Electricity information collection method and system based on internet-of-things electric energy meter

Abstract

This invention relates to the field of electricity meter data acquisition, and particularly to a method and system for acquiring electricity consumption information based on IoT electricity meters. The method includes: acquiring and preprocessing raw metering data from a first channel and a second channel; for each channel, obtaining the maximum allowable rate of change of the raw metering data and the reconstructed metering data from the previous sampling time, and calculating the physical violation degree by combining the raw metering data from the current sampling time; acquiring the physical violation degree at each sampling time within a preset sliding window to obtain a physical violation degree sequence; calculating the channel reliability based on the physical violation degree sequence; weighting and fusing the raw metering data based on the channel reliability to obtain reconstructed metering data; and outputting the reconstructed metering data to complete the acquisition of IoT electricity meter data. This invention can effectively filter out unreliable data in strong interference environments, while maintaining rapid tracking of valid data during rapid switching in real operating conditions.

Description

Technical Field

[0001] This invention relates to the field of electricity meter data acquisition. In particular, it relates to a method and system for acquiring electricity consumption information based on an IoT-based electricity meter. Background Technology

[0002] Smart IoT energy meters employing dual-mode communication (power line carrier and wireless communication) enhance communication link reliability through hardware redundancy. However, existing dual-mode communication technologies typically only achieve parallel data transmission, lacking intelligent processing and fusion of received data.

[0003] In actual operation, power line carrier channels are susceptible to periodic conducted interference introduced by the switching of power devices in chargers, while wireless channels are prone to random fading caused by shielding from metal stakes. This results in asynchronous, biased, or even erroneous data received by the host computer from both channels. Traditional data processing methods, such as simple master / slave switching or fixed-weight fusion, cannot distinguish between actual battery operating condition changes and sudden changes in channel noise, and are also difficult to dynamically assess channel reliability under strong interference. Consequently, it is difficult to balance steady-state accuracy and dynamic response in metering data, affecting metering accuracy and battery status assessment.

[0004] Therefore, there is a need in this field for a method and system for collecting electricity consumption information based on IoT energy meters to solve the problem of poor accuracy of metering data collected by existing solutions. Summary of the Invention

[0005] To address the technical problem of poor accuracy in the measurement data collected by the existing solutions, the present invention provides solutions in the following aspects.

[0006] In the first aspect, the method for collecting electricity consumption information based on IoT energy meters includes: The raw metering data of the first and second channels of the IoT energy meter are collected at a fixed frequency at the current sampling time, and the raw metering data is preprocessed. The original measurement data from the first and second channels are fused to obtain the reconstructed measurement data at the current sampling time; The reconstructed metering data at the current sampling time is stored as the reference for the next sampling time, and the reconstructed metering data is output to complete the data collection of the IoT energy meter. The method for calculating the reconstructed metrology data at the current sampling time includes: for each of the first and second channels, obtaining the maximum permissible rate of change of the original metrology data and the reconstructed metrology data at the previous sampling time, and calculating the degree of excess of the original metrology data at the current sampling time relative to the maximum permissible rate of change based on the maximum permissible rate of change, the original metrology data at the current sampling time, and the reconstructed metrology data at the previous sampling time, to obtain the physical violation degree; taking the current sampling time as the endpoint, obtaining the physical violation degree of the original metrology data at each sampling time within a preset sliding window, to obtain a physical violation degree sequence; calculating the channel credibility of the corresponding channel based on the physical violation degree sequence; and weightedly fusing the original metrology data of the first and second channels based on the channel credibility of the first and second channels to obtain the reconstructed metrology data at the current sampling time.

[0007] Preferably, the step of calculating the degree of excess of the original measurement data at the current sampling time relative to the maximum permissible rate of change to obtain the physical violation degree includes: calculating the absolute difference between the original measurement data at the current sampling time and the reconstructed measurement data at the previous sampling time; calculating the ratio of the absolute difference to the sampling time interval; subtracting the maximum permissible rate of change from the ratio to obtain a first difference; and taking the maximum value between 0 and the first difference and dividing it by the maximum permissible rate of change to obtain the physical violation degree.

[0008] Preferably, the maximum allowable rate of change is read from the local memory of the IoT energy meter, and the maximum allowable rate of change stored in the local memory is pre-configured according to the physical constraint characteristics of the monitored load connected to the IoT energy meter in the current application scenario.

[0009] Preferably, the monitored load is a battery, the physical constraint characteristics include the battery's electrochemical inertia, and the maximum permissible rate of change is pre-configured in the local memory of the IoT energy meter based on the physical limit of the battery's current change rate.

[0010] Preferably, the calculation of the channel reliability based on the physical violation sequence includes: setting the window length of the sliding window, taking the current sampling time as the endpoint, extracting the physical violations of the original measurement data at each sampling time to obtain a physical violation sequence; calculating the root mean square value of the physical violation sequence to obtain the error energy intensity; calculating the first-order autocorrelation coefficient of the physical violation sequence, and taking the absolute value of the first-order autocorrelation coefficient as the autocorrelation coefficient; calculating the difference between 2 and the autocorrelation coefficient, and multiplying the difference by the error energy intensity to obtain a first product; calculating the sum of 1 and the first product, and taking the ratio of 1 to the sum as the channel reliability.

[0011] Preferably, the first channel is a power line carrier communication channel, and the second channel is a wireless communication channel.

[0012] Preferably, the step of weighting and fusing the original measurement data of the first channel and the second channel based on the channel credibility of the first channel and the second channel to obtain reconstructed measurement data includes: fusing the channel credibility of the first channel and the second channel to obtain a first sum; segmenting the channel credibility of the first channel using the first sum, and weighting the original measurement data of the first channel using the segmented values ​​to obtain a first weighted value; segmenting the channel credibility of the second channel using the first sum, and weighting the original measurement data of the second channel using the segmented values ​​to obtain a second weighted value; and fusing the first weighted value and the second weighted value to obtain reconstructed measurement data.

[0013] Secondly, an electricity consumption information collection system based on an IoT energy meter includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned electricity consumption information collection method based on an IoT energy meter is implemented.

[0014] The present invention has the following effects: 1. This invention obtains the maximum permissible rate of change of the monitored load and calculates the physical violation degree by combining the reconstructed measurement data from the previous sampling time. This invention can effectively identify whether the current sampling data violates the physical laws of the monitored load. Compared with traditional methods that only judge data anomalies from a statistical perspective, this invention introduces a physical constraint mechanism, which can accurately distinguish between real operating condition changes and anomalies caused by channel interference, and significantly improve the ability to identify the authenticity of data.

[0015] 2. Based on physical violation degree, this invention further obtains the physical violation degree sequence through a sliding window and calculates the error energy intensity and autocorrelation coefficient to construct channel credibility. This evaluation mechanism not only considers the overall severity of channel error, but also distinguishes between regular interference and random noise through temporal structure characteristics. Under the same error energy, channels with predictable error patterns are given priority, which greatly reduces the impact of strong random interference on data reconstruction.

[0016] 3. This invention dynamically adjusts the fusion weights based on the channel credibility of each channel. The higher the channel credibility, the greater the retention in the reconstruction result, and the lower the channel credibility, the smaller the retention. This adaptive mechanism can effectively filter out untrusted data in a strong interference environment, while maintaining rapid tracking of valid data when switching rapidly in real working conditions. It solves the technical problem that traditional methods cannot balance steady-state accuracy and dynamic response. Attached Figure Description

[0017] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a flowchart of steps S1-S3 in the electricity consumption information collection method based on IoT energy meters according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of steps S20-S22 in the electricity consumption information collection method based on the Internet of Things electricity meter according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0020] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The method of the present invention is executed by the data receiving end of a dual-mode communication IoT energy meter. The data receiving end includes, but is not limited to, a charging pile controller, an edge computing gateway, or a cloud service platform. The data receiving end receives parallel data streams representing electrical parameters at the same moment from the same energy meter through a power line carrier communication module and a wireless communication module, and executes the data analysis and reconstruction steps of the present invention.

[0021] Reference Figure 1 The method for collecting electricity consumption information based on IoT energy meters includes steps S1-S3, as detailed below: Step S1: Collect and preprocess the raw metering data of the first and second channels at a fixed frequency.

[0022] The method for preprocessing the raw metering data of the first channel (PLC, Power Line Carrier, power line carrier communication channel) and the second channel (WL, Wireless, wireless communication channel) is to collect the raw metering data of the first channel and the second channel at the inherent sampling frequency of the IoT energy meter (e.g., 1kHz). The method includes: aligning the raw metering data of the first channel and the second channel based on timestamps, and using the 3σ principle (Raida criterion) to remove obvious outliers.

[0023] Aligning the raw metering data of the first channel and the second channel based on timestamps means synchronizing the acquisition time of the raw metering data of the first channel with the acquisition time of the raw metering data of the second channel. For example, the acquisition time of each raw metering data of the first channel can be obtained and used as a reference time, and then the raw metering data of the second channel at each reference time can be obtained to align the raw metering data of the first channel and the second channel based on timestamps.

[0024] The specific process for removing outliers is as follows: For the raw measurement data sequence continuously collected from a single channel, firstly, the arithmetic mean μ and standard deviation σ of the raw measurement data sequence are calculated; then, the raw measurement data at each sampling time is compared with μ. If the value of a certain raw measurement data is not within the range of [μ-3σ, μ+3σ], then the data point is determined to be an outlier; finally, the outlier is removed from the raw measurement data sequence and filled in using the raw measurement data from the previous valid sampling time or interpolation.

[0025] The raw metering data can be the raw current data, raw voltage data, or raw instantaneous power data of the monitored load connected to the IoT energy meter. Subsequent analysis will take the raw current data as an example.

[0026] It should be noted that the core concept of this invention lies in using at least two communication channels with different physical characteristics for redundant transmission, and weighting and fusing them through physical violation and channel reliability. Therefore, the first and second channels are not limited to power line carrier and wireless communication, but can be extended to any combination of two or more channels, including but not limited to: power line carrier channel, wireless communication channel, fiber optic channel, RS-485 (Recommended Standard 485) wired channel, M-Bus (Meter-Bus) bus, Bluetooth channel, infrared channel, etc.

[0027] Step S2: Fuse the original metering data of the first channel and the second channel to obtain reconstructed metering data.

[0028] Reference Figure 2 Step S2 includes steps S20-S22, as follows: Step S20: Obtain the maximum permissible rate of change of the raw metering data for each channel and the reconstructed metering data at the previous sampling time, and calculate the physical violation degree based on the maximum permissible rate of change, the raw metering data at the current sampling time and the reconstructed metering data at the previous sampling time.

[0029] The method for calculating the physical violation includes: calculating the absolute difference between the original measurement data at the current sampling time and the reconstructed measurement data at the previous sampling time; calculating the ratio of this absolute difference to the sampling time interval; subtracting the maximum allowable rate of change from this ratio to obtain the first difference; and taking the maximum value between 0 and the first difference and dividing it by the maximum allowable rate of change to obtain the physical violation.

[0030] The following description uses the collected raw current data as an example. The specific formula for calculating the physical violation of the raw current data is as follows: In the formula, Indicates the first The raw current data of the channel at the sampling time The physical violation degree, and the value of t is greater than or equal to 2. ; Indicates the sampling time The raw current data; Indicates the sampling time Reconstructed current data; Indicates the sampling time interval; This indicates the maximum permissible rate of change of current.

[0031] This embodiment takes an electric vehicle charging pile scenario as an example, where the monitored load is a battery. The maximum allowable rate of change is set based on the battery's electrochemical inertia (ion diffusion rate or electrode reaction rate), which can be obtained from the battery datasheet or through factory calibration. The unit of the maximum allowable rate of change is A / s.

[0032] It should be noted that the maximum permissible rate of change, as the core parameter of this method, is pre-configured and stored in the local memory of the IoT energy meter. The maximum permissible rate of change is set as follows: for monitored loads with specifications, the maximum rate of change index in the datasheet is directly adopted; for loads without specifications, the maximum rate of change of electrical parameters is measured during actual operating condition switching through offline calibration experiments, and then multiplied by a safety factor of 0.8~0.9 to determine the rate of change; or the setting basis of the maximum permissible rate of change is adjusted according to the physical constraint characteristics of the actual monitored load. Physical constraint characteristics refer to the inherent limitations of the monitored load on the rate of change of electrical parameters at the physical level. For example, for inductive loads, the rate of change of current is limited by the electromagnetic induction characteristics of the inductor, and the theoretical maximum rate of change of current, i.e., the maximum permissible rate of change, can be calculated by dividing the driving voltage by the inductance; for capacitive loads, the rate of change of voltage is limited by the charging and discharging characteristics of the capacitor, and the theoretical maximum rate of change of voltage can be calculated by dividing the charging and discharging current by the capacitance.

[0033] The physical violation score is used to quantify the extent to which the raw measurement data at the current sampling time exceeds the maximum permissible rate of change. When the rate of change between the raw measurement data at the current sampling time and the reconstructed measurement data at the previous sampling time does not exceed the maximum permissible rate of change, i.e., the rate of change is within the physically permissible range, the physical violation score is zero, indicating that the data point conforms to the electrochemical inertia law of the battery and has high reliability. Conversely, when the rate of change exceeds the maximum permissible rate of change, the physical violation score is positive, and its value is positively correlated with the degree of exceedance. The larger the value, the deeper the degree to which the data point violates the physical law, and the lower its reliability. In this way, the present invention can effectively identify and quantify data anomalies caused by channel interference, providing a reliable quantitative basis for subsequent channel reliability assessment.

[0034] It is understandable that when calculating the physical violation of the original voltage data and the original instantaneous power data, the maximum allowable rate of change of the original voltage data can be set according to the battery polarization time constant, and the maximum allowable rate of change of the original instantaneous power data can be derived from the maximum allowable rate of change of the original current data and the original voltage data, or set separately according to the response characteristics of the power device.

[0035] Step S21: Using the current sampling time as the endpoint, obtain the physical violation degree of the original measurement data at each sampling time within the preset sliding window to obtain the physical violation degree sequence; calculate the channel reliability of the corresponding channel based on the physical violation degree sequence.

[0036] Calculating the channel reliability based on the physical violation sequence involves: setting the window length of the sliding window (e.g., 100, i.e., 100 sampling times). This window length is a preset value configured in the system and can be obtained through calibration experiments. For example, a channel with known actual reliability at the target sampling time can be obtained. An integer within the range of greater than 2 and less than 10 can be randomly selected as the window length of the sliding window. The physical violation sequence of the channel at the target sampling time is obtained using this window length. Then, the predicted reliability of the channel is calculated based on the physical violation sequence. The deviation between the predicted reliability and the actual reliability is calculated, and it is determined whether the deviation is greater than a preset deviation threshold. If it is greater, the window length is incremented by 1, and the physical violation sequence of the channel at the target sampling time is obtained again based on the new window length. The predicted reliability of the channel is calculated, and the deviation between it and the actual reliability is calculated. If it is not greater, the current window length value is used as the preset value, and the window length of the sliding window is set to this preset value in the system.

[0037] Using the current sampling time as the endpoint, the physical violation degree of the raw measurement data at each sampling time is extracted to obtain a physical violation degree sequence; the root mean square value of the physical violation degree sequence is calculated to obtain the error energy intensity; the first-order autocorrelation coefficient of the physical violation degree sequence is calculated, and the absolute value of the first-order autocorrelation coefficient is used as the autocorrelation coefficient; the difference between the first-order autocorrelation coefficient and the second-order autocorrelation coefficient is calculated, and this difference is multiplied by the error energy intensity to obtain the first product; the sum of the first product and the second-order autocorrelation coefficient is calculated, and the ratio of the first product to the sum is used as the channel reliability. The specific formula is as follows: In the formula, Indicates the first Channel reliability; Indicates the first Channel error energy intensity; Indicates the first The autocorrelation coefficient of the channel. The calculation methods for the root mean square value and first-order autocorrelation coefficient of the physical violation sequence are existing techniques and will not be elaborated here.

[0038] As a core evaluation item, it reflects the overall severity of the channel's violation of physical laws within the current sliding window. The larger the value, the deeper the interference to the channel, and therefore the lower the channel reliability.

[0039] As a chaos penalty factor, it is used to further distinguish the impact of different types of interference on channel reliability when the error energy intensity is the same. This characterizes the temporal structure of the physical violation sequence. A value closer to 1 indicates a stronger regularity in the sequence, suggesting the interference source may be predictable; a value closer to 0 indicates stronger randomness, suggesting the interference is more likely to originate from unreliable channel noise. Therefore, when... When it approaches 0, the chaos penalty factor Approaching 2, it amplifies the error energy intensity by approximately two times, thus significantly reducing the channel reliability, i.e., prioritizing channels with predictable physical violation patterns; when When it approaches 1, the chaos penalty factor Approaching 1, it does not produce additional amplification of the error energy intensity.

[0040] Therefore, the channel reliability of the first channel and the second channel can be obtained.

[0041] During electric vehicle charging, sudden changes in operating conditions include, but are not limited to, switching from constant current to constant voltage, charging start-up, charging stop-up, and power adjustment. These sudden changes in operating conditions cause changes in raw measurement data such as current and voltage to occur within a short period of time, consistent with the physical laws of the battery. The rate of change is limited by the electrochemical inertia of the battery.

[0042] Unlike random data jumps caused by channel noise, data changes resulting from actual operating condition switching have the following characteristics: synchronous changes in the original measurement data of both channels, a rate of change within physical limits, and a brief but regular fluctuation in the physical violation sequence. This invention, through joint evaluation of physical violations and autocorrelation coefficients, can accurately identify operating condition switching, avoiding misjudgment as channel interference, thereby maintaining rapid tracking capability of actual operating condition changes while filtering out noise.

[0043] Step S22: Based on the channel credibility of the first channel and the second channel, the original measurement data of the first channel and the second channel are weighted and fused to obtain the reconstructed measurement data.

[0044] The method for calculating reconstructed measurement data includes: calculating the sum of the channel confidence scores of the first channel and the second channel, and using this sum as the first sum; calculating the ratio of the channel confidence score of the first channel to the first sum, and multiplying this ratio by the original measurement data of the first channel as the second product; calculating the ratio of the channel confidence score of the second channel to the first sum, and multiplying this ratio by the original measurement data of the second channel as the third product; and calculating the sum of the second and third products to obtain the reconstructed measurement data. The specific formula is as follows: In the formula, Indicates the current sampling time Reconstructed current data; This indicates the channel reliability of the first channel; This indicates the channel reliability of the second channel; This represents the raw current data for the first channel; This represents the raw current data of the second channel.

[0045] The channel reliability of the first and second channels is used as the weight for data reconstruction. The higher the channel reliability, the greater the weight of the original measurement data transmitted through that channel in the reconstruction result; the lower the channel reliability, the smaller the weight of the original measurement data transmitted through that channel in the reconstruction result.

[0046] Step S3: Output the reconstructed metering data to complete the data collection of the IoT energy meter.

[0047] The reconstructed current data calculated in the above steps is output to the display module of the corresponding sampling point on the receiver, completing the data acquisition from the IoT energy meter. Simultaneously, the reconstructed current data is stored for calculating the physical violation rate of the original current data at the next sampling time.

[0048] The reconstructed measurement data integrates the advantages of dual-channel information, maintaining high accuracy in steady state and achieving rapid tracking during sudden changes in operating conditions. Finally, the reconstructed measurement data is output to the display or measurement module and stored for physical violation calculation at the next sampling time, forming a closed-loop recursive structure to ensure the continuity and consistency of the entire acquisition process.

[0049] This application also discloses an electricity consumption information collection system based on an IoT energy meter. The system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the electricity consumption information collection method based on an IoT energy meter according to the above embodiments of the present invention is implemented.

[0050] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0051] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for collecting electricity consumption information based on IoT energy meters, characterized in that, include: The raw metering data of the first and second channels of the IoT energy meter are collected at a fixed frequency at the current sampling time, and the raw metering data is preprocessed. The original measurement data from the first and second channels are fused to obtain the reconstructed measurement data at the current sampling time; The reconstructed metering data at the current sampling time is stored as the reference for the next sampling time, and the reconstructed metering data is output to complete the acquisition of IoT energy meter data. The method for calculating the reconstructed metrology data at the current sampling time includes: for each of the first and second channels, obtaining the maximum permissible rate of change of the original metrology data and the reconstructed metrology data at the previous sampling time, and calculating the degree of excess of the original metrology data at the current sampling time relative to the maximum permissible rate of change based on the maximum permissible rate of change, the original metrology data at the current sampling time, and the reconstructed metrology data at the previous sampling time, to obtain the physical violation degree; taking the current sampling time as the endpoint, obtaining the physical violation degree of the original metrology data at each sampling time within a preset sliding window, to obtain a physical violation degree sequence; calculating the channel credibility of the corresponding channel based on the physical violation degree sequence; and weightedly fusing the original metrology data of the first and second channels based on the channel credibility of the first and second channels to obtain the reconstructed metrology data at the current sampling time.

2. The method for collecting electricity consumption information based on an IoT-based energy meter according to claim 1, characterized in that, The step of calculating the degree of excess of the original measurement data at the current sampling time relative to the maximum allowable rate of change to obtain the physical violation degree includes: calculating the absolute difference between the original measurement data at the current sampling time and the reconstructed measurement data at the previous sampling time; calculating the ratio of the absolute difference to the sampling time interval; subtracting the maximum allowable rate of change from the ratio to obtain the first difference; taking the maximum value between 0 and the first difference and dividing it by the maximum allowable rate of change to obtain the physical violation degree.

3. The method for collecting electricity consumption information based on an IoT-based energy meter according to claim 1, characterized in that, The maximum permissible rate of change is read from the local memory of the IoT energy meter, which is pre-configured based on the physical constraints of the monitored load connected to the IoT energy meter in the current application scenario.

4. The method for collecting electricity consumption information based on an IoT-based energy meter according to claim 3, characterized in that, The monitored load is a battery, and the physical constraint characteristics include the battery's electrochemical inertia. The maximum allowable rate of change is pre-configured in the local memory of the IoT energy meter based on the physical limit of the battery's current change rate.

5. The method for collecting electricity consumption information based on an IoT-based energy meter according to claim 1, characterized in that, The method for calculating the channel reliability based on the physical violation sequence includes: setting the window length of the sliding window, taking the current sampling time as the endpoint, extracting the physical violations of the original measurement data at each sampling time to obtain a physical violation sequence; calculating the root mean square value of the physical violation sequence to obtain the error energy intensity; calculating the first-order autocorrelation coefficient of the physical violation sequence and using the absolute value of the first-order autocorrelation coefficient as the autocorrelation coefficient; calculating the difference between 2 and the autocorrelation coefficient, and multiplying the difference by the error energy intensity to obtain the first product; calculating the sum of 1 and the first product, and using the ratio of 1 to the sum as the channel reliability.

6. The method for collecting electricity consumption information based on an IoT-based energy meter according to claim 1, characterized in that, The first channel is a power line carrier communication channel, and the second channel is a wireless communication channel.

7. The method for collecting electricity consumption information based on an IoT-enabled energy meter according to claim 1, characterized in that, The step of weighting and fusing the original measurement data of the first channel and the second channel based on the channel credibility of the first channel and the second channel to obtain reconstructed measurement data includes: fusing the channel credibility of the first channel and the second channel to obtain a first sum; segmenting the channel credibility of the first channel using the first sum, and weighting the original measurement data of the first channel using the segmented values ​​to obtain a first weighted value; segmenting the channel credibility of the second channel using the first sum, and weighting the original measurement data of the second channel using the segmented values ​​to obtain a second weighted value; and fusing the first weighted value and the second weighted value to obtain reconstructed measurement data.

8. A power consumption information collection system based on an IoT-enabled energy meter, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, implement the electricity consumption information collection method based on an IoT energy meter according to any one of claims 1-7.

Images