment of the weight, which not only improves the accuracy of signal fusion, but also enhances the system's adaptability to complex flight scenarios.

[0011] Optionally, frequency domain analysis and time domain analysis are performed on the signal quality parameters to establish a communication link quality model and generate a signal quality evaluation result, which specifically includes the following steps: Dividing the signal quality parameter into multiple data segments according to the time window, and performing Fourier transform on each data segment to obtain signal amplitude spectrum characteristics; Calculating amplitude spectrum differences of adjacent data segments according to the signal amplitude spectrum characteristics to obtain signal strength fluctuation characteristics; Identify signal fading events according to the signal strength fluctuation characteristics, and count the duration and depth of the fading; A communication link quality scoring standard is established based on the duration and depth of the fading event, and a signal quality evaluation result is generated.

[0012] By adopting the above technical solution, when the UAV 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, which is easy to cause misjudgment of 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 that takes into account the fading characteristics is established; the time-frequency domain analysis method is applied to signal quality evaluation, which can not only accurately identify various types of signal fading events, but also realize the accurate quantification of link quality, providing a reliable basis for subsequent parameter optimization.

[0013] Optionally, the luminous intensity and signal relay power of the beacon are calculated according to the signal quality evaluation result to obtain the optimal working parameters, which specifically includes the following steps: Collect the remaining battery power and current working power to calculate the sustainable working time; When the sustainable working time is less than a preset threshold, determining a 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 a preset ratio to obtain a dynamic adjustment value of the luminous intensity; According to the minimum guaranteed value of the signal relay power and the dynamic adjustment value of the luminous intensity, the optimal working parameters of the beacon are output.

[0014] By adopting the above technical solution, when operating continuously in severe weather, traditional beacon systems often consume power too quickly due to blindly increasing power, and even have faults such as communication interruption or weakening of light signals, which seriously affect flight safety; the present 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 gives priority to ensuring the minimum guaranteed power of the communication signal; then, based on the signal quality evaluation results, the remaining power is allocated to the light-emitting system in an intelligent proportion; finally, the optimal working parameters that take into account both performance and endurance are output; the combination of power constraints and signal quality evaluation not only achieves a dynamic balance between communication and lighting functions, but also significantly extends the effective working time of the system, providing reliable guarantee for long-term continuous operation.

[0015] In a second aspect, the present application provides a signal relay control system for an intelligent low-altitude take-off and landing field beacon, comprising: The signal quality parameter acquisition module 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; A signal quality assessment module, 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 assessment result; An optimal working parameter acquisition module, used to calculate the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters; The collaborative optimization module is used to apply the optimal working parameters to the signal relay system to achieve collaborative optimization of optical signals and communication signals.

[0016] Optionally, the signal quality parameter acquisition module includes: A theoretical attenuation calculation unit, used to dynamically calculate the theoretical attenuation values ​​of the communication signal and the optical signal according to the distance between the UAV and the beacon; A deviation calculation unit, used to collect actual communication signal strength and light intensity data, and calculate the degree of deviation from the theoretical attenuation value; A weighted fusion unit, used to calculate a corresponding weight coefficient according to the degree of deviation, and perform weighted fusion of the communication signal strength and the light intensity data according to the weight coefficient; The trend compensation unit is used to predict the deviation trend of the next position point according to the flight speed of the UAV, and compensate the signal quality parameter of the next position point according to the deviation trend.

[0017] Optionally, the weighted fusion unit includes: The initial weight calculation subunit is used to calculate the ratio of the deviation degree between the communication signal and the optical signal to a preset threshold value to obtain an initial weight coefficient; The influence factor calculation subunit is used to obtain the attitude angle, flight speed and weather visibility data of the UAV, calculate the influence coefficient of each parameter according to the preset rules, and obtain the weight adjustment factor; The weight correction subunit is used to correct the initial weight coefficient using the weight adjustment factor to obtain a fused signal quality parameter.

[0018] Optionally, the signal quality assessment module includes: A frequency domain analysis unit, used to divide the signal quality parameter into multiple data segments according to the time window, and perform Fourier transform on each data segment to obtain signal amplitude spectrum characteristics; A fluctuation feature extraction unit, used to calculate the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum feature to obtain a signal strength fluctuation feature; A fading event identification unit, used to identify signal fading events according to the signal strength fluctuation characteristics, and to count the duration and depth of the fading; The quality scoring unit is used to establish a communication link quality scoring standard based on the duration and depth of the fading event and generate a signal quality evaluation result.

[0019] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the signal relay control method of the above-mentioned intelligent low-altitude take-off and landing field beacon.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: This 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 positioning information; then, frequency domain analysis and time domain analysis are combined to establish a communication link quality model and generate evaluation results; then, based on the evaluation results, the optimal luminous intensity and signal relay power are dynamically calculated; finally, these parameters are applied to the system to achieve collaborative optimization; the idea of ​​optimizing optical signals and communication signals as a whole is adopted, and the intelligent collaboration of the two signals is achieved by establishing a signal quality evaluation model, which not only ensures system performance, but also improves energy efficiency; When the UAV flies quickly, relying solely on real-time collected signal strength data often leads to lags, especially under complex weather conditions, where the attenuation characteristics of optical signals and communication signals are quite different, which can easily cause parameter distortion. This application first establishes a theoretical attenuation model based on the distance between the UAV and the beacon. Then the measured data is compared with the theoretical value to obtain the degree of deviation. Then a weight coefficient mechanism is introduced to adaptively fuse the optical signal and the communication signal. The motion characteristics of the UAV are taken into account, and the parameters are adjusted in advance through a predictive compensation mechanism. The theoretical model, real-time measurement, and predictive compensation are organically combined, which not only improves the accuracy of parameter acquisition, but also solves the delay problem in fast motion scenarios, providing a reliable data basis for subsequent signal optimization. When the UAV hovers or turns quickly, the signal reception characteristics change dramatically due to the change in attitude. The traditional fixed weight fusion method is difficult to adapt to this complex scenario, which easily causes inaccurate signal quality assessment. The present application first establishes the 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, the initial weight is dynamically corrected through the weight adjustment factor; the flight dynamic characteristics and environmental factors are incorporated into the weight calculation system to achieve adaptive adjustment of the weight, 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

[0021] Figure 1 It is a flow chart of a signal relay control method of an intelligent low-altitude take-off and landing field beacon according to an embodiment of the present application; Figure 2 It is a flowchart of step S100 in a signal relay control method for an intelligent low-altitude take-off and landing field beacon according to an embodiment of the present application; Figure 3 It is a flowchart of step S130 in a signal relay control method for an intelligent low-altitude take-off and landing field beacon in an embodiment of the present application; Figure 4 It is a flowchart of step S200 in a signal relay control method for an intelligent low-altitude take-off and landing field beacon in an embodiment of the present application; Figure 5 It is a flowchart of step S300 in a signal relay control method for an intelligent low-altitude take-off and landing field beacon according to an embodiment of the present application; Figure 6 It is a module schematic diagram of a signal relay control system of an intelligent low-altitude take-off and landing field beacon in an embodiment of the present application; Figure 7 It is a diagram of the internal structure of a low altitude take-off and landing field beacon according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] 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 be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0024] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0025] In the first aspect, the present application provides a signal relay control method for an intelligent low-altitude take-off and landing field beacon, referring to Figure 1 , including the following steps: S100, collect the communication signal strength and light intensity data between the drone and the beacon in real time, and obtain real-time signal quality parameters in combination with the positioning information.

[0026] Among them, the communication signal strength refers to the received signal strength indication of the radio signal emitted by the beacon received by the drone. The beacon uses a wireless communication module to transmit the signal, and the drone is equipped with a signal strength detector to collect RSSI data in real time. Light intensity data refers to the visible light intensity emitted by the beacon measured by the photoelectric sensor on the drone. Positioning information includes the three-dimensional coordinates of the drone, which is obtained through the onboard GPS module.

[0027] Specifically, the system pre-establishes a signal attenuation mapping table, which contains theoretical signal strength values ​​at different distances. In the real-time acquisition process, the relative distance between the drone and the beacon is first obtained based on GPS positioning, and the theoretical attenuation value is obtained by looking up the table. Then, the RSSI value and light intensity value are collected every 100ms, compared with the theoretical value to calculate the deviation, and the real-time signal quality parameter is obtained. In order to reduce the impact of random fluctuations, the sliding average method is used to smooth the raw data.

[0028] S200: Perform frequency domain analysis and time domain analysis on signal quality parameters, establish a communication link quality model, and generate a signal quality evaluation result.

[0029] 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 counts 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 into a quality score of 0-100.

[0030] Specifically, the system pre-sets a scoring rule base, which contains scoring criteria corresponding to different feature combinations. In practical applications, the signal quality parameters are first segmented into 2-second time windows, and each segment of data is subjected to FFT to obtain the spectrum. Then the main features of the spectrum such as the intensity of the main frequency component and the distribution of harmonics are extracted, and the time domain statistics are calculated at the same time. Finally, each feature is assigned a score according to the rule base and the weighted sum is obtained to obtain the final quality assessment score.

[0031] S300, calculating the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters.

[0032] Among them, luminous intensity and signal relay power are two key working parameters of the beacon, which directly affect the system performance and energy consumption. The optimal working parameters refer to the parameter combination that minimizes power consumption while meeting the communication quality requirements. The system has established a simple parameter lookup table containing recommended parameter values ​​corresponding to different quality evaluation scores.

[0033] Specifically, first obtain the current remaining power according to the battery management module and calculate the sustainable working time. When the remaining working time is sufficient, directly look up the table to obtain the recommended power parameters according to the quality evaluation score; when the remaining time is insufficient, prioritize communication power and allocate the remaining power to the lighting system.

[0034] S400, applying optimal operating parameters to the signal relay system to achieve coordinated optimization of optical signals and communication signals.

[0035] The signal relay system includes a light control unit and a communication control unit. The light control unit uses PWM dimming to control the light intensity of the LED light group, and the communication control unit realizes the signal relay function by modulating the transmission power of the radio frequency module.

[0036] Specifically, the system adopts a simple closed-loop control strategy to update the working parameters every 1 second. The controller first checks the current power status, and then sends the calculated optimal power parameters to the light control unit and the communication control unit respectively. After receiving the new parameters, the two control units smoothly transition to the new working state through soft start to avoid system instability caused by sudden parameter changes. At the same time, the power adjustment records are recorded for subsequent optimization analysis.

[0037] In one embodiment, referring to Figure 2 In step S100, the communication signal strength and light intensity data between the drone 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: S110. Dynamically calculate theoretical attenuation values ​​of the communication signal and the optical signal according to the distance between the UAV and the beacon.

[0038] In this embodiment, the theoretical attenuation of the communication signal is calculated using the free space propagation model, mainly considering the path loss. The system pre-establishes a distance-attenuation comparison table, which contains 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 drone and the beacon is obtained by calculating the relative position through GPS positioning.

[0039] Specifically, the system obtains the drone's position data every 100ms and calculates the straight-line distance to the beacon. For communication signals, first look up the distance-attenuation table to obtain the standard attenuation value of two adjacent distance points, and then use linear interpolation to obtain the theoretical attenuation at the precise distance. For optical signals, directly substitute 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 -75dBm, and the optical signal is attenuated to 1 / 25 of the initial intensity.

[0040] S120: Collect actual communication signal strength and light intensity data, and calculate the degree of deviation from the theoretical attenuation value.

[0041] In this embodiment, the signal strength data is collected by the receiving module on the drone, and the sampling frequency is 100Hz. The light intensity data is collected by the photoelectric sensor, and the sampling frequency is synchronized with the signal strength. To reduce the impact of random fluctuations, the system uses a 10-point sliding average to smooth the data. The degree of deviation is defined as the relative deviation percentage between the measured value and the theoretical value.

[0042] Specifically, the system pre-sets a deviation evaluation matrix, which maps different degrees of deviation into five levels. After each sampling, the difference between the measured value and the theoretical value of the communication signal and the optical signal is calculated, and the relative deviation is obtained by dividing the difference by the theoretical value. Then the evaluation matrix is ​​queried to determine the deviation level, which is recorded as 1-5. For example, when the measured signal strength is 30% lower than the theoretical value, it is judged as a level 4 deviation; when the light intensity deviation is within ±10%, it is judged as a level 1 deviation.

[0043] S130. Calculate a corresponding weight coefficient according to the degree of deviation, and perform weighted fusion of the communication signal strength and the light intensity data according to the weight coefficient.

[0044] In this embodiment, the weight coefficient adopts the inverse proportional distribution method, and the larger the deviation, the smaller the signal weight. The system establishes a weight distribution table, which corresponds the five deviation levels to the weight interval of 0.1-0.9. The weighted fusion adopts the arithmetic average method to obtain the comprehensive quality score of 0-100.

[0045] Specifically, first, the initial weights of the two signals are obtained by looking up the table according to the deviation levels. Then the two weights are normalized so that their sum is 1. Finally, the normalized weights are multiplied and summed with the corresponding signal quality values ​​to obtain the fused quality parameters. For example, when the communication signal has a deviation of level 2 (weight 0.7) and the optical signal has a deviation of level 4 (weight 0.3), the final weighted score is the weighted average of the two.

[0046] Furthermore, the system integrates a fluxgate sensor array in the beacon base, and uses the local magnetic field changes generated by the operation of the UAV motor to obtain auxiliary positioning information. Due to the different motor layouts and power characteristics of different models of UAVs, 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 approach direction and attitude changes of the UAV when the photoelectric signal is limited. Especially in the low-altitude strong electromagnetic interference environment, the correction mechanism of the magnetic field disturbance characteristics generated by the operation of the UAV motor provides a reliable reference for weight allocation. For example, when the magnetic field disturbance characteristics are detected, indicating that the UAV is making low-altitude hovering adjustments, the system increases the communication signal weight accordingly to ensure the stable transmission of control instructions. In particular, the harmonic components of the magnetic field disturbance generated by the operation of the UAV motor are significantly correlated with its flight state. The system extracts the harmonic energy distribution of the characteristic frequency band by performing wavelet transform on the magnetic field signal. When the UAV is in a stable hovering state, its fundamental frequency harmonic energy accounts for the highest proportion; when adjusting the attitude, the energy proportion of the higher harmonics will increase significantly; and during fast flight, due to the influence of the Doppler effect, the harmonic frequency will produce a predictable offset. Furthermore, in a strong electromagnetic interference environment, the harmonic characteristics of the magnetic field of the drone motor remain relatively stable, because the interference source usually does not cause significant interference on the motor characteristic frequency. The system uses this feature to introduce a credibility assessment mechanism based on harmonic stability when calculating weights. When the environmental electromagnetic interference is strong, the system gives priority to referring to signal channels with stable harmonic characteristics, thereby ensuring the reliability of signal fusion.

[0047] S140, predicting a deviation trend of a next location point according to the flight speed of the UAV, and compensating a signal quality parameter of the next location point according to the deviation trend.

[0048] The deviation trend prediction adopts the linear extrapolation method to calculate the expected position of the next sampling point based on the velocity vector of the current position. The system maintains the deviation history data of the last 100 sampling points for analyzing the local change trend. The compensation amount is calculated using the proportional-integral method to correct the predicted deviation.

[0049] Specifically, the instantaneous speed and direction of movement of the drone are first calculated based on the GPS data, and the position coordinates after 100ms are predicted. Then, the deviation change pattern of similar position points in the historical data is analyzed to obtain the trend prediction value. Finally, the trend prediction value is combined with the current deviation value to generate a compensation coefficient to 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 the speed is fast, the system will increase the compensation coefficient accordingly and lower the signal quality parameters in advance.

[0050] In one embodiment, referring to Figure 3In step S130, the corresponding weight coefficient is calculated according to the degree of deviation, and the communication signal strength and light intensity data are weightedly fused according to the weight coefficient, which specifically includes the following steps: S131. Calculate the ratio of the deviation degree between the communication signal and the optical signal to a preset threshold value to obtain an initial weight coefficient.

[0051] 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 degree of deviation is calculated by the relative error between the measured value and the theoretical value, and is recorded as a percentage.

[0052] 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%, it is between the slight and medium thresholds, and the weight coefficient calculated by linear interpolation is 0.75.

[0053] S132, obtaining the attitude angle, flight speed and weather visibility data of the UAV, calculating the influence coefficient of each parameter according to preset rules, and obtaining the weight adjustment factor.

[0054] Among them, the attitude angle data is provided by the drone's IMU sensor, including pitch angle, roll angle and yaw angle. The flight speed is calculated by GPS position difference. The weather visibility data is obtained in real time from the nearest weather station. The system establishes an influence factor database to store the influence coefficients corresponding to different parameter intervals.

[0055] Specifically, the system presets a set of benchmark conditions, such as level flight attitude (pitch angle and roll angle within ±5°), low-speed flight (speed less than 10m / s), and good visibility (greater than 10km). When the actual parameters deviate from the benchmark conditions, the database is queried to obtain the corresponding influence coefficient. The final adjustment factor is the product of all influence coefficients. For example, when the drone climbs at a high speed with a pitch angle of 20°, the attitude influence coefficient is 0.8 and the speed influence coefficient is 0.9. The multiplication of the two gives an adjustment factor of 0.72.

[0056] S133. Correct the initial weight coefficient using the weight adjustment factor to obtain a fused signal quality parameter.

[0057] Among them, weight correction adopts multiplication adjustment method, multiplying the initial weight with the adjustment factor to get the final weight. The system sets upper and lower limits of weight to ensure that the corrected weight is within the valid range.

[0058] Specifically, the system maintains a weight correction record table to record the results of the last 100 corrections. Each time the calculation is performed, the initial weight is multiplied by the current adjustment factor. If the result exceeds the limit, it is truncated. Then the quality values ​​of the two signals are weighted averaged with the corrected weight to obtain the final fusion parameter.

[0059] In one embodiment, referring to Figure 4 In step S200, the signal quality parameters are analyzed in the frequency domain and the time domain, a communication link quality model is established, and a signal quality evaluation result is generated, which specifically includes the following steps: S210: Divide the signal quality parameter into multiple data segments according to the time window, and perform Fourier transform on each data segment to obtain signal amplitude spectrum characteristics.

[0060] Specifically, the system pre-establishes a spectrum feature database containing standard spectrum types under typical working conditions. For each 2-second data segment, a 1024-point FFT operation is first performed to obtain a spectrum in the range of 0-50Hz. Then the top three frequency components with the largest amplitudes and their amplitudes are extracted, the ratio of each harmonic to the fundamental frequency is calculated, and the noise floor level is estimated. For example, the main frequency component of a data segment is located at 5Hz, the amplitude is 0.8, the subharmonic ratio is 0.3, and the noise floor is -40dB.

[0061] S220. Calculate amplitude spectrum differences between adjacent data segments according to the signal amplitude spectrum characteristics to obtain signal strength fluctuation characteristics.

[0062] In this embodiment, the amplitude spectrum difference is obtained by calculating the Euclidean distance of the spectrum vectors of adjacent time windows. The system sets a fluctuation feature extraction table to map the spectrum difference into three states: stable, fluctuating, and drastic. To improve reliability, the system performs sliding average processing on the difference of three consecutive windows.

[0063] Specifically, the difference between each pair of adjacent windows in three dimensions, namely, the main frequency component, the harmonic ratio, and the noise floor, is first calculated. Then, the state is judged according to the preset threshold: a difference of less than 20% is a stable state, 20%-50% is a fluctuating state, and greater than 50% is a drastic change state. At the same time, the duration and frequency of each state are recorded for subsequent analysis. For example, when the spectrum difference of three consecutive windows is detected to be greater than 50%, it is determined that the signal strength has fluctuated violently.

[0064] S230: Identify signal fading events according to signal strength fluctuation characteristics, and count the duration and depth of the fading.

[0065] 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 in signal strength relative to the normal level, expressed in dB. The duration records the time interval from the start of fading to the signal recovery.

[0066] Specifically, the system uses a state machine to track the change process of the signal strength. When the detected signal strength is more than 6 dB below the normal level, the fading event detection is triggered. The fading is classified according to the strength decrease rate: fast fading shows a decrease of more than 10 dB within 0.1 seconds, slow fading shows a slow decrease within 1 second, and periodic fading shows regular strength fluctuations. The system records the type, depth, and duration of each fading and stores them in the event log.

[0067] 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.

[0068] 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 fading (type, depth, duration) to different deduction items. The system maintains a 60 - second sliding scoring window, and the fading events occurring during this period are cumulatively deducted points to obtain the final link quality score.

[0069] Specifically, the system scores each fading event: 20 points are deducted for each fast fading, 10 points for each slow fading, and 5 - 15 points are deducted for periodic fading according to the frequency. For every additional 3 dB increase in the fading depth, 5 more points are deducted, and for every additional 1 - second increase in the duration, 3 more points are deducted. The deductions of each item are weighted and superimposed to obtain the overall score of this time window. For example, if there are 2 fast fadings (depth 12 dB, duration 0.5 seconds) and 1 slow fading (depth 6 dB, duration 2 seconds) within 1 minute, the total score is 65 points.

[0070] 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 working parameters, which specifically include the following steps: S310. Collect the remaining battery power and the current working power, and calculate the sustainable working time.

[0071] In this embodiment, the system collects the real - time voltage, current, and temperature data of the battery pack through a 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 working power includes the total power consumption of the signal relay module and the LED lighting module, which is monitored in real - time through a power detection circuit.

[0072] Specifically, the system updates the estimated value of sustainable working time every 1 minute. First, read the remaining power data provided by the BMS, and look up the table to obtain the corresponding available energy value. Then divide the available energy by the current total power consumption to get the theoretical working time. Taking into account the impact of battery efficiency changes with temperature, the system looks up the correction coefficient table according to the battery temperature for compensation. For example, when the remaining power is 60%, the current power consumption is 10W, and the battery temperature is 25°C, the estimated sustainable working time is about 6 hours.

[0073] S320: When the sustainable working time is less than a preset threshold, determine a minimum guaranteed value of the signal relay power according to a signal quality evaluation result.

[0074] The system sets a warning threshold and a guarantee threshold. The minimum guarantee value refers to the minimum power required to maintain basic communication functions, which is determined by a stepped power configuration table. The configuration table divides the signal quality assessment score into 5 levels, each corresponding to a different minimum power requirement.

[0075] Specifically, when the duration is lower than the reminder threshold, the system enters 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 based on the difference between the remaining time and the guarantee threshold. The final minimum guarantee value is equal to the reference power multiplied by the adjustment coefficient.

[0076] S330, allocating the remaining available power to the luminous intensity according to a preset ratio to obtain a dynamic adjustment value of the luminous intensity.

[0077] In this embodiment, the system establishes a power allocation priority table to define the allocation ratio of communication power and lighting power under different working conditions. The remaining available power is equal to the maximum output power of the battery minus the minimum guaranteed value of the signal relay. The luminous intensity adopts PWM dimming mode, and the duty cycle is approximately linear with the power.

[0078] 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). The allocation scheme is selected according to the current power status to calculate the available power of the lighting system. Then, the power value is converted into the corresponding PWM duty cycle by looking up the table to obtain the LED drive signal.

[0079] S340: 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.

[0080] In this embodiment, the optimal operating parameters include two control quantities: transmitter power level and LED drive duty cycle. The system maintains a parameter configuration log to record historical data of power adjustment.

[0081] Specifically, the system uses a soft start strategy to achieve smooth parameter transition. First, check the time interval from the last adjustment to determine whether the parameter update is allowed. When the update conditions are met, the new power parameter is subtracted from the current value and gradually adjusted to the target value according to the maximum allowed step size. At the same time, the adjustment time and parameter value are recorded in the configuration log.

[0082] It should be understood that the size of the serial numbers of the steps in the above embodiments does 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 on the implementation process of the embodiments of the present application.

[0083] On the second aspect, the present application provides a signal relay control system for an intelligent low-altitude take-off and landing field light beacon. The signal relay control system for the intelligent low-altitude take-off and landing field light beacon of the present application is described below in combination with the signal relay control method for the above-mentioned intelligent low-altitude take-off and landing field light beacon.

[0084] Reference Figure 6 , a signal relay control system for an intelligent low altitude take-off and landing field beacon, comprising: The signal quality parameter acquisition module 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; The signal quality assessment module 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; The optimal working parameter acquisition module is used to calculate the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters; The collaborative optimization module is used to apply the optimal working parameters to the signal relay system to achieve collaborative optimization of optical signals and communication signals.

[0085] In one embodiment, the signal quality parameter acquisition module includes: A theoretical attenuation calculation unit, used to dynamically calculate the theoretical attenuation values ​​of the communication signal and the optical signal according to the distance between the UAV and the beacon; A deviation calculation unit, used to collect actual communication signal strength and light intensity data, and calculate the degree of deviation from the theoretical attenuation value; A weighted fusion unit, used to calculate a corresponding weight coefficient according to the degree of deviation, and to perform weighted fusion of the communication signal strength and light intensity data according to the weight coefficient; The trend compensation unit is used to predict the deviation trend of the next position point according to the flight speed of the UAV, and compensate the signal quality parameters of the next position point according to the deviation trend.

[0086] In one embodiment, the weighted fusion unit includes: The initial weight calculation subunit is used to calculate the ratio of the deviation degree between the communication signal and the optical signal to a preset threshold value to obtain an initial weight coefficient; The influence factor calculation subunit is used to obtain the attitude angle, flight speed and weather visibility data of the UAV, calculate the influence coefficient of each parameter according to the preset rules, and obtain the weight adjustment factor; The weight correction subunit is used to correct the initial weight coefficient using the weight adjustment factor to obtain the fused signal quality parameter.

[0087] In one embodiment, the signal quality assessment module includes: A frequency domain analysis unit, used to 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; A fluctuation feature extraction unit is used to calculate the amplitude spectrum difference of adjacent data segments according to the signal amplitude spectrum feature to obtain the signal strength fluctuation feature; A fading event recognition unit is used to recognize signal fading events according to signal strength fluctuation characteristics and to calculate the duration and depth of the fading; The quality scoring unit is used to establish a communication link quality scoring standard based on the duration and depth of the fading event and generate a signal quality evaluation result.

[0088] In one embodiment, the optimal working parameter acquisition module includes: Working time calculation unit, used to collect the remaining battery power and current working power, and calculate the sustainable working time; A power guarantee unit, used to determine a minimum guaranteed value of the signal relay power according to a signal quality evaluation result when the sustainable working time is less than a preset threshold; A light intensity adjustment unit, used to allocate the remaining available power to the luminous intensity according to a preset ratio to obtain a dynamic adjustment value of the luminous intensity; The parameter output unit is used to 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.

[0089] In one embodiment, the present application provides a low altitude take-off and landing field beacon, the internal structure of which can be as follows: Figure 7As shown. The low-altitude 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 landing field beacon is used to provide computing and control capabilities. The memory of the low-altitude 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 landing field beacon is used to store data. The network interface of the low-altitude landing field beacon is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a signal relay control method for an intelligent low-altitude landing field beacon is implemented.

[0090] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the low-altitude landing and take-off field light beacon to which the present application scheme is applied. The specific low-altitude landing and take-off field light beacon may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0091] In one embodiment, a low altitude take-off and landing field beacon is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0092] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned 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 embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0093] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A signal relay control method for an intelligent low altitude take-off and landing field beacon, characterized in that: The steps include: Collect the communication signal strength and light intensity data between the drone and the beacon in real time, and obtain real-time signal quality parameters in combination with positioning information; Performing frequency domain analysis and time domain analysis on the signal quality parameters, establishing a communication link quality model, and generating a signal quality evaluation result; Calculate the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters; The optimal operating parameters are applied to the signal relay system to achieve coordinated optimization of optical signals and communication signals.

2. The signal relay control method of the intelligent low altitude take-off and landing field beacon according to claim 1 is characterized in that: The communication signal strength and light intensity data between the drone and the beacon are collected in real time, and the real-time signal quality parameters are obtained in combination with the positioning information. The specific steps include the following: According to the distance between the drone and the beacon, the theoretical attenuation value of the communication signal and the optical signal is dynamically calculated; Collecting actual communication signal strength and light intensity data, and calculating the degree of deviation from the theoretical attenuation value; Calculating a corresponding weight coefficient according to the degree of deviation, and performing weighted fusion of the communication signal strength and the light intensity data according to the weight coefficient; The deviation trend of the next position point is predicted according to the flight speed of the UAV, and the signal quality parameter of the next position point is compensated according to the deviation trend.

3. The signal relay control method of the intelligent low altitude take-off and landing field beacon according to claim 2 is characterized in that: Calculating a corresponding weight coefficient according to the degree of deviation, and weighted fusion of the communication signal strength and the light intensity data according to the weight coefficient, specifically includes the following steps: Calculating the ratio of the degree of deviation between the communication signal and the optical signal to a preset threshold value to obtain an initial weight coefficient; Obtain the attitude angle, flight speed and weather visibility data of the UAV, calculate the influence coefficient of each parameter according to the preset rules, and obtain the weight adjustment factor; The initial weight coefficient is corrected using the weight adjustment factor to obtain a fused signal quality parameter.

4. The signal relay control method of the intelligent low altitude take-off and landing field beacon according to claim 1 is characterized in that: Performing frequency domain analysis and time domain analysis on the signal quality parameters, establishing a communication link quality model, and generating a signal quality evaluation result specifically includes the following steps: Dividing the signal quality parameter into multiple data segments according to the time window, and performing Fourier transform on each data segment to obtain signal amplitude spectrum characteristics; Calculating amplitude spectrum differences of adjacent data segments according to the signal amplitude spectrum characteristics to obtain signal strength fluctuation characteristics; Identify signal fading events according to the signal strength fluctuation characteristics, and count the duration and depth of the fading; A communication link quality scoring standard is established based on the duration and depth of the fading event, and a signal quality evaluation result is generated.

5. The signal relay control method of the intelligent low altitude take-off and landing field beacon according to claim 1 is characterized in that: Calculating the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters specifically includes the following steps: Collect the remaining battery power and current working power to calculate the sustainable working time; When the sustainable working time is less than a preset threshold, determining a 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 a preset ratio to obtain a dynamic adjustment value of the luminous intensity; According to the minimum guaranteed value of the signal relay power and the dynamic adjustment value of the luminous intensity, the optimal working parameters of the beacon are output.

6. A signal relay control system for an intelligent low altitude take-off and landing field beacon, characterized in that: include: The signal quality parameter acquisition module 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; A signal quality assessment module, 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 assessment result; An optimal working parameter acquisition module, used to calculate the luminous intensity and signal relay power of the beacon according to the signal quality evaluation result to obtain the optimal working parameters; The collaborative optimization module is used to apply the optimal working parameters to the signal relay system to achieve collaborative optimization of optical signals and communication signals.

7. According to the signal relay control system of the intelligent low-altitude take-off and landing field beacon according to claim 6, the signal quality parameter acquisition module comprises: A theoretical attenuation calculation unit, used to dynamically calculate the theoretical attenuation values ​​of the communication signal and the optical signal according to the distance between the UAV and the beacon; A deviation calculation unit, used to collect actual communication signal strength and light intensity data, and calculate the degree of deviation from the theoretical attenuation value; A weighted fusion unit, used to calculate a corresponding weight coefficient according to the degree of deviation, and perform weighted fusion of the communication signal strength and the light intensity data according to the weight coefficient; The trend compensation unit is used to predict the deviation trend of the next position point according to the flight speed of the UAV, and compensate the signal quality parameter of the next position point according to the deviation trend.

8. According to the signal relay control system of the intelligent low-altitude take-off and landing field beacon according to claim 7, the weighted fusion unit comprises: The initial weight calculation subunit is used to calculate the ratio of the deviation degree between the communication signal and the optical signal to a preset threshold value to obtain an initial weight coefficient; The influence factor calculation subunit is used to obtain the attitude angle, flight speed and weather visibility data of the UAV, calculate the influence coefficient of each parameter according to the preset rules, and obtain the weight adjustment factor; The weight correction subunit is used to correct the initial weight coefficient using the weight adjustment factor to obtain a fused signal quality parameter.

9. The signal relay control system of the intelligent low altitude take-off and landing field beacon according to claim 8 is characterized in that: The signal quality assessment module comprises: A frequency domain analysis unit, used to divide the signal quality parameter into multiple data segments according to the time window, and perform Fourier transform on each data segment to obtain signal amplitude spectrum characteristics; A fluctuation feature extraction unit, used to calculate the amplitude spectrum difference between adjacent data segments according to the signal amplitude spectrum feature to obtain a signal strength fluctuation feature; A fading event identification unit, used to identify signal fading events according to the signal strength fluctuation characteristics, and to count the duration and depth of the fading; The quality scoring unit is used to establish a communication link quality scoring standard based on the duration and depth of the fading event and generate a signal quality evaluation result.

10. 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 take-off and landing field beacon described in any one of claims 1-5 are implemented.

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