Communication method and system for online monitoring data of power transmission line

Abstract

The invention discloses a communication method and system for on-line monitoring data of a power transmission line. Relates to a signal processing method and device. In order to overcome the problem of data loss or transmission errors caused by the fact that existing power transmission line data are transmitted through a power line, the method comprises the following steps: collecting power transmission line state data, modulating the power transmission line state data to a power transmission line state carrier signal, and transmitting the power transmission line state carrier signal through the power line; detecting the real-time state of the power line at the moment t; taking the real-time state of the power line at the t moment as input, and predicting an estimated state of the power line at the t + 1 moment through a power line state prediction model; and according to the estimated state of the power line at the t + 1 moment, selecting a corresponding signal processing strategy at the t + 1 moment to process the state carrier signal of the power transmission line.

Description

Technical Field

[0001] The invention relates to a signal processing method and a device. Background Art

[0002] With the rapid development of power systems, the monitoring and data collection of transmission lines have gradually become an important part of ensuring the stable operation of power systems. Existing data collection methods for transmission lines usually rely on specially deployed sensors and data collection devices to transmit data to the control center through wireless communication, optical fiber communication, etc. However, these methods have certain limitations, especially the stability and efficiency of data transmission in the power line environment.

[0003] In recent years, power line communication (PLC), as a technology that uses existing power line networks for data transmission, has gradually attracted widespread attention from researchers and engineers. PLC technology can effectively reduce the deployment cost of additional communication facilities by modulating data onto power line carrier signals, and is particularly suitable for remote areas or environments with existing power facilities.

[0004] However, despite the significant cost advantages and convenience of PLC technology, the characteristics of power lines themselves determine that their signal transmission is easily affected by factors such as impedance mismatch, voltage fluctuation, and equipment interference, which can cause signal attenuation and noise increase, leading to data loss or transmission errors. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem that data loss or transmission error occurs when existing transmission line data is transmitted through power lines, and a communication method and system for online monitoring data of a transmission line are provided.

[0006] The present invention provides a method for communicating online monitoring data of a power transmission line, the method being as follows:

[0007] Collecting transmission line status data, and modulating the transmission line status data onto a transmission line status carrier signal for transmission through a power line;

[0008] Transmission line status data includes current, voltage, temperature, humidity, and strain;

[0009] Detect and obtain the real-time status of the power line at time t;

[0010] The states include impedance characteristics, channel gain, and noise intensity;

[0011] The real-time state of the power line at time t is used as input, and the estimated state of the power line at time t+1 is predicted by the power line state prediction model;

[0012] The channel state prediction model is a combination of LSTM long short-term memory network and Kalman filter;

[0013] The input end of LSTM is used as the input end of the channel state prediction model, the output end of LSTM is used as the Kalman filter as the prior estimation information input end of the Kalman filter, and the correction information output end of the Kalman filter is used as the output end of the channel state prediction model;

[0014] According to the estimated state of the power line at time t+1, a corresponding signal processing strategy is selected at time t+1 to process the transmission line state carrier signal;

[0015] Signal processing strategies include adjusting gain and frequency hopping.

[0016] The present invention also provides a communication system for online monitoring data of a power transmission line, comprising:

[0017] An acquisition modulator is used to acquire the state data of the power transmission line and modulate the state data of the power transmission line onto a carrier signal of the state of the power transmission line for transmission through the power line;

[0018] Transmission line status data includes current, voltage, temperature, humidity, and strain;

[0019] A repeater is used to detect the real-time status of the power line at time t;

[0020] The states include impedance characteristics, channel gain, and noise intensity;

[0021] The real-time state of the power line at time t is used as input, and the estimated state of the power line at time t+1 is predicted by the power line state prediction model;

[0022] The channel state prediction model is a combination of LSTM long short-term memory network and Kalman filter;

[0023] The input end of LSTM is used as the input end of the channel state prediction model, the output end of LSTM is used as the Kalman filter as the prior estimation information input end of the Kalman filter, and the correction information output end of the Kalman filter is used as the output end of the channel state prediction model;

[0024] According to the estimated state of the power line at time t+1, a corresponding signal processing strategy is selected at time t+1 to process the transmission line state carrier signal;

[0025] Signal processing strategies include adjusting gain and frequency hopping.

[0026] The beneficial effects of the present invention are:

[0027] The communication method and system for online monitoring data of transmission lines of the present invention adopt power line communication (PLC) technology, and effectively utilize the existing power line infrastructure by modulating the status data of the transmission line onto the power line carrier signal for transmission, avoiding the high cost required for additional deployment of communication networks, and is particularly suitable for remote areas or environments with existing power facilities.

[0028] The composite model that combines LSTM and Kalman filter can identify the channel change trend in advance and adjust the signal processing strategy in time through real-time prediction and estimation of the power line status. It helps to improve the real-time and accuracy of data transmission and reduce the incidence of data loss and transmission errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of a method for communicating online monitoring data of a power transmission line according to the present invention;

[0030] Figure 2 It is a structural schematic diagram of the communication system for online monitoring data of power transmission lines of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0033] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention. Specific implementation method 1

[0035] The communication method of online monitoring data of the power transmission line in this embodiment is as follows:

[0036] Collecting transmission line status data, and modulating the transmission line status data onto a transmission line status carrier signal for transmission through a power line;

[0037] Transmission line status data includes current, voltage, temperature, humidity, and strain;

[0038] Detect and obtain the real-time status of the power line at time t;

[0039] The states include impedance characteristics, channel gain, and noise intensity;

[0040] The real-time state of the power line at time t is used as input, and the estimated state of the power line at time t+1 is predicted by the power line state prediction model;

[0041] The channel state prediction model is a combination of LSTM long short-term memory network and Kalman filter;

[0042] The input end of LSTM is used as the input end of the channel state prediction model, the output end of LSTM is used as the Kalman filter as the prior estimation information input end of the Kalman filter, and the correction information output end of the Kalman filter is used as the output end of the channel state prediction model;

[0043] According to the estimated state of the power line at time t+1, a corresponding signal processing strategy is selected at time t+1 to process the transmission line state carrier signal;

[0044] Signal processing strategies include adjusting gain and frequency hopping.

[0045] Specifically, online detection technology is used to monitor the transmission line in real time. The types of data collected include the following:

[0046] Current and voltage, temperature and humidity, mechanical conditions (such as cable tension, vibration, deflection, etc.), meteorological data (wind speed, wind direction, air pressure, etc.).

[0047] Depending on the layout of the transmission line and the monitoring objectives, current sensors, voltage sensors, temperature sensors, humidity sensors and mechanical sensors can be installed at different key locations, including:

[0048] Current sensors can be installed at the inlet and outlet lines of substations to collect current data.

[0049] Temperature sensors can be installed in key locations such as cables and support towers to detect the cable surface temperature and determine whether there is overheating.

[0050] Mechanical sensors such as strain sensors and accelerometers can be installed on support towers, conductors, guide towers and other parts of transmission lines to monitor physical displacement, cable tension, vibration, etc.

[0051] Then, the above data is modulated onto the transmission line status carrier signal through modulation technology (such as frequency modulation, amplitude modulation, etc.) and transmitted through the power line.

[0052] The channel status of the power line can be characterized by the following parameters:

[0053] Impedance characteristic Z(t): The impedance of the power line changes with time. The transformer will cause discontinuous changes in impedance.

[0054] Channel gain G(t): indicates the attenuation of the communication signal during the transmission process.

[0055] Noise intensity N(t): The noise level in the power line, including background noise and impulse noise.

[0056] Signal bit error rate BER(t): reflects the current communication quality. Fluctuations in the bit error rate can indicate changes in channel status.

[0057] Signal power P(t): The change in signal power between the sending and receiving ends, used to measure signal strength and path loss.

[0058] Frequency selective attenuation F(t, f): The signal attenuation at different frequencies can reflect the changes in multipath effects.

[0059] Select and collect all or some of the above parameters, and use the input feature vector X of the LSTM in the channel state prediction model t , taking the impedance characteristics, channel gain and noise intensity as an example:

[0060] X t =[Z(t), G(t), N(t)]

[0061] The LSTM model is used to capture the long-term timing characteristics of the power line communication channel and output short-term prediction values ​​as the initial estimation input of the Kalman filter.

[0062] Input feature vector {X t-n , X t-n+1 , ..., X t}, where n is the length of the input sequence, representing the observed data at the past n moments. The channel state predicted by LSTM is The following forecast values ​​are included:

[0063] Predicted Impedance

[0064] Predicted Channel Gain

[0065] Predicted noise intensity

[0066] The output vector can be expressed as:

[0067]

[0068] The Kalman filter is used to correct the prediction results of LSTM in real time and deal with the rapid changes and mutations of the power line communication channel.

[0069] The state equation of the Kalman filter is as follows:

[0070] Xt+1 =Ax t +w t

[0071] in:

[0072] x t =[Z(t), G(t), N(t)] T .

[0073] A is the state transfer matrix, which is initialized by the prediction value of LSTM.

[0074] is the process noise.

[0075] The Kalman filter observation equation is as follows:

[0076] z t =Hx t +v t

[0077] in:

[0078] z t =[Z obs (t),G obs (t),N obs (t),BER obs (t)] T It is the real-time observation data, that is, the real-time status of the power line at time t.

[0079] H is the observation matrix.

[0080] is the observation noise.

[0081] The Kalman filter update steps are as follows:

[0082] Prediction Steps:

[0083]

[0084] Update steps:

[0085]

[0086] Among them, K t+1 is the Kalman gain; P t is the state covariance matrix.

[0087] The predicted value of the above LSTM As the initial state prediction input of the Kalman filter

[0088] The Kalman filter is based on the real-time observation data z tCorrect the prediction results of LSTM.

[0089] Final Output The corrected channel state estimation result, ie, the estimated state of the power line at time t+1, is sent to the repeater 2 to select a corresponding signal processing strategy to adjust the communication quality of the power line. Specific implementation method 2

[0091] This embodiment is a further explanation of the first embodiment. In this embodiment, the LSTM obtains the predicted state at time t+1 based on the real-time state prediction at time t, and uses the predicted state at time t+1 as the prior estimated state of the Kalman filter at time t+1.

[0092] The Kalman filter corrects the prior estimated state at time t+1 through the real-time state at time t to obtain the estimated state at time t+1.

[0093] The other technical features of this embodiment are exactly the same as those of the second embodiment. Specific implementation method three

[0095] This embodiment is a further description of the first or second embodiment, and this embodiment also includes:

[0096] Monitor the dynamic indicators of the power line according to the set monitoring cycle;

[0097] Dynamic indicators include the standard deviation or rate of change of impedance, the standard deviation or rate of change of gain, the mean or variance of noise power, and the difference in bit error rate;

[0098] When the dynamic index meets the preset high dynamic range, the sampling period for detecting the real-time status is set to 1s to 30s;

[0099] When the dynamic index meets the preset medium dynamic range, the sampling period for detecting the real-time status is set to 10 minutes to 30 minutes;

[0100] When the dynamic index meets the preset low dynamic range, the sampling period for detecting the real-time status is set to 1h to 24h.

[0101] The other technical features of this embodiment are exactly the same as those of embodiment one or two.

[0102] Specifically, it is necessary to pre-set several common power line communication scenarios and formulate a suitable sampling interval strategy according to the characteristics of power line channel changes and differences in communication environments.

[0103] Scenario A: Highly dynamic environment, characterized by frequent changes in power load and the presence of high-frequency interference sources (such as industrial equipment and power tools). This is often seen in industrial workshops, electric vehicle charging stations, and household high-power electrical appliances. Optimum sampling interval: 1-10 seconds.

[0104] Scenario B: Medium dynamic environment, characterized by a certain periodicity in load changes, but no significant mutations. Mainly affected by user electricity consumption behavior (such as residential areas). Mostly seen in residential areas, office buildings, shopping malls, etc.; optimal sampling interval: 10-15 minutes.

[0105] Scenario C: Low dynamic environment, characterized by stable load changes and obvious daily or weekly cycle trends. It is often seen in substation load monitoring and long-term stable industrial equipment operation environments. Optimum sampling interval: 1-24 hours.

[0106] To identify the above scenarios, the following key indicators can be selected:

[0107] Impedance change rate ΔZ: used to monitor load fluctuations and calculate the standard deviation or change rate of impedance:

[0108]

[0109] Signal gain fluctuation ΔG: used to detect signal attenuation changes and calculate the standard deviation or rate of change of gain:

[0110]

[0111] Noise power N(t): used to evaluate the stability of the noise environment and calculate the mean and variance of the noise power:

[0112]

[0113] Bit error rate change ΔBER: used to reflect the fluctuation of communication quality and calculate the change trend of bit error rate:

[0114] ΔBER=BER t -BER t-1

[0115] It can also include periodicity detection: using autocorrelation analysis to detect the periodicity of load and channel status, and determine whether there is an obvious daily or weekly cycle in the signal.

[0116] According to the changes in the monitored dynamic indicators, rule-based classification algorithms or machine learning models (such as decision trees or random forests) can be used to identify the current scenario and dynamically adjust the sampling interval:

[0117] Rule 1: If ΔZ, ΔG, and ΔBER are all high and the noise variance is large, it is considered a high-dynamic environment and a sampling interval of seconds (1-10 seconds) is selected.

[0118] Rule 2: If ΔZ and ΔG are low, the noise variance is moderate, and a significant daily cycle variation of the load is detected, it is considered to be a medium dynamic environment and a minute sampling interval (10-15 minutes) is selected.

[0119] Rule 3: If the indicator changes little and the autocorrelation analysis shows an obvious periodic trend (such as daily or weekly cycles), it is considered to be a low-dynamic environment and an hourly or daily sampling interval (1-24 hours) is selected.

[0120] At the same time, the sampling interval can also be adjusted dynamically according to the following process:

[0121] Initialization phase: When the system starts, sampling begins using the default sampling interval (such as 10 minutes) to collect initial data.

[0122] Monitoring phase: At regular intervals (e.g., every 30 minutes), the module calculates monitoring indicators (ΔZ, ΔG, ΔBER, Var(N0, etc.).

[0123] Classification phase: Based on the calculated dynamic indicator changes, the current scenario is identified using decision rules or classification models.

[0124] Adjustment phase: According to the identified scene type, the sampling interval Δt and the LSTM input sequence length n are automatically adjusted:

[0125] If a highly dynamic environment is detected, shorten the sampling interval (1-10 seconds) and reduce the input sequence length (e.g., 10-50).

[0126] If a medium dynamic environment is detected, adjust the sampling interval to minutes (10-15 minutes) and the input sequence length to 50-100.

[0127] If a low dynamic environment is detected, the sampling interval is extended to the hourly or daily level (1-24 hours), and the input sequence length is 200-365.

[0128] Feedback mechanism: If the indicator change is found to increase significantly under the adjusted new interval, it will fall back to the previous sampling strategy to avoid system instability caused by over-adjustment.

[0129] Example: Assuming the system initially uses a sampling interval of 10 minutes:

[0130] When ΔZ>0.1, ΔG>0.1, bit error rate rise ΔBER>0.01 and noise power variance is high, the system identifies it as a high dynamic environment and adjusts the sampling interval to 5 seconds.

[0131] When it is detected that ΔZ<0.01, ΔG<0.01, the bit error rate is stable and there is an obvious daily cycle, the system identifies it as a low dynamic environment and adjusts the sampling interval to 1 hour. Specific implementation method four

[0133] This embodiment is a further description of the third embodiment, and this embodiment also includes:

[0134] After sampling the transmission line state carrier signal at time t, the transmission line state carrier signal is divided into different frequencies according to a preset frequency band;

[0135] Calculate and obtain the real-time status of the power lines corresponding to each frequency band at time t;

[0136] Score and sort each frequency band according to the real-time status of the power line, and select the best N frequency bands, N ≥ 1;

[0137] At time t+1, the transmission line status carrier signal is switched from the current frequency band to N frequency bands.

[0138] Specifically, the transmission line status carrier signal can be divided into three frequency bands: low frequency band f low , mid-frequency band f mid , high frequency band f high .

[0139] The power line state prediction model is used to predict the channel state of each frequency band.

[0140] According to the prediction results, frequency hopping can be performed on signals in different frequency bands. That is, if the channel prediction of a certain frequency band is not ideal (low signal-to-noise ratio, high impedance), you can choose to hop to other frequency bands to avoid interference.

[0141] However, in addition to frequency hopping processing, the following processing methods can also be included:

[0142] Gain adjustment, that is, for the frequency band with low signal-to-noise ratio (i.e. Where S th is the signal-to-noise ratio threshold), the gain can be increased.

[0143] For high signal-to-noise ratio frequency band Keep the gain as is or reduce it slightly to save power.

[0144] Dynamic frequency band selection, that is, based on the predicted impedance, select the frequency band with lower impedance for priority transmission, avoid the frequency band with high impedance and large interference, and improve communication efficiency.

[0145] Modulation mode adjustment, that is, based on the power prediction of the channel, if the predicted power is low, switching to a more interference-resistant modulation mode (for example, switching from QAM to BPSK). Specific implementation method five

[0147] The communication system for online monitoring data of a power transmission line of this embodiment includes:

[0148] The acquisition modulator 1 is used to acquire the transmission line status data and modulate the transmission line status data onto the transmission line status carrier signal for transmission through the power line;

[0149] Transmission line status data includes current, voltage, temperature, humidity, and strain;

[0150] Repeater 2, used to detect and obtain the real-time status of the power line at time t;

[0151] The states include impedance characteristics, channel gain, and noise intensity;

[0152] The real-time state of the power line at time t is used as input, and the estimated state of the power line at time t+1 is predicted by the power line state prediction model;

[0153] The channel state prediction model is a combination of LSTM long short-term memory network and Kalman filter;

[0154] The input end of LSTM is used as the input end of the channel state prediction model, the output end of LSTM is used as the Kalman filter as the prior estimation information input end of the Kalman filter, and the correction information output end of the Kalman filter is used as the output end of the channel state prediction model;

[0155] According to the estimated state of the power line at time t+1, a corresponding signal processing strategy is selected at time t+1 to process the transmission line state carrier signal;

[0156] Signal processing strategies include adjusting gain and frequency hopping. Specific implementation method 6

[0158] This embodiment is a further explanation of the fifth embodiment. In this embodiment, in the repeater 2, the LSTM obtains the predicted state at time t+1 according to the real-time state prediction at time t, and uses the predicted state at time t+1 as the prior estimated state of the Kalman filter at time t+1;

[0159] The Kalman filter corrects the prior estimated state at time t+1 through the real-time state at time t to obtain the estimated state at time t+1.

[0160] The other technical features of this embodiment are exactly the same as those of embodiment seven. Specific implementation method seven

[0162] This embodiment is a further description of the fifth or sixth embodiment. In this embodiment, a sampling scene dynamic monitor 3 is also included;

[0163] The sampling scene dynamic monitor 3 is used to monitor the dynamic indicators of the power line according to the set monitoring period and send the dynamic indicators to the repeater 2;

[0164] Dynamic indicators include the standard deviation or rate of change of impedance, the standard deviation or rate of change of gain, the mean or variance of noise power, and the difference in bit error rate;

[0165] When the dynamic index meets the preset high dynamic range, the repeater 2 is also used to set the sampling period for detecting the real-time state to 1s to 30s;

[0166] When the dynamic index meets the preset medium dynamic range, the repeater 2 is also used to set the sampling period for detecting the real-time status to 10min to 30min;

[0167] When the dynamic index meets the preset low dynamic range, the repeater 2 is further used to set the sampling period for detecting the real-time state to 1 hour to 24 hours.

[0168] The other technical features of this embodiment are exactly the same as those of embodiment five or six. Specific implementation method eight

[0170] This implementation is a further description of the seventh implementation. In this implementation, a frequency divider 4 is also included:

[0171] Frequency divider 4, used for dividing the power transmission line status carrier signal according to a preset frequency band;

[0172] Repeater 2 is also used to calculate the real-time status of the power line corresponding to each frequency band at time t;

[0173] Score and sort each frequency band according to the real-time status of the power line, and select the best N frequency bands, N ≥ 1;

[0174] At time t+1, the transmission line status carrier signal is switched from the current frequency band to N frequency bands.

[0175] The other technical features of this embodiment are exactly the same as those of embodiment seven.

[0176] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the features of the various dependent claims and herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with the individual embodiments may be used in other embodiments.

Claims

1. A method for communicating online monitoring data of a power transmission line, characterized in that: Here’s how: Collecting power transmission line status data, and modulating the power transmission line status data onto a power transmission line status carrier signal for transmission via a power line; The transmission line status data includes current, voltage, temperature, humidity and strain; Detect and obtain the real-time status of the power line at time t; The states include impedance characteristics, channel gain, and noise intensity; Taking the real-time state of the power line at time t as input, the estimated state of the power line at time t+1 is predicted by a power line state prediction model; The channel state prediction model is a combination model of LSTM long short-term memory network and Kalman filter; The input end of the LSTM is used as the input end of the channel state prediction model, the output end of the LSTM is used as the Kalman filter as the prior estimation information input end of the Kalman filter, and the correction information output end of the Kalman filter is used as the output end of the channel state prediction model; According to the estimated state of the power line at time t+1, a corresponding signal processing strategy is selected at time t+1 to process the power line state carrier signal; The signal processing strategy includes adjusting gain and frequency hopping.

2. The method for communicating online monitoring data of a power transmission line according to claim 1, characterized in that: LSTM predicts the predicted state at time t+1 based on the real-time state at time t, and uses the predicted state at time t+1 as the prior estimated state of the Kalman filter at time t+1; The Kalman filter corrects the prior estimated state at time t+1 through the real-time state at time t to obtain the estimated state at time t+1.

3. The method for communicating online monitoring data of a power transmission line according to claim 1 or 2, characterized in that: Also includes: Monitor the dynamic indicators of the power line according to the set monitoring cycle; The dynamic indicators include the standard deviation or change rate of impedance, the standard deviation or change rate of gain, the mean or variance of noise power, and the difference in bit error rate; When the dynamic index meets the preset high dynamic range, the sampling period for detecting the real-time state is set to 1s to 30s; When the dynamic index meets the preset medium dynamic range, the sampling period for detecting the real-time status is set to 10 minutes to 30 minutes; When the dynamic index meets the preset low dynamic range, the sampling period for detecting the real-time status is set to 1 hour to 24 hours.

4. The method for communicating online monitoring data of a power transmission line according to claim 3, characterized in that: Also includes: After sampling the transmission line state carrier signal at time t, the transmission line state carrier signal is divided into different frequencies according to a preset frequency band; Calculate and obtain the real-time status of the power lines corresponding to each frequency band at time t; Scoring and sorting each frequency band according to the real-time status of the power line, and selecting the best N frequency bands, N≥1; At time t+1, the power transmission line status carrier signal is switched from the current frequency band to the N frequency bands.

5. A communication system for online monitoring data of transmission lines, characterized in that: include: An acquisition modulator (1) is used to acquire power transmission line status data and modulate the power transmission line status data onto a power transmission line status carrier signal for transmission via a power line; The transmission line status data includes current, voltage, temperature, humidity and strain; A repeater (2), used for detecting and obtaining the real-time status of the power line at time t; The states include impedance characteristics, channel gain, and noise intensity; Taking the real-time state of the power line at time t as input, the estimated state of the power line at time t+1 is predicted by a power line state prediction model; The channel state prediction model is a combination model of LSTM long short-term memory network and Kalman filter; The input end of the LSTM is used as the input end of the channel state prediction model, the output end of the LSTM is used as the Kalman filter as the prior estimation information input end of the Kalman filter, and the correction information output end of the Kalman filter is used as the output end of the channel state prediction model; According to the estimated state of the power line at time t+1, a corresponding signal processing strategy is selected at time t+1 to process the power line state carrier signal; The signal processing strategy includes adjusting gain and frequency hopping.

6. The communication system for online monitoring data of a power transmission line according to claim 5, characterized in that: LSTM predicts the predicted state at time t+1 based on the real-time state at time t, and uses the predicted state at time t+1 as the prior estimated state of the Kalman filter at time t+1; The Kalman filter corrects the prior estimated state at time t+1 through the real-time state at time t to obtain the estimated state at time t+1.

7. The communication system for online monitoring data of a power transmission line according to claim 5 or 6, characterized in that: Also included is a sampling scene dynamics monitor (3); The sampling scene dynamic monitor (3) is used to monitor the dynamic indicators of the power line according to a set monitoring cycle, and send the dynamic indicators to the repeater (2); The dynamic indicators include the standard deviation or change rate of impedance, the standard deviation or change rate of gain, the mean or variance of noise power, and the difference in bit error rate; When the dynamic index meets the preset high dynamic range, the repeater (2) is also used to set the sampling period for detecting the real-time state to 1s to 30s; When the dynamic index meets the preset medium dynamic range, the repeater (2) is also used to set the sampling period for detecting the real-time state to 10 minutes to 30 minutes; When the dynamic index meets the preset low dynamic range, the repeater (2) is also used to set the sampling period for detecting the real-time state to 1 hour to 24 hours.

8. The communication system for online monitoring data of a power transmission line according to claim 7, characterized in that: Also includes dividers (4): The frequency divider (4) is used to divide the power transmission line status carrier signal according to a preset frequency band; The repeater (2) is also used to calculate the real-time status of the power line corresponding to each frequency band at time t; Scoring and sorting each frequency band according to the real-time status of the power line, and selecting the best N frequency bands, N≥1; At time t+1, the power transmission line status carrier signal is switched from the current frequency band to the N frequency bands.

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