Drone Pilot Short-Distance Control Method and Control System Based on SparkLink Technology

Abstract

The present invention relates to the field of UAV control technology, in particular to a short-distance control method and control system for UAV pilots based on SparkLink technology. The method establishes a directional wireless connection between the real pilot layer and the dummy pilot relay layer, and optimizes the communication frequency band and signal waveform in real time; fuses the real pilot operation behavior characteristics with the preset dummy pilot behavior pattern library; performs multi-hop encryption transmission processing on the camouflaged control data stream to form an encrypted camouflaged data stream and then performs multi-path diversity transmission to the UAV execution layer; after receiving the encrypted camouflaged data stream, the UAV execution layer decrypts and authenticates the encrypted camouflaged data stream and then performs flight control operations. The present invention can not only effectively prevent the enemy from positioning the real pilot through wireless signals, but also ensure the reliable transmission of control instructions in a complex electromagnetic environment, providing an effective solution for UAV control tasks in a high-confrontation environment.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to a short-distance control method and control system for UAV pilots based on SparkLink technology. Background Art

[0002] In high-confrontation environments such as electronic countermeasures, the strike ability and concealment of UAVs are crucial. However, UAV pilots often face the risk of being located and struck by the enemy during operation. Especially during the wireless signal transmission process, the position of the pilot is likely to be exposed, leading to the indirect destruction of the UAV. Traditional UAV control methods mainly rely on a single wireless communication link, whose signal characteristics are easily detected and interfered with by the enemy, making it difficult to meet the requirements of concealment and security in high-confrontation environments. To solve this problem, there is an urgent need for a UAV control method that can achieve low latency and high concealment within a short distance to enhance the survival ability of the pilot and the mission execution efficiency of the UAV. As an emerging short-distance wireless communication technology, SparkLink technology has low latency, high anti-interference ability, and dynamic spectrum sensing ability, which can provide reliable communication link support for UAV control. Based on this, the present invention proposes a short-distance control method and control system for UAV pilots based on SparkLink technology. By constructing a hierarchical communication architecture of a real pilot layer, a dummy pilot relay layer, and a UAV execution layer, and combining dynamic camouflage and multi-hop encryption mechanisms, the concealment of the pilot's position and the security of control instructions are achieved. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a short-distance control method and control system for UAV pilots based on SparkLink technology.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows:

[0005] In the first aspect of the present invention, a short-distance control method for UAV pilots based on SparkLink technology is disclosed, including the following steps:

[0006] Establish a directional wireless connection between the real pilot layer and the dummy pilot relay layer, optimize the communication frequency band and signal waveform in real time, and generate an initial control instruction signal with dynamic camouflage attributes;

[0007] When the dummy pilot relay layer receives the initial control instruction signal, fuse the real pilot operation behavior characteristics with a preset dummy pilot behavior pattern library to generate a camouflaged control data stream;

[0008] Perform multi-hop encryption transmission processing on the camouflaged control data stream to form an encrypted camouflaged data stream, and perform multi-path diversity transmission to the UAV execution layer. At the same time, continuously optimize the spatio-temporal distribution parameters during the transmission process;

[0009] After receiving the encrypted camouflage data stream, the UAV execution layer decrypts and authenticates the encrypted camouflage data stream and then performs flight control operations.

[0010] Preferably, a directional wireless connection is established between the real pilot layer and the dummy pilot relay layer, and the communication frequency band and signal waveform are optimized in real time to generate an initial control command signal with dynamic camouflage attributes. Specifically:

[0011] Perform dynamic spectrum sensing on the electromagnetic environment within the preset range of the real pilot layer, extract the interference intensity and channel characteristic parameters of the available frequency bands in real time, and generate a dynamic spectrum sensing result;

[0012] Based on the sensing result, screen the optimal anti-interference frequency band and match the time-frequency resource allocation strategy to generate a Xingchen communication link configuration instruction including a frequency hopping sequence and waveform modulation parameters;

[0013] According to the configuration instruction, drive the Xingchen transceiver module to construct a directional beam, and combine the preset UAV control command encoding protocol to convert the original control signal of the real pilot into a Xingchen baseband signal with dynamic time-frequency parameters;

[0014] Embed random pulse position offset and pseudo-noise envelope characteristics in the Xingchen baseband signal to confuse the time-frequency domain characteristics of the signal, generate an initial control command signal with dynamic camouflage attributes, and transmit it to the dummy pilot relay layer through the Xingchen communication link.

[0015] Preferably, when the dummy pilot relay layer receives the initial control command signal, fuse the real pilot operation behavior characteristics with the preset dummy pilot behavior pattern library to generate a camouflaged control data stream. Specifically:

[0016] Configure a camouflage signal generation unit in the dummy pilot relay layer, receive the initial control command signal and parse its time-frequency domain characteristic parameters to generate an initial signal feature vector;

[0017] Based on the preset randomization rule, generate a dynamic camouflage parameter set including random delay jitter and power fluctuation characteristics, and superimpose the camouflage parameter set on the initial signal feature vector to generate a dynamic camouflage signal feature;

[0018] Deploy a behavior pattern analysis module in the dummy pilot relay layer to extract the real pilot operation behavior characteristics, including operation frequency, instruction sequence and response time, and generate a real pilot behavior feature vector;

[0019] Match the real pilot behavior feature vector with the preset dummy pilot behavior pattern library, screen out the dummy pilot behavior pattern with the highest similarity to the real pilot behavior characteristics, and generate a behavior pattern matching result;

[0020] Based on the matching results, perform behavior consistency optimization processing on the dynamic camouflage signal features to generate a camouflage control signal containing multi-node behavior features;

[0021] Fuse the features of the camouflage control signal and the initial control instruction signal to form a camouflage control data stream with multi-node behavior consistency, and transmit it to the next relay node or the UAV execution layer through the StarFlash communication link.

[0022] Preferably, perform multi-hop encryption transmission processing on the camouflage control data stream to form an encrypted camouflage data stream and perform multi-path diversity transmission to the UAV execution layer. At the same time, continuously optimize the spatio-temporal distribution parameters during the transmission process. Specifically:

[0023] Deploy quantum key distribution service nodes in the StarFlash communication network, construct a quantum key resource pool between relay nodes based on the quantum entanglement pair generation protocol, generate quantum key slices dynamically bound to each relay node, and form a hierarchically encrypted key source;

[0024] Extract the dynamic camouflage signal features in the camouflage control data stream at each relay node, generate a hierarchically encrypted key in combination with the quantum key slice, inject the hierarchically encrypted key into the encryption engine, perform segmented asymmetric encryption on the data stream, and generate a hierarchically encrypted ciphertext;

[0025] Perform spatio-temporal correlation coding on the hierarchically encrypted ciphertext and the dynamic camouflage signal features to generate a composite signal structure containing quantum key fingerprints and dynamic spatio-temporal tags;

[0026] Through the multi-path diversity transmission module of the StarFlash link, split the composite signal structure into multiple sub-signal streams carrying independent camouflage features, and perform multi-path diversity transmission of the multiple sub-signal streams carrying independent camouflage features to the UAV execution layer;

[0027] According to the real-time position feedback of the UAV execution layer, dynamically adjust the transmission timing and power spectral density distribution of the sub-signals on the transmission path of the StarFlash link to generate a spatio-temporal chaos parameter set adapted to the electromagnetic environment;

[0028] According to the spatio-temporal chaos parameter set, optimize the spatio-temporal distribution parameters of the dynamic camouflage signal features in real time, and synchronize the optimized spatio-temporal distribution parameters to the quantum key distribution service node and the behavior pattern library of the dummy pilot relay layer through a cross-layer feedback mechanism to form a closed-loop optimized encrypted camouflage cooperation mechanism.

[0029] Preferably, according to the real-time position feedback of the UAV execution layer, dynamically adjust the transmission timing and power spectral density distribution of the sub-signals on the transmission path of the StarFlash link to generate a spatio-temporal chaos parameter set adapted to the electromagnetic environment. Specifically:

[0030] Collect the real-time position nodes of the UAV and the electromagnetic interference intensity it receives; map the electromagnetic interference intensity to an initial value sensitive parameter to generate an initial chaotic parameter sequence;

[0031] Input the initial chaotic parameter sequence into the dynamic cognitive map engine, and combine it with the real-time position nodes of the UAV to perform phase chaotic modulation on the transmission timing of sub-signals. At the same time, perform dynamic shaping in the fractional Fourier transform domain on the power spectral density distribution to generate power-timing joint regulation parameters with time-varying non-stationary characteristics;

[0032] According to the channel response characteristics on the transmission path, perform multi-objective combination on the power-timing joint regulation parameters to generate an initial spatio-temporal chaotic parameter set;

[0033] Inject the initial spatio-temporal chaotic parameter set into the sub-signal transmission module, synchronously collect the signal bit error rate, channel capacity, and camouflage concealment, and construct a dynamic parameter evaluation index;

[0034] If the dynamic parameter evaluation index meets the preset index requirements, convert the initial spatio-temporal chaotic parameter set into a spatio-temporal chaotic parameter set; otherwise, repeat the above steps;

[0035] Synchronize the spatio-temporal chaotic parameter set to the quantum key distribution service node and the dynamic camouflage module through a quantum secure tunnel as the spatio-temporal reference parameters for the next round of multi-hop transmission, realizing cross-layer coordination of encryption camouflage and chaotic modulation.

[0036] Preferably, according to the spatio-temporal chaotic parameter set, the spatio-temporal distribution parameters of the dynamic camouflage signal characteristics are optimized in real time, specifically:

[0037] Perform feature decomposition on the spatio-temporal chaotic parameter set, extract the core feature parameters including time-varying phase, power spectral density, and chaotic attractor trajectory, and generate a spatio-temporal feature vector;

[0038] Based on the spatio-temporal feature vector, reconstruct the dynamic camouflage signal characteristics, map the chaotic attractor trajectory to the random jitter characteristics in the signal time-frequency domain, and generate a reconstructed camouflage signal with spatio-temporal chaotic characteristics;

[0039] Based on the UAV position feedback and electromagnetic environment change data, extract the dynamic characteristics including multipath interference intensity, spectrum occupancy rate, and channel response characteristics, and generate an environmental dynamic feature matrix;

[0040] Based on the environmental dynamic feature matrix, construct a fuzzy rule base including transmission timing, power spectral density, and signal concealment index, and generate a fuzzy control rule set;

[0041] Input the spatio-temporal distribution parameters of the reconstructed camouflage signal into the fuzzy control rule set, evaluate its matching degree with the current electromagnetic environment, and generate a parameter matching degree evaluation vector;

[0042] Dynamically optimize the matching relationship between the transmission timing and the power spectral density based on the parameter matching degree evaluation vector, and generate an optimized spatio-temporal distribution parameter set.

[0043] Preferably, perform flight control operations after decrypting and authenticating the encrypted and camouflaged data stream, specifically:

[0044] When the UAV execution layer receives the encrypted and camouflaged data stream from the dummy pilot relay layer, extract the composite signal structure containing the dynamic camouflage signal features and the quantum key fingerprint, and generate the data stream to be decrypted;

[0045] Based on the quantum key fingerprint, obtain the corresponding quantum key slice through the quantum key distribution service node, input the data stream to be decrypted into the quantum decryption engine, and perform hierarchical asymmetric decryption operations to restore the camouflaged control data stream;

[0046] Perform spatio-temporal chaotic inverse mapping processing on the camouflaged control data stream, extract its dynamic camouflage signal features, and match and verify them with the preset camouflage feature library to generate a feature matching result;

[0047] Verify the consistency between the behavior features of the control instruction and the preset dummy pilot behavior pattern library based on the feature matching result, and generate an instruction legality authentication result;

[0048] If the instruction legality authentication result is legal, input the restored control instruction into the UAV flight control module to perform flight control operations;

[0049] If the instruction legality authentication result is illegal, trigger an exception handling mechanism, generate a security warning message and suspend the flight control operation, and at the same time synchronize the warning message to the real pilot layer and the dummy pilot relay layer through the quantum security tunnel.

[0050] Wherein, the torso, limbs and head of the dummy pilot adopt a snap connection design; the dummy pilot is internally provided with a multi-layer heating sheet array, which is arranged according to the law of human thermodynamics distribution to simulate the temperature difference characteristics of the head, chest and limbs.

[0051] The second invention of the present invention discloses a short-distance control system for a UAV pilot based on the StarFlash technology. The control system includes a memory and a processor. The memory stores a program for the short-distance control method of the UAV pilot based on the StarFlash technology. When the program for the short-distance control method of the UAV pilot based on the StarFlash technology is executed by the processor, the steps of any of the short-distance control methods of the UAV pilot based on the StarFlash technology are implemented.

[0052] The present invention solves the technical defects existing in the background art and has the following beneficial effects: establishing a directional wireless connection between the real pilot layer and the dummy pilot relay layer, optimizing the communication frequency band and signal waveform in real time, and generating an initial control command signal with dynamic camouflage attributes; when the dummy pilot relay layer receives the initial control command signal, fusing the real pilot operation behavior characteristics with a preset dummy pilot behavior pattern library to generate a camouflage control data stream; performing multi-hop encryption transmission processing on the camouflage control data stream to form an encrypted camouflage data stream and performing multi-path diversity transmission to the UAV execution layer, and continuously optimizing the spatio-temporal distribution parameters during the transmission process; after receiving the encrypted camouflage data stream, the UAV execution layer decrypts and authenticates the encrypted camouflage data stream and then performs flight control operations. The present invention can not only effectively prevent the enemy from locating the real pilot through wireless signals, but also ensure the reliable transmission of control commands in a complex electromagnetic environment, providing an effective solution for UAV control tasks in a high-confrontation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 It is the first method flowchart of the short-distance control method for the UAV pilot of the present invention;

[0055] Figure 2 It is the second method flowchart of the short-distance control method for the UAV pilot of the present invention;

[0056] Figure 3 It is the system block diagram of the short-distance control system for the UAV pilot of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] In order to be able to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0058] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0059] Such as Figure 1As shown in the figure, the first aspect of the present invention discloses a short-distance control method for a drone pilot based on SparkLink technology, including the following steps:

[0060] S102. Establish a directional wireless connection between the real pilot layer and the dummy pilot relay layer, optimize the communication frequency band and signal waveform in real time, and generate an initial control command signal with dynamic camouflage attributes;

[0061] S104. After the dummy pilot relay layer receives the initial control command signal, fuse the real pilot operation behavior characteristics with the preset dummy pilot behavior pattern library to generate a camouflage control data stream;

[0062] S106. Perform multi-hop encryption transmission processing on the camouflage control data stream to form an encrypted camouflage data stream, and perform multi-path diversity transmission to the drone execution layer. At the same time, continuously optimize the spatio-temporal distribution parameters during the transmission process;

[0063] S108. After the drone execution layer receives the encrypted camouflage data stream, decrypt and authenticate the encrypted camouflage data stream and then perform flight control operations.

[0064] Preferably, establishing a directional wireless connection between the real pilot layer and the dummy pilot relay layer, optimizing the communication frequency band and signal waveform in real time, and generating an initial control command signal with dynamic camouflage attributes, as Figure 2 shown, specifically:

[0065] S202. Perform dynamic spectrum sensing on the electromagnetic environment within the preset range of the real pilot layer, extract the interference intensity and channel characteristic parameters of the available frequency bands in real time, and generate a dynamic spectrum sensing result;

[0066] It should be noted that multi-channel spectrum sensing modules are deployed in the preset area to collect spectrum data in the electromagnetic environment in real time, including the signal intensity, noise level and multipath interference characteristics of each frequency band; perform fast Fourier transform (FFT) processing on the collected spectrum data, extract frequency domain characteristic parameters, and generate a spectrum characteristic vector; analyze the interference intensity index of each frequency band based on the spectrum characteristic vector, and combine the channel response characteristics analysis to extract channel characteristic parameters, including signal-to-noise ratio, delay spread and Doppler shift, so as to generate a dynamic spectrum sensing result.

[0067] It should be noted that the dynamic spectrum sensing of the electromagnetic environment within the preset range of the real drone pilot layer refers to the process of collecting and analyzing the electromagnetic environment data in the preset area in real time through the spectrum sensing module deployed in the real drone pilot layer, including information such as the occupancy of available frequency bands, interference intensity, and channel characteristic parameters. This process can dynamically identify the best communication frequency band in the current environment and adjust the communication strategy in real time to avoid interference and optimize the signal transmission quality. Through dynamic spectrum sensing, the system can quickly adapt to the complex and changeable electromagnetic environment, providing accurate data support for subsequent frequency band screening, signal waveform optimization, and dynamic camouflage, thus ensuring the concealment and reliability of the drone control instructions in the adversarial environment. Among them, the preset area can usually be set within a range of 500 meters to 2 kilometers centered on the drone pilot, and the specific value can be flexibly adjusted according to the task requirements and equipment performance.

[0068] S204. Based on the sensing result, screen the frequency band with the best anti-interference performance and match the time-frequency resource allocation strategy to generate a StarFlash communication link configuration instruction including a frequency hopping sequence and waveform modulation parameters;

[0069] Among them, the time-frequency resource allocation strategy refers to a method in a wireless communication system that dynamically allocates time (time slots) and frequency (frequency bands) resources according to the spectrum sensing result and communication requirements to optimize communication performance and resource utilization. Specifically, it includes: determining the best frequency band according to the interference intensity and channel characteristics of the available frequency bands; reasonably allocating time slot resources in combination with the requirements of the communication task to avoid signal conflicts and resource waste; ensuring the reliability and efficiency of signal transmission by dynamically adjusting the bandwidth and modulation parameters.

[0070] It should be noted that the dynamic spectrum sensing result is analyzed to screen out the frequency band with the lowest interference intensity and the best channel characteristics as the candidate frequency band; in combination with the preset communication requirements and environmental constraint conditions, an adaptive time-frequency resource allocation strategy is adopted to allocate time slots and bandwidth resources to the candidate frequency band; a random frequency hopping sequence is generated according to the time slot and bandwidth resources allocated to the candidate frequency band, and the optimal modulation method and parameters are determined to generate a StarFlash communication link configuration instruction including a frequency hopping sequence and waveform modulation parameters; the configuration instruction is injected into the StarFlash transceiver module to construct a highly anti-interference and concealed directional communication link.

[0071] S206. Drive the StarFlash transceiver module to construct a directional beam according to the configuration instruction, and convert the original control signal of the real drone pilot into a StarFlash baseband signal with dynamic time-frequency parameters in combination with the preset drone control instruction coding protocol;

[0072] Among them, the preset UAV control instruction coding protocol refers to a set of standardized coding rules designed in advance for UAV control instructions, which is used to convert the original control signals of the pilot into digital signals suitable for wireless transmission. This protocol includes data encapsulation format, channel coding method, error correction mechanism, and instruction priority setting, etc., aiming to ensure the integrity, reliability, and efficiency of control instructions.

[0073] It should be noted that according to the frequency hopping sequence and waveform modulation parameters in the configuration instruction, initialize the RF front-end of the StarFlash transceiver module, configure the carrier frequency, modulation method, and transmit power; and generate a directional beam according to the preset beam pointing to ensure the directivity and anti-interference ability of signal transmission; input the original control signal of the real pilot into the UAV control instruction coding module, perform data encapsulation and channel coding according to the preset coding protocol to generate a baseband data stream; finally, map the baseband data stream to the dynamic time-frequency parameters to generate a StarFlash baseband signal with frequency hopping characteristics and waveform modulation characteristics, so as to prepare for subsequent transmission.

[0074] S208. Embed the randomized pulse position offset and pseudo-noise envelope characteristics in the StarFlash baseband signal to perform confusion processing on the time-frequency domain characteristics of the signal, generate an initial control instruction signal with dynamic camouflage attributes, and transmit it to the dummy pilot relay layer through the StarFlash communication link.

[0075] Exemplarily, a randomized pulse position offset sequence with a length of 1024 is generated by a pseudo-random sequence generator, and this sequence is superimposed on the time-domain pulse position of the StarFlash baseband signal to randomly offset the pulse position within the range of ±10 microseconds, and generate a pseudo-noise envelope characteristic with a bandwidth of 5 MHz and a power spectral density of -50 dBm / Hz, and perform convolution processing on its frequency-domain characteristics with the StarFlash baseband signal to make the signal spectrum show a randomized distribution. Finally, the processed signal is input into the time-frequency domain confusion module, and the time-frequency domain characteristics are reconstructed through fast Fourier transform (FFT) and inverse transform (IFFT) to generate an initial control instruction signal with dynamic camouflage attributes, where the signal characteristics are highly similar to the background noise, thereby improving the concealment and anti-detection ability of the signal.

[0076] In summary, this method solves the technical problem that the UAV pilot is located by the enemy due to the exposure of wireless signal characteristics during short-distance control. Through dynamic spectrum sensing, anti-interference frequency band screening, and dynamic time-frequency parameter optimization, combined with randomized pulse position offset and pseudo-noise envelope characteristics, an initial control instruction signal with dynamic camouflage attributes is generated, which improves the concealment and anti-interference ability of signal transmission, effectively avoids the detection of the real pilot's position by the enemy, enhances the survival ability of the pilot, and at the same time ensures the reliable transmission of control instructions in a highly adversarial environment.

[0077] Preferably, after the dummy pilot relay layer receives the initial control instruction signal, it fuses the real pilot operation behavior characteristics with the preset dummy pilot behavior pattern library to generate a camouflaged control data stream, specifically:

[0078] Configure a camouflage signal generation unit in the dummy pilot relay layer to receive the initial control instruction signal and analyze its time-frequency domain characteristic parameters to generate an initial signal feature vector;

[0079] Based on the preset randomization rules, generate a dynamic camouflage parameter set including random delay jitter and power fluctuation characteristics, and superimpose the camouflage parameter set on the initial signal feature vector to generate dynamic camouflage signal characteristics;

[0080] Among them, the preset randomization rules refer to a set of randomly generated characteristic instructions pre-configured in the dummy pilot relay layer, which are used to provide diverse random parameters for signal camouflage. It includes a linear congruential random number generator, a Gaussian distribution generator, a Poisson distribution generator, etc., which can generate various random characteristic parameters such as delay jitter, power fluctuation, and phase shift. This enables the system to dynamically generate camouflage parameters highly similar to the real signal characteristics, thereby effectively confusing the enemy's recognition and positioning of the signal and enhancing the signal's concealment and anti-detection capabilities.

[0081] Exemplarily, a linear congruential random number generator is used to generate a delay jitter parameter with a range of ±5 milliseconds, and at the same time, a Gaussian distribution is used to generate a power fluctuation parameter with a fluctuation range of ±3 dB. The generated delay jitter and power fluctuation parameters are encapsulated into a dynamic camouflage parameter set; then, the initial signal feature vector and the dynamic camouflage parameter set are input into the signal superposition module, and superimposed processing is performed through time-domain convolution and frequency-domain weighting algorithms to generate dynamic camouflage signal characteristics; finally, the processed signal characteristics are input into the verification module to ensure that they meet the preset concealment index (such as signal similarity ≥90%), and dynamic camouflage signal characteristics available for subsequent transmission are generated.

[0082] Deploy a behavior pattern analysis module in the dummy pilot relay layer to extract the real pilot operation behavior characteristics, including operation frequency, instruction sequence, and response time, and generate a real pilot behavior feature vector;

[0083] Match the real pilot behavior feature vector with the preset dummy pilot behavior pattern library, and screen out the dummy pilot behavior pattern with the highest similarity to the real pilot behavior characteristics to generate a behavior pattern matching result;

[0084] Among them, the preset behavior pattern library of dummy pilots refers to a database pre-built in the dummy pilot relay layer that contains various virtual pilot operation behavior characteristics, and its purpose is to provide diverse camouflage references for real pilot operation behaviors. This behavior pattern library includes characteristic parameters such as operation frequency, instruction sequence, and response time, and is generated by simulating the operation habits and task requirements of different pilots. During the signal camouflage process, the system screens out patterns similar to the real pilot behavior characteristics from this library and uses them to generate camouflage control signals with multi-node behavior consistency, thereby effectively confusing the enemy's recognition and positioning of real pilots and enhancing the concealment and deception of control signals.

[0085] Based on the matching result, perform behavior consistency optimization processing on the dynamic camouflage signal characteristics to generate a camouflage control signal containing multi-node behavior characteristics;

[0086] It should be noted that according to the behavior pattern matching result, extract the behavior pattern parameters of the dummy pilot with the highest similarity to the real pilot behavior characteristics, including operation frequency (such as 2Hz), instruction sequence (such as "rise - turn left - descend"), and response time (such as 500ms); secondly, input the behavior pattern parameters into the signal optimization module to adjust the dynamic camouflage signal characteristics so that its time domain and frequency domain characteristics are consistent with the dummy pilot behavior pattern.

[0087] Fuse the characteristics of the camouflage control signal and the initial control instruction signal to form a camouflage control data stream with multi-node behavior consistency, and transmit it to the next relay node or the UAV execution layer through the StarFlash communication link.

[0088] To sum up, this method generates a camouflage control data stream with multi-node behavior consistency by fusing the real pilot operation behavior characteristics with the preset behavior pattern library of dummy pilots, thereby enhancing the concealment and deception of control signals, effectively confusing the operation behavior characteristics of real pilots, making it difficult for the enemy to locate real pilots through signal analysis, and enhancing the survival ability of pilots and the mission execution security of UAVs.

[0089] Preferably, perform multi-hop encryption transmission processing on the camouflage control data stream to form an encrypted camouflage data stream and perform multi-path diversity transmission to the UAV execution layer. At the same time, continuously optimize the space-time distribution parameters during the transmission process, specifically:

[0090] Deploy quantum key distribution service nodes in the StarFlash communication network, construct a quantum key resource pool between relay nodes based on the quantum entanglement pair generation protocol, generate quantum key slices dynamically bound to each relay node, and form a hierarchical encrypted key source;

[0091] Among them, the quantum entanglement pair generation protocol refers to a standardized method that utilizes the entanglement phenomenon in quantum mechanics to generate one or more pairs of correlated quantum states (such as photon pairs) through specific physical devices and operation steps. These quantum states remain correlated even after being spatially separated, and any measurement of one quantum state will instantaneously affect the state of the other. Common protocols include the BB84 protocol and the E91 protocol, which generate secure and non-replicable quantum keys through the preparation, transmission, and measurement of quantum states.

[0092] Exemplarily, the BB84 protocol or the E91 protocol is adopted to generate quantum keys with a length of 256 bits for each pair, and the keys are distributed to each relay node through a quantum channel. In each relay node, the received quantum key is divided into multiple slices with a length of 128 bits, and each slice is dynamically bound to a specific transmission period (such as 10 seconds) and task type (such as reconnaissance, interference). The generated key slices are stored in the local key resource pool, and a dynamic identifier is added to them to ensure real-time invocation during transmission; finally, the key resource pool is monitored and updated through a quantum key management system to ensure the availability and security of the key resources, forming a hierarchically encrypted key source.

[0093] In each relay node, the dynamic camouflage signal features in the camouflage manipulation data stream are extracted, and a hierarchically encrypted key is generated in combination with the quantum key slice. The hierarchically encrypted key is injected into the encryption engine to perform segmented asymmetric encryption on the data stream, generating a hierarchically encrypted ciphertext;

[0094] Among them, the encryption engine (such as OpenSSL, Libsodium, QKD system) refers to a hardware or software module specifically used to perform encryption and decryption operations. Its core function is to convert the original data into ciphertext through a specific encryption algorithm, or restore the original data from the ciphertext. The encryption engine usually includes modules such as key management, algorithm execution, and data processing, and supports various encryption methods such as symmetric encryption (such as AES), asymmetric encryption (such as RSA), and hybrid encryption. In high-security scenarios, the encryption engine can also integrate advanced functions such as quantum encryption and hierarchical encryption to ensure the confidentiality, integrity, and anti-cracking ability of data during transmission and storage.

[0095] The hierarchically encrypted ciphertext and the dynamic camouflage signal features are subjected to spatio-temporal correlation encoding to generate a composite signal structure containing a quantum key fingerprint and a dynamic spatio-temporal label;

[0096] Exemplarily, extract the quantum key slice identifier (such as a 128-bit hash value) corresponding to each segment of the ciphertext from the hierarchical encrypted ciphertext as the quantum key fingerprint. Collect the real-time location information of the current transmission node (such as longitude 113.52634° and latitude 22.27152°) and the timestamp accurate to the millisecond level (such as 2023-09-15T14:23:05.678Z) to generate a dynamic spatio-temporal tag. Combine the hierarchical encrypted ciphertext with the dynamic camouflage signal features (such as ±3dB power fluctuation and ±5ms delay jitter), and embed the quantum key fingerprint and the dynamic spatio-temporal tag in the data header to form a composite signal structure including an encrypted data area (512 bytes), a camouflage feature area (64 bytes), and a tag header (32 bytes).

[0097] Through the multipath diversity transmission module of the StarFlash link, split the composite signal structure into multiple sub-signal streams carrying independent camouflage features, and perform multipath diversity transmission of the multiple sub-signal streams carrying independent camouflage features to the UAV execution layer;

[0098] Exemplarily, split the composite signal structure into 3 sub-signal streams (each sub-stream is 512 bytes long), and assign independent camouflage feature parameters to each sub-stream (such as sub-stream 1 loading ±5ms delay jitter, sub-stream 2 loading ±3dB power fluctuation, and sub-stream 3 loading a pseudo-noise envelope). Configure three independent transmission paths, namely the satellite link, the ground base station link, and the relay node link, in the transmission path of the StarFlash link, and each path corresponds to a sub-signal stream; then, add a transmission identifier (such as a path number and the timestamp 2023-09-15T14:23:05.678Z) to each sub-signal stream and allocate it to the corresponding path through the transmission scheduling module.

[0099] According to the real-time location feedback of the UAV execution layer, dynamically adjust the transmission timing and power spectral density distribution of the sub-signals on the transmission path of the StarFlash link to generate a spatio-temporal chaos parameter set adaptive to the electromagnetic environment;

[0100] According to the spatio-temporal chaos parameter set, real-time optimize the spatio-temporal distribution parameters of the dynamic camouflage signal features, and synchronize the optimized spatio-temporal distribution parameters to the behavior pattern libraries of the quantum key distribution service node and the dummy UAV relay layer through the cross-layer feedback mechanism to form a closed-loop optimized encryption and camouflage coordination mechanism.

[0101] In summary, to solve the problem that the UAV control signal can be cracked or recognized by the enemy due to insufficient encryption strength or single camouflage feature during the transmission process. The present invention generates an encrypted camouflage data stream with high concealment and anti-interference through multi-hop encrypted transmission, multi-path diversity transmission, and dynamic optimization of spatio-temporal distribution parameters, thereby improving the security and reliability of signal transmission, effectively preventing the enemy from cracking and locating the signal, ensuring the concealed transmission of control instructions in a high-confrontation environment, and at the same time, through a closed-loop optimization mechanism, realizing the dynamic coordination of encryption and camouflage features, further enhancing the anti-detection ability and task execution efficiency of the system.

[0102] Preferably, according to the real-time position feedback of the UAV execution layer, the transmission timing and power spectral density distribution of sub-signals are dynamically adjusted on the transmission path of the StarFlash link to generate a spatio-temporal chaos parameter set adapted to the electromagnetic environment, specifically:

[0103] Collect the real-time position nodes of the UAV and the intensity of electromagnetic interference received; map the intensity of electromagnetic interference to an initial value sensitive parameter to generate an initial chaos parameter sequence;

[0104] Exemplarily, an electromagnetic interference intensity acquisition module is deployed at the transmission node of the StarFlash link to collect the electromagnetic interference intensity value of the current environment (such as -50dBm to -30dBm) in real time; secondly, the collected interference intensity value is input into the chaos mapping model, and an initial chaos parameter sequence is generated through a preset mapping rule (such as for every 1dBm increase in interference intensity, the initial value sensitive parameter increases by 0.01), the sequence length is 1024, and the range of each parameter value is from 0 to 1. Then, the generated initial chaos parameter sequence is stored in the local parameter pool, and a timestamp (such as 2023-09-15T14:23:05.678Z) and a node identifier (such as node number 001) are added to it.

[0105] Input the initial chaos parameter sequence into the dynamic cognitive map engine, and combine with the real-time position nodes of the UAV to perform phase chaos modulation on the transmission timing of sub-signals, and at the same time perform dynamic shaping of the power spectral density distribution in the fractional Fourier transform domain to generate power-timing joint regulation parameters with time-varying non-stationary characteristics;

[0106] Exemplarily, an initial chaotic parameter sequence (length 1024, parameter range 0 to 1) is associated and mapped with the real-time position nodes of the unmanned aerial vehicle (such as longitude 113.52634° and latitude 22.27152°) to generate phase chaotic modulation parameters (such as phase offset ±10°). Through the power spectral density analysis module, the spectral characteristics of the sub-signal are dynamically shaped to adjust its power spectral density distribution (such as frequency range 2.4 GHz to 2.4835 GHz, power fluctuation ±3 dB). The phase chaotic modulation parameters and the power spectral density adjustment parameters are combined to generate power-time joint regulation parameters (such as time sequence offset ±5 ms, power spectral density dynamic range -50 dBm to -30 dBm). Then, the verification module performs real-time evaluation on the regulation parameters (such as signal bit error rate ≤1e-6), and outputs them after ensuring that they meet the preset standards, serving as the regulation basis for sub-signal transmission.

[0107] According to the channel response characteristics on the transmission path, multi-objective combination is performed on the power-time joint regulation parameters to generate an initial spatio-temporal chaotic parameter set;

[0108] Exemplarily, the channel response characteristics are collected in real time (such as signal-to-noise ratio ≥20 dB, multipath delay ≤10 ns). The channel response characteristics and the power-time joint regulation parameters (such as time sequence offset ±5 ms, power spectral density dynamic range -50 dBm to -30 dBm) are analyzed for correlation to generate multi-objective combination parameters (such as time sequence optimization weight 0.6, power optimization weight 0.4). Through the parameter combination module, the multi-objective combination parameters and the initial chaotic parameter sequence (length 1024, parameter range 0 to 1) are integrated to generate an initial spatio-temporal chaotic parameter set (such as time sequence parameter ±5 ms, power parameter ±3 dB).

[0109] The initial spatio-temporal chaotic parameter set is injected into the sub-signal transmission module, and the signal bit error rate, channel capacity, and camouflage concealment are synchronously collected to construct a dynamic parameter evaluation index;

[0110] If the dynamic parameter evaluation index meets the preset index requirements, the initial spatio-temporal chaotic parameter set is converted into a spatio-temporal chaotic parameter set; otherwise, the above steps are repeated;

[0111] It should be noted that dynamic parameter evaluation indicators such as the bit error rate of real-time collected signals, channel capacity, and camouflage concealment are evaluated. The collected evaluation indicators are compared and analyzed with the preset indicator requirements to generate an evaluation result. If the signal bit error rate, channel capacity, and camouflage concealment all meet the preset indicator requirements (such as the signal bit error rate ≤ 1e-6, the channel capacity ≥ 10 Mbps, and the camouflage concealment ≥ 90%), the initial spatio-temporal chaotic parameter set (such as the timing parameter ±5 ms and the power parameter ±3 dB) is directly converted into the spatio-temporal chaotic parameter set and stored in the local parameter pool; otherwise, the electromagnetic interference intensity and channel response characteristics are re-collected, and the initial chaotic parameter sequence, power-timing joint regulation parameters, and initial spatio-temporal chaotic parameter set are regenerated until the evaluation result meets the preset indicator requirements.

[0112] The spatio-temporal chaotic parameter set is synchronized to the quantum key distribution service node and the dynamic camouflage module through a quantum secure tunnel as the spatio-temporal reference parameters for the next round of multi-hop transmission, realizing cross-layer coordination of encryption camouflage and chaotic modulation.

[0113] To sum up, to solve the problem that the signal quality of UAV control signals decreases or the camouflage fails due to the complex and changeable electromagnetic environment during the transmission process. This method improves the anti-interference ability and concealment of signal transmission by real-time collecting the UAV position and electromagnetic interference intensity, dynamically adjusting the transmission timing and power spectral density distribution of sub-signals, and generating a spatio-temporal chaotic parameter set adaptive to the electromagnetic environment.

[0114] Preferably, the spatio-temporal distribution parameters of the dynamic camouflage signal characteristics are optimized in real time according to the spatio-temporal chaotic parameter set, specifically:

[0115] Perform eigen-decomposition on the spatio-temporal chaotic parameter set, extract the core eigen-parameters including time-varying phase, power spectral density, and chaotic attractor trajectory, and generate a spatio-temporal eigen-vector;

[0116] It should be noted that the spatio-temporal chaotic parameter set (such as the timing parameter ±5 ms and the power parameter ±3 dB) is input into the module for parsing, and the time-varying phase characteristics (such as the phase offset ±10°), power spectral density characteristics (such as the frequency range from 2.4 GHz to 2.4835 GHz and the power fluctuation ±3 dB), and chaotic attractor trajectory characteristics (such as the trajectory parameter range from 0 to 1) are extracted from the parameter set; the extracted core eigen-parameters are combined to generate a spatio-temporal eigen-vector (such as the vector length of 128, including phase offset, power fluctuation, and trajectory parameter).

[0117] Based on the spatio-temporal eigen-vector, reconstruct the dynamic camouflage signal characteristics, map the chaotic attractor trajectory to the random jitter characteristics in the time-frequency domain of the signal, and generate a reconstructed camouflage signal with spatio-temporal chaotic characteristics;

[0118] It should be noted that the spatio-temporal feature vector (such as vector length 128, including phase offset ±10°, power fluctuation ±3dB, and trajectory parameter range 0 to 1) is input into the module for analysis; according to the trajectory characteristics of the chaotic attractor (such as trajectory parameter range 0 to 1), random jitter characteristics in the time-frequency domain of the signal are generated (such as time-domain jitter ±5ms, frequency-domain jitter ±1MHz); the random jitter characteristics are superimposed with the dynamic camouflage signal characteristics (such as power spectral density dynamic range -50dBm to -30dBm) to generate a reconstructed camouflage signal with spatio-temporal chaotic characteristics (such as signal length 512 bytes, including time-frequency domain jitter characteristics); finally, the reconstructed camouflage signal is evaluated in real time through the verification module (such as signal concealment ≥90%), and after ensuring that it meets the preset standards, it is output as the camouflage signal for subsequent transmission.

[0119] Based on the UAV position feedback and electromagnetic environment change data, dynamic characteristics including multipath interference intensity, spectrum occupancy rate, and channel response characteristics are extracted to generate an environmental dynamic characteristic matrix.

[0120] Based on the environmental dynamic characteristic matrix, a fuzzy rule base including transmission timing, power spectral density, and signal concealment index is constructed to generate a fuzzy control rule set.

[0121] It should be noted that the environmental dynamic characteristic matrix (such as signal-to-noise ratio ≥20dB, multipath delay ≤10ns, spectrum occupancy rate ≤70%) is input into the module for analysis, and a fuzzy rule base (such as the number of rules is 256, and each rule contains timing, power, and concealment parameters) is generated according to the transmission timing (such as timing offset ±5ms), power spectral density (such as frequency range 2.4GHz to 2.4835GHz, power fluctuation ±3dB), and signal concealment index (such as concealment ≥90%). The fuzzy rule base is stored in the local rule pool, and a timestamp (such as 2023-09-15T14:23:05.678Z) and a node identifier (such as node number 001) are added to it.

[0122] The spatio-temporal distribution parameters of the reconstructed camouflage signal are input into the fuzzy control rule set to evaluate its matching degree with the current electromagnetic environment, and a parameter matching degree evaluation vector is generated.

[0123] It should be noted that the spatio-temporal distribution parameters of the reconstructed camouflage signal (such as timing offset ±5ms, power fluctuation ±3dB) are input into the module for analysis, fuzzy rules (such as the number of rules is 256) matching the current electromagnetic environment (such as signal-to-noise ratio ≥20dB, multipath delay ≤10ns) are extracted from the fuzzy control rule set to generate a matching rule subset, and the spatio-temporal distribution parameters are compared and analyzed with the matching rule subset to generate a parameter matching degree evaluation vector (such as vector length 128, including timing matching degree, power matching degree, and concealment matching degree).

[0124] Dynamically optimize the matching relationship between the transmission timing and the power spectral density based on the parameter matching degree evaluation vector, and generate an optimized spatio-temporal distribution parameter set.

[0125] It should be noted that the parameter matching degree evaluation vector (such as vector length 128, including timing matching degree, power matching degree, and concealment matching degree) is input into the module for parsing. According to the matching degree evaluation result, adjust the matching relationship between the transmission timing (such as the timing offset is optimized from ±5 ms to ±3 ms) and the power spectral density (such as the power fluctuation is optimized from ±3 dB to ±2 dB), and generate an optimized spatio-temporal distribution parameter set (such as timing parameter ±3 ms, power parameter ±2 dB). Then, store the optimized parameter set in the local parameter pool.

[0126] In summary, this method improves the concealment and environmental adaptability of signal transmission by real-time optimizing the spatio-temporal distribution parameters of dynamic camouflage signal features, combines environmental dynamic features with fuzzy logic control, ensures the stable transmission of control signals in a high-dynamic electromagnetic environment, effectively prevents the enemy from detecting and positioning the signals, and at the same time, through the closed-loop optimization mechanism, realizes the dynamic matching of camouflage features and the electromagnetic environment, enhancing the security and reliability of UAV mission execution.

[0127] Preferably, perform flight control operations after decrypting and authenticating the encrypted camouflage data stream, specifically:

[0128] When the UAV execution layer receives the encrypted camouflage data stream from the dummy pilot relay layer, extract the composite signal structure containing dynamic camouflage signal features and quantum key fingerprints, and generate the data stream to be decrypted;

[0129] Based on the quantum key fingerprints, obtain the corresponding quantum key slices through the quantum key distribution service node, input the data stream to be decrypted into the quantum decryption engine, and perform hierarchical asymmetric decryption operations to restore the camouflage control data stream;

[0130] Among them, the quantum decryption engine is a hardware or software module specifically used to perform decryption operations based on quantum keys. Its core function is to use quantum keys to perform hierarchical asymmetric decryption on encrypted data and restore the original data. The engine obtains the quantum key slices corresponding to the encrypted data through the quantum key distribution service node, and combines quantum decryption algorithms (such as Shor algorithm or Grover algorithm) to decrypt the ciphertext, ensuring the security and anti-cracking ability of data transmission.

[0131] Perform spatio-temporal chaotic inverse mapping processing on the camouflage control data stream, extract its dynamic camouflage signal features, and perform matching verification with the preset camouflage feature library to generate a feature matching result;

[0132] Among them, the feature matching results include but are not limited to the following: signal time-domain jitter matching degree (such as deviation within ±5 ms), frequency-domain offset matching degree (such as deviation within ±1 MHz), power fluctuation matching degree (such as fluctuation within ±3 dB), chaotic attractor trajectory matching degree (such as similarity of trajectory parameters in the range of 0 to 1), and overall signal concealment index (such as concealment ≥90%).

[0133] It should be noted that the spatio-temporal chaos inverse mapping refers to the process of restoring the signal modulated by spatio-temporal chaos to the original signal through reverse processing. Its core function is to eliminate the chaotic features (such as time-domain jitter, frequency-domain offset, etc.) introduced during signal transmission and restore the original time-frequency domain characteristics of the signal. This process performs inverse operations on characteristic parameters such as chaotic attractor trajectory, time-varying phase, and power spectral density through an inverse chaotic mapping model to restore the initial state of the signal. Spatio-temporal chaos inverse mapping is widely used in high-concealment communication systems.

[0134] It should be noted that the preset camouflage feature library refers to a database pre-constructed in the UAV control system that contains various dynamic camouflage signal features, and its purpose is to provide a reference basis for signal decryption and authentication. This library includes camouflage feature parameters such as time-domain jitter, frequency-domain offset, and power fluctuation, which are generated by simulating signal camouflage modes in different environments. During signal decryption, the system extracts the camouflage features matching the received signal from this library to verify the legitimacy and integrity of the signal.

[0135] Verify the consistency between the behavior characteristics of the control command and the preset dummy pilot behavior pattern library based on the feature matching results, and generate an instruction legality authentication result;

[0136] If the instruction legality authentication result is legal, input the restored control command into the UAV flight control module to perform flight control operations;

[0137] If the instruction legality authentication result is illegal, trigger the exception handling mechanism, generate a security warning message and suspend the flight control operation, and at the same time synchronize the warning message to the real pilot layer and the dummy pilot relay layer through the quantum security tunnel.

[0138] To sum up, to solve the technical problem that the flight control fails or there are security risks due to decryption failure or authentication failure during the transmission of UAV control commands. This method ensures the legality and reliability of control commands through multiple security mechanisms of quantum key verification, dynamic camouflage feature matching, and behavior pattern authentication, effectively preventing the enemy from tampering with or forging control commands, ensuring the safe flight of UAVs in a high-confrontation environment, and at the same time quickly responding to illegal commands through the exception handling mechanism, enhancing the anti-attack ability and task execution efficiency of the system.

[0139] Among them, the torso, limbs, and head of the dummy drone pilot adopt a snap - on connection design; the dummy drone pilot is internally equipped with a multi - layer heating element array, which is arranged according to the law of human thermodynamics distribution to simulate the temperature difference characteristics of the head, chest, and limbs; the dummy drone pilot is also internally equipped with a data transmission radio module, which forwards the received instructions to the drone, and at the same time attracts the enemy's thermal imaging / radio detection equipment through heat source and motion simulation.

[0140] It should be noted that the torso, limbs, and head of the dummy drone pilot adopt a snap - on connection design, enabling the dummy drone pilot to be quickly assembled and disassembled, facilitating transportation and deployment, while ensuring the connection stability and flexibility between components; the dummy drone pilot is internally equipped with a multi - layer heating element array, which is arranged according to the law of human thermodynamics distribution to simulate the temperature difference characteristics of the head, chest, and limbs. Specifically: heating elements are respectively arranged at key parts such as the head, chest, and limbs of the dummy drone pilot, and the temperature of each heating element is precisely controlled through distributed temperature control technology, so that the surface temperature distribution of the dummy drone pilot conforms to the thermodynamic characteristics of a real human body (such as the head temperature is slightly higher than the limbs, and the chest temperature is moderate); in addition, by wrapping the heating element array with lightweight PVC material, not only the overall weight of the dummy drone pilot is reduced, but also the heat signal conduction efficiency and simulation degree are improved; this design enables the dummy drone pilot to present heat signal characteristics highly similar to those of a real human body under infrared detection, effectively confusing the enemy's detection equipment and enhancing the concealment and survival ability of the real pilot.

[0141] In this embodiment, the short - distance control method of the drone pilot may further include the following steps:

[0142] Collect the real - time operation postures, myoelectric signals, and electroencephalogram signals of the real pilot through wearable biosensors, extract the signal amplitude, frequency, and entropy value characteristics, and generate a bio - electrical signal feature vector;

[0143] Input the bio - electrical signal feature vector into a random time - series generation module to generate a bionic electrical signal base with random time - series fluctuation characteristics;

[0144] Use a pulsed neural convolutional network to perform bio - electrical feature encoding on the real - time operation posture of the real pilot to form an operation vector;

[0145] Map the operation vector to the phase oscillation space of the bionic base signal to generate a phase - encoded signal;

[0146] Perform non - linear feature fusion on the phase - encoded signal and the bionic base signal to form a composite modulation waveform with bio - rhythm synchronization characteristics in the time - frequency domain;

[0147] It should be noted that in the signal processing module, a phase-coded signal (such as a phase offset of ±10°) and a bionic base signal (such as an amplitude range of 0 to 1 and a frequency range of 0.1 Hz to 100 Hz) are extracted to generate a signal feature vector. The signal feature vector is input into the non-linear feature fusion module, and the phase-coded signal and the bionic base signal are superimposed in the time-frequency domain through a preset fusion rule to generate a preliminary composite modulation waveform (such as a waveform length of 512 bytes, including time-frequency domain features). The preliminary composite modulation waveform is adjusted by the biological rhythm synchronization module to synchronize it with the biological rhythm characteristics of the bionic base signal (such as a period of 1 second and an amplitude fluctuation of ±10%), generating a composite modulation waveform with biological rhythm synchronization characteristics (such as a waveform length of 512 bytes, including time-frequency domain features and biological rhythm synchronization characteristics).

[0148] A multi-dimensional perturbation matrix is constructed based on the amplitude-frequency characteristic parameters of the bionic base signal, and the topological structure of the composite modulation waveform is reorganized based on the multi-dimensional perturbation matrix to generate a reorganized signal feature;

[0149] It should be noted that in the signal processing module, the amplitude-frequency characteristic parameters of the bionic base signal (such as an amplitude range of 0 to 1 and a frequency range of 0.1 Hz to 100 Hz) are extracted to generate an amplitude-frequency characteristic vector. The amplitude-frequency characteristic vector is input into the multi-dimensional perturbation matrix generation module to construct a multi-dimensional perturbation matrix including time-domain perturbation, frequency-domain perturbation, and phase perturbation (such as a matrix dimension of 3×3 and a perturbation range of ±10%). The composite modulation waveform (such as a waveform length of 512 bytes, including time-frequency domain features) and the multi-dimensional perturbation matrix are input into the topological structure reorganization module to dynamically adjust the time-frequency domain features of the waveform, generating a reorganized signal feature (such as a reorganized waveform length of 512 bytes, including a time-domain jitter of ±5 ms and a frequency-domain offset of ±1 MHz).

[0150] The reorganized signal feature is projected into the quantum state space, and a final output signal with biological dynamic camouflage is generated through quantum entanglement correlation, and is transmitted to the relay layer of the dummy drone pilot through the StarFlash link.

[0151] In summary, in the stage of generating the dynamic camouflage attribute, the present method introduces a biological noise feature mixing technology, non-linearly superimposes the operation behavior characteristics of the real drone pilot and the randomly generated bioelectric signal characteristics to form a composite signal feature with biological dynamic characteristics (the final output signal), making the signal feature highly consistent with the real biological dynamics, and effectively preventing the enemy from identifying and locating the signal.

[0152] In this embodiment, the short-distance control method for the drone pilot may further include the following steps:

[0153] The electromagnetic radiation spectrum characteristics of the real transmission channel are collected in real time through a multi-dimensional electromagnetic field probe array, and the joint distribution characteristics of carrier frequency-phase offset are extracted to form a multi-dimensional electromagnetic fingerprint map; a set of phase modulation parameters with a random quantum state distribution is dynamically generated based on the time-frequency domain parameters of the electromagnetic fingerprint map; the set of phase modulation parameters is subjected to quantum phase convolution operation with a pseudo-random noise base to generate a cluster of mirror carrier signals carrying the same electromagnetic fingerprint characteristics; the real transmission data stream and the mirror signal cluster are subjected to spiral phase interleaved modulation to generate a vortex composite waveform with electromagnetic feature isomorphism; the topological winding degree parameter of the vortex waveform is dynamically adjusted according to the environmental electromagnetic interference intensity, so that the mirror channel and the real channel form electromagnetic feature resonance coupling; a quantum teleportation identifier is implanted in the mirror channel, and the electromagnetic feature synchronization of the mirror channel and the real channel is verified in real time through the quantum entanglement correlation mechanism; when the data transmission is completed, a quantum decoherence operation is triggered, so that the electromagnetic fingerprint characteristics of the mirror channel undergo an irreversible topological structure collapse.

[0154] Through quantum phase convolution and spiral phase interleaved modulation, this method makes the mirror signal highly consistent with the real signal in electromagnetic characteristics, effectively preventing the enemy from identifying and positioning the signal; at the same time, through the quantum entanglement correlation mechanism and the topological self-destruction protocol, it ensures that the electromagnetic characteristics of the mirror channel irreversibly collapse after the data transmission is completed, enhancing the anti-reverse analysis ability and dynamic coordination of the system.

[0155] As Figure 3 shown, the second invention of the present invention discloses a short-distance control system 8 for an unmanned aerial vehicle pilot based on the SparkLink technology. The control system includes a memory 60 and a processor 80. A program for the short-distance control method of the unmanned aerial vehicle pilot based on the SparkLink technology is stored in the memory 60. When the program for the short-distance control method of the unmanned aerial vehicle pilot based on the SparkLink technology is executed by the processor 80, the steps of any of the short-distance control methods of the unmanned aerial vehicle pilot based on the SparkLink technology are implemented.

[0156] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of them should be covered by the protection scope of the present invention.

Claims

1. A short-distance control method for a UAV pilot based on star flash technology, characterized in that: The following steps are involved: Establish a directional wireless connection between the real pilot layer and the dummy pilot relay layer, optimize the communication frequency band and signal waveform in real time, and generate an initial control command signal with dynamic camouflage properties; When the dummy pilot relay layer receives the initial control command signal, it fuses the operation behavior characteristics of the real pilot with the preset dummy pilot behavior pattern library to generate a camouflaged control data stream; Implementing multi-hop encrypted transmission processing on the camouflaged control data stream to form an encrypted camouflaged data stream, and then transmitting it to the drone execution layer in a multi-path diversity manner, while continuously optimizing the spatiotemporal distribution parameters during the transmission process; After receiving the encrypted disguised data stream, the drone execution layer decrypts and authenticates the encrypted disguised data stream and then performs flight control operations; Among them, a directional wireless connection is established between the real pilot layer and the dummy pilot relay layer, the communication frequency band and signal waveform are optimized in real time, and the initial control command signal with dynamic camouflage properties is generated, specifically: Perform dynamic spectrum sensing on the electromagnetic environment within the preset range of the real pilot layer, extract the interference intensity and channel characteristic parameters of the available frequency band in real time, and generate dynamic spectrum sensing results; Based on the sensing results, the optimal frequency band for anti-interference is selected and the time-frequency resource allocation strategy is matched to generate a star flash communication link configuration instruction including a frequency hopping sequence and waveform modulation parameters; According to the configuration instructions, the Starflash transceiver module is driven to construct a directional beam, and combined with the preset UAV control instruction coding protocol, the original control signal of the real pilot is converted into a Starflash baseband signal with dynamic time-frequency parameters; Randomized pulse position offset and pseudo-noise envelope characteristics are embedded in the Starflash baseband signal to confuse the signal's time-frequency domain characteristics, generate an initial control command signal with dynamic camouflage properties, and transmit it to the dummy pilot relay layer through the Starflash communication link.

2. According to claim 1, a method for short-distance control of a drone pilot based on the star flash technology is characterized in that: When the dummy pilot relay layer receives the initial control command signal, it fuses the real pilot's operation behavior characteristics with the preset dummy pilot behavior pattern library to generate a camouflaged control data stream, specifically: A camouflage signal generating unit is configured at the dummy pilot relay layer to receive the initial control command signal and analyze its time-frequency domain characteristic parameters to generate an initial signal characteristic vector; Based on a preset randomization rule, a dynamic camouflage parameter set including random delay jitter and power fluctuation characteristics is generated, and the camouflage parameter set is superimposed with the initial signal feature vector to generate a dynamic camouflage signal feature; Deploy a behavior pattern analysis module at the dummy pilot relay layer to extract the real pilot's operation behavior characteristics, including operation frequency, command sequence, and response time, and generate a real pilot's behavior feature vector; Matching the real pilot behavior feature vector with a preset dummy pilot behavior pattern library, screening out the dummy pilot behavior pattern with the greatest similarity to the real pilot behavior feature, and generating a behavior pattern matching result; Based on the matching result, the dynamic camouflage signal feature is subjected to behavioral consistency optimization processing to generate a camouflage control signal including multi-node behavioral features; The camouflaged control signal is feature-fused with the initial control command signal to form a camouflaged control data stream with multi-node behavior consistency, and is transmitted to the next relay node or drone execution layer through the Star Flash communication link.

3. The short-distance control method for drone pilots based on Star Flash technology according to claim 1 is characterized in that: The camouflage control data stream is subjected to multi-hop encrypted transmission processing to form an encrypted camouflage data stream, and then multi-path diversity transmission is performed to the drone execution layer, and the time-space distribution parameters are continuously optimized during the transmission process, specifically: Deploy quantum key distribution service nodes in the Xingshan communication network, build a quantum key resource pool between relay nodes based on the quantum entanglement pair generation protocol, generate quantum key slices dynamically bound to each relay node, and form a key source for layered encryption; Extract the dynamic camouflage signal features in the camouflage manipulation data stream at each relay node, generate a layered encryption key in combination with the quantum key slice, inject the layered encryption key into the encryption engine, perform segmented asymmetric encryption on the data stream, and generate a layered encrypted ciphertext; Performing spatiotemporal correlation encoding on the layered encrypted ciphertext and the dynamic disguised signal feature to generate a composite signal structure including a quantum key fingerprint and a dynamic spatiotemporal label; Through the multi-path diversity transmission module of the Star Flash link, the composite signal structure is split into multiple sub-signal streams carrying independent camouflage features, and the multiple sub-signal streams carrying independent camouflage features are transmitted to the drone execution layer through multi-path diversity; According to the real-time position feedback of the UAV executive layer, the transmission timing and power spectrum density distribution of the sub-signals are dynamically adjusted on the transmission path of the star flash link to generate a spatiotemporal chaos parameter set that is adaptive to the electromagnetic environment. The spatiotemporal distribution parameters of the dynamic camouflage signal characteristics are optimized in real time according to the spatiotemporal chaos parameter set, and the optimized spatiotemporal distribution parameters are synchronized to the behavior pattern library of the quantum key distribution service node and the dummy pilot relay layer through a cross-layer feedback mechanism, forming a closed-loop optimized encryption camouflage collaborative mechanism.

4. The short-distance control method for drone pilots based on Star Flash technology according to claim 3 is characterized in that: According to the real-time position feedback of the UAV executive layer, the transmission timing and power spectrum density distribution of the sub-signal are dynamically adjusted on the transmission path of the star flash link to generate a spatiotemporal chaos parameter set that is adaptive to the electromagnetic environment. Specifically: Collect the real-time location nodes of the drone and the electromagnetic interference intensity it is subjected to; map the electromagnetic interference intensity to initial value sensitive parameters to generate an initial chaotic parameter sequence; The initial chaotic parameter sequence is input into the dynamic cognitive graph engine, and combined with the real-time position node of the drone, the transmission timing of the sub-signal is subjected to phase chaos modulation, and the power spectrum density distribution is subjected to dynamic shaping in the fractional Fourier transform domain to generate a power-timing joint control parameter with time-varying non-stationary characteristics; According to the channel response characteristics on the transmission path, the power-timing joint control parameters are combined with multiple objectives to generate an initial spatiotemporal chaos parameter set; The initial spatiotemporal chaotic parameter set is injected into the sub-signal transmission module, and the signal bit error rate, channel capacity and camouflage concealment are synchronously collected to construct dynamic parameter evaluation indicators; If the dynamic parameter evaluation index meets the preset index requirements, the initial spatiotemporal chaos parameter set is converted into a spatiotemporal chaos parameter set; otherwise, the above steps are repeated; The spatiotemporal chaotic parameter set is synchronized to the quantum key distribution service node and the dynamic camouflage module through a quantum security tunnel as the spatiotemporal reference parameter for the next round of multi-hop transmission, thereby realizing cross-layer coordination of encryption camouflage and chaotic modulation.

5. The short-distance control method for drone pilots based on Star Flash technology according to claim 3 is characterized in that: The spatiotemporal distribution parameters of the dynamic camouflage signal characteristics are optimized in real time according to the spatiotemporal chaos parameter set, specifically: Performing feature decomposition on the spatiotemporal chaotic parameter set, extracting core feature parameters including time-varying phase, power spectrum density and chaotic attractor trajectory, and generating a spatiotemporal feature vector; Based on the spatiotemporal feature vector, the dynamic camouflage signal feature is reconstructed, the chaotic attractor trajectory is mapped to the random jitter feature of the signal in the time-frequency domain, and a reconstructed camouflage signal containing spatiotemporal chaotic characteristics is generated; Based on the UAV position feedback and electromagnetic environment change data, dynamic features including multipath interference intensity, spectrum occupancy and channel response characteristics are extracted to generate an environmental dynamic feature matrix; Based on the environmental dynamic characteristic matrix, a fuzzy rule base including transmission timing, power spectrum density and signal concealment index is constructed to generate a fuzzy control rule set; Inputting the time-space distribution parameters of the reconstructed camouflage signal into the fuzzy control rule set, evaluating its matching degree with the current electromagnetic environment, and generating a parameter matching degree evaluation vector; Based on the parameter matching evaluation vector, the matching relationship between the transmission timing and the power spectrum density is dynamically optimized to generate an optimized spatiotemporal distribution parameter set.

6. The short-distance control method for drone pilots based on Star Flash technology according to claim 1 is characterized in that: After decrypting and authenticating the encrypted disguised data stream, the flight control operations are performed, specifically: When the drone execution layer receives the encrypted disguised data stream from the dummy pilot relay layer, it extracts the composite signal structure containing the dynamic disguised signal characteristics and the quantum key fingerprint to generate the data stream to be decrypted; Based on the quantum key fingerprint, the corresponding quantum key slice is obtained through the quantum key distribution service node, the data stream to be decrypted is input into the quantum decryption engine, and the layered asymmetric decryption operation is performed to restore the disguised manipulated data stream; Performing spatiotemporal chaos inverse mapping processing on the camouflage manipulation data stream, extracting its dynamic camouflage signal features, performing matching verification with a preset camouflage feature library, and generating feature matching results; Verify the consistency of the behavior characteristics of the control command with the preset dummy pilot behavior pattern library based on the feature matching result, and generate a command legitimacy authentication result; If the command legitimacy authentication result is legal, the restored control command is input into the UAV flight control module to execute the flight control operation; If the command legitimacy authentication result is illegal, the exception handling mechanism is triggered, a safety warning message is generated and the flight control operation is suspended. At the same time, the warning information is synchronized to the real pilot layer and the dummy pilot relay layer through the quantum security tunnel.

7. The short-distance control method for a UAV pilot based on the Star Flash technology according to claim 1 is characterized by: The torso, limbs and head of the dummy pilot are designed with snap-on connections; The dummy pilot has a built-in multi-layer heating element array, which is arranged according to the thermodynamic distribution law of the human body to simulate the temperature difference characteristics of the head, chest and limbs.

8. A short-distance control system for drone pilots based on Star Flash technology, characterized in that: The control system includes a memory and a processor, wherein the memory stores a program for the short-distance control method for drone pilots based on Star Flash technology. When the program for the short-distance control method for drone pilots based on Star Flash technology is executed by the processor, the steps of the short-distance control method for drone pilots based on Star Flash technology as described in any one of claims 1 to 7 are implemented.

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