Electric carbon metering device for realizing intelligent monitoring and energy efficiency management and control method thereof

Abstract

The invention relates to the technical field of carbon emission, in particular to an electric carbon metering device capable of achieving intelligent monitoring and energy efficiency management and a control method thereof, and the method comprises the steps that a processing and storage module receives power consumption parameters and carbon emission factors, calculates carbon emission and stores data, and forms a carbon emission data set; and the edge calculation module generates an energy efficiency management strategy according to the carbon emission data set and the user power consumption behavior data. According to the metering device, power consumption and carbon emission are accurately associated in real time, the metering device is suitable for various power utilization source scenes, the accuracy of carbon emission management is improved, and the effectiveness of a carbon emission reduction strategy is improved; the user is helped to know the power consumption behavior and the carbon emission condition of the user, better energy consumption suggestions are provided, and the power resource configuration and the carbon emission are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission, and in particular to an electric carbon metering device for realizing intelligent monitoring and energy efficiency management and a control method thereof. Background Art

[0002] As the global climate change problem becomes increasingly serious, the monitoring and management of carbon emissions has become the focus of attention of countries around the world. As the main source of carbon emissions in my country, the power industry monitors its carbon emissions, which is an important measure to achieve energy conservation and carbon reduction. However, traditional electricity meters can no longer meet the needs of electricity carbon monitoring and management in the evolving smart grid. On the one hand, traditional electricity meters can only provide electricity consumption data, but cannot directly reflect the corresponding carbon emissions, and cannot provide effective advice for carbon reduction management on the user side. On the other hand, the carbon emission calculation model constructed based on traditional electricity meter data information and static carbon emission factors does not accurately reflect the actual carbon emissions, which is not conducive to the accurate formulation of energy conservation and carbon reduction strategies. Summary of the invention

[0003] (I) Purpose of the invention

[0004] The purpose of the present invention is to provide an electric carbon metering device and a control method thereof that can improve the accuracy of carbon emission calculation and optimize carbon reduction strategies to realize intelligent monitoring and energy efficiency management.

[0005] (II) Technical solution

[0006] In order to solve the above problems, the present invention provides an electric carbon metering device for realizing intelligent monitoring and energy efficiency management, comprising:

[0007] Power data collection module, user power consumption behavior collection module, carbon emission factor collection module, communication module, processing and storage module and edge computing module;

[0008] The processing and storage module is respectively connected to the power data acquisition module, the carbon emission factor acquisition module, the communication module and the edge computing module;

[0009] The processing and storage module exchanges data with the edge computing module through the communication module;

[0010] The power data acquisition module is used to collect power consumption parameters and transmit them to the processing and storage module;

[0011] The carbon emission factor collection module is used to collect carbon emission factors and transmit them to the processing and storage module;

[0012] The processing and storage module receives the power consumption parameter and the carbon emission factor, calculates the carbon emission and stores the data to form a carbon emission data set;

[0013] The user electricity usage behavior collection module is used to collect user electricity usage behavior data and transmit it to the edge computing module;

[0014] The edge computing module generates an energy efficiency management strategy based on the carbon emission data set and user electricity consumption behavior data.

[0015] In another aspect of the present invention, preferably, it further comprises: a security module, which is connected to the processing and storage module, and is used for authority management, identity authentication, protocol filtering of the metering device and encryption processing of data of the metering device.

[0016] Another aspect of the present invention preferably further comprises: a display and interaction module, the display and interaction module is connected to the processing and storage module, and the display and interaction module is used to display the data of the metering device and perform user-defined query and setting.

[0017] Another aspect of the present invention preferably further comprises: a remote management module, the remote management module is connected to the processing and storage module, the remote management module is used for remote query, remote parameter setting, remote monitoring and remote software upgrade of the metering device.

[0018] In another aspect of the present invention, preferably, it further comprises: an expansion interface module, wherein the expansion interface module is connected to the processing and storage module, and the expansion interface module is used to connect to an external device.

[0019] In another aspect of the present invention, preferably, a method for controlling electric carbon metering for realizing intelligent monitoring and energy efficiency management is provided, the control method being applicable to the metering device as described above, and the control method comprising:

[0020] Real-time collection of power consumption parameters, carbon emission factors and user electricity usage behavior;

[0021] Calculate and store carbon emissions based on the power consumption parameters and carbon emission factors to form a carbon emission data set;

[0022] Predicting the user's electricity carbon trend based on the carbon emission data set and the user's electricity consumption behavior;

[0023] An energy efficiency management strategy is generated according to the user's electricity-carbon trend.

[0024] In another aspect of the present invention, preferably, the carbon emission factor is calculated using the following formula:

[0025]

[0026] Where I is the total number of energy types on the power supply side, r i,tThe carbon emission factor of the power source of the i-th energy type at time t, P i,t is the grid-connected power of the power source of the i-th energy type at time t.

[0027] In another aspect of the present invention, preferably, the carbon emissions are calculated using the following formula:

[0028] E t =r t *P t

[0029] Among them, E t represents the carbon emissions at time t, r t represents the carbon emission factor at time t, P t represents the power at time t.

[0030] In another aspect of the present invention, preferably, each data node in the carbon emission data set accumulates the carbon emissions according to a preset time period, and the accumulation is calculated using the following formula:

[0031]

[0032] Where k represents the length of the preset time period, R k Indicates the accumulated carbon emissions within the preset time period, E t Represents the carbon emissions at time t.

[0033] In another aspect of the present invention, preferably, predicting the user's electricity carbon trend based on the carbon emission data set and the user's electricity consumption behavior and optimizing the energy efficiency management strategy includes:

[0034] Predict the user's electricity consumption behavior and carbon emissions in the next stage and build a Markov decision process model;

[0035] The SA-TD3 algorithm is used to solve the Markov decision process model and optimize the energy efficiency management strategy.

[0036] In another aspect of the present invention, preferably, the control method further comprises:

[0037] Storing the energy efficiency management strategy;

[0038] Verify the user's identity and authority, interact with the user after verification, and provide corresponding energy efficiency management strategies.

[0039] (III) Beneficial effects

[0040] The above technical solution of the present invention has the following beneficial technical effects:

[0041] The metering device of the present invention accurately correlates electricity consumption with carbon emissions in real time, is applicable to a variety of electricity consumption scenarios, improves the accuracy of carbon emission management, and enhances the effectiveness of carbon reduction strategies; helps users understand their own electricity consumption behavior and carbon emissions, and provides better energy usage recommendations, optimizing electricity resource allocation and carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the overall structure of an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of a user power strategy optimization method based on SA-TD3 of the present invention;

[0044] Figure 3 It is a schematic diagram of the overall process of an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0046] The accompanying drawings show schematic diagrams of structures according to embodiments of the present invention. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clarity. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0047] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0049] The present invention will be described in more detail below with reference to the accompanying drawings. In each of the accompanying drawings, the same elements are represented by similar reference numerals. For the sake of clarity, the various parts in the accompanying drawings are not drawn to scale.

[0050] Embodiment 1

[0051] An electric carbon metering device for realizing intelligent monitoring and energy efficiency management, Figure 1 FIG. 1 shows a schematic diagram of the overall structure of an embodiment of the present invention. Figure 1 As shown, including:

[0052] Power data collection module, user power consumption behavior collection module, carbon emission factor collection module, communication module, processing and storage module and edge computing module;

[0053] The power data acquisition module uses a high-precision power metering chip to monitor power consumption data in real time. The power data acquisition module supports the metering of active and reactive power in both positive and negative directions, as well as real-time monitoring of power parameters such as voltage, current, power factor, and harmonics. The user power consumption behavior acquisition module collects data such as user power consumption habits, power consumption time periods, and peak and valley power consumption through smart meters and sensors installed on key power-consuming equipment. The carbon emission factor acquisition module is connected to a real-time database containing carbon emission factors for various energy types (such as coal, natural gas, renewable energy, etc.);

[0054] The processing and storage module is connected to the power data acquisition module, the carbon emission factor acquisition module, the communication module and the edge computing module respectively; the processing and storage module has a built-in microprocessor and a memory for basic data processing such as power consumption data processing, storage and carbon emission calculation.

[0055] The processing and storage module exchanges data with the edge computing module through the communication module; the communication module integrates multiple communication modes (such as 4G, Wi-Fi, etc.) to ensure stable, real-time upload and remote management of data such as electricity consumption data and real-time dynamic electricity carbon emission factors in different application scenarios;

[0056] The power data acquisition module is used to collect power consumption parameters and transmit them to the processing and storage module;

[0057] The carbon emission factor collection module is used to collect carbon emission factors and transmit them to the processing and storage module;

[0058] The processing and storage module receives the power consumption parameter and the carbon emission factor, calculates the carbon emission and stores the data to form a carbon emission data set;

[0059] The user electricity usage behavior collection module is used to collect user electricity usage behavior data and transmit it to the edge computing module;

[0060] The edge computing module generates energy efficiency management strategies based on the carbon emission data set and user electricity consumption behavior data. The edge computing module has a built-in high-performance processor and large-capacity memory to adapt to complex data analysis processing and storage as well as model training.

[0061] In this embodiment, further, the metering device further includes: a security module, the security module is connected to the processing and storage module, and the security module is used for authority management, identity authentication, protocol filtering of the metering device and encryption processing of the data of the metering device. The security module adopts an IIC communication chip with an encryption algorithm, mainly provides functions such as authority management, identity authentication, and protocol filtering of the metering device, and can use encryption technology to ensure data transmission and storage security, and ensure data security and privacy protection.

[0062] In this embodiment, further, the metering device also includes: a display and interaction module, the display and interaction module is connected to the processing and storage module, and the display and interaction module is used to display the data of the metering device and perform user-defined query and setting. The display and interaction module is equipped with a high-definition LCD touch screen, supports local query and setting, and provides an intuitive user experience. The display and interaction module provides intuitive display of relevant data such as power consumption data, carbon emissions, and energy consumption behavior prediction, and supports users to query, set, and export energy efficiency management strategies.

[0063] In this embodiment, further, the metering device also includes: a remote management module, which is connected to the processing and storage module, and is used to remotely query, remotely set parameters, remotely monitor and remotely upgrade the software of the metering device. Specifically, it supports remote query of metering devices, generates and exports electricity consumption data analysis, carbon emission reports, and energy-saving and emission reduction strategies to facilitate energy management; supports remote setting of metering device parameters, and can select or customize metering mode, communication method, data reporting frequency and other parameters according to application scenarios; realizes remote real-time monitoring of power quality parameters such as voltage fluctuations and current harmonics through the cloud platform, and provides fault early warning control, abnormal alarm and other functions; supports remote software upgrades, etc.

[0064] In this embodiment, the metering device further includes: an expansion interface module, the expansion interface module is connected to the processing and storage module, and the expansion interface module is used to connect to an external device. The expansion interface module can provide multiple interfaces such as USB, RS485, Ethernet, etc., which is convenient for integration with other devices or systems, such as smart homes and energy management systems.

[0065] The metering device of the present invention accurately correlates electricity consumption with carbon emissions in real time, is applicable to a variety of electricity consumption scenarios, improves the accuracy of carbon emission management, and enhances the effectiveness of carbon reduction strategies; helps users understand their own electricity consumption behavior and carbon emissions, and provides better energy usage recommendations, optimizing electricity resource allocation and carbon emissions.

[0066] Embodiment 2

[0067] A method for controlling electric carbon metering to realize intelligent monitoring and energy efficiency management, the control method is applicable to the metering device as described above, Figure 3 FIG. 1 shows a schematic diagram of the overall process of an embodiment of the present invention. Figure 3 As shown, the control method includes:

[0068] Real-time collection of power consumption parameters, carbon emission factors and user power consumption behavior; power consumption parameters, user power consumption behavior and carbon emission factors are collected through power data collection module, user power consumption behavior collection module and carbon emission factor collection module respectively; carbon emission factors can be obtained from communication module, and carbon emission factors at time t can be obtained from cloud platform in real time through communication module; in this embodiment, the collected power consumption parameters include the power consumption of the user at time t;

[0069] According to the power consumption parameters and the carbon emission factor, the carbon emission is calculated and stored to form a carbon emission data set; the real-time carbon emission calculation can be calculated through the storage and processing module;

[0070] Predicting the user's electricity carbon trend based on the carbon emission data set and the user's electricity consumption behavior;

[0071] Optimize energy efficiency management strategy based on the user's electricity-carbon trend.

[0072] Furthermore, in this embodiment, before collecting power consumption parameters in real time, the parameters of the metering device are initialized, and the parameters of the metering device are configured through an on-site display and interaction module or remotely according to application scenario requirements.

[0073] Furthermore, in this embodiment, the determination of the carbon emission factor is related to the type of power source on the power supply side, that is, the power source of the power user on the power consumption side is allocated in proportion to the power grid on the power supply side, and the carbon emissions are also allocated in proportion. The carbon emission factor is calculated using the following formula:

[0074]

[0075] Where I is the total number of energy types on the power supply side, r i,t The carbon emission factor of the power source of the i-th energy type at time t, P i,t is the grid-connected power of the power source of the i-th energy type at time t. The energy types on the power source side can include wind power, thermal power, photovoltaic power, etc.

[0076] Furthermore, in this embodiment, the carbon emissions are calculated using the following formula:

[0077] E t =r t *P t

[0078] Among them, Et represents the carbon emissions at time t, r t represents the carbon emission factor at time t, P t represents the power at time t.

[0079] Further, in this embodiment, in another aspect of the present invention, preferably, each data node in the carbon emission data set accumulates the carbon emissions according to a preset time period, and the accumulation is calculated using the following formula:

[0080]

[0081] Where k represents the length of the preset time period, R k Indicates the accumulated carbon emissions within the preset time period, E t Represents the carbon emissions at time t.

[0082] The step of predicting the user's electricity carbon trend based on the carbon emission data set and the user's electricity consumption behavior includes:

[0083] Predict the user's electricity consumption behavior and carbon emissions in the next stage and build a Markov decision process model;

[0084] The TD3 algorithm is used to solve the Markov decision process model, optimize the energy efficiency management strategy, and predict the user's electricity-carbon trend.

[0085] Predicting the user's electricity consumption behavior and carbon emissions in the next stage includes:

[0086] The user's next stage 1 The carbon emission factor at this moment is In the next stage, the user 1 Electricity plan at all times Depend on It consists of three major components, namely

[0087]

[0088] in, For users in t 1 The most basic electricity load that always maintains basic production and living needs is the non-adjustable part; For users in t 1 The load power consumption can be shifted at any time. Users can adjust the power consumption time interval, such as the charging task of battery vehicles. A Boolean variable can only take the values ​​1 and 0. A value of 1 indicates that the load that can be shifted is using electricity at the current moment. A value of 0 means that the load can be moved without electricity. sh The total duration of electricity consumption that can be shifted by the user to meet the constraints For users in t 1 The power consumption of load can be reduced at any time. Users can adjust the power consumption, such as the use of air conditioners. is the load reduction ratio that can be reduced, and its value range is 0-1.

[0089] In the next stage, the user 1 The predicted carbon emissions at the time are:

[0090]

[0091] The next stage is t 1 The predicted carbon emissions from the time of writing to the end of the day are:

[0092]

[0093] To solve the management problem of energy efficiency strategy using deep reinforcement learning method, it is necessary to first conduct Markov decision modeling on the user's electricity consumption behavior. First, divide the remaining time of the day into k time periods. Then the environmental state s at time k is k The action s executed according to the current power consumption status of the user and the current environment status k+1 Corresponding to the user's electricity consumption plan for the next moment. Then, the agent environment state is composed of s k Convert to s k+1 , corresponding to the electricity consumption plan at time k and time k+1 respectively. Finally, the agent will accumulate rewards for feedback, corresponding to indicators such as carbon emissions and user experience related to the electricity consumption plan c k Therefore, the Markov decision process model M of user electricity consumption can be expressed as:

[0094] M={s k ,a k ,c k ,s k+1}

[0095] Among them, the environmental status That is, the load adjustment amount, the environmental state at the next moment, satisfies: The agent gets the reward c k Not only carbon emissions are taken into account, but also the negative penalties caused by users cutting electricity consumption, to meet: where λ 1 , 2 are the proportion of carbon emissions and the penalty for reducing user electricity consumption in the agent’s reward function; reducing load will bring negative penalties to users satisfy in is the negative impact coefficient on users when load is reduced at time k.

[0096] The TD3 algorithm (Twin Delayed Deep Deterministic policy gradient) is introduced to solve the Markov decision process. In the TD3 algorithm, 6 network structures are involved, which are a combination of 2 Actor and dual Critic networks. Its parameters can be used It means that the structure of the Actor network is the same as that of the target Actor network, which realizes the mapping from state s to action a. The network update function of the TD3 algorithm is specifically expressed as a loss function as follows:

[0097]

[0098]

[0099] Among them, γ is the discount factor, N is the total number of samples used, is the loss function of Critic network i. The core idea of ​​the SA algorithm draws on the annealing principle of metals, comparing each solution within a search range to a molecule in the air. Each solution within the search range has the same "energy" as the molecules in the air. The present invention introduces the idea of ​​simulated annealing (SA) on the basis of the TD3 algorithm to reduce the situation of "blindly" seeking the optimal solution and thus falling into the local optimum, thereby improving the exploration efficiency of the TD3 algorithm. This method uses the first Critic network as the exploration network for the initial solution in the SA algorithm, and uses the second Critic network as the exploration network for the perturbation term in the SA algorithm. Therefore, the network update formula of the improved SA-TD3 algorithm is specifically expressed as follows:

[0100]

[0101] Where, T k Parameters set for the initial temperature in the simulated annealing algorithm.

[0102] like Figure 2 As shown in the figure, the specific steps for solving the user power consumption strategy based on the SA-TD3 algorithm are as follows:

[0103] 1) Initialize the policy network π φ , Valuation Network Target Network Experience Pool β

[0104] 2) Set the learning rate α, discount factor γ, delayed update step number d, soft update coefficient τ and maximum learning step number maxepisode, learning step number count = 0, initial annealing temperature T k

[0105] 3)For episode=1to max episode do:

[0106] 4) Randomly initialize state s 0

[0107] 5)For max episode to max episode do:

[0108] 6) According to the output of the Actor network, Gaussian noise is introduced to generate the current action a k =μ(s k |θ μ )+η

[0109] 7) Adjust the electricity consumption plan to a k Send to the environment to calculate the next state s k+1 And the current running cost C k

[0110] 8) Sample data (s k ,a k ,C k ,s k+1 ) is stored in the experience pool, let k = k + 1

[0111] 9) Update the Critic network Let count = count + 1

[0112] 10)If count mod d then:

[0113] 11) Based on the SA algorithm strategy, update the Actor network θ μ

[0114] 12) Soft update target network θ μ '←τθ μ +(1-τ)θ μ '、

[0115] 13)End if

[0116] 14) End for

[0117] 15) End for

[0118] According to the configuration, the metering device transmits the carbon emissions from the processing and storage module to the edge computing module through the selected communication method. The edge computing module performs data analysis and mining, predicts the user's electricity-carbon trend through the built-in deep learning algorithm, and gives relevant suggestions for electricity optimization strategies. The information is then fed back to the processing and storage module through the communication module.

[0119] Furthermore, in this embodiment, the control method further includes:

[0120] Storing the energy efficiency management strategy;

[0121] The user's identity and authority are verified, and information is exchanged with the user after the verification, and the corresponding energy efficiency management strategy is given. The user can obtain the interactive interface in the display and interaction module on site, and after passing the identity authentication, view the daily electricity consumption report, including the current electricity loss and carbon emissions as of the day, and then independently decide on the next stage of electricity consumption. The user can also remotely manage the electricity carbon meter through the communication module through WIFI communication. The user information is passed to the security module through the storage and processing module. After confirming that the user has the corresponding identity and authority, the corresponding data can be read and operated. For example, the user can adjust his own energy consumption behavior through the obtained optimized electricity consumption strategy.

[0122] The control method of this embodiment ensures the timeliness and accuracy of the data by real-time collection of power consumption parameters, carbon emission factors and user power consumption behavior. It provides users with instant feedback on their energy consumption and carbon emissions, which helps them adjust their power consumption behavior in time and reduce unnecessary energy waste and carbon emissions. Through the built-in deep learning algorithm and edge computing module, the system can automatically analyze the user's power consumption behavior and carbon emission trends, and generate energy efficiency management strategies without manual intervention. It not only improves work efficiency, but also reduces labor costs. According to the predicted electric carbon trend, the system can generate targeted energy efficiency management strategies, such as adjusting power consumption time, optimizing equipment use efficiency, etc., so as to effectively reduce the user's energy consumption and carbon emissions. This not only helps environmental protection, but also saves electricity bills for users. It supports multiple communication methods and edge computing modules, which can be flexibly configured according to user needs. At the same time, the system has good scalability and can be upgraded and optimized with the development of technology and changes in user needs. Through the processing and storage module and the communication module, the system can present key data such as carbon emissions and power consumption behavior to users in the form of charts, reports, etc., to help them understand energy consumption and carbon emissions more intuitively. By providing real-time feedback and energy efficiency management strategy recommendations, the system can enhance users' participation in energy use and carbon emission management, and encourage them to participate more actively in energy conservation and emission reduction actions.

[0123] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

[0124] In the above description, the technical details of patterning and etching of each layer are not described in detail. However, those skilled in the art should understand that various means in the prior art can be used to form layers, regions, etc. of desired shapes. In addition, in order to form the same structure, those skilled in the art can also design methods that are not completely the same as the methods described above.

[0125] The present invention has been described above with reference to the embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, a person skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

[0126] Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.

[0127] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. An electric carbon metering device for realizing intelligent monitoring and energy efficiency management, characterized in that: include: Power data collection module, user power consumption behavior collection module, carbon emission factor collection module, communication module, processing and storage module and edge computing module; The processing and storage module is respectively connected to the power data acquisition module, the carbon emission factor acquisition module, the communication module and the edge computing module; The processing and storage module exchanges data with the edge computing module through the communication module; The power data acquisition module is used to collect power consumption parameters and transmit them to the processing and storage module; The carbon emission factor collection module is used to collect carbon emission factors and transmit them to the processing and storage module; The processing and storage module receives the power consumption parameter and the carbon emission factor, calculates the carbon emission and stores the data to form a carbon emission data set; The user electricity usage behavior collection module is used to collect user electricity usage behavior data and transmit it to the edge computing module; The edge computing module generates an energy efficiency management strategy based on the carbon emission data set and user electricity consumption behavior data.

2. The metering device according to claim 1, characterized in that Also includes: A security module is connected to the processing and storage module, and is used for authority management, identity authentication, and protocol filtering of the metering device and for encrypting data of the metering device.

3. The metering device according to claim 1, characterized in that Also includes: A display and interaction module, wherein the display and interaction module is connected to the processing and storage module, and is used for displaying the data of the metering device and performing user-defined query and setting.

4. The metering device according to claim 1, characterized in that: Also includes: A remote management module, the remote management module is connected to the processing and storage module, and the remote management module is used to remotely query, remotely set parameters, remotely monitor and remotely upgrade the software of the metering device.

5. The metering device according to claim 1, characterized in that: Also includes: An expansion interface module, the expansion interface module is connected to the processing and storage module, and the expansion interface module is used to connect to an external device.

6. A method for realizing intelligent monitoring and energy efficiency management of electric carbon metering control, characterized in that: The control method is applicable to the metering device according to any one of claims 1 to 5, and the control method comprises: Real-time collection of power consumption parameters, carbon emission factors and user electricity usage behavior; Calculate and store carbon emissions based on the power consumption parameters and carbon emission factors to form a carbon emission data set; Predicting the user's electricity carbon trend based on the carbon emission data set and the user's electricity consumption behavior; Optimize energy efficiency management strategy based on the user's electricity-carbon trend.

7. The control method according to claim 6, characterized in that: The carbon emission factor is calculated using the following formula: Where I is the total number of energy types on the power supply side, r i,t The carbon emission factor of the power source of the i-th energy type at time t, P i,t is the grid-connected power of the power source of the i-th energy type at time t.

8. The control method according to claim 7, characterized in that: The carbon emissions are calculated using the following formula: E t =r t *P t Among them, E t represents the carbon emissions at time t, r t represents the carbon emission factor at time t, P t represents the power at time t; Each data node in the carbon emission data set accumulates the carbon emissions according to a preset time period, and the accumulation is calculated using the following formula: Where k represents the length of the preset time period, R k Indicates the accumulated carbon emissions within the preset time period, E t Represents the carbon emissions at time t.

9. The control method according to claim 6, characterized in that: The method of predicting the user's electricity carbon trend based on the carbon emission data set and the user's electricity consumption behavior and optimizing the energy efficiency management strategy includes: Predict the user's electricity consumption behavior and carbon emissions in the next stage and build a Markov decision process model; The SA-TD3 algorithm is used to solve the Markov decision process model and optimize the energy efficiency management strategy.

10. The control method according to claim 6, characterized in that: The control method further comprises: Storing the energy efficiency management strategy; Verify the user's identity and permissions, and interact with the user accordingly after verification.

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