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

Abstract

The invention discloses a power line carrier communication acceleration method, system and device and a storage medium, and the method comprises the steps: carrying out the multipath communication transmission through employing an MIMO structure, and collecting the multipath channel state data in real time; according to the collected multi-path channel state data, utilizing a machine learning algorithm to predict multi-path channel state data of the next time period; according to a preset state evaluation index, state evaluation is carried out on the predicted channel state data of each path in the next time period, a channel state evaluation value is correspondingly obtained, and a corresponding path transmission optimization strategy is determined; and adjusting the corresponding path transmission mode of the next time period according to each determined path transmission optimization strategy, and completing multi-path communication transmission of the next time period in the adjusted corresponding path transmission mode. According to the invention, the communication rate and stability of power line carrier communication in long-distance transmission can be improved, and it is ensured that high acceleration cost does not need to be 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, as a communication method that uses power lines to transmit data, has been widely used in smart grids, home automation and other fields. It has the advantages of wide coverage and no need for additional wiring, which greatly simplifies the cost and complexity of network deployment. However, the inherent characteristics of power lines, such as high noise, signal attenuation and impedance changes, make power line carrier communication face many challenges, especially in long-distance transmission. These problems are particularly prominent and seriously affect the communication quality and rate.

[0003] Existing solutions usually include increasing the transmit power, using multi-carrier modulation technology, etc. to enhance signal strength and thus improve communication quality. Increasing the transmit power can effectively increase signal strength, but it will bring about higher energy consumption problems in practical applications. Multi-carrier modulation technology can increase the communication rate to a certain extent by using multiple subcarriers to transmit data, but it also increases the equipment cost and the complexity of signal processing. In addition, there are some other conventional means, such as using more advanced filters and equalizers to reduce noise and distortion, but this will also increase the complexity and cost of hardware.

[0004] Although the above methods can improve the communication rate in some cases, it is still difficult to meet the needs of high-speed communication in long-distance transmission, especially in complex situations. Therefore, how to comprehensively consider the communication acceleration cost and the stability and reliability of communication to achieve power carrier communication acceleration is an urgent problem to be solved. 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: Adopt MIMO structure for multi-path communication transmission and collect multi-path channel status data in real time; According to the collected multipath channel state data, a machine learning algorithm is used to predict the multipath channel state data of the next period; According to the preset state evaluation index, the state evaluation is performed on each path channel state data of the next predicted time period, and the corresponding channel state evaluation value is obtained and the corresponding path transmission optimization strategy is determined; the preset state evaluation index includes: 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 in 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 optimization strategy of switching to other paths is selected; for the path where the channel state evaluation shows a moderate decrease or continues to fluctuate unstably in the second time period, a comprehensive optimization strategy of priority compensation and switching to other paths when compensation fails is selected; the channel state evaluation value decrease range is set corresponding to the 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 change rate of the channel state evaluation value is greater than the preset change rate; 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.

[0007] By adopting the above scheme, the MIMO structure is used for multi-path communication transmission, which improves the reliability of the signal and the robustness of the communication; 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 other paths is selected according to the evaluation results. It can respond to possible channel state changes in advance, improve the communication rate and stability of power carrier communication in long-distance transmission, and avoid blindly adopting measures such as improving transmission efficiency to reduce unnecessary losses.

[0008] Preferably, spectrum sensing technology is used to collect frequency band information allocated to each path in real time; According to the preset frequency band status evaluation index, the frequency band information collected corresponding to each path is respectively analyzed to obtain the frequency band status analysis evaluation value; the preset frequency band status evaluation index includes: frequency band occupancy rate and interference degree; According to the frequency band status evaluation value 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; According to 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 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 original allocated frequency band.

[0009] 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 algorithm is used to predict the frequency band status evaluation value of each path in the next time period, and spectrum adjustment optimization strategy is used to pre-adjust the frequency band resources used in the next time period, which effectively improves the utilization rate of frequency band resources and the flexibility of communication.

[0010] Preferably, it also includes: 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 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.

[0011] By adopting the above scheme, the frequency hopping pattern is dynamically designed within a determined 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, so as to better cope with sudden interference and frequency resource changes, thereby improving the communication speed.

[0012] Preferably, it also includes: For each path, the transmission performance evaluation results of the relay nodes selected in the historical communication process under the same frequency band as the communication frequency band used by the path corresponding to the next period are collected; 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; Statistically determine the relay node with the best evaluation result among all the relay node transmission performance evaluation results selected in the historical communication process, and use the determined relay node as the relay node matching the communication frequency band used by the corresponding path in the next time period; In the process of performing communication transmission 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.

[0013] By adopting the above scheme, considering that the choice of frequency band will directly affect the quality and stability of signal transmission, full use is made of historical data, and relay nodes suitable for the current communication frequency band are selected to further accelerate communication.

[0014] Preferably, it also includes: The signal modulation mode and coding mode for communication transmission on the corresponding path in the next time period are determined according to 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 high-order modulation mode, Turbo code or LDPC code coding mode is matched; for the low frequency band characteristics, the low-order modulation mode and CRC check coding mode are matched.

[0015] 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.

[0016] Preferably, the frequency hopping pattern is designed by using a hybrid frequency hopping strategy to complete the sequence design of frequency hopping.

[0017] By adopting the above scheme, a hybrid frequency hopping strategy is adopted to further increase the flexibility and security of communication and improve the anti-interference ability of communication, thereby improving the communication rate and stability of power carrier communication in long-distance transmission.

[0018] Preferably, it also includes: The transmission time of the multipath communication transmission in the next time period completed in the adjusted multipath transmission mode is collected, and the user's satisfaction with the collected transmission time is obtained. If the satisfaction is greater than the preset satisfaction, the length of the predicted time period is shortened accordingly.

[0019] By adopting the above scheme, 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.

[0020] In a second aspect, the present application provides a power carrier communication acceleration system, comprising: A multipath channel state data acquisition module is used to use a MIMO structure to perform multipath communication transmission and collect multipath channel state data in real time; A multipath channel state data prediction module is used to predict the multipath channel state data of the next period of time using a machine learning algorithm based on the collected multipath 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 in the next predicted time period according to preset state evaluation indicators, obtain the corresponding channel state evaluation value and determine the corresponding path transmission optimization strategy; 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 in the first time period, choose to adopt a transmission path compensation optimization strategy; for the path where the channel state evaluation value shows a serious decrease or continues to fluctuate unstably, choose to adopt an optimization strategy of switching to other paths; for the path where the channel state evaluation shows a moderate decrease or continues to fluctuate unstably in the second time period, choose a comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails; the channel state evaluation value shows a slight decrease, a moderate decrease and a severe decrease, and the channel state evaluation value decrease range is set accordingly; the unstable fluctuation of the channel state evaluation value means that the change rate of the channel state evaluation value is greater than the 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.

[0021] By adopting the above scheme, the machine learning algorithm is used to predict the state data of each path in the next period, the predicted multi-path state 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.

[0022] In a third aspect, the present application provides a computer-readable storage medium, wherein 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 as described above.

[0023] In a fourth aspect, the present application provides a computer device, comprising a memory, a processor, and a program stored and executable on the memory, wherein the program implements the steps of the above method when executed by the processor.

[0024] In summary, this application has the following beneficial effects: 1. By adopting MIMO structure for multi-path communication transmission and collecting multi-path channel status data in real time, the machine learning algorithm is used to predict the status data of each path in the next period, and the multi-path transmission optimization strategy is determined according to the preset status evaluation index and the multi-path transmission mode of the next period is pre-adjusted, the rate and stability of long-distance power carrier communication are improved, and the problem of low communication rate during long-distance transmission in the existing technology is solved; 2. Introduce spectrum sensing technology to collect frequency band information of each path in real time, analyze the frequency band status according to the preset frequency band status evaluation criteria, use machine learning algorithms to predict the frequency band status of the next period, and determine the spectrum adjustment optimization strategy, which improves the system's anti-interference ability and communication robustness, ensures the continuity and reliability of communication, and thus improves the rate and stability of long-distance power carrier communication; 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

[0025] Figure 1 It is a flow chart of the power line carrier communication acceleration method described in a specific embodiment; Figure 2 It is a structural schematic diagram of the power carrier communication acceleration system described in a specific embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] In order to solve the problem that the communication rate of power carrier communication is low during long-distance transmission and it is difficult to meet the demand for high-speed data transmission, this application mainly adopts 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, so as to achieve the effect of improving the communication rate of long-distance power carrier communication, and maintain stable and high-speed communication even in complex power line environments. The following is a further detailed description of this application.

[0028] Example 1 like Figure 1 As shown, the embodiment of the present application discloses a method for accelerating power carrier communication, and the specific steps include: S1. Use MIMO structure for multi-path communication transmission and collect multi-path channel status data in real time.

[0029] In order to improve the rate and stability of long-distance power carrier communication, the MIMO structure is selected for multi-path communication transmission. Specifically, multiple antennas are configured at the transmitting end, and each antenna is used to send signals independently; the frequency, phase, amplitude or specific redundant information of the signal generated by the modulation and coding of the original transmission data is changed to enhance the anti-interference ability of the signal to generate multiple signals; the generated multiple signals are transmitted using one antenna respectively; the receiving end is also configured with multiple antennas, and each antenna is used to capture signals transmitted by different paths; the received signals are synchronously processed, and the maximum ratio combination technology is used to weightedly combine the received signals, and the combined signals are further demodulated and decoded to obtain the original transmission data to complete the power carrier communication.

[0030] During the entire power carrier communication process, data acquisition devices, such as sensors and data acquisition cards, are used to collect channel status data of multiple paths in real time; the channel status data includes the signal-to-noise ratio (SNR), signal-to-interference-plus-noise ratio (SINR) and measured signal strength, etc., which characterize the channel quality. The noise level is determined by the signal-to-noise ratio and the preset signal-to-noise ratio threshold range, and the interference degree is determined by the signal-to-interference-plus-noise ratio and the preset signal-to-interference-plus-noise ratio range.

[0031] S2. Based on the collected multipath channel status data, a machine learning algorithm is used to predict the multipath channel status data for the next time period.

[0032] Specifically, the collected channel state data of each path is respectively input into the constructed first deep learning neural network model 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.

[0033] 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.

[0034] S3. Perform state evaluation on each path channel state data of the next predicted time period according to a preset state evaluation index, and obtain a corresponding channel state evaluation value.

[0035] Specifically, the preset status evaluation indicators include: noise level and interference degree, etc.

[0036] Among them, each state evaluation indicator corresponds to a preset evaluation score value matching its state, such as: the noise level determined according to the signal-to-noise ratio and the preset signal-to-noise ratio threshold range includes: 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.

[0037] A weight can be set for each state evaluation index, and the predicted state evaluation value of each path channel in the next time period can be obtained by weighting.

[0038] S4. Determine a corresponding path transmission optimization strategy based on the acquired channel state evaluation value.

[0039] 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 change of the obtained channel state evaluation value, such as: only a slight decline occurs in the entire time period (the maximum difference between the corresponding path state evaluation values ​​in the entire time period is also no higher than the first preset channel state evaluation value decline threshold); or a moderate decline occurs in the entire time period (the difference between the corresponding path state evaluation values ​​in 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 in the entire time period (the difference between the corresponding path state evaluation values ​​in the entire time period is greater than the third preset channel state evaluation value decline threshold), etc.

[0040] Or if the channel state evaluation value continues to fluctuate unstably for the first period, continues to fluctuate unstably for the second period, and continues to fluctuate unstably for the entire period, a corresponding path transmission optimization strategy is set accordingly. 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 each two adjacent time points) is greater than the preset rate of change; wherein the second period is greater than the first period.

[0041] In order to avoid blindly adopting high energy consumption such as increased transmission power or using more complex modulation technology to increase costs, the corresponding path transmission strategy can be matched according to the actual channel status, including: For paths where the channel state evaluation value shows a slight decline or continues to fluctuate unstably for the first period of time, 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 the 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 a lot of noise and interference. To avoid compensation with high energy consumption, you can quickly switch to other available paths with better status, that is, choose to adopt the optimization strategy of switching to other paths; for paths where the channel state evaluation shows a moderate decline or continues to fluctuate unstably for the second period of time, it indicates that the channel state of the current path is general and there is a certain amount of noise and interference. You can choose a comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails. Prioritize the compensation path before actual application in the next period. In the actual application process, use sensors to monitor that the channel state of the current period is still in a general state, and consider switching to other paths for data transmission in the current period.

[0042] S5. According to the determined transmission optimization strategy for each path, the corresponding path transmission mode of the next time period is adjusted, and the multi-path communication transmission of the next time period is completed with the adjusted corresponding path transmission mode.

[0043] Specifically, the predicted channel status of the 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 of 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.

[0044] 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. For cases with good results, the frequency of prediction can be further increased to further improve communication efficiency. Specifically, it includes: collecting the transmission time of the multipath communication transmission in the next time period in the adjusted multipath transmission mode, and obtaining the user's satisfaction with the collected transmission time. If the satisfaction is greater than the preset satisfaction, it means that the current transmission time effect meets the user's needs, and the length of the prediction period can be shortened accordingly, and more frequent predictions can be made. Of course, considering that the computing resources are preset with a minimum duration threshold for the prediction period, the corresponding shortening of the prediction period shall not be lower than the minimum duration threshold.

[0045] 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.

[0046] Example 2 The difference from Example 1 is that, based on the multipath channel state data prediction module, this embodiment adds spectrum sensing technology, collects the allocated frequency band information of each path in real time, and performs spectrum adjustment and optimization according to the frequency band state. Specifically, the method described in this embodiment also includes: Spectrum sensing technology is used to collect frequency band information allocated to each path in real time. Among them, common spectrum scanning units including spectrum analyzers and software radio (SDR) are used to complete frequency band information data collection; the spectrum analyzer is used to directly measure the occupancy of the frequency band allocated by the path, and the software radio is used to flexibly scan and analyze the spectrum through software programming.

[0047] In order to further improve the anti-interference capability and communication efficiency of power carrier communication, the frequency band status analysis is performed on the frequency band information collected corresponding to each path according to the preset frequency band status evaluation index to obtain the frequency band status analysis evaluation value.

[0048] Among them, the preset frequency band status evaluation indicators include: frequency band occupancy rate and interference degree, etc.; specifically, for the frequency band information collected corresponding to each path, such as: the current path is allocated with two frequency bands F1 and F2, the frequency band occupancy rate collected for the current period of F1 and F2 is compared with the preset occupancy rate range, and the score value within the corresponding preset occupancy rate range is matched according to the comparison result; and the external interference SINR collected during the communication process using the corresponding F1 and F2 frequency bands is compared with the preset interference plus noise ratio range, and the score value within the corresponding preset interference plus noise ratio range is matched according to the comparison result, and then all the preset frequency band status evaluation indicators are weightedly calculated corresponding to the obtained score values, 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.

[0049] According to the frequency band status evaluation value obtained in real time, a machine learning algorithm is used to predict the frequency band status evaluation value of each path in the next time period. Specifically, the frequency band status analysis values ​​corresponding to each path obtained in the historical time period and the current time period are respectively input into the constructed second deep learning neural network model to predict the frequency band status evaluation value of each path in the next time period, including the frequency band status value of each frequency band assigned to each path in the next time period; the second deep learning neural network is trained and generated through the status evaluation value of a single frequency band assigned to a single path in the historical time period. For example: the predicted status evaluation values ​​of the two frequency bands F1 and F2 assigned to the current path in the next time period are 58 and 86.

[0050] According to the predicted frequency band state 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 the communication transmission in the next time period is completed with the adjusted frequency band resources used by each path in the next time period; wherein the spectrum adjustment optimization strategy includes: preferentially selecting the frequency band with the highest state evaluation value and greater than the preset state evaluation value, and switching to the unoccupied frequency band after determining that there is no predicted state evaluation value greater than the preset state evaluation value in the original allocated frequency band, such as: the predicted state values ​​of the two frequency bands F1 and F2 allocated and used by the current path in the next time period are 58 and 86, and the F2 frequency band is preferentially used as the carrier frequency band to complete the communication transmission in the next time period. If the predicted state values ​​of the next time period are 58 and 55 respectively, there is no original frequency band F1 and F2 allocated for the current path. There is no frequency band whose state meets the requirement (the frequency band state value is greater than the preset frequency band state value 60), then all available frequency bands are scanned accordingly, idle frequency resources are identified, and the unoccupied frequency band F5 is switched to continue to complete the communication transmission.

[0051] 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 in advance from the users; the spectrum adjustment optimization strategy includes: directly switching to unoccupied frequency bands under high real-time performance requirements, giving priority to frequency bands with the highest status evaluation value and greater than the preset status evaluation value under non-high real-time performance requirements, and switching to unoccupied frequency bands after determining that there is no predicted status evaluation value greater than the preset status evaluation value in the originally allocated frequency bands.

[0052] 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 the spectrum adjustment through spectrum sensing technology and spectrum adjustment, thereby further improving the anti-interference capability and communication efficiency of power carrier communication.

[0053] Example 3 The difference between this embodiment and the above-mentioned embodiment 2 is that: based on the spectrum sensing technology, this embodiment introduces a frequency hopping pattern design to further improve the security and reliability of communication. The method also includes: 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.

[0054] The frequency hopping pattern is designed by adopting a hybrid frequency hopping strategy to complete the sequence design of frequency hopping, that is, the frequency modulation strategy combining rules and random frequency modulation is used to complete the sequence design of frequency hopping; for example, after the aforementioned steps, it is predicted that the frequency band corresponding to the next time period is F3, and within the F3 frequency band, a frequency hopping pattern containing 10 frequencies is designed: F3-1, F3-2, ... F3-10, and the frequency modulation sequence is to hop 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.

[0055] In addition, since the frequency band resources used in different prediction time periods in each path are different, the designed frequency hopping pattern is also dynamically updated according to the frequency band determined in each time period. For example, if the next time period of the current path is the F3 frequency band, then 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, then 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.

[0056] During the communication process using the adjusted frequency band resources used by each path in the next time period, the receiving end and the sending end change the communication frequency according to the designed frequency hopping pattern; for example: at the beginning of the communication, the sender and the receiver establish a connection on the F3-1 frequency, and then quickly switch between different frequencies in the F3 frequency band according to the frequency hopping pattern.

[0057] By using 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 the communication.

[0058] Example 4 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 also includes: For each path, the transmission performance evaluation results of the relay nodes selected in the historical communication process under the same frequency band as the communication frequency band used by the corresponding path in the next period are collected; wherein, each path deploys several relay nodes, and the transmission performance evaluation results of the relay nodes are weighted according to the historical transmission success rate and delay time evaluation to determine the final evaluation value. Among them, different transmission success rate ranges are set to match different indicator scores, and different delay time ranges are set to match different indicator scores; For example: the relay nodes A, B, and C selected for transmission in the F1 frequency band on a historical single path have corresponding performance evaluation results of 90, 75, and 86.

[0059] The relay node with the best evaluation result among all the relay node transmission performance evaluation results selected in the historical communication process is statistically determined, and the determined relay node is used as the relay node matching the communication frequency band used by the corresponding path in the next period. For example, if the best relay node according to the statistical results is A, then the relay node matching the communication transmission of the corresponding single path in the F1 frequency band is A.

[0060] In the process of performing communication transmission 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.

[0061] 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.

[0062] 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 the communication.

[0063] Example 5 The difference between this embodiment and the above embodiment is that: on the basis of optimizing the communication frequency band selection, this embodiment introduces dynamic adjustment of the signal modulation mode and the coding mode to further improve the quality and efficiency of communication. The method also includes: The signal modulation mode and coding mode for communication transmission on the corresponding path in the next time period are determined according to 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 the preset frequency band threshold, and the frequency greater than the preset frequency band threshold is the high frequency band, otherwise it is the low frequency band.

[0064] By utilizing the method described in this embodiment, the quality and efficiency of power line carrier communication are further improved through dynamic adjustment of signal modulation mode and coding mode.

[0065] like Figure 2 As shown, the embodiment of the present application provides a power carrier communication acceleration system, which specifically includes: A multipath channel state data acquisition module 101 is used to perform multipath communication transmission using a MIMO structure and collect multipath channel state data in real time; The multipath channel state data prediction module 102 is used to predict the multipath channel state data of the next period of time using a machine learning algorithm based on the collected multipath channel state data; The multi-path transmission optimization strategy acquisition module 103 is used to perform state evaluation on the channel state data of each path in the next predicted time period according to the preset state evaluation index, obtain the channel state evaluation value and determine the corresponding path transmission optimization strategy; the preset state evaluation index includes: noise level and interference degree; the path transmission strategy includes: for the path where the channel state evaluation value shows a slight decrease or continues to fluctuate unstably in the first time period, select the transmission path compensation optimization strategy; for the path where the channel state evaluation value shows a serious decrease or continues to fluctuate unstably, select the optimization strategy of switching to other paths; for the path where the channel state evaluation shows a moderate decrease or continues to fluctuate unstably in the second time period, select the comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails; the channel state evaluation value shows a slight decrease, a moderate decrease and a severe decrease, and the channel state evaluation value decrease range is set accordingly; the unstable fluctuation of the channel state evaluation value means that the change rate of the channel state evaluation value is greater than the preset change rate; the second time period is greater than the first time period; The multi-path transmission optimization strategy execution module 104 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.

[0066] The system further comprises: The multi-path frequency band optimization strategy module 105 is used to collect the frequency band information allocated to each path in real time by using spectrum sensing technology; perform frequency band status analysis on the frequency band information collected corresponding to each path according to preset frequency band status evaluation indicators, and obtain frequency band status analysis evaluation values; the preset frequency band status evaluation indicators include: frequency band occupancy rate and interference degree; according to 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; according to 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 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.

[0067] The multipath transmission optimization feedback module 106 is used to collect the transmission time of the multipath communication transmission in the next time period using the adjusted multipath transmission mode, and obtain the user's satisfaction with the collected transmission time. If the satisfaction is greater than the preset satisfaction, the length of the predicted time period is shortened accordingly.

[0068] In a specific embodiment, the multi-path frequency band optimization strategy module 105 is also used to design a frequency hopping pattern within the determined frequency band for each path corresponding to the determined frequency band to be used 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; the frequency hopping pattern defines the 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 to be 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.

[0069] In a specific embodiment, the multi-path frequency band optimization strategy module 105 is also used to collect, for each path, transmission performance evaluation results of relay nodes selected during historical communications under the same frequency band condition 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 during the historical communications 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 process of performing communication transmission of the corresponding path using the frequency band resources used by each path in the next time period after adjustment, the matching relay node is selected as the relay node selected during the communication process of the corresponding path.

[0070] In a specific embodiment, the multi-path frequency band optimization strategy module 105 is also used to determine the signal modulation method and coding method for communication transmission on the corresponding path in the next time period according to the 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 is the high-order modulation method, Turbo code or LDPC code coding method; for the low frequency band characteristics, the corresponding matching is the low-order modulation method, CRC check coding method.

[0071] The embodiment of the present application also discloses a computer-readable storage medium.

[0072] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executes the above-mentioned power carrier communication acceleration method. The computer-readable storage medium includes, for example: a U disk, 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.

[0073] The embodiment of the present application also discloses a computer device.

[0074] 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.

[0075] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for accelerating power carrier communication, characterized in that: include: Adopt MIMO structure for multi-path communication transmission and collect multi-path channel status data in real time; According to the collected multipath channel state data, a machine learning algorithm is used to predict the multipath channel state data of the next period; According to the preset state evaluation index, the state evaluation of each path channel state data of the next predicted 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 index includes: 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 period, choose to adopt the transmission path compensation optimization strategy; for the path where the channel state evaluation value shows a serious decrease or continues to fluctuate unstably, choose to adopt the optimization strategy of switching to other paths; for the path where the channel state evaluation value shows a moderate decrease or continues to fluctuate unstably for the second period, choose a comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails; the channel state evaluation value shows a slight decrease, a moderate decrease and a severe decrease, and the channel state evaluation value decrease range is set accordingly; the unstable fluctuation of the channel state evaluation value means that the change rate of the channel state evaluation value is greater than the preset change rate; the second period is greater than the first 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; According to the preset frequency band status evaluation index, the frequency band status analysis is performed on the frequency band information collected corresponding to each path to obtain the frequency band status analysis evaluation value; The preset frequency band status evaluation indicators include: frequency band occupancy rate and interference degree; According to the frequency band status evaluation value 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 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 original 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 used by each path in the next period, before adjusting the frequency band resources used by each path in the next period, designing a frequency hopping pattern 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 in each 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, the transmission performance evaluation results of the relay nodes selected in the historical communication process under the same frequency band as the communication frequency band used by the path corresponding to the next period are collected; 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; Statistically determine the relay node with the best evaluation result among all the relay node transmission performance evaluation results selected in the historical communication process, and use the determined relay node as the relay node matching the communication frequency band used by the corresponding path in the next time period; In the process of performing communication transmission 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 according to 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 high-order modulation mode, Turbo code or LDPC code coding mode is matched; for the low frequency band characteristics, the low-order modulation mode and 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 time of the multipath communication transmission in the next time period completed in the adjusted multipath transmission mode is collected, and the user's satisfaction with the collected transmission time is obtained. If the satisfaction is greater than the preset satisfaction, the length of the predicted time period is shortened accordingly.

8. A power carrier communication acceleration system, characterized in that: include: A multipath channel state data acquisition module is used to use a MIMO structure to perform multipath communication transmission and collect multipath channel state data in real time; A multipath channel state data prediction module is used to predict the multipath channel state data of the next period of time using a machine learning algorithm based on the collected multipath channel state data; A multi-path transmission optimization strategy acquisition module is used to perform state evaluation on each path channel state data of the next predicted period according to a preset state evaluation index, obtain a corresponding channel state evaluation value and determine a corresponding path transmission optimization strategy; The preset state evaluation index includes: 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 period, choose to adopt the transmission path compensation optimization strategy; for the path where the channel state evaluation value shows a serious decrease or continues to fluctuate unstably, choose to adopt the optimization strategy of switching to other paths; for the path where the channel state evaluation value shows a moderate decrease or continues to fluctuate unstably for the second period, choose a comprehensive optimization strategy of priority compensation and switching to other paths if compensation fails; the channel state evaluation value shows a slight decrease, a moderate decrease and a severe decrease, and the channel state evaluation value decrease range is set accordingly; the unstable fluctuation of the channel state evaluation value means that the change rate of the channel state evaluation value is greater than the preset change rate; the second period is greater than the first 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 comprises a memory, a processor and a program stored and executable on the memory, and the program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

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