Electric meter intelligent communication detection method and system based on power line carrier

Abstract

The invention discloses an electric meter intelligent communication detection method and system based on a power line carrier, and relates to the technical field of electric meter communication detection, and the method comprises the steps: collecting the communication data of an electric meter at different time periods, inputting an electric meter communication state dynamic sensing model, analyzing features, and obtaining an initial evaluation result; channel characteristic parameters are calculated by using a carrier channel characteristic dynamic estimation algorithm based on the result, and the channel characteristic parameters are input into a power carrier signal demodulation optimization algorithm to generate an optimized demodulation scheme; signals are demodulated and data are verified according to the scheme, related results are uploaded to an electricity meter communication performance optimization management platform, and the platform evaluates performance and generates optimization suggestions and fault early warning. The system comprises a data acquisition unit, a state analysis unit, a channel calculation unit, a demodulation optimization unit, a data processing unit and a performance management unit which operate cooperatively. The communication state can be dynamically sensed, the channel characteristics can be accurately estimated, the demodulation process can be optimized, the integrated management of the communication performance can be realized, and the accuracy and reliability of the electric meter communication can be improved.

Description

Technical Field

[0001] This invention relates to the field of electricity meter communication testing technology, and in particular to an intelligent communication testing method and system for electricity meters based on power line carrier. Background Technology

[0002] In the process of intelligent development of power systems, electricity meters, as key terminals for power data acquisition, directly affect the stability and accuracy of their communication, impacting power metering, energy consumption monitoring, and the quality of power supply services. Currently, power line carrier technology is widely used in electricity meter communication due to its advantages such as no need for additional wiring and lower cost. However, the operating environment of electricity meters is complex, and they are susceptible to problems such as communication signal fluctuations, data transmission interruptions, or distortions due to factors such as grid load fluctuations, external electromagnetic interference, and channel attenuation. This leads to delays in power data acquisition and increased metering errors, making it difficult to meet the high requirements of power systems for the real-time performance and reliability of electricity meter communication. Therefore, it is urgent to construct an intelligent detection scheme that can dynamically sense the communication status of electricity meters, accurately estimate carrier channel characteristics, optimize the signal demodulation process, and achieve comprehensive management, in order to improve the communication performance of electricity meters and ensure the stable operation of the power system.

[0003] Existing power line carrier meter communication detection technologies suffer from two significant drawbacks. Firstly, current technologies rely heavily on single parameters or static models to perceive meter communication status, failing to comprehensively analyze multi-dimensional characteristics such as signal fluctuations, data transmission stability, and interference effects. They also cannot adjust detection strategies in real-time based on dynamic changes like channel attenuation, noise, and multipath propagation, resulting in insufficient accuracy in assessing meter communication status and difficulty in predicting potential communication faults. Secondly, existing technologies lack an integrated communication performance management mechanism. Signal demodulation parameter adjustment, data verification, and communication performance evaluation are independent processes, making it impossible to integrate and analyze channel characteristic parameters, demodulation optimization results, and communication fault warning information. This leads to low efficiency in managing meter communication performance, hindering the rapid generation of targeted optimization suggestions and timely resolution of communication problems. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a smart communication detection method and system for electricity meters based on power line carrier.

[0005] The technical solution adopted in this invention is a smart communication detection method for electricity meters based on power line carrier, comprising the following steps: S1, collecting communication data of the electricity meter at different operating stages through a power line carrier transmission link, wherein the communication data includes signal transmission strength, data transmission rate, signal interference amplitude, and data frame transmission interval; S2, inputting the collected communication data into a dynamic perception model of the electricity meter communication status, and analyzing the signal fluctuation characteristics, data transmission stability characteristics, and interference impact characteristics during the electricity meter communication process through the model to obtain an initial assessment result of the electricity meter communication status; S3, based on the initial assessment result of the electricity meter communication status, using a dynamic estimation algorithm of carrier channel characteristics to analyze the attenuation characteristics, noise characteristics, multipath propagation characteristics, and signal strength of the power line carrier channel. S4. The carrier channel characteristic parameters are obtained by calculating the capacity variation law; S5. The carrier channel characteristic parameters are input into the power carrier signal demodulation optimization algorithm. The algorithm adjusts the signal sampling frequency, signal filtering parameters, demodulation threshold and signal distortion compensation parameters in the demodulation process to generate an optimized demodulation scheme; S6. The power carrier signal transmitted by the meter is demodulated according to the optimized demodulation scheme to obtain the demodulated meter data, and the integrity and accuracy of the demodulated data are verified; S7. The demodulated data verification results, accuracy verification results and carrier channel characteristic parameters are uploaded to the meter communication performance optimization management platform. The platform comprehensively evaluates the meter communication performance and generates meter communication performance optimization suggestions and communication fault early warning information.

[0006] Furthermore, the expression for the dynamic sensing model of the meter's communication status is: ,in, This is the meter's communication status assessment value. These are the model weight coefficients. The average signal transmission strength. This represents the average data transmission rate. This represents the average data frame transmission interval. This represents the average amplitude of the signal interference. For the number of data collections, For the first The signal transmission strength of the second acquisition The total average value of the signal transmission strength. For the first Data transmission rate per acquisition, For the first The data frame transmission interval for each acquisition For the first The amplitude of signal interference collected in the second sampling. This represents the interference effect coefficient.

[0007] Furthermore, the expression for the dynamic estimation algorithm of carrier channel characteristics is as follows: ,in, This is the combined value of carrier channel characteristics. For algorithm coefficients, For channel bandwidth, For channel input power, For channel gain, For channel efficiency, For noise power spectral density, For channel interference power, This represents the number of sampling points for channel attenuation. For the first Channel attenuation amplitude at each sampling point For the first Attenuation coefficient at each sampling point For time variables, For the first Channel noise at each sampling point This represents the number of sampling points for multipath propagation. For the first Multipath propagation delay at each sampling point This represents the average multipath propagation delay.

[0008] Furthermore, the expression for the power line carrier signal demodulation optimization algorithm is as follows: ,in, To demodulate and optimize the evaluation value, For algorithm weights, The average value of the signal sampling frequency. The average value of the signal filtering coefficients. The average value of the demodulation threshold. This represents the average signal distortion. This is the distortion compensation coefficient. The number of times the demodulation parameters are sampled. For the first The sampling frequency of the signal in the next sample. The average value of the signal sampling frequency. For the first The signal filtering coefficients of the next sample. For the first Demodulation threshold for each sample. For the first Signal distortion at the subsample level.

[0009] Furthermore, the expression for the comprehensive evaluation of meter communication performance by the meter communication performance optimization management platform is as follows: ,in, This is a comprehensive evaluation value for communication performance. For evaluation coefficients, This is the meter's communication status assessment value. This is the combined value of carrier channel characteristics. To demodulate and optimize the evaluation value, To assess the number of cycles, For the first The pass rate of demodulation data verification for each evaluation cycle The average pass rate for demodulated data verification. For the first The comprehensive value of carrier-channel characteristics for each evaluation period. For the first Demodulation optimization evaluation value for each evaluation period For the first The meter communication status assessment value for each assessment period. The number of communication failure warnings. This represents the fault impact coefficient.

[0010] Furthermore, the expression for generating communication fault early warning information by the electricity meter communication performance optimization management platform is as follows: ,in, This is a fault warning value. As the early warning coefficient, This represents the average amplitude of the signal interference. This represents the average signal distortion. The average signal transmission strength. This represents the average data transmission rate. To provide early warning of sampling frequency, For the first Channel attenuation magnitude of the next sample, This represents the average value of the channel attenuation amplitude. For the first The amplitude of signal interference in the next sample. For the first The signal transmission strength of the next sample For the first Signal distortion at subsampling level For the first The fault warning indicator for each sample is 1 if a warning exists and 0 if no warning exists. For early warning weighting coefficients, For the first Data transmission rate per sample This represents the total average signal transmission strength.

[0011] Further, S3 includes the following sub-steps: S31, extracting the signal transmission strength fluctuation range, data transmission rate change amplitude, and signal interference amplitude peak from the initial evaluation results of the meter communication status, and using these parameters as the initial input parameters for the dynamic estimation algorithm of carrier channel characteristics; S32, setting the time interval and sampling frequency for calculating carrier channel characteristics, and continuously sampling the attenuation signal, noise signal, and multipath propagation signal of the power carrier channel according to the set time interval to obtain multiple sets of raw channel characteristic data; S33, inputting the sampled raw channel characteristic data into the dynamic estimation algorithm of carrier channel characteristics, and using the algorithm to filter, analyze trends, and extract features from the data to calculate the channel attenuation coefficient, noise power spectral density, and multipath propagation delay at different time points; S34, summarizing and organizing the channel characteristic parameters at different time points, analyzing the changing law of channel characteristic parameters over time, and generating carrier channel characteristic change curves and characteristic parameter statistical reports.

[0012] Further, S4 includes the following sub-steps: S41, receiving the carrier channel characteristic parameters output from S3, including channel attenuation coefficient, noise power spectral density, multipath propagation delay, and channel capacity; classifying, storing, and standardizing these parameters to establish a carrier channel characteristic parameter database; S42, calling the power line carrier signal demodulation optimization algorithm; inputting the standardized carrier channel characteristic parameters into the algorithm; the algorithm determines the adjustment range of the signal sampling frequency based on the channel attenuation coefficient and sets the initial value of the signal filtering parameters based on the noise power spectral density; S43, making preliminary adjustments to the demodulation threshold based on the multipath propagation delay; calculating the optimal value of the signal distortion compensation parameters in conjunction with the channel capacity; optimizing the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters through multiple iterative calculations; S44, verifying and testing the optimized demodulation parameters; applying the optimized parameters to a simulated power line carrier signal demodulation process; recording the completeness and accuracy of the demodulation data; fine-tuning the demodulation parameters based on the test results; and generating the final optimized demodulation scheme.

[0013] Further, S5 includes the following sub-steps: S51, obtaining the optimized demodulation scheme generated in S4, extracting the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters, and configuring these parameters into the power line carrier signal demodulation module; S52, the demodulation module receives the power line carrier signal transmitted by the meter according to the configured parameters, first filtering the received signal to remove noise interference, and then sampling the filtered signal according to the set sampling frequency to obtain discrete signal sample values; S53, demodulating the discrete signal sample values ​​according to the demodulation threshold to convert the analog signal into a digital signal, and simultaneously using the distortion compensation parameters to compensate and correct the signal distortion generated during the demodulation process to obtain preliminary demodulated data; S54, performing integrity verification on the preliminary demodulated data, checking the start identifier, end identifier, and check bit of the data frame to determine whether the data is complete, verifying the accuracy of the complete data, storing the verified data, marking the verified data as abnormal data and recording the reason for the abnormality.

[0014] A smart meter communication detection system based on power line carrier is disclosed. This system is applied to a smart meter communication detection method based on power line carrier, comprising: a power line carrier communication data acquisition unit, which is connected to the meter via a power line carrier transmission link, for acquiring communication data such as signal transmission strength, data transmission rate, signal interference amplitude, and data frame transmission interval at different operating stages, and transmitting the acquired data to a meter communication status analysis unit; a meter communication status analysis unit, connected to the power line carrier communication data acquisition unit, which has a built-in dynamic sensing model of meter communication status, receives the acquired communication data and inputs it into the model, analyzes signal fluctuation characteristics, data transmission stability characteristics, and interference impact characteristics through the model, and outputs the initial evaluation result of meter communication status to a carrier channel characteristic calculation unit; and a carrier channel characteristic calculation unit, connected to both the meter communication status analysis unit and the power line carrier signal demodulation optimization unit, employing a dynamic estimation algorithm for carrier channel characteristics, receiving the initial evaluation result of meter communication status, and performing calculations on the attenuation characteristics, noise characteristics, multipath propagation characteristics, and channel capacity variation patterns of the power line carrier channel. The system performs calculations and outputs carrier channel characteristic parameters to the power line carrier signal demodulation optimization unit. The power line carrier signal demodulation optimization unit, connected to the carrier channel characteristic calculation unit and the meter data processing unit, incorporates a power line carrier signal demodulation optimization algorithm. It receives carrier channel characteristic parameters, adjusts the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters, generates an optimized demodulation scheme, and transmits it to the meter data processing unit. The meter data processing unit, connected to the power line carrier signal demodulation optimization unit and the meter communication performance management unit, demodulates the power line carrier signal according to the optimized demodulation scheme, acquires demodulated data, performs integrity and accuracy verification, and transmits the verification results to the meter communication performance management unit. The meter communication performance management unit, connected to the meter data processing unit and the carrier channel characteristic calculation unit, forms the meter communication performance optimization management platform. It receives demodulated data verification results, accuracy verification results, and carrier channel characteristic parameters, comprehensively evaluates the meter communication performance, generates meter communication performance optimization suggestions and communication fault early warning information, and monitors and manages the meter communication status in real time.

[0015] Beneficial Effects: This invention proposes a smart communication detection method and system for electricity meters based on power line carrier. By constructing a dynamic sensing model of the meter's communication status, it integrates multi-dimensional communication data such as signal transmission strength and data transmission rate to comprehensively analyze signal fluctuations, transmission stability, and interference characteristics. This replaces existing single-parameter or static model sensing methods, significantly improving the accuracy of meter communication status assessment. It can predict potential communication faults in advance, solving the problems of inaccurate assessment and difficulty in early warning in existing technologies. Furthermore, by employing a dynamic estimation algorithm for carrier channel characteristics, it calculates channel attenuation, noise, and multipath propagation characteristics in real time, combined with a power line carrier signal demodulation optimization algorithm for dynamic... Adjusting sampling frequency and filtering parameters ensures the demodulation process adapts to dynamic channel changes, avoids signal distortion, and improves data demodulation quality. By integrating demodulation verification results and channel characteristic parameters through a meter communication performance optimization management platform, it achieves integrated operation of signal acquisition, status analysis, channel estimation, demodulation optimization, data processing, and performance management. This breaks the limitations of independent processes in existing technologies, improves communication performance management efficiency, quickly generates optimization suggestions, and promptly resolves communication problems. Ultimately, it ensures the real-time performance and reliability of meter communication, meets the high requirements of the power system for meter communication, and provides stable data support for power metering, energy consumption monitoring, and power supply services. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, a smart meter communication detection method based on power line carrier communication includes the following steps: S1, collect communication data of the meter at different operating stages through power line carrier transmission link, the communication data including signal transmission strength, data transmission rate, signal interference amplitude and data frame transmission interval; Specifically, in step S1, a power line carrier transmission link acquisition architecture is first established. This architecture includes a signal acquisition module, a timing control module, and a data storage module. The timing control module is set to acquire data every 5 minutes, with each acquisition lasting 10 seconds, ensuring coverage of different operating periods of the electricity meter (e.g., peak electricity consumption 8:00-10:00, off-peak 0:00-6:00). The sampling accuracy of the signal acquisition module must meet the following requirements: signal transmission strength acquisition range 0-100dBm, accuracy ±0.5dBm; data transmission rate acquisition range 0-1000bps, accuracy ±1bps; signal interference amplitude acquisition range 0-50dBm, accuracy ±0.2dBm; and data frame transmission interval acquisition range 10-1000ms, accuracy ±1ms. During the data acquisition process, the module captures the meter communication data in real time through the power line carrier link. After each acquisition, the data is stored in the temporary storage module in the format of "signal transmission strength - data transmission rate - signal interference amplitude - data frame transmission interval". The daily data acquisition volume is no less than 2880 sets, providing continuous and accurate raw data for subsequent analysis, avoiding subsequent evaluation deviations due to missing data or insufficient accuracy, and ensuring the reliability of the data source for the entire detection process.

[0019] S2, input the collected communication data into the dynamic sensing model of the meter's communication status, and analyze the signal fluctuation characteristics, data transmission stability characteristics and interference impact characteristics in the meter's communication process through the model to obtain the initial assessment results of the meter's communication status; Specifically, step S2 first preprocesses the communication data collected in S1, removing abnormal data that exceeds the normal range (such as signal transmission strength <0dBm or >100dBm, data transmission rate <0bps or >1000bps). Then, the valid data is input into the dynamic sensing model of the meter communication status in batches. When the model is running, it first extracts the signal fluctuation characteristics of each batch of data (calculate the maximum and minimum difference in signal transmission strength within 1 hour, and take the average value as the fluctuation index), data transmission stability characteristics (count the number of times the transmission rate changes by more than 5bps in 20 consecutive sets of data, and the number of times is less than 3 times to determine stability), and interference impact characteristics (calculate the ratio of signal interference amplitude to transmission strength, and the ratio is less than 0.2 to determine low interference). The model uses multi-feature fusion analysis to output initial evaluation results including communication status levels (excellent, good, medium, and poor) and corresponding feature indicators. The "excellent" level requires fluctuation index <5dBm, number of stable events <2, and interference ratio <0.1, while the "poor" level requires fluctuation index >15dBm, number of stable events >5, and interference ratio >0.4. This preliminary judgment of the meter's communication status provides targeted input for the channel characteristic calculation of S3, avoiding the waste of resources caused by blindly conducting channel analysis.

[0020] S3. Based on the initial assessment results of the electricity meter communication status, the carrier channel characteristic dynamic estimation algorithm is used to calculate the attenuation characteristics, noise characteristics, multipath propagation characteristics and channel capacity variation law of the power carrier channel to obtain the carrier channel characteristic parameters. Specifically, before implementing step S3, key parameters are extracted from the initial evaluation results of S2: for a "good" rating, the extracted parameters are the signal transmission strength fluctuation range of 3-5 dBm, the data transmission rate variation amplitude of 2-4 bps, and the peak signal interference amplitude of 5-8 dBm; for a "poor" rating, the extracted parameters are the fluctuation range of 15-20 dBm, the variation amplitude of 8-12 bps, and the peak value of 20-25 dBm. These parameters serve as the initial inputs for the dynamic estimation algorithm of carrier channel characteristics. Then, the algorithm calculation parameters are set: the time interval is adjusted according to the evaluation level, with "good" rating set to 10 minutes / time and "poor" rating set to 5 minutes / time, and the sampling frequency is uniformly set to 20 Hz. The algorithm samples the power line carrier channel through a channel detection module, capturing 200 sets of data per sample. These data include attenuation characteristics (measuring the signal strength attenuation from the transmitter to the receiver, in dB / km), noise characteristics (measuring the channel background noise power, in dBm), multipath propagation characteristics (measuring the time delay difference of signal transmission along different paths, in μs), and channel capacity (measuring the maximum amount of data that can be transmitted per unit time, in kbps). The algorithm filters the sampled data (using a moving average filter with a window size of 5), performs trend analysis (fitting the data change curve over the past hour), and extracts features. The final output for each calculation period is the channel attenuation coefficient (3-8 dB / km is normal), noise power (-110 to -90 dBm is normal), multipath propagation delay (10-30 μs is normal), and channel capacity (200-500 kbps is normal). These parameters constitute the carrier channel characteristic parameters, accurately grasping the channel's dynamic characteristics and providing an adaptation basis for S4 demodulation optimization, ensuring that the demodulation scheme matches the actual channel conditions.

[0021] S4. Input the carrier channel characteristic parameters into the power line carrier signal demodulation optimization algorithm. The algorithm adjusts the signal sampling frequency, signal filtering parameters, demodulation threshold and signal distortion compensation parameters during the demodulation process to generate an optimized demodulation scheme. Specifically, at the beginning of step S4, the carrier channel characteristic parameters output from S3 are first received and stored in the parameter database according to the structure of "calculation period - attenuation coefficient - noise power - multipath delay - channel capacity", and then standardized (the parameter values ​​are mapped to the 0-1 range, such as 0.1 for an attenuation coefficient of 3dB / km and 0.8 for 8dB / km). Then, the power line carrier signal demodulation optimization algorithm is called, and the standardized parameters are input: the sampling frequency is adjusted according to the attenuation coefficient, with 1000Hz for coefficient < 5dB / km and 2000Hz for coefficient > 7dB / km; the filtering parameters are set according to the noise power, with a filtering coefficient of 0.8 for power > -100dBm and 0.4 for power < -105dBm; the demodulation threshold is adjusted according to the multipath delay, with 0.3 for delay < 20μs and 0.6 for delay > 25μs; and the distortion compensation parameters are calculated according to the channel capacity, with 0.2 for capacity > 400kbps and 0.5 for capacity < 300kbps. The algorithm is optimized through 10 iterations. After each iteration, the demodulation success rate is calculated (target ≥98%). The process stops when the success rate stabilizes above 98% for three consecutive iterations, generating an optimized demodulation scheme that includes the final sampling frequency, filter coefficients, demodulation threshold, and distortion compensation parameters. The scheme also needs to specify the applicable channel characteristic range for the parameters, such as "sampling frequency 1500Hz adapted attenuation coefficient 5-7dB / km, noise power -105 to -100dBm". By dynamically adjusting the demodulation parameters, the accuracy of signal demodulation is improved, and demodulation errors caused by changes in channel characteristics are reduced, laying the foundation for accurate demodulation of S5.

[0022] S5, Demodulate the power carrier signal transmitted by the meter according to the optimized demodulation scheme, obtain the demodulated meter data, and verify the integrity and accuracy of the demodulated data; Specifically, in step S5, the optimized demodulation scheme from S4 is first imported into the power line carrier signal demodulation module. The module is configured with hardware parameters according to the scheme: the sampling module is adjusted to the set sampling frequency (e.g., 1500Hz, corresponding to a sampling interval of approximately 0.67ms), the filtering module is loaded with the set filtering coefficient (e.g., 0.6), the demodulation module is set with the demodulation threshold (e.g., 0.45), and the compensation module is configured with distortion compensation parameters (e.g., 0.35). After configuration, the demodulation module captures the power line carrier signal (frequency range 3-500kHz) transmitted by the meter through the receiving port. The filtering module first removes high-frequency noise (for signals with a filtering frequency > 500kHz) and low-frequency interference (for signals with a filtering frequency < 3kHz), and then the sampling module discretizes the filtered signal to generate a digital sampling sequence. The demodulation module determines the sampled sequence based on a demodulation threshold, classifying sampled values ​​above the threshold as "1" and those below as "0", thus restoring the data frame structure. Simultaneously, the compensation module corrects for amplitude deviations caused by signal attenuation during the decision-making process using distortion compensation parameters (e.g., increasing sampled values ​​with amplitudes 10% below the standard value by 8% using compensation parameters). After demodulation, meter data (including electricity consumption, voltage, and current) is obtained. Integrity verification (checking if the data frame includes an 8-bit start code, a 32-bit data segment, and an 8-bit checksum) is then performed. Accuracy verification of the complete data is also conducted (comparing the electricity consumption data with the previous period's data; a difference <10kWh is considered accurate). Verified data is stored on the local server, while abnormal data is tagged with "frame missing" or "out of tolerance" and timestamps are recorded. The carrier signal is converted into usable meter data. Verification and validation ensure data reliability, providing effective data support for S6 performance evaluation.

[0023] S6 uploads the demodulated data verification results, accuracy verification results, and carrier channel characteristic parameters to the meter communication performance optimization management platform. The platform comprehensively evaluates the meter communication performance and generates meter communication performance optimization suggestions and communication fault early warning information.

[0024] Specifically, step S6 is carried out using the meter communication performance optimization management platform, which includes four modules: data reception, analysis, evaluation, and output. The data reception module receives demodulated data verification results (complete data volume, abnormal data volume, integrity rate) and accuracy verification results (accurate data volume, out-of-tolerance data volume, accuracy rate) from S5 every 5 minutes via an Ethernet interface. Simultaneously, it receives carrier channel characteristic parameters from S3 (attenuation coefficient per cycle, noise power, etc.), receiving approximately 5760 data entries per day. The analysis module performs statistical analysis on the received data: calculating the hourly data integrity rate (complete data volume / total data volume, target ≥95%) and accuracy rate (accurate data volume / complete data volume, target ≥98%), and plotting channel characteristic parameter change curves (such as a 24-hour attenuation coefficient trend chart). The evaluation module calls a comprehensive evaluation model, calculating a comprehensive score (out of 100 points, 85 points or above is excellent) based on the weights of integrity rate, accuracy rate, and channel characteristic parameters (30% integrity rate, 30% accuracy rate, 40% channel parameters), and generating a performance evaluation report. The output module generates optimization suggestions based on the report: When the integrity rate is <95%, it is recommended to adjust the filtering parameters; when the accuracy rate is <98%, it is recommended to optimize the demodulation threshold. When the channel attenuation coefficient increases by more than 2dB / km per hour or the accuracy rate decreases by more than 3% per hour, a communication fault warning is triggered. The warning information is pushed to the maintenance personnel's mobile phone via SMS and displayed in a pop-up window on the platform interface. The platform stores the evaluation report, optimization suggestions, and warning information in a cloud database, supporting historical data queries by meter number and time range. It also generates daily reports (including daily performance score, number of faults, and optimization measures), achieving closed-loop management of meter communication performance. Through evaluation and warnings, problems are promptly identified and resolved, ensuring the long-term stable operation of meter communication.

[0025] Preferably, the expression for the dynamic sensing model of the meter's communication status is: ,in, This is the meter's communication status assessment value. These are the model weight coefficients. The average signal transmission strength. This represents the average data transmission rate. This represents the average data frame transmission interval. This represents the average amplitude of the signal interference. For the number of data collections, For the first The signal transmission strength of the second acquisition The total average value of the signal transmission strength. For the first Data transmission rate per acquisition, For the first The data frame transmission interval for each acquisition For the first The amplitude of signal interference collected in the second sampling. This represents the interference effect coefficient.

[0026] Specifically, in implementing the dynamic sensing model for electricity meter communication status, the weighting coefficients and influence coefficients of each component in the model are first determined. The three weighting coefficients need to be adjusted according to different electricity meter operating scenarios. In residential electricity scenarios, the three weighting coefficients are set to 0.4, 0.3, and 0.3 respectively, and the interference influence coefficient is set to 0.05. In industrial electricity scenarios, the three weighting coefficients are adjusted to 0.3, 0.4, and 0.3, and the interference influence coefficient is set to 0.08. The parameters input to the model need to be extracted from the communication data collected in step S1. The average signal transmission strength needs to be calculated as the arithmetic mean of all collected data for the day. The average data transmission rate is also calculated using the arithmetic mean method. The average data frame transmission interval needs to exclude abnormal interval values ​​(such as data exceeding the 10-1000ms range) before averaging. The average signal interference amplitude needs to filter out instantaneous spike interference values ​​(such as data with a single interference amplitude exceeding 50dBm). The number of data collections is determined according to the collection cycle. If the collection cycle is once every 5 minutes, the number of collections per day is 288. The calculation process first involves summing the squared differences between each data acquisition and the overall average signal transmission strength, then combining this with the average signal interference amplitude for further calculation. Simultaneously, the ratio of the data transmission rate to the data frame transmission interval for each acquisition is calculated, and this is combined with the interference impact coefficient for exponential calculation. Finally, the results of each calculation are weighted and summed according to their respective weighting coefficients to obtain the meter communication status assessment value. This model comprehensively reflects the meter communication status through multi-parameter fusion calculation, avoiding the one-sidedness of single-parameter assessment and providing accurate status basis for subsequent channel characteristic calculations.

[0027] Preferably, the expression for the carrier channel characteristic dynamic estimation algorithm is: ,in, This is the combined value of carrier channel characteristics. For algorithm coefficients, For channel bandwidth, For channel input power, For channel gain, For channel efficiency, For noise power spectral density, For channel interference power, This represents the number of sampling points for channel attenuation. For the first Channel attenuation amplitude at each sampling point For the first Attenuation coefficient at each sampling point For time variables, For the first Channel noise at each sampling point This represents the number of sampling points for multipath propagation. For the first Multipath propagation delay at each sampling point This represents the average multipath propagation delay.

[0028] Specifically, the dynamic estimation algorithm for carrier channel characteristics is implemented by first setting three algorithm coefficients. In urban power grid environments, these coefficients are set to 0.5, 0.3, and 0.2, respectively. In rural power grid environments, due to the more complex channel environment, the coefficients are adjusted to 0.4, 0.4, and 0.2. The channel bandwidth is set to 200kHz according to the power line carrier communication standard, the channel input power is set to 10dBm, and the channel gain needs to be calibrated according to the actual transmission distance. When the transmission distance is within 1km, the gain is set to 5dB, and the gain increases by 2dB for every additional 1km of transmission distance. The channel efficiency is set to 0.8. The noise power spectral density is determined according to the power grid noise level. During normal operation, it is set to -120dBm / Hz, and adjusted to -110dBm / Hz during peak power load periods. The channel interference power needs to be measured in real time, collected once every minute by an interference detection device, and the average of 5 collections is used as the input. The channel attenuation sampling points are set to 50, with each sampling point spaced 200 meters apart. The channel attenuation amplitude is measured using a signal attenuation tester. The attenuation coefficient is determined based on the channel material: 0.1 for copper cable and 0.15 for aluminum cable. The time variable is the duration of each sampling (e.g., 10 seconds). Channel noise is measured using a noise analyzer. The multipath propagation sampling points are set to 30, with multipath propagation delay collected using a delay meter. Each sampling point is spaced 100 meters apart. The calculation first calculates the squared difference between the multipath propagation delay of each sample and the overall average, sums these squares, and then divides by the number of sampling points. Finally, the channel capacity calculation result, the combined channel attenuation and noise result, and the multipath propagation characteristic result are weighted and summed according to the algorithm coefficients to obtain the comprehensive carrier channel characteristic value. This algorithm accurately quantifies the multi-dimensional characteristics of the carrier channel, providing detailed channel data support for demodulation parameter adjustment and ensuring that the demodulation scheme adapts to the actual channel conditions.

[0029] Preferably, the expression for the power line carrier signal demodulation optimization algorithm is: ,in, To demodulate and optimize the evaluation value, For algorithm weights, The average value of the signal sampling frequency. The average value of the signal filtering coefficients. The average value of the demodulation threshold. This represents the average signal distortion. This is the distortion compensation coefficient. The number of times the demodulation parameters are sampled. For the first The sampling frequency of the signal in the next sample. The average value of the signal sampling frequency. For the first The signal filtering coefficients of the next sample. For the first Demodulation threshold for each sample. For the first Signal distortion at the subsample level.

[0030] Specifically, the power line carrier signal demodulation optimization algorithm is implemented by first determining three algorithm weights. In environments with low signal interference, the three weights are set to 0.5, 0.3, and 0.2, respectively; in environments with high signal interference, they are adjusted to 0.4, 0.4, and 0.2. The average signal sampling frequency is determined based on channel attenuation: 1000Hz when the channel attenuation coefficient is less than 5dB / km, 1500Hz when the attenuation coefficient is between 5 and 10dB / km, and 2000Hz when the attenuation coefficient is greater than 10dB / km. The average signal filtering coefficient is set based on noise power: 0.3 when the noise power is less than -110dBm, 0.5 when the noise power is between -110 and -100dBm, and 0.7 when the noise power is greater than -100dBm. The average demodulation threshold is determined based on the multipath propagation delay: 0.3 for delays less than 20 μs, 0.5 for delays between 20 and 30 μs, and 0.7 for delays greater than 30 μs. The average signal distortion is measured every 5 minutes using a distortion meter, and the average of 10 measurements is taken. The distortion compensation coefficient is set to 0.6. The demodulation parameter sampling frequency is set to 20 times, with a 1-minute interval between each sampling. The calculation first calculates the squared difference between the signal sampling frequency and the total average for each sample, then multiplies it by the corresponding signal filtering coefficient, summing the results to obtain the numerator. The denominator is the sum of the products of the demodulation threshold and the signal distortion for each sample. Finally, the results are weighted and summed to obtain the demodulation optimization evaluation value. This algorithm dynamically adjusts the demodulation parameters to optimize the demodulation effect, reduce demodulation errors caused by changes in channel characteristics, and improve the accuracy and completeness of meter data demodulation.

[0031] Preferably, the expression used by the meter communication performance optimization management platform to comprehensively evaluate the meter communication performance is: ,in, This is a comprehensive evaluation value for communication performance. For evaluation coefficients, This is the meter's communication status assessment value. This is the combined value of carrier channel characteristics. To demodulate and optimize the evaluation value, To assess the number of cycles, For the first The pass rate of demodulation data verification for each evaluation cycle The average pass rate for demodulated data verification. For the first The comprehensive value of carrier-channel characteristics for each evaluation period. For the first Demodulation optimization evaluation value for each evaluation period For the first The meter communication status assessment value for each assessment period. The number of communication failure warnings. This represents the fault impact coefficient.

[0032] Specifically, the comprehensive evaluation of the electricity meter communication performance optimization management platform initially sets three evaluation coefficients. In daily monitoring scenarios, these coefficients are set to 0.4, 0.3, and 0.3 respectively; in fault diagnosis scenarios, they are adjusted to 0.3, 0.3, and 0.4. The electricity meter communication status evaluation value is taken from the model's calculation results, the carrier channel characteristic comprehensive value is taken from the algorithm's calculation results, and the demodulation optimization evaluation value is taken from the algorithm's calculation results. The number of evaluation cycles is set to 24, with each cycle lasting 1 hour. The demodulation data verification pass rate is obtained by statistically analyzing the ratio of complete data volume to total data volume in each cycle. The calculation first calculates the squared difference between the pass rate of each cycle and the overall average, then multiplies it by the ratio of the carrier channel characteristic comprehensive value to the demodulation optimization evaluation value for the corresponding cycle, and sums the results as an intermediate result. The number of communication fault warnings is statistically recorded through the platform's fault records, with one record per evaluation cycle, and the fault impact coefficient is set to 0.1. The calculation first involves applying a first evaluation coefficient to the meter's communication status assessment value, the comprehensive carrier channel characteristic value, and the demodulation optimization assessment value. Then, it combines the throughput deviation of each cycle with the channel and demodulation parameters for further calculation. Finally, it combines the communication status assessment value of each cycle, the number of fault warnings, and the fault impact coefficient for exponential calculation. The three results are then weighted and summed according to the evaluation coefficients to obtain the comprehensive communication performance assessment value. This assessment integrates data from multiple stages to comprehensively evaluate the meter's communication performance, providing a quantitative basis for generating optimization suggestions and ensuring the relevance and effectiveness of these suggestions.

[0033] Preferably, the expression used by the meter communication performance optimization management platform to generate communication fault early warning information is: ,in, This is a fault warning value. As the early warning coefficient, This represents the average amplitude of the signal interference. This represents the average signal distortion. The average signal transmission strength. This represents the average data transmission rate. To provide early warning of sampling frequency, For the first Channel attenuation magnitude of the next sample, This represents the average value of the channel attenuation amplitude. For the first The amplitude of signal interference in the next sample. For the first The signal transmission strength of the next sample For the first Signal distortion at subsampling level For the first The fault warning indicator for each sample is 1 if a warning exists and 0 if no warning exists. For early warning weighting coefficients, For the first Data transmission rate per sample This represents the total average signal transmission strength.

[0034] Specifically, the fault early warning system of the electricity meter communication performance optimization management platform initially sets three early warning coefficients. During stable grid operation, these coefficients are set to 0.5, 0.3, and 0.2 respectively; during grid maintenance periods, they are adjusted to 0.4, 0.4, and 0.2. The average values ​​of signal interference amplitude, signal distortion, signal transmission strength, and data transmission rate are all taken from the communication data collected in step S1, and outliers are excluded before averaging. The number of early warning samples is set to 60, with a 1-minute interval between each sample. Channel attenuation amplitude is collected using a signal attenuation tester, with each sampling point spaced 50 meters apart. The total average value of channel attenuation amplitude is calculated as the arithmetic mean of 60 samples. Signal interference amplitude, signal transmission strength, signal distortion, and data transmission rate are all collected from the real-time data of the corresponding sampling points. The fault early warning indicator is set based on the platform monitoring results. A 1 is used when communication anomalies are detected (e.g., data accuracy is below 90%), and a 0 is used when normal operation is detected. The early warning weight coefficient is set to 0.05, and the total average value of signal transmission strength is calculated as the arithmetic mean of 60 samples. The calculation first calculates the product of signal interference amplitude and signal distortion, then divides it by the product of signal transmission strength and data transmission rate. Next, it calculates the squared difference between the channel attenuation amplitude of each sample and the overall average, multiplies it by the ratio of the corresponding sample's signal interference amplitude to its signal transmission strength, and sums these results as an intermediate outcome. Finally, it calculates the exponential result of the signal distortion, fault warning indicator, and warning weight coefficient for each sample, divides it by the product of the corresponding sample's data transmission rate and the overall average signal transmission strength, sums this result, and combines it with the warning coefficient. The three results are then weighted and summed to obtain the fault warning value. This warning system monitors communication anomalies in real time, predicts fault risks in advance, provides a basis for maintenance personnel to handle problems promptly, and reduces the impact of communication failures on meter data collection.

[0035] Preferably, step S3 includes the following sub-steps: S31, extracting the signal transmission strength fluctuation range, data transmission rate change amplitude, and signal interference amplitude peak from the initial evaluation results of the meter communication status, and using these parameters as initial input parameters for the dynamic estimation algorithm of carrier channel characteristics; S32, setting the time interval and sampling frequency for calculating carrier channel characteristics, and continuously sampling the attenuation signal, noise signal, and multipath propagation signal of the power carrier channel according to the set time interval to obtain multiple sets of raw channel characteristic data; S33, inputting the sampled raw channel characteristic data into the dynamic estimation algorithm of carrier channel characteristics, and using the algorithm to filter, analyze trends, and extract features from the data to calculate the channel attenuation coefficient, noise power spectral density, and multipath propagation delay at different time points; S34, summarizing and organizing the channel characteristic parameters at different time points, analyzing the changing law of channel characteristic parameters over time, and generating carrier channel characteristic change curves and characteristic parameter statistical reports.

[0036] Specifically, in the step-by-step implementation of step S3, S31 first extracts key parameters from the initial evaluation results of the meter communication status output in step S2. If the evaluation result is excellent, the extracted signal transmission strength fluctuation range needs to be controlled within 3-5dBm, the data transmission rate change amplitude is set to 2-4bps, and the peak signal interference amplitude does not exceed 8dBm. If the evaluation result is poor, the extracted signal transmission strength fluctuation range is 15-20dBm, the data transmission rate change amplitude is 8-12bps, and the peak signal interference amplitude is 20-25dBm. These parameters need to be verified by data (excluding outliers that exceed the set range) before being used as the initial input parameters for the dynamic estimation algorithm of carrier channel characteristics. S32 sets the calculation time interval and sampling frequency. The evaluation result is set to 10 minutes for excellent time, 8 minutes for good time, and 5 minutes for medium or poor time. The sampling frequency is uniformly set to 20Hz. Then, the channel detection module continuously samples the attenuation signal (measurement accuracy ±0.1dB), noise signal (measurement range -120 to -80dBm), and multipath propagation signal (measurement accuracy ±1μs) of the power line carrier channel according to the set parameters, collecting 200 sets of raw data at each interval. S33 inputs the raw data into the algorithm. First, a moving average filter (window size 5) is used to process the data to eliminate instantaneous noise. Then, a linear trend analysis algorithm is used to calculate the data change trend and extract the channel attenuation coefficient (unit dB / km), noise power spectral density (unit dBm / Hz), and multipath propagation delay (unit μs) for each time node. S34 summarizes the characteristic parameters of each time point by hour, plots the characteristic change curves using a line graph, and counts the maximum, minimum and average values ​​of the parameters within each hour, generating a characteristic parameter statistical report that includes the curves and statistical data. This step provides accurate channel characteristic data for subsequent demodulation optimization, ensuring that the demodulation scheme adapts to dynamic channel changes.

[0037] Preferably, step S4 includes the following sub-steps: S41, receiving the carrier channel characteristic parameters output from S3, including channel attenuation coefficient, noise power spectral density, multipath propagation delay, and channel capacity; classifying, storing, and standardizing these parameters to establish a carrier channel characteristic parameter database; S42, calling the power line carrier signal demodulation optimization algorithm; inputting the standardized carrier channel characteristic parameters into the algorithm; the algorithm determines the adjustment range of the signal sampling frequency based on the channel attenuation coefficient and sets the initial value of the signal filtering parameters based on the noise power spectral density; S43, making preliminary adjustments to the demodulation threshold based on the multipath propagation delay; calculating the optimal value of the signal distortion compensation parameters in conjunction with the channel capacity; and optimizing the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters through multiple iterative calculations; S44, verifying and testing the optimized demodulation parameters; applying the optimized parameters to a simulated power line carrier signal demodulation process; recording the completeness and accuracy of the demodulation data; fine-tuning the demodulation parameters based on the test results; and generating the final optimized demodulation scheme.

[0038] Specifically, in the step-by-step implementation of step S4, S41 first receives the carrier channel characteristic parameters output in step S3, including the channel attenuation coefficient (range 0-50dB / km), noise power spectral density (-120 to -80dBm / Hz), multipath propagation delay (0-100μs), and channel capacity (100-1000kbps). These parameters are then classified and stored in a MySQL database according to the structure of "calculation period-parameter type-parameter value". At the same time, the parameter values ​​are mapped to the 0-1 range using the min-max normalization method (e.g., 0dB / km corresponds to 0, and 50dB / km corresponds to 1), thus establishing a normalized carrier channel characteristic parameter database. S42 invokes the power line carrier signal demodulation optimization algorithm. After inputting standardized parameters, the algorithm determines the sampling frequency adjustment range based on the channel attenuation coefficient. When the attenuation coefficient is <10dB / km, the range is 800-1200Hz; when it is 10-30dB / km, the range is 1200-1800Hz; and when it is >30dB / km, the range is 1800-2200Hz. The initial value of the filtering parameters is set according to the noise power spectral density. When the power is <-110dBm / Hz, the initial value is 0.3; when it is -110 to -90dBm / Hz, the initial value is 0.5; and when it is >-90dBm / Hz, the initial value is 0.7. S43 initially adjusts the demodulation threshold based on multipath propagation delay. The threshold is set to 0.3 when the delay is <30μs, 0.5 when it is 30-60μs, and 0.7 when it is >60μs. Combined with the channel capacity (compensation parameter 0.2 when it is >500kbps, 0.4 when it is 300-500kbps, and 0.6 when it is <300kbps), the optimal value of the distortion compensation parameter is calculated. The optimized parameter is calculated through 10 iterations (the demodulation success rate is calculated after each iteration, and the process stops when the success rate is ≥98% for 3 consecutive iterations). S44 applies optimized parameters to simulated demodulation tests (using a signal generator to generate carrier signals with different channel characteristics), records the integrity rate (target ≥ 95%) and accuracy rate (target ≥ 98%) of the demodulated data, fine-tunes the filtering parameters when the integrity rate is < 95%, and fine-tunes the demodulation threshold when the accuracy rate is < 98%, and finally generates an optimized demodulation scheme including sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters. This step ensures optimal demodulation parameters and improves signal demodulation accuracy through multi-stage optimization.

[0039] Preferably, step S5 includes the following sub-steps: S51, obtaining the optimized demodulation scheme generated in S4, extracting the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters, and configuring these parameters into the power line carrier signal demodulation module; S52, the demodulation module receives the power line carrier signal transmitted by the meter according to the configured parameters, first filtering the received signal to remove noise interference, and then sampling the filtered signal according to the set sampling frequency to obtain discrete signal sample values; S53, demodulating the discrete signal sample values ​​according to the demodulation threshold to convert the analog signal into a digital signal, and simultaneously using the distortion compensation parameters to compensate and correct the signal distortion generated during the demodulation process to obtain preliminary demodulated data; S54, performing integrity verification on the preliminary demodulated data, judging whether the data is complete by checking the start identifier, end identifier, and check bit of the data frame, verifying the accuracy of the complete data, storing the verified data, marking the verified data as abnormal data and recording the reason for the abnormality.

[0040] Specifically, in the step-by-step implementation of step S5, S51 first extracts the signal sampling frequency (e.g., 1500Hz), filtering parameters (e.g., 0.6), demodulation threshold (e.g., 0.5), and distortion compensation parameters (e.g., 0.4) from the optimized demodulation scheme generated in step S4. These parameters are then configured into the register of the power line carrier signal demodulation module (e.g., PL3105) via the RS485 interface. After configuration, the module returns a "parameter configuration successful" feedback signal. If configuration fails, the parameters are retransmitted until successful. In step S52, the demodulation module receives the power line carrier signal (frequency 3-500kHz) transmitted from the meter via a coupler. It first enters the filtering module and uses an infinite impulse response filter to filter high-frequency noise (>500kHz) and low-frequency interference (<3kHz) in the signal according to the set filtering parameters (0.6). Then, the sampling module discretizes the filtered signal at a sampling frequency of 1500Hz (sampling interval approximately 0.67ms) to generate a 16-bit precision digital sampling sequence. The S53 demodulation module determines the sampled sequence based on a demodulation threshold of 0.5 ("1" for values ​​above the threshold, "0" for values ​​below the threshold), converting the analog signal into a digital signal. Simultaneously, the compensation module adjusts the amplitude of sampled values ​​with amplitude deviations (e.g., below 10% of the standard value) using a distortion compensation parameter of 0.4 (increasing the amplitude by 8%) and performs phase calibration, obtaining preliminary demodulated data including meter consumption, voltage, and current. The S54 module performs integrity verification on the preliminary demodulated data (checking for the inclusion of an 8-bit start code, a 32-bit data segment, and an 8-bit CRC checksum). Complete data undergoes accuracy verification (accuracy is determined by a difference of less than 10 kWh between the electricity consumption and the previous cycle). Verified data is stored on a local server (storage path such as D: / meter_data / ). Incomplete data is marked as "frame missing," and inaccurate data is marked as "out of tolerance" and a timestamp (accurate to the second) is recorded. This step converts the carrier signal into reliable meter data, providing effective data support for subsequent performance evaluation.

[0041] The dynamic sensing model for electricity meter communication status in this invention is used to comprehensively analyze multi-dimensional characteristics and evaluate the communication status of electricity meters during the communication process. It first extracts parameters such as signal transmission strength, data transmission rate, signal interference amplitude, and data frame transmission interval from the communication data collected in step S1. After removing outliers, it calculates the average value and fluctuation range of each parameter. Then, it sets model weight coefficients and interference impact coefficients according to the electricity meter's operating scenario (e.g., residential electricity consumption, industrial electricity consumption). In the residential scenario, the three weight coefficients are set to 0.4, 0.3, and 0.3, and the interference impact coefficient is set to 0.05. In the industrial scenario, the weight coefficients are adjusted to 0.3, 0.4, and 0.3, and the interference impact coefficient is set to 0.08. Finally, the feature values ​​are input into the model, and through multi-feature fusion calculations, the model outputs an evaluation result including the communication status level (excellent, good, medium, poor) and corresponding feature indicators. The "excellent" level requires a signal transmission strength fluctuation range of 3-5 dBm, a data transmission rate variation of 2-4 bps, and a peak signal interference amplitude of <8 dBm. This model replaces single-parameter or static analysis methods, accurately sensing the communication status of electricity meters and providing targeted input for subsequent carrier channel characteristic calculations. It avoids deviations in subsequent processes caused by one-sided evaluations, ensuring the accuracy of electricity meter communication detection and laying the foundation for the power system to obtain reliable electricity meter communication data.

[0042] The dynamic estimation algorithm for carrier channel characteristics in this invention is used to calculate multi-dimensional characteristic parameters of power line carrier channels and quantify channel status in real time. Its implementation first extracts key parameters from the initial assessment results of the meter's communication status. For excellent assessment results, the signal transmission strength fluctuation range of 3-5 dBm is extracted; for poor assessment results, the fluctuation range of 15-20 dBm is extracted. These parameters are verified and used as the initial input to the algorithm. Next, the calculation time interval and sampling frequency are set: 10 minutes for excellent levels, 5 minutes for medium or poor levels, and a uniform sampling frequency of 20 Hz. The channel detection module collects channel attenuation, noise, and multipath propagation signals according to the set parameters, collecting 200 sets of raw data at each interval. Subsequently, the raw data undergoes moving average filtering (window size 5) and linear trend analysis to extract the channel attenuation coefficient (dB / km), noise power spectral density (dBm / Hz), and multipath propagation delay (μs) for each time point. The parameters are then summarized hourly, change curves are plotted, and a statistical report is generated. This algorithm accurately captures dynamic changes in the carrier channel, providing detailed channel data support for power carrier signal demodulation optimization; it solves the problem that existing technologies cannot adapt to channel changes in real time, ensuring that subsequent demodulation schemes match the actual channel conditions, and improving the stability and reliability of electricity meter communication data transmission.

[0043] The power line carrier signal demodulation optimization algorithm in this invention is an algorithm used to dynamically adjust signal demodulation parameters and improve the demodulation quality of power line carrier signals. The process first receives carrier channel characteristic parameters, including channel attenuation coefficient (0-50dB / km) and noise power spectral density (-120 to -80dBm / Hz), stores them in a database according to their structure, and maps them to the 0-1 range using min-max normalization. Then, it calls an algorithm to determine the sampling frequency adjustment range based on the channel attenuation coefficient (e.g., 800-1200Hz when the attenuation coefficient is <10dB / km), sets the initial value of the filtering parameters based on the noise power spectral density (e.g., 0.3 when the power is <-110dBm / Hz), adjusts the demodulation threshold based on the multipath propagation delay (e.g., 0.3 when the delay is <30μs), and calculates the distortion compensation parameters based on the channel capacity (e.g., 0.2 when the capacity is >500kbps). Subsequently, it calculates and optimizes the parameters through 10 iterations, calculates the demodulation success rate after each iteration, and stops when the success rate is ≥98% for 3 consecutive iterations. The optimized parameters are then applied to simulated demodulation tests, and the parameters are fine-tuned based on the integrity rate (target ≥95%) and accuracy rate (target ≥98%) to finally generate an optimized demodulation scheme. This algorithm reduces demodulation errors caused by changes in channel characteristics by dynamically adjusting demodulation parameters, thereby improving the accuracy and completeness of meter data demodulation. It breaks through the limitation of fixed demodulation parameters in existing technologies, ensuring efficient demodulation even in complex channel environments, and provides key technical support for power systems to obtain accurate meter data.

[0044] The meter communication performance optimization management platform of this invention is an integrated platform for integrating data from all aspects of meter communication and realizing comprehensive evaluation and management of communication performance. It relies on four modules: data reception, analysis, evaluation, and output. The data reception module receives demodulated data verification results (completeness rate, accuracy rate) and carrier channel characteristic parameters every 5 minutes, receiving approximately 5760 data points per day. The analysis module calculates the hourly completeness rate (target ≥95%) and accuracy rate (target ≥98%), and plots the channel characteristic parameter change curves. The evaluation module calls a comprehensive evaluation model to calculate a comprehensive score (out of 100, with 85 points or above considered excellent) based on the completeness rate, accuracy rate, and channel parameters according to weights (30%, 30%, 40%), generating a performance evaluation report. The output module generates optimization suggestions based on the report (e.g., adjusting filter parameters when the completeness rate is <95%). When the channel attenuation coefficient increases by more than 2dBm per hour or the accuracy rate decreases by more than 3% per hour, a fault warning is triggered, pushing warning information via SMS and pop-up windows. The report, suggestions, and warnings are stored in a cloud database, supporting historical data queries and daily report generation. This platform enables integrated management of meter communication data integration, performance evaluation, fault early warning, and optimization suggestion generation; it solves the problems of independent links and low management efficiency in existing technologies, provides real-time monitoring and decision support for operation and maintenance personnel, ensures the long-term stable operation of meter communication, and helps upgrade the intelligent management of the power system.

[0045] like Figure 2 As shown, a smart meter communication detection system based on power line carrier is applied to a smart meter communication detection method based on power line carrier. The system includes: a power line carrier communication data acquisition unit, which is connected to the meter via a power line carrier transmission link. This unit is used to acquire communication data such as signal transmission strength, data transmission rate, signal interference amplitude, and data frame transmission interval at different operating stages of the meter, and transmits the acquired data to a meter communication status analysis unit; a meter communication status analysis unit, connected to the power line carrier communication data acquisition unit, which has a built-in dynamic sensing model of meter communication status. It receives the acquired communication data and inputs it into the model, analyzes signal fluctuation characteristics, data transmission stability characteristics, and interference impact characteristics through the model, and outputs the initial evaluation result of meter communication status to a carrier channel characteristic calculation unit; and a carrier channel characteristic calculation unit, connected to both the meter communication status analysis unit and the power line carrier signal demodulation optimization unit. This unit uses a dynamic estimation algorithm for carrier channel characteristics, receives the initial evaluation result of meter communication status, and calculates the attenuation characteristics, noise characteristics, multipath propagation characteristics, and channel capacity variation of the power line carrier channel. The system performs calculations and outputs carrier channel characteristic parameters to the power line carrier signal demodulation optimization unit. The power line carrier signal demodulation optimization unit, connected to the carrier channel characteristic calculation unit and the meter data processing unit, has a built-in power line carrier signal demodulation optimization algorithm. It receives carrier channel characteristic parameters, adjusts the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters, generates an optimized demodulation scheme, and transmits it to the meter data processing unit. The meter data processing unit, connected to the power line carrier signal demodulation optimization unit and the meter communication performance management unit, demodulates the power line carrier signal according to the optimized demodulation scheme, acquires demodulated data, performs integrity and accuracy verification, and transmits the verification results to the meter communication performance management unit. The meter communication performance management unit, connected to the meter data processing unit and the carrier channel characteristic calculation unit, forms the meter communication performance optimization management platform. It receives demodulated data verification results, accuracy verification results, and carrier channel characteristic parameters, comprehensively evaluates the meter communication performance, generates meter communication performance optimization suggestions and communication fault early warning information, and monitors and manages the meter communication status in real time.

[0046] A method and system for intelligent communication detection of electricity meters based on power line carrier communication is proposed. The constructed dynamic sensing model for the meter's communication status no longer relies on single parameters or static analysis as in existing technologies. Instead, it integrates multi-dimensional communication data such as signal transmission strength, data transmission rate, and signal interference amplitude to comprehensively analyze signal fluctuations, transmission stability, and interference characteristics. This significantly improves the accuracy of meter communication status assessment and can identify potential communication faults in advance, effectively solving the problems of inaccurate assessment results and difficulty in predicting faults in existing technologies. Simultaneously, a dynamic estimation algorithm for carrier channel characteristics is used to calculate channel attenuation, noise, and multipath propagation characteristics in real time. Combined with a power line carrier signal demodulation optimization algorithm, the sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters are dynamically adjusted. This allows the demodulation process to adapt to dynamic channel changes in real time, avoiding signal distortion and significantly improving data demodulation quality. This overcomes the shortcomings of existing technologies, such as the inability to adjust detection strategies according to channel changes and poor demodulation performance.

[0047] This method and system have significant advantages in management integration and overall efficiency improvement. Through the meter communication performance optimization management platform, it organically integrates signal acquisition, status analysis, channel estimation, demodulation optimization, data processing, and performance management, breaking down the barriers of independent operation of each link in the existing technology. The platform can centrally process demodulation verification results, accuracy verification results, and carrier channel characteristic parameters, quickly complete the comprehensive evaluation of meter communication performance, and generate optimization suggestions and fault warning information in a timely manner, greatly improving the efficiency of communication performance management. It can quickly solve problems that occur during communication, effectively improving the situation of low management efficiency and inability to respond to communication problems in a timely manner in the existing technology, and ultimately ensuring the real-time performance and reliability of meter communication, meeting the high requirements of the power system for meter communication.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting smart communication in electricity meters based on power line carrier, characterized in that, Includes the following steps: S1. Collect communication data of the electricity meter during different operating periods via a power line carrier transmission link. This communication data includes signal transmission strength, data transmission rate, signal interference amplitude, and data frame transmission interval. S2. Input the collected communication data into a dynamic sensing model of the electricity meter's communication status. The model analyzes the signal fluctuation characteristics, data transmission stability characteristics, and interference impact characteristics during the meter's communication process to obtain an initial assessment result of the meter's communication status. S3. Based on the initial assessment result of the meter's communication status, use a dynamic estimation algorithm for carrier channel characteristics to calculate the attenuation characteristics, noise characteristics, multipath propagation characteristics, and channel capacity variation law of the power line carrier channel to obtain the carrier channel characteristics. Parameters; S4, input the carrier channel characteristic parameters into the power carrier signal demodulation optimization algorithm, and adjust the signal sampling frequency, signal filtering parameters, demodulation threshold, and signal distortion compensation parameters during the demodulation process to generate an optimized demodulation scheme; S5, demodulate the power carrier signal transmitted by the meter according to the optimized demodulation scheme, obtain the demodulated meter data, and perform integrity verification and accuracy verification on the demodulated data; S6, upload the demodulated data verification results, accuracy verification results, and carrier channel characteristic parameters to the meter communication performance optimization management platform, and the platform comprehensively evaluates the meter communication performance and generates meter communication performance optimization suggestions and communication fault early warning information.

2. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, The expression for the dynamic sensing model of the electricity meter communication status is: ,in, This is the meter's communication status assessment value. These are the model weight coefficients. The average signal transmission strength. This represents the average data transmission rate. This represents the average data frame transmission interval. This represents the average amplitude of the signal interference. For the number of data collections, For the first The signal transmission strength of the second acquisition The total average value of the signal transmission strength. For the first Data transmission rate per acquisition, For the first The data frame transmission interval for each acquisition For the first The amplitude of signal interference collected in the second sampling. This represents the interference effect coefficient.

3. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, The expression for the dynamic estimation algorithm of carrier channel characteristics is: ,in, This is the combined value of carrier channel characteristics. For algorithm coefficients, For channel bandwidth, For channel input power, For channel gain, For channel efficiency, For noise power spectral density, For channel interference power, This represents the number of sampling points for channel attenuation. For the first Channel attenuation amplitude at each sampling point For the first Attenuation coefficient at each sampling point For time variables, For the first Channel noise at each sampling point This represents the number of sampling points for multipath propagation. For the first Multipath propagation delay at each sampling point This represents the average multipath propagation delay.

4. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, The expression for the power line carrier signal demodulation optimization algorithm is as follows: ,in, To demodulate and optimize the evaluation value, For algorithm weights, The average value of the signal sampling frequency. The average value of the signal filtering coefficients. The average value of the demodulation threshold. This represents the average signal distortion. This is the distortion compensation coefficient. The number of times the demodulation parameters are sampled. For the first The sampling frequency of the signal in the next sample. The average value of the signal sampling frequency. For the first The signal filtering coefficients of the next sample. For the first Demodulation threshold for each sample. For the first Signal distortion at the subsample level.

5. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, The expression used by the electricity meter communication performance optimization management platform to comprehensively evaluate the electricity meter communication performance is as follows: ,in, This is a comprehensive evaluation value for communication performance. For evaluation coefficients, This is the meter's communication status assessment value. This is the combined value of carrier channel characteristics. To demodulate and optimize the evaluation value, To assess the number of cycles, For the first The pass rate of demodulation data verification for each evaluation cycle The average pass rate for demodulated data verification. For the first The comprehensive value of carrier-channel characteristics for each evaluation period. For the first Demodulation optimization evaluation value for each evaluation period For the first The meter communication status assessment value for each assessment period. The number of communication failure warnings. This represents the fault impact coefficient.

6. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, The expression used by the electricity meter communication performance optimization management platform to generate communication fault early warning information is: ,in, This is a fault warning value. As the early warning coefficient, This represents the average amplitude of the signal interference. This represents the average signal distortion. The average signal transmission strength. This represents the average data transmission rate. To provide early warning of sampling frequency, For the first Channel attenuation magnitude of the next sample, This represents the average value of the channel attenuation amplitude. For the first The amplitude of signal interference in the next sample. For the first The signal transmission strength of the next sample For the first Signal distortion at subsampling level For the first The fault warning indicator for each sample is 1 if a warning exists and 0 if no warning exists. For early warning weighting coefficients, For the first Data transmission rate per sample This represents the total average signal transmission strength.

7. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, S3 includes the following sub-steps: S31, extracting the signal transmission strength fluctuation range, data transmission rate change amplitude, and signal interference amplitude peak from the initial assessment results of the meter communication status, and using these parameters as the initial input parameters for the carrier channel characteristic dynamic estimation algorithm; S32, setting the time interval and sampling frequency for carrier channel characteristic calculation, and continuously sampling the attenuation signal, noise signal, and multipath propagation signal of the power carrier channel according to the set time interval to obtain multiple sets of raw channel characteristic data; S33, inputting the sampled raw channel characteristic data into the carrier channel characteristic dynamic estimation algorithm, and using the algorithm to filter, analyze trends, and extract features from the data to calculate the channel attenuation coefficient, noise power spectral density, and multipath propagation delay at different time points; S34, summarizing and organizing the channel characteristic parameters at different time points, analyzing the changing law of channel characteristic parameters over time, and generating carrier channel characteristic change curves and characteristic parameter statistical reports.

8. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, The S4 includes the following steps: S41, receiving the carrier channel characteristic parameters output by S3, including channel attenuation coefficient, noise power spectral density, multipath propagation delay and channel capacity, classifying, storing and standardizing these parameters, and establishing a carrier channel characteristic parameter database; S42, invoke the power line carrier signal demodulation optimization algorithm, input the standardized carrier channel characteristic parameters into the algorithm, the algorithm determines the adjustment range of the signal sampling frequency based on the channel attenuation coefficient, and sets the initial value of the signal filtering parameters based on the noise power spectral density; S43, make preliminary adjustments to the demodulation threshold based on the multipath propagation delay, calculate the optimal value of the signal distortion compensation parameters in combination with the channel capacity, and optimize the signal sampling frequency, filtering parameters, demodulation threshold and distortion compensation parameters through multiple iterative calculations; S44, verify and test the optimized demodulation parameters, apply the optimized parameters to the simulated power line carrier signal demodulation process, record the integrity and accuracy of the demodulation data, fine-tune the demodulation parameters based on the test results, and generate the final optimized demodulation scheme.

9. The method for detecting smart communication in an electricity meter based on power line carrier as described in claim 1, characterized in that, S5 includes the following sub-steps: S51, obtaining the optimized demodulation scheme generated in S4, extracting the signal sampling frequency, filtering parameters, demodulation threshold and distortion compensation parameters from it, and configuring these parameters into the power line carrier signal demodulation module; S52, the demodulation module receives the power line carrier signal transmitted by the meter according to the configured parameters. First, it filters the received signal to remove noise interference. Then, it samples the filtered signal according to the set sampling frequency to obtain discrete signal sample values. S53, it demodulates the discrete signal sample values ​​according to the demodulation threshold, converting the analog signal into a digital signal. At the same time, it uses distortion compensation parameters to compensate and correct the signal distortion generated during demodulation to obtain preliminary demodulated data. S54, it performs integrity verification on the preliminary demodulated data. It checks the start identifier, end identifier, and check bit of the data frame to determine whether the data is complete. It verifies the accuracy of the complete data, stores the data that passes the verification, and marks the data that fails the verification as abnormal data and records the reason for the abnormality.

10. A smart communication detection system for electricity meters based on power line carrier communication, characterized in that, This system is applied to the smart meter communication detection method based on power line carrier as described in claim 1, comprising: a power line carrier communication data acquisition unit, which is connected to the meter via a power line carrier transmission link, for acquiring communication data such as signal transmission strength, data transmission rate, signal interference amplitude, and data frame transmission interval of the meter at different operating stages, and transmitting the acquired data to the meter communication status analysis unit; a meter communication status analysis unit, connected to the power line carrier communication data acquisition unit, having a built-in dynamic sensing model of meter communication status, receiving the acquired communication data and inputting it into the model, analyzing signal fluctuation characteristics, data transmission stability characteristics, and interference impact characteristics through the model, and outputting the initial evaluation result of the meter communication status to the carrier channel characteristic calculation unit; and a carrier channel characteristic calculation unit, connected to both the meter communication status analysis unit and the power line carrier signal demodulation optimization unit, employing a dynamic estimation algorithm for carrier channel characteristics, receiving the initial evaluation result of the meter communication status, calculating the attenuation characteristics, noise characteristics, multipath propagation characteristics, and channel capacity variation law of the power line carrier channel, and outputting... The carrier channel characteristic parameters are sent to the power line carrier signal demodulation optimization unit. The power line carrier signal demodulation optimization unit, connected to the carrier channel characteristic calculation unit and the meter data processing unit, has a built-in power line carrier signal demodulation optimization algorithm. It receives the carrier channel characteristic parameters, adjusts the signal sampling frequency, filtering parameters, demodulation threshold, and distortion compensation parameters, generates an optimized demodulation scheme, and transmits it to the meter data processing unit. The meter data processing unit, connected to the power line carrier signal demodulation optimization unit and the meter communication performance management unit, demodulates the power line carrier signal according to the optimized demodulation scheme, acquires the demodulated data, performs integrity verification and accuracy verification, and transmits the verification results to the meter communication performance management unit. The meter communication performance management unit, connected to the meter data processing unit and the carrier channel characteristic calculation unit, constitutes the meter communication performance optimization management platform. It receives the demodulation data verification results, accuracy verification results, and carrier channel characteristic parameters, comprehensively evaluates the meter communication performance, generates meter communication performance optimization suggestions and communication fault early warning information, and monitors and manages the meter communication status in real time.

Images