Communication data transmission method and system for intelligent electric energy meter and concentrator

Abstract

The invention relates to the field of data processing, in particular to a communication data transmission method and system for an intelligent electric energy meter and a concentrator, and the method comprises the steps: obtaining an FSK modulation signal sequence transmitted by the intelligent electric energy meter, extracting instantaneous and previous frame estimation power sequences window by window, and carrying out the difference, calculating a chaos degree based on sequence characteristics of the two, performing weight fusion after normalization to obtain a current estimated power spectrum, dividing a central frequency band neighborhood frequency band according to an FSK carrier frequency and an initial bandwidth, calculating channel purity according to a power difference between the central frequency band and the neighborhood frequency band, and adjusting the bandwidth in real time based on the channel purity to obtain a high-frequency band and a low-frequency band; and the FSK modulation signal after adaptive filtering is demodulated based on an FSK demodulator, and high-reliability transmission of communication data is completed. According to the method, the three types of interference are accurately distinguished through the product of the fluctuation degree and the hopping degree of the power spectrum, the bandwidth is adaptively scaled by estimating the channel purity millisecond level driven by the power spectrum, and both signal integrity and noise suppression are considered.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a communication data transmission method and system between a smart electric energy meter and a concentrator. Background Art

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

[0003] During data transmission between smart energy 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 via the power line. Upon receiving the carrier signal, the concentrator demodulates it and extracts the data. However, in actual transmission, power line channels are complex and highly variable, making them susceptible to various interference and noise signals, such as electromagnetic interference from electrical equipment and impedance mismatches within the power line itself. These factors can lead to data errors or loss.

[0004] When processing noise in carrier communications, traditional filters usually use instantaneous power spectrum analysis to identify and remove noise. However, there are still some limitations. They cannot accurately distinguish the instantaneous changes in the interference characteristics of continuous signals and short-term signals. When encountering continuous signal interference, traditional filters will identify it as noise and perform excessive denoising, resulting in excessive filtering of real signal components and data distortion. For some short-term signal interference, it may not be removed in a timely and effective manner, resulting in more noise retention and affecting communication quality. Summary of the Invention

[0005] To address the problem that traditional filters, when processing carrier communication noise, have difficulty accurately distinguishing between persistent and transient signal interference characteristics, often causing persistent interference to be misidentified as noise and over-denoised, distorting the true signal, or failing to effectively remove transient interference, resulting in a high level of residual noise, thus affecting communication quality, the present invention provides solutions in the following aspects.

[0006] In the first aspect, a communication data transmission method between a smart electric energy meter and a concentrator comprises: obtaining an FSK modulated signal sequence transmitted by the smart electric energy meter according to a power line coupler in the concentrator; extracting an instantaneous power sequence and an instantaneous power difference sequence and an estimated power sequence and an estimated power difference sequence of the previous frame from the FSK modulated signal sequence frame by frame according to a preset window length, calculating the degree of disorder of the instantaneous power spectrum and the estimated power spectrum at the previous moment according to the two sets of sequence characteristics, and obtaining the significant disorder degree of the instantaneous power spectrum and the significant disorder degree of the estimated power spectrum at the previous moment after normalization; and taking the current The instantaneous power spectrum at the moment and the estimated power spectrum at the previous moment are weighted with their respective significant confusion levels to obtain the estimated power spectrum at the current moment; the estimated power spectrum is used as a benchmark, and the center frequency band and the neighboring frequency band are divided based on 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 a 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 FSK modulated signal after adaptive filtering, and it is determined whether there is any abnormality in the communication data transmission, thereby completing the data transmission method.

[0007] After obtaining the FSK modulated signal through the concentrator power line coupler, the instantaneous power sequence and its differential sequence are first generated using a sliding window, and the significant degree of confusion of the instantaneous and historical power spectra is calculated and normalized based on this. The current instantaneous power spectrum and the estimated power spectrum at the previous moment are then weighted and fused according to the degree of confusion 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 power difference between the two is used to obtain the channel purity and perform real-time scaling of the filter bandwidth. After adaptive filtering, the signal is demodulated and verified by the FSK demodulator to achieve accurate noise suppression and highly reliable data transmission.

[0008] Preferably, the step of extracting the instantaneous power sequence and the instantaneous power difference sequence and the previous frame estimated power sequence and the estimated power difference sequence comprises: The window length of the FSK debugging signal sequence is preset 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 value of the instantaneous power spectrum at each discrete frequency point is extracted in ascending order of frequency to form an instantaneous power sequence, and the instantaneous power sequence is subjected to first-order difference to obtain an instantaneous difference sequence; The power value of the estimated power spectrum at each discrete frequency point is extracted in ascending order of frequency to form an estimated power sequence, and the estimated power sequence is subjected to first-order difference to obtain an estimated difference sequence.

[0009] Preferably, the instantaneous power spectrum represents the instantaneous frequency domain power distribution, which is used to reflect the signal characteristics at the current moment. The current instantaneous power spectrum is fused with the estimated power spectrum at the previous moment to obtain the estimated power spectrum at the current moment, wherein the estimated power spectrum at the first moment is the instantaneous power spectrum at the first moment.

[0010] Preferably, the method for calculating the degree of disorder of the instantaneous power spectrum comprises the steps of: Taking the elements in the instantaneous power sequence as the target elements, the sum of the absolute deviations between the values ​​of the target elements in the instantaneous power sequence and the average value of the instantaneous power sequence is calculated to obtain the degree of power spectrum fluctuation. Taking any jump element in the instantaneous difference sequence as the target jump, the sum of the absolute deviations between the target jump and the average value of the instantaneous difference sequence is calculated to obtain the local jump degree of the power spectrum. The product of the power spectrum fluctuation degree and the power spectrum local jump degree is taken as the degree of disorder of the instantaneous power spectrum.

[0011] Preferably, the method for calculating the degree of disorder of the estimated power spectrum comprises the steps of: Taking the elements in the estimated power sequence as the marking elements, the sum of the absolute deviations between the values ​​of the marking elements in the estimated power sequence and the average value of the estimated power sequence is calculated to obtain the degree of fluctuation of the estimated power spectrum. Taking any jump element in the estimated difference sequence as the marking jump, the sum of the absolute deviations between the marking jump and the average value of the estimated difference sequence is calculated to obtain the local jump degree of the estimated power spectrum. The product of the estimated power spectrum fluctuation degree and the estimated power spectrum local jump degree is taken as the degree of disorder of the estimated power spectrum.

[0012] Preferably, the calculation method of the corrected estimated power spectrum at the current moment includes: The instantaneous power spectrum at any moment is taken as the target moment power spectrum, and the chaos degree of the target moment power spectrum and the chaos degree of the estimated power spectrum at the previous moment are used as their respective weights for weighted summation to obtain the corrected estimated power spectrum at the target moment.

[0013] Preferably, the dividing of the central frequency band and the neighboring frequency band based on the FSK modulation frequency and the initial bandwidth comprises the steps of: The modulation frequency is taken as the center frequency of the center frequency band, the initial bandwidth is taken as the reference width, the frequency band with a length of half the bandwidth on the left and right of the center frequency is taken as the center frequency band, and the remaining frequency bands are all neighboring frequency bands.

[0014] Preferably, the channel purity is calculated by: The difference between the mean power of the center frequency band and the mean power of the neighboring frequency band 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.

[0015] Preferably, the step of determining whether there is an abnormality in communication data transmission comprises the following steps: Use FSK demodulator to restore the analog signal after adaptive filtering to bit stream, then intercept the valid data field and calculate the check code according to the preset depacketization protocol; In response to the verification being passed, the complete frame is written into the local database; if the verification fails, an abnormal alarm is triggered, thereby completing the data transmission method for communication data between the smart electric energy meter and the concentrator.

[0016] In a second aspect, a communication and data transmission system between a smart energy meter and a concentrator is provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the communication and data transmission method between the smart energy meter and the concentrator is implemented.

[0017] The present invention has the following effects: 1. The present invention uses the nonlinear product index of the power spectrum fluctuation degree and the power spectrum local jump degree to accurately distinguish continuous harmonics, transient spikes and stable carriers within the same quantization dimension, thereby avoiding excessive denoising or noise residual caused by traditional single criterion.

[0018] 2. The present invention calculates the channel purity by using the power difference between the center frequency band and the neighboring frequency band based on the estimated power spectrum, and linearly scales the initial bandwidth accordingly to achieve millisecond-level adaptive adjustment of the filter passband, ensuring that the carrier signal is fully retained and neighboring interference is effectively suppressed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a method flow chart of steps S1 to S4 in a communication and data transmission method between a smart electric energy meter and a concentrator according to an embodiment of the present invention.

[0020] Figure 2 This is a structural block diagram of a communication and data transmission system between a smart electric energy meter and a concentrator according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0022] Reference Figure 1 A communication data transmission method between a smart electric energy meter and a concentrator includes steps S1 to S4, which are specifically as follows: S1: Obtain the FSK modulation signal sequence transmitted by the smart energy meter according to the power line coupler in the concentrator.

[0023] In communication and data transmission between smart meters and concentrators, the smart meters typically package and process the collected data, converting it into an FSK (Frequency-Shift Keying) modulated signal, which is then transmitted via the power lines to the concentrator. Therefore, the FSK modulated signal sequence transmitted by the smart meters is collected by the power line coupler in the concentrator.

[0024] In other words, an FSK modulated signal sequence uses frequency shift keying (FSK) modulation to convert a digital signal into a series of signal sequences with different frequencies. By changing the frequency of the signal, a "0" or "1" in the digital signal is represented. For example, the original digital signal sequence is "1011001." Based on a preset rule, each binary bit is mapped to a corresponding frequency, with frequency 1 representing binary 0 and frequency 2 representing binary 1. The corresponding FSK modulated signal sequence is generated, that is, within each bit period, a sine wave of the corresponding frequency is output according to the current bit value.

[0025] It should be noted that due to the different user behaviors of different smart meter users at different times, different types of electromagnetic interference appear on the power lines, causing the transmitted communication data to be distorted by the noise. For example, starting high-power appliances such as air conditioners and water heaters generates impactful transient pulses, using variable-frequency air conditioners generates high-frequency harmonics and switching noise, and daily stable electricity consumption generates low-amplitude power frequency harmonic noise. Existing methods use instantaneous power spectrum analysis to treat all instantaneous changes as noise, resulting in the mistaken elimination of sustained normal signals as noise, while missing truly short-term interference. In order to further amplify the duration characteristics of the interference in time, a window is set for the FSK modulated signal sequence. The local steps are as follows: 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 from the FSK modulated signal sequence frame by frame according to the preset window length, calculate the degree of disorder of the instantaneous power spectrum and the estimated power spectrum at the previous moment based on the two sets of sequence characteristics, and obtain the significant disorder degree of the instantaneous power spectrum and the significant disorder degree of the estimated power spectrum at the previous moment after normalization.

[0026] The window length of the FSK debugging signal sequence is preset 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 value of the instantaneous power spectrum at each discrete frequency point is extracted in ascending order of frequency to form an instantaneous power sequence, and the instantaneous power sequence is subjected to first-order difference to obtain an instantaneous difference sequence; The power value of the estimated power spectrum at each discrete frequency point is extracted in ascending order of frequency to form an estimated power sequence, and the estimated power sequence is subjected to first-order difference to obtain an estimated difference sequence.

[0027] In this embodiment, the time-frequency analysis of the window sequence can be performed by using short-time Fourier transform or wavelet transform. To further illustrate, the preset window length is 256 points and the sliding step size is If the window length of the starting time is insufficient, the corresponding window is obtained by repeating the first value filling method. For example, the length of the FSK debugging signal sequence is , the corresponding signal value is: , where each element represents the signal value corresponding to each moment, if the window length is , then the window sequence corresponding to the first moment is: , the window sequence corresponding to the second moment is: , the window sequence corresponding to the third moment is: , the signal value sequence in the window corresponding to the fourth moment is: .

[0028] The instantaneous power spectrum represents the instantaneous frequency domain power distribution, which is used to reflect the signal characteristics at the current moment. The current instantaneous power spectrum is fused with the estimated power spectrum at the previous moment to obtain the estimated power spectrum at the current moment, where the estimated power spectrum at the first moment is the instantaneous power spectrum at the first moment.

[0029] It should be noted that estimated power spectrum is a well-known technology in the field of signal processing, which is used to extract the true spectrum characteristics of a signal from noise and will not be described in detail here.

[0030] The calculation method of the degree of disorder of the instantaneous power spectrum includes the following steps: Taking the elements in the instantaneous power sequence as the target elements, the sum of the absolute deviations between the values ​​of the target elements in the instantaneous power sequence and the average value of the instantaneous power sequence is calculated to obtain the degree of power spectrum fluctuation. Taking any jump element in the instantaneous difference sequence as the target jump, the sum of the absolute deviations between the target jump and the average value of the instantaneous difference sequence is calculated to obtain the local jump degree of the power spectrum. The product of the power spectrum fluctuation degree and the power spectrum local jump degree is taken as the degree of disorder of the instantaneous power spectrum.

[0031] Specifically, the degree of chaos satisfies the following relationship: ; Where, Indicates the The degree of disorder of the instantaneous power spectrum at time represents the length of the instantaneous power sequence, represents the length of the instantaneous power difference sequence, Indicates the instantaneous power sequence The value of the element, represents the average value of the instantaneous power series, represents the instantaneous difference sequence The value of the element, represents the mean value of the instantaneous difference series.

[0032] According to the degree of chaos, the system distinguishes between stable carriers, continuous harmonics and transient pulses. When the power spectrum fluctuation degree and the power spectrum local jump degree are both small, it belongs to the stable carrier scenario; when only the power spectrum fluctuation degree is large (the continuous harmonic background is high but the waveform is smooth) or only the power spectrum local jump degree is large (isolated spikes but low energy), the product only grows linearly to "small and medium values", which will not trigger a large bandwidth contraction, and avoid treating normal signals as noise; when the power spectrum fluctuation degree and the power spectrum local jump degree increase sharply at the same time, the scenario of transient pulses superimposed on high background noise is immediately marked, and the bandwidth is quickly narrowed to suppress interference.

[0033] The degree of chaos is calculated by the degree of power spectrum fluctuation and the degree of local jumps in the power spectrum. Compared with traditional methods based on a single standard deviation or spectral entropy, the standard deviation of the instantaneous power series itself can assess the discreteness of the power spectrum, and the standard deviation of the instantaneous power difference series can assess the severity of power spectrum fluctuations in the frequency domain. When instantaneous spike interference occurs, the power spectrum jitters, and the standard deviation of the instantaneous difference series changes significantly. However, the standard deviation of the instantaneous power series is insensitive to local changes, which can lead to misjudgment. The comprehensive indicator is highly sensitive to both local changes and global discreteness, and can simultaneously analyze spectral shape and changes to assess whether the power spectrum is stable and chaotic.

[0034] Further explanation: the formula does not show the standard deviation of the instantaneous difference series. It is the overall fluctuation of the instantaneous difference sequence, that is, the L1 norm is calculated. The function of the L1 norm is exactly the same as that of the standard deviation, both of which measure the severity of the spectrum fluctuation. In this embodiment, the absolute deviation is used instead of the square sum, which saves calculations and can capture the local jump caused by the instantaneous peak.

[0035] In other words, the degree of power spectrum fluctuation is sensitive to global strength, but insensitive to local mutations; the degree of local jump in the power spectrum is sensitive to local mutations, but easily affected by the overall amplitude.

[0036] The calculation method for estimating the degree of disorder of the power spectrum includes the following steps: Taking the elements in the estimated power sequence as the marking elements, the sum of the absolute deviations between the values ​​of the marking elements in the estimated power sequence and the average value of the estimated power sequence is calculated to obtain the degree of fluctuation of the estimated power spectrum. Taking any jump element in the estimated difference sequence as the marking jump, the sum of the absolute deviations between the marking jump and the average value of the estimated difference sequence is calculated to obtain the local jump degree of the estimated power spectrum. The product of the estimated power spectrum fluctuation degree and the estimated power spectrum local jump degree is taken as the degree of disorder of the estimated power spectrum.

[0037] Specifically, the degree of disorder of the estimated power spectrum at the next moment satisfies the following relationship: ; Where, Indicates the Estimate the degree of disorder of the power spectrum at every moment, represents the length of the estimated power sequence, represents the length of the estimated power difference sequence, Represents the estimated power sequence The value of the element, represents the mean value of the estimated power series, Represents the estimated difference sequence The value of the element, represents the mean of the estimated difference series.

[0038] Specifically, the significant disorder degree of the instantaneous power spectrum satisfies the following relationship: ; Where, Indicates the The degree of significant disorder of the instantaneous power spectrum at time Indicates the The degree of disorder of the instantaneous power spectrum at time Indicates the The degree of disorder of the estimated power spectrum at the previous moment.

[0039] Similarly, specifically, the significant disorder degree of the estimated power spectrum satisfies the following relationship: ; Where, Indicates the Estimate the significant degree of disorder in the power spectrum at each moment, Indicates the The degree of disorder of the instantaneous power spectrum at time Indicates the The degree of disorder of the estimated power spectrum at the previous moment.

[0040] S3: Using the significant degree of confusion of the instantaneous power spectrum at the current moment and the estimated power spectrum at the previous moment as weights, the revised estimated power spectrum at the current moment is obtained; using the revised estimated power spectrum as a benchmark, the center frequency band and the neighboring frequency band are divided based on the FSK modulation frequency and the initial bandwidth, the power difference between the center frequency band and the neighboring frequency band is calculated, the channel purity is determined, and the channel purity is used as the scaling factor of the initial filter bandwidth to obtain the adjusted bandwidth, and the FSK modulated signal is filtered.

[0041] The instantaneous power spectrum at any moment is taken as the target moment power spectrum, and the chaos degree of the target moment power spectrum and the chaos degree of the estimated power spectrum at the previous moment are used as their respective weights for weighted summation to obtain the corrected estimated power spectrum at the target moment.

[0042] When the degree of chaos of the power spectrum at the target moment is higher than the threshold, the system will correspondingly increase the weight coefficient of the instantaneous power spectrum at that moment to quickly respond to signal mutations or noise surges; conversely, when the degree of chaos of the estimated power spectrum at the previous moment is higher than the threshold, the system will correspondingly increase the weight 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 moment.

[0043] Specifically, the corrected estimated power spectrum at the current moment satisfies the following relationship: ; Where, Indicates the The corrected estimated power spectrum at time , Indicates the The degree of significant disorder of the instantaneous power spectrum at time Indicates the The instantaneous power spectrum at time , Indicates the Estimate the significant degree of disorder in the power spectrum at each moment, Indicates the The estimated power spectrum at time .

[0044] It can be further explained that the higher the degree of chaos, the more changes the signal characteristics have undergone and the more information they contain. Therefore, the weight of the power spectrum with high chaos needs to be increased. When there is a possible signal mutation or new noise, the instantaneous power spectrum should have a larger weight to quickly track the change, while the weight of the estimated power spectrum at the previous moment should be smaller to avoid lag and improve response speed. When , it means that the signal is relatively stable or dominated by noise. The instantaneous power spectrum should have a smaller weight to avoid overfitting, while the weight of the estimated power spectrum at the previous moment should be larger to fully suppress random fluctuations and smooth noise.

[0045] By weighting and fusing the instantaneous power spectrum with the estimated power spectrum at the previous moment according to their respective normalized chaos levels to form the estimated power spectrum at the current moment, it is possible to significantly suppress the distortion effects of spike noise, power disturbances or instantaneous jitter on the single-frame spectrum, and prevent demodulation frequency misjudgment and bit errors caused by "false high" at a certain frequency point in a certain frame; at the same time, the time accumulation characteristics of the historical power spectrum are used to smooth random fluctuations, allowing the system to adaptively track the real signal trend, effectively improving the reliability of carrier identification and reducing the error rate in a low signal-to-noise ratio environment.

[0046] The modulation frequency is taken as the center frequency of the center frequency band, the initial bandwidth is taken as the reference width, the frequency band with a length of half the bandwidth on the left and right of the center frequency is taken as the center frequency band, and the remaining frequency bands are all neighboring frequency bands.

[0047] Channel purity is calculated using: The difference between the mean power of the center frequency band and the mean power of the neighboring frequency band 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.

[0048] It should be noted that the 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, that is, the net excess power of the main frequency band relative to the neighboring bands, which is used to measure whether the signal truly dominates the energy.

[0049] Specifically, the channel purity satisfies the following relationship: ; Where, Indicates the Channel purity at the moment, It represents the average power of all frequencies in the center frequency band. represents the average power of all frequencies in the neighborhood band, Indicates the maximum power value.

[0050] That is to say, since the modulation frequency in the FSK modulation frequency setting represents the main frequency of the communication signal, when When it is greater than 0, it means that the power of the main frequency is large and the noise content is small. The bandwidth should be large to avoid signal filtering. When it is less than 0, it means that the noise content is large and the bandwidth should be smaller to avoid excessive noise retention.

[0051] S4: Use the FSK demodulator to demodulate the FSK modulated signal after adaptive filtering, and determine whether there is any abnormality in the communication data transmission, thereby completing the data transmission method.

[0052] Use FSK demodulator to restore the analog signal after adaptive filtering to bit stream, then intercept the valid data field and calculate the check code according to the preset depacketization protocol; In response to the verification being passed, the complete frame is written into the local database; if the verification fails, an abnormal alarm is triggered, thereby completing the data transmission method for communication data between the smart electric energy meter and the concentrator.

[0053] The present invention also provides a communication and data transmission system between a smart energy meter and a concentrator. Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions. When executed by the processor, the computer program instructions implement a communication and 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 familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are well known in the art and are therefore not described in detail here.

[0054] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A communication data transmission method between a smart electric energy meter and a concentrator, characterized in that: include: Obtaining the FSK modulation signal sequence transmitted by the smart energy meter according to the power line coupler in the concentrator; 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 frame by frame from the FSK modulated signal sequence according to the preset window length. The degree of disorder of the instantaneous power spectrum and the estimated power spectrum at the previous moment are calculated based on the two sets of sequence characteristics. After normalization, the significant disorder degree of the instantaneous power spectrum and the significant disorder degree of the estimated power spectrum at the previous moment are obtained. The current moment's instantaneous power spectrum and the previous moment's estimated power spectrum are weighted by their respective significant confusion levels to obtain the current moment's revised estimated power spectrum. Based on the modified 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 filter bandwidth to obtain the adjusted bandwidth, and the FSK modulated signal is filtered. The FSK demodulator is used to demodulate the FSK modulated signal after adaptive filtering, and it is determined whether there is any abnormality in the communication data transmission, thereby completing the data transmission method.

2. The communication data transmission method between a smart electric energy meter and a concentrator according to claim 1, characterized in that: The step of extracting the instantaneous power sequence and the instantaneous power difference sequence and the previous frame estimated power sequence and the estimated power difference sequence comprises: The window length of the FSK debugging signal sequence is preset 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 value of the instantaneous power spectrum at each discrete frequency point is extracted in ascending order of frequency to form an instantaneous power sequence, and the instantaneous power sequence is subjected to first-order difference to obtain an instantaneous difference sequence; The power value of the estimated power spectrum at each discrete frequency point is extracted in ascending order of frequency to form an estimated power sequence, and the estimated power sequence is subjected to first-order difference to obtain an estimated difference sequence.

3. The communication data transmission method between a smart electric energy meter and a concentrator according to claim 1, characterized in that: The instantaneous power spectrum represents the instantaneous frequency domain power distribution, which is used to reflect the signal characteristics at the current moment. The current instantaneous power spectrum is fused with the estimated power spectrum at the previous moment to obtain the estimated power spectrum at the current moment, wherein 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 electric energy meter and a concentrator according to claim 1, characterized in that: The calculation method of the degree of disorder of the instantaneous power spectrum comprises the steps of: Taking the elements in the instantaneous power sequence as the target elements, the sum of the absolute deviations between the values ​​of the target elements in the instantaneous power sequence and the average value of the instantaneous power sequence is calculated to obtain the degree of power spectrum fluctuation. Taking any jump element in the instantaneous difference sequence as the target jump, the sum of the absolute deviations between the target jump and the average value of the instantaneous difference sequence is calculated to obtain the local jump degree of the power spectrum. The product of the power spectrum fluctuation degree and the power spectrum local jump degree is taken as the degree of disorder of the instantaneous power spectrum.

5. The communication data transmission method between a smart electric energy meter and a concentrator according to claim 1, characterized in that: The method for calculating the degree of disorder of the estimated power spectrum comprises the steps of: Taking the elements in the estimated power sequence as the marking elements, the sum of the absolute deviations between the values ​​of the marking elements in the estimated power sequence and the average value of the estimated power sequence is calculated to obtain the degree of fluctuation of the estimated power spectrum. Taking any jump element in the estimated difference sequence as the marking jump, the sum of the absolute deviations between the marking jump and the average value of the estimated difference sequence is calculated to obtain the local jump degree of the estimated power spectrum. The product of the estimated power spectrum fluctuation degree and the estimated power spectrum local jump degree is taken as the degree of disorder of the estimated power spectrum.

6. The communication data transmission method between a smart electric energy meter and a concentrator according to claim 1, characterized in that: The calculation method of the corrected estimated power spectrum at the current moment includes: The instantaneous power spectrum at any moment is taken as the target moment power spectrum, and the chaos degree of the target moment power spectrum and the chaos degree of the estimated power spectrum at the previous moment are used as their respective weights for weighted summation to obtain the corrected estimated power spectrum at the target moment.

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

8. The communication data transmission method between a smart electric energy meter and a concentrator according to claim 1, characterized in that: The channel purity is calculated as follows: The difference between the mean power of the center frequency band and the mean power of the neighboring frequency band 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.

9. The communication data transmission method between a smart electric energy meter and a concentrator according to claim 1, characterized in that: The method of determining whether there is an abnormality in communication data transmission comprises the steps of: Use FSK demodulator to restore the analog signal after adaptive filtering to bit stream, then intercept the valid data field and calculate the check code according to the preset depacketization protocol; In response to the verification being passed, the complete frame is written into the local database; if the verification fails, an abnormal alarm is triggered, thereby completing the data transmission method for communication data between the smart electric energy meter and the concentrator.

10. A communication data transmission system between a smart electric energy meter and a concentrator, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the communication and data transmission method between the smart electric energy meter and the concentrator according to any one of claims 1 to 9 is implemented.

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