Auxiliary power supply method and system applied to electric energy meter and collection terminal

Abstract

The application discloses an auxiliary power supply method and system applied to an electric energy meter and a collection terminal, relates to the electric energy meter power supply field, and the method comprises the following steps: acquiring real-time operation data of the electric energy meter and energy storage state data of an energy storage unit; determining task power consumption information of the electric energy meter based on the real-time operation data; acquiring photovoltaic power generation data through a photovoltaic power generation module, and determining a light power generation trend according to the photovoltaic power generation data; judging whether the energy storage unit is connected; if it is determined that the energy storage unit is connected, controlling the energy storage unit to enter a pre-activation support state, and establishing a parallel discharge path in combination with the task power consumption information, the photovoltaic power generation module, the energy storage unit and a capacitor support module; adjusting the output power of the photovoltaic power generation module in combination with the energy storage state data, the parallel discharge path and the task power consumption information; and determining a photovoltaic power supply strategy of the electric energy meter according to the output power of the photovoltaic power generation module, the energy storage state data and the task power consumption information. The application can effectively improve the stability of the electric energy meter power supply.

Description

Technical Field

[0001] This application relates to the field of power supply for electricity meters, and more particularly to an auxiliary power supply method and system for electricity meters and data acquisition terminals. Background Technology

[0002] With the development of smart grids and smart energy systems, electricity meters play a crucial role in data acquisition and monitoring within the power system. Electricity meters not only accurately measure users' electricity consumption but also provide fundamental data support for grid dispatching, energy management, and electricity consumption analysis. Therefore, the accuracy and continuity of electricity meter data acquisition directly affect the reliability of electricity metering and the safety of grid operation.

[0003] However, in practical applications, electricity meters are often constrained by external power fluctuations, making them prone to insufficient power supply or short-term power outages. Once the power supply to the electricity meter is interrupted, the meter and data acquisition terminal cannot upload data, making it impossible to detect and analyze on-site problems in a timely manner, and also impossible to collect and upload data to the backend. This affects the timeliness of electricity data collection. Long-term or frequent power outages may lead to missing electricity meter operation data and incomplete electricity consumption information, thereby affecting the accurate metering and dispatch management of the power grid, increasing the risks of power operation and maintenance, and causing inaccurate electricity metering and decreased energy management efficiency. Summary of the Invention

[0004] This application provides an auxiliary power supply method and system for electricity meters and data acquisition terminals, which improves the stability of electricity meter power supply.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: In a first aspect, an auxiliary power supply method is provided for use in electricity meters and data acquisition terminals. The data acquisition terminal includes a photovoltaic power generation module, an energy storage unit, and a capacitor support module. The data acquisition terminal is equipped with multiple types of sensors, including: Acquire real-time operating data from the electricity meter and energy storage status data from the energy storage unit; The task power consumption information of the energy meter is determined based on real-time operation data and a pre-built task timing power consumption model. Photovoltaic power generation data is obtained through photovoltaic power generation modules, and the trend of solar power generation is determined based on the photovoltaic power generation data; Determine whether to connect an energy storage unit by combining solar power generation trends and task power consumption information; If it is determined that the energy storage unit is connected, the energy storage unit is controlled to enter the pre-activated support state, and a parallel discharge path is established by combining the task power consumption information, photovoltaic power generation module, energy storage unit and capacitor support module; The output power of the photovoltaic power generation module is adjusted by combining energy storage status data, parallel discharge paths, and task power consumption information. The photovoltaic power supply strategy of the electricity meter is determined based on the output power of the photovoltaic power generation module, the energy storage status data, and the task power consumption information.

[0006] In one possible implementation of the first aspect, the method further includes: The task execution stage is determined by the task power consumption information from the electricity meter. When the task execution phase of the electricity meter is the data upload phase, determine the amount of data to be uploaded and the upload rate. The transient power consumption change value corresponding to each moment of the power meter is calculated by using the power consumption information of the power meter, and the pulse transient characteristics are determined based on the transient power consumption change value. The maximum power consumption requirement during data upload is obtained by using a preset power consumption prediction model to calculate the transient power consumption change value corresponding to the pulse transient characteristics. The maximum power consumption requirement triggers the capacitor support module to determine the energy reserve amount, which ensures that the energy storage unit will not experience micro-circulation discharge during peak upload periods. Adjust the output power and duration of the capacitor support module according to the amount and rate of uploaded data; The power supply strategy for the data upload phase is determined by output power, duration, and energy reserves.

[0007] In one possible implementation of the first aspect, adjusting the output power and duration of the capacitor support module based on the amount of uploaded data and the upload rate includes: Obtain the initial capacitor voltage, minimum preset voltage, maximum output power, and real-time capacitance of the capacitor support module; The transient energy demand is calculated based on the amount of uploaded data and the preset energy consumption per unit bit. The energy that can be released is calculated based on the initial voltage, minimum preset voltage and real-time capacitance of the capacitor, and the usable energy of the capacitor is obtained through the preset safety margin factor and the energy that can be released. When the available energy of the capacitor is greater than or equal to the transient energy demand, the ideal duration is determined by the ratio between the amount of data uploaded and the upload rate. The ideal average output power is calculated based on the transient energy demand and the ideal duration. If the ideal average output power is less than or equal to the maximum output power, then the maximum output power of the capacitor support module is set to the output power and the ideal duration of the capacitor support module is set to the duration.

[0008] In one possible implementation of the first aspect, if it is determined that an energy storage unit is connected, the energy storage unit is controlled to enter a pre-activated support state, and a parallel discharge path is established by combining task power consumption information, photovoltaic power generation module, energy storage unit, and capacitor support module, including: When it is determined that the energy meter is connected to the energy storage unit, the output voltage and output current of the energy storage unit are obtained; The output voltage of the energy storage unit is converted to the first target voltage range of the rated operating voltage of the energy meter through the conversion circuit inside the energy storage unit. Convert the output current of the energy storage unit to the first target current range of the rated operating current of the energy meter; When the output voltage of the energy storage unit is in the first target voltage range and the output current is in the first target current range, the output voltage of the energy storage unit is converted to the second target voltage range of the rated operating voltage of the energy meter and the output current of the energy storage unit is converted to the second target current range of the rated operating current of the energy meter. When the output voltage of the energy storage unit is stable within the third target voltage range of the rated operating voltage of the energy meter and the output current is stable within the third target current range of the rated current of the energy meter, the energy storage unit is determined to have completed the pre-activation support state. When the energy storage unit completes the pre-activation support state, a parallel discharge path is established by combining the task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module; Among them, the first target voltage range is smaller than the second target voltage range and the second target voltage range is smaller than the third target voltage range, and the first target current range is larger than the second target current range and the second target current range is larger than the third target current range.

[0009] In one possible implementation of the first aspect, establishing a parallel discharge path by combining task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module includes: The task type of the energy meter within a preset time period is determined based on the task power consumption information, and a power consumption demand curve is generated based on the task type. The task type includes stable power consumption type and pulse power consumption type. A photovoltaic power generation capacity curve is generated by analyzing the solar power generation trend of photovoltaic power generation modules. The health status of energy storage units is calculated based on energy storage status data. The health status of energy storage units includes the state of charge of the energy storage unit and the health status of the energy storage unit. Based on task type, power demand curve, photovoltaic power generation capacity curve and energy storage unit health, the power supply weight of photovoltaic power generation module, energy storage unit and capacitor support module in each task type is determined respectively. When the task type is stable power consumption, the photovoltaic power generation module and the energy storage unit are connected in a stable power consumption parallel path, and the first power consumption sharing ratio of the photovoltaic power generation module and the energy storage unit is determined in combination with the power supply weight corresponding to the stable power consumption type. The first parallel path is determined by the first power consumption sharing ratio and the stable power consumption parallel path; When the task type is pulse power consumption type, the photovoltaic power generation module, energy storage unit and capacitor support module are connected in parallel path for pulse power consumption, and the second power consumption sharing ratio of the photovoltaic power generation module, energy storage unit and capacitor support module is determined according to the power supply weight corresponding to the pulse power consumption type. The second parallel path is determined by the second power consumption sharing ratio and the pulse power consumption parallel path; The first parallel path and the second parallel path are used to characterize the parallel discharge path.

[0010] In one possible implementation of the first aspect, determining the power supply weights of the photovoltaic power generation module, energy storage unit, and capacitor support module in each task type based on task type, power consumption demand curve, photovoltaic power generation capacity curve, and energy storage unit health status includes: When the task type is a stable power consumption type, the total power demand corresponding to the stable power consumption type is determined by the power demand curve. Determine the first actual power supply of the current photovoltaic power generation module based on the photovoltaic power generation capacity curve; The power deficit value is determined by the difference between the total demand power and the actual supply power. The first actual power supply of the current energy storage unit is determined by combining the health status of the energy storage unit and the power deficit value. The photovoltaic power supply weight is obtained by dividing the actual power supply of the photovoltaic power generation module by the total demand power corresponding to the stable power consumption type. The energy storage power supply weight is obtained by dividing the actual power supplied by the energy storage unit by the total power demand corresponding to the stable power consumption type.

[0011] In one possible implementation of the first aspect, the method further includes: When the task type is pulse power consumption type, the instantaneous peak power and stable base power are determined by the power consumption demand curve corresponding to the pulse power consumption type. The instantaneous peak power and stable base power constitute the total pulse power demand of the pulse power consumption type. Determine the second actual power supply of the current photovoltaic power generation module based on the photovoltaic power generation capacity curve; The power gap value of the stable base value power is determined based on the real-time power and stable base value power of the photovoltaic power generation module; The second actual available power of the energy storage unit is determined by combining the power gap value based on the stable baseline power and the health status of the energy storage unit; The power supply weight of the photovoltaic power generation module is obtained by dividing the real-time power of the photovoltaic power generation module by the stable base power. The power supply weight of the energy storage unit is obtained by dividing the actual available power of the energy storage unit by the stable base power. Obtain the current maximum instantaneous discharge capability of the capacitor support module, and determine the instantaneous power requirement of the capacitor support module during the pulse phase based on the instantaneous peak power and the maximum instantaneous discharge capability; The power supply weight of the capacitor support module is obtained by dividing the instantaneous power demand by the instantaneous peak power.

[0012] In one possible implementation of the first aspect, adjusting the output power of the photovoltaic power generation module by combining energy storage state data, parallel discharge paths, and task power consumption information includes: The target output power of the photovoltaic power generation module is obtained by inputting energy storage status data, parallel discharge paths, and task power consumption information into a pre-trained power allocation model.

[0013] Secondly, this application provides a machine-readable storage medium storing instructions that cause a machine to execute the aforementioned auxiliary power supply method applied to electricity meters and data acquisition terminals.

[0014] Thirdly, an auxiliary power supply system for electricity meters and data acquisition terminals includes: The memory is configured to store instructions; and The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned auxiliary power supply method applied to electricity meters and data acquisition terminals.

[0015] By employing the aforementioned technical solutions, and by understanding the real-time operating status and power consumption requirements of the electricity meter, as well as the current power level and health status of the energy storage unit, the most basic and real-time data input can be provided for subsequent intelligent scheduling and energy management. This enables the power supply system to dynamically adjust according to actual needs and available energy, improving power supply accuracy. Based on real-time operating data and a pre-built task-sequence power consumption model, the task power consumption information of the electricity meter is determined, allowing for timely understanding of photovoltaic power generation trends and improving photovoltaic energy utilization. Intelligent decision-making regarding the timing of energy storage unit participation in power supply avoids ineffective discharge or over-reliance on energy storage units, extending their lifespan and improving overall system efficiency. If an energy storage unit is selected for connection, it is controlled to enter a pre-activated support state. A parallel discharge path is established by combining task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module. This parallel path allows for rapid response to load changes, ensuring stable power supply even during sudden power surges or insufficient sunlight. By combining energy storage status data, parallel discharge paths, and task power consumption information to adjust the output power of photovoltaic (PV) modules, the distribution of PV output power can be optimized, taking into account energy storage health, system power requirements, and discharge path constraints. This improves power supply stability, reduces energy storage unit losses, and achieves intelligent regulation. By comprehensively considering PV output, energy storage status, and task power consumption information, the power supply strategy can be dynamically adjusted to ensure that the electricity meter can continuously obtain a stable power supply under conditions of fluctuating sunlight, sudden load changes, or limited energy storage capacity, thereby improving power supply stability and reliability.

[0016] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] Figure 1 A schematic flowchart illustrating an auxiliary power supply method for an energy meter and a data acquisition terminal, provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the photovoltaic power supply process during the data upload phase of an electricity meter, as provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0020] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0021] Figure 1 This illustration schematically shows a flowchart of an auxiliary power supply method for an energy meter and a data acquisition terminal according to an embodiment of this application. Figure 1 As shown in the figure, this application provides an auxiliary power supply method for an electricity meter and a data acquisition terminal. The data acquisition terminal includes a photovoltaic power generation module, an energy storage unit, and a capacitor support module. The method may include the following steps.

[0022] S110. Obtain real-time operating data of the electricity meter and energy storage status data of the energy storage unit; S120. Determine the task power consumption information of the energy meter based on real-time operating data and a pre-built task timing power consumption model; S130. Obtain photovoltaic power generation data through the photovoltaic power generation module, and determine the solar power generation trend based on the photovoltaic power generation data; S140. Determine whether to connect the energy storage unit by combining the solar power generation trend and task power consumption information; S150. If it is determined that the energy storage unit is connected, the energy storage unit is controlled to enter the pre-activated support state, and a parallel discharge path is established in combination with the task power consumption information, photovoltaic power generation module, energy storage unit and capacitor support module. S160. Adjust the output power of the photovoltaic power generation module by combining energy storage status data, parallel discharge path and task power consumption information; S170. Determine the photovoltaic power supply strategy of the electricity meter based on the output power of the photovoltaic power generation module, energy storage status data, and task power consumption information.

[0023] In this embodiment, the data acquisition terminal may also include photovoltaic panels, lithium batteries, industrial-grade controllers, integrated brackets, inverters, auxiliary cables, conversion housings, and other equipment.

[0024] First, the system acquires real-time operating data of the electricity meter and energy storage status data of the energy storage unit by collecting data from various types of sensor modules and data acquisition units installed inside the terminal. The real-time operating data of the electricity meter includes parameters such as voltage, current, power, energy consumption, and communication status; the energy storage status data of the energy storage unit includes information such as state of charge, voltage, current, temperature, and health status.

[0025] Secondly, by collecting and updating real-time operating data from the electricity meter, this data is input into a pre-built task time-series power consumption model. The model outputs corresponding task power consumption information based on the current task stage and power consumption trends. This information can include the instantaneous power consumption, predicted power curve, and task type. The pre-built task time-series power consumption model is trained using historical task execution data, which includes the electricity meter's instantaneous power data, voltage, current, load type, and task start and end timestamps for each task cycle. This historical data can be obtained by collecting data from various types of sensors within the terminal at fixed sampling intervals within each task cycle and uploading it to a local database. Subsequently, the historical data is labeled and categorized by task type, and statistical feature extraction methods are used to calculate the main characteristic parameters for each task type. After obtaining the characteristic parameters, a task time-series power consumption training sample set is constructed, where each sample corresponds to the multi-dimensional power consumption characteristics of a specific task type within a time series window. To address the nonlinear fluctuations in power over time, this embodiment preferably employs a Long Short-Term Memory (LSTM) time series prediction algorithm for training. During model training, the learning rate η = 0.001, the number of iterations (epochs) = 200, the number of hidden layer nodes = 64, the activation function is the tanh function, and the mean squared error is used as the loss function for model optimization. After training, if the average error rate is below 5%, the model training is considered complete. Finally, a pre-constructed task time-series power consumption model is obtained. The training parameters, such as the learning rate, number of iterations, and number of hidden layer nodes, can be set according to the required prediction accuracy. This task time-series power consumption model can indirectly and significantly contribute to improving the stability of power supply from electricity meters.

[0026] By installing photovoltaic (PV) power generation modules at the data acquisition terminal, real-time data on the output voltage, current, and power generation of the PV modules are collected. Simultaneously, a light sensor collects the corresponding light intensity information. The acquisition terminal processes the time-series data of the PV power generation module's output power to determine the rate of change in power generation and the trend of light intensity change, thus determining the direction of change in the current PV power generation capacity. When the PV output power shows an upward trend over multiple consecutive sampling periods, it is determined that the current light intensity is in an enhancing phase; when the rate of change in power is negative or the light intensity decreases, it is determined that the light intensity is in a decaying phase. Based on the trend of PV output power change, the short-term trend of PV power generation is determined, and multiple short-term trends are summarized to determine the overall solar power generation trend.

[0027] Subsequently, the system will compare the solar power generation trend with the electricity meter's task power consumption information to determine whether to connect an energy storage unit. If the output power of the photovoltaic module can continuously meet or exceed the electricity meter's power consumption requirements in the future, the system may choose to prioritize power supply from the photovoltaic module alone to reduce the charge-discharge cycles of the energy storage unit. Conversely, if it is predicted that photovoltaic power generation is insufficient to support the electricity meter's power consumption, or if a power consumption peak is expected that the photovoltaic module cannot handle independently, the system will decide to connect an energy storage unit for auxiliary power supply.

[0028] If the system determines that an energy storage unit needs to be connected based on the solar power generation trend and task power consumption information, the system controls the energy storage unit to enter a pre-activated support state. Specifically, the pre-activated support state refers to the control module gradually increasing the output voltage of the energy storage unit before it officially participates in discharge, ensuring voltage matching with the bus voltage or the output voltage of the photovoltaic module within a set voltage difference range. This avoids surge currents and impact voltages that would occur during direct connection. After the energy storage unit reaches the voltage matching condition, based on the task power consumption information, the system jointly controls the photovoltaic module, energy storage unit, and capacitor support module to establish a parallel discharge path. The parallel discharge path dynamically adjusts the output ratio of the three components through an intelligent power allocation algorithm, allowing the photovoltaic module to bear the main stable power, the energy storage unit to provide auxiliary support power, and the capacitor support module to buffer instantaneous load fluctuations, thereby achieving smooth energy transition and dynamic compensation.

[0029] In parallel power supply mode, the output power of the photovoltaic (PV) power generation modules is dynamically adjusted to optimize power supply. The system collects real-time state of charge, health, and output voltage and current data of the energy storage units to determine their current available energy and discharge capacity. Simultaneously, it monitors the conduction status and equivalent impedance characteristics of each module in the parallel discharge path to determine the current energy flow direction and allocation ratio. For example, when the energy storage unit's charge is low, the system may adjust the PV modules to operate in maximum power point tracking (MPPT) mode to maximize their output power and prioritize charging the energy storage unit. When the energy storage unit has sufficient charge and the electricity meter's power consumption is low, the PV output power may be optimized to meet current demand and reduce energy loss. Based on this, the system uses task power consumption information as the target power demand input and dynamically corrects the PV power point tracking control parameters of the PV power generation modules according to real-time power balance results, ensuring that the PV output power meets load requirements while also considering the energy security of the energy storage unit and optimal system energy efficiency.

[0030] After obtaining real-time output power, energy storage status data of the energy storage unit, and task power consumption information of the electricity meter, the photovoltaic power supply strategy of the electricity meter is determined based on the power balance relationship among the three. The system first determines the irradiance conditions and available energy level based on the output power of the photovoltaic power generation module; then, based on the state of charge, health, and charge / discharge limitations of the energy storage unit, it determines the available supporting power of the energy storage unit; simultaneously, the task power consumption information of the electricity meter is used as the load demand input to calculate the system's real-time energy gap. Based on this, by comprehensively evaluating the photovoltaic power supply capacity and energy storage support capacity, the optimal photovoltaic power supply strategy is generated, such as photovoltaic-primary power supply mode, photovoltaic and energy storage hybrid power supply mode, energy storage compensation mode, and photovoltaic charging-storage mode.

[0031] By understanding the current operating status and power consumption requirements of the electricity meter, as well as the current power level and health status of the energy storage unit in real time, the most basic and real-time data input can be provided for subsequent intelligent scheduling and energy management. This enables the power supply system to dynamically adjust according to actual needs and available energy, improving power supply accuracy. Based on real-time operating data and a pre-built task timing power consumption model, the task power consumption information of the electricity meter is determined, allowing for timely understanding of the changing trends in photovoltaic power generation and improving photovoltaic energy utilization. By intelligently deciding when the energy storage unit should participate in power supply, ineffective discharge or over-reliance on the energy storage unit is avoided, extending the lifespan of the energy storage unit and improving the overall system efficiency. If the connection of the energy storage unit is determined, the energy storage unit is controlled to enter a pre-activated support state, and a parallel discharge path is established by combining task power consumption information, photovoltaic power generation module, energy storage unit, and capacitor support module. Through the parallel path, a rapid response to load changes is achieved, ensuring that the electricity meter can still provide stable power supply during power surges or insufficient sunlight. By combining energy storage status data, parallel discharge paths, and task power consumption information to adjust the output power of photovoltaic (PV) modules, the distribution of PV output power can be optimized, taking into account energy storage health, system power requirements, and discharge path constraints. This improves power supply stability, reduces energy storage unit losses, and achieves intelligent regulation. By comprehensively considering PV output, energy storage status, and task power consumption information, the power supply strategy can be dynamically adjusted to ensure that the electricity meter can continuously obtain a stable power supply under conditions of fluctuating sunlight, sudden load changes, or limited energy storage capacity, thereby improving power supply stability and reliability.

[0032] In one embodiment of this example, Figure 2 A schematic diagram of a photovoltaic power supply process during the data upload phase of an electricity meter, provided as an embodiment of this application, specifically includes the following steps: S210. Determine the task execution stage based on the task power consumption information of the electricity meter; S220. When the task execution phase of the electricity meter is the data upload phase, determine the amount of data to be uploaded and the upload rate. S230. Calculate the transient power consumption change value corresponding to each moment of the power meter through the task power consumption information of the power meter, and determine the pulse transient characteristics based on the transient power consumption change value; S240. The maximum power consumption requirement during data upload is obtained by using a preset power consumption prediction model to calculate the transient power consumption change value corresponding to the pulse transient characteristics. S250: Based on the maximum power consumption requirement, the capacitor support module is triggered to determine the energy reserve amount, which ensures that the energy storage unit will not experience micro-circulation discharge during peak upload periods. S260. Adjust the output power and duration of the capacitor support module according to the amount of uploaded data and the upload rate; S270. Determine the power supply strategy for the data upload phase by considering output power, duration, and energy reserve.

[0033] In this embodiment, the system determines the task execution stage through the task power consumption information of the electricity meter. That is, the system obtains task power consumption information by analyzing the operating current, voltage, and power data of the electricity meter and matches this information with a pre-built database. The pre-built database contains typical power consumption characteristic ranges for different task stages, such as standby, data acquisition, data processing, communication reporting, and anomaly detection stages. The pre-built database is constructed based on the electricity meter's operating data at different task stages. Task event logs are used to annotate the collected operating data with task stages, and power consumption characteristic parameters, including average power consumption, peak power consumption, power change rate, voltage fluctuation range, and task duration, are extracted from the annotated data. Based on statistical clustering analysis or time-series pattern recognition algorithms, the power consumption characteristics of different task stages are clustered and templated to obtain typical power consumption characteristic ranges for each task stage. These characteristic ranges and their corresponding stage identification information are stored in the database to form a pre-built task power consumption characteristic database. By comparing the operating current, voltage, and power data of the electricity meter with the feature intervals in a pre-built database, the similarity between the two features can be calculated using cosine similarity, thus identifying the current task execution stage of the electricity meter and outputting the corresponding task stage identifier.

[0034] In practical applications of electricity meters, the meters periodically perform data upload operations, sending collected energy data and operational status information to the main station system via the communication module. During the data upload phase of the electricity meter's task execution, the communication module experiences a momentary surge in power consumption during startup and high-frequency data transmission, leading to short-term voltage fluctuations and a momentary increase in supply current within the meter. To prevent voltage fluctuations from affecting the stable operation of the main control chip and metering module, a capacitor support module is needed. This module rapidly releases stored energy to provide short-term power support, stabilizing the meter's internal voltage and ensuring the continuity and reliability of the data upload process. First, the transmission duration and signal strength of the current communication task are obtained by detecting the communication module's operational status parameters and output current variation curve. Simultaneously, by combining the electricity meter's communication protocol stack parameters, the data frame length, number of transmitted packets, and communication frequency information are extracted to calculate the amount of data uploaded for the current task. Furthermore, the upload rate is determined based on the communication module's transmission power and modulation method.

[0035] After obtaining the amount and rate of uploaded data, the system collects real-time power data from the electricity meter during task execution and calculates the power difference between adjacent moments as the transient power consumption change value. Further, the system identifies pulse-type transient characteristics based on these transient power consumption changes. Pulse-type transient characteristics refer to the short-duration, non-steady-state power consumption change pattern exhibited by the electricity meter during data upload and other tasks, characterized by a sharp rise in instantaneous power consumption, reaching a peak value, and then rapidly decreasing. When the transient power consumption change value exceeds a preset threshold, it is identified as a pulse-type transient event, and characteristic parameters such as pulse amplitude, pulse duration, and rise and fall rates are extracted.

[0036] The transient power consumption change value corresponding to the pulse transient characteristics is used as input, and a pre-set power consumption prediction model is used to calculate the maximum power consumption requirement of the energy meter during the data upload phase. In other words, the pulse transient characteristics extracted during the upload task, such as power consumption peak value, pulse duration, and rise and fall rates, are input into the pre-trained power consumption prediction model. The pre-trained power consumption prediction model can be trained based on historical task power consumption data and can predict the maximum instantaneous power consumption value for power consumption fluctuations during the data upload phase. The pre-trained power consumption prediction model is trained using historical task power consumption data. Feature parameters, including transient power consumption change values ​​and power consumption peak values, are extracted from the historical task power consumption data, and these feature parameters are used as training samples input into the power consumption prediction model. The power consumption prediction model can employ models such as Long Short-Term Memory networks, learning the mapping relationship between historical power consumption data and task phases to form a model capable of predicting transient power consumption fluctuations during the task phase.

[0037] Subsequently, the system calculates the required energy storage capacity based on the maximum power consumption demand and the duration of the peak upload period. It then controls the capacitor support module to store energy in advance, enabling rapid energy release during peak upload periods. The maximum power consumption demand is multiplied by the peak upload duration, and combined with the capacitor module's discharge efficiency and a preset safety factor, the energy reserve is determined. The preset safety factor can be set based on factors such as power consumption prediction errors, the response characteristics of power meter task power consumption fluctuations, and the system's power supply reliability level. The energy reserve is designed to meet the instantaneous power demand during peak data upload periods while ensuring stable output from the energy storage unit during uploads. This avoids micro-circulation discharge due to frequent responses to instantaneous power consumption, thereby extending the lifespan of the energy storage unit and improving the overall system power supply stability.

[0038] Based on the energy stored in the capacitor, the output power and duration of the capacitor support module are adjusted according to the amount of data uploaded and the upload rate. Specifically, the system first obtains the initial voltage, minimum preset voltage, maximum output power, and real-time capacitance of the capacitor support module. Then, the system calculates the transient energy demand value based on the amount of data uploaded and the preset energy consumption per unit bit. This value quantifies the energy demand on the capacitor module during peak upload periods. The preset energy consumption per unit bit refers to a pre-set baseline value of electrical energy consumed per bit of data transmitted or processed. Further, the system calculates the releaseable energy based on the initial voltage, minimum preset voltage, and real-time capacitance, and determines the actual available energy of the capacitor module by combining this with a preset safety margin coefficient. This ensures that the capacitor voltage does not drop too low during discharge. The preset safety margin coefficient can be set based on factors such as power consumption prediction error, the response characteristics of power meter task power consumption fluctuations, and the system power supply reliability level. Subsequently, when the available energy of the capacitor is greater than or equal to the transient energy demand value, the ideal duration is determined by the ratio of the amount of data uploaded to the upload rate, i.e., the length of time the capacitor module needs to support the system. The ideal average output power is calculated based on the transient energy demand value and the ideal duration, and then compared with the maximum output power of the capacitor module. When the ideal average output power is less than or equal to the maximum output power, the output power of the capacitor support module is set to the ideal value, and the discharge duration is set to the ideal duration.

[0039] Finally, by combining the output power of the capacitor support module, the discharge duration, and the energy reserve, the power supply strategy for the data upload phase is determined. That is, the system calculates the power and duration required by the capacitor support module and energy storage unit during peak periods based on the predicted transient power consumption, data volume, and upload rate of the upload task, and determines the actual releaseable energy based on the available stored energy. The power supply strategy can be that during peak data upload periods, the capacitor support module provides short-term high-power output based on the predicted transient power peak, while the energy storage unit provides continuous support with lower power to ensure stable electricity meter voltage. For example, when the uploaded data volume is large and the upload rate is high, the capacitor module can provide 3 seconds of peak power support to cope with the instantaneous power peak, while the energy storage unit provides continuous power with a lower average power, covering the entire upload task cycle, ensuring the electricity meter completes data upload smoothly, while avoiding frequent micro-cycle discharge of the energy storage unit.

[0040] By determining the task stage and power supply strategy based on the power consumption information of the electricity meter, the system effectively extends the service life of the capacitor support module and the energy storage unit while ensuring stable power supply and meeting power consumption requirements during the data upload phase of the electricity meter. This improves the operational reliability and efficiency of the electricity meter during data upload.

[0041] In one embodiment of this invention, adjusting the output power and duration of the capacitor support module based on the amount of uploaded data and the upload rate includes: S310. Obtain the initial voltage, minimum preset voltage, maximum output power, and real-time capacitance of the capacitor support module. S320. Calculate the transient energy demand value based on the amount of uploaded data and the preset energy consumption per unit bit. S330: Calculate the releaseable energy based on the capacitor's initial voltage, minimum preset voltage, and real-time capacitance, and obtain the capacitor's usable energy through the preset safety margin factor and the releaseable energy. S340. When the available energy of the capacitor is greater than or equal to the transient energy demand, the ideal duration is determined by the ratio between the amount of data uploaded and the upload rate. S350: The ideal average output power is calculated based on the transient energy demand value and the ideal duration. S360. If the ideal average output power is less than or equal to the maximum output power, then set the maximum output power of the capacitor support module to the output power and set the ideal duration of the capacitor support module to the duration.

[0042] In this embodiment, the system first acquires the initial voltage, minimum preset voltage, maximum output power, and real-time capacitance of the capacitor support module. The initial voltage reflects the actual energy storage state of the capacitor before the task begins. The minimum preset voltage and maximum output power are factory standard parameters of the capacitor module. The minimum preset voltage limits the lower discharge limit of the capacitor module to prevent over-discharge from damaging the capacitor dielectric or causing system voltage instability. The maximum output power limits the instantaneous output capability of the capacitor module to avoid exceeding its rated discharge capacity during high-power pulses or peak loads. The minimum preset voltage and maximum output power can precisely manage and optimize the charging and discharging behavior of the capacitor module, thereby significantly improving the power supply stability of the energy meter under various grid conditions. The real-time capacitance reflects the currently available stored energy of the capacitor module and, combined with a preset safety margin coefficient, determines the actual releaseable energy. The real-time capacitance can be measured using a capacitance meter. The preset safety margin coefficient can be determined by conducting discharge experiments on the capacitor module under different load conditions to obtain the discharge termination voltage and rated voltage, and by determining the ratio of the discharge termination voltage to the rated voltage.

[0043] Subsequently, the total amount of data uploaded in this task is multiplied by the pre-set energy consumption per bit to calculate the instantaneous energy required to complete the entire data upload task. The energy consumption per bit can be determined experimentally or preset based on historical upload data to accurately reflect the energy consumption of the electricity meter when transmitting each bit of data. The transient energy demand value can be used to guide the determination of the output power and discharge duration of the capacitor support module and energy storage unit during peak upload periods, thereby ensuring that the electricity meter can achieve stable and continuous power supply during data upload.

[0044] The releaseable energy is calculated based on the capacitor's initial voltage, minimum preset voltage, and real-time capacitance. The usable energy of the capacitor is then obtained using a preset safety margin factor and the releaseable energy. Specifically, the releaseable energy between the minimum preset voltage and the capacitor's initial voltage is calculated using the capacitor's electrical formula, which is shown below: Where E represents the energy that can be released, V initial This represents the initial voltage across the capacitor, V. min C represents the minimum preset voltage, and C represents the real-time capacitance.

[0045] The releasable energy is calculated using the electrical formula of the capacitor. Then, the usable energy of the capacitor is obtained by using a preset safety margin factor and the releasable energy. The preset safety margin factor can be set according to the type and characteristics of the capacitor. Specifically, it is the actual energy value obtained by multiplying the releasable energy by the safety margin factor. The actual energy value is the energy that the auxiliary power supply system can safely obtain from the capacitor support module when formulating the power supply strategy.

[0046] Next, when the available energy of the capacitor is greater than or equal to the transient energy demand, the ideal duration is determined by the ratio between the uploaded data volume and the upload rate. This means obtaining the total data volume and preset upload rate for this data upload task, then calculating the ratio to obtain the time required to complete the entire data upload task. The ideal duration guides the discharge process of the capacitor module during peak data upload periods, ensuring that while meeting transient power consumption requirements, the available energy of the capacitor is fully utilized to achieve stable and continuous power supply from the energy meter, and preventing over-discharge of the capacitor module, thereby improving the lifespan of the capacitor module and the reliability of the system power supply.

[0047] Subsequently, the ideal average output power of the capacitor support module or energy storage unit is calculated based on the transient energy demand value of the data upload task and the predetermined ideal duration. This involves calculating the ratio of the transient energy demand value to the ideal duration to obtain the average power required to complete the entire data upload task. The ideal average output power guides the discharge process of the capacitor module or energy storage unit during peak data upload periods, ensuring a stable power output throughout the upload phase to meet the transient power consumption requirements of the energy meter.

[0048] When the calculated ideal average output power is less than or equal to the maximum output power of the capacitor support module, it means that the capacitor support module can meet the transient energy requirements of data upload or high-power tasks within a safe operating range. There is no need to derating the output power or extending the discharge time; it can stably supply power according to the calculated ideal value. The output power of the capacitor module is set to the calculated ideal average output power, and the discharge duration of the capacitor module is set to the ideal duration. Through this setting, the capacitor support module can output power safely and stably during data upload or high-power tasks, fully meeting the transient power consumption requirements of the energy meter, while avoiding over-discharge or overload of the capacitor module.

[0049] By determining the output power and duration of the capacitor support module, the output power and duration of the capacitor support module can be adjusted according to the requirements of the upload task, ensuring that the capacitor support module provides stable power support for the data upload phase under the premise of safety.

[0050] In one embodiment of this invention, if it is determined that an energy storage unit is connected, the energy storage unit is controlled to enter a pre-activated support state, and a parallel discharge path is established by combining task power consumption information, photovoltaic power generation module, energy storage unit, and capacitor support module, including: S410. When it is determined that the energy meter is connected to the energy storage unit, obtain the output voltage and output current of the energy storage unit; S420: The output voltage of the energy storage unit is converted to the first target voltage range of the rated operating voltage of the energy meter through the conversion circuit inside the energy storage unit. S430: Convert the output current of the energy storage unit to the first target current range of the rated operating current of the energy meter. S440. When the output voltage of the energy storage unit is in the first target voltage range and the output current is in the first target current range, the output voltage of the energy storage unit is converted to the second target voltage range of the rated operating voltage of the energy meter and the output current of the energy storage unit is converted to the second target current range of the rated operating current of the energy meter. S450. When the output voltage of the energy storage unit is stable within the third target voltage range of the rated operating voltage of the energy meter and the output current is stable within the third target current range of the rated current of the energy meter, the energy storage unit is determined to have completed the pre-activation support state. S460. When the energy storage unit completes the pre-activation support state, a parallel discharge path is established by combining the task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module. When the system determines that the energy meter needs to connect to the energy storage unit for auxiliary power, it acquires the output voltage and current of the energy storage unit in real time using voltage and current sensors installed on the unit. The voltage sensor measures the actual output voltage at the energy storage unit's port, and the current sensor measures the output current provided by the unit. Based on the collected voltage and current data, the system can calculate the instantaneous output power of the energy storage unit and, combined with the energy meter's power consumption information and the power supply status of the photovoltaic power generation module and capacitor support module, dynamically adjust the energy storage unit's discharge strategy.

[0051] In this embodiment, the output voltage of the energy storage unit typically does not match the rated operating voltage of the electricity meter. Therefore, it is necessary to adjust the output voltage of the energy storage unit to the rated operating voltage range of the electricity meter, i.e., the first target voltage range, through an internal conversion circuit. The voltage conversion circuit within the energy storage unit gradually adjusts the output voltage to the first target voltage range corresponding to the rated operating voltage of the electricity meter. This voltage conversion circuit can be a DC-DC buck-boost converter module, which may include a DC-DC converter or a voltage regulator module. It is used to boost or buck the output voltage of the energy storage unit, stabilizing it within the safe operating voltage range of the electricity meter. The first target voltage range can be 40% to 60% of the rated operating voltage. A soft-start process is achieved through gradual voltage increase to avoid direct high-voltage impact on the electricity meter. By converting the electricity meter's voltage, direct high-voltage impact on the electricity meter can be avoided, achieving a safe soft start, equivalent to the first stage of low-voltage pre-excitation.

[0052] Simultaneously with voltage conversion, the control module synchronously adjusts the output current based on feedback signals, limiting the energy storage unit's output current within the first target current range of the energy meter's rated operating current. This control module is the BMS (Battery Management System) within the energy storage unit. In other words, while the energy storage unit's internal circuitry is adjusting its output voltage to match the initial voltage required by the energy meter, the intelligent control module is also simultaneously monitoring the actual output current of the energy storage unit in real time. Based on this monitoring data, it proactively adjusts the energy storage unit's output to control the first target current range of the energy meter's rated operating current, preventing sudden current fluctuations. This first target current range can be less than or equal to 0.1 times the rated current. Current limiting control prevents overcurrent during the initial pre-activation phase, avoiding internal polarization or cell damage within the energy storage unit.

[0053] Once the output voltage and output current of the energy storage unit are detected to be stable within the first target voltage range and the first target current range, the controller further drives the voltage conversion circuit to raise the output voltage to the second target voltage range of the energy meter's rated operating voltage. This is the intermediate stage of pre-activation, namely the high-voltage pre-excitation stage. After the low-voltage operation stabilizes, the system continues to raise the output voltage to 80%–90% of the rated voltage, while simultaneously increasing the output current, but still keeping it within the safety threshold. By implementing the high-voltage pre-excitation stage, the performance of the energy storage unit is gradually activated, allowing the energy meter to gradually obtain an input closer to its rated value, and establishing a power balance between the energy meter and the energy storage unit. This allows the output characteristics of the energy storage unit to gradually approach the rated power supply requirements of the energy meter.

[0054] After the energy storage unit undergoes the pre-activation process, when its output voltage stabilizes within the third target voltage range of the electricity meter's rated operating voltage, and its output current stabilizes within the third target current range of the electricity meter's rated current, the system determines that the energy storage unit's output characteristics meet the electricity meter's safe power supply requirements, thus confirming that the energy storage unit has completed the pre-activation support state. At this time, the energy storage unit can be connected to the DC bus as an auxiliary energy source to provide stable voltage support and current compensation for the electricity meter, enabling parallel and coordinated operation with the photovoltaic power generation module.

[0055] When the output voltage of the energy storage unit stabilizes within the third target voltage range of the rated operating voltage of the electricity meter and the output current stabilizes within the third target current range of the rated current of the electricity meter, it indicates that the energy storage unit has reached a stable output state, i.e., the pre-activation support state is completed. At this time, the system will connect the energy storage unit to the energy collaborative control link through the control module, and establish a parallel discharge path based on the task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module. The control module will determine the task type of the electricity meter within a preset time period based on the task power consumption information and generate the corresponding power consumption demand curve; then, combining the real-time power generation capacity curve of the photovoltaic power generation module and the health parameters of the energy storage unit, it will determine the power supply weight of each energy module; furthermore, when the task type is a stable power consumption type, a stable power consumption parallel path between the photovoltaic power generation module and the energy storage unit is constructed; when the task type is a pulse power consumption type, a pulse power consumption parallel path between the photovoltaic power generation module, the energy storage unit, and the capacitor support module is constructed, realizing the dynamic power collaborative output of multiple energy modules.

[0056] In this embodiment, the first target voltage range is smaller than the second target voltage range, and the second target voltage range is smaller than the third target voltage range; while the first target current range is larger than the second target current range, and the second target current range is larger than the third target current range. This ensures the safety of the energy storage unit during the transition from low voltage to high voltage and avoids overvoltage or overcurrent surges in the system.

[0057] By controlling the energy storage unit to enter the pre-activated support state, adjusting the output voltage and current of the energy storage unit to the range required by the electricity meter, and then stabilizing it, a parallel discharge path is established by combining the task power consumption information, photovoltaic power generation module, energy storage unit and capacitor support module. This can effectively ensure the stable operation of the electricity meter under different power supply conditions, improve the reliability and flexibility of the system power supply, and realize the efficient management and utilization of electricity.

[0058] In one embodiment of this invention, a parallel discharge path is established by combining task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module, including: S510. Determine the task type of the energy meter within a preset time period based on the task power consumption information, and generate a power consumption demand curve based on the task type. The task type includes stable power consumption type and pulse power consumption type. S520: Generate a photovoltaic power generation capacity curve based on the solar power generation trend of the photovoltaic power generation module; S530. Calculate the health status of the energy storage unit based on the energy storage status data. The health status of the energy storage unit includes the state of charge of the energy storage unit and the health status of the energy storage unit. S540. Based on task type, power consumption demand curve, photovoltaic power generation capacity curve and energy storage unit health, determine the power supply weight of photovoltaic power generation module, energy storage unit and capacitor support module in each task type. S550. When the task type is stable power consumption type, the photovoltaic power generation module and the energy storage unit form a stable power consumption parallel path, and the first power consumption sharing ratio of the photovoltaic power generation module and the energy storage unit is determined in combination with the power supply weight corresponding to the stable power consumption type. S560, determine the first parallel path by the first power consumption sharing ratio and the stable power consumption parallel path; S570. When the task type is pulse power consumption type, the photovoltaic power generation module, energy storage unit and capacitor support module are connected in parallel path for pulse power consumption, and the second power consumption sharing ratio of the photovoltaic power generation module, energy storage unit and capacitor support module is determined according to the power supply weight corresponding to the pulse power consumption type. S580, determine the second parallel path by the second power consumption sharing ratio and the pulse power consumption parallel path; First, the task type of the electricity meter within a preset time period is determined by the task power consumption information. In this embodiment, the task types include stable power consumption type and pulse power consumption type. The stable power consumption type refers to a load task in which the power remains basically constant or changes slowly within the time period, while the pulse power consumption type refers to a load task in which the power experiences short-term peaks or rapid fluctuations within the time period. The preset time period is the time window for the system to plan future power supply strategies, such as the next 5 minutes, 1 hour, or 1 day. Specifically, the task power consumption information of the electricity meter within the time period is obtained, including real-time power data and historical power data sequences. Each data point represents the power value of the electricity meter at a specific time point. Subsequently, feature analysis is performed on the power data sequence, such as power fluctuation amplitude, instantaneous peak value, and power change frequency. Based on the feature information, the task type is determined by a preset judgment rule. When the power change amplitude is low and short-term peaks are absent, the task is judged as a stable power consumption type; when the power changes drastically or experiences instantaneous peaks within a short period of time, the task is judged as a pulse power consumption type. Subsequently, a power demand curve is generated based on the task type. The power demand curve is a continuous curve used to characterize the change of electricity meter power over time. The curve generation strategy can be selected according to the task type. When the task type is a stable power consumption type, an approximately constant or slowly changing power curve is generated by calculating the power average or smoothing the power data to reflect the stable power demand of the base load. When the task type is a pulse power consumption type, features such as instantaneous peak value, short-term power change amplitude and duration are extracted and fitted or interpolated to generate a power curve that can accurately reflect the instantaneous peak value and short-term changes to characterize the pulse power demand of the load.

[0059] Secondly, a photovoltaic power generation capacity curve is generated based on the solar power generation trend of the photovoltaic modules. The power generation capacity of the photovoltaic modules is closely related to solar conditions. The solar power generation trend refers to the changes in solar-related factors such as solar intensity and duration over time. Then, based on this solar power generation trend, the solar intensity corresponding to each moment is determined, and a pre-set solar intensity-power generation capacity table is used to determine the rated power. The pre-set solar intensity-power generation capacity table is obtained by testing the photovoltaic modules under different solar intensities, temperatures, and other conditions in advance, recording the corresponding power generation data, and creating a table. After obtaining the rated power at each moment through the pre-set solar intensity-power generation capacity table, these rated powers that change over time are plotted together to form the photovoltaic power generation capacity curve.

[0060] The energy storage unit's health is calculated based on its state data. The energy storage unit's health comprehensively characterizes its energy storage capacity and performance degradation level. It includes the energy storage unit's state of charge (SOC) and its health status. The SOC reflects the proportion of remaining charge to rated capacity at the current moment, while the health status reflects the degree to which the energy storage unit retains its initial capacity or performance. In this embodiment, the energy storage unit's state data includes electrical parameters such as initial SOC, rated capacity, and charging / discharging current. The SOC is calculated using the ampere-hour integral method, the specific expression of which is shown below: Where SOC represents the state of charge of the energy storage unit, SOC(t0) represents the initial state of charge, and C n The value represents the rated capacity of the energy storage unit, I is the charging and discharging current, and η is the coulombic efficiency (less than 1 during charging and close to 1 during discharging, affected by temperature and current).

[0061] The state of charge (SOC) of the energy storage unit is determined using the above method. Subsequently, the health status of the energy storage unit is calculated. The health status represents the degree of degradation of the battery's current performance relative to its initial performance. This can be calculated based on the capacity decay method, which determines the degree of battery capacity decay by the ratio of the current usable capacity to the initial rated capacity, thereby determining the health status of the energy storage unit. The specific expression is as follows: Where SOH represents the energy storage unit's health status, C a This is the battery's current actual usable capacity; C r This is the battery's initial rated capacity.

[0062] To determine the current usable capacity of a battery, it's possible to integrate the charging and discharging current within a specific SOC range during battery operation and then correct for it using the open-circuit voltage. For example, during constant-current charging, the capacity can be estimated using the relationship between the rate of voltage change and capacity. This involves integrating the charging and discharging current over time within a specific SOC range to obtain the change in charge capacity of the energy storage unit during that period. The integrated calculation result is then corrected using the open-circuit voltage (OCV) characteristic curve or equivalent circuit model of the energy storage unit to eliminate errors caused by factors such as internal resistance, temperature, and aging, thereby accurately estimating the current capacity. For instance, during constant-current charging, the remaining charge capacity of the energy storage unit can be estimated based on the functional relationship between the rate of voltage change and capacity, enabling dynamic assessment of the state of charge. After obtaining the SOC and SOH of the energy storage unit, different weight values ​​are assigned to SOC and SOH, which can be set according to the different energy storage system applications' requirements for energy storage and battery life. The energy storage unit's health is then calculated by combining the SOC and SOH and their corresponding weight values.

[0063] After identifying the task's power consumption characteristics and assessing its energy storage status, the system determines the power supply weights of the photovoltaic (PV) power generation module, energy storage unit, and capacitor support module for each task type based on the task type, power demand curve, PV power generation capacity curve, and energy storage unit health status. Specifically, when the task type is a stable power consumption type, the system determines the total power demand corresponding to the stable power consumption type through the power demand curve and obtains the first actual power supply of the PV power generation module based on the PV power generation capacity curve. Subsequently, it calculates the power gap value using the difference between the total power demand and the actual PV power supply, and determines the first actual power supply of the energy storage unit based on the energy storage unit's health status. The ratio of the actual power supply of the PV power generation module to the total power demand is the PV power supply weight, and the ratio of the actual power supply of the energy storage unit to the total power demand is the energy storage power supply weight. When the task type is a pulse power consumption type, the system determines the instantaneous peak power and stable base power through the power demand curve corresponding to the pulse power consumption type, where the instantaneous peak power and stable base power together constitute the total pulse power demand. Subsequently, the second actual power supply of the photovoltaic power generation module is determined based on the photovoltaic power generation capacity curve. The power gap value is calculated based on the real-time photovoltaic power and the stable baseline power, and then the second actual available power of the energy storage unit is determined by combining the energy storage unit's health status. The ratio of the real-time power of the photovoltaic power generation module to the stable baseline power is the photovoltaic power supply weight, and the ratio of the actual available power of the energy storage unit to the stable baseline power is the energy storage power supply weight. Furthermore, the system obtains the current maximum instantaneous discharge capacity of the capacitor support module and determines the instantaneous power demand value of the capacitor support module during the pulse phase based on the instantaneous peak power and the maximum instantaneous discharge capacity. The ratio of its instantaneous power demand value to the instantaneous peak power is the power supply weight of the capacitor support module.

[0064] When the task type is stable power consumption, the photovoltaic (PV) power generation module and energy storage unit form a parallel path for stable power consumption. The first power consumption sharing ratio between the PV power generation module and the energy storage unit is determined based on the power supply weight corresponding to the stable power consumption type. Stable power consumption indicates that the power demand of the electricity meter is relatively constant, with small fluctuations and low frequency over a period of time. Once a stable power consumption task is determined, the system designates the PV power generation module and energy storage unit as the main power supply to meet this demand. Because capacitor-supported modules have very low energy density, they are not suitable for long-term stable power supply. Their advantage lies in instantaneously releasing large power to cope with pulsed loads, rather than continuous stable output. Therefore, in stable power consumption scenarios, capacitors typically do not participate in the main power supply. By forming a parallel path for stable power consumption with the PV power generation module and energy storage unit, the power consumption sharing ratio between the PV power generation module and the energy storage unit is calculated based on the previously determined PV power supply weight and energy storage power supply weight corresponding to the stable power consumption type. This achieves dynamic allocation and energy balance control of their output power. The PV power generation module undertakes the main stable power output, while the energy storage unit provides auxiliary power compensation, thereby ensuring the continuous power supply capability of the stable load under different lighting conditions. For example, if the total power demand is 10W, according to the previous calculations, the photovoltaic power supply weight is 0.7W, the energy storage power supply weight is 0.3, the photovoltaic module output power is 7W, and the energy storage unit output power is 3W. That is, the first power consumption sharing ratio is 7:3, which represents the power sharing relationship between photovoltaic and energy storage in the stable power supply path.

[0065] Subsequently, the control module establishes a stable power consumption parallel path between the photovoltaic power generation module and the energy storage unit according to the first power consumption sharing ratio. It dynamically adjusts their respective conduction states, voltage matching, and output current, ensuring that the photovoltaic power generation module and the energy storage unit each bear the load power according to their respective power consumption sharing ratios, thus forming the first parallel path. This first parallel path is a logical combination of the photovoltaic power generation module and the energy storage unit coordinating power supply under stable power consumption tasks. This path configuration enables optimal power allocation and balanced energy output under stable load conditions, avoiding the impact of excessive discharge of the energy storage unit or fluctuations in photovoltaic output on the system's power supply stability, and improving the energy efficiency and reliability of the auxiliary power supply system during stable operation.

[0066] When the task type is pulse power consumption, the system logically connects the photovoltaic power generation module, energy storage unit, and capacitor support module in parallel to form a power supply path, creating a pulse power consumption parallel path. This path characterizes the power collaborative allocation among the photovoltaic power generation module, energy storage unit, and capacitor support module under pulse power consumption tasks. Pulse power consumption refers to a load power demand exhibiting pulse-like changes, where power suddenly increases or decreases within a short period, then returns to a relatively stable lower or higher level. In this path, the system calculates the second power consumption sharing ratio for each module based on the photovoltaic power supply weight, energy storage power supply weight, and instantaneous power weight of the capacitor support module corresponding to the pulse power consumption type. This achieves dynamic response to transient peak power and effective supply of stable base power. The photovoltaic power generation module primarily handles stable power output, the energy storage unit provides power compensation, and the capacitor support module meets the load's instantaneous high power demand, thus ensuring the system's power supply stability and response speed under pulse power consumption tasks.

[0067] Under pulsed power consumption tasks, after calculating the power sharing ratios of the photovoltaic module, energy storage unit, and capacitor support module, and defining their logical parallel relationships (i.e., pulsed power consumption parallel paths), the system will, based on this sharing ratio and parallel relationship, form a practically executable power supply path in control or logic—the second parallel path. This allows the three types of modules to collaboratively supply power to the load according to the calculated power sharing ratios, meeting the load's transient peak and base power requirements. Through this second parallel path, the system can adjust the output power of each module according to the power sharing ratios, achieving stable output from the photovoltaic module, power compensation from the energy storage unit, and instantaneous peak support from the capacitor module, thereby ensuring power continuity and system power supply stability under pulsed power consumption tasks. In this embodiment, the first and second parallel paths are used to characterize parallel discharge paths.

[0068] By intelligently analyzing task power consumption information, dynamically distinguishing between stable and pulse load characteristics, and integrating photovoltaic power generation capacity curves, energy storage health assessments, and capacitor support status, power supply weights are precisely allocated for different task types. This achieves the dual effect of long-term, low-loss power supply under stable operating conditions and instantaneous high-current support without drop-down under pulse operating conditions, significantly improving the stability of meter power supply, photovoltaic self-generation and self-consumption rate, and energy storage cycle life.

[0069] In one embodiment of this invention, the power supply weights of the photovoltaic power generation module, energy storage unit, and capacitor support module in each task type are determined based on the task type, power consumption demand curve, photovoltaic power generation capacity curve, and energy storage unit health status, including: S610. When the task type is a stable power consumption type, the total power demand corresponding to the stable power consumption type is determined by the power demand curve. S620. Determine the first actual power supply of the current photovoltaic power generation module based on the photovoltaic power generation capacity curve; S630. Determine the power deficit value by the difference between the total demand power and the actual power supplied. S640. Determine the first actual power supply of the current energy storage unit by combining the energy storage unit's health status and power deficit value; S650. Divide the actual power supply of the photovoltaic power generation module by the total power demand corresponding to the stable power consumption type to obtain the photovoltaic power supply weight. S660. Divide the actual power supplied by the energy storage unit by the total power demand corresponding to the stable power consumption type to obtain the energy storage power supply weight.

[0070] When a task is identified as a stable power consumption type, the power demand information for that task within the current time period is obtained through the power demand curve, and the total power demand corresponding to the stable power consumption type is calculated accordingly. Specifically, the power demand data for the stable power consumption type task within a preset time period is obtained through the power demand curve, and the instantaneous power values ​​within that time period are averaged to determine the total power demand corresponding to the task. That is, when the power demand curve is a continuous curve, the total power demand is obtained by integrating and averaging the power curve over the time period; when the power demand curve is discrete sampled data, the total power demand is obtained by averaging the power values ​​at each sampling point within the time period. The total power demand is used to guide the power allocation and output control of the photovoltaic power generation module and the energy storage unit under stable power consumption tasks, thereby ensuring that the load obtains the required power during the stable operation phase, while realizing efficient collaborative power supply between the photovoltaic power generation and energy storage units.

[0071] Subsequently, the first actual power supply of the photovoltaic power generation module is determined through the photovoltaic power generation capacity curve. In this embodiment, the photovoltaic power generation capacity curve is the PV curve, i.e., the power-voltage characteristic curve. The PV curve shows the power that the photovoltaic module can provide at different output voltages under a given irradiance and temperature. The PV curve has a clear peak, which is the maximum power point. The power is extracted from the PV curve. For example, if the current time is 14:30, the actual output power corresponding to 14:30 is found on the generated photovoltaic power generation capacity curve, thus obtaining the first actual power supply of the photovoltaic power generation module.

[0072] The total power demand corresponding to the task is compared with the actual power supplied by the photovoltaic power generation modules, and the difference between the two is calculated to determine the power gap value. The power gap value represents the additional power required by the load when the photovoltaic modules are insufficient to supply power. It is used to guide the power allocation of energy storage units and other auxiliary modules, thereby ensuring that the power supply requirements of the task are met under stable or pulsed power consumption conditions.

[0073] When deciding how much power to supply to the grid or load, an energy storage unit first determines the current system's power requirement, then checks its own capacity under its current health, state of charge, and temperature to ensure safe and reliable power delivery. The final actual power supply will be a reasonable balance between these two factors: not exceeding its own capacity while meeting system demands as much as possible. Specifically, the system calculates the maximum allowable discharge power of the energy storage unit based on its health status and compares the power deficit with this maximum allowable discharge power to determine the actual power the unit can provide in the current time period. Calculating the maximum allowable discharge power based on the energy storage unit's health status can be achieved by multiplying the unit's health status by its rated power. If the power deficit exceeds the unit's output capacity, the energy storage provides its maximum capacity; if the power deficit is less than the output capacity, the energy storage only provides the power required to fill the deficit. For example, if the power deficit is 40W and the maximum allowable discharge power is 30W, the actual power supplied by the energy storage unit is 30W, and the remaining 10W can be supplemented by other modules (such as capacitor support modules) or other energy storage units.

[0074] By comparing the actual power supplied by the photovoltaic (PV) power generation module with the total power demand of the stable power consumption task and dividing the two, the power supply weight of the PV power generation module under that task is obtained. The power supply weight is used to determine the power allocation ratio of the PV module in the stable power consumption task and guide the energy storage unit and other auxiliary modules to supplement the remaining power, thereby achieving multi-source coordinated power supply and ensuring load power continuity.

[0075] Subsequently, by comparing the actual power supplied by the energy storage unit with the total power demand of the stable power consumption type task and dividing the two, the power supply weight of the energy storage unit under that task is obtained. The energy storage power supply weight is used to guide the power allocation ratio of the energy storage unit in the stable power consumption task. Combined with the power supply weight of the photovoltaic module, multi-source coordinated power supply is achieved, thereby ensuring the continuity of load power and the stability of system power supply.

[0076] By determining the corresponding power supply weight for each task type, we can not only ensure that photovoltaic power generation prioritizes meeting demand and reduce dependence on external power, but also rationally allocate power supply tasks based on the health status of the energy storage unit, avoiding overcharging or over-discharging. This achieves efficient, stable, and accurate power supply weight allocation, ensuring that the electricity meter operates reliably in stable power consumption tasks, effectively extending the lifespan of the energy storage unit, and optimizing overall energy utilization efficiency.

[0077] In one embodiment of this invention, the method further includes: S710. When the task type is pulse power consumption type, the instantaneous peak power and stable base power are determined by the power consumption demand curve corresponding to the pulse power consumption type. The instantaneous peak power and stable base power constitute the total pulse power demand of the pulse power consumption type. S720. Determine the second actual power supply of the current photovoltaic power generation module based on the photovoltaic power generation capacity curve; S730. Determine the power gap value of the stable base value power based on the real-time power and stable base value power of the photovoltaic power generation module; S740. Combine the power gap value based on the stable baseline power and the health status of the energy storage unit to determine the second actual available power of the energy storage unit; S750. Divide the real-time power of the photovoltaic power generation module by the stable base power to obtain the power supply weight of the photovoltaic power generation module. S760. Divide the actual available power of the energy storage unit by the stable base power to obtain the power supply weight of the energy storage unit; S770. Obtain the current maximum instantaneous discharge capability of the capacitor support module, and determine the instantaneous power requirement of the capacitor support module in the pulse phase based on the instantaneous peak power and the maximum instantaneous discharge capability. S780: Divide the instantaneous power demand value by the instantaneous peak power to obtain the power supply weight of the capacitor support module.

[0078] When the task type is pulse power consumption, the power demand information of the load within a preset time period is obtained through the power demand curve corresponding to the pulse power consumption type, and the instantaneous peak power and stable base value power are determined accordingly. In this implementation, each task type corresponds to a different power demand curve, and the pulse power consumption type corresponds to a dedicated power demand curve. The instantaneous peak power and stable base value power are determined from the power demand curve corresponding to the pulse power consumption type. Histograms can be used for analysis, and all power data can be plotted into a histogram. For pulse power consumption, the histogram usually presents two or more peaks: one or more lower power peaks (corresponding to base value power and possible intermediate states), and one or more higher power peaks (corresponding to pulse peak value). The base value power corresponds to the center value of the peak with the highest frequency and lowest power value in the histogram. Among them, the instantaneous peak power represents the maximum power required by the load during the pulse, while the stable base value power represents the continuous power consumption between pulses or within the pulse cycle. The two are combined to form the total pulse demand power, which is used to guide the power allocation and power supply control of photovoltaic power generation modules, energy storage units, and capacitor support modules in pulse power consumption tasks.

[0079] The second actual power supply of the photovoltaic power generation module is determined based on the photovoltaic power generation capacity curve. In this embodiment, the photovoltaic power generation capacity curve is the PV curve, i.e., the power-voltage characteristic curve. The PV curve shows the power that the photovoltaic module can provide at different output voltages under a given irradiance and temperature. The PV curve has a significant peak, which is the maximum power point. The power is extracted from the PV curve to obtain the second actual power supply of the photovoltaic power generation module.

[0080] Subsequently, the power gap value of the stable base power is determined based on the real-time power and stable base power of the photovoltaic power generation module. That is, the power gap value is determined by using the difference between the real-time power of the photovoltaic power generation module and the stable base power. This power gap value indicates whether the current output of the photovoltaic module meets the system's stable power requirements. Based on this power gap value, the system can dynamically adjust the output of the energy storage unit or other auxiliary power sources to compensate for insufficient photovoltaic power generation or regulate excess power, thereby ensuring stable operation and reliable power supply under different lighting conditions.

[0081] Furthermore, by combining the power gap value based on the stable baseline power and the energy storage unit's health status, the second actual available power of the energy storage unit is determined. This means that when allocating energy storage power to the stable baseline load, the power gap value is calculated based on the difference between the real-time power of the photovoltaic power generation module and the preset stable baseline power. The power gap value represents the additional energy required by the photovoltaic module to meet the stable baseline power under the current irradiance conditions. Subsequently, the health information of the energy storage unit is obtained, which reflects the available capacity, charging and discharging capability, and degradation status of the energy storage unit. Combining the power gap value and the energy storage unit's health status, the system determines the second actual available power of the energy storage unit, that is, the power that the energy storage unit can actually output in the current state to supplement the photovoltaic power gap.

[0082] Subsequently, after acquiring the real-time power of the photovoltaic (PV) power generation module, this real-time power is compared with the previously obtained stable baseline power to obtain the power supply weight of the PV power generation module. This power supply weight quantifies the relative proportion of load borne by the PV module in the entire power supply system. Combining the health information and power deficit value of the energy storage unit, the system further determines the second actual available power of the energy storage unit, thereby achieving coordinated scheduling of the PV power generation module and the energy storage unit, ensuring stable power supply under various load conditions.

[0083] Next, the real-time power of the photovoltaic (PV) module is compared with the stable baseline power to obtain the power supply weight of the PV module. This power supply weight is used to quantify the relative proportion of the load borne by the PV module in the entire power supply system. By calculating the power supply weight of the PV module, the system can dynamically evaluate and schedule the output power of the PV module, thereby ensuring the stable operation of the system under various load conditions.

[0084] Simultaneously, the system calculates the ratio of the actual available power of the energy storage unit to the stable baseline power to obtain the power supply weight of the energy storage unit. This power supply weight is used to quantify the relative proportion of the load borne by the energy storage unit in the entire power supply system. By calculating the power supply weight of the energy storage unit, the system can achieve dynamic evaluation and scheduling of the output power of the energy storage unit, thereby ensuring the stability and reliability of the system under multi-energy collaborative power supply conditions.

[0085] The maximum instantaneous discharge capacity of the capacitor support module is obtained, reflecting the maximum instantaneous power that the module can output under the current voltage, capacitance, and health conditions. Then, the instantaneous power requirement of the capacitor support module during the pulse phase is determined by matching the instantaneous peak power of the load during the pulse phase with the module's maximum instantaneous discharge capacity. When the module's maximum instantaneous discharge capacity is greater than or equal to the load's instantaneous peak power, the module fully meets the load's instantaneous power consumption requirements. When the module's maximum instantaneous discharge capacity is less than the peak power, the module only provides its maximum capacity, with the remaining power supplemented by other capacitor support units. This method allows the system to safely and efficiently utilize the instantaneous high-power output characteristics of the capacitor support module, achieving dynamic response to pulsed loads while ensuring the stability and reliability of the entire power supply system.

[0086] Finally, the instantaneous power demand value is compared with the instantaneous peak power of the load to obtain the power supply weight of the capacitor support module during the pulse phase. This power supply weight is used to quantify the proportion of power undertaken by the capacitor module in the entire instantaneous peak power. By calculating the power supply weight of the capacitor support module, the system can dynamically evaluate and schedule the instantaneous output power of the capacitor module, thereby achieving multi-energy coordinated power supply under pulse load conditions and ensuring the stability and reliability of the entire power supply system.

[0087] By determining the power supply weight allocation for pulse power consumption types, not only can the reliable supply of critical instantaneous power be ensured, but also the overuse of energy storage and capacitor modules can be avoided. This improves the system's response speed, power supply accuracy, and overall lifespan under pulse tasks, and enables efficient coordination and stable operation of the power supply of the energy meter under complex load scenarios.

[0088] In one embodiment of this invention, adjusting the output power of the photovoltaic power generation module by combining energy storage status data, parallel discharge paths, and task power consumption information includes: S810: Input the energy storage status data, parallel discharge path and task power consumption information into the pre-trained power allocation model to obtain the target output power of the photovoltaic power generation module.

[0089] The system acquires energy storage status data for the energy storage unit, including its voltage, current, charge / discharge status, and health information. It also acquires parallel discharge paths reflecting the conduction status, maximum output capacity, and line characteristics of the photovoltaic (PV) power generation module, energy storage unit, and capacitor support module. Furthermore, it acquires task power consumption information, including the load's stable power consumption and pulse power consumption requirements for the current time period. Subsequently, the aforementioned energy storage status data, parallel discharge path parameters, and task power consumption information are input into a pre-trained power allocation model. The model calculates the target output power for the PV power generation module based on the capabilities of each module and the load requirements. This target output power guides the PV module to dynamically adjust its output, achieving coordinated operation with the energy storage unit and capacitor module. This ensures that while meeting the load power consumption requirements, each module operates within a safe range, thereby improving the overall stability and reliability of the system's power supply. The pre-trained power allocation model is obtained by training a neural network model using historical operating data. This historical operating data can include information such as the voltage, current, SOC, and health status of the energy storage unit; real-time and historical power data of the photovoltaic module; the discharge capacity and status of the capacitor module; parallel discharge path parameters; and the power demand of the load during stable power consumption and pulse power consumption phases. The training data comes from the data acquisition terminal operating system, which collects the above data at a fixed sampling period. The data is then preprocessed, with each feature normalized and outlier and noise samples removed to form a standardized input sample set. In terms of model structure, this embodiment uses a neural network model for training. The model includes an input layer, three hidden layers, and an output layer. The activation function is the ReLU function, and the output layer uses the Softmax function to ensure the normalization constraint of the power allocation ratio. The model's loss function uses the mean absolute error form, and its specific expression is shown below: Among them, It is the power allocation predicted by the model, y i, where L represents the optimal power allocation in the training data; L represents the mean absolute error; and N represents the total number of samples in the dataset. The optimal power allocation refers to the power allocation scheme that minimizes the overall energy loss of the system and the lifespan degradation rate of the energy storage units, given the load power demand and the current state constraints of the photovoltaic, energy storage, and capacitor modules. During training, the Adam optimizer can be used with a learning rate of 0.0005 and 300 iterations, and the model convergence performance can be monitored using a validation set. The learning rate and number of iterations can be specifically set according to the model's accuracy requirements. When the output power allocation satisfies the constraints of energy conservation and power supply stability, the model training is considered complete, resulting in a pre-trained power allocation model. This pre-trained power allocation model, through intelligent and predictive management of the internal power resources of the electricity meter, can operate more stably and reliably, ensuring metering accuracy, communication reliability, and data integrity, and improving the adaptability and robustness of the electricity meter to changes in the power supply environment.

[0090] This application provides a machine-readable storage medium storing instructions that cause a machine to execute the auxiliary power supply method described above for use in electricity meters and data acquisition terminals.

[0091] This application also provides an auxiliary power supply system for electricity meters and data acquisition terminals, including: The memory is configured to store instructions; and The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the aforementioned auxiliary power supply method for electricity meters and data acquisition terminals.

[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0098] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0100] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for supplying auxiliary power to electricity meters and data acquisition terminals, characterized in that, Applied to electricity meters and data acquisition terminals, the data acquisition terminal includes a photovoltaic power generation module, an energy storage unit, and a capacitor support module, comprising: Acquire real-time operating data from the electricity meter and energy storage status data from the energy storage unit; The task power consumption information of the energy meter is determined based on real-time operation data and a pre-built task timing power consumption model. Photovoltaic power generation data is obtained through photovoltaic power generation modules, and the trend of solar power generation is determined based on the photovoltaic power generation data; Determine whether to connect an energy storage unit by combining solar power generation trends and task power consumption information; If it is determined that the energy storage unit is connected, the energy storage unit is controlled to enter the pre-activated support state, and a parallel discharge path is established by combining the task power consumption information, photovoltaic power generation module, energy storage unit and capacitor support module; The output power of the photovoltaic power generation module is adjusted by combining energy storage status data, parallel discharge paths, and task power consumption information. The photovoltaic power supply strategy of the electricity meter is determined based on the output power of the photovoltaic power generation module, the energy storage status data, and the task power consumption information.

2. The method according to claim 1, characterized in that, The method further includes: The task execution stage is determined by the task power consumption information from the electricity meter. When the task execution phase of the electricity meter is the data upload phase, determine the amount of data to be uploaded and the upload rate. The transient power consumption change value corresponding to each moment of the power meter is calculated by using the power consumption information of the power meter, and the pulse transient characteristics are determined based on the transient power consumption change value. The maximum power consumption requirement during data upload is obtained by using a preset power consumption prediction model to calculate the transient power consumption change value corresponding to the pulse transient characteristics. The maximum power consumption requirement triggers the capacitor support module to determine the energy reserve amount, which ensures that the energy storage unit will not experience micro-circulation discharge during peak upload periods. Adjust the output power and duration of the capacitor support module according to the amount and rate of uploaded data; The power supply strategy for the data upload phase is determined by output power, duration, and energy reserves.

3. The method according to claim 2, characterized in that, The adjustment of the output power and duration of the capacitor support module based on the amount of uploaded data and the upload rate includes: Obtain the initial capacitor voltage, minimum preset voltage, maximum output power, and real-time capacitance of the capacitor support module; The transient energy demand is calculated based on the amount of uploaded data and the preset energy consumption per unit bit. The energy that can be released is calculated based on the initial voltage, minimum preset voltage and real-time capacitance of the capacitor, and the usable energy of the capacitor is obtained through the preset safety margin factor and the energy that can be released. When the available energy of the capacitor is greater than or equal to the transient energy demand, the ideal duration is determined by the ratio between the amount of data uploaded and the upload rate. The ideal average output power is calculated based on the transient energy demand and the ideal duration. If the ideal average output power is less than or equal to the maximum output power, then the maximum output power of the capacitor support module is set to the output power and the ideal duration of the capacitor support module is set to the duration.

4. The method according to claim 1, characterized in that, If it is determined that an energy storage unit is connected, the energy storage unit is controlled to enter a pre-activated support state, and a parallel discharge path is established based on the task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module, including: When it is determined that the energy meter is connected to the energy storage unit, the output voltage and output current of the energy storage unit are obtained; The output voltage of the energy storage unit is converted to the first target voltage range of the rated operating voltage of the energy meter through the conversion circuit inside the energy storage unit. Convert the output current of the energy storage unit to the first target current range of the rated operating current of the energy meter; When the output voltage of the energy storage unit is in the first target voltage range and the output current is in the first target current range, the output voltage of the energy storage unit is converted to the second target voltage range of the rated operating voltage of the energy meter and the output current of the energy storage unit is converted to the second target current range of the rated operating current of the energy meter. When the output voltage of the energy storage unit is stable within the third target voltage range of the rated operating voltage of the energy meter and the output current is stable within the third target current range of the rated current of the energy meter, the energy storage unit is determined to have completed the pre-activation support state. When the energy storage unit completes the pre-activation support state, a parallel discharge path is established by combining the task power consumption information, the photovoltaic power generation module, the energy storage unit, and the capacitor support module; Among them, the first target voltage range is smaller than the second target voltage range and the second target voltage range is smaller than the third target voltage range, and the first target current range is larger than the second target current range and the second target current range is larger than the third target current range.

5. The method according to claim 4, characterized in that, The establishment of a parallel discharge path by combining task power consumption information, photovoltaic power generation module, energy storage unit and capacitor support module includes: The task type of the energy meter within a preset time period is determined based on the task power consumption information, and a power consumption demand curve is generated based on the task type. The task type includes stable power consumption type and pulse power consumption type. A photovoltaic power generation capacity curve is generated by analyzing the solar power generation trend of photovoltaic power generation modules. The health status of energy storage units is calculated based on energy storage status data. The health status of energy storage units includes the state of charge of the energy storage unit and the health status of the energy storage unit. Based on task type, power demand curve, photovoltaic power generation capacity curve and energy storage unit health, the power supply weight of photovoltaic power generation module, energy storage unit and capacitor support module in each task type is determined respectively. When the task type is stable power consumption, the photovoltaic power generation module and the energy storage unit are connected in a stable power consumption parallel path, and the first power consumption sharing ratio of the photovoltaic power generation module and the energy storage unit is determined in combination with the power supply weight corresponding to the stable power consumption type. The first parallel path is determined by the first power consumption sharing ratio and the stable power consumption parallel path; When the task type is pulse power consumption type, the photovoltaic power generation module, energy storage unit and capacitor support module are connected in parallel path for pulse power consumption, and the second power consumption sharing ratio of the photovoltaic power generation module, energy storage unit and capacitor support module is determined according to the power supply weight corresponding to the pulse power consumption type. The second parallel path is determined by the second power consumption sharing ratio and the pulse power consumption parallel path; The first parallel path and the second parallel path are used to characterize the parallel discharge path.

6. The method according to claim 5, characterized in that, The power supply weights of the photovoltaic power generation module, energy storage unit, and capacitor support module in each task type are determined based on task type, power consumption demand curve, photovoltaic power generation capacity curve, and energy storage unit health, including: When the task type is a stable power consumption type, the total power demand corresponding to the stable power consumption type is determined by the power demand curve. Determine the first actual power supply of the current photovoltaic power generation module based on the photovoltaic power generation capacity curve; The power deficit value is determined by the difference between the total demand power and the actual supply power. The first actual power supply of the current energy storage unit is determined by combining the health status of the energy storage unit and the power deficit value. The photovoltaic power supply weight is obtained by dividing the actual power supply of the photovoltaic power generation module by the total demand power corresponding to the stable power consumption type. The energy storage power supply weight is obtained by dividing the actual power supplied by the energy storage unit by the total power demand corresponding to the stable power consumption type.

7. The method according to claim 6, characterized in that, The method further includes: When the task type is pulse power consumption type, the instantaneous peak power and stable base power are determined by the power consumption demand curve corresponding to the pulse power consumption type. The instantaneous peak power and stable base power constitute the total pulse power demand of the pulse power consumption type. Determine the second actual power supply of the current photovoltaic power generation module based on the photovoltaic power generation capacity curve; The power gap value of the stable base value power is determined based on the real-time power and stable base value power of the photovoltaic power generation module; The second actual available power of the energy storage unit is determined by combining the power gap value based on the stable baseline power and the health status of the energy storage unit; The power supply weight of the photovoltaic power generation module is obtained by dividing the real-time power of the photovoltaic power generation module by the stable base power. The power supply weight of the energy storage unit is obtained by dividing the actual available power of the energy storage unit by the stable base power. Obtain the current maximum instantaneous discharge capability of the capacitor support module, and determine the instantaneous power requirement of the capacitor support module during the pulse phase based on the instantaneous peak power and the maximum instantaneous discharge capability; The power supply weight of the capacitor support module is obtained by dividing the instantaneous power demand by the instantaneous peak power.

8. The method according to claim 1, characterized in that, The adjustment of the photovoltaic power generation module's output power by combining energy storage status data, parallel discharge paths, and task power consumption information includes: The target output power of the photovoltaic power generation module is obtained by inputting energy storage status data, parallel discharge paths, and task power consumption information into a pre-trained power allocation model.

9. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the auxiliary power supply method for an energy meter and a data acquisition terminal according to any one of claims 1 to 8.

10. An auxiliary power supply system for use in electricity meters and data acquisition terminals, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the auxiliary power supply method for an energy meter and a data acquisition terminal according to any one of claims 1 to 8.

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