High-precision low-power consumption metering method and system for smart meter

Abstract

The present application relates to the field of electric energy metering, and discloses a high-precision low-power consumption metering method for a smart meter, comprising the following steps: step one, predicting an electricity mode in a future preset time period; step two, dynamically setting a trigger sensitivity threshold based on the electricity mode; step three, monitoring a transient electrical event on a line in a low-power consumption state according to the trigger sensitivity threshold; and step four, performing a preset metering operation in response to monitoring the transient electrical event. In the step two, dynamically setting a trigger sensitivity threshold based on the electricity mode comprises determining an expected load fluctuation from the electricity mode. The present application can actively increase the trigger threshold to reduce the sensitivity of the system to small fluctuations during a period when the electricity is predicted to be stable, thereby avoiding unnecessary wake-up and calculation and maximizing the duration of the low-power consumption monitoring state.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, and in particular to a high-precision, low-power metering method and system for smart meters. Background Technology

[0002] With the development of smart grid technology, the functions of electricity metering devices have far exceeded traditional electricity accumulation. Modern smart meters are required to provide more refined electricity consumption data to support advanced applications such as demand-side management, load identification, fault diagnosis, and energy efficiency analysis. The foundation for achieving these functions lies in the meter's ability to accurately monitor and capture transient electrical events on the line, such as the start-up and shutdown of various appliances and the switching of operating modes. The waveform characteristics of these events contain rich load information. To ensure that no key information is missed and to obtain sufficient resolution for feature analysis, the metering chip typically needs to continuously sample voltage and current signals at frequencies of several kilohertz (kHz) or even higher.

[0003] However, as a widely deployed public metering device, smart meters are subject to strict power consumption limits. Maintaining high-frequency sampling and high-performance computing for extended periods would lead to excessive power consumption, failing to meet relevant industry standards and energy efficiency requirements, and increasing operating costs for power grid companies. Therefore, minimizing the average power consumption of smart meters while ensuring high accuracy and fidelity in capturing critical electrical events has become a pressing technical challenge. To address this challenge, existing technologies typically employ an event-triggered low-power operating mode. In this mode, the system remains in sleep or standby mode most of the time, only being awakened and switched to high-frequency sampling to perform accurate metering when a change in a certain electrical parameter (such as current or power) exceeds a preset fixed threshold.

[0004] While this method achieves a balance between power consumption and performance to some extent, its inherent limitations are also quite apparent. Load behavior in power systems exhibits significant time-varying and complex characteristics, with normal load fluctuation levels varying greatly across different times of day and between residential and industrial users. The fixed trigger thresholds used in existing technologies cannot adapt to this dynamically changing power environment. If the fixed threshold is set too low to ensure sensitivity to minor events, normal, non-critical load fluctuations in the lines will frequently and unnecessarily wake up the system during peak power consumption periods, leading to a significant increase in meter power consumption and negating the purpose of the low-power mode. Conversely, if the threshold is set too high to avoid frequent interference during peak power consumption periods, many low-power but analytically valuable electrical events (such as the startup of standby appliances) will be ignored during stable power consumption periods because they will not meet the trigger conditions, resulting in the permanent loss of critical data. Therefore, due to the static and non-adaptive nature of its monitoring sensitivity, existing technologies consistently fail to achieve an ideal balance between power consumption and metering integrity, exhibiting significant limitations. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision, low-power metering method and system for smart meters, which solves the problem that existing technologies, which use fixed trigger thresholds, cannot achieve a balance between power consumption and the integrity of metering event capture.

[0006] To achieve the above objectives, the present invention provides a high-precision, low-power metering method for smart meters, comprising the following steps:

[0007] Step 1: Predict electricity consumption patterns within a preset time period;

[0008] Step 2: Based on the power consumption pattern, dynamically determine a trigger sensitivity threshold;

[0009] Step 3: Based on the aforementioned trigger sensitivity threshold, monitor transient electrical events on the line under low power consumption conditions;

[0010] Step 4: In response to the detection of the transient electrical event, execute the preset metering operation.

[0011] Preferably, in step two, based on the power consumption pattern, a trigger sensitivity threshold is dynamically determined, including:

[0012] Determine the expected load fluctuations from the electricity consumption patterns;

[0013] Furthermore, the trigger sensitivity threshold is set based on the load variability, such that the trigger sensitivity threshold is inversely correlated with the load variability.

[0014] Preferably, the trigger sensitivity threshold is determined by the following formula:

[0015]

[0016] Among them, T base Based on the trigger threshold, V p The load fluctuation is denoted by α, which is a preset sensitivity calibration coefficient.

[0017] Preferably, in step four, in response to detecting the transient electrical event, a preset metering operation is performed, including:

[0018] High-fidelity capture of waveform data containing the transient electrical events;

[0019] The waveform data is analyzed using a preset feature recognition model to identify the load type.

[0020] Preferably, in step four, after identifying the load type, the method further includes:

[0021] Based on the identified load type, an optimal metering strategy is selected and executed from a preset strategy library.

[0022] Preferably, the step of analyzing the waveform data using a preset feature recognition model to identify the load type includes:

[0023] Extract a feature vector containing at least one electrical parameter from the waveform data;

[0024] The feature vector is input into the feature recognition model to obtain the load type.

[0025] Preferably, the feature vector includes a current crest factor:

[0026] It is determined by the following formula:

[0027]

[0028] Among them, I peak For peak current, I rms This is the effective value of the current.

[0029] Preferably, after capturing the waveform data containing the transient electrical event with high fidelity, the method further includes:

[0030] The waveform data is used to update and optimize the prediction model or the feature recognition model used in step one.

[0031] Preferably, in step one, predicting the electricity consumption pattern within a preset time period is achieved by analyzing the stored historical electricity consumption data of smart meters.

[0032] This invention also provides a high-precision, low-power metering system for smart meters, comprising:

[0033] The electricity consumption pattern prediction module is used to predict electricity consumption patterns within a preset time period in the future.

[0034] A dynamic calibration module is used to dynamically calibrate a trigger sensitivity threshold based on the power consumption pattern;

[0035] An event monitoring module is used to monitor transient electrical events on the line in a low-power state based on the trigger sensitivity threshold; and a metering execution module is used to execute a preset metering operation in response to the detection of the transient electrical event.

[0036] In summary, the present invention has at least one of the following beneficial technical effects:

[0037] 1. This invention achieves proactive power management by first predicting future power consumption patterns and then dynamically setting the trigger sensitivity threshold for transient electrical events based on these predictions. Compared to techniques that rely solely on reactive adjustments based on current load levels, this invention can proactively increase the trigger threshold during periods of predicted stable power consumption to reduce the system's sensitivity to minor fluctuations. This avoids unnecessary wake-ups and calculations, maximizes the duration of low-power monitoring, and significantly reduces the system's average power consumption.

[0038] 2. This invention ensures high-precision metering of critical electrical events by employing an event triggering mechanism based on dynamic thresholds. This method can respond instantly and capture the complete event waveform with high fidelity the moment the load condition changes, rather than reacting only after detecting power exceeding a fixed threshold. This instantaneous capture capability of transient processes effectively avoids metering oversights of short-term, high-frequency energy such as motor starting surges and switching power supply impacts caused by sampling frequency switching lag, thus improving metering accuracy in complex power consumption scenarios.

[0039] 3. This invention, by introducing load type identification and model feedback optimization mechanisms, endows the system with good adaptability and long-term stability. The system can not only match the optimal metering strategy according to the identified specific load type to achieve refined resource allocation, but also continuously update its prediction and identification models using newly captured waveform data. This closed-loop self-optimization capability enables the meter to adapt to changes in user electricity consumption habits or the addition of new appliances, thus maintaining high efficiency and accuracy throughout its entire lifecycle, improving the system's intelligence level and environmental adaptability. Attached Figure Description

[0040] Figure 1 This is a flowchart of the method of the present invention;

[0041] Figure 2 This is a schematic diagram of the main framework of the present invention. Detailed Implementation

[0042] The following is in conjunction with the appendix Figure 1 - Appendix Figure 2 The present invention will be further described in detail below.

[0043] This invention provides a high-precision, low-power metering method for smart meters. By first predicting future electricity consumption patterns and then dynamically setting the trigger sensitivity threshold for transient electrical events based on the prediction results, a proactive power management approach is achieved. Compared to techniques that only react to changes based on the current load, this invention can proactively increase the trigger threshold during predicted periods of stable electricity consumption to reduce the system's sensitivity to minor fluctuations. This avoids unnecessary wake-ups and calculations, maximizes the duration of low-power monitoring, and significantly reduces the system's average power consumption.

[0044] like Figure 1 As shown, the high-precision, low-power metering method for smart meters may include the following steps:

[0045] Step 1: Predict electricity consumption patterns within a preset time period;

[0046] Step 2: Based on the power consumption pattern, dynamically determine a trigger sensitivity threshold;

[0047] Step 3: Based on the trigger sensitivity threshold, monitor transient electrical events on the line under low power consumption conditions;

[0048] Step 4: In response to the detection of a transient electrical event, execute the preset metering operation.

[0049] This method can be implemented in a smart meter device. Besides conventional hardware components such as voltage and current sampling circuits, analog-to-digital converters (ADCs), and communication interfaces, the core of this smart meter device includes a processor and a memory connected to it. The memory stores computer program instructions, which the processor executes to implement the method provided by this invention.

[0050] In this embodiment, the system specifically includes the following functional modules: an electricity consumption pattern prediction module, a dynamic calibration module, an event monitoring module, and a metering execution module. These modules are functional modules and can be implemented by the processor by executing specific program segments.

[0051] The electricity consumption pattern prediction module is configured to analyze historical electricity consumption data stored in memory to predict electricity consumption patterns over a preset time period. The module's output is electricity consumption pattern data that includes information such as expected load fluctuations.

[0052] The dynamic calibration module has its input connected to the output of the power consumption pattern prediction module. This module is configured to receive power consumption pattern data and, based on this data, calculate and calibrate a trigger sensitivity threshold for subsequent event monitoring.

[0053] The event monitoring module has its input connected to the output of the dynamic calibration module. This module is configured to receive a trigger sensitivity threshold and, based on this threshold, continuously monitor transient electrical parameters on the power line during low-power operation. When a monitored parameter exceeds the threshold, the module outputs an event trigger signal.

[0054] The metering execution module has its input connected to the output of the event monitoring module. This module is configured to switch from a low-power state to a full-function operating state upon receiving an event trigger signal, and then perform a series of operations such as high-fidelity waveform capture, load identification, and metering strategy selection.

[0055] When the system executes the high-precision, low-power metering method for smart meters, its overall workflow may include the following steps:

[0056] S100, the electricity consumption pattern prediction module is activated to predict future electricity consumption patterns;

[0057] S200, the dynamic calibration module sets the trigger sensitivity threshold based on the prediction results of S100;

[0058] S300, the event monitoring module performs low-power transient event monitoring based on the threshold set in S200;

[0059] S400, the metering execution module responds to the trigger signal of S300 and executes the preset metering operation.

[0060] In actual operation, the output data of the electricity consumption pattern prediction module is transmitted to the dynamic calibration module. After processing the data, the dynamic calibration module writes the generated trigger sensitivity threshold parameter into a designated register or memory address. During operation, the event monitoring module reads the threshold parameter in the register or memory address as its judgment basis. Once the judgment condition of the event monitoring module is met, it transfers control to the metering execution module through an interrupt or status flag to complete the subsequent accurate metering task. Through the orderly data transfer and functional cooperation between the above modules, a complete execution chain of the method of this invention is formed.

[0061] In one specific embodiment, the method begins with the prediction of future electricity consumption behavior. An electricity consumption pattern prediction module is configured to perform this prediction task periodically, for example, once per hour. This module retrieves historical electricity consumption data from memory, which is time-series data containing parameters such as timestamps, active power, and RMS current values. The module uses a pre-defined prediction model to analyze the historical electricity consumption data to output an electricity consumption pattern for a predetermined future time period. The electricity consumption pattern is a set of one or more parameters, including at least one quantitative parameter characterizing the expected drastic change in load, i.e., load volatility.

[0062] After obtaining the power consumption pattern, the dynamic calibration module dynamically calibrates a trigger sensitivity threshold based on this pattern. Specifically, the module extracts the load fluctuation parameter (V0) from the received power consumption pattern. p Based on a preset functional relationship, a trigger sensitivity threshold (T) is calculated and set using load fluctuation parameters. d The functional relationship is configured such that T d With V p They show an inverse correlation. When the predicted load fluctuation is high, the system sets a lower trigger sensitivity threshold to improve the ability to monitor minute changes; when the predicted load fluctuation is low, the system sets a higher trigger sensitivity threshold to reduce the probability of false triggering caused by non-electrical events such as circuit noise.

[0063] In one implementation, this functional relationship can be expressed by the following formula:

[0064]

[0065] Among them, T d T represents the final generated trigger sensitivity threshold. base V is a preset base trigger threshold used by a system when there is no predictive information. p α is the quantified value of load variability extracted from the electricity consumption pattern; α is a preset, non-negative sensitivity calibration coefficient used to adjust the weight of the influence of load variability on the trigger threshold. After calculation, the dynamic calibration module will obtain T. d The value is written to a specified register or memory address for use in subsequent steps.

[0066] After setting the trigger sensitivity threshold, the system enters a low-power operating state. In this state, most of the processor's computing units and the high-power digital signal processing units in the metering chip are in sleep or clock-gated states. The event monitoring module is activated, which continuously monitors one or more transient electrical parameters on the power line, such as the instantaneous rate of change of current (di / dt), through a low-power comparator circuit.

[0067] The event monitoring module continuously compares the absolute value of the instantaneous rate of change of the monitored current with the trigger sensitivity threshold (T) set in the previous step. d This comparison is performed. In discrete digital systems, this comparison operation specifically involves:

[0068]

[0069] Where i[k] is the instantaneous current value at the current sampling point, i[k-1] is the instantaneous current value at the previous sampling point, and Δt s This is the sampling time interval during low-power monitoring. When the above conditions are met, the event monitoring module determines that a transient electrical event has occurred and immediately generates an event trigger signal.

[0070] Upon receiving an event trigger signal, the metering execution module is activated, switching the processor and metering chip from a low-power state to a full-function operating state. Subsequently, the metering execution module performs a series of preset metering operations, including but not limited to: capturing voltage and current waveform data with high fidelity at a high sampling frequency for a period before and after the event; analyzing the captured waveform data to identify the load type; and selecting and executing an optimal metering strategy based on the identification results. The specific implementation methods of these operations will be detailed later.

[0071] In one specific embodiment, the process of dynamically calibrating the trigger sensitivity threshold essentially involves establishing a direct mapping relationship from macroscopic electricity consumption pattern prediction to microscopic event monitoring sensitivity. This process is executed by the dynamic calibration module, the core of which is to first determine a key quantitative parameter from the predicted electricity consumption pattern, namely the expected load fluctuation.

[0072] Load volatility is a numerical value used to characterize the degree of drastic change in electricity load over a predetermined future time period. This parameter can be calculated in various ways; for example, it can be defined as the variance of the power consumption signal within the prediction time window, or as the mathematical expectation of the square of the instantaneous rate of change of the current signal. By calculating this parameter, the system can transform an abstract electricity consumption trend into a forward-looking quantitative indicator that can be used for precise calculations.

[0073] After determining the load fluctuation parameter, the system sets a trigger sensitivity threshold based on this parameter to achieve an inverse correlation between the two. This inverse correlation setting is an important technical feature of the present invention, and its technical effect is as follows: when the system predicts that the load will fluctuate frequently and drastically in the future, the system will automatically set a lower trigger sensitivity threshold, making the event monitoring module more sensitive to minor changes on the line, thereby ensuring that no important electrical events are missed; conversely, when the system predicts that the future power consumption will be stable and the load changes will be infrequent and gradual, the system will set a higher trigger sensitivity threshold to effectively filter out minor disturbances caused by non-real power consumption behaviors such as inherent line noise, avoiding unnecessary system wake-ups and computational resource consumption.

[0074] In a preferred embodiment of the present invention, the functional relationship used to determine the trigger sensitivity threshold contains several key parameters with clear technical definitions. For example, the basic trigger threshold is a system parameter preset according to the hardware characteristics of the meter. It represents the baseline monitoring sensitivity of the system, and its value can be determined based on the noise level of the circuit itself or relevant metrological specifications. It defines the upper limit or baseline of the dynamic threshold.

[0075] For example, the functional relationship also includes a sensitivity calibration coefficient. This coefficient is a configurable parameter used to adjust the weight of load fluctuations on the final threshold (i.e., the steepness of the adjustment curve). By setting this coefficient, the method of the present invention can be adapted to different power grid environments or meet specific metering accuracy requirements. For example, in an industrial environment with high electrical noise, this coefficient can be appropriately reduced to decrease the drastic changes in the threshold and enhance the stability of the system.

[0076] By employing an exponentially decaying function to construct this inverse correlation, a non-linear and efficient regulation effect can be achieved. When the load volatility parameter increases from zero, the trigger threshold decreases rapidly, allowing the system to quickly adapt to the transition from stable to active power consumption. However, when the load volatility parameter is very large, the rate of threshold decrease slows and approaches a minimum rather than zero. This ensures that the system maintains necessary monitoring capabilities even under extremely active power consumption scenarios, preventing the threshold from being set to an invalid value and enhancing the robustness of the method.

[0077] After the dynamic calibration module sets and loads the trigger sensitivity threshold, the system enters a low-power operating state. In this state, to minimize energy consumption, the core units in the processor that perform the main computational functions, as well as the circuit modules in the metering chip responsible for high-frequency sampling and complex digital signal processing (DSP), are placed in clock-gated or deep sleep mode. At this time, only the minimum functional units necessary to maintain basic system activity remain running, including a low-power timer and an event monitoring module.

[0078] In this low-power state, the event monitoring module is responsible for continuously monitoring one or more preset transient electrical parameters on the power line. In one specific embodiment, the module samples the line current using a low-power analog-to-digital converter (ADC) at a sampling rate (e.g., several hundred hertz) much lower than the normal metering frequency to obtain a discrete sequence of instantaneous current values ​​(i[k]).

[0079] The core function of this module is to calculate the instantaneous rate of change of current in real time and compare it with a trigger sensitivity threshold stored in a designated hardware register. This instantaneous rate of change of current is calculated by dividing the difference in current values ​​between two adjacent sampling points by the sampling time interval. The trigger condition is met when the absolute value of this instantaneous rate of change of current exceeds the trigger sensitivity threshold.

[0080] The fulfillment of this trigger condition indicates the occurrence of a transient electrical event on the line with sufficient energy or characteristics that exceeds the normal fluctuation range under the current prediction mode. This event indicates a change in the state of the electrical load, such as the starting or stopping of a high-power appliance.

[0081] Once the event monitoring module detects that the trigger condition has been met, it immediately generates a hardware interrupt signal. This interrupt signal is transmitted to the system's processor. Upon receiving the interrupt signal, the processor is immediately awakened from its sleep state and simultaneously de-gated from clock gating of the metering chip and other related peripherals. This allows the entire system to return to full-function operation in a very short time, preparing for subsequent high-fidelity waveform capture and accurate metering operations. In this way, the system can maintain extremely low power consumption most of the time, only utilizing full system resources when electrical events of practical metering significance occur, thus achieving an effective balance between power consumption and accuracy.

[0082] When the event monitoring module outputs an event trigger signal, the metering execution module is activated, and the system switches from a low-power state to a full-function operating state. Subsequently, the module executes a series of preset metering operations to complete the accurate analysis and metering of the transient electrical event.

[0083] First, the metering execution module performs high-fidelity waveform data capture. To ensure complete capture of information containing the transient TAI electrical event, the system utilizes a circular buffer to continuously store low-frequency sampled data from the most recent period. Upon receiving the event trigger signal, the system immediately increases the sampling frequency of the analog-to-digital converter (ADC) to a preset high sampling frequency (e.g., several kilohertz or higher) and continues sampling for a period. Simultaneously, the system concatenates the data stored in the circular buffer prior to the trigger point with the newly acquired high-frequency sampled data subsequent to the trigger point. In this way, the system obtains a high-fidelity voltage and current waveform data segment encompassing the complete process before, during, and after the event.

[0084] After obtaining high-fidelity waveform data, the metering execution module uses a pre-defined feature recognition model to analyze the waveform data to identify the load type that triggered the event. This identification process specifically includes extracting a feature vector containing at least one electrical parameter from the high-fidelity waveform data. This feature vector is a multi-dimensional vector used to describe the electrical characteristics of the load.

[0085] In one embodiment, the feature vector may include a current crest factor (CF) parameter. The current crest factor is defined as the ratio of the peak current value to the RMS current value, and is determined by the following formula:

[0086]

[0087] Among them, I peak For the peak current extracted from waveform data, I rms This is the RMS current value calculated from the same waveform data. This parameter is significant in distinguishing different types of loads; for example, the current crest factor of a purely resistive load is close to... Electronic devices containing switching power supplies typically have a higher current crest factor.

[0088] After the feature vectors are extracted, they are fed as input data to a pre-defined feature recognition model (e.g., a pre-trained support vector machine or neural network model). The model processes the input feature vectors and outputs a recognition result, which is the type of load that caused the transient event, such as "inductive load," "capacitive load," or "nonlinear load."

[0089] After identifying the load type, the metering execution module further performs an optimal metering strategy selection operation based on the identification result. The system has a pre-set strategy library that stores the mapping relationships between multiple metering strategies and different load types. Based on the identified load type, the metering execution module queries this strategy library, selects the corresponding optimal metering strategy, and executes it immediately. For example, if the load is identified as a motor starting, the optimal strategy might define maintaining high-frequency sampling for the next few seconds to accurately calculate its starting energy; if it is identified as a resistive load being applied, the strategy might define resuming a lower sampling frequency after completing one accurate power calculation.

[0090] Furthermore, this invention includes a feedback optimization mechanism. High-fidelity captured waveform data, whose load types have been successfully identified, constitute a high-quality, labeled data sample. This data sample can be stored and used to update and optimize the prediction model or feature recognition model used by the electricity consumption pattern prediction module. For example, by adding these new data samples to the training set to incrementally train the feature recognition model, its recognition accuracy can be continuously improved, enabling the system to have adaptive and self-evolving capabilities.

[0091] In another specific embodiment, a more detailed implementation of the technical solution of the present invention is provided.

[0092] The transient electrical parameters monitored by the event monitoring module can include not only the instantaneous rate of change of current, but also the rate of change of active power. By monitoring the rate of change of power, the system can directly respond to changes in the energy consumption of the load, providing an alternative or supplementary triggering basis for certain specific application scenarios.

[0093] The feature recognition model can specifically be a Support Vector Machine (SVM) model. This model is trained offline using a large number of labeled waveform data samples to establish a hyperplane decision boundary for distinguishing different load types. The trained model parameters are then firmware-based and stored in memory for direct use by the metering execution module during runtime.

[0094] The optimal metering strategy library can specifically be a lookup table. This lookup table exists in memory as a static data structure, establishing a direct mapping between load type identifiers and the entry addresses of one or more metering procedures. When the metering execution module queries this lookup table based on the output of the feature recognition model (i.e., a specific load type identifier), it can directly obtain the starting address of the specific metering subroutine to be executed, thereby enabling rapid switching and execution of metering strategies.

[0095] The self-optimization mechanism is implemented through the following steps: An internal counter accumulates the number of newly acquired and successfully labeled waveform data samples. When the counter reaches a preset threshold (e.g., 100 samples), the processor initiates a model retraining program as a background task during a low-resource idle period. This program incorporates the newly accumulated sample data into the existing training dataset, iteratively updates the internal parameters of the feature recognition model, and overwrites the original model parameters in memory with the updated parameters. This process requires no external intervention, allowing the device's recognition accuracy to gradually improve with runtime.

[0096] The high-precision, low-power metering system for smart meters described below can be referenced and correspond to the high-precision, low-power metering method for smart meters described above.

[0097] Please see the appendix Figure 2 The present invention also provides a high-precision, low-power metering system for smart meters, comprising:

[0098] The electricity consumption pattern prediction module is used to predict electricity consumption patterns within a preset time period in the future.

[0099] The dynamic calibration module is used to dynamically calibrate a trigger sensitivity threshold based on power consumption patterns.

[0100] An event monitoring module is used to monitor transient electrical events on the line in a low-power state based on a trigger sensitivity threshold; and a metering execution module is used to execute a preset metering operation in response to the detection of a transient electrical event.

[0101] The device in this embodiment can be used to execute the above method embodiments, and its principle and technical effects are similar, so they will not be described again here.

[0102] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various 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 high-precision, low-power metering method for smart meters, characterized in that, Includes the following steps: Step 1: Predict electricity consumption patterns within a preset time period; Step 2: Based on the power consumption pattern, dynamically determine a trigger sensitivity threshold; Step 3: Based on the aforementioned trigger sensitivity threshold, monitor transient electrical events on the line under low power consumption conditions; Step 4: In response to the detection of the transient electrical event, execute the preset metering operation; In step two, based on the power consumption pattern, a trigger sensitivity threshold is dynamically determined, including: Determine the expected load fluctuations from the electricity consumption patterns; Furthermore, the trigger sensitivity threshold is set based on the load variability, such that the trigger sensitivity threshold is inversely correlated with the load variability; The trigger sensitivity threshold is determined by the following formula: ； in, Based on the trigger threshold, For the aforementioned load fluctuation, These are the preset sensitivity calibration coefficients.

2. The high-precision, low-power metering method for smart meters according to claim 1, characterized in that, In step four, in response to the detection of the transient electrical event, a preset metering operation is performed, including: High-fidelity capture of waveform data containing the transient electrical events; The waveform data is analyzed using a preset feature recognition model to identify the load type.

3. The high-precision, low-power metering method for smart meters according to claim 2, characterized in that, Step four, after identifying the load type, also includes: Based on the identified load type, a corresponding metering strategy is selected and executed from a preset strategy library.

4. The high-precision, low-power metering method for smart meters according to claim 2, characterized in that, The step of using a preset feature recognition model to analyze the waveform data to identify the load type includes: Extract a feature vector containing at least one electrical parameter from the waveform data; The feature vector is input into the feature recognition model to obtain the load type.

5. The high-precision, low-power metering method for smart meters according to claim 4, characterized in that, The feature vector includes the current peak factor; It is determined by the following formula: ； in, This is the peak current. This is the effective value of the current.

6. The high-precision, low-power metering method for smart meters according to claim 4, characterized in that, After capturing the waveform data containing the transient electrical event with high fidelity, the method further includes: The waveform data is used to update and optimize the prediction model or the feature recognition model used in step one.

7. The high-precision, low-power metering method for smart meters according to claim 1, characterized in that, In step one, predicting the electricity consumption pattern within a preset time period is achieved by analyzing the stored historical electricity consumption data of smart meters.

8. A high-precision, low-power metering system for smart meters, used to execute the high-precision, low-power metering method for smart meters as described in any one of claims 1-7, characterized in that, include: The electricity consumption pattern prediction module is used to predict electricity consumption patterns within a preset time period in the future. A dynamic calibration module is used to dynamically calibrate a trigger sensitivity threshold based on the power consumption pattern; The event monitoring module is used to monitor transient electrical events on the line in a low-power state based on the trigger sensitivity threshold. And a metering execution module, used to perform preset metering operations in response to the detection of the transient electrical event.

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