Unmanned aerial vehicle identity authentication charging management method

Abstract

The invention relates to the technical field of unmanned aerial vehicle identity recognition and automatic charging management, and discloses an unmanned aerial vehicle identity authentication charging management method. The method comprises the following steps: sampling environmental noise through an acoustic receiving system, counting a mean value, a variance and a standard deviation, and adaptively setting a resolution amplitude, an emission peak value and a dispreading threshold; generating a unique spread spectrum sequence based on a preset chip length; selecting carrier frequency and chip duration, and transmitting spread spectrum sound waves by aligning a rectangular window with a carrier period; the ground and the unmanned aerial vehicle are in time sequence synchronization, identity codes are distributed, and phase reflection echoes are mapped; sampling echoes on the ground, removing direct current, de-spreading and judging one by one, and terminating if the judgment fails; the identity code is restored and compared after the judgment is passed, and the authentication is passed if matching; and after successful authentication, calculating a matching score and normalizing the matching score into credibility, and distributing a charging starting priority according to the credibility.

Description

Technical Field

[0001] This invention relates to the field of drone identification and automatic charging management technology, specifically a drone identification authentication and charging management method. Background Technology

[0002] With the rapid development and widespread application of drone technology, drones are playing an increasingly important role in many fields such as logistics, security patrols, emergency rescue, and agricultural production. The coordinated operation of large numbers of drones places higher demands on the automation capabilities of ground infrastructure, especially in areas such as identity authentication and energy replenishment. Currently, ground charging stations and drone ports have become key infrastructure for the automation and intelligence of drone missions, and their operational safety and charging management efficiency directly affect the reliability and economy of drone systems.

[0003] In existing technologies, drone authentication typically relies on traditional wireless communication protocols (such as Wi-Fi, Bluetooth, RFID, and cellular networks) for data-level identity information exchange. These methods generally employ mechanisms such as digital encryption, key negotiation, or QR code / visual recognition. However, they are vulnerable to security risks such as signal hijacking, man-in-the-middle attacks, forgery, and strong network dependence, which can easily lead to problems like identity theft, unauthorized access, and energy theft. Furthermore, traditional authentication methods often require multiple communication handshakes between the drone and the ground station, resulting in a complex authentication process with high processing latency, making them unsuitable for large-scale, low-power, real-time automatic charging scenarios for drones. On the other hand, existing drone charging scheduling generally employs simple strategies such as static queues, first-come-first-served charging, or timed grouping, lacking effective integration of identity credibility, behavioral history, and current task priority, which can easily lead to resource waste, service conflicts, and increased operational risks. In recent years, with advancements in IoT security and physical layer signal processing technologies, device uniqueness authentication based on physical layer features (such as acoustic waves, radio waves, and RF fingerprints) has gained increasing attention. However, existing methods are mostly based on fixed fingerprints or device defects, making it difficult to achieve real-time, dynamic challenge-response authentication models. Furthermore, how to organically integrate physical layer authentication with the drone automatic charging management system to achieve efficient, dynamic, and scalable charging scheduling driven by identity remains a challenge and hot topic in the industry's technological development.

[0004] Therefore, this case aims to propose a method for managing the identity authentication charging of unmanned aerial vehicles (UAVs). Through key steps such as continuous environmental noise adaptive analysis, spreading code sequence generation, acoustic challenge signal transmission and reflection modulation, echo discrete sampling and lossless preprocessing, independent chip-level despreading decision, identity code reconstruction and comparison, and trust-based charging priority allocation, a complete closed loop is formed. Summary of the Invention

[0005] This invention provides a method for managing the identity authentication and charging of unmanned aerial vehicles (UAVs), which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: a method for managing the identity authentication and charging of unmanned aerial vehicles (UAVs), comprising:

[0007] By continuously sampling ambient acoustic signals through an acoustic receiving system, a noise amplitude sequence is obtained, and statistical characteristics such as mean, variance, and standard deviation of the noise are analyzed to determine the minimum resolution amplitude, peak amplitude of the transmitted signal, and despreading decision threshold of the acoustic system.

[0008] Based on the preset number and length of chips, a corresponding binary spread spectrum chip sequence is generated and stored in the ground base station system;

[0009] Select the carrier frequency and chip duration, construct a rectangular window function to extract a single chip time period, perform spread spectrum processing on the carrier signal, and transmit the spread spectrum sound waves to the UAV through a ground loudspeaker array;

[0010] Before the session starts, the ground base station synchronizes with the drone in time, assigns a unique identification code to the target drone, and maps the identification code to phase modulation values ​​in binary bits. Within each chip time window, after receiving the spread spectrum sound wave, the drone modulates the sound wave according to the mapped phase value to generate an echo signal.

[0011] Configure the sampling frequency and number of sampling points of the ground receiver, perform discrete sampling of the UAV reflected echo signal, store it as a digital sequence, and remove the DC component;

[0012] For each chip, a sampling interval is set, the despread value in each interval is calculated, and the despread value is judged according to the preset normalization threshold. If the judgment result of any chip is failure, the authentication process is terminated and authentication failure is reported.

[0013] The drone's identification code is reconstructed based on the judgment results of each chip and compared with the pre-assigned identification code. If they match, the authentication is deemed successful; otherwise, the authentication is reported as failed.

[0014] After authentication is successful, the matching score is calculated sequentially, the matching score is normalized to a trust level, the charging start-up waiting time is set and the current time is recorded, and the charging start-up priority of the drone is assigned based on the trust level.

[0015] Optionally, the step of continuously sampling ambient acoustic signals through an acoustic receiving system to obtain a noise amplitude sequence, and performing statistical characteristic analysis on the noise, such as mean, variance, and standard deviation, to determine the minimum resolution amplitude, peak amplitude of the transmitted signal, and despreading decision threshold of the acoustic system, specifically includes:

[0016] Configure an acoustic receiving system with a sampling frequency of Sampling time is Then the number of noise samples ;

[0017] Continuous sampling of the ambient acoustic channel was performed to obtain the noise amplitude sequence. ;in, For the first The amplitude of each noise sample; For sample index;

[0018] Calculate the mean of the noise sequence. ;

[0019] For the The noise samples are mean-reduced to obtain the mean-reduced noise amplitude:

[0020] ;

[0021] Calculate the variance of the mean-free noise-free sequences respectively. With noise standard deviation :

[0022] , ;

[0023] Setting the minimum resolution of the acoustic system ,when season ;

[0024] Based on the standard deviation, the peak amplitude of the spread spectrum transmission signal is set as... ;

[0025] Based on the standard deviation, the despreading decision threshold is set as follows: .

[0026] Optionally, the step of generating a corresponding binary spreading chip sequence based on a preset number and length of chips and storing it in the ground base station system specifically includes:

[0027] The total number of spread spectrum chips selected is ,and ;in, It is the set of positive integers;

[0028] For each chip number Generate a binary sequence:

[0029] ;in, For the first The spreading value of each spreading chip;

[0030] will sequence Stored at ground base stations.

[0031] Optionally, the selected carrier frequency and chip duration are used to construct a rectangular window function to extract a single chip time period, the carrier signal is spread spectrum processed, and the spread spectrum sound waves are transmitted to the UAV by a ground loudspeaker array, specifically including:

[0032] Selected carrier frequency and chip duration And make each chip contain an integer number of carrier cycles, i.e. ;

[0033] Constructing a rectangular window function ;in, A rectangular window function is used to truncate the signal, retaining only the current chip time period;

[0034] In the time domain Internally transmitted spread spectrum signal:

[0035] ;in, To transmit signals in time The value; It is a time variable, and ;

[0036] Ground loudspeaker array will transmit signals Transmitted to the drone receiver.

[0037] Optionally, the ground base station synchronizes with the UAV before the session starts, assigns a unique identification code to the target UAV, and maps the identification code to phase modulation values ​​in binary order. Within each chip time window, after receiving the spread spectrum acoustic wave, the UAV modulates the acoustic wave according to the mapped phase values ​​to generate an echo signal, specifically including:

[0038] Before the session begins, the ground base station and the drone first communicate via... Perform timing synchronization;

[0039] For the first Assign a unique identification code to each drone ;in, Index for drones;

[0040] Will Convert to length of binary vector: ;in, To be The first, obtained by binary decomposition Bits; This is the modulo operation;

[0041] The reflection phase is set based on the binary bits: ;in, For the first The reflection phase modulation value corresponding to the chip;

[0042] exist Inside the window, the drone receives the spread spectrum signal. According to the phase sequence Modulation reflection generates an echo:

[0043] ;in, This is the echo signal from the drone.

[0044] Optionally, configuring the sampling frequency and number of sampling points at the ground receiver, discretely sampling the UAV reflected echo signal, storing it as a digital sequence, and removing the DC component specifically includes:

[0045] Configure the ground receiver sampling frequency as follows: And satisfy , , ;in, The number of sampling points per chip; This represents the total number of sampling points;

[0046] Acquire and store discrete sequences of echo signals ;in, For the first The amplitude of the acquired echo signal; ;

[0047] After removing the DC component from the echo signal, we get:

[0048] , ;in, The mean of the echo sampling sequence; For the first The echo signal after DC transmission.

[0049] Optionally, the step of setting a sampling interval for each chip, calculating the despreading value within each interval, and judging the despreading value according to a preset normalization threshold, and terminating the authentication process and reporting authentication failure if any chip fails to be judged, specifically includes:

[0050] For each chip Set the sampling interval index: , ;in, For the first Chip start sampling point; For the first End of chip sampling point;

[0051] Calculate the first Despreading value of the chip ;

[0052] Set normalization decision threshold ;

[0053] Judge each piece separately:

[0054] ;in, For the first Chip despreading decision result;

[0055] If any chip If the authentication fails, the authentication process will be terminated immediately and the authentication failure will be reported.

[0056] Optionally, the step of restoring the UAV identification code based on the judgment results of each chip and comparing it with the pre-assigned identification code, and determining that the authentication is successful if they match, otherwise reporting the authentication failure, specifically includes:

[0057] According to the decision sequence Restore the binary bits: ;in, For the first The binary bits recovered from the chip;

[0058] Reconstruct the identity code: ;in, The drone identification code recovered after descaling;

[0059] like If the authentication passes, the authentication is successful; otherwise, the authentication fails and the authentication process is immediately terminated and a failure report is submitted.

[0060] Optionally, after authentication, the following steps are performed: calculating the matching score, normalizing the matching score to a trust level, setting the charging start-up waiting time and recording the current moment, and allocating the charging start-up priority of the drone based on the trust level. Specifically, this includes:

[0061] When authentication is successful, steps S801 to S804 are executed sequentially:

[0062] S801. Calculate the original matching score: ;in, For the first The drone's identity authentication matching score, and ;

[0063] S802. Normalize the matching score into a trust level: ;in, For the first Normalize the trust level of drones, and ;

[0064] S803, Set the maximum charging start-up waiting time to And record the current moment. ;

[0065] S804, Assign charging start time: ;

[0066] When trust Approaching When the trust level is low, the drone will have priority to get a charging opportunity; when the trust level is low, the charging order will be postponed to the back of the queue.

[0067] The present invention has the following beneficial effects:

[0068] 1. Based on the real-time statistical characteristics of environmental noise, the system sensitivity and decision threshold are dynamically calibrated. Traditional acoustic certification often operates under fixed parameters, making it susceptible to noise variations that can lead to misjudgments or missed judgments. This solution, however, continuously samples environmental noise and calculates its statistical characteristics, automatically setting the minimum resolution amplitude, emission peak value, and decision threshold. This enables adaptive handling of quiet scenarios and sudden noise, improving the system's reliability in complex working environments. Simultaneously, parameter calibration allows for flexible adjustment of the sampling strategy based on on-site noise bandwidth requirements, balancing real-time performance and accuracy. This resolves the risk of misjudgment caused by fixed thresholds and the delay issues resulting from over-calibration. Real-time noise analysis provides a robust benchmark for subsequent spread spectrum and despreading, giving the entire certification process higher fault tolerance and stability.

[0069] 2. Combining system capacity and anti-interference requirements, this solution dynamically generates and securely stores spread spectrum chip sequences, rather than using fixed pseudo-random sequences. Traditional spread spectrum authentication often uses publicly available standard sequences, which are easily predicted or imitated by attackers. This solution flexibly determines the total number and length of chips based on the preset total number of devices and the actual scenario. It generates unique and irreversible binary sequences through its proprietary algorithm, increasing the complexity of identity mapping and enhancing the ability to resist relay attacks and signal spoofing. The sequences are centrally stored at ground base stations, ensuring consistency between the transmitter and receiver. Simultaneously, the sequence generation process balances code space size and system real-time performance, avoiding excessive latency caused by long codes, achieving an optimal balance between authentication speed and security.

[0070] 3. A rectangular window is used to precisely truncate the chip time slots and closely align them with the carrier to transmit the acoustic spread spectrum signal, instead of a traditional continuous wave or random burst signal. This design ensures that each chip time-domain window contains an integer number of carrier cycles, avoiding common phase truncation errors and suppressing signal crosstalk between adjacent time slots. Compared to the demodulation distortion caused by inaccurate application of the time-domain window function in existing technologies, this scheme improves the time-domain clarity and spectral purity of the echo signal through fine coupling in the time and frequency domains. Distributed radiation using a loudspeaker array expands the radiation range and equalizes the sound pressure distribution, solving the problem of insufficient signal coverage from a single point of transmission.

[0071] 4. Before the challenge begins, bidirectional timing synchronization is performed to strictly align the working windows of the base station and the drones, and a unique identification code is assigned to each drone to map phase modulation values. Traditional acoustic authentication often relies on unidirectional synchronization or no synchronization, resulting in propagation delay and sampling misalignment that severely affect despreading accuracy. This scheme corrects delay through bidirectional feedback, achieving millisecond-level synchronization accuracy, and accurately maps the identification code to a phase reflection sequence within each time slot, so that the echo signal simultaneously carries identification information and time slot labels. This method effectively eliminates the impact of delay jitter on authentication accuracy, improving the credibility and security boundary of challenge-response authentication.

[0072] 5. This approach combines echo sampling with DC removal preprocessing. High-fidelity echo data is captured through discrete sampling at the top of the hour, and DC components are removed in real time to highlight phase characteristics. Existing technologies often suffer from baseline drift due to the failure to remove DC components, affecting subsequent despreading and identification. This solution balances sampling rate and channel bandwidth requirements at the hardware level, ensuring data satisfies the Nyquist theorem. Simultaneously, digital filtering and zero-bias correction eliminate environmental or equipment biases. This preserves subtle phase changes in the echo while reducing the computational complexity of subsequent signal processing, providing high-quality input for the despreading stage and improving the robustness of identity determination.

[0073] 6. The despreading operation is completely decoupled from chip-level decision-making. Despread values ​​are extracted and standardized for comparison in independent time slots, instead of the traditional method of despreading the entire chip and then performing segmented statistics. This design allows each chip's despreading to trigger fast failure independently, avoiding the accumulation of errors in the entire data processing and reducing overall authentication latency. Simultaneously, the normalized threshold adapts to different signal-to-noise ratio scenarios, achieving portability of the decision threshold. Compared to existing overall decision-making based on fixed thresholds, this scheme's chip-by-chip judgment improves tolerance for individual distorted chips and immediately interrupts the process upon failure of a single chip decision, improving system resource utilization and security.

[0074] 7. The binary identity code is meticulously reconstructed and compared bit by bit. Uniqueness is confirmed through bit equality, rather than a simple overall verification. Existing authentication methods often rely on a single overall verification, failing to pinpoint faulty bits. This solution performs independent comparisons on each bit and records the reasons for failures, improving accuracy and providing data support for subsequent fault diagnosis and algorithm optimization. Furthermore, the bit-by-bit comparison method increases the difficulty of forgery. Even with some echo noise interference, the overall identity validity can still be determined based on information from other correct bits, reducing the probability of false rejection and false acceptance.

[0075] 8. The authentication matching quality is converted into a normalized trust level, and charging priority is dynamically allocated based on the trust level, achieving a fusion of identity security and resource allocation. Compared with the traditional "first-come, first-served" or "fixed priority" methods, trust-based scheduling balances authentication accuracy and fairness, and is highly suitable for scenarios where multiple devices simultaneously apply for charging. The trust level design can adapt to the current charging pile service duration and system capacity requirements, balancing response speed and differentiated latency; at the same time, it can provide a delayed retry mechanism for low-trust devices to avoid permanent loss of charging opportunities due to temporary interference. This innovation effectively improves the security, controllability, and user experience of charging management. Attached Figure Description

[0076] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0077] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] Example, refer to Figure 1 A method for managing drone identity authentication and charging, comprising:

[0079] By continuously sampling ambient acoustic signals through an acoustic receiving system, a noise amplitude sequence is obtained, and statistical characteristics such as mean, variance, and standard deviation of the noise are analyzed to determine the minimum resolution amplitude, peak amplitude of the transmitted signal, and despreading decision threshold of the acoustic system.

[0080] Based on the preset number and length of chips, a corresponding binary spread spectrum chip sequence is generated and stored in the ground base station system;

[0081] Select the carrier frequency and chip duration, construct a rectangular window function to extract a single chip time period, perform spread spectrum processing on the carrier signal, and transmit the spread spectrum sound waves to the UAV through a ground loudspeaker array;

[0082] Before the session starts, the ground base station synchronizes with the drone in time, assigns a unique identification code to the target drone, and maps the identification code to phase modulation values ​​in binary bits. Within each chip time window, after receiving the spread spectrum sound wave, the drone modulates the sound wave according to the mapped phase value to generate an echo signal.

[0083] Configure the sampling frequency and number of sampling points of the ground receiver, perform discrete sampling of the UAV reflected echo signal, store it as a digital sequence, and remove the DC component;

[0084] For each chip, a sampling interval is set, the despread value in each interval is calculated, and the despread value is judged according to the preset normalization threshold. If the judgment result of any chip is failure, the authentication process is terminated and authentication failure is reported.

[0085] The drone's identification code is reconstructed based on the judgment results of each chip and compared with the pre-assigned identification code. If they match, the authentication is deemed successful; otherwise, the authentication is reported as failed.

[0086] After authentication is successful, the matching score is calculated sequentially, the matching score is normalized to a trust level, the charging start-up waiting time is set and the current time is recorded, and the charging start-up priority of the drone is assigned based on the trust level.

[0087] By continuously monitoring the acoustic background and calculating its statistical characteristics during environmental noise acquisition and parameter calibration, the noise mean, variance, and standard deviation are dynamically obtained. This allows for adaptive setting of the acoustic system's minimum resolution amplitude, peak transmitted signal, and despreading decision threshold, solving the problem of missed or false detections that easily occur in traditional fixed-threshold methods under different scenarios. By pre-setting the number and length of chips and generating a unique spreading sequence, the security risks of publicly available pseudo-random sequences being easily cracked or misused are addressed. By precisely truncating each spreading chip time period using a rectangular window function and aligning it with the carrier period for transmission, synergistic optimization of time-domain spreading and frequency-domain noise suppression is achieved, avoiding signal crosstalk and phase truncation errors. Furthermore, through bidirectional pre-session communication... The timing synchronization and phase mapping reflection mechanism solves the sampling asynchrony problem caused by propagation delay jitter, ensuring that the echo carries complete identity information. Discrete sampling and DC removal of the echo signal eliminates baseline drift and DC bias, guaranteeing high accuracy in despreading decisions. Independent despreading and threshold decision-making with a fast failure mechanism for each chip avoids overall authentication misjudgments caused by the accumulation of errors in a single chip. Bit-by-bit reconstruction of the identity code and precise comparison with the pre-assigned code eliminates impersonation and fraud. Finally, normalizing the matching score to a trust level and allocating charging priorities based on the trust level solves the problems of inconsistent identity verification and unfair resource allocation when multiple drones share charging resources. Through these eight systematic steps, a complete closed loop from environmental perception to charging scheduling is formed, demonstrating overall advantages in anti-interference capability, identity security, and fairness of resource allocation.

[0088] The process of continuously sampling ambient acoustic signals through an acoustic receiving system to obtain a noise amplitude sequence, and performing statistical characteristic analysis on the noise, such as mean, variance, and standard deviation, to determine the minimum resolution amplitude, peak amplitude of the transmitted signal, and despreading decision threshold of the acoustic system, specifically includes:

[0089] Configure an acoustic receiving system with a sampling frequency of Sampling time is Then the number of noise samples Determine the sampling rate and duration of the noise data to ensure accurate sample size and avoid errors in subsequent statistical calculations due to non-integer samples.

[0090] Sampling frequency is The sampling rate of an acoustic receiving system for ambient noise determines its ability to capture the noise's spectral characteristics and transient changes. If... A lower value may result in the omission of high-frequency noise components, leading to an underestimation of the noise standard deviation in the calculation; if Higher values ​​increase data volume, storage, and processing overhead, but offer limited improvement in noise characteristics. The optimal value is determined based on system bandwidth requirements; it should at least cover the highest significant frequency components of ambient noise; and it should satisfy the Nyquist sampling theorem. ,in This represents the highest frequency of the noise to be monitored. Recommended value: If the environmental noise is mainly concentrated in... , can be set If it is necessary to capture The above ingredients can be set as follows: To balance real-time performance and accuracy, the following is typically chosen: A sampling rate that is times higher than the highest noise frequency.

[0091] Sampling duration is The length of the statistical time window used to determine environmental noise affects the stability of the estimates of the noise mean and variance. If... Shorter durations mean that noise statistics are easily affected by transient disturbances, leading to unstable standard deviation estimates; if The longer duration results in delayed parameter updates, reducing the system's response speed to sudden environmental changes. Value selection is based on the time-varying characteristics of environmental noise and the expected minimum change period; ensuring at least several cycles of typical noise fluctuations are included. Recommended values: For indoor or relatively stable scenarios, For dynamic environments, it is advisable to... ; to obtain at least It is advisable to collect sampling data for a stable period.

[0092] Continuous sampling of the ambient acoustic channel was performed to obtain the noise amplitude sequence. ;in, For the first The amplitude of each noise sample; This serves as a sample index; it acquires acoustic noise data from real-world environments to provide raw input for subsequent noise feature analysis.

[0093] Calculate the mean of the noise sequence. The DC offset is eliminated by averaging, providing a benchmark for mean removal and variance calculation.

[0094] For the The noise samples are mean-reduced to obtain the mean-reduced noise amplitude:

[0095] The DC component of noise is removed to highlight the fluctuation characteristics, so that the subsequent variance calculation can accurately reflect the noise intensity.

[0096] Calculate the variance of the mean-free noise-free sequences respectively. With noise standard deviation :

[0097] , Quantifying the energy distribution and average amplitude of environmental noise provides a basis for determining the amplitude of transmitted signals and decision thresholds.

[0098] Setting the minimum resolution of the acoustic system ,when season To prevent the standard deviation from being too small or zero under extremely quiet conditions, thereby avoiding the failure of subsequent threshold and amplitude calculations;

[0099] Set the lower limit of noise standard deviation To prevent extremely quiet operation Too small a value will cause subsequent calculations to malfunction. If... Too small; for extremely low noise, real [technology] is still used. This may trigger subsequent division by zero or a threshold that is too low; if An excessively high value would normalize the noise level, potentially leading to an overestimation of the emission amplitude or threshold in low-noise environments. The value should be based on the lowest noise measurement accuracy of the acoustic front-end hardware (sensor noise floor), such as the minimum detectable signal level of the microphone. Recommended value: If the hardware noise floor is... , can be set The lower limit should be twice the value specified in the equipment manual or calibration results to ensure stability and reliability.

[0100] Based on the standard deviation, the peak amplitude of the spread spectrum transmission signal is set as... The transmission amplitude is designed based on the signal-to-noise ratio to ensure that the echo signal is much higher than the noise level, thereby improving detection reliability.

[0101] Based on the standard deviation, the despreading decision threshold is set as follows: Set an amplitude threshold for despreading decision to distinguish between valid echoes and residual noise.

[0102] By precisely determining the sampling frequency and duration in the environmental noise sampling stage and strictly adhering to the sampling theorem requirements, the error fluctuation problem caused by insufficient or excessive sampling in noise statistical analysis is solved. By continuously acquiring the amplitude of real environmental noise and performing mean removal, variance and standard deviation calculations, the acoustic signal fluctuation characteristics are highlighted, solving the problem that traditional single or short-time-window sampling cannot reflect long-term environmental changes. By setting a lower limit for the noise standard deviation and a minimum resolution amplitude threshold, decision failures caused by division by zero or excessively low thresholds in extremely low noise or silent scenarios are avoided. By designing the peak transmission amplitude based on the signal-to-noise ratio, sufficient signal-to-noise ratio is ensured for the echo signal above the noise, solving the problem of balancing signal strength and receiving sensitivity. By adaptively adjusting the despreading decision threshold based on the signal-to-noise ratio, the drawbacks of fixed thresholds leading to misjudgments or missed judgments in various environments are overcome. Based on the synergistic effect of the above steps, accurate response to dynamic changes in environmental noise is achieved, providing a robust signal foundation for subsequent spread spectrum verification and improving the reliability and robustness of the entire identity authentication system in variable acoustic scenarios.

[0103] The process of generating a corresponding binary spread spectrum chip sequence based on a preset number and length of chips and storing it in the ground base station system specifically includes:

[0104] The total number of spread spectrum chips selected is ,and ;in, It is a set of positive integers; the length of the spreading code is specified, which affects the size and security of the code space for identity mapping;

[0105] Total number of spread spectrum chips The length of the spreading sequence determines the size of the identification code mapping space and the system's anti-interference performance. The space is relatively small, the identity space and pseudo-randomness are insufficient, and it is easily guessed or interfered with; if A larger value increases transmission or despreading latency and computational burden, impacting real-time performance. Value selection is based on: Identification code bit length requirements: The required total number of devices must be met. The maximum allowable latency of the system Recommended value: For dozens of drones, For large-scale deployment, it is advisable to... Taking into account launch duration It should be kept within an acceptable few seconds.

[0106] For each chip number Generate a binary sequence:

[0107] ;in, For the first The spreading value of each spreading chip; generating a self-created spreading sequence, giving the system natural anti-interference and anti-spoofing capabilities, and ensuring the uniqueness of identity feature mapping;

[0108] will sequence It is stored at the ground base station; the same sequence is used for subsequent transmission and despreading to achieve synchronous spread or despreading.

[0109] By pre-determining the total number and length of spreading chips based on system scale and security requirements, and dynamically selecting the spreading code length in conjunction with equipment capacity and real-time requirements, the contradiction between fixed small-capacity code sequences being easily predicted or interfered with by attackers and excessively long code sequences causing high latency that affects real-time performance is resolved. By generating an independent and irreversible binary spreading sequence for each chip, the unforgeability of identity mapping is improved, addressing the security vulnerabilities of traditional public sequences in resisting relay and spoofing attacks. By uniformly storing the chip sequences at the ground base station and always using the same sequence during transmission and despreading, consistency between the transmitter and receiver is ensured, avoiding verification failures caused by asynchronous or sequence mismatches. The combined effect of these steps ensures both the uniqueness and security of the spreading sequence while taking into account the system's real-time response and storage and computational overhead, achieving secure and efficient operation of the identity authentication phase from sequence generation to modulation and despreading.

[0110] The selected carrier frequency and chip duration are used to construct a rectangular window function to extract a single chip time period. The carrier signal is then spread spectrum processed, and the spread spectrum sound waves are transmitted to the UAV by a ground loudspeaker array. Specifically, this includes:

[0111] Selected carrier frequency and chip duration And make each chip contain an integer number of carrier cycles, i.e. Ensure precise alignment between the chip time-domain window and the carrier waveform, prevent phase truncation errors, and optimize time-domain characteristics;

[0112] carrier frequency Determining the frequency of the sound wave affects signal propagation characteristics and environmental penetration. If Lower (several hundred) It has a long propagation distance and good attenuation resistance, but is susceptible to low-frequency noise interference; if High (tens) High positioning accuracy and large bandwidth, but rapid attenuation and poor penetration. Value selection criteria: based on scene noise spectrum, speaker or microphone bandwidth, and desired authentication distance. Recommended values: typically suitable for outdoor use. Indoor short-range options available ;make sure Within the efficient operating bandwidth of the speaker and sensor.

[0113] Chip duration The duration of each spreading chip affects the time-domain resolution and system latency. Shorter duration, lower latency, higher resolution, but less local correlated signal energy, resulting in poor despreading performance; if Longer length increases correlation energy and despreading accuracy, but overall authentication latency increases. Value selection is based on ensuring each chip contains at least several dozen carrier cycles. (This satisfies the system's real-time requirements. Recommended value: If...) Optional (correspond When high real-time requirements are needed, the time can be shortened to... If the bias accuracy can be increased to .

[0114] Constructing a rectangular window function ;in, Using a rectangular window function, the signal is truncated, retaining only the current chip time slot; this ensures that the spread spectrum signal is activated only within the current chip time slot, suppressing signal interference between adjacent chips and achieving time-domain spread spectrum separation;

[0115] In the time domain Internally transmitted spread spectrum signal:

[0116] ;in, To transmit signals in time The value; It is a time variable, and ; to realize time-domain spread spectrum modulation based on spreading code and carrier, providing a distinguishable signal basis for subsequent reflection and despreading discrimination;

[0117] Ground loudspeaker array will transmit signals Transmitted to the drone receiver; ensuring that the ground signal is transmitted completely and accurately to the drone in the air, enabling acoustic challenges and subsequent authentication interactions.

[0118] By precisely selecting the acoustic carrier frequency and chip duration, and ensuring that each chip contains a complete carrier cycle, perfect alignment between the time-domain window function and the carrier phase is achieved, solving the phase truncation and spectral leakage problems that occur when cutting traditional random window functions. By constructing a rectangular time-domain window function to truncate individual chip pulses, signal overlap and crosstalk between adjacent chips are avoided, ensuring the independence of each chip in the transmission and despreading process. Through distributed transmission via a loudspeaker array, more uniform sound pressure coverage and a longer propagation distance are achieved, solving the problem of insufficient reception caused by signal attenuation at long distances or under obstruction conditions in single-point transmission. The organic combination of the above steps not only ensures the high time-frequency purity of the spread spectrum acoustic challenge signal, but also improves the detectability and despreading recognition rate of the acoustic signal, providing a solid signal foundation for subsequent identity verification.

[0119] Before the session starts, the ground base station synchronizes with the UAV, assigns a unique identification code to the target UAV, and maps this identification code to phase modulation values ​​in binary order. Within each chip time window, after receiving the spread spectrum acoustic wave, the UAV modulates the acoustic wave according to the mapped phase values ​​to generate an echo signal, specifically including:

[0120] Before the session begins, the ground base station and the drone first communicate via... Perform timing synchronization; ensure that the working window of the drone is strictly aligned with that of the ground base station to avoid authentication failure caused by reflection or sampling misalignment;

[0121] For the first Assign a unique identification code to each drone ;in, Index the drones; assign a unique identification code to each drone as an authentication credential;

[0122] Will Convert to length of binary vector: ;in, To be The first, obtained by binary decomposition Bits; For the modulo operation; the decimal identification code is converted to binary to facilitate subsequent mapping of phase information;

[0123] The reflection phase is set based on the binary bits: ;in, For the first The reflection phase modulation value corresponding to the chip; uniquely mapping the identity code to the phase of the spread spectrum signal, facilitating unique reflection modulation at the physical layer;

[0124] exist Inside the window, the drone receives the spread spectrum signal. According to the phase sequence Modulation reflection generates an echo:

[0125] ;in, This is the drone echo signal; after receiving the challenge signal, the drone only reflects the signal according to a preset phase sequence to form the echo carrier identity information.

[0126] By implementing bidirectional timing synchronization before session initiation, the working time windows of the ground base station and the UAV are strictly aligned, solving the signal synchronization problem caused by propagation delay and sampling misalignment. By assigning a unique identification code to each UAV and converting it into a binary phase map, the unique carrying of identity information at the physical layer is ensured, solving the problem of traditional one-way mapping being unable to prevent bit order disorder and impersonation. By reflecting and modulating the acoustic wave according to the mapped phase within each chip time window, the UAV avoids misreceiving or reflecting other challenging signals, solving the security risk of cross-interference in concurrent environments. This method enables the echo signal to not only carry acoustic energy but also a unique identification mark, providing a clear and identifiable physical layer authentication basis for the despreading stage and improving the system's anti-tampering and anti-spoofing capabilities.

[0127] The configuration of the ground receiver's sampling frequency and number of sampling points, discrete sampling of the UAV reflected echo signal, storage as a digital sequence, and removal of the DC component specifically includes:

[0128] Configure the ground receiver sampling frequency as follows: And satisfy , , ;in, The number of sampling points per chip; The total number of sampling points is given; the Nyquist sampling theorem is guaranteed to be satisfied, and the chip time slots are sampled at the top of the hour;

[0129] The digitization sampling rate of the echo signal determines the despreading accuracy and the accuracy of synchronous sampling. If... If the value is too low, the Nyquist theorem cannot be satisfied, leading to signal distortion; if... Too high a value increases hardware and computational overhead. The value should be determined based on the following criteria: Considering hardware processing capabilities and DC power removal requirements, the recommended value is: If... Optional ;like Optional .

[0130] Acquire and store discrete sequences of echo signals ;in, For the first The amplitude of the acquired echo signal; ; Obtain discrete samples of UAV reflected echoes to provide data for despreading decision;

[0131] After removing the DC component from the echo signal, we get:

[0132] , ;in, The mean of the echo sampling sequence; For the first The echo signal after DC removal is used to eliminate the bias that may be introduced during the echo sampling process, thereby improving the signal despreading resolution.

[0133] By configuring the sampling frequency and number of sampling points at the ground receiver to satisfy the sampling theorem, complete and accurate discrete sampling of the reflected echo signal is ensured, solving the problems of signal distortion caused by too low a sampling rate or computational pressure caused by too high a sampling rate. By removing DC components from the sampled echo sequence, interference from DC components introduced by hardware bias or environmental baseline drift is eliminated, resolving decision errors caused by baseline offset during despreading. By storing high-fidelity digital sequences in real time, the subsequent despreading operation is able to sensitively capture minute phase changes. The synergistic effect of the above steps enables the echo signal to be transmitted and processed efficiently while maintaining complete phase characteristics, providing a clean and stable input data source for subsequent decision-making.

[0134] The process involves setting a sampling interval for each chip, calculating the despread value within each interval, and making a judgment on the despread value based on a preset normalization threshold. If any chip fails the judgment, the authentication process is terminated and an authentication failure is reported. Specifically, this includes:

[0135] For each chip Set the sampling interval index: , ;in, For the first Chip start sampling point; For the first The sampling point ends at the chip; the overall sampled data is divided into the sampling intervals corresponding to each chip to ensure that each chip is despread independently and effectively.

[0136] Calculate the first Despreading of chips Multiply and accumulate the echo signal and the local reference signal to extract the first... The chip's identity is modulated with phase information;

[0137] Set normalization decision threshold Standardize the threshold scale and compare it directly with the despread correlation value to improve the consistency and portability of decisions;

[0138] Judge each piece separately:

[0139] ;in, For the first Chip despreading decision result;

[0140] If any chip If the authentication fails, the authentication process is immediately terminated and an authentication failure is reported. For each despread value decision phase, the legitimate identity information is checked. If any chip is judged as "authentication failed", the process is terminated.

[0141] By precisely dividing the overall sampled data into sampling intervals for each chip and independently calculating the despread value within each interval, the cumulative error problem caused by slicing after overall despreading is avoided. By setting a normalized decision threshold, the despread values ​​under different signal-to-noise ratio scenarios are mapped to a unified discrimination scale, solving the problem of inherent thresholds failing in dynamic environments or requiring frequent manual calibration. By making individual decisions for each chip and terminating upon failure, the adverse effects of a few distorted chips on the overall authentication result are avoided, saving system resources and shortening authentication latency. This method effectively improves the accuracy of identity determination and system response speed, and enhances the tolerance and protection against some interference or distorted signals.

[0142] The process of reconstructing the UAV's identity code based on the judgment results of each chip and comparing it with the pre-assigned identity code, determines that authentication is successful if they match, otherwise reports authentication failure, specifically including:

[0143] According to the decision sequence Restore the binary bits: ;in, For the first The binary bits of the chip are restored; the decision phase is converted back to the identity binary bits for identity code concatenation;

[0144] Reconstruct the identity code: ;in, To recover the drone's identity code after descaling, all binary decision bits are concatenated into an integer code for easy and accurate comparison with the registered identity code.

[0145] like If the authentication is successful, the authentication process will be completed; otherwise, the authentication process will fail and the authentication failure will be immediately terminated and reported. This completes the uniqueness verification of the drone's identity to prevent impersonation and misjudgment.

[0146] By restoring the decision results of each chip bitwise into an identity binary sequence, the problem of being unable to locate errors due to local misjudgments in the overall identity restoration is solved. By concatenating all decision bits and comparing them precisely with the pre-assigned identity, the problem of simple overall hash verification failing to detect subtle errors is eliminated. Through a bit-by-bit comparison reporting mechanism for failure reasons, proactive diagnosis of authentication faults and a basis for performance optimization are realized. This method ensures the uniqueness and traceability of identity authentication. Even with a small amount of chip noise interference, reliable discrimination can be completed based on other correct bits, thereby improving the balance between false rejection rate and false acceptance rate.

[0147] After successful authentication, the process involves calculating a matching score, normalizing the matching score to a trust level, setting a charging start-up waiting time and recording the current moment, and allocating the drone's charging start-up priority based on the trust level. Specifically, this includes:

[0148] When authentication is successful, steps S801 to S804 are executed sequentially:

[0149] S801. Calculate the original matching score: ;in, For the first The drone's identity authentication matching score, and The consistency between the actual reflection phase and the decision phase is statistically analyzed to quantify the authentication quality.

[0150] S802. Normalize the matching score into a trust level: ;in, For the first Normalize the trust level of drones, and Map the matching score to This allows for unified sorting and allocation of charging resources;

[0151] S803, Set the maximum charging start-up waiting time to And record the current moment. Clearly define the charging scheduling window and time start point;

[0152] Define the maximum charging delay time for low-trust drones to dynamically balance fairness and efficiency. Smaller size, faster overall response, but loses the space for differentiated latency based on trust level; if

[0153] Larger dimensions enhance differentiation, but long waiting times for low-trust drones negatively impact user experience. Value basis: Based on the average service time of charging stations and the system's maximum acceptable waiting time. Recommendation: If the average charging time per session is... , can be set For high-density deployment scenarios, it can scale to .

[0154] S804, Assign charging start time: ;

[0155] When trust Approaching When the trust level is low, the drone will have priority to receive a charging opportunity; when the trust level is low, the charging order will be postponed to the back of the queue; the charging order will be dynamically allocated according to the trust level, with high-trust drones receiving priority to charge, to ensure safety and fairness.

[0156] By continuously calculating the matching score after successful authentication and normalizing it into a trust level, the problem of relying solely on binary results of pass or fail to reflect differences in authentication quality is solved. By dynamically setting the charging start-up waiting time based on the trust level, differentiated scheduling of resource allocation in multi-drone environments is achieved, avoiding resource waste and safety hazards caused by first-come, first-served charging. By recording the current moment and defining a maximum waiting threshold, charging efficiency and fairness are balanced, solving the problem of poor user experience caused by long waiting times for low-trust devices. By linking the above steps, the identity authentication results are organically combined with charging management, providing a quantifiable, controllable, and traceable solution for the secure access of drone swarms to the charging network, improving the system's security level and overall operational efficiency.

[0157] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0158] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1.A method for drone identity authentication and charging management, characterized in that, include: By continuously sampling ambient acoustic signals through an acoustic receiving system, a noise amplitude sequence is obtained, and statistical characteristics such as mean, variance, and standard deviation of the noise are analyzed to determine the minimum resolution amplitude, peak amplitude of the transmitted signal, and despreading decision threshold of the acoustic system. Based on the preset number and length of chips, a corresponding binary spread spectrum chip sequence is generated and stored in the ground base station system; Select the carrier frequency and chip duration, construct a rectangular window function to extract a single chip time period, perform spread spectrum processing on the carrier signal, and transmit the spread spectrum sound waves to the UAV through a ground loudspeaker array; Before the session starts, the ground base station synchronizes with the drone in time, assigns a unique identification code to the target drone, and maps the identification code to phase modulation values ​​in binary bits. Within each chip time window, the drone receives the spread spectrum sound wave and modulates the sound wave according to the mapped phase value to generate an echo signal. Configure the sampling frequency and number of sampling points of the ground receiver, perform discrete sampling of the UAV reflected echo signal, store it as a digital sequence, and remove the DC component; For each chip, a sampling interval is set, the despread value in each interval is calculated, and the despread value is judged according to the preset normalization threshold. If the judgment result of any chip is failure, the authentication process is terminated and authentication failure is reported. The drone's identification code is reconstructed based on the judgment results of each chip and compared with the pre-assigned identification code. If they match, the authentication is deemed successful; otherwise, the authentication is reported as failed. After authentication is successful, the matching score is calculated sequentially, the matching score is normalized to a trust level, the charging start-up waiting time is set and the current time is recorded, and the charging start-up priority of the drone is assigned based on the trust level. 2.The UAV identity authentication and charging management method of claim 1, wherein, The process of continuously sampling ambient acoustic signals through an acoustic receiving system to obtain a noise amplitude sequence, and performing statistical characteristic analysis on the noise, such as mean, variance, and standard deviation, to determine the minimum resolution amplitude, peak amplitude of the transmitted signal, and despreading decision threshold of the acoustic system, specifically includes: The acoustic receiving system is configured to have a sampling frequency of , and a sampling time length of , so that the number of noise samples is ; Continuous sampling of the ambient acoustic channel was performed to obtain the noise amplitude sequence. ;in, For the first The amplitude of each noise sample; For sample index; Computing the mean of the noise sequence ; The first noise sample is de-meaned to obtain a de-meaned noise amplitude: ； Calculate the variance of the de-meaned noise sequence with the noise standard deviation : ， ； Setting minimum resolution amplitude of an acoustic system When , let ; Based on the standard deviation, the peak amplitude of the spread spectrum transmission signal is set to ; Based on the standard deviation, set the despreading decision threshold to . 3.The UAV identity authentication and charging management method of claim 2, wherein, The process of generating a corresponding binary spread spectrum chip sequence based on a preset number and length of chips and storing it in the ground base station system specifically includes: The total number of selected spreading chips is , and ; wherein, is a set of positive integers; for each chip number generating a binary sequence: ; wherein is a spreading value of the jth spreading chip; The sequence is saved at the ground base station. 4.The method of claim 3, wherein, The selected carrier frequency and chip duration are used to construct a rectangular window function to extract a single chip time period. The carrier signal is then spread spectrum processed, and the spread spectrum sound waves are transmitted to the UAV by a ground loudspeaker array. Specifically, this includes: Selected carrier frequency And chip duration And make each chip is an integer number of carrier cycles, namely ; Constructing a rectangular window function ; wherein is a rectangular window function, truncating the signal, keeping only the current chip time period; In time domain Intra-time domain spread spectrum signal ; wherein, is the value of the transmitted signal at time ; is a time variable, and ; The ground speaker array transmits the signal to the drone receiving end. 5.The UAV identity authentication and charging management method of claim 4, wherein, Before the session starts, the ground base station synchronizes with the UAV, assigns a unique identification code to the target UAV, and maps this identification code to phase modulation values ​​in binary order. Within each chip time window, after receiving the spread spectrum acoustic wave, the UAV modulates the acoustic wave according to the mapped phase values ​​to generate an echo signal, specifically including: The ground base station and the unmanned aerial vehicle perform timing synchronization before session initiation ; For the A drone is assigned a unique identity code ; wherein, is a drone index; Converts to a binary vector of length : ; where is the th bit obtained by binary decomposition of ; is the modulo operation; The reflection phase is set according to the binary bits: ; wherein, is the reflection phase modulation value corresponding to the first chip; In Within the window, the drone modulates the received spread spectrum signal According to the phase sequence Modulates the reflection, producing an echo: ; wherein, is a drone echo signal. 6.The UAV identity authentication and charging management method of claim 5, wherein, The configuration of the ground receiver's sampling frequency and number of sampling points, discrete sampling of the UAV reflected echo signal, storage as a digital sequence, and removal of the DC component specifically includes: The sampling frequency of the ground receiving end is configured as , and satisfies , , ; wherein, is the number of sampling points per chip; is the total number of sampling points; Acquiring and storing discrete sequences of echo signals ; wherein is the th acquired echo signal amplitude; ; After removing the DC component from the echo signal, we get: , ; wherein, is the mean of the echo sample sequence; is the de-DCed echo signal of the th echo signal. 7.The method of claim 6, wherein, The sampling interval is set for each chip respectively, the despread values in each interval are calculated, and the despread values are judged according to a preset normalization threshold; if the judgment result of any chip is failure, the authentication process is terminated and authentication failure is reported; specifically comprising: for each chip , set the sampling interval index: , ; wherein, is the start sample point of the th chip; is the end sample point of the th chip; Computing the first Despreading values of chips ; Setting a normalization decision threshold ; Respectively, each piece is judged: ; wherein is the chip despreading decision result; If any chip If the decision is that the authentication has failed, then the authentication process is terminated immediately and the authentication failure is reported. 8.The method of claim 7, wherein, The identity code of the unmanned aerial vehicle is restored according to the judgment result of each chip, and is compared with the pre-allocated identity code; if they are consistent, it is determined that the authentication is passed, otherwise, authentication failure is reported; specifically comprising: According to the sequence of decisions Reduced binary bits: ; wherein is the chip-reduced binary bits; Reconstructed identity code: ; wherein is the recovered drone identity code after despreading. If then the authentication is passed; otherwise, the authentication is failed and the authentication process is terminated immediately and reports the authentication failure. 9.The method of claim 8, wherein, After the authentication is passed, the matching score is calculated in turn, the matching score is normalized as trust degree, the charging start waiting time is set and the current time is recorded, and the charging start priority of the unmanned aerial vehicle is allocated based on the trust degree; specifically comprising: When the authentication is passed, steps S801 to S804 are executed in turn: S801, calculate an original matching score: ; wherein, is the identity authentication matching score of the first frame unmanned aerial vehicle, and ; S802, normalizing the matching score to a trust score: ; wherein, is the normalized trust score for the i-th drone, ;​ S803, set the maximum charging start waiting time length as , and record the current time ; S804, allocate the charging start time ; When the trust degree approaches to a certain value, the UAVs will get the charging opportunity in priority; when the trust degree is low, the charging sequence will be postponed to the rear of the queue.

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