A device and method for retrofitting smart energy meters with carbon metering function

Abstract

This invention discloses a device and method for upgrading smart energy meters to include carbon metering functionality. It relates to the field of smart energy meter function expansion and carbon factor metering prediction technology. The method includes adding a UART interface to the carrier communication unit module of the energy meter; connecting an edge computing module to the UART interface to acquire frozen energy data from the energy meter; dividing the frozen energy data into preset time intervals; calculating the increase in energy within each preset interval to obtain the carbon emissions for each interval; accumulating the carbon emissions between intervals to obtain monthly and annual carbon emissions; and dynamically acquiring and storing carbon emissions based on a local carbon factor prediction algorithm during the accumulation process. The device of this invention enables low-cost upgrading of traditional energy meters, achieves full-lifecycle storage and traceability of carbon emissions, realizes local calculation and compensation of carbon emissions, and improves system robustness and data reliability.

Description

Technical Field

[0001] This invention relates to the field of smart energy meter function expansion and carbon factor measurement and prediction technology, specifically to a device and method for modifying smart energy meters to add carbon measurement function. Background Technology

[0002] In recent years, smart meters have gradually become an important infrastructure for the transformation of the power system towards low-carbon, digital, and intelligent operation. The new generation of smart meters not only supports electricity collection but also undertakes functions such as time-of-use metering, load analysis, and remote control, and is highly anticipated for its role in green electricity consumption and carbon emission monitoring. Especially against the backdrop of the growing demand for carbon trading markets and carbon emission accounting, electricity meter data is considered a crucial source for supporting carbon emission factor calculations and electricity carbon monitoring.

[0003] However, the communication modules of currently widely used smart meters are still limited to traditional electricity metering functions, lacking the ability to collect and process carbon factors, and thus failing to meet the power system's needs for refined calculation and traceability of carbon emissions. Existing smart meter MCUs have limited computing power and cannot simultaneously handle complex data cleaning, factor prediction, and local storage, resulting in carbon emission data still relying on a centralized master station for processing, leading to problems such as high latency, high bandwidth pressure, data loss, and insufficient anomaly monitoring. Furthermore, current smart meters lack local calculation and redundancy backup mechanisms for carbon factors, making it impossible to independently calculate carbon emissions when the upper-level network is interrupted or common factors are missing. Therefore, how to endow smart meters with carbon metering functions through modular modification without altering their existing legal metering functions, achieving integrated calculation of electricity and carbon emission data and full-process traceability, has become a pressing technical problem to be solved. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing smart meters have problems such as the inability to support carbon metering in terms of communication modules, lack of local prediction and redundant storage of carbon factors, and insufficient storage space of the meter itself; at the same time, the computing power of existing smart meter MCUs is limited and cannot undertake the new carbon data calculation and traceability functions, making it difficult to realize carbon emission monitoring and full life-cycle data tracking; in addition, the traditional method of replacing the entire meter brings huge costs, material and financial waste.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a device for upgrading smart energy meters to include carbon metering functionality, comprising adding a UART interface to the carrier communication unit module of the energy meter; an edge computing module connected to the UART interface to acquire the energy meter's frozen data; dividing the energy meter's frozen data into preset time intervals, calculating the increase in energy within the preset intervals to obtain the carbon emissions between intervals, and accumulating the carbon emissions between intervals to obtain monthly and annual carbon emissions; during the accumulation of carbon emissions between intervals, dynamically acquiring and storing carbon emissions based on a local prediction algorithm for the carbon factor.

[0007] As a preferred embodiment of the device for upgrading the carbon metering function of a smart energy meter according to the present invention, the carrier communication unit module includes an additional UART interface for communication with edge computing.

[0008] The first LDO is used to convert the power supply level of the energy meter and power the MCU chip of the uplink channel. The second LDO is used to power the MCU chip, the first memory unit, and the second memory unit.

[0009] The first backup power source is a supercapacitor with a capacity of 10F. When the electricity meter loses power, the first backup power source supports the electricity meter in reporting events.

[0010] As a preferred embodiment of the device for upgrading a smart energy meter to include carbon metering function as described in this invention, the edge computing module includes a UART interface that shares data with the MCU chip via transparent transmission, and the edge computing module obtains the energy meter's frozen data through the UART interface.

[0011] As a preferred embodiment of the device for upgrading the carbon metering function of a smart energy meter according to the present invention, the edge computing module further includes a program simulation method for local upgrading and development of the program for the non-metering part through a program burning interface.

[0012] The encryption chip uses the Zhixin SC1763Y model chip for hardware encryption.

[0013] The first storage unit uses EEPROM for storage, and the second storage unit uses Flash for storage, thus providing dual backup of the carbon data.

[0014] The second backup power source is a supercapacitor with a capacity greater than 1.5F.

[0015] The MCU chip uses a 32-bit MCU with computing power no less than that of the ARM0 core.

[0016] When the edge computing module is running normally, the first indicator light is solid green. When the power supply to the UART interface of the electricity meter is damaged or the edge computing module circuit is abnormal, the first indicator light goes out.

[0017] The second indicator light is the signal transceiver indicator light for the edge computing module. It flashes green when receiving signals normally and flashes red when sending data normally.

[0018] Another objective of this invention is to provide a method for upgrading smart energy meters to include carbon metering functionality. This method can achieve data acquisition of frozen electricity consumption, calculation of carbon factors, and local storage by adding a UART interface and combining it with an edge computing module without changing the original carrier interface definition of the energy meter. This solves the problems of traditional energy meters lacking carbon metering functionality, having insufficient MCU computing power, and limited storage capacity, which prevent them from performing full life-cycle carbon emission tracing and local carbon factor prediction.

[0019] As a preferred embodiment of the method for enhancing the carbon metering function of smart energy meters according to the present invention, the step of dividing the frozen energy data of the energy meter into preset intervals according to time includes dividing the frozen energy data of the energy meter into 96 intervals per day according to time, and calculating the increase in energy in the 96 intervals by combining the carbon factor obtained from the main station system, as expressed as:

[0020] ,

[0021] in, This refers to daily carbon emissions. As an electrocarbon factor, This is used to freeze the electricity consumption data of the electricity meter in the m-th preset interval.

[0022] As a preferred embodiment of the method for modifying smart energy meters to include carbon metering function as described in this invention, the method for obtaining monthly and annual carbon emissions includes summing up the cumulative daily carbon emissions for corresponding time periods in the month and year to obtain monthly and annual carbon emissions.

[0023] As a preferred embodiment of the method for upgrading smart energy meters to include carbon metering functionality as described in this invention, the local carbon emission factor prediction algorithm includes, during the accumulation of carbon emissions between zones, when the carbon emission factor cannot be obtained, using the local carbon emission factor to calculate the carbon emissions locally and storing them in chronological order, as shown below:

[0024] ,

[0025] in, This is the first local predicted assessment value for the electrocarbon factor. This is the period coefficient for the low-carbon factor. The coefficient for the medium carbon factor period. This represents the period coefficient for the high-carbon factor. This represents the instantaneous power of the electricity meter. The electrocarbon factor is obtained from the higher-level metering network. This is the daily frozen data for electricity meters. This is the daily frozen data from the electricity meter. This represents the average of the differences in the minute-by-minute freezing values ​​of the electricity meter. Divide the time period into boundary points, For time intervals.

[0026] The first local predicted evaluation value of the carbon factor The first local predicted evaluation value of the electrocarbon factor is stored in the first storage unit and in the second storage unit. Perform a backup.

[0027] As a preferred embodiment of the method for enhancing the carbon metering function of smart energy meters according to the present invention, the local prediction algorithm for the carbon factor further includes a constraint condition that must be satisfied during the calculation of the carbon factor, expressed as follows:

[0028] ,

[0029] when When using the low-carbon factor time period coefficient.

[0030] when At that time, the medium carbon factor time period coefficient is used.

[0031] when At that time, the high carbon factor time period coefficient is used.

[0032] in, This represents the maximum power value of the electricity meter within a daily cycle.

[0033] Another object of the present invention is to provide a device for retrofitting smart energy meters with carbon metering function, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of retrofitting smart energy meters with carbon metering function.

[0034] Another object of the present invention is to provide a storage medium for modifying a smart energy meter to include carbon metering functionality, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of modifying the device to include carbon metering functionality are implemented.

[0035] The beneficial effects of this invention are as follows: The device for upgrading smart energy meters to include carbon metering function provides a low-cost upgrade for traditional energy meters. It does not require replacing the entire meter; the upgrade can be completed simply by plugging and unplugging the communication module, maximizing cost savings while avoiding the risk of electric shock. It enables full-lifecycle storage and traceability of carbon emissions, supports long-term accumulation of carbon data and peak event analysis, and can still generate carbon factors using local algorithms when the upper-level system is offline, communication is interrupted, or data is abnormal, thereby achieving local calculation and compensation of carbon emissions and improving system robustness and data reliability. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is an overall schematic diagram of a device for modifying a smart energy meter to add carbon metering function, as provided in Embodiment 1 of the present invention.

[0038] Figure 2 This is a schematic diagram of the actual device implementation of a device for modifying a smart energy meter to add carbon metering function, as provided in Embodiment 1 of the present invention.

[0039] Figure 3 This is a flowchart of the single-phase meter carbon metering data processing of a device for upgrading a smart energy meter to include carbon metering function, as provided in Embodiment 1 of the present invention.

[0040] Figure 4 This is a flowchart of the three-phase meter carbon metering data processing of a device for upgrading a smart energy meter to include carbon metering function, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0041] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0042] Example 1, referring to Figures 1-4 As one embodiment of the present invention, a device for modifying a smart energy meter to include carbon metering functionality is provided, comprising:

[0043] Furthermore, the carrier communication unit module 100 includes an additional UART interface for communication with edge computing.

[0044] The first LDO101 is used to convert the power supply level of the energy meter and power the MCU chip 201 of the uplink channel. The second LDO101' is used to power the MCU chip 201, the first storage unit 204, and the second storage unit 204'.

[0045] The first backup power supply 102 is a supercapacitor with a power capacity of 10F. When the electricity meter loses power, the first backup power supply supports the electricity meter in reporting events.

[0046] It should be noted that, as Figure 1 As shown, the carrier communication unit module 100 is completely consistent with the traditional intelligent carrier communication unit module, characterized by the addition of a UART interface for communication with the edge computing module 200. A first LDO101 (low dropout linear regulator) is used to convert the 12V power supply level of the energy meter to a 3.3V level to power the MCU devices in the uplink channel. A second LDO101' (low dropout linear regulator) is used to convert the 12V power supply level of the energy meter to a 5V level to power the MCU chip 201, the first storage unit 204, and the second storage unit 204'. The main purpose of this independent design is to ensure the independence of power supply between different functional circuits while dispersing heat sources, thereby improving the overall hardware lifespan of the device. The first backup power supply 102 is a supercapacitor with a capacity of 10F, primarily intended to ensure sufficient backup power to support the energy meter event reporting function after a power outage.

[0047] It should also be noted that a communication interface UART for the edge computing module 200 is added to the carrier communication unit module 100, ensuring that the traditional energy meter does not need to be replaced as a whole. Data interaction with the edge computing module 200 can be achieved simply by expanding the interface, which significantly reduces the system transformation cost and improves the functional expandability of the energy meter. By using the first LDO101 and the second LDO101' to supply power separately, independent power supply for different functional circuits is achieved, avoiding functional coupling problems caused by voltage fluctuations. At the same time, heat dissipation is achieved, improving the overall stability of the device and the lifespan of the hardware. The first backup power supply can still provide sufficient power to the energy meter when the energy meter loses power, ensuring that the event reporting function is executed normally. Reliable data transmission and event traceability are achieved in the power outage scenario, ensuring the integrity and traceability of carbon metering data.

[0048] Furthermore, the edge computing module 200 includes a UART interface that uses transparent transmission to share data with the MCU chip 201. The edge computing module 200 obtains the power meter's power freeze data through the UART interface.

[0049] It should be noted that the UART interface operates in transparent transmission mode, meaning that no additional encapsulation or parsing is performed on the transmitted data; instead, it forwards the data directly in its original format. This ensures the real-time performance and integrity of data transmission, avoiding delays and data loss caused by protocol conversion. When the electricity meter generates a power freeze, the data is first output to the UART interface through the electricity meter's built-in carrier communication unit module 100, and then transmitted to the edge computing module 200 via transparent transmission. The edge computing module 200 shares the received data with the MCU chip 201 through its internal bus. The MCU chip 201 can directly access the data to complete subsequent power increment calculations and local calculations of the carbon factor. The UART interface serves as a high-speed data channel between the electricity meter's carrier communication unit module 100 and the edge computing module 200. The application of transparent transmission mode allows the data output by the electricity meter to be transmitted to the edge computing module 200 within milliseconds, thus ensuring timely data processing.

[0050] It should also be noted that by operating in transparent transmission mode via the UART interface, the latency and data loss caused by traditional encapsulation, parsing, and protocol conversion are avoided. This enables millisecond-level transmission of frozen electricity data between the electricity meter and the edge computing module. The UART interface is not merely a communication medium, but is designed as a high-speed direct data channel between the electricity meter's carrier communication unit module 100 and the edge computing module 200. Through transparent transmission, the data output by the electricity meter can be transmitted to the edge computing module without any additional processing, thus forming a low-cost, low-latency, high-speed local communication mechanism.

[0051] The encryption chip 203 uses the Zhixin SC1763Y chip for hardware encryption.

[0052] The first storage unit 204 uses EEPROM for storage, and the second storage unit 204' uses Flash for storage, thus performing dual backup of the carbon data through the first storage unit 204 and the second storage unit 204'.

[0053] The second backup power supply 102' is a supercapacitor with a power capacity greater than 1.5F.

[0054] The MCU chip 201 is a 32-bit MCU with computing power no less than that of the ARM0 core.

[0055] When the edge computing module 200 is running normally, the first indicator light 300 is constantly green. When the power supply of the electricity meter UART interface is damaged or the circuit of the edge computing module 200 is abnormal, the first indicator light 300 is turned off.

[0056] The second indicator light 300' is the signal transceiver indicator light of the edge computing module 200. It flashes green when receiving signals normally and flashes red when sending data normally.

[0057] It should be noted that the program programming interface 202 is used to locally upgrade and simulate the metering program during the equipment operation or design phase. This interface effectively prevents tampering with unauthorized metering programs and supports rapid iteration during subsequent maintenance. The encryption chip 203 uses a Zhixin SC1763Y chip for hardware encryption, ensuring the security and tamper-proof nature of the carbon data during storage and transmission. EEPROM stores data by byte, while Flash stores data by block. The first storage unit 204 uses an EEPROM device for storage. EEPROM has a fast read speed but a higher cost. Flash is cheaper for the same space, so the second storage unit 204' uses Flash for large-capacity storage. The combination of these two devices achieves dual-device redundant storage of the carbon data, allowing users to access two... Each memory unit backs up the others to store important data, thus improving data reliability and redundancy. Through the collaborative work of the first memory unit 204 and the second memory unit 204', real-time backup of the carbon dioxide data can be achieved, preventing data loss due to the failure of a single memory unit. The second backup power supply 102' is a supercapacitor with a capacity greater than 1.5F, used to ensure the edge computing module 200 can maintain normal operation for a period of time after the main power supply fails. The MCU chip 201 is a 32-bit MCU with an ARM0 architecture or higher, ensuring sufficient processing power to complete the carbon dioxide factor calculation and data interaction. The first indicator light 300 displays the module's operating status: when the system is working normally, indicator light L is solid green; when the power supply to the electricity meter's UART interface is damaged or the edge computing module 200 circuit malfunctions, indicator light L turns off, prompting maintenance personnel to perform timely repairs. The second indicator light 300 indicates the signal transmission and reception status: when receiving signals normally, the green light flashes; when transmitting data signals normally, the red light flashes. This dual-light indication method intuitively reflects the module's operating status in both power and communication states, improving the maintainability of the equipment and on-site visualization capabilities.

[0058] It should also be noted that local upgrades and hardware protection of the carbon data are achieved through the program burning interface 202 and encryption chip 203 in the edge computing module 200; the dual storage unit redundancy backup mechanism, constructed by the first storage unit 204 using EEPROM and the second storage unit 204' using Flash, ensures long-term data stability and high-speed read and write; at the same time, the second backup power supply 102' is configured as a large-capacity supercapacitor for continuous power supply after the main power supply fails; the MCU chip 201 adopts a 32-bit ARM architecture to provide high-performance computing and data interaction support; and the working status and signal transmission and reception status are displayed in real time through the first indicator light 300 and the second indicator light 300', realizing visual feedback on the module's operation status, thereby achieving significant improvements in security, reliability, continuity, processing power and maintainability compared to existing technologies.

[0059] It should also be noted that the actual circuit edge computing module 200 of the edge computing device in this embodiment can be integrated with the internal hardware circuit carrier communication unit module 100 or dual-mode communication module of the traditional smart energy meter communication module onto a single circuit board. Its housing structure can completely utilize the housing of the energy meter carrier communication module or dual-mode communication module. Actual device modifications can be implemented as follows: Figure 2 As shown, the edge computing device of this embodiment can be directly inserted into the communication interface based on the principle of a single-phase meter or a three-phase meter.

[0060] Furthermore, the electricity meter's frozen data is divided into preset time intervals. Specifically, the daily frozen data is divided into 96 time intervals. Combined with the carbon factor obtained from the main station system, the increase in electrical energy across these 96 intervals is calculated and represented as follows:

[0061] ,

[0062] in, This refers to daily carbon emissions. As an electrocarbon factor, This is used to freeze the electricity consumption data of the electricity meter in the m-th preset interval.

[0063] It should be noted that, in terms of edge computing hardware, the electricity consumption data is read from the UART interface at the electricity meter's carrier interface. The electricity consumption data is then divided into 96 time intervals daily. Combined with the carbon factor obtained from the main station system, the energy consumption data for each of the 96 intervals is increased. The single-phase meter carbon metering data processing flow is as follows: Figure 3 The data processing flow for carbon metering of the three-phase meter is shown below. Figure 4As shown, the electricity meter's frozen data is processed by dividing it into preset time intervals. Daily frozen data is collected at 15-minute intervals, resulting in 96 intervals over a 24-hour period. The data for each interval represents the electricity meter's frozen value for that time period. The system uses an edge computing module to read the frozen data from each of the 96 intervals one by one, combining it with the carbon factor obtained from the main station system. The daily carbon emissions are calculated by cumulatively calculating the increased load on electricity consumption within 96 intervals.

[0064] It should also be noted that by precisely dividing the daily electricity consumption data into 96 intervals, with a minimum calculation granularity of 15 minutes, the dynamic distribution of electricity consumption can be reflected in real time, improving the time-series accuracy of carbon emission calculations. This utilizes the electricity carbon factor issued by the main station. It corresponds one-to-one with the electricity data of each interval, enabling regional and time-based carbon emission accounting, thus overcoming the distortion problem caused by the unified processing of carbon emission factors in existing methods.

[0065] Furthermore, obtaining monthly and annual carbon emissions involves summing up the cumulative daily carbon emissions for the corresponding time periods of the month and year to obtain the monthly and annual carbon emissions.

[0066] It should be noted that after the daily carbon emissions are calculated, a daily carbon emissions record table is created in chronological order. At the end of each calendar month, all daily carbon emissions data for that month are added up to obtain the monthly carbon emissions. At the end of each calendar year, all monthly carbon emissions for that year are added up to obtain the annual carbon emissions.

[0067] It should also be noted that by calculating daily carbon emissions data in time intervals and establishing a daily carbon emissions record table arranged in chronological order, the daily or monthly carbon emissions for the corresponding time period can be automatically accumulated at the end of the natural month and natural year to form complete monthly and annual carbon emissions. This not only ensures the continuity and completeness of carbon emissions data statistics, but also avoids the data loss and calculation delay problems that may occur when relying on centralized calculation in traditional schemes.

[0068] Furthermore, the local prediction algorithm for the electric carbon factor includes, during the process of accumulating carbon emissions across partitions, when the electric carbon factor is unavailable, using the local carbon emission factor to calculate carbon emissions locally and storing them in chronological order, as shown below:

[0069] ,

[0070] in, This is the first local predicted assessment value for the electrocarbon factor. This is the period coefficient for the low-carbon factor. The coefficient for the medium carbon factor period. This represents the period coefficient for the high-carbon factor. This represents the instantaneous power of the electricity meter. The electrocarbon factor is obtained from the higher-level metering network. This is the daily frozen data for electricity meters. This is the daily frozen data from the electricity meter. This represents the average of the differences in the minute-by-minute freezing values ​​of the electricity meter. Divide the time period into boundary points, For time intervals.

[0071] The first local predicted evaluation value of the carbon factor The first local predicted evaluation value of the electrocarbon factor is stored in the first storage unit 204 and in the second storage unit 204'. Perform a backup.

[0072] It should be noted that during the accumulation of carbon emissions across different time zones, when communication is abnormal or the upper-level carbon emission factor cannot be obtained, a local carbon emission factor prediction algorithm is activated to ensure continuous calculation of carbon emissions. This algorithm stores the local carbon emission factor based on the user's historical frozen data and the remaining lifespan of the electricity meter calculated since the installation of the edge computing device. It fully utilizes historical load data from the electricity meter, frozen data, and historically obtained carbon emission factors to perform fitting calculations to determine the current local carbon emission factor, which is then stored. During normal operation, this embodiment obtains the public carbon emission factor from the main station system and prioritizes its use for calculations. When the public carbon emission factor cannot be obtained, the local carbon emission factor is activated for local carbon emission calculation, and the first local predicted value of the carbon emission factor is recorded. The first local predicted evaluation value of the electrocarbon factor is stored in the first storage unit 204 and in the second storage unit 204'. Perform a backup.

[0073] It should also be noted that by replacing external data with local predictors, the continuity and integrity of carbon emission calculations are ensured in the event of communication interruption or unavailability of the upper-level system. This avoids the interruption of the entire calculation due to a single point of failure in the data link. The dual storage mechanism ensures that the local prediction results will not be lost due to the failure of a single storage unit, thus providing redundancy for long-term carbon emission measurement.

[0074] It should also be noted that the internal storage space of current single-phase and three-phase meters is insufficient to support the storage of historical data frozen throughout the entire lifecycle of the electricity meter. Since this edge computing device needs to support carbon emission calculation and predict carbon emission factors, it requires a larger storage space. Given that the 2013 version of the electricity meter can store 64 days of daily frozen data, and the 2020 version of the smart electricity meter can store up to 365 days of daily frozen data, traditional smart electricity meters will recycle and reuse their storage space after the standard time limit to fully utilize the internal storage space of the electricity meter. The edge computing device in this embodiment can have a storage space of several hundred megabytes, so data exceeding the storage capacity of the electricity meter can be stored locally in the edge computing module. If maintenance personnel of power companies in various regions encounter abnormal electricity meter malfunctions, they can not only read the data stored in the electricity meter itself, but also read other historical data of the electricity meter stored locally within the edge computing device of this embodiment. By comparing the two sets of data, the cause of historical problems with the electricity meter can be traced. The edge computing device itself has MCU functionality, enabling it to detect, monitor, and save electricity meter data. This allows it to monitor the accuracy of meter data in addition to the meter's own reported fault events when an anomaly occurs. For example, if the frozen data of the electricity meter suddenly becomes extremely large or small, the software program itself cannot determine whether this is normal. In this case, the MCU of the edge computing device in this embodiment can detect the anomaly in the meter's internal data and provide early warning through a carrier channel or dual-mode channel, eliminating the need to rely entirely on the main station system to read historical data to discover the problem. This improves the timeliness of fault warnings and generates good economic benefits. After the electricity meter is upgraded, in actual operation, when events such as meter failure occur, the communication priority of traditional frozen data uploads and actively reported events is higher than that of carbon data from this embodiment, thus avoiding congestion of carbon metering-related data in traditional communication networks due to the upgrade.

[0075] Furthermore, the local prediction algorithm for the electric carbon factor also includes constraints that must be met during the calculation of the electric carbon factor, expressed as:

[0076] ,

[0077] when When using the low-carbon factor time period coefficient.

[0078] when At that time, the medium carbon factor time period coefficient is used.

[0079] when At that time, the high carbon factor time period coefficient is used.

[0080] in, This represents the maximum power value of the electricity meter within a daily cycle.

[0081] It should be noted that the daily electricity usage time is divided into three periods, namely... , , The sum of the three time periods meets the 24-hour requirement, thus ensuring full coverage of the daily electricity load. Within each time period, the electricity meter collects the instantaneous power value in real time. And based on power and maximum power value Based on the relationship, select the corresponding carbon factor time period coefficient and maximum power value. The maximum power value of the electricity meter within a daily cycle is used as a reference standard for segmented thresholds. Through this time-of-use constraint and threshold division mechanism, the carbon factor can be dynamically adjusted in different time periods, making the carbon emission calculation results more closely match the actual electricity load characteristics of users, thus forming an accurate time-of-use carbon emission figure.

[0082] It should also be noted that by dividing the daily electricity consumption time into three time periods and ensuring that the sum of these three periods meets 24-hour requirements, full coverage of the daily load is achieved. This time-segmentation structure not only allows for real-time collection of instantaneous power values ​​through the electricity meter, but also... And combined with the maximum power value Dynamic segmentation allows for the selection of low-carbon, medium-carbon, or high-carbon factor time period coefficients based on the correlation between power and different threshold ranges. This ensures both the continuity and real-time nature of carbon emission calculations while improving the precision of carbon emission estimations. This enables carbon emission accounting results to more accurately reflect differentiated electricity usage behavior across different time periods, thereby providing data support for precise carbon management.

Claims

1. A method for increasing the carbon metering function of a smart meter, characterized in that, The application relates to an energy meter and a carbon emission calculation method thereof. A UART interface is added to a carrier wave communication unit module (100) of an energy meter; An edge computing module (200) is connected with the UART interface to obtain frozen power data of the energy meter; The frozen power data of the energy meter is divided into preset intervals according to time, the power increase data of the preset intervals is calculated, the carbon emission of the preset intervals is obtained, and the monthly and annual carbon emissions are obtained by accumulating the carbon emission of the preset intervals; During the accumulation of the carbon emission of the preset intervals, the carbon emission is dynamically obtained and stored based on a local electric carbon factor prediction algorithm; The edge computing module (200) further comprises a program simulation during local upgrading and development design of a program of an illegal metering part through a program burning interface (202); An encryption chip (203) is an SC1763Y chip for hardware encryption; A first storage unit (204) is stored by using an EEPROM, and a second storage unit (204') is stored by using a Flash, and the electric carbon data is double-backed by the first storage unit (204) and the second storage unit (204'); A second backup power supply (102') is a super capacitor, and the power supply capacity is greater than 1.5F; An MCU chip (201) is a 32-bit MCU with a computing capacity of not less than ARM0 core; When the edge computing module (200) normally operates, a first indicator light (300) is green and constantly on, when the UART interface power supply of the energy meter is damaged and the circuit of the edge computing module (200) is abnormal, the first indicator light (300) is extinguished; A second indicator light (300') is a signal receiving and transmitting indicator light of the edge computing module (200), and is green and flashes when normally receiving signals and is red and flashes when normally transmitting data.

2. The retrofit method for adding carbon metering functionality to a smart meter of claim 1, wherein: The carrier wave communication unit module (100) comprises a UART interface for communication with the edge computing module; A first LDO (101) is used to convert the power supply level of the energy meter, and a second LDO (101') is used to supply power to the MCU chip (201), the first storage unit (204) and the second storage unit (204'); A first backup power supply (102) is a super capacitor, and the power supply capacity is 10F, and the first backup power supply is used to support the energy meter to report events after power failure.

3. The retrofit method for adding carbon metering functionality to a smart meter of claim 1 or 2, wherein: The edge computing module (200) comprises a UART interface which is used to share data with the MCU chip (201) in a transparent transmission mode, and the edge computing module (200) obtains the frozen power data of the energy meter through the UART interface.

4. The retrofit method for adding carbon metering functionality to a smart meter of claim 1, wherein: The frozen power data of the energy meter is divided into 96 intervals according to time every day, the electric carbon factor obtained from a master station system is combined, the power increase data of the 96 intervals is calculated, and the power increase data is represented as: ， wherein, is the daily carbon emission amount, is the electricity carbon factor, is the electricity quantity frozen data of the electricity meter in the mth preset interval.

5. The retrofit method for adding carbon metering functionality to a smart meter of claim 4, wherein: The monthly and annual carbon emissions are obtained by accumulating the daily carbon emissions of the corresponding time periods of the month and the year.

6. The retrofit method for adding carbon metering functionality to a smart meter of claim 4, wherein: The local prediction algorithm of the electricity-carbon factor includes, in the accumulation process of carbon emissions in the partition, when the electricity-carbon factor cannot be obtained, enabling the local carbon emission factor to perform local calculation of carbon emissions and storing in time sequence, expressed as: ， wherein, is a first local prediction estimate of the carbon factor, is a low carbon factor period coefficient, is a medium carbon factor period coefficient, is a high carbon factor period coefficient, is an instantaneous power of the energy meter, is an electrical carbon factor obtained from a superior metering network, is a daily freeze data of the energy meter, is a daily freeze data of the previous day of the energy meter, is an average of the difference of the minute freeze of the energy meter, is a period division boundary point, is a time interval; a first local prediction estimate of the electrical carbon factor is stored in the first storage unit (204) and backed up in the second storage unit (204') for the first local prediction estimate of the electrical carbon factor is stored in the first storage unit (204) and backed up in the second storage unit (204') for the first local prediction estimate of the electrical carbon factor 7. The retrofit method for adding carbon metering functionality to a smart meter of claim 6, wherein: The local prediction algorithm of the electricity-carbon factor also includes, in the calculation process of the electricity-carbon factor, needing to satisfy the constraint condition, expressed as: ， When a low carbon factor period coefficient is employed; When a medium carbon factor period coefficient is employed; When a high carbon factor period coefficient is employed; wherein, is the maximum power value of the electric energy meter within a day cycle.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the retrofit method for adding the carbon metering function of the smart electric energy meter in any one of claims 1 to 7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the retrofit method for adding the carbon metering function of the smart electric energy meter in any one of claims 1 to 7.

Images