An adaptive optical remote control interaction method for quantum communication

Abstract

This invention discloses an adaptive optics remote control interaction method for quantum communication, relating to the field of quantum communication technology. The method includes the following steps: S1, system initialization and video stream generation. This invention integrates a CPU and an FPGA on a single board. When handling highly repetitive calculations, such as Hartmann calculations and centroid calculations, the FPGA exhibits lower control latency and power consumption compared to a GPU, thereby increasing the correction frequency of the adaptive optics system. This enhances the stability and integrity of the beam in quantum communication. Simultaneously, the CPU+FPGA hardware architecture retains the advantages of the CPU's Von Leigh architecture while introducing the instruction-free, low-latency, and reprogrammable characteristics of the FPGA, overcoming the complexity of the instruction flow control logic of CPUs and GPUs. Ultimately, this achieves the goal of increasing the beam correction frequency and reducing correction latency in quantum communication, while also reducing device power consumption and improving the reliability and efficiency of the quantum communication system in practical applications.

Description

Technical Field

[0001] This invention relates to the field of quantum communication technology, specifically to an adaptive optics remote control interaction method for quantum communication. Background Technology

[0002] In the field of modern communication, quantum communication has attracted much attention due to its ultra-high security and has become an important direction for the development of communication technology. The core of quantum communication lies in the transmission of quantum states, and the integrity and stability of the light beam, as the carrier of quantum states, play a decisive role in the quality and reliability of quantum communication. In the process of quantum communication, the light beam needs to propagate in the atmospheric environment. However, the atmosphere is not a uniform and stable medium. Factors such as atmospheric turbulence can cause the wavefront of the light wave to be distorted, that is, wavefront distortion. This wavefront distortion will cause phase changes and intensity fluctuations in the light beam, which will seriously affect the accurate transmission of quantum states and thus reduce the performance of quantum communication. Adaptive optics correction system has emerged to address this issue. It aims to solve the problem of the distortion of the light wavefront caused by atmospheric turbulence. Its working principle is mainly to first detect the wavefront distortion and then use deformable mirrors to correct the wavefront in real time.

[0003] In existing adaptive optics systems, CPUs and GPUs are commonly used to implement adaptive algorithms to control AO correction. For example, a hardware architecture for a real-time adaptive optics controller based on a multi-core CPU and GPU utilizes the multi-core CPU and GPU to perform slope calculations, wavefront reconstruction, and wavefront control of sub-apertures of a Shaker-Hartmann wavefront sensor in parallel. Specifically, using a general-purpose multi-core CPU and GPU as the computing platform, one CPU core transmits a portion of the sub-aperture image data to the GPU via the PCIe bus, where the GPU performs the wavefront calculation for that portion of the sub-aperture. Simultaneously, the remaining CPU cores perform the wavefront calculation for the remaining sub-apertures in parallel.

[0004] However, the above-mentioned solutions have many problems. Since CPUs and GPUs are based on the von Neumann architecture, they decode and execute instructions and share memory. The execution unit can execute arbitrary instructions, making the instruction flow control logic extremely complex. This complex instruction flow control makes it difficult to reduce control latency, failing to meet the requirements for rapid beam correction in quantum communication. Furthermore, the GPU needs to frequently read off-chip DRAM during operation, which not only increases data transmission latency but also leads to higher power consumption, hindering the long-term stable operation of quantum communication devices. Therefore, it is necessary to propose an adaptive optics remote control interaction method for quantum communication to address the problems in existing technologies. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an adaptive optics remote control interaction method for quantum communication. It can improve the integrity and stability of the beam during quantum communication, reduce control delay, reduce device power consumption, and thus improve the overall performance of quantum communication by optimizing the hardware architecture and algorithm process.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an adaptive optics remote control interaction method for quantum communication, the method comprising the following steps:

[0007] S1. System initialization and video stream generation: After the device is powered on, the CPU and FPGA integrated on a board are initialized. The FPGA acquires image data from the light source detection camera, performs buffer processing and generates a video stream. The video stream is transmitted to the CPU's Linux system. The FPGA initialization includes configuring the internal buffer size and data transmission format according to the precision requirements of quantum communication.

[0008] S2. Light Source Detection and Adaptive Optical Correction Startup: After receiving the video stream, the Linux system starts the light source detection program. When a light source is detected, the FPGA performs the following operations:

[0009] S201, Hartmann Calculation: Based on data acquired by a Hartmann wavefront sensor, the sensor divides the beam wavefront into multiple sub-apertures. For each sub-aperture, the slope is determined by calculating the offset of the spot centroid position relative to a reference position. The coordinates of the spot centroid in the two-dimensional plane are defined as (x...). c ,y c ), the reference position coordinates are (x r ,y r If the sub-aperture size parameter is d, then the slope S x =(x c -x r ) / d, S y =(y c -y r During the calculation process, the light spot signal is weak and the noise interference is large. An adaptive weighted filtering algorithm is adopted to filter the light spot by assigning different weights according to the distance between the pixel and the center of the light spot.

[0010] S202. Centroid Calculation: Based on the spot size calculated by Hartmann, calculate the centroid coordinates (x, y) for the spot size within each sub-aperture. c ,y c The calculation method is as follows: Where (x) i ,y i Let p be the coordinates of the i-th pixel within the light spot. iLet n be the intensity value of the pixel and n be the total number of pixels in the spot. The spot shape is irregular and there is partial occlusion. A morphological optimization algorithm based on deep learning is used to train a neural network to identify the effective area of ​​the spot and optimize its shape before calculating the centroid coordinates.

[0011] S203. Control Matrix Generation and Transmission: Based on the slope calculated by Hartmann and the centroid coordinates calculated by the centroid, the control matrices for the deformable mirror and the tilting mirror are generated using a multi-parameter fitting algorithm. The slope deviation is defined as ΔS. x ΔS y The centroid coordinate deviation is Δx c Δy c Control matrix element M ij =f(ΔS) x ,ΔS y ,Δx c ,Δy c ), where f is a complex functional relationship. After generating the control matrix, it is transmitted to the Linux system and the beam correction stage via the PCIE bus. During the transmission process, error correction coding verification algorithm is used to ensure data accuracy.

[0012] S3, Beam Correction: After receiving the control matrix, the beam correction stage controls the deformable mirror and the tilt mirror. For the deformable mirror, a voltage signal is applied through a high-voltage amplifier according to the element values ​​of the control matrix to deform the mirror surface. The relationship between the degree of deformation and the voltage value is determined according to the physical model. For the tilt mirror, the tilt angle is adjusted through the motor drive system according to the angle information in the control matrix. The motor drive system adopts an intelligent control algorithm.

[0013] S4. Continuous Quantum Communication Repair: During the quantum communication process from the transmitter to the receiver, steps S2 and S3 are continuously repeated. If abnormalities occur in beam energy loss and quantum state fidelity, the Hartmann calculation and centroid calculation frequencies are adjusted according to the monitoring and evaluation algorithm, and the light source and optical components are checked. At the same time, the parameters of the adaptive optics system are dynamically adjusted according to the environmental adaptation algorithm.

[0014] Furthermore, in step S1, the CPU initialization process includes loading a Linux operating system specifically optimized for quantum communication. This operating system kernel is customized and has efficient scheduling capabilities for quantum communication data processing. Its scheduling strategy dynamically allocates computing resources based on quantum state priority and data real-time requirements. Upon startup, it automatically detects the communication interface status with the FPGA and other hardware devices. If an abnormality is detected, an interface repair algorithm is used for automatic repair. This algorithm locates the fault point by sending a specific detection signal sequence and analyzing the feedback signal. Specifically, it performs spectral analysis on the feedback signal, extracts characteristic frequencies, compares them with a preset fault frequency library, and then performs targeted repair operations according to the fault type. At the same time, it records the repair process and result information. The repair record is stored in a specific non-volatile storage area, and the storage capacity is managed using a cyclic overwrite method.

[0015] Furthermore, in step S201, the Hartmann calculation, the adaptive weighted filtering algorithm specifically defines the distance between pixel (x, y) and the center of the light spot (x0, y0) as... Assign weights w(r) = 1 / (1+αr) β +γr δ ), where α, β, γ, and δ are parameters determined based on the noise characteristics of the quantum communication environment. The formula for calculating the filtered pixel value p′(x,y) is:

[0016] N(x,y) is the set of neighborhood pixels centered at (x,y), and the neighborhood size is dynamically adjusted according to the spot size and noise distribution. This algorithm effectively removes noise and retains spot details based on the characteristics of spot noise distribution in quantum communication. At the same time, the filtering effect is monitored in real time during the algorithm execution. If the centroid offset of the filtered spot exceeds the preset threshold, the weight parameters are automatically adjusted to optimize the filtering performance.

[0017] Furthermore, in step S202, the centroid calculation, the deep learning-based morphological optimization algorithm includes the following steps:

[0018] A deep learning model is constructed. This model takes a spot image as input and outputs an optimized spot shape. The model structure includes convolutional layers, pooling layers, and fully connected layers. The kernel function of the convolutional layer adopts a function form optimized for the edge features of the spot. The pooling layer adopts an adaptive stride strategy. The fully connected layer adopts a neuron activation function.

[0019] The training process utilizes a large number of light spot images with different shapes and occlusion conditions, employing a loss function during training. Where S i The original light spot shape, S′ i To optimize the light spot shape, W jHere, C represents the model weights, λ and μ are regularization parameters, and C represents the model weights. l Statistical features of the feature maps of the intermediate layers of the model;

[0020] After training, the actual collected spot images are input into the model to obtain the optimized spot shape, and then the centroid coordinates are calculated. This algorithm adapts to complex spot shape changes, and continuously collects new spot samples for incremental learning during model use to continuously improve model performance.

[0021] Furthermore, in step S203, the generation and transmission of the control matrix, the multi-parameter fitting algorithm is specifically defined as follows: Where a1-a n and b1-b m p k q k r k s k To optimize the parameters determined based on the optical characteristics and correction requirements of the quantum communication system, the algorithm comprehensively considers various combinations of slope and centroid coordinate deviation to generate a more accurate control matrix. At the same time, an adaptive parameter adjustment mechanism is introduced into the algorithm to dynamically adjust some parameters according to the real-time monitoring results of beam quality during quantum communication to adapt to different atmospheric disturbances.

[0022] Furthermore, in step S203, control matrix generation and transmission, the error correction coding verification algorithm includes: grouping and encoding the control matrix, adding a check bit to each group of data, the value of the check bit being generated according to the data bits and encoding rules, and defining the data bit as D. i The check digit is P. j P j =g(D1,D2,…,D) n ), where g is a functional relationship. At the receiving end, the check bit is recalculated based on the received data and compared with the transmitted check bit. If they are inconsistent, an error location and correction algorithm is used. This algorithm determines the error location and corrects it by analyzing the logical relationship between the check bit difference and the data bits. Specifically, it constructs an error location discrimination matrix, calculates the matrix element values ​​based on the check bit comparison results, and then determines the error location. In addition, the data is encrypted during transmission. The encryption algorithm adopts a quantum key distribution-based encryption method.

[0023] Furthermore, in step S3, the deformable mirror control for beam correction, the physical model is defined as follows: the element value of the control matrix is ​​M. ij The output voltage of the high-voltage amplifier is V. out The deformation displacement of the deformable mirror surface is d.

[0024]

[0025] Where k1-k 13 Based on the parameters determined by the material properties and optical performance of the deformable mirror, this model is obtained through experimental measurement and theoretical calculation. It accurately describes the relationship between the control matrix element values ​​and the deformation of the deformable mirror surface, and monitors the mirror surface deformation in real time during the operation of the deformable mirror, and fine-tunes the physical model parameters based on the feedback information.

[0026] Furthermore, in step S3, the tilt mirror control for beam correction, the intelligent control algorithm includes: the motor drive system calculates the number of motor rotation steps based on the angle information in the control matrix, defines the target angle of the tilt mirror as θ, and the number of motor rotation steps as N, N = h(θ), where h is a piecewise function relationship. Different calculation coefficients are used according to different angle ranges. During the motor rotation process, the actual rotation angle of the motor is monitored in real time. If the deviation from the target angle exceeds a preset threshold, a dynamic adjustment strategy is adopted. The motor drive voltage and pulse frequency are adjusted according to the magnitude and direction of the deviation. The adjustment method is to use a fuzzy logic control algorithm to determine the voltage and frequency adjustment amount based on the deviation and the rate of change of the deviation. At the same time, the motor operating status is monitored in real time, including temperature and current parameters. If an abnormality is detected, the motor drive parameters are adjusted in time and an alarm is issued.

[0027] Furthermore, in step S4, the anomaly handling of continuous quantum communication repair, the monitoring and evaluation algorithm is as follows: Define the beam energy loss rate as η, the quantum state fidelity as F, and calculate the evaluation value. α, β, γ, and δ are parameters determined based on the characteristics of the quantum communication system. The magnitude of the evaluation value determines whether the Hartmann calculation and centroid calculation frequencies need to be adjusted. If E exceeds a preset threshold, the calculation frequency is increased. Meanwhile, the environmental adaptation algorithm adjusts the correction parameters of the adaptive optics system in real time based on atmospheric turbulence intensity, temperature, and humidity environmental parameters. The adjustment rules are based on a mapping relationship established by a large amount of experimental data and machine learning models. Detailed anomaly information and processing procedures are recorded during anomaly handling.

[0028] Compared with existing technologies, this adaptive optics remote control interaction method for quantum communication has the following advantages:

[0029] This invention integrates a CPU and an FPGA on a single board. When handling repetitive calculations, such as Hartmann calculations and centroid calculations, the FPGA exhibits lower control latency and power consumption compared to a GPU, enabling an increase in the correction frequency of the adaptive optics system. This enhances the stability and integrity of the beam in quantum communication. Furthermore, the CPU+FPGA hardware architecture retains the advantages of the CPU's Von Leigh architecture while incorporating the instruction-free, low-latency, and reprogrammable characteristics of the FPGA. This overcomes the complexity of the instruction flow control logic of CPUs and GPUs, ultimately achieving the goals of increasing the beam correction frequency and reducing correction latency in quantum communication, while also reducing device power consumption and improving the reliability and efficiency of the quantum communication system in practical applications.

[0030] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of an adaptive optics remote control interaction method for quantum communication;

[0033] Figure 2 This is a timing diagram of an adaptive optics remote control interaction method for quantum communication. Detailed Implementation

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below. 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.

[0035] Example 1

[0036] In a practical quantum communication scenario, a system is constructed that includes a quantum communication transmitter, a receiver, and an intermediate transmission link. An adaptive optics correction device based on the present invention is deployed in the transmission link.

[0037] After the device is powered on, it first performs system initialization. The CPU and FPGA integrated on a single board start their respective initialization processes. The FPGA configures the internal buffer size and sets the data transmission format according to the requirements of the quantum communication system for beam processing accuracy, preparing to receive image data from the light source detection camera. The CPU loads a Linux operating system optimized for quantum communication. Its kernel has efficient scheduling capabilities for quantum communication data processing and can dynamically allocate computing resources according to quantum state priority and data real-time requirements. At the same time, when the CPU starts up, it automatically detects the communication interface status with the FPGA and other hardware devices (such as high-voltage amplifiers, deformable mirrors, tilt mirrors, etc.).

[0038] The light source detection and adaptive optics correction are initiated. After the FPGA completes image buffering, it generates a video stream and transmits it to the Linux system on the CPU. The Linux system then starts the light source detection program and uses the light source detection camera to collect light source information.

[0039] Once the light source is detected, the FPGA begins to perform critical adaptive optics correction calculations.

[0040] Based on data acquired by a Hartmann wavefront sensor, the sensor divides the beam wavefront into multiple sub-apertures. For each sub-aperture, the slope is determined by calculating the offset of the spot centroid position relative to a reference position. Let the coordinates of the spot centroid in the two-dimensional plane be (x...). c ,y c ), the reference position coordinates are (x r ,y r If the sub-aperture size parameter is d, then the slope S x =(x c -x r ) / dS y =(y c -y r ) / d.

[0041] During the calculation process, if the light spot signal is weak or there is significant noise interference, a novel adaptive weighted filtering algorithm is used. Let the distance between the pixel (x,y) and the center of the light spot (x0,y0) be r=√(x-x0). 2 +(y-y0) 2 Assign weights w(r) = 1 / (1+αr) β +γr δ (where α, β, γ, and δ are parameters determined based on the noise characteristics of the quantum communication environment), the filtered pixel values.

[0042] N(x,y) is a set of neighboring pixels centered at (x,y), and the size of the neighborhood is dynamically adjusted according to the spot size and noise distribution to improve the accuracy of slope calculation.

[0043] Based on the light spot calculated by Hartmann, the centroid coordinates (x, y) are calculated for the light spot within each sub-aperture. c ,y c The calculation method is as follows: (where (x) i ,y i Let p be the coordinates of the i-th pixel within the light spot. i (where n is the intensity value of the pixel and n is the total number of pixels in the spot). If the spot shape is irregular or partially occluded, a morphological optimization algorithm based on deep learning is used.

[0044] First, a deep learning model is constructed. This model takes a spot image as input and outputs the optimized spot shape. The model structure includes convolutional layers, pooling layers, and fully connected layers. The kernel function of the convolutional layers adopts a function optimized for spot edge features, the pooling layers use an adaptive stride strategy, and the fully connected layers use a novel neuron activation function. The model is trained using a large number of spot images with different shapes and occlusion conditions, and a novel loss function is used during training. (where S) i The original light spot shape, S′ i To optimize the light spot shape, W j Here, C represents the model weights, λ and μ are regularization parameters, and C represents the model weights. l (Statistical features of the intermediate layer feature map of the model). After training, the actual collected spot image is input into the model to obtain the optimized spot shape, and then the centroid coordinates are calculated to improve the accuracy of centroid calculation.

[0045] Control Matrix Generation and Transmission: Based on the slope calculated by Hartmann and the centroid coordinates calculated by the centroid, a novel multi-parameter fitting algorithm is used to generate the control matrices for the deformable mirror and the tilting mirror. Let the slope deviation be ΔS. x ΔS y The centroid coordinate deviation is Δx c Δy c Control matrix elements

[0046] (where a1-a) n and b1-b m p k q k r k s k(The parameters are determined based on the optical characteristics and correction requirements of the quantum communication system). This algorithm can comprehensively consider various combinations of slope and centroid coordinate deviation to generate a more accurate control matrix.

[0047] After generating the control matrix, it is transmitted to the Linux system and the beam correction stage via the PCIe bus. During transmission, a newly developed error correction coding and verification algorithm is used to group and encode the control matrix. A check bit is added to each group of data. The value of the check bit is generated based on the data bits and the newly developed coding rules. Let the data bits be D. i The check digit is P. j P j =g(D1,D2,…,D) n (where g is a newly created functional relationship), at the receiving end, the check bit is recalculated based on the received data and compared with the transmitted check bit. If they are inconsistent, a newly created error location and correction algorithm is used to determine the error location and correct it by analyzing the difference between the check bit and the logical relationship between the data bits. At the same time, the data is encrypted during transmission. The encryption algorithm adopts a quantum key distribution-based encryption method to ensure the security of data transmission.

[0048] After receiving the control matrix during the beam correction stage, the deformable mirror and tilt mirror are controlled.

[0049] For deformable mirrors, based on the control matrix element value M ij A voltage signal is applied by a high-voltage amplifier to deform the mirror surface. Let the output voltage of the high-voltage amplifier be V. o ut, the deformation displacement of the deformable mirror surface is d, (where k1-k 13 Based on the parameters determined according to the material properties and optical performance of the deformable mirror, this model can accurately describe the relationship between the control matrix element values ​​and the deformation of the deformable mirror surface, achieve more precise beam wavefront phase adjustment, monitor the mirror surface deformation in real time during the operation of the deformable mirror, and fine-tune the physical model parameters according to the feedback information to compensate for the influence of factors such as material aging on deformation control.

[0050] For the tilt mirror, the tilt angle is adjusted by the motor drive system based on the angle information in the control matrix. The motor drive system calculates the number of motor rotation steps based on the angle information in the control matrix. Let the target angle of the tilt mirror be θ, and the number of motor rotation steps be N, N = h(θ) (where h is a newly created piecewise function relationship, and different calculation coefficients are used according to different angle ranges). During the motor rotation, the actual rotation angle of the motor is monitored in real time. If the deviation from the target angle exceeds the preset threshold, a newly created dynamic adjustment strategy is adopted. The motor drive voltage and pulse frequency are adjusted according to the magnitude and direction of the deviation. The adjustment method adopts a fuzzy logic control algorithm to determine the voltage and frequency adjustment amount based on the deviation and the rate of change of the deviation, so that the tilt mirror can be quickly and accurately adjusted to the target angle, improving the accuracy and speed of beam propagation direction correction. At the same time, the motor operating status is monitored in real time, including parameters such as temperature and current. If an abnormality is detected, the motor drive parameters are adjusted in time or an alarm is issued.

[0051] Throughout the entire quantum communication process from transmitter to receiver, the aforementioned adaptive optics correction process is continuously repeated. The system monitors the beam state in real time. If any abnormalities occur in beam energy loss or quantum state fidelity, a judgment is made based on the newly developed monitoring and evaluation algorithm. Let the beam energy loss rate be η. i The quantum state fidelity is F. Calculate the evaluation value. (where α, β, γ, and δ are parameters determined based on the characteristics of the quantum communication system). The Hartmann calculation and centroid calculation frequencies need to be adjusted based on the magnitude of the evaluation value. If E exceeds the preset threshold, the calculation frequency is increased. At the same time, the newly created environmental adaptation algorithm adjusts the correction parameters of the adaptive optics system in real time based on environmental parameters such as atmospheric turbulence intensity, temperature, and humidity, such as the sub-aperture division method in the Hartmann calculation and the parameters in the control matrix generation algorithm. The adjustment rules are based on the mapping relationship established by a large amount of experimental data and machine learning models to adapt to different quantum communication environments and ensure that the beam is always in a good correction state.

[0052] Through the application of this embodiment, interference from factors such as atmospheric turbulence on the light beam is effectively overcome during quantum communication. First, due to the low latency and low power consumption characteristics of FPGA in processing critical computing tasks, the correction frequency of the adaptive optics system is significantly improved, enabling timely correction of wavefront distortion, thereby greatly enhancing the stability and integrity of the light beam. This ensures the accuracy of quantum states during transmission, reduces the bit error rate, and improves the reliability of quantum communication. Second, the CPU+FPGA hardware architecture and the application of various innovative algorithms overcome the problems of complex instruction flow control logic and high power consumption in traditional technologies. The entire system can operate efficiently and stably, adapting to the complex and ever-changing quantum communication environment, providing strong technical support for the large-scale practical application of quantum communication technology, and promoting the further development of quantum communication technology in real-world scenarios. At the same time, data encryption and error correction mechanisms ensure the accuracy and security of control signal transmission, further improving the overall performance of the quantum communication system.

[0053] Example 2

[0054] In satellite quantum communication scenarios, a communication link is established between the ground station and the quantum communication satellite, and the adaptive optics correction system of this invention is integrated into the receiving equipment of the ground station.

[0055] After the device is powered on, the system initializes and starts. The CPU of the ground station loads a customized Linux operating system. This system is optimized for the characteristics of satellite quantum communication and has efficient data processing and task scheduling capabilities. It can prioritize the processing of data related to key information of quantum states. At the same time, the CPU detects the connection status with the FPGA and other hardware (such as optical antennas for receiving satellite signals, signal amplifiers, deformable mirrors and tilting mirrors related to beam correction, etc.). The FPGA configures the internal buffer size and data transmission format according to the expected signal strength and accuracy requirements of satellite communication, and prepares to receive optical signals from the optical antenna and convert them into image data for processing.

[0056] The optical antenna receives quantum communication signal light from the satellite. The FPGA converts it into image data, caches it, and generates a video stream that is transmitted to the CPU's Linux system. The Linux system starts a signal detection program to analyze the signal characteristics in the video stream and determine whether a valid quantum communication signal has been successfully captured.

[0057] Once a valid signal is detected, the FPGA performs adaptive optics correction.

[0058] The received beam wavefront is divided into multiple sub-apertures using a Hartmann wavefront sensor. The offset of the beam centroid relative to the reference position within each sub-aperture is calculated to determine the slope. Let the beam centroid coordinates (x...) be... c ,y c) and reference coordinates (x) r ,y r Given the sub-aperture size parameter d, the slope S x =(x c -x r ) / d, S y =(y c -y r If the signal is weak or atmospheric noise affects the calculation, an adaptive weighted filtering algorithm is used, where the distance between pixel (x,y) and the center of the light spot (x0,y0) is set. Weight w(r) = 1 / (1+αr) β +γr δ (α, β, γ, and δ are determined based on the noise characteristics of the satellite communication environment), the filtered pixel values.

[0059] N(x,y) is a dynamically adjusted set of neighboring pixels to improve the accuracy of slope calculation.

[0060] Based on the light spot obtained from Hartmann's calculation, the centroid coordinates (x) are calculated. c ,y c The formula is: p represents the pixel coordinates of the light spot. i (where n is the pixel intensity and n is the total number of light spot pixels). If the light spot is irregularly shaped due to cloud cover or other factors, a morphological optimization algorithm based on deep learning is used.

[0061] A deep learning model is constructed, which takes a light spot image as input and outputs an optimized shape. It includes specially designed convolutional layers, pooling layers, and fully connected layers, and employs a loss function during training.

[0062] (where S) i The original light spot shape, S′ i To optimize the light spot shape, W j Here, C represents the model weights, λ and μ are regularization parameters, and C represents the model weights. l (Statistical features of the intermediate layer feature map of the model), after training, the actual light spot image is input to obtain the optimized shape and then the centroid coordinates are calculated to improve the calculation accuracy.

[0063] The coordinate deviation ΔS is calculated based on the slope calculated by Hartmann and the centroid. x ΔS y Δx c Δy c The control matrix M is generated through a multi-parameter fitting algorithm. ij ,

[0064] (a1-a n (Parameters are determined based on the characteristics of the satellite communication optical system). After generation, the data is transmitted to the CPU and beam correction stage via the PCIe bus. The transmission uses an error correction coding and verification algorithm, and a check bit P is added to the block coding. j =g(D1,D2,…,D) n (g is a newly created function relationship), the receiving end compares the check bits, and if they are inconsistent, it uses an error location correction algorithm to handle them. At the same time, quantum encryption is used to ensure transmission security.

[0065] After receiving the control matrix, the beam correction section controls the deformable mirror according to the control matrix element M. ij High voltage amplifier output voltage V out The deformation displacement d of the deformable mirror surface is related to the following: Where k1-k 13 The model is determined based on the material and optical properties of the variable-opening mirror, and the deformation is monitored in real time and the model parameters are finely adjusted to adapt to environmental changes.

[0066] For the tilting mirror, based on the angle information of the control matrix, the motor drive system calculates the number of motor rotation steps N, N = h(θ) (h is a piecewise function with coefficients determined according to the angle range). During rotation, the actual angle is monitored. When the deviation exceeds the threshold, the fuzzy logic control algorithm is used to adjust the motor drive voltage and pulse frequency. The adjustment amount is determined according to the deviation and rate of change. At the same time, the motor temperature, current, etc. are monitored. When abnormalities occur, the parameters are adjusted or an alarm is triggered.

[0067] Throughout the satellite quantum communication process, the aforementioned adaptive optics correction procedure is continuously repeated. The system monitors beam performance in real time. If beam energy loss is abnormal or quantum state fidelity decreases, the system determines the energy loss rate (η) and the fidelity (F) based on the monitoring and evaluation algorithm, and calculates the evaluation value. (Parameters such as α are determined according to system characteristics). If the threshold is exceeded, the Hartmann and centroid calculation frequencies are increased. The newly created environmental adaptation algorithm adjusts the correction parameters according to atmospheric turbulence, temperature, humidity, etc., such as Hartmann calculation of sub-aperture division, control matrix generation parameters, etc. Based on the mapping relationship between experimental data and machine learning models, the good correction state of the beam is guaranteed.

[0068] In satellite quantum communication scenarios, this embodiment offers significant advantages. First, the high-efficiency computing capabilities of the FPGA ensure that the adaptive optics system can quickly respond to wavefront distortions caused by changes in the atmospheric and space environment, promptly correcting the beam and greatly improving the stability and reliability of the quantum communication link. This enables quantum communication between the satellite and the ground station to maintain high accuracy in complex space environments, effectively reducing signal interruptions and errors. Second, the CPU+FPGA architecture combined with innovative algorithms solves the high latency and high power consumption problems of traditional technologies when processing complex satellite communication signals. The system can adjust in real time according to changes in satellite orbit, atmospheric conditions, and other factors, ensuring continuous stability of quantum communication. Data encryption and error correction mechanisms effectively guarantee the security and accuracy of control signal transmission between the satellite and the ground, preventing signal theft or tampering, further enhancing the overall performance of the satellite quantum communication system and laying a solid foundation for the construction and application of future satellite quantum communication networks.

[0069] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the same elements of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An adaptive optics remote control interaction method for quantum communication, characterized in that, The method includes the following steps: S1. System initialization and video stream generation: After the device is powered on, the CPU and FPGA integrated on a board are initialized. The FPGA acquires image data from the light source detection camera, performs buffer processing and generates a video stream. The video stream is transmitted to the CPU's Linux system. The FPGA initialization includes configuring the internal buffer size and data transmission format according to the precision requirements of quantum communication. S2. Light Source Detection and Adaptive Optical Correction Startup: After receiving the video stream, the Linux system starts the light source detection program. When a light source is detected, the FPGA performs the following operations: S201, Hartmann Calculation: Based on data acquired by a Hartmann wavefront sensor, the sensor divides the beam wavefront into multiple sub-apertures. For each sub-aperture, the slope is determined by calculating the offset of the spot centroid position relative to a reference position. The coordinates of the spot centroid in the two-dimensional plane are defined as (x...). c ,y c ), the reference position coordinates are (x r ,y r If the sub-aperture size parameter is d, then the slope S x =(x c -x r ) / d, S y =(y c -y r During the calculation process, the light spot signal is weak and the noise interference is large. An adaptive weighted filtering algorithm is adopted to filter the light spot by assigning different weights according to the distance between the pixel and the center of the light spot. S202. Centroid Calculation: Based on the spot size calculated by Hartmann, calculate the centroid coordinates (x, y) for the spot size within each sub-aperture. c ,y c The calculation method is as follows: , , where (x i ,y i Let p be the coordinates of the i-th pixel within the light spot. i Let n be the intensity value of the pixel and n be the total number of pixels in the spot. The spot shape is irregular and there is partial occlusion. A morphological optimization algorithm based on deep learning is used to train a neural network to identify the effective area of ​​the spot and optimize its shape before calculating the centroid coordinates. S203. Control Matrix Generation and Transmission: Based on the slope calculated by Hartmann and the centroid coordinates calculated by the centroid, the control matrices for the deformable mirror and the tilting mirror are generated using a multi-parameter fitting algorithm. The slope deviation is defined as ΔS. x ΔS y The centroid coordinate deviation is Δx c Δy c Control matrix element M ij =f(ΔS) x ,ΔS y ,Δx c ,Δy c ), where f is a complex functional relationship. After generating the control matrix, it is transmitted to the Linux system and the beam correction stage via the PCIE bus. During the transmission process, error correction coding verification algorithm is used to ensure data accuracy. S3, Beam Correction: After receiving the control matrix, the beam correction stage controls the deformable mirror and the tilt mirror. For the deformable mirror, a voltage signal is applied through a high-voltage amplifier according to the element values ​​of the control matrix to deform the mirror surface. The relationship between the degree of deformation and the voltage value is determined according to the physical model. For the tilt mirror, the tilt angle is adjusted through the motor drive system according to the angle information in the control matrix. The motor drive system adopts an intelligent control algorithm. S4. Continuous Quantum Communication Repair: During the quantum communication process from the transmitter to the receiver, steps S2 and S3 are continuously repeated. If abnormalities occur in beam energy loss and quantum state fidelity, the Hartmann calculation and centroid calculation frequencies are adjusted according to the monitoring and evaluation algorithm, and the light source and optical components are checked. Simultaneously, the parameters of the adaptive optics system are dynamically adjusted according to the environmental adaptation algorithm. The monitoring and evaluation algorithm is as follows: Define the beam energy loss rate as η, the quantum state fidelity as F, and calculate the evaluation value. α, β, γ and δ are parameters determined based on the characteristics of the quantum communication system. The Hartmann calculation and centroid calculation frequencies need to be adjusted based on the magnitude of the evaluation value. If E exceeds the preset threshold, the calculation frequency is increased. At the same time, the environmental adaptation algorithm adjusts the correction parameters of the adaptive optics system in real time based on atmospheric turbulence intensity, temperature and humidity environmental parameters. The adjustment rules are based on the mapping relationship established by a large amount of experimental data and machine learning models. In addition, detailed abnormal information and processing procedures are recorded during the anomaly handling process.

2. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S1, the CPU initialization process includes loading a Linux operating system specifically optimized for quantum communication. This operating system kernel is customized and has efficient scheduling capabilities for quantum communication data processing. Its scheduling strategy dynamically allocates computing resources based on quantum state priority and data real-time requirements. At startup, it automatically detects the communication interface status with the FPGA and other hardware devices. If an abnormality is detected, an interface repair algorithm is used for automatic repair. This algorithm locates the fault point by sending a specific detection signal sequence and analyzing the feedback signal. Specifically, it performs spectrum analysis on the feedback signal, extracts characteristic frequencies, compares them with a preset fault frequency library, and then performs targeted repair operations according to the fault type. At the same time, it records the repair process and result information. The repair record is stored in a specific non-volatile storage area, and the storage capacity is managed using a cyclic overwrite method.

3. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S201, the Hartmann calculation, the adaptive weighted filtering algorithm is as follows: The distance between pixel (x, y) and the center of the light spot (x0, y0) is defined as... Assign weights Where α, β, γ, and δ are parameters determined based on the noise characteristics of the quantum communication environment, the formula for calculating the filtered pixel value p′(x,y) is: ， The algorithm is a set of neighborhood pixels centered at (x,y), and the size of the neighborhood is dynamically adjusted according to the spot size and noise distribution. Based on the characteristics of spot noise distribution in quantum communication, this algorithm effectively removes noise and retains spot details. At the same time, the filtering effect is monitored in real time during the algorithm execution. If the centroid offset of the filtered spot exceeds the preset threshold, the weight parameters are automatically adjusted to optimize the filtering performance.

4. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S202, centroid calculation, the deep learning-based morphological optimization algorithm includes the following steps: A deep learning model is constructed. This model takes a spot image as input and outputs an optimized spot shape. The model structure includes convolutional layers, pooling layers, and fully connected layers. The kernel function of the convolutional layer adopts a function form optimized for the edge features of the spot. The pooling layer adopts an adaptive stride strategy. The fully connected layer adopts a neuron activation function. The training process utilizes a large number of light spot images with different shapes and occlusion conditions, employing a loss function during training. ,in S i The original light spot shape, S′ i To optimize the shape of the light spot, W j Here, λ and μ are the model weights, and λ and μ are the regularization parameters. C l Statistical features of the feature maps of the intermediate layers of the model; After training, the actual collected spot images are input into the model to obtain the optimized spot shape, and then the centroid coordinates are calculated. This algorithm adapts to complex spot shape changes, and continuously collects new spot samples for incremental learning during model use to continuously improve model performance.

5. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S203, control matrix generation and transmission, the multi-parameter fitting algorithm is specifically defined as follows: , in a 1 -a n and b 1 -b m , p k , q k , r k , s k To optimize the parameters determined based on the optical characteristics and correction requirements of the quantum communication system, the algorithm comprehensively considers various combinations of slope and centroid coordinate deviation to generate a more accurate control matrix. At the same time, an adaptive parameter adjustment mechanism is introduced into the algorithm to dynamically adjust some parameters according to the real-time monitoring results of beam quality during quantum communication to adapt to different atmospheric disturbances.

6. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S203, control matrix generation and transmission, the error correction coding verification algorithm includes: grouping and encoding the control matrix, adding a check bit to each group of data, the value of the check bit being generated according to the data bits and encoding rules, and defining the data bit as D. i The check digit is P. j P j =g(D1,D2,…,D) n ), where g is a functional relationship. At the receiving end, the check bit is recalculated based on the received data and compared with the transmitted check bit. If they are inconsistent, an error location and correction algorithm is used. This algorithm determines the error location and corrects it by analyzing the logical relationship between the check bit difference and the data bits. Specifically, it constructs an error location discrimination matrix, calculates the matrix element values ​​based on the check bit comparison results, and then determines the error location. In addition, the data is encrypted during transmission. The encryption algorithm adopts a quantum key distribution-based encryption method.

7. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S3, the deformable mirror control for beam correction, the physical model is defined as follows: the element value of the control matrix is ​​M. ij The output voltage of the high-voltage amplifier is V. out The deformation displacement of the deformable mirror surface is d. , ,in k 1 -k 13 Based on the parameters determined by the material properties and optical performance of the deformable mirror, this model is obtained through experimental measurement and theoretical calculation. It accurately describes the relationship between the control matrix element values ​​and the deformation of the deformable mirror surface, and monitors the mirror surface deformation in real time during the operation of the deformable mirror, and fine-tunes the physical model parameters based on the feedback information.

8. The adaptive optics remote control interaction method for quantum communication according to claim 1, characterized in that, In step S3, the tilt mirror control for beam correction, the intelligent control algorithm includes: the motor drive system calculates the number of motor rotation steps based on the angle information in the control matrix, defines the target angle of the tilt mirror as θ, and the number of motor rotation steps as N, N = h(θ), where h is a piecewise function relationship. Different calculation coefficients are used according to different angle ranges. During the motor rotation process, the actual rotation angle of the motor is monitored in real time. If the deviation from the target angle exceeds a preset threshold, a dynamic adjustment strategy is adopted. The motor drive voltage and pulse frequency are adjusted according to the magnitude and direction of the deviation. The adjustment method is to use a fuzzy logic control algorithm to determine the voltage and frequency adjustment amount based on the deviation and the rate of change of the deviation. At the same time, the motor operating status is monitored in real time, including temperature and current parameters. If an abnormality is detected, the motor drive parameters are adjusted in time and an alarm is issued.

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