Power carrier communication acceleration method, system, device and storage medium

Abstract

The present application discloses a method, system, device and storage medium for accelerating power carrier communication, the method comprising: using a MIMO structure for multi-path communication transmission, and collecting multi-path channel state data in real time; using a machine learning algorithm to predict the multi-path channel state data of the next time period based on the collected multi-path channel state data; performing a state evaluation on each path channel state data of the predicted next time period according to a preset state evaluation index, obtaining a corresponding channel state evaluation value and determining a corresponding path transmission optimization strategy; adjusting the corresponding path transmission mode of the next time period according to the determined transmission optimization strategy for each path, and completing the multi-path communication transmission of the next time period with the adjusted corresponding path transmission mode. The present application can improve the communication rate and stability of power carrier communication in long-distance transmission, and ensure that no high acceleration cost is consumed.

Description

Technical Field

[0001] The present application relates to the technical field of power line carrier communication, and in particular to a power line carrier communication acceleration method, system, device and storage medium. Background Art

[0002] Power line carrier communication (PLC), a communication method that uses power lines to transmit data, has been widely used in fields such as smart grids and home automation. It offers wide coverage and eliminates the need for additional wiring, significantly simplifying the cost and complexity of network deployment. However, inherent characteristics of power lines, such as high noise levels, signal attenuation, and impedance variations, pose numerous challenges to PLC communication, particularly over long distances, where these challenges can significantly impact communication quality and speed.

[0003] Existing solutions typically include increasing transmit power and using multi-carrier modulation techniques to enhance signal strength and thus improve communication quality. Increasing transmit power can effectively increase signal strength, but in practice this can lead to higher energy consumption. Multi-carrier modulation technology, by using multiple subcarriers to transmit data, can increase communication rates to a certain extent, but it also increases equipment cost and signal processing complexity. Other conventional approaches, such as using more advanced filters and equalizers to reduce noise and distortion, also increase hardware complexity and cost.

[0004] While these methods can improve communication rates in some cases, they still struggle to meet the demands of high-speed communication over long distances, especially in complex environments. Therefore, achieving power carrier communication acceleration while balancing the cost of communication acceleration with the stability and reliability of communication is an urgent issue that needs to be addressed. Summary of the Invention

[0005] In order to improve the communication rate and stability of power carrier communication in long-distance transmission without incurring high acceleration costs, the present application provides a power carrier communication acceleration method, system, device and storage medium.

[0006] In a first aspect, the present application provides a method for accelerating power line carrier communication, comprising:

[0007] Adopt MIMO structure for multi-path communication transmission and collect multi-path channel status data in real time;

[0008] Based on the collected multipath channel state data, a machine learning algorithm is used to predict the multipath channel state data for the next period;

[0009] According to the preset state evaluation indicators, the state evaluation of each path channel state data of the next predicted time period is performed respectively, and the corresponding channel state evaluation value is obtained and the corresponding path transmission optimization strategy is determined; the preset state evaluation indicators include: noise level and interference degree; the path transmission optimization strategy includes: for the path where the channel state evaluation value shows a slight decrease or continues to fluctuate unstably for the first time period, the transmission path compensation optimization strategy is selected; for the path where the channel state evaluation value shows a serious decrease or continues to fluctuate unstably, the switching optimization strategy of other paths is selected; for the path where the channel state evaluation value shows a moderate decrease or continues to fluctuate unstably for the second time period, a comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails is selected; the channel state evaluation value decrease range is set for slight decrease, moderate decrease and severe decrease; the unstable fluctuation of the channel state evaluation value means that the rate of change of the channel state evaluation value is greater than the preset rate of change; the second time period is greater than the first time period;

[0010] The transmission mode of the corresponding path in the next period is adjusted according to the determined transmission optimization strategy for each path, and the multi-path communication transmission in the next period is completed with the adjusted transmission mode of the corresponding path.

[0011] By adopting the above scheme, the MIMO structure is used for multi-path communication transmission, which improves the signal reliability and communication robustness; the machine learning algorithm is used to predict and evaluate the state data of each path in the next period, and the path optimization strategy of compensating or switching to other paths is selected according to the evaluation results. This not only responds to possible channel state changes in advance, improves the communication rate and stability of power carrier communication in long-distance transmission, but also avoids blindly adopting measures such as improving transmission efficiency to reduce unnecessary losses.

[0012] Preferably, spectrum sensing technology is used to collect frequency band information allocated to each path in real time;

[0013] Performing frequency band status analysis on the frequency band information collected corresponding to each path according to preset frequency band status evaluation indicators to obtain a frequency band status analysis evaluation value; the preset frequency band status evaluation indicators include: frequency band occupancy rate and interference level;

[0014] Based on the frequency band status evaluation values ​​obtained in real time, a machine learning algorithm is used to predict the frequency band status evaluation value of each path in the next period;

[0015] Based on the predicted frequency band status evaluation value of each path in the next time period, the frequency band resources used by each path in the next time period are adjusted according to the spectrum adjustment optimization strategy; the spectrum adjustment optimization strategy includes: giving priority to selecting the frequency band with the highest status evaluation value and greater than the preset status evaluation value and switching to an unoccupied frequency band after determining that there is no predicted status evaluation value greater than the preset status evaluation value in the originally allocated frequency band.

[0016] By adopting the above solution, spectrum sensing technology is used to collect the frequency band resources corresponding to each path in real time and perform frequency band status evaluation. Machine learning algorithms are used to predict the frequency band status evaluation value of each path in the next time period. Spectrum adjustment optimization strategies are used to pre-adjust the frequency band resources used in the next time period, effectively improving the utilization rate of frequency band resources and the flexibility of communication.

[0017] Preferably, it also includes:

[0018] After determining the frequency band resources used by each path in the next time period, before adjusting the frequency band resources used by each path in the next time period, a frequency hopping pattern is designed within the determined frequency band for each path; the frequency hopping pattern defines a sequence of frequency hopping during communication using the current path; wherein the designed frequency hopping pattern is dynamically updated according to the frequency band determined for each time period; during communication using the adjusted frequency band resources used by each path in the next time period, the receiving end and the transmitting end change the communication frequency according to the designed frequency hopping pattern.

[0019] By adopting the above scheme, the frequency hopping pattern is dynamically designed within a certain frequency band, so that the frequency hopping sequence during the communication process can be flexibly adjusted according to the frequency band characteristics of each time period, better coping with sudden interference and frequency resource changes, thereby improving communication speed.

[0020] Preferably, it also includes:

[0021] For each path, collect the transmission performance evaluation results of relay nodes selected during historical communications under the same frequency band as the communication frequency band used by the path corresponding to the next period. Each path deploys several relay nodes, and the relay node transmission performance evaluation results are determined based on historical transmission success rates and delay times.

[0022] Statistically determine the relay node with the best evaluation result among all the relay node transmission performance evaluation results collected during the historical communication process, and use the determined relay node as the relay node that matches the communication frequency band used by the corresponding path in the next period;

[0023] In the communication transmission process of the corresponding path using the adjusted frequency band resources used by each path in the next time period, a matching relay node is selected as the relay node selected in the communication process of the corresponding path.

[0024] By adopting the above scheme, considering that the selection of frequency band will directly affect the quality and stability of signal transmission, we can make full use of historical data and select relay nodes that are suitable for the current communication frequency band to further accelerate communication.

[0025] Preferably, it also includes:

[0026] The signal modulation mode and coding mode for communication transmission on the corresponding path in the next time period are determined based on the obtained frequency band characteristics of the frequency band resources used by the corresponding path in the next time period; wherein, for high frequency band characteristics, a high-order modulation mode, a Turbo code or an LDPC code coding mode is matched; for low frequency band characteristics, a low-order modulation mode and a CRC check coding mode are matched.

[0027] By adopting the above scheme, the signal modulation and coding methods are dynamically adjusted according to the characteristics of different frequency bands, so that the performance of the entire communication system in different environments is optimized, the flexibility and robustness of communication are improved, and thus the communication rate and stability of power carrier communication in long-distance transmission are improved.

[0028] Preferably, the frequency hopping pattern is designed by using a hybrid frequency hopping strategy to complete the frequency hopping sequence design.

[0029] By adopting the above scheme, a hybrid frequency hopping strategy is adopted to further increase the flexibility and security of communication, improve the anti-interference ability of communication, and thus improve the communication rate and stability of power carrier communication in long-distance transmission.

[0030] Preferably, it also includes:

[0031] The transmission duration of the multipath communication transmission completed in the next period using the adjusted multipath transmission mode is collected, and the user's satisfaction with the collected transmission duration is obtained. If the satisfaction is greater than the preset satisfaction, the length of the predicted period is shortened accordingly.

[0032] By adopting the above solution, the transmission path adjustment optimization is completed according to the predicted path state data. When it is determined that the optimized transmission path helps to accelerate communication, more frequent path optimization can be performed to accelerate communication with more efficient path optimization.

[0033] In a second aspect, the present application provides a power line carrier communication acceleration system, comprising:

[0034] A multipath channel state data acquisition module is used to use a MIMO structure for multipath communication transmission and collect multipath channel state data in real time;

[0035] A multipath channel state data prediction module is used to predict the multipath channel state data of the next period using a machine learning algorithm based on the collected multipath channel state data;

[0036] A multi-path transmission optimization strategy acquisition module is used to perform state evaluation on the channel state data of each path predicted for the next time period according to preset state evaluation indicators, obtain corresponding channel state evaluation values ​​and determine corresponding path transmission optimization strategies; the preset state evaluation indicators include: noise level and interference degree; the path transmission optimization strategies include: for paths where the channel state evaluation values ​​show a slight decrease or continuous unstable fluctuations for the first time period, selecting a transmission path compensation optimization strategy; for paths where the channel state evaluation values ​​show a severe decrease or continuous unstable fluctuations, selecting an optimization strategy of switching to other paths; for paths where the channel state evaluation values ​​show a moderate decrease or continuous unstable fluctuations for the second time period, selecting a comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails; a channel state evaluation value decrease range is set for slight decreases, moderate decreases and severe decreases in the channel state evaluation values; unstable fluctuations in the channel state evaluation values ​​refer to a change rate of the channel state evaluation values ​​being greater than a preset change rate; the second time period is greater than the first time period;

[0037] The multi-path transmission optimization strategy execution module is used to adjust the corresponding path transmission mode of the next period according to each determined path transmission optimization strategy, and complete the multi-path communication transmission of the next period with the adjusted corresponding path transmission mode.

[0038] By adopting the above scheme, a machine learning algorithm is used to predict the status data of each path in the next period, the predicted multi-path status data is evaluated, the appropriate multi-path transmission optimization strategy is determined, and the multi-path transmission mode for the next period is pre-adjusted, so as to maintain stable high-speed data transmission in a complex power line environment.

[0039] In a third aspect, the present application provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the method as described above.

[0040] In a fourth aspect, the present application provides a computer device, which includes a memory, a processor, and a program stored and executable on the memory, and the program implements the steps of the above method when executed by the processor.

[0041] In summary, this application has the following beneficial effects:

[0042] 1. By adopting a MIMO structure for multipath communication transmission and collecting multipath channel status data in real time, a machine learning algorithm is used to predict the status data of each path in the next period. The multipath transmission optimization strategy is determined based on the preset status evaluation index and the multipath transmission mode for the next period is pre-adjusted. This improves the rate and stability of long-distance power carrier communication, solving the problem of low communication rate during long-distance transmission in existing technologies.

[0043] 2. Spectrum sensing technology is introduced to collect frequency band information of each path in real time. The frequency band status is analyzed according to the preset frequency band status evaluation criteria. The machine learning algorithm is used to predict the frequency band status of the next period and determine the spectrum adjustment optimization strategy. This improves the system's anti-interference capability and communication robustness, ensures the continuity and reliability of communication, and thus improves the rate and stability of long-distance power carrier communication.

[0044] 3. By designing a dynamically updated frequency-hopping pattern and adjusting the communication frequency according to the determined frequency band during the communication process, the reliability and anti-interference capability of signal transmission are further improved, the data packet loss rate is reduced, and the speed and stability of long-distance power carrier communication are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the power line carrier communication acceleration method described in a specific embodiment;

[0046] Figure 2 It is a structural diagram of the power carrier communication acceleration system described in a specific embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] To address the problem of low communication rates in power line carrier communication over long distances, making it difficult to meet the demands of high-speed data transmission, this application primarily employs the following solutions: multipath transmission strategy and MIMO architecture, spectrum sensing technology and dynamic frequency allocation mechanism, relay node deployment, adaptive modulation and demodulation technology, and error control coding technology. This approach improves the communication rate of long-distance power line carrier communication, maintaining stable and high-speed communication even in complex power line environments. The following further describes this application in detail.

[0049] Example 1

[0050] like Figure 1 As shown, the embodiment of the present application discloses a method for accelerating power carrier communication, the specific steps of which include:

[0051] S1. Use MIMO structure for multi-path communication transmission and collect multi-path channel status data in real time.

[0052] To improve the speed and stability of long-distance power carrier communications, a MIMO structure is chosen for multi-path communication transmission. Specifically, multiple antennas are configured at the transmitting end, with each antenna used to independently transmit signals. The signal generated by the modulation and encoding of the original transmission data is modified in frequency, phase, and amplitude, or specific redundant information is added to enhance the signal's anti-interference ability to generate multiple signals. The generated multiple signals are each transmitted using an antenna. The receiving end is also configured with multiple antennas, each used to capture signals transmitted along different paths. The received signals are synchronously processed, weightedly combined using maximum ratio combining technology, and further demodulated and decoded to obtain the original transmission data to complete the power carrier communication.

[0053] Throughout the power carrier communication process, data acquisition devices, such as sensors and data acquisition cards, are used to collect real-time channel status data from multiple paths. This channel status data includes the signal-to-noise ratio (SNR), signal-to-interference-plus-noise ratio (SINR), and measured signal strength, which characterize channel quality. The noise level is determined by comparing the SNR with a preset SNR threshold range, while the interference level is determined by comparing the SINR with a preset SNR range.

[0054] S2. Based on the collected multipath channel state data, a machine learning algorithm is used to predict the multipath channel state data for the next time period.

[0055] Specifically, the collected channel state data of each path is input into the constructed first deep learning neural network model respectively to predict the channel state data of the path in the next time period, including the channel state data corresponding to multiple time points in the next time period.

[0056] Among them, the length of the next time period can be set manually according to user needs; the input of the first deep learning neural network model constructed is the single path channel state data of the previous time period, and the output of the model is the corresponding path channel state data of the next time period, which is generated through training of several historical single path channel state data.

[0057] S3. Perform state evaluation on each path channel state data of the predicted next time period according to a preset state evaluation index, and obtain a corresponding channel state evaluation value.

[0058] Specifically, the preset status evaluation indicators include: noise level and interference degree, etc.

[0059] Among them, each status evaluation indicator has a preset evaluation score value matching its status. For example, the noise level is determined according to the signal-to-noise ratio and the preset signal-to-noise ratio threshold range, including: weak, relatively weak, general, severe, and very severe, and the corresponding matching scores are: 90, 80, 70, 50, 30, etc. Similarly, the interference degree is determined by the signal-to-interference-plus-noise ratio and the preset signal-to-interference-plus-noise ratio range, including: weak, relatively weak, general, severe, and very severe, and the corresponding matching scores are: 90, 80, 70, 50, 30, etc.

[0060] A weight can be set for each state evaluation indicator, and the weighted channel state evaluation value of each path in the next period of time is obtained.

[0061] S4. Determine a corresponding path transmission optimization strategy based on the acquired channel state evaluation value.

[0062] Specifically, since the predicted channel state evaluation value of each path in the next time period is a state evaluation corresponding to multiple time points, the corresponding path transmission optimization strategy can be set according to the changes in the obtained channel state evaluation values, such as: only a slight decline occurs during the entire time period (the maximum difference between the corresponding path state evaluation values ​​during the entire time period is also not higher than the first preset channel state evaluation value decline threshold); or a moderate decline occurs during the entire time period (the difference between the corresponding path state evaluation values ​​during the entire time period is greater than the second preset channel state evaluation value decline threshold and less than the third preset channel state evaluation value decline threshold) or a serious decline occurs during the entire time period (the difference between the corresponding path state evaluation values ​​during the entire time period is greater than the third preset channel state evaluation value decline threshold).

[0063] Alternatively, if the channel state evaluation value fluctuates unstably for the first period, fluctuates unstably for the second period, or fluctuates unstably for the entire period, a corresponding path transmission optimization strategy will be set. Unstable fluctuation of the channel state evaluation value means that the rate of change of the channel state evaluation value (the rate of change between the channel state evaluation values ​​corresponding to two adjacent time points) is greater than a preset rate of change; the rate of change is greater during the second period than during the first period.

[0064] To avoid blindly adopting high energy consumption such as increased transmit power or more complex modulation technologies that increase costs, the corresponding path transmission strategy can be matched according to the actual channel status, including:

[0065] For paths where the channel state evaluation value shows a slight decline or continues to fluctuate unstably for the first period, it indicates that the channel state of the current path is good and there is a little noise and interference. Some compensation with low energy consumption can be performed to optimize the current path selection, and a transmission path compensation optimization strategy is adopted; for paths where the channel state evaluation value shows a serious decline or continues to fluctuate unstably, it indicates that the channel state of the current path is poor and there is large noise and interference. To avoid compensation with high energy consumption, it is possible to quickly switch to other available paths with better conditions, that is, to adopt the switching to other paths optimization strategy; for paths where the channel state evaluation shows a moderate decline or continues to fluctuate unstably for the second period, it indicates that the channel state of the current path is average and there is a certain amount of noise and interference. A comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails can be selected. Prioritize the compensation path before actual application in the next period. During actual application, if the sensor monitors that the channel state in the current period is still in an average state, it can be considered to switch to other paths for data transmission in the current period.

[0066] S5. Adjust the corresponding path transmission mode for the next period according to the determined transmission optimization strategy for each path, and complete the multi-path communication transmission for the next period with the adjusted corresponding path transmission mode.

[0067] Specifically, the channel status of the predicted next time period is evaluated through the above-mentioned paths and the corresponding optimization strategy is matched, and the signal transmission mode of the corresponding path in the next time period is adjusted accordingly, such as compensating the channel of the corresponding path, continuing the signal transmission in the next time period with the path after channel status compensation, or switching the signal propagated by the corresponding path to another available and good path for signal transmission in the next time period, and continuing to complete the communication transmission in the next time period.

[0068] In addition, considering the feedback results of communication transmission, the effect of the current accelerated communication can be further determined based on the feedback results. If the effect is good, the frequency of prediction can be further increased to further improve communication efficiency. Specifically, it includes: collecting the transmission time of the multi-path communication transmission in the next period of time using the adjusted multi-path transmission method, obtaining the user's satisfaction with the collected transmission time, and if the satisfaction is greater than the preset satisfaction, it indicates that the current transmission time effect meets the user's needs. The length of the prediction period can be shortened accordingly, and more frequent predictions can be made. Of course, considering that the computing resources have a preset minimum duration threshold for the prediction period, the corresponding shortening of the prediction period must not be lower than the minimum duration threshold.

[0069] In summary, this embodiment significantly improves the rate and stability of long-distance power line carrier communication based on a series of measures such as multi-path communication transmission, channel state prediction, state evaluation, and optimization strategy generation.

[0070] Example 2

[0071] The difference from Example 1 is that this embodiment adds spectrum sensing technology to the multipath channel state data prediction module, collects the allocated frequency band information of each path in real time, and adjusts and optimizes the spectrum according to the frequency band status. Specifically, the method described in this embodiment also includes:

[0072] Spectrum sensing technology is used to collect real-time information about the frequency bands allocated to each path. This is accomplished using common spectrum scanning units, including spectrum analyzers and software-defined radios (SDRs). The spectrum analyzer directly measures the occupancy of the frequency bands allocated to each path, while the SDR flexibly scans and analyzes the spectrum through software programming.

[0073] In order to further improve the anti-interference capability and communication efficiency of power carrier communication, the frequency band information collected corresponding to each path is analyzed according to the preset frequency band status evaluation index to obtain the frequency band status analysis evaluation value.

[0074] Among them, the preset frequency band status evaluation indicators include: frequency band occupancy and interference level, etc.; specifically, for the frequency band information collected corresponding to each path, such as: the current path allocates two frequency bands F1 and F2, the frequency band occupancy collected in the current period of F1 and F2 will be compared with the preset occupancy range, and the score value within the corresponding preset occupancy range will be matched according to the comparison result; and the external interference SINR collected during the communication process using the corresponding F1 and F2 frequency bands will be compared with the preset interference plus noise ratio range, and the score value within the corresponding preset interference plus noise ratio range will be matched according to the comparison result, and then all the preset frequency band status evaluation indicators corresponding to the obtained score values ​​are weightedly calculated, and finally the frequency band status value of each path is obtained, such as: the status values ​​of the current path allocating and using the two frequency bands F1 and F2 are 65 and 85.

[0075] Based on the frequency band status evaluation values ​​obtained in real time, a machine learning algorithm is used to predict the frequency band status evaluation values ​​for each path in the next time period. Specifically, the frequency band status analysis values ​​corresponding to each path obtained in the historical and current time periods are input into the constructed second deep learning neural network model to predict the frequency band status evaluation values ​​for each path in the next time period, including the frequency band status values ​​for each frequency band assigned to each path in the next time period. The second deep learning neural network is trained and generated using the status evaluation values ​​of individual frequency bands assigned to individual paths in the historical time period. For example, the predicted status evaluation values ​​for the two frequency bands F1 and F2 assigned to the current path in the next time period are 58 and 86.

[0076] Based on the predicted frequency band status evaluation value of each path in the next time period, the frequency band resources used by each path in the next time period are determined and adjusted according to the spectrum adjustment optimization strategy, and communication transmission in the next time period is completed using the adjusted frequency band resources used by each path in the next time period. The spectrum adjustment optimization strategy includes: preferentially selecting a frequency band with the highest status evaluation value that is greater than a preset status evaluation value, and switching to an unoccupied frequency band after determining that no originally allocated frequency band has a predicted status evaluation value greater than the preset status evaluation value. For example, if the predicted status values ​​of frequency bands F1 and F2 allocated and used by the current path in the next time period are 58 and 86, frequency band F2 is preferentially used as the carrier frequency band for communication transmission in the next time period. If the predicted status values ​​of the next time period are 58 and 55, respectively, and no frequency band of the original frequency bands F1 and F2 allocated to the current path has a status that meets the requirement (frequency band status value greater than the preset frequency band status value of 60), all available frequency bands are scanned accordingly, idle frequency resources are identified, and switching is performed to the unoccupied frequency band F5 to continue communication transmission.

[0077] In addition, considering that some users have high requirements for the real-time performance of communications and that the available frequency band resources are relatively abundant (for example, the number of available frequency bands is greater than the number of preset frequency bands), before determining and adjusting the frequency band resources used by each path in the next time period according to the spectrum adjustment optimization strategy, the spectrum adjustment optimization strategy corresponding to each path is determined in combination with the real-time performance requirements of communications received from users in advance; the spectrum adjustment optimization strategy includes: directly switching to an unoccupied frequency band under high real-time requirements, giving priority to selecting a frequency band with the highest status evaluation value and greater than a preset status evaluation value under non-high real-time requirements, and switching to an unoccupied frequency band after determining that there is no predicted status evaluation value greater than the preset status evaluation value in the originally allocated frequency band.

[0078] By using the method described in this embodiment, spectrum sensing technology can be added to monitor the frequency band information of each path in real time, evaluate the frequency band status of each path, determine the optimal spectrum adjustment strategy, and optimize spectrum adjustment through spectrum sensing technology and spectrum adjustment, thereby further improving the anti-interference capability and communication efficiency of power carrier communication.

[0079] Example 3

[0080] The difference between this embodiment and the above-mentioned embodiment 2 is that this embodiment introduces frequency hopping pattern design based on spectrum sensing technology to further improve the security and reliability of communication. The method further includes:

[0081] After determining the frequency band resources used by each path in the next time period, before adjusting the frequency band resources used by each path in the next time period, a frequency hopping pattern is designed within the determined frequency band for each path; the frequency hopping pattern defines the sequence of frequency hopping during communication using the current path.

[0082] The frequency hopping pattern is designed by adopting a hybrid frequency hopping strategy to complete the frequency hopping sequence design, that is, the frequency hopping sequence design is completed by a frequency modulation strategy that combines regular and random frequency modulation; for example: after the aforementioned steps, it is predicted that the corresponding frequency band to be used in the next time period is F3. Within the F3 frequency band, a frequency hopping pattern containing 10 frequencies is designed: F3-1, F3-2, ... F3-10. The frequency modulation sequence is to jump in the order of F3-1, F3-2, ... F3-10 in a certain period of time, and to randomly select F3-1, F3-2, ... F3-10 for hopping after a certain period of time.

[0083] In addition, since the frequency band resources used in different predicted time periods in each path are different, the designed frequency hopping pattern is also dynamically updated based on the frequency band determined in each time period. For example, if the next time period of the current path is the F3 frequency band, the corresponding designs are F3-1, F3-2,... F3-10. If the next time period after the next time period of the current path is the F5 frequency band, the corresponding designs are F5-1, F5-2,... F5-8. The specific number of pattern sequences designed for each frequency band can be determined according to the size of the frequency band.

[0084] During communication using the adjusted frequency band resources for each path in the next time period, the receiver and transmitter change the communication frequency according to the designed frequency hopping pattern. For example, at the beginning of communication, the sender and receiver establish a connection on the F3-1 frequency, and then quickly switch between different frequencies within the F3 band according to the frequency hopping pattern.

[0085] By utilizing the method described in this embodiment, a frequency hopping pattern design is introduced to dynamically adjust the frequency hopping sequence during the communication process, thereby improving the security and reliability of communication.

[0086] Example 4

[0087] The difference between this embodiment and the above-mentioned embodiment 2 is that this embodiment introduces relay node selection optimization to further improve the stability and efficiency of communication. The method further includes:

[0088] For each path, the transmission performance evaluation results of relay nodes selected during historical communications using the same frequency band as the corresponding path in the next period are collected. Each path deploys several relay nodes, and the relay node transmission performance evaluation results are weighted based on historical transmission success rates and latency assessments to determine the final evaluation value. Different transmission success rate ranges correspond to different indicator scores, and different latency ranges correspond to different indicator scores. For example, for a single path transmitting in the F1 frequency band, relay nodes A, B, and C would have performance evaluation results of 90, 75, and 86, respectively.

[0089] The relay node with the best transmission performance evaluation result from all relay nodes selected during historical communication processes is statistically determined. This relay node is then used as the relay node that matches the communication frequency band used by the corresponding path in the next period. For example, if the statistically best relay node is A, then the corresponding relay node for communication transmission on the F1 frequency band for the corresponding single path is A.

[0090] In the communication transmission process of the corresponding path using the adjusted frequency band resources used by each path in the next time period, a matching relay node is selected as the relay node selected in the communication process of the corresponding path.

[0091] In addition, in order to further avoid the overload of the relay node matched in the next time period, before selecting the matched relay node as the relay node selected in the corresponding path communication process, the load rate of the currently matched relay node is monitored. If the load rate is greater than the preset load rate, the currently matched relay node is ignored from the statistical results and the relay node is matched again.

[0092] By utilizing the method described in this embodiment and introducing relay node selection optimization, the optimal relay node can be dynamically selected during the communication process, thereby improving the stability and efficiency of communication.

[0093] Example 5

[0094] The difference between this embodiment and the above embodiment is that: based on the optimization of communication frequency band selection, this embodiment introduces dynamic adjustment of signal modulation and coding methods to further improve the quality and efficiency of communication. The method also includes:

[0095] The signal modulation mode and coding mode for communication transmission on the corresponding path in the next time period are determined based on the obtained frequency band characteristics of the frequency band resources used by the corresponding path in the next time period; wherein, for the high frequency band characteristics, the corresponding matching high-order modulation mode, Turbo code or LDPC code coding mode; for the low frequency band characteristics, the corresponding matching low-order modulation mode, CRC check coding mode; wherein, the high frequency band and the low frequency band are distinguished and determined by comparing with a preset frequency band threshold, and if it is greater than the preset frequency band threshold, it is a high frequency band, otherwise it is a low frequency band.

[0096] By utilizing the method described in this embodiment, the quality and efficiency of power line carrier communication are further improved through dynamic adjustment of the signal modulation mode and the coding mode.

[0097] like Figure 2 As shown, the embodiment of the present application provides a power carrier communication acceleration system, specifically including:

[0098] A multipath channel state data acquisition module 101 is configured to acquire multipath channel state data in real time using a MIMO structure for multipath communication transmission;

[0099] The multipath channel state data prediction module 102 is configured to predict the multipath channel state data of the next time period using a machine learning algorithm based on the collected multipath channel state data;

[0100] The multi-path transmission optimization strategy acquisition module 103 is used to perform state evaluation on the channel state data of each path predicted for the next time period according to preset state evaluation indicators, obtain corresponding channel state evaluation values ​​and determine corresponding path transmission optimization strategies; the preset state evaluation indicators include: noise level and interference degree; the path transmission strategies include: for paths where the channel state evaluation value shows a slight decrease or continues to fluctuate unstably for the first time period, selecting a transmission path compensation optimization strategy; for paths where the channel state evaluation value shows a severe decrease or continues to fluctuate unstably, selecting a switching to other paths optimization strategy; for paths where the channel state evaluation value shows a moderate decrease or continues to fluctuate unstably for the second time period, selecting a comprehensive optimization strategy of prioritizing compensation and switching to other paths if compensation fails; a channel state evaluation value decrease range is set for each of a slight decrease, a moderate decrease and a severe decrease in the channel state evaluation value; unstable fluctuation of the channel state evaluation value means that the rate of change of the channel state evaluation value is greater than the preset rate of change; the second time period is greater than the first time period;

[0101] The multi-path transmission optimization strategy execution module 104 is configured to adjust the corresponding path transmission mode of the next period according to each determined path transmission optimization strategy, and complete the multi-path communication transmission of the next period with the adjusted corresponding path transmission mode.

[0102] The system further comprises:

[0103] The multi-path frequency band optimization strategy module 105 is used to use spectrum sensing technology to collect frequency band information allocated to each path in real time; perform frequency band status analysis on the frequency band information collected corresponding to each path according to preset frequency band status evaluation indicators to obtain frequency band status analysis evaluation values; the preset frequency band status evaluation indicators include: frequency band occupancy rate and interference level; based on the frequency band status evaluation values ​​obtained in real time, use a machine learning algorithm to predict the frequency band status evaluation value of each path in the next time period; based on the predicted frequency band status evaluation value of each path in the next time period, determine and adjust the frequency band resources used by each path in the next time period according to the spectrum adjustment optimization strategy; the spectrum adjustment optimization strategy includes: giving priority to selecting the frequency band with the highest status evaluation value that is greater than the preset status evaluation value and switching to an unoccupied frequency band after determining that there is no predicted status evaluation value greater than the preset status evaluation value in the originally allocated frequency band.

[0104] The multipath transmission optimization feedback module 106 is used to collect the transmission duration of the multipath communication transmission completed in the next time period using the adjusted multipath transmission mode, obtain the user's satisfaction with the collected transmission duration, and shorten the length of the predicted time period accordingly if the satisfaction is greater than a preset satisfaction.

[0105] In a specific embodiment, the multi-path frequency band optimization strategy module 105 is further used to design a frequency hopping pattern within the determined frequency band for each path after determining the frequency band resources used by each path in the next time period and before adjusting the frequency band resources used by each path in the next time period; the frequency hopping pattern defines a sequence of frequency hopping during communication using the current path; wherein the designed frequency hopping pattern is dynamically updated according to the frequency band determined in each time period; during communication using the adjusted frequency band resources used by each path in the next time period, the receiving end and the transmitting end change the communication frequency according to the designed frequency hopping pattern.

[0106] In a specific embodiment, the multi-path frequency band optimization strategy module 105 is also used to collect, for each path, the transmission performance evaluation results of the relay nodes selected in the historical communication process under the same frequency band conditions as the communication frequency band used by the path corresponding to the next time period; wherein, a number of relay nodes are deployed on each path, and the transmission performance evaluation results of the relay nodes are determined based on the historical transmission success rate and delay time evaluation; the relay node with the best evaluation result among all the collected transmission performance evaluation results of the relay nodes selected in the historical communication process is statistically determined, and the determined relay node is used as the relay node that matches the communication frequency band used by the path corresponding to the next time period; in the communication transmission process of the corresponding path using the adjusted frequency band resources used by each path in the next time period, the matching relay node is selected as the relay node selected in the communication process of the corresponding path.

[0107] In a specific embodiment, the multi-path frequency band optimization strategy module 105 is also used to determine the signal modulation mode and coding mode for communication transmission on the corresponding path in the next time period based on the obtained frequency band characteristics of the frequency band resources used by the corresponding path in the next time period; wherein, for high frequency band characteristics, the corresponding matching is a high-order modulation mode, a Turbo code or a LDPC code coding mode; for low frequency band characteristics, the corresponding matching is a low-order modulation mode, a CRC check coding mode.

[0108] The embodiment of the present application also discloses a computer-readable storage medium.

[0109] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed as the above-mentioned power carrier communication acceleration method. The computer-readable storage medium includes, for example: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0110] The embodiment of the present application also discloses a computer device.

[0111] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned power carrier communication acceleration method.

[0112] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise stated, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A method for accelerating power carrier communication, characterized in that: include: The MIMO structure is used for multi-path communication transmission, and multi-path channel status data is collected in real time; the method includes: configuring multiple antennas at the transmitting end, each antenna is used to independently transmit signals; the frequency, phase, amplitude of the signal generated by the modulation and coding of the original transmission data is changed, or specific redundant information is added to enhance the anti-interference ability of the signal to generate multiple signals; the multiple generated signals are transmitted using one antenna respectively; the receiving end is also configured with multiple antennas, each antenna is used to capture signals transmitted by different paths; the received signals are synchronously processed, the received signals are weightedly combined using the maximum ratio combining technology, the combined signals are further demodulated and decoded, and the original transmission data is obtained to complete the power carrier communication; Based on the collected multipath channel state data, a machine learning algorithm is used to predict the multipath channel state data for the next time period; the method includes: inputting the collected channel state data for each path into a constructed first deep learning neural network model, and predicting the channel state data for the path for the next time period, including the channel state data corresponding to multiple time points within the next time period; wherein the length of the next time period can be manually set according to user needs; the input of the constructed first deep learning neural network model is the channel state data of a single path in the previous time period, and the output of the model is the channel state data of the path corresponding to the next time period, which is generated by training with a plurality of historical single path channel state data; According to the preset state evaluation indicators, the state evaluation of each path channel state data of the next time period is performed respectively, and the corresponding channel state evaluation value is obtained and the corresponding path transmission optimization strategy is determined; the preset state evaluation indicators include: noise level and interference degree; the path transmission optimization strategy includes: for the path where the channel state evaluation value shows a slight decrease or continues to fluctuate unstably for the first time period, the transmission path compensation optimization strategy is selected; for the path where the channel state evaluation value shows a serious decrease or continues to fluctuate unstably, the switching to other paths optimization strategy is selected; for the path where the channel state evaluation shows a moderate decrease or continues to fluctuate unstably for the second time period, the comprehensive optimization strategy of prioritizing the transmission path compensation optimization strategy and adopting the switching to other paths optimization strategy after compensation fails is selected; the channel state evaluation value decrease range is set for slight decrease, moderate decrease and severe decrease of the channel state evaluation value; the unstable fluctuation of the channel state evaluation value means that the rate of change of the channel state evaluation value is greater than the preset rate of change; the second time period is greater than the first time period; The transmission mode of the corresponding path in the next period is adjusted according to the determined transmission optimization strategy for each path, and the multi-path communication transmission in the next period is completed with the adjusted transmission mode of the corresponding path.

2. The power carrier communication acceleration method according to claim 1, characterized in that: Also includes: Use spectrum sensing technology to collect frequency band information allocated to each path in real time; Perform frequency band status analysis on the frequency band information collected corresponding to each path according to the preset frequency band status evaluation index to obtain a frequency band status analysis evaluation value; The preset frequency band status evaluation indicators include: frequency band occupancy rate and interference level; Based on the frequency band status evaluation values ​​obtained in real time, a machine learning algorithm is used to predict the frequency band status evaluation value of each path in the next period; Based on the predicted frequency band status evaluation value of each path in the next time period, the frequency band resources used by each path in the next time period are determined and adjusted according to the spectrum adjustment optimization strategy; the spectrum adjustment optimization strategy includes: giving priority to selecting the frequency band with the highest status evaluation value and greater than the preset status evaluation value and switching to an unoccupied frequency band after determining that there is no predicted status evaluation value greater than the preset status evaluation value in the originally allocated frequency band.

3. The power carrier communication acceleration method according to claim 2, characterized in that: Also includes: After determining the frequency band resources to be used by each path in the next time period, and before adjusting the frequency band resources to be used by each path in the next time period, a frequency hopping pattern is designed within the determined frequency band for each path; the frequency hopping pattern defines a sequence of frequency hopping during communication using the current path; the designed frequency hopping pattern is dynamically updated based on the frequency band determined for each time period; During the communication process using the adjusted frequency band resources used by each path in the next time period, the receiving end and the transmitting end change the communication frequency according to the designed frequency hopping pattern.

4. The power carrier communication acceleration method according to claim 2, characterized in that: Also includes: For each path, collect the transmission performance evaluation results of relay nodes selected during historical communications under the same frequency band as the communication frequency band used by the path corresponding to the next period. Each path deploys several relay nodes, and the relay node transmission performance evaluation results are determined based on historical transmission success rates and delay times. Statistically determine the relay node with the best evaluation result among all the relay node transmission performance evaluation results collected during the historical communication process, and use the determined relay node as the relay node that matches the communication frequency band used by the corresponding path in the next period; In the communication transmission process of the corresponding path using the adjusted frequency band resources used by each path in the next time period, a matching relay node is selected as the relay node selected in the communication process of the corresponding path.

5. The power carrier communication acceleration method according to claim 2, characterized in that: Also includes: The signal modulation mode and coding mode for communication transmission on the corresponding path in the next time period are determined based on the obtained frequency band characteristics of the frequency band resources used by the corresponding path in the next time period; wherein, for high frequency band characteristics, a high-order modulation mode, a Turbo code or an LDPC code coding mode is matched; for low frequency band characteristics, a low-order modulation mode and a CRC check coding mode are matched.

6. The power carrier communication acceleration method according to claim 3, characterized in that: The frequency hopping pattern is designed by using a hybrid frequency hopping strategy to complete the sequence design of frequency hopping.

7. The power carrier communication acceleration method according to claim 1, characterized in that: Also includes: The transmission duration of the multipath communication transmission completed in the next period using the adjusted multipath transmission mode is collected, and the user's satisfaction with the collected transmission duration is obtained. If the satisfaction is greater than the preset satisfaction, the length of the predicted period is shortened accordingly.

8. A power carrier communication acceleration system, characterized in that: include: The multipath channel state data acquisition module is used to adopt the MIMO structure for multipath communication transmission and collect multipath channel state data in real time; it includes: configuring multiple antennas at the transmitting end, each antenna is used to independently transmit signals; for the signal generated by the modulation and coding of the original transmission data, the frequency, phase, amplitude of the signal is changed or specific redundant information is added to enhance the anti-interference ability of the signal to generate multiple signals; the multiple generated signals are transmitted using one antenna respectively; the receiving end is also configured with multiple antennas, each antenna is used to capture signals transmitted by different paths; the received signals are synchronously processed, the received signals are weightedly combined using the maximum ratio combining technology, the combined signals are further demodulated and decoded, and the original transmission data is obtained to complete the power carrier communication; The multi-path channel state data prediction module is used to predict the multi-path channel state data of the next time period using a machine learning algorithm based on the collected multi-path channel state data; including: inputting the collected channel state data of each path into the constructed first deep learning neural network model, predicting the channel state data of the path in the next time period, including the channel state data corresponding to multiple time points in the next time period; wherein the length of the next time period can be manually set according to user needs; the input of the constructed first deep learning neural network model is the single path channel state data of the previous time period, and the output of the model is the path channel state data corresponding to the next time period, which is generated by training with several historical single path channel state data A multi-path transmission optimization strategy acquisition module is used to perform state evaluation on the channel state data of each path predicted for the next time period according to preset state evaluation indicators, obtain corresponding channel state evaluation values ​​and determine corresponding path transmission optimization strategies; the preset state evaluation indicators include: noise level and interference degree; the path transmission optimization strategies include: for paths where the channel state evaluation values ​​show a slight decrease or continuous unstable fluctuations for the first time period, selecting a transmission path compensation optimization strategy; for paths where the channel state evaluation values ​​show a severe decrease or continuous unstable fluctuations, selecting a switching to other path optimization strategy; for paths where the channel state evaluation values ​​show a moderate decrease or continuous unstable fluctuations for the second time period, selecting a comprehensive optimization strategy of preferentially adopting the transmission path compensation optimization strategy and adopting the switching to other path optimization strategy after compensation fails; a channel state evaluation value decrease range is set for slight decreases, moderate decreases and severe decreases in the channel state evaluation values; unstable fluctuations in the channel state evaluation values ​​refer to a change rate of the channel state evaluation values ​​being greater than a preset change rate; the second time period is greater than the first time period; The multi-path transmission optimization strategy execution module is used to adjust the corresponding path transmission mode of the next period according to each determined path transmission optimization strategy, and complete the multi-path communication transmission of the next period with the adjusted corresponding path transmission mode.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device includes a memory, a processor, and a program stored and executable on the memory, and when the program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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