Real-time adaptive control method and system for link power of unmanned aerial vehicle

Abstract

The invention discloses a real-time adaptive control method and system for the link power of an unmanned aerial vehicle, and the main layout is that high-precision signal intensity sensors are arranged around an unmanned aerial vehicle body, signal intensity values are collected every 50 milliseconds, and the link quality is evaluated through weighted calculation in combination with a signal-to-noise ratio and a multipath fading condition. And according to the link quality, the flight parameters and the environment information, a power adjustment value is calculated by using a decision model, the transmitting power is adjusted at a specific step length according to the link condition, and an instruction is transmitted to the power control module. The system comprises a data acquisition module, a processing module, a power control module and a communication module. The data acquisition module acquires multi-source data in an omnibearing manner; the data processing module integrates an algorithm to evaluate the link quality and calculate a power adjustment value; the power control module accurately regulates and controls the transmitting power; according to the method and the system, the link power of the unmanned aerial vehicle can be accurately controlled in real time, and the communication stability and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle communication technology, and in particular to a real-time adaptive control method and system for unmanned aerial vehicle link power. Background Art

[0002] In the increasingly widespread application scenarios of drones, link power control between drones and ground control stations has become a key link to ensure stable and efficient communication. Traditional power control methods are mostly fixed power output modes, or only roughly adjusted based on simple signal strength feedback. However, the environment in which drones fly is extremely complex and changeable. For example, when encountering obstacles such as mountains and tall buildings, the signal is easily blocked and attenuated; different weather conditions, such as rain and dust, will significantly change the propagation characteristics of the signal; in addition, the surrounding electromagnetic environment is always in an unstable state, and various interference sources appear frequently. The fixed power output method is completely unable to adapt to these complex changes, causing unnecessary energy waste in areas with good signals, and unable to guarantee communication quality in areas with poor signals. The adjustment method based on simple signal strength feedback fails to fully consider a variety of influencing factors, making it difficult to achieve accurate and timely power control, resulting in the stability and energy utilization efficiency of the drone communication link far from ideal. Summary of the invention

[0003] The purpose of the present invention is to provide a real-time adaptive control method for UAV link power, which can achieve accurate and timely power control and solve the problem that the stability and energy utilization efficiency of the UAV communication link are far from ideal.

[0004] Another object of the present invention is to provide a real-time adaptive control system for UAV link power, which can dynamically and accurately adaptively control power according to the real-time flight status and link status of the UAV.

[0005] In order to solve the above technical problems, the technical solution adopted in this application is:

[0006] In the first aspect, the embodiment of the present application provides a real-time adaptive control method for the link power of a drone, including: step 1. four strictly calibrated high-precision signal strength sensors are evenly arranged at the front, rear, left and right positions of the drone body, and the link signal strength value between the drone and the ground control station is synchronously collected every 50 milliseconds. Step 2. Based on the collected signal strength value, combined with the signal-to-noise ratio and multipath fading, the link quality is evaluated through a preset weighted calculation model, wherein the signal strength accounts for 40%, the signal-to-noise ratio accounts for 30%, and the multipath fading accounts for 30%. The signal strength calculation formula is shown in Formula 1:

[0007] RSSI adjusted=RSSI-α distance log(d)+γ env ·EF Formula 1,

[0008] In the formula, RSSI is the received signal strength, α distance is the signal attenuation coefficient, d is the distance between the device and the signal source, γ env is the environmental factor coefficient, and the environmental effect of the adjustment signal is shown in Formula 2:

[0009] EF=α temp ·T+α humidity ·H+α obstacle ·O+α emi ·I Formula 2,

[0010] Where: T is the ambient temperature, α temp is the influence coefficient of temperature; H is the ambient humidity, α humidity is the influence coefficient of humidity; O is the obstacle density or obstacle type, α obstacle is the influence coefficient of the obstacle; I is the electromagnetic interference intensity, α emi is the influence coefficient of electromagnetic interference;

[0011] Signal-to-noise ratio: SNR, multipath fading is shown in equation 3:

[0012]

[0013] In the formula, PathLoss i is the loss of the ith path, γ fade is the multipath fading influence coefficient, ∈ time is the time attenuation coefficient, which is used to consider the change of fading over time, TF is the time factor, which is the change of environment or signal quality over time;

[0014] The calculation formula of link quality score LQ is shown in formula 4:

[0015] LQ=0.4·RSSI adjusted +0.3·SNR+0.3·Fading adjusted Formula 4;

[0016] Step 3. Calculate the power adjustment value using the pre-built decision model based on the link quality assessment results, UAV flight parameters and environmental information. Step 4. If the link quality deteriorates sharply, increase the transmit power by 2 dBm at a time. If the link quality is stable, adjust the transmit power by 0.5 dBm at a time. Step 5. Convert the power adjustment decision result into a power adjustment instruction, transmit it to the power control module through the internal data bus of the UAV, and the power control module adjusts the power amplifier gain to achieve precise control of the transmit power.

[0017] In some embodiments of the present invention, when the power adjustment value is calculated using the pre-built decision model, the input parameters of the decision model include the flight altitude H, speed V, heading D, ambient temperature T, and electromagnetic interference EI of the drone, and the calculation formula is shown in Formula 5:

[0018]

[0019] In some embodiments of the present invention, before the signal strength sensor collects data, the sensor is subjected to zero point calibration and sensitivity calibration to ensure the accuracy of the collected data.

[0020] In some embodiments of the present invention, before evaluating the link quality, the collected signal strength values ​​are filtered to remove noise interference in the signals.

[0021] The hybrid filtering algorithm combines the advantages of low-pass filtering and adaptive weighted filtering, and is designed to effectively process dynamically changing signals, especially in environments where noise intensity is not constant. The core idea of ​​the algorithm is to use the original value S of the signal to raw (n) is optimized, and the optimized filtering formula is shown in Formula 6:

[0022]

[0023] Where N is the size of the sliding window, which controls the smoothness of the average value, α, β are dynamic weights, satisfying α + β = 1, and their values ​​depend on the noise intensity N r Dynamic changes are made to ensure that when the noise is strong, more reliance is placed on the historical average, and when the noise is weak, more attention is paid to the current signal. The specific adjustment formula is shown in Formula 7:

[0024]

[0025] This adaptive hybrid filtering method can not only effectively suppress noise, but also flexibly respond to dynamic changes of signals. It is particularly suitable for processing signals with time-varying noise characteristics.

[0026] Furthermore, multi-source data collection is used in step 1: in order to accurately obtain the link signal strength between the UAV and the ground control station, four strictly calibrated high-precision signal strength sensors are evenly arranged in the front, back, left and right directions of the UAV body. These sensors have excellent sensitivity and can accurately capture subtle fluctuations in signal strength. Every 50 milliseconds is set as a collection cycle. In each cycle, the four sensors are started synchronously to quickly collect link signal strength values. With this all-round, high-frequency collection strategy, the signal strength situation of the UAV in different directions and at different times can be fully and timely grasped, providing solid and reliable basic data for subsequent power adjustments.

[0027] In step 2-3, the link quality assessment uses a model algorithm, which comprehensively weighs multiple key factors to quantify the link quality. Signal strength is the basic consideration factor. When the signal strength is within the normal range, but the signal-to-noise ratio (SNR) is low, it indicates that the signal is seriously interfered by noise and the link quality decreases accordingly. At the same time, multipath fading is also an important consideration indicator. Multipath fading will cause the signal to produce delay spread and frequency selective fading, which seriously affects the integrity of the signal. For example, by deeply analyzing the delay time and amplitude changes of the signal on different propagation paths, the degree of multipath fading can be accurately assessed. The model performs weighted calculations on these factors, and after repeated verification by a large amount of experimental data and actual application scenarios, it is determined that signal strength accounts for 40%, signal-to-noise ratio accounts for 30%, and multipath fading accounts for 30%. By performing weighted sum operations on each factor, a link quality assessment value that intuitively reflects the health of the current link is finally obtained. This value provides a key decision basis for power adjustment decisions.

[0028] In step 4-5, the decision on power adjustment is as follows:

[0029] Decision model establishment: Based on massive flight test data and in-depth theoretical analysis, a power adjustment decision model is constructed. The model uses link quality assessment results, UAV flight parameters such as altitude, speed, heading, etc., and environmental information including meteorological data, electromagnetic interference conditions, etc. as input parameters, and outputs accurate power adjustment values. For example, when the UAV's flight altitude increases and causes signal attenuation, the decision model uses a preset algorithm model based on the altitude change, current signal strength, and link quality assessment results to accurately calculate the amount of power that needs to be increased.

[0030] Dynamic adjustment strategy: According to the power adjustment value output by the decision model, dynamic control is performed according to the pre-set power adjustment step. When the link quality deteriorates sharply, the power adjustment step is appropriately increased to quickly improve the signal strength; when the link quality remains stable, the power adjustment step is reduced to prevent unnecessary power fluctuations. For example, if the link quality assessment shows that the signal strength drops rapidly and the signal-to-noise ratio is lower than the set threshold, the power adjustment step is set to increase by 2dBm each time; if the link quality is stable, the step is set to increase or decrease by 0.5dBm each time.

[0031] Power adjustment execution:

[0032] Command transmission: The power adjustment decision result is converted into a power adjustment command, which is quickly transmitted to the power control module through the high-speed and stable data bus inside the drone. The command contains key information such as the direction and amplitude of the power adjustment.

[0033] Power control implementation: After receiving the command, the power control module adjusts the gain of the power amplifier to accurately control the change of the drone's transmission power. The power amplifier adopts advanced high-precision digital control technology, which can respond to power adjustment commands quickly and accurately, ensuring that the transmission power reaches the target value in a very short time.

[0034] In the second aspect, an embodiment of the present application provides a real-time adaptive control system for UAV link power, which includes: a data acquisition module, which is composed of four high-precision signal strength sensors evenly distributed around the UAV body, and a built-in gyroscope, accelerometer, GPS module, meteorological sensor and electromagnetic spectrum monitoring equipment, which is used to collect link signal strength values, flight parameters, meteorological data and electromagnetic interference information, and transmit them to the data processing module through the data acquisition circuit and the data transmission bus. The data processing module adopts a high-performance embedded processor, and internally integrates a link quality assessment model algorithm module and a power adjustment decision model algorithm module, which is used to receive data from the data acquisition module, perform link quality assessment and power adjustment value calculation, generate a power adjustment instruction and send it to the power control module. The power control module is composed of a power amplifier and a digital control circuit. The digital control circuit receives the power adjustment instruction and adjusts the power amplifier gain according to the instruction. The communication module supports multiple communication protocols such as WiFi, 4G, 5G, etc., and adopts adaptive modulation and coding technology to dynamically adjust the transmission rate and encoding method according to the link quality.

[0035] In order to improve the adaptability and performance of the communication module under various communication protocols, this model dynamically adjusts the transmission rate and coding method through adaptive modulation and coding technology to ensure that the transmission rate can be maximized and the power consumption is minimized under different link quality conditions. The model considers the cooperation factor between devices, link quality and energy consumption protection mechanism in the dynamic adjustment process to achieve more efficient communication. The optimized dynamic transmission rate formula is shown in Equation 7:

[0036]

[0037] Where R is the dynamic transmission rate in bits per second; C is the device cooperation factor; β is the energy conservation coefficient, and low energy state gives priority to energy saving; E represents the current energy state of the device, indicating the remaining energy of the device. A higher EEE value means that the device has enough energy to support a higher transmission rate; R min The minimum transmission rate refers to the minimum rate under the worst link quality or the lowest cooperation factor; R max The maximum transmission rate indicates the highest rate under the best link quality and the strongest device cooperation factor.

[0038] In order to dynamically select the modulation mode according to the link quality LQ, the system adopts a hierarchical modulation scheme. The change of link quality determines the selected modulation mode, thereby optimizing the spectrum efficiency and energy consumption while ensuring the communication quality. The modulation mode is dynamically selected according to the link quality as shown in Equation 8:

[0039]

[0040] γ1,γ2,γ3 dynamically adjust the link quality threshold as shown in formula 9:

[0041] γ i =γ i,0 +η(LQ avg -LQ threshold ) Formula 9,

[0042] In the formula, γ i,0 is the initial link quality threshold, which indicates the preset threshold under standard conditions; η is the adjustment factor, which is used to control the sensitivity of threshold adjustment. A larger η value will make the threshold more sensitive to link quality changes; LQ avg It is the average value of link quality, usually the average of historical link quality, used to smooth sudden link quality fluctuations; LQ threshold It is a preset link quality reference threshold, usually the design standard value of the system, representing a benchmark link quality level.

[0043] In some embodiments of the present invention, the data acquisition circuit in the above-mentioned data acquisition module has signal amplification and analog-to-digital conversion functions, and converts the analog signal collected by the sensor into a digital signal and transmits it to the data processing module.

[0044] In some embodiments of the present invention, the high-performance embedded processor in the above-mentioned data processing module has a hardware multiplier and a high-speed cache.

[0045] In some embodiments of the present invention, the power amplifier is a linear power amplifier having high linearity, high efficiency and fast response characteristics.

[0046] In some embodiments of the present invention, the communication module uses an AES encryption algorithm to encrypt the transmission data.

[0047] In some embodiments of the present invention, the above system further includes a fault diagnosis module for monitoring the working status of each module in real time, and when a fault is detected, an alarm is issued and fault information is recorded.

[0048] Furthermore, the data acquisition module is composed of four high-precision signal strength sensors evenly distributed around the drone body, as well as built-in gyroscopes, accelerometers, GPS modules, meteorological sensors and electromagnetic spectrum monitoring equipment. These sensors and devices are connected to the high-speed data transmission bus through a carefully designed data acquisition circuit, and can transmit all kinds of collected data to the data processing module in real time and accurately. The selection and layout of each sensor and device have been repeatedly verified and optimized to ensure that multi-source data closely related to drone link power control can be fully and accurately collected.

[0049] The above-mentioned data processing module adopts a high-performance embedded processor with powerful computing power and sufficient memory resources. The processor integrates the link quality assessment model algorithm module and the power adjustment decision model algorithm module. The data processing module receives the data from the data acquisition module through the data transmission bus, generates power adjustment instructions after efficient processing by each algorithm module, and quickly sends the instructions to the power control module. The high-performance processor and optimized algorithm module ensure the efficiency and accuracy of data processing, and can complete complex data processing and decision-making processes in a very short time.

[0050] The power control module is mainly composed of a power amplifier and a digital control circuit. The digital control circuit receives the power adjustment command sent by the data processing module, and accurately controls the gain of the power amplifier according to the power adjustment direction and amplitude in the command to achieve precise control of the UAV transmission power. The power amplifier has the characteristics of high linearity, high efficiency and fast response. It can quickly adjust the transmission power while ensuring the signal quality, meeting the stringent requirements of real-time adaptive control of UAV link power.

[0051] The above-mentioned communication module is responsible for data transmission between the UAV and the ground control station, and supports multiple communication protocols such as WiFi, 4G, and 5G. The communication module uses adaptive modulation and coding technology to dynamically adjust the transmission rate and coding method according to the link quality to improve data transmission efficiency and reliability. At the same time, the communication module has advanced data encryption and decryption functions to effectively ensure the security of data transmission. During the power adjustment process, the communication module can stably transmit data to ensure that the communication between the UAV and the ground control station is always smooth.

[0052] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0053] 1. The present invention evenly arranges high-precision and strictly calibrated signal strength sensors on the four positions of the UAV body, and synchronously collects signal strength values ​​at a high-frequency period of every 50 milliseconds, which can obtain link signal strength in an all-round, timely and accurate manner, providing a reliable data basis for subsequent link quality evaluation and power adjustment, and effectively improving the accuracy of power control.

[0054] 2. The present invention combines the signal-to-noise ratio and multipath fading conditions, evaluates the link quality through a preset weighted calculation model, and comprehensively considers multiple key factors, making the evaluation of link quality more comprehensive and scientific, thereby providing a more accurate basis for power adjustment decisions.

[0055] 3. The present invention calculates the power adjustment value based on the link quality evaluation results, flight parameters and environmental information using a pre-built decision model, ensuring that the power adjustment strategy fully considers the actual flight status and external environmental factors of the UAV, thereby achieving more reasonable and effective power control.

[0056] 4. The present invention adopts different power adjustment steps according to different conditions of link quality. When the link quality deteriorates sharply, the transmission power can be quickly increased to ensure communication. When the link quality is stable, the power is finely adjusted to avoid unnecessary power fluctuations and improve energy utilization efficiency.

[0057] 5. The present invention converts the power adjustment decision result into an instruction, and transmits it to the power control module through the internal data bus, so as to realize the precise control of the transmission power, ensure the timeliness and accuracy of the power adjustment, and improve the stability of the communication link.

[0058] 6. The decision model of the present invention incorporates input parameters such as flight altitude, speed, heading, meteorological data, and electromagnetic interference conditions, so that the power adjustment decision can comprehensively consider more factors affecting signal transmission, further improve the pertinence and effectiveness of power adjustment, and adapt to more complex and changeable flight environments.

[0059] 7. The present invention performs zero point calibration and sensitivity calibration before the signal strength sensor collects data, which can effectively eliminate the sensor's own errors and ensure that the collected data is accurate and reliable, thereby improving the accuracy and stability of the entire power control process.

[0060] 8. The present invention filters the collected signal strength values ​​before evaluating the link quality to remove noise interference, which can improve the signal quality, make the link quality evaluation results more accurate, and further improve the reliability of the power adjustment decision.

[0061] 9. The data acquisition module involved in the present invention integrates multiple sensors, which can comprehensively collect link signal strength values, flight parameters, meteorological data and electromagnetic interference information, and transmit them through specific circuits and buses, providing rich and accurate data support for the system, which helps to achieve comprehensive and accurate power control.

[0062] 10. The data processing module involved in the present invention adopts a high-performance embedded processor and integrates a link quality evaluation and power adjustment decision algorithm module, which can process data quickly and accurately, efficiently complete link quality evaluation and power adjustment value calculation, and ensure the real-time performance and decision accuracy of the system.

[0063] 11. The present invention adjusts the power amplifier gain according to instructions through a digital control circuit in the power control module to achieve precise control of the transmission power, ensure the accuracy and stability of power adjustment, and meet the strict requirements of the UAV for power control.

[0064] 12. The present invention supports multiple communication protocols in the communication module, adopts adaptive modulation and coding technology, and dynamically adjusts the transmission rate and coding method according to the link quality, thereby ensuring the security and stability of data transmission between the UAV and the ground control station, and improving the reliability and adaptability of the communication system.

[0065] 13. The data acquisition circuit involved in the present invention has signal amplification and analog-to-digital conversion functions, and can convert the weak analog signals collected by the sensor into digital signals suitable for processing by the data processing module, ensuring the accuracy and integrity of data transmission and providing a reliable data source for subsequent data processing and decision-making.

[0066] 14. The high-performance embedded processor in the data processing module of the present invention is equipped with a hardware multiplier and a high-speed cache, which can significantly improve the data processing speed and computing efficiency, enabling the system to complete complex data processing and decision-making tasks in a short time and meet the needs of real-time power control of drones.

[0067] 15. The present invention adopts a linear power amplifier, whose high linearity, high efficiency and fast response characteristics ensure that the signal is not distorted during the power adjustment process, ensuring the communication quality, while improving the energy utilization efficiency and reducing power loss.

[0068] 16. The communication module involved in the present invention adopts AES encryption algorithm to encrypt the transmitted data, effectively preventing the data from being stolen or tampered with during the transmission process, ensuring the security of data transmission and enhancing the application reliability of the system in complex environments.

[0069] 17. The system involved in the present invention is equipped with a fault diagnosis module, which can monitor the working status of each module in real time, discover and handle faults in time, issue alarms and record fault information, which is convenient for subsequent maintenance and troubleshooting, improves the stability and reliability of the system, and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 It is a principle block diagram of the present invention;

[0072] Figure 2 It is a flow chart of the real-time adaptive control method of the UAV link power in the present invention;

[0073] Figure 3 A structural block diagram of an electronic device provided by an embodiment of the present invention.

[0074] Icon: 100 - memory; 102 - processor; 103 - communication interface. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0076] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0077] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0078] Example 1

[0079] See also Figure 1-2 , Figure 1 It is a principle block diagram of the present invention; Figure 2 It is a flow chart of the real-time adaptive control method of the UAV link power in the present invention.

[0080] This embodiment provides a real-time adaptive control method for link power of a UAV, including: step 1. four strictly calibrated high-precision signal strength sensors are evenly arranged at the front, rear, left and right positions of the UAV body, and the link signal strength value between the UAV and the ground control station is synchronously collected every 50 milliseconds. Step 2. Based on the collected signal strength value, combined with the signal-to-noise ratio and multipath fading, the link quality is evaluated through a preset weighted calculation model, wherein the signal strength accounts for 40%, the signal-to-noise ratio accounts for 30%, and the multipath fading accounts for 30%. The signal strength calculation formula is shown in Formula 1:

[0081] RSSI adjusted =RSSI-α distance log(d)+γ env ·EF Formula 1,

[0082] In the formula, RSSI is the received signal strength, α distance is the signal attenuation coefficient, d is the distance between the device and the signal source, γ env is the environmental factor coefficient, and the environmental effect of the adjustment signal is shown in Formula 2:

[0083] EF=α temp ·T+α humidity ·H+αobstacle ·O+α emi ·I Formula 2,

[0084] Where: T is the ambient temperature, α temp is the influence coefficient of temperature; H is the ambient humidity, α humidity is the influence coefficient of humidity; O is the obstacle density or obstacle type, α obstacle is the influence coefficient of the obstacle; I is the electromagnetic interference intensity, α emi is the influence coefficient of electromagnetic interference;

[0085] Signal-to-noise ratio: SNR, multipath fading is shown in equation 3:

[0086]

[0087] In the formula, PathLoss i is the loss of the ith path, γ fade is the multipath fading influence coefficient, ∈ time is the time attenuation coefficient, which is used to consider the change of fading over time, TF is the time factor, which is the change of environment or signal quality over time;

[0088] The calculation formula of link quality score LQ is shown in formula 4:

[0089] LQ=0.4·RSSI adjusted +0.3·SNR+0.3·Fading adjusted Formula 4;

[0090] Step 3. Calculate the power adjustment value using the pre-built decision model based on the link quality assessment results, UAV flight parameters and environmental information. Step 4. If the link quality deteriorates sharply, increase the transmit power by 2 dBm at a time. If the link quality is stable, adjust the transmit power by 0.5 dBm at a time. Step 5. Convert the power adjustment decision result into a power adjustment instruction, transmit it to the power control module through the internal data bus of the UAV, and the power control module adjusts the power amplifier gain to achieve precise control of the transmit power.

[0091] In some embodiments of the present invention, when the power adjustment value is calculated using the pre-built decision model, the input parameters of the decision model include the flight altitude H, speed V, heading D, ambient temperature T, and electromagnetic interference EI of the drone, and the calculation formula is shown in Formula 5:

[0092]

[0093] In some embodiments of the present invention, before the signal strength sensor collects data, the sensor is subjected to zero point calibration and sensitivity calibration to ensure the accuracy of the collected data.

[0094] In some embodiments of the present invention, before evaluating the link quality, the collected signal strength values ​​are filtered to remove noise interference in the signals.

[0095] The hybrid filtering algorithm combines the advantages of low-pass filtering and adaptive weighted filtering, and is designed to effectively process dynamically changing signals, especially in environments where noise intensity is not constant. The core idea of ​​the algorithm is to use the original value S of the signal to raw (n) is optimized, and the optimized filtering formula is shown in Formula 6:

[0096]

[0097] Where N is the size of the sliding window, which controls the smoothness of the average value, α, β are dynamic weights, satisfying α + β = 1, and their values ​​depend on the noise intensity N r Dynamic changes are made to ensure that when the noise is strong, more reliance is placed on the historical average, and when the noise is weak, more attention is paid to the current signal. The specific adjustment formula is shown in Formula 7:

[0098]

[0099] This adaptive hybrid filtering method can not only effectively suppress noise, but also flexibly respond to dynamic changes of signals. It is particularly suitable for processing signals with time-varying noise characteristics.

[0100] Example 2

[0101] On the basis of Example 1, this Example 2 provides a real-time adaptive control system for UAV link power, including: a data acquisition module, which is composed of four high-precision signal strength sensors evenly distributed around the UAV body, and a built-in gyroscope, accelerometer, GPS module, meteorological sensor and electromagnetic spectrum monitoring equipment, and is used to collect link signal strength values, flight parameters, meteorological data and electromagnetic interference information, and transmit them to the data processing module through the data acquisition circuit and the data transmission bus. The data processing module adopts a high-performance embedded processor, and internally integrates a link quality assessment model algorithm module and a power adjustment decision model algorithm module, which is used to receive data from the data acquisition module, perform link quality assessment and power adjustment value calculation, generate a power adjustment instruction and send it to the power control module. The power control module is composed of a power amplifier and a digital control circuit. The digital control circuit receives the power adjustment instruction and adjusts the power amplifier gain according to the instruction. The communication module supports multiple communication protocols such as WiFi, 4G, and 5G, and adopts adaptive modulation and coding technology to dynamically adjust the transmission rate and encoding method according to the link quality.

[0102] In order to improve the adaptability and performance of the communication module under various communication protocols, this model dynamically adjusts the transmission rate and coding method through adaptive modulation and coding technology to ensure that the transmission rate can be maximized and the power consumption is minimized under different link quality conditions. The model considers the cooperation factor between devices, link quality and energy consumption protection mechanism in the dynamic adjustment process to achieve more efficient communication. The optimized dynamic transmission rate formula is shown in Equation 7:

[0103]

[0104] Where R is the dynamic transmission rate in bits per second; C is the device cooperation factor; β is the energy conservation coefficient, and low energy state gives priority to energy saving; E represents the current energy state of the device, indicating the remaining energy of the device. A higher EEE value means that the device has enough energy to support a higher transmission rate; R min The minimum transmission rate refers to the minimum rate under the worst link quality or the lowest cooperation factor; R max The maximum transmission rate indicates the highest rate under the best link quality and the strongest device cooperation factor.

[0105] In order to dynamically select the modulation mode according to the link quality LQ, the system adopts a hierarchical modulation scheme. The change of link quality determines the selected modulation mode, thereby optimizing the spectrum efficiency and energy consumption while ensuring the communication quality. The modulation mode is dynamically selected according to the link quality as shown in Equation 8:

[0106]

[0107] γ1,γ2,γ3 dynamically adjust the link quality threshold as shown in formula 9:

[0108] γ i =γ i,0 +η(LQ avg -LQ threshold ) Formula 9,

[0109] In the formula, γ i,0 is the initial link quality threshold, which indicates the preset threshold under standard conditions; η is the adjustment factor, which is used to control the sensitivity of threshold adjustment. A larger η value will make the threshold more sensitive to link quality changes; LQ avg It is the average value of link quality, usually the average of historical link quality, used to smooth sudden link quality fluctuations; LQ threshold It is a preset link quality reference threshold, usually the design standard value of the system, representing a benchmark link quality level.

[0110] In some embodiments of the present invention, the data acquisition circuit in the above-mentioned data acquisition module has signal amplification and analog-to-digital conversion functions, and converts the analog signal collected by the sensor into a digital signal and transmits it to the data processing module.

[0111] In some embodiments of the present invention, the high-performance embedded processor in the above-mentioned data processing module has a hardware multiplier and a high-speed cache.

[0112] In some embodiments of the present invention, the power amplifier is a linear power amplifier having high linearity, high efficiency and fast response characteristics.

[0113] In some embodiments of the present invention, the communication module uses an AES encryption algorithm to encrypt the transmission data.

[0114] In some embodiments of the present invention, the system further includes a fault diagnosis module for real-time monitoring of the working status of each module, and when a fault is detected, an alarm is issued and the fault information is recorded.

[0115] Implementation scenario: In a bustling city with densely populated high-rise buildings, drones perform express delivery tasks. The electromagnetic environment in this area is complex, the signals generated by various electronic devices interfere with each other, and the communication signals between the drone and the ground control station are frequently blocked by high-rise buildings, posing a severe challenge to communication stability.

[0116] Implementation process:

[0117] Multi-source data acquisition: According to the design of the present invention, strictly calibrated high-precision signal strength sensors are deployed at four positions of the drone body to synchronously collect signal strength values ​​at a frequency of 50 milliseconds. When the drone is 100 meters away from a high-rise building, the signal strength drops from -60dBm to -80dBm. At the same time, the built-in gyroscope, accelerometer, and GPS module obtain flight attitude, speed, heading, and location information in real time, and meteorological sensors and electromagnetic spectrum monitoring equipment collect temperature, humidity, air pressure, and surrounding electromagnetic interference data, providing a comprehensive data basis for subsequent analysis.

[0118] Link quality assessment: After receiving the data, the data processing module starts the link quality assessment model algorithm of the present invention. The algorithm evaluates the link quality based on the preset weighted calculation model by comprehensively considering factors such as signal-to-noise ratio and multipath fading. The multipath fading is serious due to the occlusion of high-rise buildings, the signal-to-noise ratio is reduced, and the link quality is judged to be deteriorated.

[0119] Power adjustment decision and execution: The power adjustment decision model algorithm module accurately calculates the need to increase the transmission power based on the link quality assessment results, flight parameters and environmental information. After receiving the command, the power control module adjusts the power amplifier gain to increase the transmission power from 20dBm to 23dBm, thereby enhancing the signal transmission strength and overcoming the signal attenuation caused by high-rise building obstruction.

[0120] Communication performance optimization: The communication module adopts the adaptive modulation and coding technology of the present invention. According to the change of link quality, the modulation mode is adjusted from 16QAM to 64QAM, and the transmission rate is increased from 10Mbps to 12Mbps, thereby improving the efficiency and reliability of data transmission.

[0121] Implementation effect: Through the method and system of the present invention, the drone can achieve stable communication with the ground control station in the densely populated area of ​​urban high-rise buildings. During the express delivery process, the data transmission packet loss rate is controlled within 5%, and the bit error rate is less than 3%. The delivery task is successfully completed, demonstrating the effectiveness and advantages of the present invention in complex urban environments.

[0122] Comparative Example 1

[0123] Comparison scenario: In a bustling city with densely populated high-rise buildings, drones perform express delivery tasks and face complex electromagnetic environments and signal obstruction from high-rise buildings.

[0124] Implementation process: The drone uses a fixed power output mode, with a constant transmission power of 20dBm. When the drone is 100 meters away from a high-rise building, the signal is blocked by the building, and the strength drops from -60dBm to -90dBm. Due to the lack of a dynamic power adjustment mechanism, the transmission power cannot be adjusted according to signal changes.

[0125] Comparison effect: As the drone approaches high-rise buildings, the quality of the communication link deteriorates. The data transmission packet loss rate reaches 30%, and the bit error rate is 20%. The ground control station cannot accurately obtain drone information, express delivery tasks are hindered, and some orders cannot be completed, reflecting the limitations of the fixed power output mode in complex urban environments.

[0126] Example 4

[0127] Implementation scenario: In mountainous areas with complex terrain, drones are used to conduct power inspections. The terrain in this area is undulating, the signal transmission distance is long, and the weather conditions are changeable, which poses a great threat to the stability of drone communications.

[0128] Implementation process:

[0129] Continuous data collection: Based on the technical architecture of the present invention, the signal strength sensor continuously collects signal strength values. Within 2 minutes of the drone flying over the valley, the signal strength dropped from -70dBm to -75dBm. At the same time, other sensors obtain information such as flight altitude, speed, heading, weather, and electromagnetic interference, providing accurate support for data analysis.

[0130] Link quality assessment: The data processing module uses the link quality assessment model of the present invention to conduct an assessment based on factors such as signal strength, signal-to-noise ratio, and multipath fading. After rigorous calculation and analysis, it is determined that although the link quality is in a stable state, fine-tuning is required to ensure reliable communication.

[0131] Power fine-tuning decision and execution: The power adjustment decision model calculates the transmit power that needs to be fine-tuned based on the link quality assessment results and related information. The power control module adjusts the transmit power from 22dBm to 22.5dBm by precisely controlling the power amplifier gain according to the instructions, achieving fine power control.

[0132] Communication strategy adjustment: The communication module uses adaptive modulation and coding technology based on changes in link quality, adjusts the modulation mode from QPSK to 16QAM, and increases the transmission rate from 8Mbps to 9Mbps, thereby improving the stability and efficiency of data transmission.

[0133] Implementation effect: Relying on the technology of the present invention, the UAV can fly stably in the complex environment of mountainous areas. During the power inspection, the data transmission packet loss rate is less than 8%, and the bit error rate is about 5%, providing accurate data for the maintenance of power facilities and ensuring the stability of power supply in mountainous areas.

[0134] Comparative Example 2

[0135] Comparison scenario: In mountainous areas with complex terrain, drones conduct power inspections, facing complex terrain and changeable weather conditions.

[0136] Implementation process: This drone only adjusts power based on signal strength feedback. When the drone flew over a valley, the signal strength dropped from -70dBm to -85dBm within 2 minutes. The drone increased the transmit power from 20dBm to 23dBm, but did not consider the combined impact of multipath fading, meteorological and electromagnetic environment in the valley on the signal.

[0137] Comparison results: Due to unscientific power adjustment, the UAV communication link is unstable. The data transmission packet loss rate is 20%, the bit error rate is 15%, the power inspection data is incomplete, and the subsequent power facility maintenance work cannot be carried out smoothly due to the lack of accurate data, reflecting the shortcomings of the simple signal strength feedback power adjustment method.

[0138] Example 5

[0139] Implementation scenario: In farmland areas, drones are used for agricultural plant protection operations. Electromagnetic interference generated by surrounding irrigation systems, agricultural monitoring equipment and other electronic equipment seriously affects the communication quality between drones and ground control stations.

[0140] Implementation process:

[0141] Real-time data collection: According to the technical solution of the present invention, the signal strength sensor collects the signal strength value in real time. When the drone is close to the irrigation system, the electromagnetic spectrum monitoring device detects strong electromagnetic interference, and the signal-to-noise ratio drops from 30dB to 20dB. At the same time, other sensors collect flight parameters and environmental information to provide key data for link quality assessment and power adjustment.

[0142] Link quality assessment: The data processing module uses the link quality assessment model of the present invention to comprehensively analyze factors such as signal strength, signal-to-noise ratio, multipath fading, etc. to determine whether the link quality has decreased due to electromagnetic interference.

[0143] Power increase decision and execution: The power adjustment decision model calculates the required increase in transmit power based on relevant information. After receiving the instruction, the power control module adjusts the power amplifier gain to increase the transmit power from 21dBm to 23dBm to enhance the signal's anti-interference ability.

[0144] Communication mode switching: The communication module uses adaptive modulation and coding technology to adjust the modulation mode from 8PSK to 16QAM according to the link quality, and the transmission rate is increased from 9Mbps to 10Mbps, ensuring stable and efficient data transmission.

[0145] Implementation effect: Through the technology of the present invention, the UAV maintains stable communication with the ground control station in an electromagnetic interference environment. The data transmission rate is stable at more than 9Mbps, and the data error rate is controlled within 6%, which can efficiently complete agricultural plant protection operations and provide technical support for agricultural production.

[0146] Comparative Example 3

[0147] Comparison scenario: When performing agricultural plant protection operations in farmland, the communication quality of the drone is affected by electromagnetic interference from surrounding electronic equipment.

[0148] Implementation process: The drone communication module does not have adaptive modulation and coding technology. When the drone is close to the irrigation system, the signal-to-noise ratio drops from 30dB to 10dB, and the link quality decreases. However, the communication module still transmits data with a fixed 8PSK modulation mode and 9Mbps transmission rate, which cannot resist the damage of electromagnetic interference to the signal.

[0149] Comparison effect: Under strong electromagnetic interference, the transmission performance of the communication module is reduced. The data transmission rate is reduced to 4Mbps, the transmission efficiency is low, the data error rate is as high as 25%, the plant protection operation instructions cannot be accurately transmitted, the pesticide spraying position is deviated, the accuracy of the plant protection operation is reduced, and the pesticide is wasted, highlighting the disadvantage of the lack of adaptive modulation and coding technology.

[0150] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor 102, a system as in any one of the second aspects described above is implemented. If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk.

[0151] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0152] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A real-time adaptive control method for UAV link power, characterized in that: The control steps include: Step 1. Four high-precision signal strength sensors are evenly arranged at the front, rear, left and right positions of the UAV body, and the link signal strength value between the UAV and the ground control station is synchronously collected at a cycle of every 50 milliseconds; Step 2. Based on the collected signal strength values, combined with the signal-to-noise ratio and multipath fading, the link quality is evaluated through a preset weighted calculation model, where the signal strength accounts for 40%, the signal-to-noise ratio accounts for 30%, and the multipath fading accounts for 30%. The signal strength calculation formula is shown in Formula 1: RSSI adjusted =RSSI-a distance ·log(d)+γ env ·EF formula 1, Where RSSI is the received signal strength, α distance is the signal attenuation coefficient, d is the distance between the device and the signal source, γ env is the environmental factor coefficient, and the environmental effect of the adjustment signal is shown in Formula 2: EF = α temp ·T + α humidity ·H + α obstacle ·O + α emi ·I Formula 2, Where: T is the ambient temperature, α temp is the influence coefficient of temperature; H is the ambient humidity, α humidity is the influence coefficient of humidity; O is the obstacle density or obstacle type, α obstacle is the influence coefficient of the obstacle; I is the electromagnetic interference intensity, α emi is the influence coefficient of electromagnetic interference; Signal-to-noise ratio: SNR; The multipath fading situation is shown in Equation 3: In the formula, PathLoss i is the loss of the ith path, γ fade is the multipath fading influence coefficient, ∈ time is the time attenuation coefficient, which is used to consider the change of fading over time, and TF is the time factor; The calculation formula of link quality score LQ is shown in formula 4: LQ = 0.4·RSSI adjusted + 0.3·SNR + 0.3·Fading adjusted Equation 4; Step 3. Calculate the power adjustment value using the pre-built decision model based on the link quality evaluation results, UAV flight parameters, and environmental information; Step 4. If the link quality deteriorates sharply, increase the transmit power by 2 dBm at a time; if the link quality is stable, adjust the transmit power by 0.5 dBm at a time; Step 5. Convert the power adjustment decision result into a power adjustment instruction, transmit it to the power control module through the internal data bus of the UAV, and the power control module adjusts the power amplifier gain to achieve precise control of the transmission power.

2. The real-time adaptive control method of UAV link power according to claim 1 is characterized in that: When the power adjustment value is calculated using the pre-built decision model in step 3, the input parameters of the decision model include the flight altitude H, speed V, heading D, ambient temperature T and electromagnetic interference EI of the UAV, and the calculation formula is shown in Formula 5:

3. The real-time adaptive control method of UAV link power according to claim 1 is characterized in that: Before the signal strength sensor collects data, the sensor is calibrated for zero point and sensitivity to ensure the accuracy of the collected data.

4. The real-time adaptive control method of UAV link power according to claim 1 is characterized in that: Before evaluating the link quality, the collected signal strength values ​​are filtered to remove noise interference in the signal. A hybrid filtering algorithm is used to filter the original value S of the signal. raw (n) is optimized, and the optimized filtering formula is shown in Formula 6: Where N is the size of the sliding window, which controls the smoothness of the average value, α, β are dynamic weights, satisfying α + β = 1, and their values ​​depend on the noise intensity N r Dynamic changes are made to ensure that when the noise is strong, more reliance is placed on the historical average, and when the noise is weak, more attention is paid to the current signal. The specific adjustment formula is shown in Formula 7:

5. A real-time adaptive control system for UAV link power, characterized in that: include: The data acquisition module consists of four high-precision signal strength sensors evenly distributed around the drone body, as well as a built-in gyroscope, accelerometer, GPS module, meteorological sensor and electromagnetic spectrum monitoring equipment. It is used to collect link signal strength values, flight parameters, meteorological data and electromagnetic interference information, and transmit them to the data processing module through the data acquisition circuit and data transmission bus; The data processing module adopts a high-performance embedded processor, and internally integrates a link quality assessment model algorithm module and a power adjustment decision model algorithm module, which is used to receive data from the data acquisition module, perform link quality assessment and power adjustment value calculation, generate a power adjustment instruction and send it to the power control module; The power control module is composed of a power amplifier and a digital control circuit. The digital control circuit receives a power adjustment instruction and adjusts the power amplifier gain according to the instruction. The communication module supports WiFi, 4G, and 5G communication protocols, and uses adaptive modulation and coding technology to dynamically adjust the transmission rate and coding method according to the link quality; The optimized dynamic transmission rate formula is shown in Formula 7: Where R is the dynamic transmission rate in bits per second; C is the device cooperation factor; β is the energy consumption protection coefficient; E represents the current energy state of the device, indicating the remaining energy of the device; R min is the minimum transmission rate; R max is the maximum transmission rate; The modulation mode is dynamically selected according to the link quality as shown in Equation 8: γ1,γ2,γ3 dynamically adjust the link quality threshold as shown in formula 9: γ i =γ i,0 +η(LQ avg -LQ threshold ) Formula 9, In the formula, γ i,0 is the initial link quality threshold; η is the adjustment factor used to control the sensitivity of the threshold adjustment; LQ avg It is the average value of link quality, which is used to smooth sudden link quality fluctuations; LQ threshold It is the preset link quality reference threshold, which is the design standard value of the system.

6. A real-time adaptive control system for UAV link power according to claim 5, characterized in that: The data acquisition circuit in the data acquisition module has signal amplification and analog-to-digital conversion functions, and converts the analog signal collected by the sensor into a digital signal and transmits it to the data processing module.

7. The real-time adaptive control system of UAV link power according to claim 5, characterized in that: The high-performance embedded processor in the data processing module is equipped with a hardware multiplier and a high-speed cache.

8. The real-time adaptive control system of UAV link power according to claim 5, characterized in that: The power amplifier is a linear power amplifier with high linearity, high efficiency and fast response characteristics.

9. The real-time adaptive control system of UAV link power according to claim 5, characterized in that: The communication module uses the AES encryption algorithm to encrypt the transmission data.

10. The real-time adaptive control system of UAV link power according to claim 5, characterized in that: The system also includes a fault diagnosis module for real-time monitoring of the working status of each module, and when a fault is detected, an alarm is issued and fault information is recorded.

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