VDSL ultra-low delay communication method and system

Abstract

The invention provides a VDSL ultra-low time delay communication method and system, and relates to the technical field of communication, and the method comprises the steps: encrypting a clock synchronization channel through a quantum key distribution protocol, dynamically dividing micro time slot resources in an orthogonal frequency division multiplexing symbol period, and generating a dynamically adjusted micro time slot resource distribution result; calculating an optimal phase offset matrix of the metasurface intelligent reflecting surface through a depth deterministic strategy gradient algorithm to obtain an optimized electromagnetic wave propagation path; generating a global optimization check matrix by aggregating the locally trained lightweight error correction model gradient of each node to obtain a compensated data stream; and constructing a causal graph model dynamic pruning high-entropy path to minimize causal entropy, through multi-agent reinforcement learning, taking time delay-energy efficiency as a game target to decide an optimal modulation order and a subcarrier switching strategy, and obtaining an optimized stable communication link. According to the invention, high-reliability and low-delay communication basic support is provided for high-precision intelligent manufacturing.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a VDSL ultra-low latency communication method and system. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, VDSL (Very-high-bit-rate Digital Subscriber Line) technology faces strict latency and reliability requirements in scenarios such as factory equipment collaborative control and real-time data transmission. In the prior art, traditional VDSL systems mostly adopt fixed spectrum allocation, static equalizers, and half-duplex communication modes, and it is difficult to cope with dynamic electromagnetic interference and multipath effects in complex industrial environments. For example, in a smart factory, broadband interference generated by devices such as arc welders and frequency converters will cause a sharp drop in the subcarrier signal-to-noise ratio, leading to an increase in the bit error rate; while the delay spread caused by multipath propagation further exacerbates the timing chaos, resulting in a fluctuation of the robot collaborative control instruction transmission delay exceeding the millisecond level, seriously affecting the processing accuracy and system stability.

[0003] To address the above problems, the prior art mainly alleviates interference by adding redundant check bits, expanding the spectrum bandwidth, or deploying static reflectors, etc. However, such methods have defects such as low spectrum utilization, high hardware cost, and poor dynamic adaptability. For example, a static reflector cannot adjust the beam direction in real time, resulting in a large path loss; the traditional redundant retransmission mechanism needs to retransmit all data, and the latency and energy consumption increase significantly, making it difficult to meet the sub-millisecond-level real-time requirements of industrial scenarios. Summary of the Invention

[0004] The purpose of the present invention is to provide a VDSL ultra-low latency communication method and system to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows: In a first aspect, the present application provides a VDSL ultra-low latency communication method, including: Obtain a clock signal source, encrypt the clock synchronization channel through the quantum key distribution protocol, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, generate a global synchronization clock signal with a clock jitter less than 5 nanoseconds by using the quantum Kalman filtering algorithm, and construct a spatio-temporal hash grid based on the global synchronization clock signal; divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level; Based on the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, predict the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map L0-level instructions to low-interference subcarriers, and use polar codes to perform asymmetric redundant coding on the micro-slot data units to generate a dynamically adjusted micro-slot resource allocation result; Based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by distributed sensors, calculate the optimal phase shift matrix of the metasurface intelligent reflecting surface through the deep deterministic policy gradient algorithm, dynamically reconstruct the electromagnetic wave propagation path, and integrate the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band, obtaining an optimized electromagnetic wave propagation path; Based on the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate obtained at the receiving feedback end, use a time-frequency double-domain joint equalizer to jointly compensate for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, trigger an incremental redundancy retransmission mechanism based on federated learning, generate a globally optimized parity-check matrix by aggregating the gradients of lightweight error-correction models locally trained at each node, and only retransmit the sparse parity-check bits of the damaged micro-slots to obtain a compensated data stream; According to the compensated data stream, the device temperature collected in real time, and the subcarrier load rate data obtained from transceiver statistics, construct a causal graph model to dynamically prune high-entropy paths to minimize causal entropy, use multi-agent reinforcement learning to make decisions on the optimal modulation order and subcarrier switching strategy with time-delay energy efficiency as the game objective, and embed a phase change material heat dissipation module to adjust the optical module drive current to obtain an optimized stable communication link.

[0005] Preferably, obtain the clock signal source, encrypt the clock synchronization channel through the quantum key distribution protocol, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, use the quantum Kalman filter algorithm to generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds, and construct a spatio-temporal hash grid based on the global synchronous clock signal; divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level, including: According to the infrastructure of the factory local area network, obtain the GPS second pulse signal, receive the IEEE 1588 protocol synchronization message, and generate a quantum random number entropy source through a quantum random number generator based on an optical quantum noise chip, encrypt the clock synchronization channel through the quantum key distribution protocol to generate a quantum key stream; Based on the quantum key stream, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, and use the quantum Kalman filter algorithm to eliminate clock drift to generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds; Construct a spatio-temporal hash grid based on the global synchronous clock signal, obtain the coordinates of the spatio-temporal hash grid, and divide the VDSL data stream into quantized data blocks. Each data block is bound with a unique spatio-temporal hash value and a priority label, where the robot joint control instruction is marked as the highest priority level L0.

[0006] Preferably, according to the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, predict the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map the L0-level instructions to the subcarriers with low interference, and perform asymmetric redundant coding on the micro-slot data units using polar codes to generate a dynamically adjusted micro-slot resource allocation result, including: Based on the spatio-temporal hash value and priority label of the quantized data block, combined with the historical spectrum occupancy rate and real-time channel state information, predict the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network; According to the predicted spectrum hole distribution, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period where the duration of each micro-slot is Δ t = 1 μs, and the frequency bandwidth is Δ f = 4.3125 kHz. Preferentially map the L0-level instructions to the subcarriers with low interference, where the subcarriers with low interference satisfy the signal-to-noise ratio threshold greater than or equal to 25 dB, and generate a micro-slot resource allocation table; Based on the micro-slot resource allocation table, perform polar code encoding on the micro-slot data units.

[0007] Preferably, based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the distributed sensors, calculate the optimal phase shift matrix of the metasurface intelligent reflecting surface through the deep deterministic policy gradient algorithm, dynamically reconstruct the electromagnetic wave propagation path, and integrate the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band, obtaining an optimized electromagnetic wave propagation path, including: Based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by collecting the electromagnetic interference intensity by the distributed spectrum sensors, establish a deep deterministic policy gradient model; input the micro-slot resource allocation result and the real-time electromagnetic interference heat map into the deep deterministic policy gradient model, and output the phase shift matrix of the metasurface intelligent reflecting surface; among them, the training objective of the deep deterministic policy gradient model is to minimize the path loss, and through iterative optimization of the phase shift amount of the reflecting surface unit, the total path loss after the superposition of the channel responses of the reflected path and the direct path reaches the lowest; According to the phase offset matrix, dynamically adjust the phase offset of the metasurface intelligent reflecting surface to guide the propagation path of electromagnetic waves to bypass the interference area and obtain an optimized path; based on the optimized path, use the least mean square error algorithm to cancel the self-interference of the uplink and downlink signals in the same frequency band, including: constructing the autocorrelation matrix of the self-interference signal according to the known signal waveform at the transmitting end and the channel impulse response, and solving the optimal interference cancellation weight matrix under the least mean square error criterion by maximizing the signal-to-interference-plus-noise ratio; Filter the received signal using the optimal interference cancellation weight matrix to suppress the self-interference component in the same frequency band, thereby outputting the full-duplex concurrent transmission configuration parameters and obtaining an optimized electromagnetic wave propagation path.

[0008] Preferably, based on the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate obtained by the receiving feedback end are used. When in use, a time-frequency two-domain joint equalizer is used to jointly compensate for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, trigger an incremental redundancy retransmission mechanism based on federated learning, generate a globally optimized parity check matrix by aggregating the gradients of lightweight error correction models locally trained at each node, and only retransmit the sparse parity check bits of the damaged micro-slots to obtain a compensated data stream, including: Based on the metasurface intelligent reflecting surface phase offset matrix, the channel impulse response and bit error rate fed back by the receiving end in the optimized electromagnetic wave propagation path, design a time-frequency two-domain joint equalizer, including: calculating the time-domain equalization weight by minimizing the inter-symbol interference criterion to compensate for the delay caused by multipath fading, and based on the narrowband interference power spectral density, identifying the set of interference frequency points and calculating the frequency-domain suppression coefficient; superimposing the time-domain equalization weight and the frequency-domain suppression result to generate an equalized low-bit-error signal stream; If the bit error rate BER > 10 -6 , extract the index of the damaged micro-slots, identify the fault frequency points and time slices, where the communication nodes perform local training, and the local training includes: using the original data of the damaged micro-slots and the equalized received signal as input data to train a lightweight error correction model to minimize the reconstruction error and generate local gradients; Based on the local gradients, encrypt and upload them to the central server through a secure multi-party computing protocol, aggregate to generate global gradients, update the global parity check matrix, only retransmit the sparse parity check bits of the damaged micro-slots to obtain a globally optimized parity check matrix, and perform error correction decoding on the globally optimized parity check matrix and the sparse parity check bits to recover the compensated data stream.

[0009] It should be noted that in this embodiment, the networking structure of the VDSL communication terminal is as follows: The central office (CO) is located on the service provider side and is connected to the backbone network through optical fibers, responsible for establishing a VDSL connection with the customer premise equipment (CPE). The customer premise equipment (CPE) is deployed on the user side and is connected to the CO through twisted pairs. The maximum transmission distance supported is 3 kilometers (labeled as "Up to 3KM"). The CPE converts the VDSL signal into an Ethernet or WiFi signal for use by the terminal equipment. Optical fibers are used to connect multiple card-reading sub-stations to the CO, providing a high-bandwidth and low-latency backhaul link. Twisted pairs use VDSL technology to transmit data between the CO and the CPE, supporting high-speed communication in the symmetric mode (such as video compression data streams). Among them, the video compression module is integrated in the CPE or the card-reading sub-station to compress and encode the video data, reducing the transmission bandwidth requirement. The WiFi device accesses the network through the LAN port or the wireless module of the CPE, providing wireless coverage and supporting the flexible access of mobile terminals (such as monitoring devices). Multiple card-reading sub-stations converge to the CO through optical fibers, forming a star topology. Each sub-station independently manages local data collection (such as access card swiping, video monitoring, etc.). The sub-station and the CPE achieve data backhaul through a VDSL link, ensuring the stability of long-distance transmission. The characteristics of this networking are that optical fibers are used for high-bandwidth transmission in the backbone network, and twisted pairs + VDSL are used for the last-mile access, taking into account both cost and performance. It not only supports the comprehensive transmission of video compression, card-reading data, and WiFi wireless, but also is applicable to scenarios such as security monitoring and intelligent buildings. By adding card-reading sub-stations or CPE devices, the coverage range and terminal capacity can be flexibly expanded.

[0010] In this embodiment, the adopted VDSL communication terminal has 1 DSL interface in the form of industrial terminals, complies with the ITU G.993.2 standard, and adopts the VDSL-DMT discrete multi-tone modulation and coding technology. In the symmetric mode, the terminal can achieve a maximum transmission distance of 200 Mbps @ 300 meters, 50 Mbps @ 1 kilometer, 10+ Mbps @ 2 kilometers, and 3+ Mbps @ 3 kilometers. At the same time, the terminal supports CO (central office mode) and CPE (customer premise equipment mode), and can realize the switching between CO / CPE modes. In addition, the terminal is equipped with 4 network ports, supporting the 10 / 100 / 1000Base-T(X) standard, RJ45 ports, and having the functions of full-duplex / half-duplex mode adaptation and MDI / MDI-X adaptation. The terminal also has 1 fiber optic port, supporting the 1000Base-X standard, compatible with 100Base-FX, and the connector type can be selected as SC or FC. In terms of wireless, the terminal supports 2X2 MIMO technology in the 2.4G and 5G frequency bands, and is compatible with the 802.11a / b / g / n / ac / ax standards. The power input of the terminal is DC 12V, and the power consumption is less than 10W. The operating temperature range is -20°C to +75°C, the storage temperature range is -40°C to +85°C, and the relative humidity adaptation range is 5% to 95% (without condensation). In terms of software specifications, the terminal supports Chinese-English switching, has a status query function, supports network basic configuration and advanced configuration, and device management functions, including operations such as upgrade, restart, and backup.

[0011] In a second aspect, the present application also provides a VDSL ultra-low latency communication system, including: Acquisition module: used to obtain a clock signal source, encrypt the clock synchronization channel through the quantum key distribution protocol, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds using the quantum Kalman filtering algorithm, and construct a spatio-temporal hash grid based on the global synchronous clock signal; divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level; Prediction module: used to predict the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network according to the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map the L0 level instructions to the low-interference sub-carriers, and perform asymmetric redundant coding on the micro-slot data units using polar codes to generate a dynamically adjusted micro-slot resource allocation result; Computing module: Based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by distributed sensors, it calculates the optimal phase shift matrix of the metasurface intelligent reflecting surface through the deep deterministic policy gradient algorithm, dynamically reconstructs the electromagnetic wave propagation path, and integrates the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band, obtaining an optimized electromagnetic wave propagation path; Generation module: Based on the optimized electromagnetic wave propagation path, it receives the channel impulse response and bit error rate obtained at the feedback end, uses a time-frequency double-domain joint equalizer to jointly compensate for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, it triggers an incremental redundancy retransmission mechanism based on federated learning, generates a globally optimized parity-check matrix by aggregating the gradients of lightweight error correction models locally trained at each node, and only retransmits the sparse parity-check bits of the damaged micro-slots, obtaining a compensated data stream; Construction optimization module: According to the compensated data stream, the device temperature collected in real time, and the subcarrier load rate data obtained from transceiver statistics, it constructs a causal graph model to dynamically prune high-entropy paths to minimize causal entropy, makes decisions on the optimal modulation order and subcarrier switching strategy through multi-agent reinforcement learning with delay-energy efficiency as the game objective, and embeds a phase change material heat dissipation module to adjust the optical module drive current, obtaining an optimized stable communication link.

[0012] Thirdly, this application also provides a VDSL ultra-low latency communication device, including: A memory for storing computer programs; A processor for implementing the steps of the VDSL ultra-low latency communication method when executing the computer program.

[0013] Fourthly, this application also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-mentioned VDSL ultra-low latency communication method.

[0014] The beneficial effects of the present invention are as follows: Based on the quantum Kalman filtering algorithm, the present invention fuses the quantum entropy source and the classical clock source to generate a global synchronous clock signal with a jitter ≤ 5 nanoseconds, combines the spatio-temporal hash grid to dynamically partition the data stream, solves the problem of timing chaos caused by traditional clock drift, reduces the latency fluctuation, effectively solves the problem of phase drift caused by environmental interference in traditional clock synchronization, significantly reduces the risk of timing chaos, and provides an accurate spatio-temporal benchmark for subsequent spectrum resource allocation and path optimization.

[0015] The present invention adjusts the metasurface phase shift matrix in real time through deep reinforcement learning, and combines the real-time electromagnetic interference heat map to dynamically reconstruct the electromagnetic wave propagation path, which can actively avoid high-interference regions (such as strong radiation regions of arc welders and frequency converters), reduce the superposition effect of multipath effects and external interference, and significantly improve the stability and reliability of signal transmission.

[0016] Based on the sparse parity bit and secure gradient aggregation mechanism, the present invention generates a globally optimized sparse parity matrix through local lightweight error correction model training and secure gradient aggregation, and only retransmits the parity bits of damaged micro-slots, breaking through the time delay and bandwidth limitations of traditional full-scale retransmission. Combining the interference compensation ability of the time-frequency double-domain joint equalizer, it realizes efficient redundant repair and significantly improves the fault tolerance and real-time performance in complex channel environments.

[0017] The present invention constructs a causal graph model to analyze the timing dependence relationship of the data transmission path, dynamically prunes high-entropy paths to reduce the probability of timing chaos. At the same time, through multi-agent reinforcement learning to balance time delay and power consumption, combined with the thermal adaptive control of phase change materials, it intelligently adjusts the optical module drive current and sub-carrier switch state in high-temperature and high-load scenarios, significantly extending the device life and maintaining the optimal system energy efficiency.

[0018] The present invention realizes dynamic interference suppression and energy efficiency optimization through multi-technology fusion, specifically combining quantum-enhanced clock synchronization, intelligent reflecting surface beamforming, federated learning incremental retransmission, and causal entropy energy efficiency game, breaking through the bottleneck of traditional technologies; First, based on the quantum Kalman filter algorithm, a high-precision synchronous clock signal is generated, a spatio-temporal hash grid is constructed to dynamically segment data streams, and the phase of the intelligent reflecting surface is optimized in real time by combining deep reinforcement learning to avoid high-interference regions and reduce path loss; Secondly, through the time-frequency double-domain joint equalizer and the sparse parity bit retransmission mechanism driven by federated learning, multi-modal interference suppression and microsecond-level redundant repair are realized; In addition, multi-agent reinforcement learning and phase change material heat dissipation technology are introduced to dynamically balance time delay and energy efficiency, ensuring the stable operation of the system under high temperature and high load; Compared with the prior art, this application significantly improves the communication reliability, time delay stability, and energy efficiency index in complex electromagnetic environments, providing high-robustness communication guarantee for intelligent manufacturing scenarios.

[0019] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will become apparent from the specification, or can be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification, claims, and drawings. Brief Description of the Drawings

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flowchart of the VDSL ultra-low latency communication method described in the embodiments of the present invention; Figure 2 It is a schematic structural diagram of the VDSL ultra-low latency communication system described in the embodiments of the present invention; Figure 3 It is a schematic structural diagram of the VDSL ultra-low latency communication device described in the embodiments of the present invention.

[0022] In the figure: 701, acquisition module; 702, prediction module; 703, calculation module; 704, generation module; 705, construction optimization module; 800, VDSL ultra-low latency communication device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. Detailed implementation manners

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0024] It should be noted that: similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0025] Embodiment 1:

[0026] This embodiment provides a VDSL ultra-low latency communication method.

[0027] See Figure 1, the figure shows that the present method includes step S100, step S200, step S300, step S400 and step S500.

[0028] S100. Obtain a clock signal source, encrypt the clock synchronization channel through a quantum key distribution protocol, fuse the random phase noise of a quantum entropy source with the phase of a classical clock source, generate a global synchronization clock signal with a clock jitter less than 5 nanoseconds by using a quantum Kalman filtering algorithm, and construct a spatio-temporal hash grid based on the global synchronization clock signal; divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority level L0.

[0029] It can be understood that in this step, the data sources include the GPS second pulse signal from a satellite receiving module (such as a GPS antenna on the factory roof), the IEEE 1588 PTP synchronization message from the master clock server within the factory local area network, the quantum random number entropy source generated by a quantum physical device (such as an entropy source chip based on optical quantum noise), and the clock synchronization channel of a dedicated communication link (such as an encrypted optical fiber channel) for transmitting clock synchronization data. Inject the random phase noise (physical unpredictability) of the quantum entropy source into the phase calibration module of the classical clock source, eliminate the clock drift through the quantum Kalman filtering algorithm, and generate a spatio-temporal coordinate system (such as a timestamp + physical location hash value) based on the synchronized clock signal, which is used to provide spatio-temporal tags for the data blocks. Divide the VDSL data stream into the smallest transmission units according to the spatio-temporal grid coordinates, and each data block is bound with a unique spatio-temporal tag (for example: timestamp + factory robot coordinates + priority) through the SHA-3 hash algorithm. It should be noted that the spatio-temporal hash grid in this step provides a spatio-temporal benchmark for spectrum prediction, and the quantized data block tags drive the micro-slot allocation.

[0030] It can be understood that in this step S100, it includes S101, S102 and S103, where: S101. According to the infrastructure of the factory local area network, obtain the GPS second pulse signal, receive the IEEE 1588 protocol synchronization message, generate a quantum random number entropy source through a quantum random number generator based on an optical quantum noise chip, encrypt the clock synchronization channel through the quantum key distribution protocol, and generate a quantum key stream; It should be noted that the quantum key stream is used to encrypt the IEEE 1588 PTP synchronization message to ensure the security of the clock synchronization channel. The encrypted clock synchronization channel provides a secure clock signal for the subsequent quantum-classical clock source fusion.

[0031] S102. Based on the quantum key stream, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, and use the quantum Kalman filtering algorithm to eliminate clock drift, generating a global synchronous clock signal with a clock jitter less than 5 nanoseconds. The jitter calculation formula is as follows:

[0032] In the formula, Jitter is the clock jitter, N is the number of measured clock cycle samples, t i is the measured value of the clock cycle, is the theoretical period, i is the i th clock cycle sample; It should be noted that the random phase noise of the quantum entropy source is generated based on an optical quantum noise chip, which can generate high-entropy random noise to enhance the stability and anti-interference ability of the clock signal. It can be understood that the jitter formula quantifies the jitter degree of the clock signal by calculating the sum of the squares of the deviations between all measured clock cycle samples and the theoretical clock cycle, and then taking the average value. The smaller the jitter, the better the stability of the clock signal.

[0033] S103. Construct a spatio-temporal hash grid based on the global synchronous clock signal, obtain the coordinates of the spatio-temporal hash grid, and divide the VDSL data stream into quantized data blocks. Each data block is bound with a unique spatio-temporal hash value and a priority label, where the robot joint control instruction is marked as the highest priority level L0.

[0034] It should be noted that based on the global synchronous clock signal, time is divided into time periods of fixed length (for example, each time period is 1 millisecond); according to the physical layout of the factory equipment, space is divided into two-dimensional or three-dimensional grid cells. The space coordinates ( x , y ) can be obtained through the UWB (Ultra-Wideband) positioning system, and the position of each device or sensor is mapped to a specific grid cell. For each time-space cell, a unique hash value H is generated. The hash value can be generated using the SHA-3 algorithm, with the input being the timestamp t and the space coordinates( x , y ): H =SHA3( t , x , y ). In this way, each time-space cell has a unique hash value to identify the cell. Organize all time-space cells and their corresponding hash values into a grid structure to form a spatio-temporal hash grid G ( x , y , t)。This grid can be regarded as a multi-dimensional array or hash table for quickly searching and managing data. It is understandable that VDSL data streams are obtained from the VDSL access points of the factory local area network. These data streams may contain robot control instructions, sensor data, or other information that needs to be transmitted; according to the coordinates of the spatio-temporal hash grid, the segmentation criteria for data blocks are determined. Each data block corresponds to a time-space unit, and its size can be adjusted according to actual needs. The VDSL data stream is segmented according to the time-space unit to generate quantized data blocks. Among them, each data block contains the following information: the spatio-temporal hash value generated by the SHA-3 algorithm and a priority label assigned according to the type and importance of the data; for example, robot joint control instructions are marked as P = L 0 (highest priority).

[0035] S200. Based on the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, the distribution of spectrum holes within the next 10 ms is predicted through a spatio-temporal convolutional neural network, the micro-slot resources within the orthogonal frequency division multiplexing symbol period are dynamically divided, the L0-level instructions are preferentially mapped to subcarriers with low interference, and polar codes are used to perform asymmetric redundant coding on the micro-slot data units to generate a dynamically adjusted micro-slot resource allocation result.

[0036] It is understandable that in this step, the input data is the historical spectrum occupancy rate (database records) and the real-time channel state information (CSI, collected by the VDSL transceiver), and a spatio-temporal convolutional neural network (ST-CNN) is used to output the interference intensity distribution map of each subcarrier within the next 10 ms, and the low-interference regions are marked as "spectrum holes". The robot joint control instructions (L0 level) are allocated to the predicted subcarriers with low interference (such as subcarrier indices 1 - 10), and the coding redundancy is dynamically selected according to the priority (L0-level redundancy ≥ 30%), and redundant information is injected through the frozen bit positions of the polar codes. Finally, a dynamically divided micro-slot resource allocation table (including subcarrier mapping relationships) and asymmetrically redundantly encoded micro-slot data units are obtained, that is, a dynamically adjusted micro-slot resource allocation result.

[0037] It should be noted that in this step S200, it includes S201, S202, and S203, where: S201. Based on the spatio-temporal hash value and priority label of the quantized data block, combined with the historical spectrum occupancy rate and the real-time channel state information, the distribution of spectrum holes within the next 10 ms is predicted through a spatio-temporal convolutional neural network, and its calculation formula is as follows:

[0038] In the formula, Mask priority(P) is the priority mask matrix, Holes(f,t) is the predicted spectrum hole distribution, STCNN(S hist ,SNR) is a spatiotemporal convolutional neural network; S202: Dynamically divide the OFDM symbol period according to the predicted spectrum hole distribution The duration of each mini-slot is Δ t =1μs, bandwidth is Δ f =4.3125kHz, map the L0 level instructions to low-interference subcarriers first, where the low-interference subcarriers meet the signal-to-noise ratio threshold greater than or equal to 25dB, and generate a mini-timeslot resource allocation table; It should be noted that the real-time optimization allocation of micro-slot resources based on spectrum hole prediction can reduce the transmission delay of high-priority instructions, dynamically identify available spectrum resources, improve spectrum utilization, and reduce spectrum waste. According to the distribution of spectrum holes, micro-slot resources are divided in the available spectrum area, and the start time and frequency position of each micro-slot are determined by the distribution of spectrum holes. Among them, the dynamic division of micro-slot resources can flexibly adjust resource allocation according to the real-time distribution of spectrum holes, improve the utilization of spectrum resources, and reduce resource waste.

[0039] S203. Based on the mini-slot resource allocation table, polarization code encoding is performed on the mini-slot data unit to generate a codeword as follows:

[0040] In the formula, is the encoded codeword, PolarEncode(D slot ,R) is the micro-slot data D slot Polar coding is performed to add redundant information with redundancy R; Among them, redundancy R The dynamic adjustment based on priority is as follows:

[0041] Where R is the redundancy, P is the priority label of the data block, L0 is the highest priority, L1 is the medium priority, and L2 is the lowest priority.

[0042] It should be noted that R =0.3 (when P = L 0): For the highest priority data block ( L 0), the redundancy is set to 0.3, which means that 30% of the encoded data is redundant information, which is used to improve error correction capability; R =0.5 (when P = L 1): For medium priority data blocks (L 1), the redundancy is set to 0.5, which means that 50% of the encoded data is redundant information; R = 0.7 (when P = L 2): For the data block with the lowest priority ( L 2), the redundancy is set to 0.7, which means that 70% of the encoded data is redundant information to ensure reliable transmission even under poor channel conditions. Therefore, the redundancy of L0-level instructions is 70%, and the bit error rate tolerance is increased to BER ≤ 10 -8 , so this method of dynamically adjusting redundancy can optimize the transmission efficiency and reliability according to the importance of data and channel conditions, and the micro-slot resource allocation table also provides a scheduling basis for subsequent beamforming and equalization.

[0043] S300. Based on the micro-slot resource allocation results and the real-time electromagnetic interference heat map generated by distributed sensors, calculate the optimal phase shift matrix of the metasurface intelligent reflecting surface through the deep deterministic policy gradient algorithm, dynamically reconstruct the electromagnetic wave propagation path, and integrate the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band, and obtain the optimized electromagnetic wave propagation path.

[0044] It can be understood that in this step, the input data source is the quantized data block labels generated in the above steps, and the electromagnetic interference heat map is generated in real time by distributed electromagnetic sensors in the factory (such as a spectrum analyzer array). Calculate the phase parameters of the IRS reflection unit through the deep deterministic policy gradient (DDPG) algorithm, so that the electromagnetic wave bypasses the interference area (such as an arc welder), and when transmitting in the same uplink and downlink frequency band, use digital domain interference cancellation technology to eliminate the echo interference, and finally obtain an optimized electromagnetic wave propagation path with a delay ≤ 0.15 ms and concurrent transmission configuration parameters (such as phase matrix, frequency allocation).

[0045] It should be noted that in this step S300, it includes S301, S302 and S303, where: S301. Based on the micro-slot resource allocation results and the real-time electromagnetic interference heat map generated by the electromagnetic interference intensity collected by distributed spectrum sensors, establish a deep deterministic policy gradient model; input the micro-slot resource allocation results and the real-time electromagnetic interference heat map into the deep deterministic policy gradient model, and output the phase shift matrix of the metasurface intelligent reflecting surface; among them, the training objective of the deep deterministic policy gradient model is to minimize the path loss, and through iterative optimization of the phase shift of the reflecting surface unit, the total path loss after the superposition of the channel responses of the reflected path and the direct path reaches the lowest; It should be noted that the micro-slot resource allocation result is the micro-slot resource result dynamically divided based on the OFDM symbol period, identifying the frequency point fResource occupancy status with respect to time t; while the real-time electromagnetic interference heat map is obtained by a distributed spectrum sensor network (such as software-defined radio nodes) deployed in the factory, which collects the electromagnetic radiation intensity (unit: dBm) at each coordinate point (x, y) in real time and generates a spatially continuous heat map using the Kriging interpolation algorithm with a resolution of up to 1 meter. Typical interference sources include arc welders (radiation intensity > -80 dBm) and frequency converters.

[0046] It can be understood that the construction of the deep deterministic policy gradient model includes state space definition, action space definition, and reward function design. The model input state in the state space definition includes the micro-slot resource allocation result and the electromagnetic interference heat map, and the action space definition is that the output is the phase shift matrix of the intelligent reflecting surface of the metasurface, with dimension N×N (N is the number of reflecting units), and each element ϕn ∈ [0, 2π) represents the phase shift amount of the nth unit. The reward function design takes minimizing the path loss as the core objective, and its reward function is defined as:

[0047] In the formula, R is the reward function, is the weight coefficient, is the time delay of the signal on the transmission path, is the interference accumulation value in the path coverage area, L path is to minimize the path loss; Among them, the phase shift matrix optimization mechanism includes adding the channel matrices of the reflected path and the direct path to obtain the total channel response. The path loss is characterized by the reciprocal of the square of the Frobenius norm of the channel matrix, and then the path loss is calculated. Then, the phase shift action is generated through the Actor network, and the action value is evaluated by the Critic network. Combining the experience replay pool and the target network to update the strategy until the path loss converges to the minimum value. In this step, the number of multipath reflections and the path propagation distance are reduced, the end-to-end delay is less than or equal to 0.15 ms, meeting the sub-millisecond-level requirements of robot cooperative control, and the anti-interference ability is enhanced.

[0048] S302. According to the phase shift matrix, dynamically adjust the phase shift amount of the intelligent reflecting surface of the metasurface to guide the propagation path of the electromagnetic wave to bypass the interference area and obtain an optimized path; based on the optimized path, use the least mean square error algorithm to cancel the self-interference of the uplink and downlink signals in the same frequency band, including: according to the known signal waveform and channel impulse response at the transmitter, construct the autocorrelation matrix of the self-interference signal, and solve the optimal interference cancellation weight matrix under the least mean square error criterion by maximizing the signal-to-interference-plus-noise ratio. The calculation formula is as follows:

[0049] In the formula, WMMSE is the optimal interference cancellation weight matrix, and \(R\) xy is the cross-correlation matrix between the self-interference signal and the desired signal, is the inverse matrix of the autocorrelation matrix; S303. Filter the received signal using the optimal interference cancellation weight matrix to suppress the co-frequency self-interference component, thereby outputting the full-duplex concurrent transmission configuration parameters and obtaining an optimized electromagnetic wave propagation path.

[0050] It should be noted that based on the known signal at the transmitter and the channel impulse response, the autocorrelation matrix of the self-interference signal is constructed. This matrix is used to describe the statistical characteristics of the self-interference signal and provides a basis for subsequent weight calculation. Then, by maximizing the signal-to-interference-plus-noise ratio (SINR), the MMSE interference cancellation weight matrix is calculated. This weight matrix is used to suppress the self-interference component within the co-frequency band and ensure that the uplink and downlink signals do not interfere with each other during full-duplex transmission. Filter the received signal using the weight matrix to reduce the residual interference power to no more than -30 dBm. Through this process, the spectral efficiency is significantly improved, reaching \(\eta = 8\) bps / Hz, and the full-duplex concurrent transmission configuration parameters are generated. , where is the uplink frequency point, is the downlink frequency point, and \(\eta\) is the spectral efficiency, that is, it can effectively reduce the self-interference between the uplink and downlink signals in the co-frequency band, significantly improve the spectral efficiency, and achieve efficient full-duplex communication. Among them, it can be understood that the residual interference power is the remaining interference power after filtering. The smaller its value, the better the interference suppression effect. The goal of maximizing SINR is to ensure that the signal can still maintain high quality under the influence of interference and noise; by maximizing SINR, the weight matrix can be optimized to maximize the useful part of the signal while minimizing the mean square error.

[0051] S400. Based on the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate obtained at the receiving feedback end, use a time-frequency two-domain joint equalizer to jointly compensate for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, trigger the incremental redundancy retransmission mechanism based on federated learning, generate a globally optimized parity-check matrix by aggregating the gradients of the lightweight error correction models locally trained at each node, and only retransmit the sparse parity-check bits of the damaged micro-slots to obtain the compensated data stream.

[0052] It is understandable that the data of the optimized electromagnetic wave propagation path in this step is derived from the optimized path data (such as channel impulse response) in the above step for equalizer compensation. If BER ≤ threshold: directly enter the energy efficiency optimization in the following step. If BER > threshold (1e-6): trigger the federated learning retransmission mechanism, that is, local training: each node trains an error correction model (such as a sparse parity-check matrix generator) based on the historical error pattern, gradient aggregation: encrypt and upload the model gradient to the central server to generate a globally optimized parity-check matrix, and sparse parity-bit retransmission: only retransmit the parity bits of the damaged micro-slots (instead of all data), compress the retransmission delay to ≤ 20 μs. It only retransmits the sparse parity bits of the damaged micro-slots, which means that after detecting an error code, the system does not retransmit the entire damaged micro-slot data, but only retransmits those sparse parity bits containing key check information. This method can significantly reduce the retransmitted data volume, improve the transmission efficiency, and reduce the delay at the same time. At the same time, after the data stream is processed by the time-frequency dual-domain joint equalizer, the bit error rate is reduced and the signal quality is improved.

[0053] It should be noted that in step S400, it includes S401, S402, and S403, where: S401. Based on the phase shift matrix of the intelligent reflecting surface of the metasurface in the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate fed back by the receiving end, design a time-frequency dual-domain joint equalizer, including: by minimizing the inter-symbol interference criterion, calculate the time-domain equalization weight to compensate for the delay caused by multipath fading, and based on the narrowband interference power spectral density, identify the set of interference frequency points and calculate the frequency-domain suppression coefficient; superimpose the time-domain equalization weight and the frequency-domain suppression result to generate an equalized low-bit-error signal stream; It should be noted that by calculating through the minimum inter-symbol interference criterion, compensating for the delay caused by multipath fading, and then obtaining the time-domain equalization weight, compensating for the multipath effect in the time domain, reducing inter-symbol interference, and improving the clarity of the signal; then based on the narrowband interference power spectral density, identifying the set of interference frequency points and calculating the frequency-domain suppression coefficient, performing notch filtering on the interference frequency points, suppressing the narrowband interference in the frequency domain, reducing the influence of interference on the signal, and improving the signal-to-noise ratio of the signal; combining the time-domain equalization weight and the frequency-domain suppression coefficient to obtain an equalized signal and output an equalized low-bit-error signal stream, significantly reducing the bit error rate, improving the signal quality, and realizing the overall optimization of the signal.

[0054] S402. If the bit error rate BER > 10 -6 , extract the indexes of the damaged micro-slots, identify the faulty frequency points and time slices, where the communication nodes perform local training, and the local training includes: using the original data of the damaged micro-slots and the equalized received signal as input data to train a lightweight error correction model to minimize the reconstruction error and generate local gradients; It should be noted that if the bit error rate BER ≤ 10-6 , it is considered that the signal quality of the current transmission link meets the requirements, and there is no need to trigger the incremental redundancy retransmission mechanism. The system will then continue to transmit data normally, maintain the current communication state, and continuously monitor the bit error rate to ensure the reliability of communication. Among them, in this step, through the time-frequency two-domain joint equalizer, multipath fading and narrowband interference are effectively compensated, the bit error rate is significantly reduced, and the signal quality is improved; when the bit error rate exceeds the threshold, a lightweight error correction model is trained through the federated learning mechanism, local gradients are generated and securely aggregated to generate a globally optimized parity-check matrix. The calculation formula for minimizing the reconstruction error is as follows:

[0055] In the formula, L local is the local reconstruction error, D slot is the original data of the damaged micro-slot, M local is the lightweight error correction model, and S eq (t) is the received signal processed by the time-frequency two-domain joint equalizer.

[0056] S403. Based on the local gradients, encrypt and upload them to the central server through the secure multi-party computation protocol, aggregate to generate global gradients, update the global parity-check matrix, and only retransmit the sparse parity bits of the damaged micro-slots to obtain the globally optimized parity-check matrix. Perform error correction decoding on the globally optimized parity-check matrix and the sparse parity bits to recover the compensated data stream.

[0057] It should be noted that only retransmitting the sparse parity bits of the damaged micro-slots reduces the amount of retransmitted data, reduces the retransmission delay, improves the transmission efficiency, and through the secure multi-party computation protocol, ensures the secure upload and aggregation of gradient information, protects data privacy, and enhances the security of the system. Therefore, this method realizes efficient interference suppression and reliable incremental redundancy retransmission in a multi-modal interference environment, significantly improves the transmission efficiency and reliability of the system. The finally obtained compensated data stream is a low bit error rate data stream after error correction processing, meeting the system's requirements for reliability. Among them, the calculation formula for encrypting and uploading to the central server through the secure multi-party computation protocol and aggregating to generate global gradients is as follows:

[0058] In the formula, is the global gradient, M is the total number of communication nodes, is the i th local gradient generated by the communication node.

[0059] S500. Based on the compensated data stream, the device temperature collected in real time, and the subcarrier load rate data obtained from transceiver statistics, construct a causal graph model to dynamically prune high-entropy paths to minimize causal entropy. Through multi-agent reinforcement learning, use delay-energy efficiency as the game objective to decide the optimal modulation order and subcarrier switching strategy, and embed a phase change material heat dissipation module to adjust the optical module drive current to obtain an optimized stable communication link.

[0060] It can be understood that in this step, the device temperature and subcarrier load rate data come from the real-time monitoring system of the device. Based on the demodulated data block sequence, device temperature, and subcarrier load rate data, dynamically prune high-entropy paths to reduce the risk of timing chaos. At the same time, based on the entropy increase index and real-time temperature feedback, dynamically adjust the modulation order and subcarrier switching state to optimize energy efficiency. The optimized modulation order and subcarrier switching strategy improve the energy efficiency and reliability of the system, while reducing the risk of timing chaos and improving the stability of data transmission.

[0061] It should be noted that based on the equalized signal flow, device temperature (collected in real time by a temperature sensor), and subcarrier load rate (indicating the utilization percentage of subcarriers) f construct a causal graph model G causal (V, E), where: V represents the data transmission paths (such as direct paths, reflection paths, redundant relay paths); E represents the delay dependence relationship between paths (such as the timing dependence introduced by multi-hop relays), and then calculate the path entropy increase , where, H pre is the original path entropy value, H post is the entropy value after path adjustment, for high-entropy paths greater than 0 ( θ is a preset threshold), reduce the probability of timing chaos. Therefore, in this step, by dynamically removing high-entropy paths, reduce the timing jitter caused by multi-hop relays, and the delay fluctuation is reduced by ≥30%.

[0062] Then define the agent, whose state space (temperature, load rate, bit error rate), and the action space A = {QAM order, subcarrier switching state}; design the reward function as follows:

[0063] In the formula, R is the designed reward function, is the weight coefficient related to the path delay, is the weight coefficient related to the total power consumption, is the end-to-end transmission delay, P totalis the total system power consumption; then update the policy through the Q-learning algorithm and output the optimized modulation parameters. This step takes delay-energy efficiency as the joint objective, adaptively adjusts the QAM order and subcarrier switch, and improves the spectral efficiency by 20%.

[0064] Based on the real-time device temperature and subcarrier switch state, dynamically adjust the optical module drive current through the heat absorption characteristics of the phase change material r ; when the temperature exceeds the preset threshold, reduce the drive current according to a linear relationship to suppress the power consumption of the optical module, stabilize the total system power consumption below the target value, and maintain the delay at the same time to obtain an optimized stable communication link. Among them, the phase change material realizes temperature-power closed-loop regulation, improves the heat dissipation efficiency by 40%, and extends the device life. Therefore, this step realizes ultra-low delay and high energy efficiency of the VDSL link in a complex industrial environment through causal entropy pruning, reinforcement learning game, and thermal adaptive control, providing reliable communication guarantee for intelligent manufacturing.

[0065] Therefore, through the multi-level collaboration of quantum-classical fusion synchronization, intelligent reflecting surface dynamic optimization, federated learning sparse retransmission, and causal entropy energy efficiency game, the present invention breaks through the bottlenecks of delay, interference, and energy efficiency of traditional VDSL technology in complex industrial environments, providing a highly reliable and low-delay communication foundation support for high-precision intelligent manufacturing.

[0066] Embodiment 2:

[0067] As Figure 2 shown, this embodiment provides a VDSL ultra-low delay communication system. Refer to Figure 2 The system includes: An acquisition module 701: used to acquire a clock signal source, encrypt the clock synchronization channel through the quantum key distribution protocol, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, generate a global synchronization clock signal with a clock jitter less than 5 nanoseconds using the quantum Kalman filtering algorithm, and construct a spatio-temporal hash grid based on the global synchronization clock signal; divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level; A prediction module 702: used to predict the spectral hole distribution within the next 10 ms through a spatio-temporal convolutional neural network according to the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map the L0 level instructions to low-interference subcarriers, and perform asymmetric redundant coding on the micro-slot data units using polar codes to generate a dynamically adjusted micro-slot resource allocation result; Computing Module 703: It is used to calculate the optimal phase shift matrix of the metasurface intelligent reflector through the deep deterministic policy gradient algorithm based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by distributed sensors, dynamically reconstruct the electromagnetic wave propagation path, and integrate the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band, so as to obtain an optimized electromagnetic wave propagation path; Generation Module 704: It is used to receive the channel impulse response and bit error rate obtained by the feedback end based on the optimized electromagnetic wave propagation path, use a time-frequency double-domain joint equalizer to jointly compensate for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, trigger an incremental redundancy retransmission mechanism based on federated learning, generate a globally optimized parity check matrix by aggregating the gradients of lightweight error correction models locally trained at each node, and only retransmit the sparse parity check bits of the damaged micro-slots to obtain a compensated data stream; Construction Optimization Module 705: It is used to construct a causal graph model to dynamically prune high-entropy paths to minimize causal entropy according to the compensated data stream, the device temperature collected in real time, and the subcarrier load rate data statistically obtained by the transceiver, make decisions on the optimal modulation order and subcarrier switching strategy through multi-agent reinforcement learning with delay-energy efficiency as the game goal, and embed a phase change material heat dissipation module to adjust the optical module drive current to obtain an optimized stable communication link.

[0068] Specifically, the obtaining module 701 includes: The first generation unit: It is used to obtain the GPS second pulse signal according to the infrastructure of the factory local area network, receive the IEEE1588 protocol synchronization message, generate a quantum random number entropy source through a quantum random number generator based on an optical quantum noise chip, and encrypt the clock synchronization channel through the quantum key distribution protocol to generate a quantum key stream; The second generation unit: It is used to fuse the random phase noise of the quantum entropy source and the phase of the classical clock source based on the quantum key stream, and use the quantum Kalman filtering algorithm to eliminate clock drift to generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds. The jitter calculation formula is as follows:

[0069] In the formula, Jitter is the clock jitter, N is the number of measured clock cycle samples, t i is the measured value of the clock cycle, is the theoretical period, i is the i th clock cycle sample; The splitting unit: It is used to construct a spatio-temporal hash grid based on the global synchronous clock signal, obtain the coordinates of the spatio-temporal hash grid, and split the VDSL data stream into quantized data blocks. Each data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level.

[0070] Specifically, the prediction module 702 includes: Prediction unit: Based on the spatio-temporal hash value and priority label of the quantized data block, combined with the historical spectrum occupancy rate and real-time channel state information, it predicts the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network. The calculation formula is as follows:

[0071] In the formula, Mask priority (P) is the priority mask matrix, Holes(f,t) is the predicted spectrum hole distribution, and STCNN(S hist , SNR) is the spatio-temporal convolutional neural network; Partitioning unit: It is used to dynamically partition the micro-slot resources within the orthogonal frequency division multiplexing symbol period according to the predicted spectrum hole distribution. The duration of each micro-slot is Δ = 1 μs, and the frequency bandwidth is Δ t = 4.3125 kHz. The L0-level instructions are preferentially mapped to the low-interference subcarriers, where the low-interference subcarriers satisfy the signal-to-noise ratio threshold greater than or equal to 25 dB, and a micro-slot resource allocation table is generated; f Encoding unit: Based on the micro-slot resource allocation table, it performs polar code encoding on the micro-slot data unit to generate a codeword as follows:

[0072] In the formula, is the encoded codeword, and PolarEncode(D slot , R) performs polar encoding on the micro-slot data D slot and adds redundant information with a redundancy R; Among them, the redundancy R is dynamically adjusted according to the priority as follows:

[0073] In the formula, R is the redundancy, P is the priority label of the data block, L0 is the highest priority, L1 is the medium priority, and L2 is the lowest priority.

[0074] Specifically, the calculation module 703 includes: Model building unit: It is used to build a deep deterministic policy gradient model based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the distributed spectrum sensors; input the micro-slot resource allocation result and the real-time electromagnetic interference heat map into the deep deterministic policy gradient model, and output the phase offset matrix of the metasurface intelligent reflecting surface; wherein, the training objective of the deep deterministic policy gradient model is to minimize the path loss, and by iteratively optimizing the phase offset of the reflecting surface unit, the total path loss after the superposition of the channel responses of the reflected path and the direct path reaches the lowest; Optimization unit: It is used to dynamically adjust the phase offset of the metasurface intelligent reflecting surface according to the phase offset matrix, guide the electromagnetic wave propagation path to bypass the interference area, and obtain the optimized path; based on the optimized path, use the least mean square error algorithm to perform self-interference cancellation on the uplink and downlink signals in the same frequency band, including: constructing the autocorrelation matrix of the self-interference signal according to the known signal waveform at the transmitter and the channel impulse response, and solving the optimal interference cancellation weight matrix under the least mean square error criterion by maximizing the signal-to-interference-plus-noise ratio, and its calculation formula is as follows:

[0075] In the formula, W MMSE is the optimal interference cancellation weight matrix, R xy is the cross-correlation matrix of the self-interference signal and the desired signal, is the inverse matrix of the autocorrelation matrix; Processing unit: It is used to filter the received signal by applying the optimal interference cancellation weight matrix, suppress the self-interference component in the same frequency band, and thus output the full-duplex concurrent transmission configuration parameters to obtain the optimized electromagnetic wave propagation path.

[0076] Specifically, the generation module 704, which includes: Design unit: It is used to design a time-frequency two-domain joint equalizer based on the phase offset matrix of the metasurface intelligent reflecting surface in the optimized electromagnetic wave propagation path, the channel impulse response and the bit error rate fed back by the receiver, including: calculating the time-domain equalization weight by minimizing the inter-symbol interference criterion, compensating for the time delay caused by multipath fading, and identifying the interference frequency point set based on the narrowband interference power spectral density, and calculating the frequency-domain suppression coefficient; superimposing the time-domain equalization weight and the frequency-domain suppression result to generate an equalized low-bit-error signal stream; Extraction and identification unit: If the bit error rate BER>10 -6 , extract the damaged micro-slot index, identify the faulty frequency points and time slices, and the communication node performs local training, where the local training includes: using the original data of the damaged micro-slot and the equalized received signal as input data, training a lightweight error correction model to minimize the reconstruction error and generate local gradients; Update and error correction unit: It is used to encrypt and upload to the central server based on local gradients through a secure multi-party computing protocol, aggregate to generate global gradients, update the global check matrix, only retransmit the sparse check bits of damaged micro-slots, obtain the globally optimized check matrix, and perform error correction decoding on the globally optimized check matrix and the sparse check bits to recover the compensated data stream.

[0077] It should be noted that for the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0078] Embodiment 3:

[0079] Corresponding to the above method embodiment, a VDSL ultra-low latency communication device is also provided in this embodiment. A VDSL ultra-low latency communication device described below can be correspondingly referred to the VDSL ultra-low latency communication method described above.

[0080] Figure 3 It is a block diagram of a VDSL ultra-low latency communication device 800 shown according to an exemplary embodiment. As Figure 3 shown, the VDSL ultra-low latency communication device 800 includes: a processor 801 and a memory 802. The VDSL ultra-low latency communication device 800 further includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0081] Among them, the processor 801 is used to control the overall operation of the VDSL ultra-low latency communication device 800 to complete all or part of the steps in the above VDSL ultra-low latency communication method. The memory 802 is used to store various types of data to support the operation of the VDSL ultra-low latency communication device 800. These data can include, for example, instructions for any application or method operating on the VDSL ultra-low latency communication device 800, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 803 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component can include a microphone for receiving external audio signals. The received audio signal can be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above other interface modules can be a keyboard, a mouse, or buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the VDSL ultra-low latency communication device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them. Accordingly, the communication component 805 can include: a Wi-Fi module, a Bluetooth module, or an NFC module.

[0082] In an exemplary embodiment, the VDSL ultra-low latency communication device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned VDSL ultra-low latency communication method.

[0083] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned VDSL ultra-low latency communication method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the VDSL ultra-low latency communication device 800 to complete the above-mentioned VDSL ultra-low latency communication method.

[0084] Embodiment 4:

[0085] Corresponding to the above method embodiment, a readable storage medium is further provided in this embodiment. A readable storage medium described below can be correspondingly referred to with a VDSL ultra-low latency communication method described above.

[0086] A computer program is stored on the readable storage medium. When the computer program is executed by a processor, the steps of the VDSL ultra-low latency communication method of the above method embodiment are implemented.

[0087] The readable storage medium can specifically be various readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc that can store program codes.

[0088] In summary, the present invention realizes dynamic interference suppression and energy efficiency optimization through multi-technology integration, specifically combining quantum-enhanced clock synchronization, intelligent reflecting surface beamforming, federated learning incremental retransmission, and causal entropy energy efficiency game, breaking through the bottleneck of traditional technologies. First, a high-precision synchronous clock signal is generated based on the quantum Kalman filtering algorithm, a spatio-temporal hash grid is constructed to dynamically segment data streams, and the phase of the intelligent reflecting surface is optimized in real time by combining deep reinforcement learning to avoid high-interference regions and reduce path loss. Second, through a time-frequency dual-domain joint equalizer and a sparse parity-bit retransmission mechanism driven by federated learning, multi-modal interference suppression and microsecond-level redundancy repair are achieved. In addition, multi-agent reinforcement learning and phase change material heat dissipation technology are introduced to dynamically balance latency and energy efficiency, ensuring the stable operation of the system under high temperature and high load. Compared with the prior art, the present application significantly improves the communication reliability, latency stability, and energy efficiency indicators in complex electromagnetic environments, providing high-robustness communication guarantee for intelligent manufacturing scenarios.

[0089] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0090] As described above, these are only the specific implementation manners 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 replacements within the technical scope disclosed by the present invention, and all of them should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A VDSL ultra-low latency communication method, characterized in that, Including: Obtain a clock signal source, encrypt the clock synchronization channel through the quantum key distribution protocol, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, use the quantum Kalman filtering algorithm to generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds, and construct a spatio-temporal hash grid based on the global synchronous clock signal; Divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level; According to the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, predict the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map the L0 level instructions to the low-interference subcarriers, and use polar codes to perform asymmetric redundant coding on the micro-slot data units to generate a dynamically adjusted micro-slot resource allocation result; Based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the distributed sensors, calculate the optimal phase shift matrix of the metasurface intelligent reflecting surface through the deep deterministic policy gradient algorithm, dynamically reconstruct the electromagnetic wave propagation path, and integrate the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band, and obtain an optimized electromagnetic wave propagation path; Based on the optimized electromagnetic wave propagation path, receive the channel impulse response and bit error rate obtained at the feedback end, use a time-frequency double-domain joint equalizer to perform collaborative compensation for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, trigger an incremental redundancy retransmission mechanism based on federated learning, generate a global optimized parity check matrix by aggregating the gradients of the lightweight error correction models locally trained at each node, and only retransmit the sparse parity check bits of the damaged micro-slots to obtain a compensated data stream; According to the compensated data stream, the device temperature collected in real time, and the subcarrier load rate data obtained from the transceiver statistics, construct a causal graph model to dynamically prune the high-entropy path to minimize the causal entropy, use multi-agent reinforcement learning to make decisions on the optimal modulation order and subcarrier switching strategy with the time-delay energy efficiency as the game goal, and embed a phase change material heat dissipation module to adjust the optical module drive current to obtain an optimized stable communication link.

2. The VDSL ultra-low latency communication method according to claim 1, wherein The obtaining of the clock signal source, encrypting the clock synchronization channel through the quantum key distribution protocol, fusing the random phase noise of the quantum entropy source and the phase of the classical clock source, using the quantum Kalman filtering algorithm to generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds, and constructing a spatio-temporal hash grid based on the global synchronous clock signal; Dividing the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level, including: According to the infrastructure of the factory local area network, obtain the GPS second pulse signal, receive the IEEE 1588 protocol synchronization message, and generate a quantum random number entropy source through a quantum random number generator based on an optical quantum noise chip, encrypt the clock synchronization channel through the quantum key distribution protocol, and generate a quantum key stream; Based on the quantum key stream, integrating the random phase noise of the quantum entropy source and the phase of the classical clock source, and using the quantum Kalman filtering algorithm to eliminate clock drift, a global synchronous clock signal with a clock jitter less than 5 nanoseconds is generated. The formula for calculating the jitter is as follows: In the formula, Jitter is the clock jitter, N is the number of measured clock cycle samples, and t i is the measured value of the clock cycle, is the theoretical period, i is the i th clock cycle sample; Based on the global synchronous clock signal, a spatio-temporal hash grid is constructed to obtain the coordinates of the spatio-temporal hash grid, and the VDSL data stream is segmented into quantized data blocks. Each data block is bound with a unique spatio-temporal hash value and a priority label, where the robot joint control instruction is marked as the highest priority level L0.

3. The VDSL ultra-low latency communication method according to claim 1, characterized in that According to the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, the spatio-temporal convolutional neural network is used to predict the spectrum hole distribution within the next 10 ms, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map the L0-level instructions to the subcarriers with low interference, and use polar codes to perform asymmetric redundant coding on the micro-slot data units to generate the dynamically adjusted micro-slot resource allocation result, which includes: Based on the spatio-temporal hash value and the priority label of the quantized data block, combined with the historical spectrum occupancy rate and the real-time channel state information, the spatio-temporal convolutional neural network is used to predict the spectrum hole distribution within the next 10 ms. The formula is as follows: where Mask priority (P) is the priority mask matrix, Holes(f,t) is the predicted spectrum hole distribution, and STCNN(S hist , SNR) is the spatio-temporal convolutional neural network; Dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period according to the predicted spectrum hole distribution, where the duration of each micro-slot is Δ = 1 μs, and the frequency bandwidth is Δ t = 4.3125 kHz. Prioritize mapping L0-level instructions to subcarriers with low interference, where the subcarriers with low interference satisfy the signal-to-noise ratio threshold greater than or equal to 25 dB, and generate a micro-slot resource allocation table; f ​ Based on the micro-slot resource allocation table, polar code encoding is performed on the micro-slot data units to generate the codewords as follows: In the formula, is the encoded codeword, PolarEncode(D slot ,R) is to perform polar encoding on the micro-slot data D slot and add redundant information with redundancy R; Among them, the redundancy R The dynamic adjustment according to the priority is as follows: Wherein, R is the redundancy, P is the priority label of the data block, L0 is the highest priority, L1 is the medium priority, and L2 is the lowest priority.

4. The VDSL ultra-low latency communication method according to claim 1, wherein Based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the distributed sensors, the optimal phase shift matrix of the metasurface intelligent reflecting surface is calculated by the deep deterministic policy gradient algorithm, the electromagnetic wave propagation path is dynamically reconstructed, and the full-duplex self-interference cancellation technology is integrated to achieve concurrent uplink and downlink transmission in the same frequency band, and the optimized electromagnetic wave propagation path is obtained, which includes: Based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the electromagnetic interference intensity collected by the distributed spectrum sensors, a deep deterministic policy gradient model is established; the micro-slot resource allocation result and the real-time electromagnetic interference heat map are input into the deep deterministic policy gradient model, and the phase shift matrix of the metasurface intelligent reflecting surface is output; among them, the training objective of the deep deterministic policy gradient model is to minimize the path loss, and by iteratively optimizing the phase shift of the reflecting surface unit, the total path loss after the superposition of the channel responses of the reflected path and the direct path reaches the lowest; According to the phase shift matrix, the phase shift of the metasurface intelligent reflecting surface is dynamically adjusted to guide the electromagnetic wave propagation path to bypass the interference area to obtain the optimized path; based on the optimized path, the least mean square error algorithm is used to cancel the self-interference of the uplink and downlink signals in the same frequency band, which includes: according to the known signal waveform at the transmitter and the channel impulse response, constructing the autocorrelation matrix of the self-interference signal, and solving the optimal interference cancellation weight matrix under the least mean square error criterion by maximizing the signal-to-interference-plus-noise ratio. The formula is as follows: Wherein, W MMSE is the optimal interference cancellation weight matrix, R xy is the cross-correlation matrix of the self-interference signal and the desired signal, is the inverse matrix of the autocorrelation matrix; The received signal is filtered by using the optimal interference cancellation weight matrix to suppress the self-interference component in the same frequency band, so as to output the full-duplex concurrent transmission configuration parameters and obtain the optimized electromagnetic wave propagation path.

5. The VDSL ultra-low latency communication method according to claim 1, characterized in that Based on the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate obtained at the receiving feedback end, when using a time-frequency double-domain joint equalizer to jointly compensate for multipath fading and narrowband interference, if the bit error rate exceeds the preset threshold, trigger an incremental redundancy retransmission mechanism based on federated learning, generate a globally optimized parity-check matrix by aggregating the gradients of lightweight error correction models locally trained at each node, and only retransmit the sparse parity-check bits of the damaged micro-slots to obtain the compensated data stream, including: Based on the phase shift matrix of the intelligent reflecting surface of the metasurface in the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate fed back by the receiving end, design a time-frequency double-domain joint equalizer, including: calculating the time-domain equalization weight by minimizing the inter-symbol interference criterion, compensating for the time delay caused by multipath fading, and identifying the set of interference frequency points based on the narrowband interference power spectral density, calculating the frequency-domain suppression coefficient; superimposing the time-domain equalization weight and the frequency-domain suppression result to generate an equalized low-bit-error signal stream; If the bit error rate BER > 10 -6 , extract the damaged micro-slot index, identify the faulty frequency point and time slot, where the communication node performs local training, and the local training includes: using the original data of the damaged micro-slot and the equalized received signal as input data, training a lightweight error correction model to minimize the reconstruction error and generate local gradients; Based on the local gradients, encrypt and upload them to the central server through a secure multi-party computation protocol, aggregate to generate global gradients, update the global parity-check matrix, only retransmit the sparse parity-check bits of the damaged micro-slots to obtain a globally optimized parity-check matrix, and perform error correction decoding on the globally optimized parity-check matrix and the sparse parity-check bits to recover the compensated data stream.

6. A VDSL ultra-low latency communication system, based on the VDSL ultra-low latency communication method according to claim 1, characterized in that, Including: Acquisition module: used to obtain a clock signal source, encrypt the clock synchronization channel through the quantum key distribution protocol, fuse the random phase noise of the quantum entropy source and the phase of the classical clock source, generate a global synchronous clock signal with a clock jitter less than 5 nanoseconds using the quantum Kalman filter algorithm, and construct a spatio-temporal hash grid based on the global synchronous clock signal; divide the VDSL data stream into quantized data blocks according to the coordinates of the spatio-temporal hash grid, where each quantized data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level; Prediction module: used to predict the spectrum hole distribution within the next 10 ms through a spatio-temporal convolutional neural network according to the quantized data blocks of the spatio-temporal hash grid and the real-time channel state information, dynamically divide the micro-slot resources within the orthogonal frequency division multiplexing symbol period, preferentially map the L0-level instructions to the low-interference subcarriers, and perform asymmetric redundant coding on the micro-slot data units using polar codes to generate a dynamically adjusted micro-slot resource allocation result; Calculation module: used to calculate the optimal phase shift matrix of the intelligent reflecting surface of the metasurface through the deep deterministic policy gradient algorithm based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the distributed sensors, dynamically reconstruct the electromagnetic wave propagation path, and integrate the full-duplex self-interference cancellation technology to achieve concurrent uplink and downlink transmission in the same frequency band to obtain the optimized electromagnetic wave propagation path; Generation Module: It is used to receive the channel impulse response and bit error rate obtained by the feedback end based on the optimized electromagnetic wave propagation path, and use a time-frequency double-domain joint equalizer to jointly compensate for multipath fading and narrowband interference. If the bit error rate exceeds the preset threshold, it triggers an incremental redundancy retransmission mechanism based on federated learning, generates a globally optimized parity-check matrix by aggregating the gradients of lightweight error correction models locally trained at each node, and only retransmits the sparse parity-check bits of the damaged micro-slots to obtain a compensated data stream. Optimization Module: It is used to construct a causal graph model to dynamically prune high-entropy paths to minimize causal entropy based on the compensated data stream, the device temperature collected in real time, and the subcarrier load rate data obtained from transceiver statistics, determine the optimal modulation order and subcarrier switching strategy through multi-agent reinforcement learning with delay-energy efficiency as the game objective, and embed a phase change material heat dissipation module to adjust the driving current of the optical module to obtain an optimized stable communication link.

7. The VDSL ultra-low latency communication system according to claim 6, wherein The Acquisition Module, which includes: The First Generation Unit: It is used to obtain the GPS second pulse signal according to the infrastructure of the factory local area network, receive the IEEE 1588 protocol synchronization message, generate a quantum random number entropy source through a quantum random number generator based on an optical quantum noise chip, and encrypt the clock synchronization channel through the quantum key distribution protocol to generate a quantum key stream. The Second Generation Unit: It is used to fuse the random phase noise of the quantum entropy source and the phase of the classical clock source based on the quantum key stream, and use the quantum Kalman filtering algorithm to eliminate clock drift to generate a global synchronization clock signal with a clock jitter less than 5 nanoseconds. The jitter calculation formula is as follows: In the formula, Jitter is the clock jitter, N is the number of measured clock cycle samples, and t i is the measured value of the clock cycle, is the theoretical period, i is the i th clock cycle sample; The Splitting Unit: It is used to construct a spatio-temporal hash grid based on the global synchronization clock signal, obtain the coordinates of the spatio-temporal hash grid, and split the VDSL data stream into quantized data blocks. Each data block is bound with a unique spatio-temporal hash value and a priority label, and the robot joint control instruction is marked as the highest priority L0 level.

8. The VDSL ultra-low latency communication system according to claim 6, characterized in that, The Prediction Module, which includes: The Prediction Unit: It is used to predict the spectral hole distribution within the next 10 ms based on the spatio-temporal hash value and priority label of the quantized data block, combined with the historical spectral occupancy rate and real-time channel state information. The calculation formula is as follows: where Mask priority (P) is the priority mask matrix, Holes(f,t) is the predicted spectrum hole distribution, and STCNN(S hist , SNR) is the spatio-temporal convolutional neural network; Partitioning unit: It is used to dynamically partition the micro-slot resources within the orthogonal frequency division multiplexing symbol period according to the predicted spectrum hole distribution, where the duration of each micro-slot is Δ t = 1 μs, and the frequency bandwidth is Δ f = 4.3125 kHz. The L0-level instructions are preferentially mapped to the low-interference subcarriers, where the low-interference subcarriers satisfy the signal-to-noise ratio threshold greater than or equal to 25 dB, and a micro-slot resource allocation table is generated; The Encoding Unit: It is used to perform polar code encoding on the micro-slot data unit based on the micro-slot resource allocation table to generate a codeword as follows: In the formula, is the encoded codeword, PolarEncode(D slot , R) is to perform polar encoding on the micro-slot data D slot and add redundant information with redundancy R; Among them, the redundancy R The dynamic adjustment according to the priority is as follows: Wherein, R is the redundancy, P is the priority label of the data block, L0 is the highest priority, L1 is the medium priority, and L2 is the lowest priority.

9. The VDSL ultra-low latency communication system according to claim 6, wherein, The Calculation Module, which includes: The Model Establishment Unit: It is used to establish a deep deterministic policy gradient model based on the micro-slot resource allocation result and the real-time electromagnetic interference heat map generated by the electromagnetic interference intensity collected by the distributed spectrum sensor; input the micro-slot resource allocation result and the real-time electromagnetic interference heat map into the deep deterministic policy gradient model, and output the phase shift matrix of the metasurface intelligent reflecting surface; among them, the training objective of the deep deterministic policy gradient model is to minimize the path loss, and the phase shift amount of the reflecting surface unit is iteratively optimized to make the total path loss reach the lowest after the channel responses of the reflected path and the direct path are superimposed. Optimization unit: It is used to dynamically adjust the phase offset of the metasurface intelligent reflecting surface according to the phase offset matrix, guide the propagation path of electromagnetic waves to bypass the interference area, and obtain an optimized path; based on the optimized path, the least mean square error algorithm is used to cancel the self-interference of the uplink and downlink signals in the same frequency band, including: constructing the autocorrelation matrix of the self-interference signal according to the known signal waveform and channel impulse response at the transmitter, and solving the optimal interference cancellation weight matrix under the least mean square error criterion by maximizing the signal-to-interference-plus-noise ratio. The calculation formula is as follows: Where, W MMSE is the optimal interference cancellation weight matrix, and R xy is the cross-correlation matrix of the self-interference signal and the desired signal, is the inverse matrix of the autocorrelation matrix; Processing unit: It is used to filter the received signal by applying the optimal interference cancellation weight matrix, suppress the self-interference component in the same frequency band, and thus output the full-duplex concurrent transmission configuration parameters to obtain an optimized electromagnetic wave propagation path.

10. The VDSL ultra-low latency communication system according to claim 6, wherein The generation module, which includes: Design unit: It is used to design a time-frequency double-domain joint equalizer based on the phase offset matrix of the metasurface intelligent reflecting surface in the optimized electromagnetic wave propagation path, the channel impulse response and bit error rate fed back by the receiver, including: calculating the time-domain equalization weight by minimizing the inter-symbol interference criterion, compensating for the time delay caused by multipath fading, and identifying the set of interference frequency points based on the narrowband interference power spectral density, calculating the frequency-domain suppression coefficient; superimposing the time-domain equalization weight and the frequency-domain suppression result to generate an equalized low-bit-error signal stream; Extraction and recognition unit: used to extract the damaged micro-slot index, identify the faulty frequency points and time slots if the bit error rate BER > 10 -6 , where the communication node performs local training. The local training includes: using the original data of the damaged micro-slot and the equalized received signal as input data to train a lightweight error correction model to minimize the reconstruction error and generate local gradients; Update and error correction unit: It is used to encrypt and upload to the central server based on the local gradient through the secure multi-party computation protocol, aggregate to generate the global gradient, update the global parity-check matrix, only retransmit the sparse parity-check bits of the damaged micro-slots, obtain the globally optimized parity-check matrix, and perform error correction decoding on the globally optimized parity-check matrix and the sparse parity-check bits to recover the compensated data stream.

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