Electricity utilization information acquisition equipment of electric energy meter metering pulse simulation system

Abstract

The invention discloses an electricity utilization information acquisition device of an electric energy meter metering pulse simulation system, and relates to the technical field of monitoring analysis, and the method comprises the steps: carrying out the ripple disturbance simulation of an initial pulse characteristic data set, and generating an interference pulse waveform matrix; performing multi-source interference signal separation processing on the waveform matrix with the interference pulses, constructing a three-dimensional clock error compensation network, generating a clock synchronization calibration pulse set, and extracting dynamic characteristic parameters corresponding to the clock synchronization calibration pulse set; performing multi-modal data fusion processing on the dynamic characteristic parameters to form compressed pulse characteristic data; establishing a simulation load feature matching model, outputting a simulation load adaptation parameter group, and converting the simulation load adaptation parameter group into a wireless transmission data frame; and carrying out transmission protocol self-adaptive packaging on the wireless transmission data frame, dynamically adjusting a data packaging strategy based on a channel quality evaluation index, and executing remote power utilization information acquisition and transmission. The method has the effect of improving the power utilization information acquisition efficiency.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to an electricity consumption information collection device for an electric energy meter pulse simulation system. Background Art

[0002] An important function of electricity consumption information collection equipment is to measure the electricity and power of the electricity meter by collecting the metering pulses of the electricity meter. With the rapid advancement of smart grid construction, the accuracy and reliability requirements of the electricity metering system are increasing.

[0003] In related technologies, traditional electricity meter pulse metering simulation systems are susceptible to interference from high-frequency ripples, power frequency harmonics, and other factors in complex electromagnetic environments, resulting in widespread distortion of pulse signals. 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 consumption information collection and leaves room for improvement. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present application provides an electricity consumption information collection device for an electricity meter pulse simulation system.

[0005] In a first aspect, the present application provides a method for collecting electricity consumption information of an electric energy meter pulse simulation system, comprising the following steps: Step S1: Generate a standard metrology pulse signal through a reference clock source, synchronously construct a pulse waveform topology relationship model, output an initial pulse feature data set, perform ripple disturbance simulation on the initial pulse feature data set, and generate an interference pulse waveform matrix; Step S2: performing multi-source interference signal separation processing on the interference pulse waveform matrix to obtain a valid pulse component sequence, and performing signal impedance matching optimization on the valid pulse component sequence to form a pre-calibration pulse data set; Step S3: constructing a three-dimensional clock error compensation network, performing error compensation on the pre-calibration pulse data set in combination with the carrier phase offset characteristics, generating a clock synchronization calibration pulse set, and extracting dynamic characteristic parameters corresponding to the clock synchronization calibration pulse set; Step S4: performing multimodal data fusion processing on the dynamic characteristic parameters to generate a multidimensional pulse characteristic vector, and then performing data dimensionality reduction optimization on the multidimensional pulse characteristic vector to form compressed pulse characteristic data; Step S5: establishing a simulated load feature matching model, matching and mapping the compressed pulse feature data with a preset load condition database, outputting a simulated load adaptation parameter group, and converting the simulated load adaptation parameter group into a wireless transmission data frame; Step S6: Construct a multi-protocol communication compatible gateway, perform transmission protocol adaptive encapsulation on wireless transmission data frames, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then perform remote power consumption information collection and transmission.

[0006] Preferably, the step S1 specifically includes the following steps: Step S11: generating an initial square wave signal having a frequency of a preset standard value through a crystal oscillator corresponding to a reference clock source, and performing duty cycle correction on the initial square wave signal to generate a standard metering pulse signal; Step S12: constructing a pulse waveform topology relationship model based on pulse parameter standard data corresponding to the standard metering pulse signal, wherein the pulse parameter standard data corresponding to the standard metering pulse signal includes rising edge, falling edge and pulse width parameters; Step S13: collecting parasitic inductance data and distributed capacitance parameters corresponding to the standard metrology pulse signal on the transmission path based on the pulse waveform topology relationship model, and then generating an initial pulse feature data set based on the parasitic inductance data and distributed capacitance parameters, wherein the initial pulse feature data set includes a pulse amplitude distortion rate, a phase jitter coefficient, and a peak offset; Step S14: injecting an interference signal into the initial pulse feature data set to perform ripple disturbance simulation, thereby generating an interference pulse waveform matrix.

[0007] Preferably, the step S2 specifically includes the following steps: Step S21: performing wavelet packet decomposition processing on the interference pulse waveform matrix to extract energy distribution characteristic data of sub-band signals in different frequency bands, and screening the energy distribution characteristic data of sub-band signals based on a preset energy threshold to screen out effective frequency band signal components; Step S22: analyzing the effective frequency band signal components, and separating effective pulse component sequences based on the analysis results; Step S23: performing impedance matching optimization on the effective pulse component sequence based on the equivalent impedance model corresponding to the transmission path, and forming a pre-calibration pulse data set based on the result of the impedance matching optimization.

[0008] Preferably, the step S3 specifically includes the following steps: Step S31: constructing a three-dimensional clock error compensation network and obtaining carrier phase offset characteristics, and then capturing the corresponding phase jitter and frequency drift in the pre-calibration pulse data set based on the three-dimensional clock error compensation network and the carrier phase offset characteristics, and then generating a dynamic phase correction coefficient based on the phase jitter and frequency drift; Step S32: performing clock synchronization calibration on the pre-calibration pulse data set using the dynamic phase correction coefficient to generate a clock synchronization calibration pulse set, and then extracting dynamic characteristic parameters based on the clock synchronization calibration pulse set.

[0009] Preferably, the step S5 specifically includes the following steps: Step S51: establishing a simulated load feature matching model, wherein the simulated load feature matching model includes a load type classifier, a power curve fitting unit, and an impedance characteristic mapping unit; Step S52: inputting the compressed pulse characteristic data into the load type classifier to identify the type and operating status of the electrical appliance corresponding to the load; Step S53: performing matching and mapping processing based on the typical load power curve and impedance spectrum in the preset load condition database and the electrical appliance type and operating state corresponding to the load, thereby matching and generating a simulated load adaptation parameter group; Step S54: converting the data encapsulation format of the simulated load adaptation parameter group, adding a timestamp and a device identification field, and generating a wireless transmission data frame.

[0010] Preferably, step S6 specifically includes the following steps: Step S61: constructing a multi-protocol communication compatible gateway, parsing the payload type identifier corresponding to the wireless transmission data frame, and selecting the message header and check field of the corresponding protocol for adaptive encapsulation based on the payload type identifier; Step S62: monitoring channel quality evaluation indicators in real time, wherein the channel quality evaluation indicators include signal strength, bit error rate and delay jitter, evaluating the channel quality evaluation indicators, and dynamically adjusting the data encapsulation strategy based on the evaluation results.

[0011] In a second aspect, the present application provides an electricity consumption information collection device for an electric energy meter pulse simulation system, comprising: A signal generation module is used to generate a standard metrology pulse signal through a reference clock source, synchronously construct a pulse waveform topology relationship model, output an initial pulse feature data set, perform ripple disturbance simulation on the initial pulse feature data set, and generate an interference pulse waveform matrix; An analysis module is used to separate the multi-source interference signals from the interference pulse waveform matrix, obtain the effective pulse component sequence, and perform signal impedance matching optimization on the effective pulse component sequence to form a pre-calibration pulse data set; The calibration module is used to build a three-dimensional clock error compensation network, perform error compensation on the pre-calibration pulse data set in combination with the carrier phase offset characteristics, generate a clock synchronization calibration pulse set, and extract the dynamic characteristic parameters corresponding to the clock synchronization calibration pulse set; an optimization module, configured to perform multimodal data fusion processing on the dynamic characteristic parameters to generate a multidimensional pulse characteristic vector, and then perform data dimensionality reduction optimization on the multidimensional pulse characteristic vector to form compressed pulse characteristic data; A data conversion module is used to establish a simulated load feature matching model, match and map the compressed pulse feature data with a preset load condition database, output a simulated load adaptation parameter group, and convert the simulated load adaptation parameter group into a wireless transmission data frame; The transmission evaluation module is used to build a multi-protocol communication compatible gateway, perform transmission protocol adaptive encapsulation on wireless transmission data frames, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then perform remote power consumption information collection and transmission.

[0012] In a third aspect, the present application provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes any one of the above-mentioned methods for collecting electricity consumption information of an electricity meter pulse simulation system.

[0013] In summary, this application has the following beneficial technical effects: 1. The present application provides a method for collecting electricity consumption information of an electric energy meter pulse simulation system, which generates an interference pulse waveform matrix by performing ripple disturbance simulation on an initial pulse feature data set, and performs multi-source interference signal separation processing on the interference pulse waveform matrix to obtain an effective pulse component sequence, and then performs signal impedance matching optimization on the effective pulse component sequence to form a pre-calibration pulse data set; A three-dimensional clock error compensation network is constructed, and the error compensation of the pre-calibration pulse data set is performed in combination with the carrier phase offset characteristics to generate a clock synchronization calibration pulse set. The dynamic characteristic parameters corresponding to the clock synchronization calibration pulse set are extracted, thereby effectively reducing the distortion of the pulse signal. 2. Generate multi-dimensional pulse feature vectors by performing multimodal data fusion processing on dynamic feature parameters, perform data dimensionality reduction optimization on the multi-dimensional pulse feature vectors, and form compressed pulse feature data; establish a simulated load feature matching model, match and map the compressed pulse feature data with the preset load condition database, output a simulated load adaptation parameter group, and convert the simulated load adaptation parameter group into a wireless transmission data frame; build a multi-protocol communication compatible gateway, perform transmission protocol adaptive encapsulation on the wireless transmission data frame, dynamically adjust the data encapsulation strategy based on the channel quality evaluation index, and then execute remote power consumption information collection and transmission, thereby effectively optimizing the strategy for different channel qualities, thereby effectively improving the efficiency of power consumption information collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a flow chart of a method for collecting electricity consumption information in an electric energy meter pulse simulation system according to an embodiment of the present application.

[0016] Figure 2 It is a system schematic diagram of electricity consumption information collection of the electric energy meter measurement pulse simulation system in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following is combined with Figure 1-2 This application is described in further detail.

[0018] Example 1 The embodiment of the present application discloses a method for collecting electricity consumption information of an electric energy meter pulse simulation system.

[0019] Reference Figure 1 A method for collecting electricity consumption information of an electric energy meter pulse simulation system comprises the following steps: Step S1: Generate a standard metrology pulse signal through a reference clock source, synchronously construct a pulse waveform topology relationship model, output an initial pulse feature data set, perform ripple disturbance simulation on the initial pulse feature data set, and generate an interference pulse waveform matrix; Step S2: performing multi-source interference signal separation processing on the interference pulse waveform matrix to obtain a valid pulse component sequence, and performing signal impedance matching optimization on the valid pulse component sequence to form a pre-calibration pulse data set; Step S3: constructing a three-dimensional clock error compensation network, performing error compensation on the pre-calibration pulse data set in combination with the carrier phase offset characteristics, generating a clock synchronization calibration pulse set, and extracting dynamic characteristic parameters corresponding to the clock synchronization calibration pulse set; Step S4: performing multimodal data fusion processing on the dynamic characteristic parameters to generate a multidimensional pulse characteristic vector, and then performing data dimensionality reduction optimization on the multidimensional pulse characteristic vector to form compressed pulse characteristic data; Step S5: establishing a simulated load feature matching model, matching and mapping the compressed pulse feature data with a preset load condition database, outputting a simulated load adaptation parameter group, and converting the simulated load adaptation parameter group into a wireless transmission data frame; Step S6: Construct a multi-protocol communication compatible gateway, perform transmission protocol adaptive encapsulation on wireless transmission data frames, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then perform remote power consumption information collection and transmission.

[0020] It should be noted that the step S1 specifically includes the following steps: Step S11: generating an initial square wave signal having a frequency of a preset standard value through a crystal oscillator corresponding to a reference clock source, and performing duty cycle correction on the initial square wave signal to generate a standard metering pulse signal; Step S12: constructing a pulse waveform topology relationship model based on pulse parameter standard data corresponding to the standard metering pulse signal, wherein the pulse parameter standard data corresponding to the standard metering pulse signal includes rising edge, falling edge and pulse width parameters; The pulse waveform topological relationship model is used to determine the correlation weight matrix of the standard metrology pulse signal in the time domain and the frequency domain; Step S13: collecting parasitic inductance data and distributed capacitance parameters corresponding to the standard metrology pulse signal on the transmission path based on the pulse waveform topology relationship model, and then generating an initial pulse feature data set based on the parasitic inductance data and distributed capacitance parameters, wherein the initial pulse feature data set includes a pulse amplitude distortion rate, a phase jitter coefficient, and a peak offset; Step S14: injecting an interference signal into the initial pulse feature data set to perform ripple disturbance simulation, thereby generating an interference pulse waveform matrix.

[0021] The interference pulse waveform matrix is ​​provided with a plurality of interference modes, and the plurality of interference modes include but are not limited to power supply fluctuation, electromagnetic coupling and ground loop interference.

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

[0023] Based on the rising edge time, falling edge time and pulse width parameters of the standard metrology pulse signal, a pulse waveform topology relationship model is constructed: The pulse waveform topology relationship model quantifies the time-frequency domain relationship, providing a mathematical basis for subsequent interference analysis. For example, it can predict the attenuation of high-frequency components when the rise time increases. On a 50Ω transmission path, parasitic inductance and distributed capacitance were measured using a vector network analyzer. The initial pulse feature dataset was generated using the pulse waveform topology relationship model: Amplitude distortion rate: Calculate the change in pulse amplitude before and after transmission. For example, if it drops from 2.0V to 1.92V, the distortion rate is 4%. Phase jitter coefficient: Phase offset is detected by a phase-locked loop; Peak offset: The measured peak occurrence time is 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: performing wavelet packet decomposition processing on the interference pulse waveform matrix to extract energy distribution characteristic data of sub-band signals in different frequency bands, and screening the energy distribution characteristic data of sub-band signals based on a preset energy threshold to screen out effective frequency band signal components; Step S22: analyzing the effective frequency band signal components, and separating effective pulse component sequences based on the analysis results; The effective pulse component sequence is related to the standard metering pulse signal, and the influence of power frequency harmonics and random pulse noise needs to be filtered out; Step S23: performing impedance matching optimization on the effective pulse component sequence based on the equivalent impedance model corresponding to the transmission path, and forming a pre-calibration pulse data set based on the result of the impedance matching optimization.

[0025] In the embodiment of the present application, a pulse width consistency check needs to be performed on the pre-calibration pulse data set to eliminate abnormal pulse data whose timing offset exceeds a preset threshold.

[0026] Specifically, a three-layer wavelet packet decomposition is performed on the pulse waveform matrix containing noise, and the signal frequency band from 0-20MHz is divided into 8 sub-bands (each band is 2.5MHz). Taking Daubechies wavelet (db4) as an example: Decomposition process: The first layer decomposition: low frequency band (0-10MHz), high frequency band (10-20MHz); Second-level 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: ultimately resulting in eight 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 (for example, the main frequency energy of a 10 MHz pulse is concentrated in the 7.5-12.5 MHz band, accounting for 65% of the energy; the energy of a 50 Hz power frequency interference is concentrated in the 0-0.1 MHz band, accounting for 8%; the energy of a 2.4 GHz RF noise is concentrated in the 2.3-2.5 GHz band, accounting for 15%). Threshold screening: Set the energy threshold to 5% and filter out frequency bands with energy proportions greater than 5% (e.g., 7.5-12.5MHz, 2.3-2.5GHz). The 2.3-2.5GHz band, although high in energy, is an interference band and is therefore eliminated. The 7.5-12.5MHz band is retained as the primary effective frequency band, and wavelet packet decomposition is used to achieve refined separation of multi-band noise. Perform inverse wavelet packet transform on the retained effective frequency band signal (7.5-12.5MHz) to reconstruct the signal components that do not contain low-frequency power frequency and high-frequency radio frequency interference: Time-frequency domain analysis: By reconstructing the signal's time-frequency diagram, we confirmed that the rising and falling edge timings of the 10 MHz pulse were clear, while the noise components in the interference frequency band had been filtered out. Pulse sequence extraction: An adaptive threshold detection algorithm is used to binarize the reconstructed signal and extract the pulse edge timing. For example, the pulse period detected is 100ns and the duty cycle is 50%, which is consistent with the standard metrology pulse, indicating that the effective component separation is successful. Impedance matching optimization is performed on the effective pulse component sequence based on an equivalent impedance model of the transmission path, impedance matching is performed using a Smith chart, and then a pre-calibration pulse data set 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: constructing a three-dimensional clock error compensation network and obtaining carrier phase offset characteristics, and then capturing the corresponding phase jitter and frequency drift in the pre-calibration pulse data set based on the three-dimensional clock error compensation network and the carrier phase offset characteristics, and then generating a dynamic phase correction coefficient 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 compensate for the time base, carrier phase and ambient temperature drift of the pulse signal; Step S32: Perform clock synchronization calibration on the pre-calibration pulse data set using the dynamic phase correction coefficient to generate a clock synchronization calibration pulse set, and then extract dynamic characteristic parameters based on the clock synchronization calibration pulse set, wherein the dynamic characteristic parameters include the pulse repetition frequency deviation rate and the peak fluctuation variance.

[0028] Furthermore, in an embodiment of the present application, step S4 performs time series alignment processing on the dynamic feature parameters, and fuses the 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, extracts a low-dimensional embedding representation in the high-dimensional space, and then uses principal component analysis to remove redundant features of the low-dimensional embedding representation, retains feature dimensions with variance contribution rates greater than a preset value, forms a compressed pulse feature number, and performs data normalization processing on the compressed pulse feature data to ensure dimensional consistency of each feature dimension; It should be noted that step S5 specifically includes the following steps: Step S51: establishing a simulated load feature matching model, wherein the simulated load feature matching model includes a load type classifier, a power curve fitting unit, and an impedance characteristic mapping unit; Step S52: inputting the compressed pulse characteristic data into the load type classifier to identify the type and operating status of the electrical appliance corresponding to the load; Step S53: performing matching and mapping processing based on the typical load power curve and impedance spectrum in the preset load condition database and the electrical appliance type and operating state corresponding to the load, thereby matching and generating a simulated load adaptation parameter group; Step S54: converting the data encapsulation format of the simulated load adaptation parameter group, adding a timestamp and a device identification field, and generating a wireless transmission data frame.

[0029] Specifically, while converting the data encapsulation format of the simulated load adaptation parameter group, a timestamp and a device identification field are added to generate a wireless transmission data frame, which complies with the wireless communication protocol.

[0030] It should be noted that step S6 specifically includes the following steps: Step S61: constructing a multi-protocol communication compatible gateway, parsing the payload type identifier corresponding to the wireless transmission data frame, and selecting the message header and check field of the corresponding protocol for adaptive encapsulation based on the payload type identifier; Step S62: monitoring channel quality evaluation indicators in real time, wherein the channel quality evaluation indicators include signal strength, bit error rate and delay jitter, evaluating the channel quality evaluation indicators, and dynamically adjusting the data encapsulation strategy based on the evaluation results.

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

[0032] Strategy adjustment logic: High-quality channel (RSSI>-80dBm, BER<10 -6 ): Use the efficient MQTT protocol (12-byte header), enable QoS0, and improve throughput; Bad channel (RSSI<-95dBm, BER>10 -3 ): Switch to Modbus TCP protocol with ARQ retransmission, add 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 The embodiment of the present application also discloses an electricity consumption information collection device for an electricity meter pulse simulation system.

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

[0035] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the concept of the invention, they should all fall within the scope of protection of the present invention.

[0036] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0037] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention.

Claims

1. A method for collecting electricity consumption information of an electric energy meter pulse simulation system, characterized in that: The following steps are involved: Step S1: Generate a standard metrology pulse signal through a reference clock source, synchronously construct a pulse waveform topology relationship model, output an initial pulse feature data set, perform ripple disturbance simulation on the initial pulse feature data set, and generate an interference pulse waveform matrix; Step S2: performing multi-source interference signal separation processing on the interference pulse waveform matrix to obtain a valid pulse component sequence, and performing signal impedance matching optimization on the valid pulse component sequence to form a pre-calibration pulse data set; Step S3: constructing a three-dimensional clock error compensation network, performing error compensation on the pre-calibration pulse data set in combination with the carrier phase offset characteristics, generating a clock synchronization calibration pulse set, and extracting dynamic characteristic parameters corresponding to the clock synchronization calibration pulse set; Step S4: performing multimodal data fusion processing on the dynamic characteristic parameters to generate a multidimensional pulse characteristic vector, and then performing data dimensionality reduction optimization on the multidimensional pulse characteristic vector to form compressed pulse characteristic data; Step S5: establishing a simulated load feature matching model, matching and mapping the compressed pulse feature data with a preset load condition database, outputting a simulated load adaptation parameter group, and converting the simulated load adaptation parameter group into a wireless transmission data frame; Step S6: Construct a multi-protocol communication compatible gateway, perform transmission protocol adaptive encapsulation on wireless transmission data frames, dynamically adjust the data encapsulation strategy based on channel quality evaluation indicators, and then perform remote power consumption information collection and transmission.

2. The method for collecting electricity consumption information of an electric energy meter pulse simulation system according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S11: generating an initial square wave signal having a frequency of a preset standard value through a crystal oscillator corresponding to a reference clock source, and performing duty cycle correction on the initial square wave signal to generate a standard metering pulse signal; Step S12: constructing a pulse waveform topology relationship model based on pulse parameter standard data corresponding to the standard metering pulse signal, wherein the pulse parameter standard data corresponding to the standard metering pulse signal includes rising edge, falling edge and pulse width parameters; Step S13: collecting parasitic inductance data and distributed capacitance parameters corresponding to the standard metrology pulse signal on the transmission path based on the pulse waveform topology relationship model, and then generating an initial pulse feature data set based on the parasitic inductance data and distributed capacitance parameters, wherein the initial pulse feature data set includes a pulse amplitude distortion rate, a phase jitter coefficient, and a peak offset; Step S14: injecting an interference signal into the initial pulse feature data set to perform ripple disturbance simulation, thereby generating an interference pulse waveform matrix.

3. The method for collecting electricity consumption information of an electric energy meter pulse simulation system according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S21: performing wavelet packet decomposition processing on the interference pulse waveform matrix to extract energy distribution characteristic data of sub-band signals in different frequency bands, and screening the energy distribution characteristic data of sub-band signals based on a preset energy threshold to screen out effective frequency band signal components; Step S22: analyzing the effective frequency band signal components, and separating effective pulse component sequences based on the analysis results; Step S23: performing impedance matching optimization on the effective pulse component sequence based on the equivalent impedance model corresponding to the transmission path, and forming a pre-calibration pulse data set based on the result of the impedance matching optimization.

4. The method for collecting electricity consumption information of an electric energy meter pulse simulation system according to claim 1, characterized in that: The step S3 specifically includes the following steps: Step S31: constructing a three-dimensional clock error compensation network and obtaining carrier phase offset characteristics, and then capturing the corresponding phase jitter and frequency drift in the pre-calibration pulse data set based on the three-dimensional clock error compensation network and the carrier phase offset characteristics, and then generating a dynamic phase correction coefficient based on the phase jitter and frequency drift; Step S32: performing clock synchronization calibration on the pre-calibration pulse data set using the dynamic phase correction coefficient to generate a clock synchronization calibration pulse set, and then extracting dynamic characteristic parameters based on the clock synchronization calibration pulse set.

5. The method for collecting electricity consumption information of an electric energy meter pulse simulation system according to claim 1, characterized in that: The step S5 specifically includes the following steps: Step S51: establishing a simulated load feature matching model, wherein the simulated load feature matching model includes a load type classifier, a power curve fitting unit, and an impedance characteristic mapping unit; Step S52: inputting the compressed pulse characteristic data into the load type classifier to identify the type and operating status of the electrical appliance corresponding to the load; Step S53: performing matching and mapping processing based on the typical load power curve and impedance spectrum in the preset load condition database and the electrical appliance type and operating state corresponding to the load, thereby matching and generating a simulated load adaptation parameter group; Step S54: converting the data encapsulation format of the simulated load adaptation parameter group, adding a timestamp and a device identification field, and generating a wireless transmission data frame.

6. The method for collecting electricity consumption information of an electric energy meter pulse simulation system according to claim 1, characterized in that: The step S6 specifically includes the following steps: Step S61: constructing a multi-protocol communication compatible gateway, parsing the payload type identifier corresponding to the wireless transmission data frame, and selecting the message header and check field of the corresponding protocol for adaptive encapsulation based on the payload type identifier; Step S62: monitoring channel quality evaluation indicators in real time, wherein the channel quality evaluation indicators include signal strength, bit error rate and delay jitter, evaluating the channel quality evaluation indicators, and dynamically adjusting the data encapsulation strategy based on the evaluation results.

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

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the method for collecting electricity consumption information of an electric energy meter pulse simulation system according to any one of claims 1 to 6.

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