Fast synchronous transmission method for medium voltage carrier communication

Abstract

The present application relates to a method for fast synchronous transmission of medium-voltage carrier communication. The method includes: establishing a connection between the client and the server through a three-way handshake mechanism; deploying multiple relay routers between the client and the server to extend the communication distance through the multiple relay routers; the client modulates the medium-voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers; the server uses phase-locked loop technology to perform carrier synchronization on the received modulation to ensure that the carrier frequency of the server is consistent with the carrier frequency of the client. In this way, more intelligent medium-voltage carrier communication synchronous transmission can be achieved, and the stability and reliability of data transmission can be ensured in various environments and conditions, thereby improving the overall performance of the medium-voltage carrier communication system.

Description

Technical Field

[0001] The present application relates to the technical field of medium voltage carrier communication, and in particular, to a medium voltage carrier communication fast synchronous transmission method. Background Art

[0002] Medium voltage carrier communication is a communication technology that uses power lines to transmit data. It can achieve synchronous data transmission during power transmission. Compared with traditional wireless communication technology, medium voltage carrier communication has the advantages of low cost, wide coverage, and strong anti-interference ability. It is widely used in smart grids, home automation, and remote monitoring. However, with the increase in user demand and the complexity of the network environment, traditional medium voltage carrier communication technology faces some challenges, especially in terms of the stability and reliability of data transmission.

[0003] In medium voltage carrier communications, the reliability and real-time performance of data transmission are crucial. Synchronous transmission is a key link to ensure accurate data transmission, especially when there are adverse conditions such as multipath propagation, interference, and channel attenuation. Traditional synchronous transmission methods usually rely on fixed coding rates and simple modulation and demodulation technologies, and lack the ability to adapt to dynamically changing channel conditions. This results in a decrease in data transmission quality and even packet loss when the channel state is poor, reducing the stability and reliability of medium voltage carrier communication data transmission.

[0004] Therefore, an optimized medium voltage carrier communication fast synchronous transmission scheme is desired. Summary of the invention

[0005] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] In a first aspect, the present application provides a medium voltage carrier communication fast synchronous transmission method, the method comprising:

[0007] The client and server establish a connection through a three-way handshake mechanism;

[0008] deploying a plurality of relay routers between the client and the server to extend the communication distance through the plurality of relay routers;

[0009] The client modulates the medium voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers;

[0010] The server uses a phase-locked loop technology to perform carrier synchronization on the received modulation to ensure that the carrier frequency of the server is consistent with the carrier frequency of the client.

[0011] Optionally, the client modulates a medium voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers, including: obtaining a time series of channel state parameters, wherein the channel state parameters include a signal-to-noise ratio, a channel gain value, and a multipath effect; embedding and encoding each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors; inputting the time series of the channel state parameter embedded coding vectors into a sequence encoder based on a Bi-LSTM model to obtain a time series of channel state local time series implicitly associated feature vectors; inputting the time series of the channel state local time series implicitly associated feature vectors into a feature aggregation network based on feature energy local significance gating to obtain a channel state time series aggregation representation vector; and generating a recommended coding rate based on the channel state time series aggregation representation vector.

[0012] Optionally, embedding and encoding each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors, including: embedding and encoding each channel state parameter in the time series of the channel state parameters using a channel parameter embedding coding matrix to obtain a time series of channel state parameter embedded coding vectors.

[0013] Optionally, the time series of the local temporal implicit correlation feature vectors of the channel state is input into a feature aggregation network based on feature energy local significance gating to obtain a channel state temporal aggregate representation vector, including: based on the maximum value, minimum value, mean value and variance of each local temporal implicit correlation feature vector of the channel state in the time series of the local temporal implicit correlation feature vector of the channel state, determining the feature energy level coefficients of each local temporal implicit correlation feature vector of the channel state to obtain a sequence of local temporal feature energy level coefficients of the channel state; determining the scale of the local neighborhood of the local temporal implicit correlation feature of the channel state, and calculating the local temporal feature energy level of the channel state. The local neighborhood significance descriptors of each channel state local temporal feature energy level coefficient in the coefficient are used to obtain a sequence of channel state local temporal feature energy level significance descriptors; the sequence of channel state local temporal feature energy level significance descriptors is input into a mask module based on a gating function to obtain a sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors; the sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors is used as a sequence of weights, and the position-weighted sum of the time series of the channel state local temporal implicitly associated feature vectors is calculated to obtain the channel state temporal aggregate representation vector.

[0014] Optionally, based on the maximum value, minimum value, mean value and variance of each channel state local time series implicitly associated feature vector in the time series of the channel state local time series implicitly associated feature vector, the characteristic energy level coefficient of each channel state local time series implicitly associated feature vector is determined to obtain a sequence of channel state local time series characteristic energy level coefficients, including: calculating the maximum value, minimum value, mean value and variance of the channel state local time series implicitly associated feature vector to obtain the channel state local time series maximum value, the channel state local time series minimum value, the channel state local time series mean value and the channel state local time series variance; calculating the sum of the channel state local time series variance and the hyperparameter to obtain the first channel state local time series local energy level coefficient. Quantity statistical factor; calculate the difference between the maximum local timing value of the channel state and the average local timing value of the channel state to obtain the maximum local timing deviation value of the channel state; calculate the difference between the minimum local timing value of the channel state and the average local timing value of the channel state to obtain the minimum local timing deviation value of the channel state; calculate the sum of the squares of the maximum local timing deviation value of the channel state and the minimum local timing deviation value of the channel state and add it to the hyperparameter to obtain the second local timing local energy statistical factor of the channel state; calculate the division between the first local timing local energy statistical factor of the channel state and the second local timing local energy statistical factor of the channel state to obtain the local timing characteristic energy level coefficient of the channel state.

[0015] Optionally, the scale of the local neighborhood of the local temporal sequence of the channel state is determined, and the local neighborhood significance descriptors of each channel state local temporal sequence feature energy level coefficient in the channel state local temporal sequence feature energy level coefficient are calculated to obtain a sequence of channel state local temporal sequence feature energy level significance descriptors, including: using the scale of the local neighborhood of the local temporal sequence of the channel state as the mean calculation range, and respectively calculating the local neighborhood mean of each of the local temporal sequence feature energy level coefficients of the channel state to obtain a sequence of the channel state local temporal sequence feature energy level significance descriptors.

[0016] Optionally, the sequence of the channel state local temporal feature energy level significance descriptors is input into a mask module based on a gating function to obtain a sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors, including: taking the negative number of each channel state local temporal feature energy level significance descriptor in the sequence of the channel state local temporal feature energy level significance descriptors as an exponential power, calculating the exponential function value with the natural constant e as the base to obtain a sequence of channel state local temporal feature energy level class support significance descriptors; calculating the positional sum between the sequence of the channel state local temporal feature energy level class support significance descriptors and the constant 1 to obtain a sequence of linear modulation channel state local temporal feature energy level class support significance descriptors; calculating the positional sum between the sequence of the channel state local temporal feature energy level class support significance descriptors and the constant 1 to obtain a sequence of linear modulation channel state local temporal feature energy level class support significance descriptors; calculating the positional sum between each linear modulation channel state local temporal feature energy level class support significance descriptors in the sequence of the linear modulation channel state local temporal feature energy level class support significance descriptors. descriptor to obtain a sequence of channel state local temporal gated probabilistic feature energy level significance descriptors; masking is performed on each channel state local temporal gated probabilistic feature energy level significance descriptor in the sequence of channel state local temporal gated probabilistic feature energy level significance descriptors to obtain a sequence of masked gated probabilistic channel state local temporal feature energy level significance descriptors; wherein, masking is performed on each channel state local temporal gated probabilistic feature energy level significance descriptor in the sequence of channel state local temporal gated probabilistic feature energy level significance descriptors to obtain a sequence of masked gated probabilistic channel state local temporal feature energy level significance descriptors, comprising: in response to the channel state local temporal gated probabilistic feature energy level significance descriptor being greater than a predetermined threshold, taking the original value of the channel state local temporal gated probabilistic feature energy level significance descriptor, otherwise setting it to 0.

[0017] Optionally, generating a recommended coding rate based on the channel state timing aggregate representation vector includes: inputting the channel state timing aggregate representation vector into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value.

[0018] The above technical solution is adopted to establish a connection between the client and the server through a three-way handshake mechanism; multiple relay routers are deployed between the client and the server to extend the communication distance through the multiple relay routers; the client modulates the medium-voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers; the server uses the phase-locked loop technology to synchronize the received modulation to ensure that the carrier frequency of the server is consistent with the carrier frequency of the client. In this way, more intelligent medium-voltage carrier communication synchronous transmission can be achieved, and the stability and reliability of data transmission can be ensured in various environments and conditions, thereby improving the overall performance of the medium-voltage carrier communication system.

[0019] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings:

[0021] Figure 1 The present invention is a flow chart showing a method for fast synchronous transmission of medium voltage carrier communication according to an exemplary embodiment.

[0022] Figure 2 The figure is a schematic diagram of a data downlink collaborative initial transmission and retransmission mechanism according to an exemplary embodiment.

[0023] Figure 3 The figure is a schematic diagram of a data downlink cooperative retransmission mechanism according to an exemplary embodiment.

[0024] Figure 4 is based on Figure 1 The illustrated embodiment shows a flow chart of step S103 of a medium voltage carrier communication fast synchronous transmission method.

[0025] Figure 5 The invention is a block diagram of a medium voltage carrier communication fast synchronous transmission system according to an exemplary embodiment.

[0026] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0027] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0028] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0029] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0030] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0031] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0032] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0033] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0034] Figure 1 is a flow chart showing a method for fast synchronous transmission of medium voltage carrier communication according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0035] Step S101: A connection is established between the client and the server through a three-way handshake mechanism;

[0036] Step S102: deploying a plurality of relay routers between the client and the server to extend the communication distance through the plurality of relay routers;

[0037] Step S103: the client modulates the medium voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers;

[0038] Step S104: The server uses a phase-locked loop technology to perform carrier synchronization on the received modulation to ensure that the carrier frequency of the server is consistent with the carrier frequency of the client.

[0039] A phase-locked loop is an electronic circuit that ensures that the frequency of the received signal is synchronized with the frequency of the transmitted signal. In medium-voltage carrier communications, the server uses phase-locked loop technology to synchronize the received modulated signal with the carrier to ensure the accuracy of data transmission.

[0040] Through the noiseless token passing protocol, the necessary deterministic control is provided to maximize data throughput and reduce latency. Based on the "three-way handshake token transfer", the control is transferred in an orderly manner without losing the token, and a 63-level relay routing method is proposed to achieve the goal of a maximum communication distance of more than 50km. Figure 2 and Figure 3 shown.

[0041] First handshake: When establishing a connection, the client sends a syn packet (syn=j) to the server and enters the SYN_SEND state, waiting for the server to confirm;

[0042] Second handshake: After receiving the SYN packet, the server must confirm the client's SYN (ack=j+1) and send a SYN packet (syn=k), i.e., a SYN+ACK packet. At this time, the server enters the SYN_RECV state.

[0043] The third handshake: The client receives the SYN+ACK packet from the server and sends a confirmation packet ACK (ack=k+1) to the server. After this packet is sent, the client and server enter the ESTABLISHED state, completing the three-way handshake.

[0044] Based on the "three-party handshake transfer token", the 63-level data encryption transmission relay routing function is completed.

[0045] In one embodiment of the present application, the terminal-related data of the distribution automation equipment is uploaded to the distribution automation master station through the medium-voltage high-speed analog communication terminal equipment for system analysis. In order to be able to play the function of the distribution automation master station, the real-time and accuracy of the data must be guaranteed. Therefore, the medium-voltage high-speed analog communication terminal equipment should have the characteristics of high speed, low latency, fast synchronization, large data throughput, and long-distance transmission. In order to achieve this overall goal, the theoretical research is divided into 3 steps, namely:

[0046] (1) Solve the problem of low transmission rate of distribution network automation terminals through long-distance distribution lines and point-to-point communication, and study fast synchronous transmission technology to solve the problems of low transmission rate and asynchrony.

[0047] (2) Due to the large number of distribution automation terminals and the large amount of data generated, the medium-voltage high-speed analog communication terminal equipment should solve the problem of low speed and asynchrony, and also solve the problem of data throughput to achieve the transmission of large amounts of data. This project is to study the communication 63-level relay routing method through the noiseless token passing protocol to maximize the data throughput.

[0048] (3) Based on the frequency-based hierarchical design, the modulation method of each layer is determined according to the signal-to-noise ratio, and a nanosecond-level broadband communication data transmission delay reduction algorithm is proposed to achieve the goal of low-latency data transmission.

[0049] After the above three interrelated theoretical studies, the medium voltage high speed analog communication terminal equipment can meet the requirements of various index data.

[0050] Research on 63-level relay routing methods for medium-voltage high-speed analog communications. During the development and testing phase, the main tasks were to complete research on fast synchronous transmission technology for communications, research on 63-level relay routing methods for medium-voltage high-speed analog communications, research on algorithms for shortening data transmission delays for broadband communications, and design of related hardware and analog circuits.

[0051] Furthermore, the medium-voltage high-speed analog communication terminal is industrial-grade designed and complies with the IEEE1901 standard, meeting the needs of stable and high-performance communication of smart grids. All user devices connected to the analog communication module are built into a data network through medium-voltage distribution lines, which can be interconnected with existing intelligent distribution automation terminals DTU, FTU, TTU and load control devices, etc., supporting data transmission and networking communication between the central station and the distribution automation terminal, and can operate in the extremely cold weather in Northeast China and is easy to install on site. It includes the following functions: the medium-voltage high-speed analog communication terminal has a point-to-point communication distance of 2.5KM-5KM in the medium-voltage distribution network, and the communication rate of the medium-voltage high-speed analog communication terminal is guaranteed to be 300Mbps under the standard line impedance of 75 ohms; the medium-voltage high-speed analog communication terminal has a 63-level relay routing function, and the farthest communication distance is greater than 50KM; the frequency of the medium-voltage high-speed analog communication terminal is 0KHz ~ 100MHz, divided into 512 frequency-division channels, the data transmission delay is nanoseconds, the high-speed communication rate is fast, and the physical layer can reach 1.2Gbps.

[0052] Furthermore, the high-speed simulated communication terminal environmental conditions are compressed to meet the operating conditions of the extremely cold weather in Northeast China. Among them, the working temperature is -40℃ to 85℃; the storage temperature is -40℃ to 85℃; the ambient humidity is less than or equal to 95%.

[0053] The main performance indicators of high-speed analog communication terminals include:

[0054] (1) Structure: Check the appearance. The outer shell should not have any dents, scratches, cracks or burrs. The metal parts should have anti-rust and anti-corrosion coatings or plating. The words and symbols on the signs should be clear and durable, and the wiring should be firm.

[0055] (2) Working frequency band: The working frequency band of the medium voltage high-speed analog communication terminal is in the range of 0 kHz to 100 MHz. The signal analyzer is used for analysis: f1 = 1.606300 MHz, f2 = 67.670350 MHz, and the working frequency band = 66.064050 MHz.

[0056] (3) Transmitted power spectrum density: The signal output power spectrum density is not greater than -45dBm / Hz within the working frequency band, and is not greater than -75dBm / Hz outside the working frequency band (integral bandwidth 100kHz) to pass the inspection. Actual measurement results: The power spectrum density in the frequency band of 1.6MHz to 67.6MHz is ≤-50.99dBm / Hz, the power spectrum density in the frequency band <1.6MHz is ≤-89.55dBm / Hz, and the power spectrum density in the frequency band >67.6MHz is ≤-85.27dBm / Hz.

[0057] (4) Communication rate (throughput): Under the test condition of a frame length of 1024 bytes, the average throughput of a medium-voltage high-speed analog communication terminal shall not be less than 200 Mbit / s for upstream and downstream transmission and not less than 100 Mbit / s for bidirectional transmission.

[0058] (5) Network delay: For a medium-voltage high-speed analog communication terminal, under the test condition of a frame length of 1024 bytes, the average uplink delay and the average downlink delay should not exceed 3 ms.

[0059] (6) Long-term transmission performance: The medium-voltage high-speed analog communication terminal is tested under the conditions of a frame length of 1518 bytes, a load (bidirectional) of 110 Mbit / s, a test time of 2 hours, and a frame loss rate of 0 for both uplink and downlink.

[0060] (7) Power supply influence: The power supply voltage is DC 24 V. The medium-voltage high-speed analog communication terminal under test should be able to operate normally and reliably when the rated voltage varies within the range of -20% to +20%. The data frame length is 1518 bytes, the data flow rate is 110 Mbit / s bidirectional, and there is no frame loss in data transmission during the power supply voltage adjustment process.

[0061] (8) Power consumption: The power consumption of the medium voltage high speed analog communication terminal during normal operation is 3.4W.

[0062] (9) Climate influence: Low temperature influence: The medium-voltage high-speed analog communication terminal under test was placed in the center of the environmental test chamber with power on. The temperature was lowered to -40°C and kept warm for 2 hours. The test frame length was 1518 bytes, the load (bidirectional) was 110 Mbit / s, the uplink frame loss rate was 0, and the downlink frame loss rate was 0. High temperature influence: The medium-voltage high-speed analog communication terminal under test was placed in the center of the environmental test chamber with power on. The temperature was raised to +70°C and kept warm for 2 hours. The test frame length was 1518 bytes, the load (bidirectional) was 110 Mbit / s, the uplink frame loss rate was 0, and the downlink frame loss rate was 0.

[0063] (10) Insulation performance: The insulation resistance of the DC power input to the ground is >500MΩ. When the dielectric strength is 0.5kV and the impulse voltage is 1kV, the medium-voltage high-speed analog communication terminal has no breakdown, flashover, or component damage. The insulation resistance of the signal input and output terminals to the ground is >500MΩ. When the dielectric strength is 2.0kV and the impulse voltage is 5kV, the medium-voltage high-speed analog communication terminal has no breakdown, flashover, or component damage. The insulation resistance of the Ethernet interface to the ground is >500MΩ. When the dielectric strength is 0.5kV and the impulse voltage is 1kV, the medium-voltage high-speed analog communication terminal has no breakdown, flashover, or component damage.

[0064] (11) Design and manufacturing requirements: Dimensions: 168 mm × 120 mm × 44 mm (length × width × height) (including connectors and mounting holes, excluding mounting brackets); Weight: ≤ 1 kg (including assembly screws and mounting brackets) (excluding packaging box).

[0065] (12) Electromagnetic compatibility: Electrostatic discharge immunity test; in accordance with GB / T 17626.2-2018 Electromagnetic compatibility test and measurement technology electrostatic discharge immunity test, air discharge: ±2kV, ±4kV, ±8kV; contact discharge / indirect discharge: ±2kV, ±4kV, ±6kV. Radio frequency electromagnetic field radiation immunity test; in accordance with GB / T 17626.3-2016 Electromagnetic compatibility test and measurement technology radio frequency electromagnetic field radiation immunity test, test level: 10V / m; frequency range: 80MHz~1GHz; modulation: 80%AM; modulation frequency: 1KHz; automatic scanning frequency interval: 1% of the previous frequency; each frequency point holding time: 0.5s.

[0066] (13) Immunity to electrical fast transients and pulses: Based on GB / T 17626.4-2018 Electromagnetic compatibility test and measurement technology for immunity to electrical fast transients and pulses, test level: ±2kV (power supply, carrier input and output ports); repetition frequency of interference signal: 5kHz; duration of interference signal: 60s for both positive and negative polarities.

[0067] (14) Surge (impact) immunity test: Based on GB / T 17626.5-2019 Electromagnetic compatibility test and measurement technology surge (impact) immunity test, test level: line-to-ground ±0.5kV, ±1kV, ±2kV; line-to-line ±0.5kV, ±1kV; number of test pulses: 5 times each for positive and negative; pulse interval: 30s.

[0068] (15) Damped oscillation wave immunity test: Based on GB / T 17626.18-2016 Electromagnetic compatibility test and measurement technology damped oscillation wave immunity test, test voltage: common mode 2.5kV, differential mode 1kV; test frequency: 1MHz, 100kHz; pulse group duration: greater than 2s; repetition rate: 400 times / s, 40 times / s.

[0069] In the above-mentioned medium-voltage carrier communication fast synchronous transmission method, the client modulates the medium-voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers. Since the channel conditions of the power line carrier communication may change due to various factors, such as electromagnetic interference in the surrounding environment, load changes in the power line, etc., the signal transmission through multiple relay routers may cause signal delay spread and phase difference, affecting the demodulation of the signal and the correctness of the data.

[0070] That is to say, in medium voltage carrier communication, changes in channel conditions have a significant impact on the quality of data transmission. Factors such as signal-to-noise ratio, channel gain, and multipath effect directly determine the quality and transmission rate of the signal. Therefore, in order to ensure the stability of data transmission and the correctness of subsequent data synchronization, in the technical solution of this application, the coding rate is dynamically adjusted according to the real-time channel status and condition information to adapt to different communication environments.

[0071] Specifically, the technical concept of the present application is to collect the time series data of channel state parameters, including the time series of signal-to-noise ratio, channel gain value and multipath effect, and then introduce an analysis algorithm based on artificial intelligence and data processing at the back end to perform collaborative analysis and time series information association aggregation on these channel state parameter time series data, so as to derive the recommended coding rate value, so as to adaptively adjust the coding rate according to the channel conditions and status. In this way, more intelligent medium-voltage carrier communication synchronous transmission can be achieved, and the stability and reliability of data transmission can be ensured in various environments and conditions, thereby improving the overall performance of the medium-voltage carrier communication system.

[0072] Figure 4 is based on Figure 1 The embodiment shown is a flowchart of step S103 of a medium voltage carrier communication fast synchronous transmission method, as shown in FIG. Figure 4 As shown, step S103, the client modulates the medium voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers, including:

[0073] Step S1031, obtaining a time series of channel state parameters, wherein the channel state parameters include a signal-to-noise ratio, a channel gain value, and a multipath effect;

[0074] Step S1032, embedding and coding each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors;

[0075] Step S1033, embedding the channel state parameter into the time series of the coding vector into a sequence encoder based on the Bi-LSTM model to obtain a time series of the local temporal implicit correlation feature vector of the channel state;

[0076] Step S1034, inputting the time series of the local temporal implicit correlation feature vector of the channel state into a feature aggregation network based on feature energy local significance gating to obtain a channel state temporal aggregation representation vector;

[0077] Step S1035: Generate a recommended coding rate based on the channel state timing aggregation representation vector.

[0078] More specifically, in the technical solution of the present application, first, a time series of channel state parameters is obtained, wherein the channel state parameters include a signal-to-noise ratio, a channel gain value, and a multipath effect. It should be understood that channel state parameters such as signal-to-noise ratio (SNR), channel gain, and multipath effect, each provide information on different aspects of the channel state, and there is a complex and nonlinear correlation between these channel state parameters. At the same time, these channel state parameter data have different dimensions and ranges, which makes it difficult to fuse and correlate these data when performing collaborative analysis of channel state parameters. Therefore, in order to better perform correlation analysis on different channel state parameters to capture the complex correlation therein, in the technical solution of the present application, a channel parameter embedding coding matrix is ​​used to embed code each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors. It should be understood that the original channel state parameters (such as signal-to-noise ratio, channel gain, etc.) can be embedded and mapped into a common space by means of embedded coding, and the collaborative correlation feature information between these parameters can be captured, thereby converting them into a low-dimensional vector representation. Using the channel parameters to embed the coding matrix can enhance the nonlinear expression ability of the model, so that the model can learn more complex feature interactions, which is particularly important for coding rate adjustment under dynamic channel conditions.

[0079] In one embodiment of the present application, each channel state parameter in the time series of the channel state parameters is embedded and encoded to obtain a time series of channel state parameter embedded coding vectors, including: using a channel parameter embedding coding matrix to embed code each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors.

[0080] Then, the time series of the channel state parameter embedded coding vector is input into the sequence encoder based on the Bi-LSTM model to obtain the time series of the local temporal implicit correlation feature vector of the channel state. The Bi-LSTM model can simultaneously consider the previous and next information in the sequence. Through the processing of the sequence encoder based on the Bi-LSTM model, the dynamic feature information of the channel state embedded coding feature changing over time can be effectively captured. This is crucial for understanding and analyzing the temporal changes of the channel state, and is helpful for the subsequent adaptive control of the coding rate.

[0081] It should be understood that each channel state local time series implicit correlation feature vector in the time series of the channel state local time series implicit correlation feature vector respectively contains the forward and backward time series correlation features related to the channel state parameters at each time point, and although this channel state time series state feature reflects the forward and backward correlation relationship in the time dimension, it does not pay attention to the importance of the channel state at different time points for the coding rate adjustment task. In other words, the channel state time series correlation relationship at different time points has different contributions and importance for the subsequent coding rate adjustment task, which needs to be paid attention to in the process of aggregating the channel state time series feature information. Based on this, in the technical solution of the present application, the time series of the channel state local time series implicit correlation feature vector is further input into the feature aggregation network based on the feature energy local significance gating to obtain the channel state time series aggregation representation vector. The feature aggregation network based on the feature energy local significance gating can refine the feature representation by simulating the decay process of the time node energy, quantizing and aggregating the local energy distribution of the channel state local time series, significantly improving the model's ability to understand the channel state time series characteristics and dynamic changes, and is beneficial to the subsequent coding rate adjustment task.

[0082] In one embodiment of the present application, the time series of the local temporal implicit correlation feature vectors of the channel state is input into a feature aggregation network based on feature energy local significance gating to obtain a channel state temporal aggregate representation vector, including: based on the maximum value, minimum value, mean value and variance of each local temporal implicit correlation feature vector of the channel state in the time series of the local temporal implicit correlation feature vector of the channel state, determining the feature energy level coefficients of each local temporal implicit correlation feature vector of the channel state to obtain a sequence of local temporal feature energy level coefficients of the channel state; determining the scale of the local neighborhood of the local temporal implicit correlation feature vector of the channel state, and calculating the local temporal implicit correlation feature vector of the channel state. The local neighborhood significance descriptors of each channel state local temporal feature energy level coefficient in the feature energy level coefficient are used to obtain a sequence of channel state local temporal feature energy level significance descriptors; the sequence of channel state local temporal feature energy level significance descriptors is input into a mask module based on a gating function to obtain a sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors; the sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors is used as a sequence of weights, and the position-weighted sum of the time series of the channel state local temporal implicitly associated feature vectors is calculated to obtain the channel state temporal aggregate representation vector.

[0083] Specifically, firstly, by analyzing the maximum value, minimum value, mean value and variance of each vector in the time series of the local temporal implicit correlation feature vector of the channel state, the characteristic energy level coefficient is calculated to quantify the energy distribution of the local temporal characteristics of the channel state and evaluate the energy level of the feature. Then, the scale of the local neighborhood of each local temporal implicit correlation feature vector of the channel state is determined, and the local neighborhood significance descriptor of the characteristic energy level coefficient of each local temporal implicit correlation feature vector of the channel state is calculated to capture the energy distribution characteristics of each local temporal implicit correlation feature vector of the channel state in the local neighborhood. Furthermore, the local neighborhood significance descriptor is input into the mask module based on the gating function to perform nonlinear probabilistic transformation through the gating function and adaptive feature screening through the mask function to realize the dynamic adjustment of the significance of the local temporal characteristics of the channel state. Then, using the mask-gated probabilistic channel state local temporal feature energy level significance descriptor as the weight, the weighted sum of the time series of the channel state local temporal implicit correlation feature vector is calculated to obtain the channel state temporal aggregate representation vector, thereby strengthening the local significance features of each channel state local temporal feature and generating a comprehensive feature representation with rich expressiveness, which is conducive to more accurate subsequent coding rate recommendation.

[0084] In one embodiment of the present application, based on the maximum value, minimum value, mean value and variance of each channel state local time series implicitly associated feature vector in the time series of the channel state local time series implicitly associated feature vector, determining the characteristic energy level coefficient of each channel state local time series implicitly associated feature vector to obtain a sequence of channel state local time series characteristic energy level coefficients, including: calculating the maximum value, minimum value, mean value and variance of the channel state local time series implicitly associated feature vector to obtain the channel state local time series maximum value, the channel state local time series minimum value, the channel state local time series average value and the channel state local time series variance; calculating the sum of the channel state local time series variance and the hyperparameter to obtain the first channel state local time series; The local energy statistical factor of the second channel state local sequence is calculated; the difference between the local energy statistical factor of the first channel state local sequence and the local energy statistical factor of the second channel state local sequence is calculated to obtain the local energy statistical factor of the second channel state local sequence; the difference between the local energy statistical factor of the first channel state local sequence and the local energy statistical factor of the second channel state local sequence is calculated to obtain the local energy level coefficient of the channel state local sequence.

[0085] Furthermore, in one embodiment of the present application, the scale of the local neighborhood of the local temporal sequence of the channel state is determined, and the local neighborhood significance descriptors of each channel state local temporal sequence feature energy level coefficient in the channel state local temporal sequence feature energy level coefficient are calculated to obtain a sequence of channel state local temporal sequence feature energy level significance descriptors, including: using the scale of the local neighborhood of the local temporal sequence of the channel state as the mean calculation range, and respectively calculating the local neighborhood mean of each of the local temporal sequence feature energy level coefficients of the channel state to obtain a sequence of the channel state local temporal sequence feature energy level significance descriptors.

[0086] Furthermore, in one embodiment of the present application, the sequence of the channel state local temporal feature energy level significance descriptors is input into a mask module based on a gating function to obtain a sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors, including: taking the negative number of each channel state local temporal feature energy level significance descriptor in the sequence of the channel state local temporal feature energy level significance descriptors as an exponential power, calculating the exponential function value with the natural constant e as the base to obtain a sequence of channel state local temporal feature energy level class support significance descriptors; calculating the positional sum between the sequence of the channel state local temporal feature energy level class support significance descriptors and the constant 1 to obtain a sequence of linear modulation channel state local temporal feature energy level class support significance descriptors; calculating each linear modulation channel state local temporal feature energy level in the sequence of linear modulation channel state local temporal feature energy level class support significance descriptors. The class supports the inverse of the significance descriptor to obtain a sequence of channel state local temporal gated probabilistic feature energy level significance descriptors; each channel state local temporal gated probabilistic feature energy level significance descriptor in the sequence of channel state local temporal gated probabilistic feature energy level significance descriptors is masked to obtain a sequence of masked gated probabilistic channel state local temporal feature energy level significance descriptors; wherein, each channel state local temporal gated probabilistic feature energy level significance descriptor in the sequence of channel state local temporal gated probabilistic feature energy level significance descriptors is masked to obtain a sequence of masked gated probabilistic channel state local temporal feature energy level significance descriptors, including: in response to the channel state local temporal gated probabilistic feature energy level significance descriptor being greater than a predetermined threshold, taking the original value of the channel state local temporal gated probabilistic feature energy level significance descriptor, otherwise setting it to 0.

[0087] Specifically, the time series of the local temporal implicit correlation feature vector of the channel state is input into the feature aggregation network based on the local significance gating of feature energy for processing using the following feature aggregation formula to obtain a channel state temporal aggregation representation vector;

[0088] Among them, the feature aggregation formula is:

[0089] ,

[0090] in, X is the time series of the local temporal implicit correlation feature vector of the channel state, is the time series of the local temporal implicit correlation feature vector of the channel state The local temporal implicit correlation feature vector of the channel state, For the The mean of the local temporal implicit correlation feature vectors of the channel states, For the said The variance of the local temporal implicit correlation feature vector of the channel state, is a hyperparameter, Indicates the The maximum value of the local temporal implicit correlation feature vector of the channel state, Indicates the The minimum value of the local temporal implicit correlation feature vector of the channel state, For the said The characteristic energy level coefficient of the local temporal implicit correlation characteristic vector of the channel state, Indicated by the characteristic energy level coefficient The number of characteristic energy coefficients in the local neighborhood centered at For the q The characteristic energy level coefficient of the local temporal implicit correlation characteristic vector of the channel state, For the said The feature energy level saliency descriptor of the local temporal implicit correlation feature vector of the channel state, For the said The gated probabilistic feature energy level significance descriptor of the local temporal implicit correlation feature vector of the channel state, For masking operation, is the predetermined threshold, For the said The mask-gated probabilistic feature energy level saliency descriptor of the local temporal implicit correlation feature vector of the channel state, For the said The saliency enhanced channel state local temporal implicit correlation feature vector corresponding to the channel state local temporal implicit correlation feature vector, To significantly enhance the number of sequences of implicitly correlated feature vectors of the local temporal sequence of the channel state, f A time series aggregation representation vector for the channel state.

[0091] Furthermore, the channel state timing aggregation representation vector is input into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value. In other words, the decoding process is performed using the timing aggregation characteristics of the channel state, so as to perform adaptive recommendation adjustment on the coding rate. In this way, the coding rate can be adaptively adjusted according to the channel conditions and states to ensure the stability and reliability of data transmission under various environments and conditions. In this way, more intelligent medium voltage carrier communication synchronous transmission can be achieved, and the overall performance of the medium voltage carrier communication system can be improved.

[0092] In one embodiment of the present application, generating a recommended coding rate based on the channel state timing aggregation representation vector includes: inputting the channel state timing aggregation representation vector into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value.

[0093] In a preferred example, considering that the time series of the local temporal implicit association feature vector of the channel state expresses the temporal contextual semantic association features of the channel state parameters, after the time series of the local temporal implicit association feature vector of the channel state is subjected to a feature aggregation network based on local significance gating of feature energy, the channel state temporal aggregation representation vector will also cause differences in local significance gating of feature energy due to the heterogeneity of the feature distribution manifold of each channel state local temporal implicit association feature vector, resulting in insufficient feature aggregation coverage, thereby causing outlier decoding regression inference mapping deviation, affecting the accuracy of the recommended coding rate decoding value obtained by the channel state temporal aggregation representation vector through the decoder-based coding rate adaptive adjuster.

[0094] Based on this, the channel state timing aggregate representation vector is input into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value, including:

[0095] Calculating the sum of the absolute values ​​of each eigenvalue of the channel state time series aggregation representation vector to obtain a first channel state time series aggregation spatial structure value, and calculating the square root of the sum of the squares of each eigenvalue of the channel state time series aggregation representation vector to obtain a second channel state time series aggregation spatial structure value;

[0096] Multiply each eigenvalue of the channel state temporal aggregation representation vector by the first channel state temporal aggregation spatial structure value and the second channel state temporal aggregation spatial structure value respectively to obtain a first channel state temporal aggregation structure reference value and a second channel state temporal aggregation structure reference value corresponding to each eigenvalue;

[0097] Multiplying each eigenvalue of the channel state time series aggregate representation vector by the length of the channel state time series aggregate representation vector and the square root of the length to obtain a first channel state time series aggregate scale transformation value and a second channel state time series aggregate scale transformation value corresponding to each eigenvalue;

[0098] Dividing the first channel state timing aggregation structure reference value by the difference between the first channel state timing aggregation space structure value and the first channel state timing aggregation scale transformation value to obtain a first channel state timing aggregation transformation adjustment value;

[0099] Dividing the second channel state timing aggregation structure reference value by the difference between the second channel state timing aggregation space structure value and the second channel state timing aggregation scale transformation value to obtain a second channel state timing aggregation transformation adjustment value;

[0100] Calculating a weighted sum of the first channel state timing aggregate transformation adjustment value and the second channel state timing aggregate transformation adjustment value to obtain each eigenvalue of the optimized channel state timing aggregate representation vector;

[0101] The optimized channel state timing aggregation representation vector is input into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value.

[0102] Here, the channel state time series aggregation represents a vector, denoted as V The optimization expression is:

[0103] ,

[0104] in, V is the channel state time series aggregation representation vector, represents the set of real numbers, represents the channel state time series aggregation representation vector The eigenvalues ​​at the positions, represents the length of the channel state timing aggregation representation vector, represents the first channel state time series aggregation space structure value, represents the second channel state time series aggregation space structure value, It means point multiplication by position. It means adding by position. represents the second channel state timing aggregation transformation adjustment vector, represents the second channel state timing aggregation transformation adjustment value, represents the first channel state timing aggregation transformation adjustment vector, represents the first channel state timing aggregation transformation adjustment value, represents the weighted hyperparameter, Represents the optimized channel state temporal aggregation representation vector.

[0105] Therefore, in the preferred example, for the spatial structure information of the feature set of the channel state time series aggregation representation vector in the high-dimensional space, the scale-based frame transformation of each eigenvalue of the channel state time series aggregation representation vector is performed by using the class norm space structured representation of the channel state time series aggregation representation vector as a reference window, and the spatial structure-based frame attention weight adjustment of each eigenvalue of the channel state time series aggregation representation vector is realized to ensure the invariance of the spatial transformation (translation, scaling and rotation) of the channel state time series aggregation representation vector under the interaction of feature space, thereby improving the convergence and generalization effect of the decoding regression of the feature set of the channel state time series aggregation representation vector under the complex spatial structure representation, and improving the accuracy of the recommended coding rate decoding value obtained by the decoder-based coding rate adaptive adjuster. In this way, the coding rate can be more accurately adjusted according to the channel conditions and states to ensure the stability and reliability of data transmission under various environments and conditions.

[0106] In summary, the above scheme is adopted to collect the time series data of channel state parameters, including the time series of signal-to-noise ratio, channel gain value and multipath effect, and then introduce an analysis algorithm based on artificial intelligence and data processing at the back end to perform collaborative analysis and time series information association aggregation on these channel state parameter time series data, so as to obtain the recommended coding rate value, so as to adaptively adjust the coding rate according to the channel conditions and status. In this way, more intelligent medium-voltage carrier communication synchronous transmission can be achieved, and the stability and reliability of data transmission can be ensured in various environments and conditions, thereby improving the overall performance of the medium-voltage carrier communication system.

[0107] Figure 5 FIG. 1 is a block diagram of a medium voltage carrier communication fast synchronous transmission system according to an exemplary embodiment. Figure 5 As shown, the system 200 includes:

[0108] A connection establishment module 201 is used to establish a connection between a client and a server through a three-way handshake mechanism;

[0109] A relay router deployment module 202, configured to deploy a plurality of relay routers between the client and the server to extend the communication distance through the plurality of relay routers;

[0110] A signal modulation module 203, configured for the client to modulate the medium voltage power line carrier signal to obtain a modulated signal, and transmit the modulated signal to the server through the multiple relay routers;

[0111] The carrier synchronization module 204 uses a phase-locked loop technology with the server to perform carrier synchronization on the received modulation to ensure that the carrier frequency of the server is consistent with the carrier frequency of the client.

[0112] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0113] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0114] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0115] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0116] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0117] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0118] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0119] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the test parameter acquisition module can also be described as a "module for acquiring device test parameters corresponding to the target device".

[0122] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0123] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0124] In the application scenario of the medium-voltage carrier communication fast synchronous transmission method, first, a time series of channel state parameters is obtained, wherein the channel state parameters include a signal-to-noise ratio, a channel gain value, and a multipath effect; then, the obtained signal-to-noise ratio, channel gain value, and multipath effect are input into a server deployed with a medium-voltage carrier communication fast synchronous transmission algorithm, wherein the server is capable of processing the signal-to-noise ratio, the channel gain value, and the multipath effect based on the medium-voltage carrier communication fast synchronous transmission algorithm to generate a recommended coding rate.

[0125] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0126] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0127] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely exemplary forms of implementation. With respect to the apparatus in the above-mentioned embodiments, the specific manner in which each module performs the operation has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims

1. A medium voltage carrier communication fast synchronous transmission method, characterized in that: include: The client and server establish a connection through a three-way handshake mechanism; deploying a plurality of relay routers between the client and the server to extend the communication distance through the plurality of relay routers; The client modulates the medium voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers; The server uses a phase-locked loop technology to perform carrier synchronization on the received modulated signal to ensure that the carrier frequency of the server is consistent with the carrier frequency of the client; The client modulates the medium voltage power line carrier signal to obtain a modulated signal, and transmits the modulated signal to the server through the multiple relay routers, including: Acquire a time series of channel state parameters, wherein the channel state parameters include a signal-to-noise ratio, a channel gain value, and a multipath effect; Embedding and encoding each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors; The channel state parameter is embedded in the time series of the coding vector and input into the sequence encoder based on the Bi-LSTM model to obtain the time series of the local temporal implicit correlation feature vector of the channel state; Inputting the time series of the local temporal implicit correlation feature vector of the channel state into a feature aggregation network based on local significant gating of feature energy to obtain a channel state temporal aggregation representation vector; Generating a recommended coding rate based on the channel state timing aggregate representation vector; Generating a recommended coding rate based on the channel state timing aggregate representation vector, comprising: inputting the channel state timing aggregate representation vector into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value; Inputting the channel state timing aggregate representation vector into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value, including: Calculating the sum of the absolute values ​​of each eigenvalue of the channel state time series aggregation representation vector to obtain a first channel state time series aggregation spatial structure value, and calculating the square root of the sum of the squares of each eigenvalue of the channel state time series aggregation representation vector to obtain a second channel state time series aggregation spatial structure value; Multiply each eigenvalue of the channel state temporal aggregation representation vector by the first channel state temporal aggregation spatial structure value and the second channel state temporal aggregation spatial structure value respectively to obtain a first channel state temporal aggregation structure reference value and a second channel state temporal aggregation structure reference value corresponding to each eigenvalue; Multiplying each eigenvalue of the channel state time series aggregate representation vector by the length of the channel state time series aggregate representation vector and the square root of the length to obtain a first channel state time series aggregate scale transformation value and a second channel state time series aggregate scale transformation value corresponding to each eigenvalue; Dividing the first channel state timing aggregation structure reference value by the difference between the first channel state timing aggregation space structure value and the first channel state timing aggregation scale transformation value to obtain a first channel state timing aggregation transformation adjustment value; Dividing the second channel state timing aggregation structure reference value by the difference between the second channel state timing aggregation space structure value and the second channel state timing aggregation scale transformation value to obtain a second channel state timing aggregation transformation adjustment value; Calculating a weighted sum of the first channel state time series aggregation transformation adjustment value and the second channel state time series aggregation transformation adjustment value to obtain each eigenvalue of an optimized channel state time series aggregation representation vector; The optimized channel state timing aggregation representation vector is input into a decoder-based coding rate adaptive adjuster to obtain a recommended coding rate decoding value.

2. The medium voltage carrier communication fast synchronous transmission method according to claim 1, characterized in that: Embedding and encoding each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors, including: using a channel parameter embedded coding matrix to embedding and encoding each channel state parameter in the time series of the channel state parameters to obtain a time series of channel state parameter embedded coding vectors.

3. The medium voltage carrier communication fast synchronous transmission method according to claim 2, characterized in that: Inputting the time series of the local temporal implicit correlation feature vector of the channel state into a feature aggregation network based on feature energy local significance gating to obtain a channel state temporal aggregation representation vector, including: Determine the characteristic energy level coefficients of each channel state local time series implicitly associated characteristic vector based on the maximum value, minimum value, mean value and variance of each channel state local time series implicitly associated characteristic vector in the time series of the channel state local time series implicitly associated characteristic vector to obtain a sequence of channel state local time series characteristic energy level coefficients; Determining the scale of the local neighborhood of the channel state local temporal sequence, calculating the local neighborhood significance descriptor of each channel state local temporal sequence feature energy level coefficient in the channel state local temporal sequence feature energy level coefficient to obtain a sequence of channel state local temporal sequence feature energy level significance descriptors; Inputting the sequence of channel state local temporal feature energy level significance descriptors into a mask module based on a gating function to obtain a sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors; The sequence of the masked gated probabilistic channel state local temporal feature energy level significance descriptors is used as a sequence of weights, and the position-weighted sum of the time series of the channel state local temporal implicit association feature vectors is calculated to obtain the channel state temporal aggregate representation vector.

4. The medium voltage carrier communication fast synchronous transmission method according to claim 3, characterized in that: Based on the maximum value, minimum value, mean value and variance of each channel state local time series implicitly associated feature vector in the time series of the channel state local time series implicitly associated feature vector, determining the characteristic energy level coefficient of each channel state local time series implicitly associated feature vector to obtain a sequence of channel state local time series characteristic energy level coefficients, including: Calculating the maximum value, minimum value, average value and variance of the implicitly associated characteristic vector of the local time series of the channel state to obtain the local time series maximum value of the channel state, the local time series minimum value of the channel state, the local time series average value of the channel state and the local time series variance of the channel state; Calculating the sum of the local time series variance of the channel state and the hyperparameter to obtain a first channel state local time series local energy statistical factor; Calculating the difference between the maximum value of the local timing of the channel state and the average value of the local timing of the channel state to obtain the maximum deviation value of the local timing of the channel state; Calculating the difference between the local timing minimum value of the channel state and the local timing average value of the channel state to obtain the local timing minimum deviation value of the channel state; Calculate the square sum of the maximum deviation value of the local time series of the channel state and the minimum deviation value of the local time series of the channel state, and add the result to the super parameter to obtain a second channel state local time series local energy statistical factor; The division between the first channel state local time series local energy statistical factor and the second channel state local time series local energy statistical factor is calculated to obtain a channel state local time series characteristic energy level coefficient.

5. The medium voltage carrier communication fast synchronous transmission method according to claim 4, characterized in that: Determine the scale of the local neighborhood of the local temporal sequence of the channel state, calculate the local neighborhood significance descriptor of each channel state local temporal sequence feature energy level coefficient in the channel state local temporal sequence feature energy level coefficient to obtain a sequence of channel state local temporal sequence feature energy level significance descriptors, including: using the scale of the local neighborhood of the local temporal sequence of the channel state as the mean calculation range, respectively calculating the local neighborhood mean of each of the local temporal sequence feature energy level coefficients of the channel state to obtain a sequence of the channel state local temporal sequence feature energy level significance descriptors.

6. The medium voltage carrier communication fast synchronous transmission method according to claim 5, characterized in that: Inputting the sequence of the channel state local temporal feature energy level significance descriptors into a mask module based on a gating function to obtain a sequence of mask-gated probabilistic channel state local temporal feature energy level significance descriptors, comprising: Taking the negative number of each channel state local temporal feature energy level significance descriptor in the sequence of the channel state local temporal feature energy level significance descriptor as an exponential power, calculating the exponential function value with the natural constant e as the base to obtain a sequence of channel state local temporal feature energy level class support significance descriptors; Calculating the positional sum of the sequence of the channel state local temporal feature energy level class support significance descriptor and a constant 1 to obtain a sequence of linear modulation channel state local temporal feature energy level class support significance descriptor; Calculating the inverse of each linear modulation channel state local temporal sequence feature energy level class support significance descriptor in the sequence of linear modulation channel state local temporal sequence feature energy level class support significance descriptors to obtain a sequence of channel state local temporal sequence gated probabilistic feature energy level significance descriptors; Performing masking processing on each channel state local temporal gated probabilistic feature energy level significance descriptor in the sequence of the channel state local temporal gated probabilistic feature energy level significance descriptors to obtain the sequence of the masked gated probabilistic channel state local temporal feature energy level significance descriptors; Among them, performing masking processing on each channel state local temporal gated probabilistic feature energy level significance descriptor in the sequence of the channel state local temporal gated probabilistic feature energy level significance descriptors to obtain the sequence of the masked gated probabilistic channel state local temporal feature energy level significance descriptors includes: In response to the channel state local temporal gated probabilistic feature energy level significance descriptor being greater than a predetermined threshold, the original value of the channel state local temporal gated probabilistic feature energy level significance descriptor is taken, otherwise it is set to 0.

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