A power consumption information acquisition device of an electric energy metering pulse simulation system

Abstract

The application discloses a kind of electric energy metering pulse simulation system's power utilization information acquisition equipment, it is related to monitoring and analysis technical field, it includes ripple disturbance simulation to initial pulse feature dataset, generates interference pulse waveform matrix;Interference pulse waveform matrix is separated to multi-source interference signal processing, constructs three-dimensional clock error compensation network, generates clock synchronization calibration pulse set, and extracts the dynamic characteristic parameter corresponding to clock synchronization calibration pulse set;Multi-modal data fusion processing is carried out to dynamic characteristic parameter, and compressed pulse feature data is formed;Establish simulated load feature matching model, output simulated load adaptation parameter group, convert simulated load adaptation parameter group into wireless transmission data frame;Transmission protocol adaptive packaging is carried out to wireless transmission data frame, based on channel quality evaluation index dynamic adjustment data packaging strategy, executes remote power utilization information acquisition transmission.The application has the effect of improving power utilization information acquisition efficiency.

Description

Technical Field

[0001] This application relates to the field of monitoring and analysis technology, and in particular to an electricity consumption information acquisition device for an electricity meter pulse simulation system. Background Technology

[0002] One of the important functions of electricity information collection equipment is to measure the electricity consumption and power of electricity meters by collecting the metering pulses of the electricity meters. With the rapid advancement of smart grid construction, the requirements for the accuracy and reliability of electricity metering systems are increasing day by day.

[0003] In related technologies, traditional energy meter pulse metering simulation systems are susceptible to interference from high-frequency ripples and power frequency harmonics in complex electromagnetic environments, resulting in widespread distortion of pulse signals. Furthermore, existing data acquisition systems mostly use fixed communication protocols to transmit load information and lack dynamic optimization strategies for different channel qualities, which reduces the efficiency of electricity information acquisition and needs improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides an electricity consumption information acquisition device for an electricity meter pulse simulation system.

[0005] In a first aspect, this application provides a method for collecting electricity consumption information in an electricity meter pulse simulation system, comprising the following steps: Step S1: Generate a standard measurement pulse signal through a reference clock source, synchronously construct a pulse waveform topology model, output an initial pulse feature dataset, simulate ripple disturbance on the initial pulse feature dataset, and generate a pulse waveform matrix with interference. Step S2: Perform multi-source interference signal separation processing on the interference pulse waveform matrix to obtain the effective pulse component sequence, and optimize the signal impedance matching of the effective pulse component sequence to form a pulse data set before calibration; Step S3: Construct a three-dimensional clock error compensation network, combine carrier phase offset features to perform error compensation on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and extract the dynamic feature parameters corresponding to the clock synchronization calibration pulse set; Step S4: Perform multimodal data fusion processing on the dynamic feature parameters to generate a multidimensional pulse feature vector, and then perform data dimensionality reduction optimization on the multidimensional pulse feature vector to form compressed pulse feature data; Step S5: Establish a simulated load feature matching model, match and map the compressed pulse feature data with the preset load condition database, output the simulated load adaptation parameter set, and convert the simulated load adaptation parameter set into wireless transmission data frames. Step S6: Construct a multi-protocol communication compatible gateway, perform adaptive encapsulation of wireless transmission data frames according to transmission protocols, dynamically adjust the data encapsulation strategy based on channel quality assessment indicators, and then execute remote electricity information collection and transmission.

[0006] Preferably, step S1 specifically includes the following steps: Step S11: Generate an initial square wave signal with a preset standard value through the crystal oscillator corresponding to the reference clock source, and correct the duty cycle of the initial square wave signal to generate a standard measurement pulse signal. Step S12: Construct a pulse waveform topology model based on the standard pulse parameter data corresponding to the standard measurement pulse signal. The standard pulse parameter data corresponding to the standard measurement pulse signal includes rising edge, falling edge, and pulse width parameters. Step S13: Based on the pulse waveform topology model, collect the parasitic inductance data and distributed capacitance parameters of the standard metering pulse signal on the transmission path, and then generate an initial pulse feature dataset based on the parasitic inductance data and distributed capacitance parameters. The initial pulse feature dataset includes pulse amplitude distortion rate, phase jitter coefficient and peak offset. Step S14: Inject interference signals into the initial pulse feature dataset to simulate ripple disturbance, thereby generating a pulse waveform matrix with interference.

[0007] Preferably, step S2 specifically includes the following steps: Step S21: Perform wavelet packet decomposition on the interference pulse waveform matrix to extract the energy distribution feature data of sub-band signals in different frequency bands, and filter the energy distribution feature data of sub-band signals based on a preset energy threshold to filter out effective frequency band signal components; Step S22: Analyze the effective frequency band signal components and separate the effective pulse component sequence based on the analysis results; Step S23: Based on the equivalent impedance model corresponding to the transmission path, perform impedance matching optimization on the effective pulse component sequence, and form a pulse data set before calibration based on the result of impedance matching optimization.

[0008] Preferably, step S3 specifically includes the following steps: Step S31: Construct a three-dimensional clock error compensation network and obtain carrier phase offset features. Then, based on the three-dimensional clock error compensation network and carrier phase offset features, capture the corresponding phase jitter and frequency drift in the pre-calibration pulse data set. Then, generate dynamic phase correction coefficients based on the phase jitter and frequency drift. Step S32: Use the dynamic phase correction coefficient to perform clock synchronization calibration on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and then extract dynamic feature parameters based on the clock synchronization calibration pulse set.

[0009] Preferably, step S5 specifically includes the following steps: Step S51: Establish a simulated load characteristic matching model, which includes a load type classifier, a power curve fitting unit, and an impedance characteristic mapping unit; Step S52: Input the compressed pulse feature data into the load type classifier to identify the electrical appliance type and operating status corresponding to the load; Step S53: Based on the typical load power curves and impedance spectra in the preset load condition database, perform matching mapping processing with the electrical appliance type and operating status corresponding to the load, and then generate a set of simulated load adaptation parameters. Step S54: Convert the data encapsulation format of the simulated load adaptation parameter group, add timestamp and device identification fields, and generate wireless transmission data frames.

[0010] Preferably, step S6 specifically includes the following steps: Step S61: Construct a multi-protocol communication compatible gateway, parse the payload type identifier corresponding to the wireless transmission data frame, and select the corresponding protocol's message header and check field for adaptive encapsulation based on the payload type identifier; Step S62: Monitor channel quality assessment indicators in real time. The channel quality assessment indicators include signal strength, bit error rate and delay jitter. Evaluate the channel quality assessment indicators and dynamically adjust the data encapsulation strategy based on the evaluation results.

[0011] Secondly, this application provides an electricity consumption information acquisition device for an electricity meter metering pulse simulation system, comprising: The signal generation module is used to generate a standard measurement pulse signal through a reference clock source, synchronously construct a pulse waveform topology model, output an initial pulse feature dataset, simulate ripple disturbance on the initial pulse feature dataset, and generate a pulse waveform matrix with interference. The analysis module is used to perform multi-source interference signal separation processing on the interference pulse waveform matrix, obtain the effective pulse component sequence, and optimize the signal impedance matching of the effective pulse component sequence to form a pulse data set before calibration. The calibration module is used to construct a three-dimensional clock error compensation network, combine carrier phase offset characteristics to perform error compensation on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and extract the dynamic feature parameters corresponding to the clock synchronization calibration pulse set. The optimization module is used to perform multimodal data fusion processing on the dynamic feature parameters to generate a multidimensional pulse feature vector, and then perform data dimensionality reduction optimization on the multidimensional pulse feature vector to form compressed pulse feature data. The data conversion module is used to establish a simulated load characteristic matching model, match and map compressed pulse characteristic data with a preset load condition database, output a simulated load adaptation parameter set, and convert the simulated load adaptation parameter set into wireless transmission data frames. The transmission evaluation module is used to build a multi-protocol communication compatible gateway, perform adaptive encapsulation of wireless transmission data frames according to transmission protocols, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then execute remote electricity information collection and transmission.

[0012] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the electricity consumption information acquisition method of an electricity meter metering pulse simulation system as described in any of the above claims.

[0013] In summary, this application includes the following beneficial technical effects: 1. This application provides a method for collecting electricity consumption information in an electricity meter metering pulse simulation system. By simulating ripple disturbance on an initial pulse feature dataset, an interference pulse waveform matrix is ​​generated. The interference pulse waveform matrix is ​​then processed to separate multi-source interference signals to obtain an effective pulse component sequence. The effective pulse component sequence is then optimized by signal impedance matching to form a pulse data set before calibration. A three-dimensional clock error compensation network is constructed, and error compensation is performed on the pulse data set before calibration by combining carrier phase offset characteristics. A clock synchronization calibration pulse set is generated, and the dynamic feature parameters corresponding to the clock synchronization calibration pulse set are extracted, thereby effectively reducing the occurrence of pulse signal distortion. 2. By performing multimodal data fusion processing on dynamic feature parameters, a multidimensional pulse feature vector is generated. This multidimensional pulse feature vector is then optimized for dimensionality reduction to form compressed pulse feature data. A simulated load feature matching model is established, and the compressed pulse feature data is matched and mapped with a preset load condition database to output a simulated load adaptation parameter set. This simulated load adaptation parameter set is then converted into wireless transmission data frames. A multi-protocol communication compatible gateway is constructed to adaptively encapsulate the wireless transmission data frames according to the transmission protocol. Based on channel quality assessment indicators, the data encapsulation strategy is dynamically adjusted to execute remote electricity consumption information collection and transmission. This effectively optimizes strategies for different channel qualities, thereby significantly improving the efficiency of electricity consumption information collection. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the method for collecting electricity consumption information in the electricity meter metering pulse simulation system according to an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of the electricity consumption information collection system of the electricity meter metering pulse simulation system in the embodiment of this application. Detailed Implementation

[0017] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0018] Example 1 This application discloses a method for collecting electricity consumption information in an electricity meter pulse simulation system.

[0019] Reference Figure 1 A method for collecting electricity consumption information in an electricity meter pulse simulation system includes the following steps: Step S1: Generate a standard measurement pulse signal through a reference clock source, synchronously construct a pulse waveform topology model, output an initial pulse feature dataset, simulate ripple disturbance on the initial pulse feature dataset, and generate a pulse waveform matrix with interference. Step S2: Perform multi-source interference signal separation processing on the interference pulse waveform matrix to obtain the effective pulse component sequence, and optimize the signal impedance matching of the effective pulse component sequence to form a pulse data set before calibration; Step S3: Construct a three-dimensional clock error compensation network, combine carrier phase offset features to perform error compensation on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and extract the dynamic feature parameters corresponding to the clock synchronization calibration pulse set; Step S4: Perform multimodal data fusion processing on the dynamic feature parameters to generate a multidimensional pulse feature vector, and then perform data dimensionality reduction optimization on the multidimensional pulse feature vector to form compressed pulse feature data; Step S5: Establish a simulated load feature matching model, match and map the compressed pulse feature data with the preset load condition database, output the simulated load adaptation parameter set, and convert the simulated load adaptation parameter set into wireless transmission data frames. Step S6: Construct a multi-protocol communication compatible gateway, perform adaptive encapsulation of wireless transmission data frames according to transmission protocols, dynamically adjust the data encapsulation strategy based on channel quality assessment indicators, and then execute remote electricity information collection and transmission.

[0020] It should be noted that step S1 specifically includes the following steps: Step S11: Generate an initial square wave signal with a preset standard value through the crystal oscillator corresponding to the reference clock source, and correct the duty cycle of the initial square wave signal to generate a standard measurement pulse signal. Step S12: Construct a pulse waveform topology model based on the standard pulse parameter data corresponding to the standard measurement pulse signal. The standard pulse parameter data corresponding to the standard measurement pulse signal includes rising edge, falling edge, and pulse width parameters. The pulse waveform topology model is used to determine the correlation weight matrix of the standard metrological pulse signal in the time domain and frequency domain; Step S13: Based on the pulse waveform topology model, collect the parasitic inductance data and distributed capacitance parameters of the standard metering pulse signal on the transmission path, and then generate an initial pulse feature dataset based on the parasitic inductance data and distributed capacitance parameters. The initial pulse feature dataset includes pulse amplitude distortion rate, phase jitter coefficient and peak offset. Step S14: Inject interference signals into the initial pulse feature dataset to simulate ripple disturbance, thereby generating a pulse waveform matrix with interference.

[0021] The interference pulse waveform matrix is ​​configured with multiple interference modes, including but not limited to power fluctuations, electromagnetic coupling, and ground loop interference.

[0022] Specifically, a temperature-controlled crystal oscillator generates an initial square wave signal with a duty cycle of 48% ± 2%, which is then compensated for using a digital duty cycle correction circuit. The compensation steps are as follows: Sampling and detection: A high-speed comparator was used to detect the zero-crossing point of the square wave, and the actual duty cycle was measured to be 47.8%. PID control: The PID controller calculates the correction value and drives the MOSFET switch to adjust the charging and discharging time constant; Closed-loop calibration: After multiple cycles of adjustment, the duty cycle is stabilized at 50.0%±0.1%, generating a standard metrological pulse signal that conforms to the standard.

[0023] Based on the rise time, fall time, and pulse width parameters of the standard measurement pulse signal, a pulse waveform topology model is constructed: The pulse waveform topology model quantifies the time-frequency domain relationship, providing a mathematical basis for subsequent interference analysis. For example, it can predict the degree of attenuation of high-frequency components as the rise time increases. On a 50Ω transmission path, parasitic inductance and distributed capacitance are measured using a vector network analyzer, and an initial pulse feature dataset is generated by combining this with a pulse waveform topology model. Amplitude distortion rate: Calculates the change in pulse amplitude before and after transmission. For example, if the amplitude drops from 2.0V to 1.92V, the distortion rate is 4%. Phase jitter coefficient: Detected by phase shift via phase-locked loop; Peak offset: The measured peak occurrence time was delayed from 50ns to 50.2ns, with an offset of 0.2ns.

[0024] It should be noted that step S2 specifically includes the following steps: Step S21: Perform wavelet packet decomposition on the interference pulse waveform matrix to extract the energy distribution feature data of sub-band signals in different frequency bands, and filter the energy distribution feature data of sub-band signals based on a preset energy threshold to filter out effective frequency band signal components; Step S22: Analyze the effective frequency band signal components and separate the effective pulse component sequence based on the analysis results; The effective pulse component sequence is related to the standard measurement pulse signal and the influence of power frequency harmonics and random pulse noise needs to be filtered out. Step S23: Based on the equivalent impedance model corresponding to the transmission path, perform impedance matching optimization on the effective pulse component sequence, and form a pulse data set before calibration based on the result of impedance matching optimization.

[0025] In this embodiment of the application, it is necessary to perform pulse width consistency verification on the pulse data set before calibration and remove abnormal pulse data with timing offset exceeding a preset threshold.

[0026] Specifically, a three-level wavelet packet decomposition is performed on the noisy pulse waveform matrix, dividing the signal frequency band from 0-20MHz into 8 sub-bands (each band 2.5MHz), taking the Daubechies wavelet (db4) as an example: Decomposition process: First-level decomposition: Low frequency band (0-10MHz), High frequency band (10-20MHz); The second layer of decomposition: the low frequency band is further divided into 0-5MHz and 5-10MHz, and the high frequency band is divided into 10-15MHz and 15-20MHz; The third level of decomposition results in 8 sub-bands (e.g., 0-2.5MHz, 2.5-5MHz, ..., 17.5-20MHz). Energy distribution calculation: Calculate the normalized energy of each sub-band signal (e.g., the main frequency energy of a 10MHz pulse is concentrated in the 7.5-12.5MHz band, accounting for 65% of the energy; the power frequency interference energy of 50Hz is concentrated in the 0-0.1MHz band, accounting for 8%; the radio frequency noise energy of 2.4GHz is concentrated in the 2.3-2.5GHz band, accounting for 15%). Threshold screening: An energy threshold of 5% is set to screen out frequency bands with an energy percentage greater than 5% (such as 7.5-12.5MHz and 2.3-2.5GHz). Among them, the 2.3-2.5GHz frequency band, although high in energy, is an interference band and is therefore excluded; the 7.5-12.5MHz frequency band is retained as the main effective frequency band, and fine separation of multi-band noise is achieved through wavelet packet decomposition. The retained effective frequency band signal (7.5-12.5MHz) is subjected to inverse wavelet packet transform to reconstruct the signal components free from low-frequency power frequency and high-frequency radio frequency interference: Time-frequency domain analysis: By reconstructing the time-frequency diagram of the signal, it was confirmed that the rise time, fall time and other characteristics of the 10MHz pulse were clear, while the noise components of the interference frequency band had been filtered out. Pulse sequence extraction: The reconstructed signal is binarized using an adaptive threshold detection algorithm to extract the pulse edge time. For example, the pulse period is detected to be 100ns with a duty cycle of 50%, which is consistent with the standard measurement pulse, indicating that the effective component has been successfully separated. The effective pulse component sequence is impedance matched and optimized based on the equivalent impedance model of the transmission path. The Smith chart is used for impedance matching, and then the pulse data set before calibration is formed based on the result of the impedance matching optimization.

[0027] It should be noted that step S3 specifically includes the following steps: Step S31: Construct a three-dimensional clock error compensation network and obtain carrier phase offset features. Then, based on the three-dimensional clock error compensation network and carrier phase offset features, capture the corresponding phase jitter and frequency drift in the pre-calibration pulse data set. Then, generate dynamic phase correction coefficients based on the phase jitter and frequency drift. The three-dimensional clock error compensation network includes a time base error correction layer, a phase offset compensation layer, and a temperature drift suppression layer, which respectively provide collaborative compensation for the time base, carrier phase, and ambient temperature drift of the pulse signal. Step S32: Use the dynamic phase correction coefficient to perform clock synchronization calibration on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and then extract dynamic feature parameters based on the clock synchronization calibration pulse set. The dynamic feature parameters include pulse repetition frequency deviation rate and peak fluctuation variance.

[0028] Furthermore, in this embodiment, step S4 performs time series alignment processing on the dynamic feature parameters and fuses pulse width, amplitude, phase, and frequency features to generate a multidimensional pulse feature vector; and uses a local linear embedding algorithm to perform manifold learning on the multidimensional pulse feature vector, extracting low-dimensional embedding representations in the high-dimensional space, and then uses principal component analysis to remove redundant features from the low-dimensional embedding representations, retaining feature dimensions with a variance contribution rate greater than a preset value to form compressed pulse feature numbers, and performs data normalization processing on the compressed pulse feature data to ensure the consistency of the dimensions of each feature dimension; It should be noted that step S5 specifically includes the following steps: Step S51: Establish a simulated load characteristic matching model, which includes a load type classifier, a power curve fitting unit, and an impedance characteristic mapping unit; Step S52: Input the compressed pulse feature data into the load type classifier to identify the electrical appliance type and operating status corresponding to the load; Step S53: Based on the typical load power curves and impedance spectra in the preset load condition database, perform matching mapping processing with the electrical appliance type and operating status corresponding to the load, and then generate a set of simulated load adaptation parameters. Step S54: Convert the data encapsulation format of the simulated load adaptation parameter group, add timestamp and device identification fields, and generate wireless transmission data frames.

[0029] Specifically, while converting the data encapsulation format of the simulated load adaptation parameter group, timestamp and device identification fields are added to generate wireless transmission data frames, which conform to wireless communication protocols.

[0030] It should be noted that step S6 specifically includes the following steps: Step S61: Construct a multi-protocol communication compatible gateway, parse the payload type identifier corresponding to the wireless transmission data frame, and select the corresponding protocol's message header and check field for adaptive encapsulation based on the payload type identifier; Step S62: Monitor channel quality assessment indicators in real time. The channel quality assessment indicators include signal strength, bit error rate and delay jitter. Evaluate the channel quality assessment indicators and dynamically adjust the data encapsulation strategy based on the evaluation results.

[0031] Specifically, build a multi-protocol communication compatible gateway that supports multiple protocols and implements the following functions: Load type identification: Parse the payload type identifier in the wireless transmission data frame (such as 0x01 in the frame header field representing sensor data, and 0x02 representing control commands). For example, when data with the identifier 0x03 is received, the gateway determines that the Modbus protocol should be used for transmission. Protocol adaptive encapsulation: For sensor data (load type 0x01), select the lightweight CoAP protocol, encapsulate the header of 4 bytes (including source / destination port and version number), and adapt to low power wide area network (LPWAN). For control commands (load type 0x02), the Modbus RTU protocol with CRC-16 checksum is used to ensure command reliability. The encapsulation header includes 8 bytes of fields such as function code and register address. For example: the electricity consumption data (load type 0x01) uploaded by a smart meter is encapsulated into a CoAP message by the gateway, reducing transmission overhead by 60%, which is suitable for low-bandwidth scenarios in NB-IoT networks; Real-time monitoring of channel quality assessment metrics and dynamic optimization of encapsulation strategies: Indicator monitoring: Signal strength: If it drops from -70dBm to -90dBm, it indicates that the link attenuation has worsened; Bit error rate: from 10 -5 Up to 10 -3 This indicates increased noise interference. Delay jitter: increased from 10ms to 50ms, reflecting network congestion.

[0032] Strategy adjustment logic: High-quality channel (RSSI > -80dBm, BER < 10) -6 ): Employs the efficient MQTT protocol (12-byte header), enables QoS0, and improves throughput; Poor channel performance (RSSI < -95 dBm, BER > 10) -3 ): Switch to the Modbus TCP protocol with ARQ retransmission, add a sequence number and checksum field (20 bytes) to the encapsulation header, and reduce the transmission rate to 50% of the original to improve reliability.

[0033] Example 2 This application also discloses an electricity consumption information acquisition device for an electricity meter metering pulse simulation system.

[0034] Reference Figure 2 An electricity consumption information acquisition device for an electricity meter pulse simulation system, comprising: The signal generation module is used to generate a standard measurement pulse signal through a reference clock source, synchronously construct a pulse waveform topology model, output an initial pulse feature dataset, simulate ripple disturbance on the initial pulse feature dataset, and generate a pulse waveform matrix with interference. The analysis module is used to perform multi-source interference signal separation processing on the interference pulse waveform matrix, obtain the effective pulse component sequence, and optimize the signal impedance matching of the effective pulse component sequence to form a pulse data set before calibration. The calibration module is used to construct a three-dimensional clock error compensation network, combine carrier phase offset characteristics to perform error compensation on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and extract the dynamic feature parameters corresponding to the clock synchronization calibration pulse set. The optimization module is used to perform multimodal data fusion processing on the dynamic feature parameters to generate a multidimensional pulse feature vector, and then perform data dimensionality reduction optimization on the multidimensional pulse feature vector to form compressed pulse feature data. The data conversion module is used to establish a simulated load characteristic matching model, match and map compressed pulse characteristic data with a preset load condition database, output a simulated load adaptation parameter set, and convert the simulated load adaptation parameter set into wireless transmission data frames. The transmission evaluation module is used to build a multi-protocol communication compatible gateway, perform adaptive encapsulation of wireless transmission data frames according to transmission protocols, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then execute remote electricity information collection and transmission.

[0035] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0036] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0037] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for collecting electricity consumption information in an electricity meter pulse simulation system, characterized in that, Includes the following steps: Step S1: Generate a standard measurement pulse signal through a reference clock source, synchronously construct a pulse waveform topology model, output an initial pulse feature dataset, simulate ripple disturbance on the initial pulse feature dataset, and generate a pulse waveform matrix with interference. Step S2: Perform multi-source interference signal separation processing on the interference pulse waveform matrix to obtain the effective pulse component sequence, and optimize the signal impedance matching of the effective pulse component sequence to form a pulse data set before calibration; Step S3: Construct a three-dimensional clock error compensation network, combine carrier phase offset features to perform error compensation on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and extract the dynamic feature parameters corresponding to the clock synchronization calibration pulse set; Step S3 specifically includes the following steps: Step S31: Construct a three-dimensional clock error compensation network and obtain carrier phase offset features. Then, based on the three-dimensional clock error compensation network and carrier phase offset features, capture the corresponding phase jitter and frequency drift in the pre-calibration pulse data set. Then, generate dynamic phase correction coefficients based on the phase jitter and frequency drift. The three-dimensional clock error compensation network includes a time base error correction layer, a phase offset compensation layer, and a temperature drift suppression layer, which respectively provide collaborative compensation for the time base, carrier phase, and ambient temperature drift of the pulse signal. Step S32: Use the dynamic phase correction coefficient to perform clock synchronization calibration on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and then extract dynamic feature parameters based on the clock synchronization calibration pulse set. The dynamic feature parameters include pulse repetition frequency deviation rate and peak fluctuation variance. Step S4: Perform multimodal data fusion processing on the dynamic feature parameters to generate a multidimensional pulse feature vector, and then perform data dimensionality reduction optimization on the multidimensional pulse feature vector to form compressed pulse feature data; Step S5: Establish a simulated load feature matching model, match and map the compressed pulse feature data with the preset load condition database, output the simulated load adaptation parameter set, and convert the simulated load adaptation parameter set into wireless transmission data frames. Step S6: Construct a multi-protocol communication compatible gateway, perform adaptive encapsulation of wireless transmission data frames according to transmission protocols, dynamically adjust the data encapsulation strategy based on channel quality assessment indicators, and then execute remote electricity information collection and transmission.

2. The method for collecting electricity consumption information in an electricity meter pulse simulation system according to claim 1, characterized in that, Step S1 specifically includes the following steps: Step S11: Generate an initial square wave signal with a preset standard value through the crystal oscillator corresponding to the reference clock source, and correct the duty cycle of the initial square wave signal to generate a standard measurement pulse signal. Step S12: Construct a pulse waveform topology model based on the standard pulse parameter data corresponding to the standard measurement pulse signal. The standard pulse parameter data corresponding to the standard measurement pulse signal includes rising edge, falling edge, and pulse width parameters. Step S13: Based on the pulse waveform topology model, collect the parasitic inductance data and distributed capacitance parameters of the standard metering pulse signal on the transmission path, and then generate an initial pulse feature dataset based on the parasitic inductance data and distributed capacitance parameters. The initial pulse feature dataset includes pulse amplitude distortion rate, phase jitter coefficient and peak offset. Step S14: Inject interference signals into the initial pulse feature dataset to simulate ripple disturbance, thereby generating a pulse waveform matrix with interference.

3. The method for collecting electricity consumption information in an electricity meter pulse simulation system according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S21: Perform wavelet packet decomposition on the interference pulse waveform matrix to extract the energy distribution feature data of sub-band signals in different frequency bands, and filter the energy distribution feature data of sub-band signals based on a preset energy threshold to filter out effective frequency band signal components; Step S22: Analyze the effective frequency band signal components and separate the effective pulse component sequence based on the analysis results; Step S23: Based on the equivalent impedance model corresponding to the transmission path, perform impedance matching optimization on the effective pulse component sequence, and form a pulse data set before calibration based on the result of impedance matching optimization.

4. The method for collecting electricity consumption information in an electricity meter pulse simulation system according to claim 1, characterized in that, Step S5 specifically includes the following steps: Step S51: Establish a simulated load characteristic matching model, which includes a load type classifier, a power curve fitting unit, and an impedance characteristic mapping unit; Step S52: Input the compressed pulse feature data into the load type classifier to identify the electrical appliance type and operating status corresponding to the load; Step S53: Based on the typical load power curves and impedance spectra in the preset load condition database, perform matching mapping processing with the electrical appliance type and operating status corresponding to the load, and then generate a set of simulated load adaptation parameters. Step S54: Convert the data encapsulation format of the simulated load adaptation parameter group, add timestamp and device identification fields, and generate wireless transmission data frames.

5. The method for collecting electricity consumption information in an electricity meter pulse simulation system according to claim 1, characterized in that, Step S6 specifically includes the following steps: Step S61: Construct a multi-protocol communication compatible gateway, parse the payload type identifier corresponding to the wireless transmission data frame, and select the corresponding protocol's message header and check field for adaptive encapsulation based on the payload type identifier; Step S62: Monitor channel quality assessment indicators in real time. The channel quality assessment indicators include signal strength, bit error rate and delay jitter. Evaluate the channel quality assessment indicators and dynamically adjust the data encapsulation strategy based on the evaluation results.

6. A power consumption information acquisition device for an energy meter metering pulse simulation system, used to implement the power consumption information acquisition method of the energy meter metering pulse simulation system according to any one of claims 1-5, characterized in that, include: The signal generation module is used to generate a standard measurement pulse signal through a reference clock source, synchronously construct a pulse waveform topology model, output an initial pulse feature dataset, simulate ripple disturbance on the initial pulse feature dataset, and generate a pulse waveform matrix with interference. The analysis module is used to perform multi-source interference signal separation processing on the interference pulse waveform matrix, obtain the effective pulse component sequence, and optimize the signal impedance matching of the effective pulse component sequence to form a pulse data set before calibration. The calibration module is used to construct a three-dimensional clock error compensation network, combine carrier phase offset characteristics to perform error compensation on the pulse data set before calibration, generate a clock synchronization calibration pulse set, and extract the dynamic feature parameters corresponding to the clock synchronization calibration pulse set. The optimization module is used to perform multimodal data fusion processing on the dynamic feature parameters to generate a multidimensional pulse feature vector, and then perform data dimensionality reduction optimization on the multidimensional pulse feature vector to form compressed pulse feature data. The data conversion module is used to establish a simulated load characteristic matching model, match and map compressed pulse characteristic data with a preset load condition database, output a simulated load adaptation parameter set, and convert the simulated load adaptation parameter set into wireless transmission data frames. The transmission evaluation module is used to build a multi-protocol communication compatible gateway, perform adaptive encapsulation of wireless transmission data frames according to transmission protocols, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then execute remote electricity information collection and transmission.

7. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform a method for collecting electricity consumption information in an electricity meter metering pulse simulation system as described in any one of claims 1 to 5.

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