A communication data transmission method and system for an intelligent electric energy meter and a concentrator

Abstract

This invention relates to the field of data processing, and more particularly to a communication data transmission method and system for smart meters and concentrators. The method includes: acquiring the FSK modulated signal sequence transmitted by the smart meter; extracting and differentiating the instantaneous and previous frame estimated power sequences window by window; calculating the disorder degree based on the sequence characteristics of the two sequences, normalizing and then weighting and fusing them to obtain the current estimated power spectrum; dividing the center frequency band and neighboring frequency bands according to the FSK carrier frequency and initial bandwidth; calculating the channel purity based on the power difference between the center frequency band and the neighboring frequency bands; adjusting the bandwidth in real time based on the channel purity; and demodulating the adaptively filtered FSK modulated signal based on the FSK demodulator to complete the high-reliability transmission of communication data. This invention accurately distinguishes three types of interference by using the product of power spectrum fluctuation and jump degree, and adaptively scales the bandwidth at the millisecond level using the estimated power spectrum-driven channel purity, balancing signal integrity and noise suppression.

Description

Technical Field

[0001] This invention relates to the field of data processing. In particular, it relates to a communication data transmission method and system for a smart energy meter and a concentrator. Background Technology

[0002] Smart meters are responsible for collecting users' electricity consumption data in real time and transmitting the data to a concentrator via carrier communication and other means. The concentrator then uploads the aggregated data to the power company's main station system, thereby enabling functions such as remote meter reading, load monitoring, and electricity consumption analysis, which greatly improves the operating efficiency and management level of the power system.

[0003] In the communication data transmission process between smart meters and concentrators, data is transmitted via power line carrier communication technology. The smart meter modulates the collected data onto a high-frequency carrier signal and transmits it to the concentrator through the power line. After receiving the carrier signal, the concentrator demodulates it and extracts the data information. However, in actual transmission, the power line channel is complex and variable, and is easily affected by various interference and noise signals, such as electromagnetic interference from electrical equipment and impedance mismatch of the power line itself. These factors can all lead to data transmission errors or loss.

[0004] Traditional filters typically use instantaneous power spectrum analysis to identify and remove noise in carrier communication. However, this method still has limitations. It cannot accurately distinguish between the instantaneous changes in the characteristics of continuous and transient signal interference. When encountering continuous signal interference, traditional filters will identify it as noise and perform excessive denoising, resulting in the over-filtering of real signal components and causing data distortion. On the other hand, for some transient signal interference, it may not be able to remove it in a timely and effective manner, leaving more noise and affecting communication quality. Summary of the Invention

[0005] To address the problem that traditional filters often fail to accurately distinguish between continuous and transient signal interference when processing carrier communication noise, leading to continuous interference being misjudged as noise and resulting in excessive denoising and distortion of the true signal, or transient interference not being effectively removed and leaving a large amount of residual noise, thus affecting communication quality, this invention provides solutions in the following aspects.

[0006] In a first aspect, a communication data transmission method between a smart energy meter and a concentrator includes: acquiring an FSK modulated signal sequence transmitted by the smart energy meter based on a power line coupler in the concentrator; extracting instantaneous power sequence and instantaneous power difference sequence, as well as the estimated power sequence and estimated power difference sequence of the previous frame, frame by frame from the FSK modulated signal sequence according to a preset window length; calculating the disorder of the instantaneous power spectrum and the estimated power spectrum of the previous moment based on the characteristics of the two sets of sequences; and obtaining the significant disorder of the instantaneous power spectrum and the significant disorder of the estimated power spectrum of the previous moment after normalization; using the current... The estimated power spectrum at the current moment is obtained by weighting the degree of significant disorder between the instantaneous power spectrum at time 1 and the estimated power spectrum at the previous moment. Based on the estimated power spectrum, the center band and the neighborhood band are divided according to the FSK modulation frequency and the initial bandwidth. The power difference between the center band and the neighborhood band is calculated to determine the channel purity. The channel purity is used as a scaling factor for the initial filtering bandwidth to obtain the adjusted bandwidth, and the FSK modulated signal is filtered. The adaptively filtered FSK modulated signal is demodulated using an FSK demodulator, and it is determined whether there are any abnormalities in the communication data transmission, thus completing the data transmission method.

[0007] After acquiring the FSK modulated signal through the power line coupler of the concentrator, an instantaneous power sequence and its differential sequence are first generated using a sliding window. Based on this, the degree of significant disorder between the instantaneous and historical power spectra is calculated and normalized. Then, the current instantaneous power spectrum and the estimated power spectrum at the previous moment are weighted and fused according to the degree of disorder to obtain the current estimated power spectrum. The center frequency band and the neighboring frequency band are then divided according to the FSK modulation frequency and the initial bandwidth. The channel purity is obtained by using the power difference between the two and the filtering bandwidth is scaled in real time. After adaptive filtering, the signal is demodulated and verified by the FSK demodulator to achieve accurate noise suppression and high-reliability data transmission.

[0008] Preferably, the step of extracting the instantaneous power sequence and the instantaneous power difference sequence, as well as the estimated power sequence and the estimated power difference sequence of the previous frame, includes:

[0009] The FSK debugging signal sequence is preset with a window length to obtain the corresponding window sequence. Time-frequency analysis is performed on each window sequence to obtain the instantaneous power spectrum at each moment. The horizontal axis of the instantaneous power spectrum is the frequency, and the vertical axis is the power corresponding to the frequency.

[0010] The power values ​​at each discrete frequency point of the instantaneous power spectrum are extracted in ascending order of frequency to form an instantaneous power sequence. The instantaneous power sequence is then subjected to first-order difference to obtain an instantaneous power difference sequence.

[0011] The power values ​​at each discrete frequency point of the estimated power spectrum are extracted in ascending order of frequency to form an estimated power sequence. The estimated power sequence is then subjected to first-order difference to obtain the estimated power difference sequence.

[0012] Preferably, the instantaneous power spectrum represents the instantaneous frequency domain power distribution to reflect the signal characteristics at the current moment. The estimated power spectrum at the current moment is obtained by fusing the current instantaneous power spectrum with the estimated power spectrum at the previous moment, wherein the estimated power spectrum at the first moment is the instantaneous power spectrum at the first moment.

[0013] Preferably, the calculation method for the disorder level of the instantaneous power spectrum includes the following steps:

[0014] Using elements in the instantaneous power sequence as target elements, calculate the sum of absolute deviations between the value of the target element and the average value of the instantaneous power sequence to obtain the power spectrum fluctuation degree. Using any jump element in the instantaneous power difference sequence as the target jump, calculate the sum of absolute deviations between the target jump and the average value of the instantaneous power difference sequence to obtain the local jump degree of the power spectrum. The product of the power spectrum fluctuation degree and the local jump degree of the power spectrum is taken as the disorder degree of the instantaneous power spectrum.

[0015] Preferably, the calculation method for the degree of disorder in the estimated power spectrum includes the following steps:

[0016] Using elements in the estimated power sequence as labeled elements, the sum of absolute deviations between the values ​​of the labeled elements and the average value of the estimated power sequence is calculated to obtain the degree of fluctuation of the estimated power spectrum. Using any jump element in the estimated power difference sequence as a labeled jump, the sum of absolute deviations between the labeled jump and the average value of the estimated power difference sequence is calculated to obtain the degree of local jump of the estimated power spectrum. The product of the degree of fluctuation of the estimated power spectrum and the degree of local jump of the estimated power spectrum is taken as the degree of disorder of the estimated power spectrum.

[0017] Preferably, the calculation method for the corrected estimated power spectrum at the current moment includes:

[0018] Using the instantaneous power spectrum at any given time as the target power spectrum, the disorder of the target power spectrum and the disorder of the estimated power spectrum at the previous time are weighted and summed to obtain the corrected estimated power spectrum at the target time.

[0019] Preferably, the step of dividing the center frequency band and the neighboring frequency band based on the FSK modulation frequency and the initial bandwidth includes the following steps:

[0020] The modulation frequency is used as the center frequency of the center band. The initial bandwidth is used as the reference width. The frequency band with a length of half the bandwidth to the left and right of the center frequency is the center band, and the remaining frequency bands are the neighborhood bands.

[0021] Preferably, the method for calculating the channel purity includes:

[0022] The difference between the average power of the center frequency band and the average power of the neighboring frequency bands is taken as the net advantage of the main frequency power relative to the noise; the net advantage is divided by the maximum power of the entire frequency band to obtain the channel purity at the current moment.

[0023] Preferably, determining whether there is an anomaly in the communication data transmission includes the following steps:

[0024] The FSK demodulator is used to restore the adaptively filtered analog signal to a bit stream, and then the valid data fields are extracted and the checksum is calculated according to the preset unpacking protocol.

[0025] If the verification passes, the complete frame is written to the local database; if the verification fails, an exception alarm is triggered, thus completing the data transmission method for communication data between the smart energy meter and the concentrator.

[0026] Secondly, a communication data transmission system between a smart energy meter and a concentrator includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned communication data transmission method between the smart energy meter and the concentrator is implemented.

[0027] The present invention has the following effects:

[0028] 1. This invention uses the nonlinear product index of the power spectrum fluctuation degree and the local jump degree of the power spectrum to accurately distinguish continuous harmonics, transient spikes and stationary carriers within the same quantization dimension, thereby avoiding excessive denoising or noise residue caused by traditional single criteria.

[0029] 2. This invention calculates channel purity by using the estimated power spectrum as a benchmark and the power difference between the center frequency band and the neighboring frequency band, and linearly scales the initial bandwidth accordingly, thereby achieving millisecond-level adaptive adjustment of the filter passband, ensuring that the carrier signal is completely preserved and neighboring interference is effectively suppressed. Attached Figure Description

[0030] Figure 1 This is a flowchart of steps S1-S4 in a communication data transmission method between a smart energy meter and a concentrator according to an embodiment of the present invention.

[0031] Figure 2 This is a structural block diagram of a communication data transmission system between a smart energy meter and a concentrator according to an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0033] Reference Figure 1A communication data transmission method between a smart energy meter and a concentrator includes steps S1-S4, as detailed below:

[0034] S1: Obtain the FSK modulated signal sequence transmitted by the smart energy meter based on the power line coupler in the concentrator.

[0035] In the communication data transmission between smart meters and concentrators, the smart meters typically package and convert the collected data into FSK (Frequency-shift keying) modulated signals, which are then transmitted to the concentrator via the power line. Therefore, the concentrator collects the FSK modulated signal sequence transmitted by the smart meters through a power line coupler.

[0036] In other words, an FSK modulated signal sequence refers to a digital signal that is converted into a series of signals with different frequencies using frequency shift keying modulation. The frequency of the signal represents "0" or "1" in the digital signal. For example, if the original digital signal sequence is "1011001", each binary bit is mapped to a corresponding frequency according to a preset rule; frequency 1 can represent binary 0, and frequency 2 can represent binary 1. The resulting FSK modulated signal sequence outputs a sine wave of the corresponding frequency within each bit period, based on the current bit value.

[0037] It should be noted that due to varying user behaviors at different times for different smart meter users, different types of electromagnetic interference occur on power lines, causing distortion of transmitted communication data due to noise. For example, starting high-power appliances such as air conditioners and water heaters generates impulsive transient pulses; using inverter air conditioners generates high-frequency harmonics and switching noise; and stable daily power consumption generates low-amplitude power frequency harmonic noise. Existing methods treat all instantaneous changes as noise in instantaneous power spectrum analysis, leading to the incorrect rejection of continuous normal signals as noise while missing truly transient interference. To further amplify the duration characteristics of the interference in time, a window is set for the FSK modulated signal sequence. The partial steps are as follows:

[0038] S2: Extract the instantaneous power sequence and instantaneous power difference sequence, as well as the estimated power sequence and estimated power difference sequence of the previous frame, frame by frame according to the preset window length of the FSK modulated signal sequence. Calculate the disorder of the instantaneous power spectrum and the estimated power spectrum of the previous moment based on the characteristics of the two sets of sequences. After normalization, obtain the significant disorder of the instantaneous power spectrum and the significant disorder of the estimated power spectrum of the previous moment.

[0039] The FSK debugging signal sequence is preset with a window length to obtain the corresponding window sequence. Time-frequency analysis is performed on each window sequence to obtain the instantaneous power spectrum at each moment. The horizontal axis of the instantaneous power spectrum is the frequency, and the vertical axis is the power corresponding to the frequency.

[0040] The power values ​​at each discrete frequency point of the instantaneous power spectrum are extracted in ascending order of frequency to form an instantaneous power sequence. The instantaneous power sequence is then subjected to first-order difference to obtain an instantaneous power difference sequence.

[0041] The power values ​​at each discrete frequency point of the estimated power spectrum are extracted in ascending order of frequency to form an estimated power sequence. The estimated power sequence is then subjected to first-order difference to obtain the estimated power difference sequence.

[0042] In this embodiment, time-frequency analysis of the window sequence can be performed using short-time Fourier transform or wavelet transform. Further explanation is provided: the preset window length is 256 points, and the sliding step size is... If the window length at the initial time point is insufficient, the corresponding window is obtained by repeatedly padding with the first value. For example, the length of the FSK debug signal sequence is... The corresponding signal value is: Each element represents the signal value at each time step. If the window length is... Then the window sequence corresponding to the first time step is: The window sequence corresponding to the second time step is: The window sequence corresponding to the third time step is: The signal value sequence within the window corresponding to the fourth time point is as follows: .

[0043] The instantaneous power spectrum represents the instantaneous frequency domain power distribution to reflect the signal characteristics at the current moment. The estimated power spectrum at the current moment is obtained by fusing the current instantaneous power spectrum with the estimated power spectrum at the previous moment. The estimated power spectrum at the first moment is the instantaneous power spectrum at the first moment.

[0044] It should be noted that the estimated power spectrum is a well-known technique in the field, applied in signal processing to extract the true spectral characteristics of a signal from noise, and will not be described in detail here.

[0045] The calculation of the disorder level of the instantaneous power spectrum includes the following steps:

[0046] Using elements in the instantaneous power sequence as target elements, calculate the sum of absolute deviations between the value of the target element and the average value of the instantaneous power sequence to obtain the power spectrum fluctuation degree. Using any jump element in the instantaneous power difference sequence as the target jump, calculate the sum of absolute deviations between the target jump and the average value of the instantaneous power difference sequence to obtain the local jump degree of the power spectrum. The product of the power spectrum fluctuation degree and the local jump degree of the power spectrum is taken as the disorder degree of the instantaneous power spectrum.

[0047] Specifically, the degree of disorder satisfies the following relationship:

[0048] ;

[0049] In the formula, Indicates the first The degree of disorder in the instantaneous power spectrum at any given moment. Indicates the length of the instantaneous power sequence. Indicates the length of the instantaneous power difference sequence. Represents the instantaneous power sequence. The value of each element, This represents the average value of the instantaneous power sequence. Represents the instantaneous power difference sequence. The value of each element, This represents the average value of the instantaneous power difference sequence.

[0050] Based on the degree of disorder, we can distinguish between stationary carriers, continuous harmonics, and transient pulses. When both the power spectrum fluctuation and the local power spectrum jump are small, it belongs to the scenario of a stationary carrier. Only when the power spectrum fluctuation is large (high background of continuous harmonics but smooth waveform) or only the local power spectrum jump is large (isolated spikes but low energy), the product only increases linearly to a "small to medium value", which will not trigger a large bandwidth contraction and avoid treating normal signals as noise. When both the power spectrum fluctuation and the local power spectrum jump increase sharply at the same time, the scenario of transient pulse superimposed with high noise floor is immediately marked, and the bandwidth is quickly narrowed to suppress interference.

[0051] The degree of disorder is calculated by measuring the fluctuation and local jumps in the power spectrum. Compared to traditional methods based on a single standard deviation or spectral entropy, the standard deviation of the instantaneous power sequence itself can assess the discreteness of the power spectrum, while the standard deviation of the instantaneous power difference sequence can assess the severity of power spectrum fluctuations in the frequency domain. When instantaneous spike interference causes power spectrum jitter, the standard deviation of the instantaneous power difference sequence changes significantly. However, the standard deviation of the instantaneous power sequence is not sensitive to local changes, and its small change can lead to misjudgment. The comprehensive index has high sensitivity to both local changes and global dispersion, and can simultaneously analyze spectral shape and changes to assess whether the power spectrum is stable or disordered.

[0052] To further clarify, the formula does not show the standard deviation of the instantaneous power difference sequence. The overall fluctuation of the instantaneous power difference sequence is calculated as the L1 norm. The L1 norm has the same function as the standard deviation, both measuring the severity of spectral fluctuations. In this embodiment, the sum of absolute deviations is used instead of the sum of squares, which saves computation and can capture the local jumps caused by instantaneous peaks.

[0053] In other words, the degree of fluctuation in the power spectrum is sensitive to the overall strength, but insensitive to local abrupt changes; the degree of local jump in the power spectrum is sensitive to local abrupt changes, but easily affected by the overall amplitude.

[0054] The calculation method for estimating the disorder of the power spectrum includes the following steps:

[0055] Using elements in the estimated power sequence as labeled elements, the sum of absolute deviations between the values ​​of the labeled elements and the average value of the estimated power sequence is calculated to obtain the degree of fluctuation of the estimated power spectrum. Using any jump element in the estimated power difference sequence as a labeled jump, the sum of absolute deviations between the labeled jump and the average value of the estimated power difference sequence is calculated to obtain the degree of local jump of the estimated power spectrum. The product of the degree of fluctuation of the estimated power spectrum and the degree of local jump of the estimated power spectrum is taken as the degree of disorder of the estimated power spectrum.

[0056] Specifically, the degree of disorder in the estimated power spectrum at the next time step satisfies the following relationship:

[0057] ;

[0058] In the formula, Indicates the first Estimate the degree of disorder in the power spectrum at all times. This indicates the length of the estimated power sequence. This indicates the length of the estimated power difference sequence. Indicates the estimated power sequence number of the first power series. The value of each element, This represents the average value of the estimated power sequence. Represents the estimated power difference sequence. The value of each element, This represents the average value of the estimated power difference sequence.

[0059] Specifically, the degree of significant disorder in the instantaneous power spectrum satisfies the following relationship:

[0060] ;

[0061] In the formula, Indicates the first The degree of significant disorder in the instantaneous power spectrum at any given time. Indicates the first The degree of disorder in the instantaneous power spectrum at any given moment. Indicates the first The degree of disorder in the power spectrum is estimated at the previous time step.

[0062] Similarly, specifically, the estimated degree of significant disorder in the power spectrum satisfies the following relationship:

[0063] ;

[0064] In the formula, Indicates the first Estimate the significant disorder of the power spectrum at any given time. Indicates the first The degree of disorder in the instantaneous power spectrum at any given moment. Indicates the first The degree of disorder in the power spectrum is estimated at the previous time step.

[0065] S3: Using the degree of significant disorder between the instantaneous power spectrum at the current moment and the estimated power spectrum at the previous moment as weights, obtain the corrected estimated power spectrum at the current moment; using the corrected estimated power spectrum as a benchmark, divide the center frequency band and the neighboring frequency band based on the FSK modulation frequency and the initial bandwidth, calculate the power difference between the center frequency band and the neighboring frequency band, determine the channel purity, use the channel purity as the scaling factor of the initial filtering bandwidth to obtain the adjusted bandwidth, and perform filtering processing on the FSK modulated signal.

[0066] Using the instantaneous power spectrum at any given time as the target power spectrum, the disorder of the target power spectrum and the disorder of the estimated power spectrum at the previous time are weighted and summed to obtain the corrected estimated power spectrum at the target time.

[0067] When the disorder of the power spectrum at the target time exceeds the threshold, the system increases the weighting coefficient of the instantaneous power spectrum at that time to quickly respond to signal changes or noise surges; conversely, when the disorder of the estimated power spectrum at the previous time exceeds the threshold, the system increases the weighting coefficient of the historical estimated power spectrum to suppress random fluctuations and smooth noise, thereby achieving real-time and robust estimation of the power spectrum at the current time.

[0068] Specifically, the corrected power spectrum at the current moment satisfies the following relationship:

[0069] ;

[0070] In the formula, Indicates the first Corrected power spectrum at time [time]. Indicates the first The degree of significant disorder in the instantaneous power spectrum at any given time. Indicates the first Instantaneous power spectrum at time t. Indicates the first Estimate the significant disorder of the power spectrum at any given time. Indicates the first Estimated power spectrum at time t.

[0071] To further explain, a higher degree of disorder indicates more changes in signal characteristics and contains more information, thus requiring a higher weighting for the power spectrum with high disorder. When This indicates the possibility of sudden signal changes or new noise influence. The instantaneous power spectrum should have a larger weight to quickly track changes, while the weight of the power spectrum estimated at the previous moment should be smaller to avoid lag and improve response speed. The instantaneous power spectrum should have a smaller weight to avoid overfitting, while the estimated power spectrum of the previous moment should have a larger weight to fully suppress random fluctuations and smooth noise.

[0072] By weighting and fusing the instantaneous power spectrum with the estimated power spectrum of the previous moment according to their respective normalization disorder levels, the estimated power spectrum of the current moment can be formed. This can significantly suppress the distortion effect of spike noise, power disturbance or instantaneous jitter on the single frame spectrum, and prevent demodulation frequency misjudgment and bit error caused by "false high" frequency points in a certain frame. At the same time, by using the time accumulation characteristics of historical power spectrum to smooth random fluctuations, the system can adaptively track the trend of real signal, effectively improve the reliability of carrier identification and reduce the misjudgment rate in low signal-to-noise ratio environments.

[0073] The modulation frequency is used as the center frequency of the center band. The initial bandwidth is used as the reference width. The frequency band with a length of half the bandwidth to the left and right of the center frequency is the center band, and the remaining frequency bands are the neighborhood bands.

[0074] The methods for calculating channel purity include:

[0075] The difference between the average power of the center frequency band and the average power of the neighboring frequency bands is taken as the net advantage of the main frequency power relative to the noise; the net advantage is divided by the maximum power of the entire frequency band to obtain the channel purity at the current moment.

[0076] It should be noted that net advantage refers to the difference between the average signal power in the center frequency band and the average noise power in the neighboring frequency bands. In other words, the net power excess of the main frequency band relative to the neighboring bands is used to measure whether the signal truly dominates the energy.

[0077] Specifically, channel purity satisfies the following relation:

[0078] ;

[0079] In the formula, Indicates the first Channel purity at time t. This represents the average power across all frequencies in the center band. This represents the average power across all frequencies in the neighboring frequency band. This indicates the maximum power value.

[0080] In other words, since the modulation frequency in the FSK modulation frequency setting represents the main frequency of the communication signal, when A value greater than 0 indicates that the main frequency has high power and low noise content; therefore, a large bandwidth is needed to prevent signal filtering. A value less than 0 indicates a high noise level, and the bandwidth should be smaller to avoid excessive noise retention.

[0081] S4: Use an FSK demodulator to demodulate the adaptively filtered FSK modulated signal, determine if there are any abnormalities in the communication data transmission, and complete the data transmission method.

[0082] The FSK demodulator is used to restore the adaptively filtered analog signal to a bit stream, and then the valid data fields are extracted and the checksum is calculated according to the preset unpacking protocol.

[0083] If the verification passes, the complete frame is written to the local database; if the verification fails, an exception alarm is triggered, thus completing the data transmission method for communication data between the smart energy meter and the concentrator.

[0084] This invention also provides a communication data transmission system for a smart energy meter and a concentrator. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a communication data transmission method between a smart energy meter and a concentrator according to the first aspect of the present invention. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their configuration and functions are known in the art and will not be described further here.

[0085] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A communication data transmission method between a smart energy meter and a concentrator, characterized in that, include: The FSK modulated signal sequence transmitted by the smart energy meter is obtained from the power line coupler in the concentrator; For each frame of the FSK modulated signal sequence, the instantaneous power sequence and instantaneous power difference sequence, as well as the estimated power sequence and estimated power difference sequence of the previous frame are extracted. The disorder of the instantaneous power spectrum and the estimated power spectrum of the previous moment are calculated based on the characteristics of the two sets of sequences. After normalization, the significant disorder of the instantaneous power spectrum and the significant disorder of the estimated power spectrum of the previous moment are obtained. The corrected estimated power spectrum for the current moment is obtained by weighting the degree of significant disorder between the instantaneous power spectrum at the current moment and the estimated power spectrum at the previous moment. Based on the corrected estimated power spectrum, the center frequency band and the neighboring frequency band are divided according to the FSK modulation frequency and the initial bandwidth. The power difference between the center frequency band and the neighboring frequency band is calculated to determine the channel purity. The channel purity is used as the scaling factor of the initial filtering bandwidth to obtain the adjusted bandwidth, and the FSK modulated signal is filtered. The FSK demodulator is used to demodulate the adaptively filtered FSK modulated signal and to determine whether there are any abnormalities in the communication data transmission, thus completing the data transmission method. Specifically, the power spectrum fluctuation degree is obtained by taking the elements in the instantaneous power sequence as the target elements and calculating the sum of absolute deviations between the value of the target element and the average value of the instantaneous power sequence. The power spectrum fluctuation degree is obtained by taking any jump element in the instantaneous power difference sequence as the target jump and calculating the sum of absolute deviations between the target jump and the average value of the instantaneous power difference sequence. The product of the power spectrum fluctuation degree and the power spectrum local jump degree is used as the disorder degree of the instantaneous power spectrum. Using elements in the estimated power sequence as labeled elements, the sum of absolute deviations between the values ​​of the labeled elements and the average value of the estimated power sequence is calculated to obtain the degree of fluctuation of the estimated power spectrum. Using any jump element in the estimated power difference sequence as a labeled jump, the sum of absolute deviations between the labeled jump and the average value of the estimated power difference sequence is calculated to obtain the degree of local jump of the estimated power spectrum. The product of the degree of fluctuation of the estimated power spectrum and the degree of local jump of the estimated power spectrum is taken as the degree of disorder of the estimated power spectrum.

2. The communication data transmission method between a smart energy meter and a concentrator according to claim 1, characterized in that, The steps of extracting the instantaneous power sequence and the instantaneous power difference sequence, as well as the estimated power sequence and the estimated power difference sequence of the previous frame, include: The FSK debugging signal sequence is preset with a window length to obtain the corresponding window sequence. Time-frequency analysis is performed on each window sequence to obtain the instantaneous power spectrum at each moment. The horizontal axis of the instantaneous power spectrum is the frequency, and the vertical axis is the power corresponding to the frequency. The power values ​​at each discrete frequency point of the instantaneous power spectrum are extracted in ascending order of frequency to form an instantaneous power sequence. The instantaneous power sequence is then subjected to first-order difference to obtain an instantaneous power difference sequence. The power values ​​at each discrete frequency point of the estimated power spectrum are extracted in ascending order of frequency to form an estimated power sequence. The estimated power sequence is then subjected to first-order difference to obtain the estimated power difference sequence.

3. The communication data transmission method between a smart energy meter and a concentrator according to claim 1, characterized in that, The instantaneous power spectrum represents the instantaneous frequency domain power distribution to reflect the signal characteristics at the current moment. The estimated power spectrum at the current moment is obtained by fusing the current instantaneous power spectrum with the estimated power spectrum at the previous moment. The estimated power spectrum at the first moment is the instantaneous power spectrum at the first moment.

4. The communication data transmission method between a smart energy meter and a concentrator according to claim 1, characterized in that, The calculation method for the corrected estimated power spectrum at the current moment includes: Using the instantaneous power spectrum at any given time as the target power spectrum, the disorder of the target power spectrum and the disorder of the estimated power spectrum at the previous time are weighted and summed to obtain the corrected estimated power spectrum at the target time.

5. The communication data transmission method between a smart energy meter and a concentrator according to claim 1, characterized in that, The method of dividing the center frequency band and the neighboring frequency band based on the FSK modulation frequency and the initial bandwidth includes the following steps: The modulation frequency is used as the center frequency of the center band. The initial bandwidth is used as the reference width. The frequency band with a length of half the bandwidth to the left and right of the center frequency is the center band, and the remaining frequency bands are the neighborhood bands.

6. The communication data transmission method between a smart energy meter and a concentrator according to claim 1, characterized in that, The calculation method for channel purity includes: The difference between the average power of the center frequency band and the average power of the neighboring frequency bands is taken as the net advantage of the main frequency power relative to the noise; the net advantage is divided by the maximum power of the entire frequency band to obtain the channel purity at the current moment.

7. The communication data transmission method between a smart energy meter and a concentrator according to claim 1, characterized in that, The process of determining whether there is an anomaly in the communication data transmission includes the following steps: The FSK demodulator is used to restore the adaptively filtered analog signal to a bit stream, and then the valid data fields are extracted and the checksum is calculated according to the preset unpacking protocol. If the verification passes, the complete frame is written to the local database; if the verification fails, an exception alarm is triggered, thus completing the data transmission method for communication data between the smart energy meter and the concentrator.

8. A communication data transmission system for a smart energy meter and a concentrator, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the communication data transmission method between the smart energy meter and the concentrator according to any one of claims 1-7.

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