Energy digital metering monitoring and tracking method and system

Abstract

The application relates to an energy digital metering monitoring and tracking method and system, and relates to the technical field of energy digital management. The application comprises the following steps: deploying a plurality of energy metering terminals in a physical energy network to collect energy consumption data in real time, and performing local energy carbon emission metering through an embedded carbon emission calculation component; uploading the metering results to a cloud monitoring center, constructing a virtual mapping model based on digital twin technology and in combination with the topological information of the physical energy network; synchronizing the metering data to the virtual mapping model, visually tracking the metering data in combination with topological analysis and particle tracking technology, and generating an energy flow dynamic visualization atlas; tracing the carbon emission footprint according to the atlas, and realizing energy digital metering based on the tracing results. The application solves the problems that the traditional energy metering monitoring is not differentiated and adapted in combination with metering characteristics, lacks dynamic visual tracking means, and thus leads to insufficient energy metering accuracy and incomplete carbon footprint tracing.

Description

Technical Field

[0001] This application relates to the field of digital energy management, and in particular to a digital energy metering, monitoring and tracking method and system. Background Technology

[0002] With the development of digital energy management technologies, the accuracy of energy metering and carbon footprint tracking has become a key foundation for improving energy management efficiency and carbon cost control capabilities. Currently, traditional energy metering and monitoring methods mainly rely on unified metering models and static data recording modes, which suffer from problems such as poor terminal adaptability, insufficient metering accuracy, and unintuitive carbon footprint tracking. This not only increases data redundancy and management costs, but also exacerbates inaccurate carbon cost accounting due to incomplete tracking, making it difficult to meet the needs of refined metering and carbon management in complex energy networks. Summary of the Invention

[0003] To address the aforementioned technical issues, this application provides a digital energy metering, monitoring, and tracking method and system, which improves upon the current situation in traditional energy metering and monitoring, which suffers from poor terminal compatibility, insufficient metering accuracy, and unintuitive carbon footprint tracking, resulting in excessive data redundancy, high management costs, and inaccurate carbon cost accounting.

[0004] The embodiments of this application disclose the following technical solutions:

[0005] In a first aspect, embodiments of this application provide a method for digital energy metering, monitoring, and tracking, the method comprising:

[0006] Energy consumption data is collected in real time through multiple energy metering terminals deployed in the physical energy network, and local energy carbon emission metering is performed through the carbon emission calculation component embedded in the energy metering terminal.

[0007] Upload local energy carbon emission measurement results to the cloud monitoring center, and construct a virtual mapping model based on digital twin technology and the topology information of the physical energy network;

[0008] The local energy carbon emission metering is synchronized to the virtual mapping model, and the metered data is visualized and tracked using topology analysis and particle tracking technology to generate a dynamic visualization map of energy flow.

[0009] Carbon emission footprints are traced and tracked based on dynamic visualization maps of energy flows, and digital energy metering is achieved based on the traceability results.

[0010] Secondly, embodiments of this application provide an energy digital metering, monitoring, and tracking system, the system comprising:

[0011] The local energy carbon emission metering module is used to collect energy consumption data in real time through multiple energy metering terminals deployed in the physical energy network, and to perform local energy carbon emission metering through the carbon emission calculation component embedded in the energy metering terminal.

[0012] The cloud-based virtual mapping modeling module is used to upload local energy carbon emission measurement results to the cloud monitoring center and construct a virtual mapping model based on digital twin technology and the topology information of the physical energy network.

[0013] The energy flow dynamic visualization module is used to synchronize the local energy carbon emission measurement to the virtual mapping model, and combine topology analysis and particle tracking technology to visualize and track the data obtained by measurement, and generate a dynamic visualization map of energy flow.

[0014] The carbon footprint traceability module is used to trace carbon emission footprints based on dynamic visualization maps of energy flows, and to achieve digital energy metering based on the traceability results.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0016] This application proposes a method and system for digital energy metering, monitoring, and tracking. It achieves local energy carbon emission metering by configuring suitable carbon emission calculation components for energy metering terminals with different characteristics. A virtual mapping model is constructed using digital twin technology. Metering results are synchronized, and a dynamic visualization map of energy flow is generated using topology analysis and particle tracking technology. Finally, based on the map, a collaborative operation of carbon footprint tracing and digital metering is performed, realizing digital energy metering, monitoring, and tracking. First, the metering characteristics of the energy metering terminals are acquired, typical virtual terminals are defined by clustering, a differentiated carbon emission calculation model library is constructed, and carbon emission calculation components are optimized and acquired. The terminals then perform local energy carbon emission metering. Next, the metering results are uploaded to a cloud monitoring center, and a virtual mapping model is constructed based on digital twin technology and topology information. Then, the metering results are synchronized to the virtual mapping model, and a dynamic visualization map of energy flow is generated using topology analysis and particle tracking technology. Finally, carbon footprint tracing is performed by traversing the metering object nodes according to the map, extracting the carbon footprint, and performing quantitative statistics to measure the digital carbon emission cost.

[0017] The technical solution of this application solves the problems of measurement deviation caused by differences in terminal characteristics, lack of intuitiveness in the abstract energy flow process, and incomplete carbon footprint traceability in traditional energy measurement by integrating local energy carbon emission measurement with measurement characteristic analysis and differential model construction, virtual mapping model combining digital twin and topology analysis, dynamic visualization map of energy flow by particle tracking, and carbon footprint traceability and cost measurement throughout the entire process. It realizes the full-process digital management of energy from consumption measurement to carbon cost accounting. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0019] Figure 1 A flowchart illustrating a digital energy metering, monitoring, and tracking method provided in this application embodiment;

[0020] Figure 2 This is a schematic diagram of the structure of an energy digital metering, monitoring and tracking system provided in an embodiment of this application.

[0021] The components represented by each number in the attached diagram are explained below:

[0022] Local energy carbon emission metering module 01, cloud virtual mapping modeling module 02, energy flow dynamic visualization module 03, carbon footprint traceability module 04. Detailed Implementation

[0023] This application provides a digital energy metering, monitoring and tracking method and system to solve the technical problems in the prior art, such as insufficient energy metering accuracy and incomplete carbon footprint traceability due to the lack of differentiated adaptation to metering characteristics and the lack of dynamic visualization tracking methods.

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0027] Example 1, as shown in the appendix Figure 1 As shown, this application provides a method for digital energy metering, monitoring, and tracking, the method comprising the following steps:

[0028] S110: Energy consumption data is collected in real time through multiple energy metering terminals deployed in the physical energy network, and local energy carbon emission metering is performed through the carbon emission calculation component embedded in the energy metering terminal.

[0029] In this embodiment of the application, in the scenario of digital energy metering, monitoring and tracking, in order to achieve accurate local energy carbon emission metering, it is necessary to first configure an appropriate carbon emission calculation component for each energy metering terminal to solve the problem of carbon emission metering deviation caused by differences in sampling frequency, sampling accuracy and data transmission capability among different metering terminals.

[0030] Specifically, the metering characteristics of the energy metering terminal at each energy metering point are first obtained. These metering characteristics include at least the sampling frequency, sampling accuracy, and data transmission capability, which serve as the basis for terminal differentiation analysis.

[0031] Meanwhile, based on metering characteristics, multiple energy metering terminals are clustered, and terminals with similar metering characteristics are grouped into one category. Several typical virtual terminals are defined accordingly, providing targeted objects for subsequent model construction.

[0032] Furthermore, differentiated carbon emission calculation models are constructed for multiple typical virtual terminals. By defining the initial model category mapping relationship, models such as rule, regression analysis, and lightweight machine learning are constructed. After performance verification and optimization, a differentiated carbon emission calculation model library is formed to adapt to the metering characteristics of different energy metering terminals.

[0033] Based on this, the adaptability of each model in the differentiated carbon emission calculation model library to individual terminals under corresponding typical virtual terminals is evaluated. The accuracy and timeliness of the models are verified by constructing a verification dataset. When the threshold is not met, reinforcement learning optimization or knowledge distillation compression are performed respectively. Finally, multiple adapted carbon emission calculation components are obtained and embedded into the corresponding energy metering terminals.

[0034] Furthermore, once the corresponding energy metering terminal is deployed in the physical energy network, it can collect energy consumption data in real time and perform local energy carbon emission metering through the embedded carbon emission calculation component to obtain accurate local carbon emission metering results, providing basic data for subsequent uploading to the cloud monitoring center and full-process metering monitoring.

[0035] This step, through technical solutions such as energy metering terminal metering characteristic analysis, clustering modeling, and adaptation optimization, provides accurate local metering data support for subsequent cloud-based modeling and traceability, ensuring the reliability of metering monitoring throughout the entire process.

[0036] Step S110 in the method provided in this application embodiment includes:

[0037] Obtain the metering characteristics of the energy metering terminal at each energy metering point, wherein the metering characteristics include at least sampling frequency, sampling accuracy, and data transmission capability;

[0038] Based on the metering characteristics, multiple energy metering terminals are clustered, and multiple typical virtual terminals are defined accordingly.

[0039] Differentiated carbon emission calculation models are constructed for multiple typical virtual terminals, forming a differentiated carbon emission calculation model library;

[0040] The adaptability of each differentiated carbon emission calculation model in the differentiated carbon emission calculation model library to the corresponding energy metering terminal under the typical virtual terminal is evaluated, and the typical virtual terminal is adaptively adjusted by combining the threshold discrimination method to obtain multiple carbon emission calculation components.

[0041] In this embodiment of the application, in order to achieve accurate measurement of local energy carbon emissions by each energy metering terminal, it is necessary to build a carbon emission calculation component that is adapted to the metering characteristics of different terminals. Through differentiated model design and optimization, the metering deviation problem caused by the difference in terminal characteristics is solved, so as to ensure the accuracy and efficiency of local carbon emission metering results.

[0042] Specifically, the first step is to comprehensively acquire the metering characteristics of the energy metering terminal at each energy metering point. These metering characteristics include at least the sampling frequency, sampling accuracy, and data transmission capability.

[0043] For example, smart meters in an industrial area sample 10 times per second with a sampling accuracy of 0.01 kWh and support 5G high-speed data transmission; while smart meters in a residential area sample once per minute with a sampling accuracy of 0.1 kWh and only support Bluetooth Low Energy transmission. By recording these parameters in detail, basic data is provided for subsequent classification of metering terminals and model construction.

[0044] Furthermore, based on the aforementioned metering characteristics, cluster analysis is performed on multiple energy metering terminals. Specifically, the existing K-means clustering algorithm is used, with sampling frequency, sampling accuracy, and data transmission capability as feature dimensions, to group metering terminals with similar characteristics into one category.

[0045] For example, metering terminals with a sampling frequency higher than 5 times per second, a sampling accuracy higher than 0.05kWh and supporting high-speed transmission are grouped into "high-frequency high-precision class", and metering terminals with a sampling frequency lower than 1 time per minute, a sampling accuracy lower than 0.1kWh and limited transmission capability are grouped into "low-frequency basic class". Multiple typical virtual terminals are defined accordingly, and each typical virtual terminal represents the common characteristics of a class of terminals.

[0046] Based on this, differentiated carbon emission calculation models are constructed for multiple typical virtual terminals to form a differentiated carbon emission calculation model library.

[0047] The method provided in this application embodiment includes the following steps: "Constructing differentiated carbon emission calculation models for multiple typical virtual terminals to form a differentiated carbon emission calculation model library":

[0048] Based on historical prior knowledge, define the initial model category mapping relationship;

[0049] Multiple differentiated carbon emission calculation models are constructed based on the initial model category mapping relationship, and model performance is verified. The multiple differentiated carbon emission calculation models include at least:

[0050] Computational models based on pre-configured rules; computational models based on regression analysis; computational models based on lightweight machine learning algorithms;

[0051] Based on the model performance verification results, multiple differentiated carbon emission calculation models are expanded and constructed, and model performance verification is repeated. The differentiated carbon emission calculation model library is selected and output with the optimal model performance verification results as the goal.

[0052] In this embodiment of the application, in order to achieve accurate matching between different typical virtual terminals and carbon emission calculation models, it is necessary to build a differentiated carbon emission calculation model library based on historical experience and model performance verification. Through targeted design and iterative optimization, it is ensured that the model can adapt to the metering characteristics of various terminals and provide efficient and accurate calculation support for local carbon emission metering.

[0053] First, an initial model category mapping relationship is defined based on historical prior knowledge. Then, by combining past operational data from energy metering terminals with carbon emission calculation practices, the appropriate model direction for different types of typical virtual terminals is clarified.

[0054] For example, for "basic" virtual terminals with low sampling frequency and limited data transmission capabilities, the initial model category is mapped to a computational model adapted to pre-configured rules because of their high requirements for computational efficiency and low requirements for complex analysis.

[0055] In addition, for "standard" virtual terminals with medium sampling accuracy and certain regularity of data, the initial model category is mapped to a computational model based on regression analysis; for "high-precision" virtual terminals with high sampling frequency, rich data dimensions and complex fluctuations, the initial model category is mapped to a computational model based on lightweight machine learning algorithms.

[0056] Furthermore, multiple differentiated carbon emission calculation models are constructed based on the initial model category mapping relationship.

[0057] Specifically, for calculation models based on pre-configured rules, carbon emission calculation can be achieved through preset fixed formulas, such as directly using "carbon emissions = energy consumption × carbon emission factor of the corresponding energy type". The model structure is simple, the calculation speed is fast, and it is suitable for metering terminals with low accuracy requirements and simple data characteristics.

[0058] Furthermore, for calculation models based on regression analysis, historical energy consumption data and actual carbon emissions are selected as samples. By fitting the functional relationship through linear or multiple regression, this calculation model can capture the linear correlation between data points and is suitable for metering terminals with relatively stable data patterns. For example, the formula y = a1x1 + a2x2 + b can be used, where y is carbon emissions, x1 is electricity consumption, x2 is natural gas consumption, a1 and a2 are regression coefficients, and b is a constant term.

[0059] Furthermore, for computational models based on lightweight machine learning algorithms, lightweight algorithms such as random forests and shallow neural networks can be used. These can be input with multi-dimensional features such as energy consumption, time, ambient temperature, and equipment operating status, and the nonlinear relationships between the data can be learned through training. For example, a neural network with three hidden layers (16 neurons per layer) can be constructed, and the parameters can be optimized using the cross-entropy loss function. This model can handle complex data correlations and is suitable for metering terminals requiring high precision.

[0060] Furthermore, after the construction of multiple differentiated carbon emission calculation models is completed, model performance verification will be conducted.

[0061] Specifically, 5,000 sets of data (including raw energy consumption data and actual carbon emissions) covering different metering terminal types and different operating scenarios were selected from the historical database and divided into training set and validation set in a 7:3 ratio.

[0062] The performance verification metrics include computational accuracy (measured by the relative deviation rate between predicted and actual values) and computational timeliness (measured by the average computation time for a single set of data).

[0063] Based on this, the constructed differentiated carbon emission calculation models were expanded and built according to the model performance verification results to improve the adaptability and stability of the models in diverse scenarios and ensure that they can better meet the metering needs of different typical virtual terminals.

[0064] Specifically, if a certain carbon emission calculation model shows insufficient accuracy in a specific scenario during verification, the amount of sample data for that scenario will be increased, and the model input features will be optimized to improve the model's adaptability to that scenario, reduce measurement bias, and ensure that the calculation accuracy meets the preset requirements.

[0065] For example, the carbon emission measurement deviation rate of the calculation model based on pre-configured rules exceeds 8% during peak industrial production periods (the preset threshold is 5%). Analysis reveals that the instantaneous energy consumption of equipment fluctuates greatly in this scenario, and the original fixed carbon emission factor does not consider the impact of load changes. In this case, it is necessary to supplement the data with hourly energy consumption data, equipment load rate, and corresponding actual carbon emissions during the peak industrial production periods of the past 6 months as samples. The "equipment load rate" is incorporated into the model input features, and the rule formula is optimized to reduce the measurement deviation rate in this scenario to 4.2%.

[0066] In addition, if the calculation time of a certain carbon emission calculation model exceeds expectations, the model structure can be simplified to reduce computational complexity, shorten calculation time, and adapt to the limited computing power and data transmission capabilities of typical virtual terminals.

[0067] For example, the computational model based on the lightweight machine learning algorithm takes 1.2 seconds for a single computation in a typical "standard" virtual terminal (the preset threshold is 0.8 seconds). It can simplify the decision tree depth of the random forest model and reduce the dimension of input features. After optimization, the computation time is reduced to 0.7 seconds, while keeping the measurement deviation rate stable within 3%.

[0068] Furthermore, the model performance was repeatedly validated. After each validation, the version of the calculation model with the lower deviation rate and shorter calculation time was retained until the model performance tended to stabilize. Finally, the model of each type was selected to form a differentiated carbon emission calculation model library with the goal of achieving the best validation results.

[0069] Ultimately, the completed differentiated carbon emission calculation model library can provide adapted calculation models based on the measurement characteristics of different typical virtual terminals, laying the foundation for subsequent evaluation of the adaptability of models to individual terminals and obtaining accurate carbon emission calculation components.

[0070] Based on this, the adaptability of each model in the differentiated carbon emission calculation model library to individual energy metering terminals under corresponding typical virtual terminals is evaluated, and adaptive adjustment is performed in combination with threshold discrimination method to obtain multiple carbon emission calculation components.

[0071] The method provided in this application embodiment includes the following steps: "evaluating the adaptability of each differentiated carbon emission calculation model in the differentiated carbon emission calculation model library to the corresponding energy metering terminal under the typical virtual terminal, and performing adaptive model adjustment on the typical virtual terminal in combination with a threshold discrimination method to obtain multiple carbon emission calculation components".

[0072] A validation dataset is constructed, and the model performance is validated by combining the individual configuration information of the energy metering terminals, including computational accuracy and computational timeliness.

[0073] If the calculation accuracy does not meet the preset threshold, then corresponding data is collected, and reinforcement learning is performed on the differentiated carbon emission calculation model to obtain the carbon emission calculation component;

[0074] If the timeliness of the calculation does not meet the preset threshold, the differentiated carbon emission calculation model is compressed based on knowledge distillation to obtain the carbon emission calculation component.

[0075] In this embodiment of the application, in order to ensure that the differentiated carbon emission calculation model can accurately adapt to the metering characteristics of each individual energy metering terminal, it is necessary to carry out performance verification by constructing a verification dataset and make targeted adjustments in combination with preset threshold discrimination, so as to finally obtain the carbon emission calculation component with the best adaptability, so as to solve the metering deviation or inefficiency problem caused by the mismatch between the model and the characteristics of the individual terminal.

[0076] Specifically, a validation dataset is first constructed, which includes historical energy consumption data, actual carbon emissions, and detailed individual configuration information for each energy metering terminal, such as hardware model, firmware version, computing power limit, and memory capacity.

[0077] For example, for a certain type of smart meter (sampling frequency 5 times / second, sampling accuracy 0.02kWh, supporting 4G transmission), its hourly energy consumption records for the past 6 months, the corresponding measured carbon emission values, and the terminal's CPU model, running memory and other configuration parameters are collected to provide a comprehensive basis for model performance verification.

[0078] Furthermore, the performance of the differentiated carbon emission calculation model was verified bidirectionally using the validation dataset, specifically verifying the model's computational accuracy and timeliness.

[0079] Among them, the verification of calculation accuracy is based on the relative deviation rate between the carbon emission measurement value output by the carbon emission calculation model and the actual carbon emission value. The specific calculation formula is "relative deviation rate = |model prediction value - actual carbon emission amount| / actual carbon emission amount × 100%".

[0080] Simultaneously, a preset threshold is set. If the relative deviation rate of a model prediction for an individual energy metering terminal exceeds the corresponding threshold, the calculation accuracy is deemed substandard. For example, the relative deviation rate threshold can be set at 3% for basic virtual terminals, 2% for standard virtual terminals, and 1% for high-precision virtual terminals.

[0081] Furthermore, the verification of computational timeliness uses the time taken for the energy metering terminal to execute the carbon emission calculation model in a single operation as an indicator. A threshold is set based on the terminal's data transmission capacity. The energy metering terminal's built-in timer records the complete time taken from receiving data to outputting the result. If the time exceeds the threshold, the computational timeliness is deemed unsatisfactory. For example, the transmission capacity threshold can be set as ≤0.1 seconds for basic virtual terminals, ≤0.5 seconds for standard virtual terminals, and ≤1 second for high-precision virtual terminals.

[0082] Based on this, if the calculation accuracy does not meet the preset threshold, the corresponding collected data will be used to perform reinforcement learning on the differentiated carbon emission calculation model.

[0083] Specifically, for individual energy metering terminals with excessive relative deviation rates, real-time energy consumption data, environmental parameters, and equipment operating status for the past 30 days are collected to construct a specialized training subset. Using an online learning approach, the newly collected energy consumption data is divided into an incremental training set and a validation set in an 8:2 ratio. The underlying parameters of the model are frozen, the top-level weights are fine-tuned, and the prediction bias is minimized using a gradient descent algorithm.

[0084] Secondly, if the computational timeliness does not meet the preset threshold, the differentiated carbon emission calculation model is compressed based on knowledge distillation. That is, the output of the complex model is used as a supervision signal to train a more streamlined and lightweight model, retaining the core computational capabilities by transferring "knowledge".

[0085] For example, when a basic virtual terminal uses a pre-configured rule-based calculation model, the initial calculation time is 0.08 seconds (meeting the preset threshold of 0.1 seconds), but the prediction deviation rate during peak electricity consumption periods reaches 4% (the preset threshold is 3%). By collecting 500 sets of energy consumption data during this period and optimizing the segmented carbon emission factors in the rules, such as adjusting the peak period factor from 0.785 to 0.802, the prediction deviation rate is reduced to 2.2% after reinforcement learning.

[0086] In addition, the computation time of the lightweight machine learning algorithm in another high-precision virtual terminal was 1.2 seconds (the preset threshold was 1 second). By reducing the number of neural network layers from 5 to 3 and the number of decision trees from 100 to 50 through knowledge distillation, the computation time was shortened to 0.9 seconds, and the prediction bias rate was maintained at 0.9%.

[0087] Ultimately, through the adaptability evaluation and adaptive adjustment of the above steps, each individual energy metering terminal was provided with a dedicated carbon emission calculation component. This ensured the accuracy of local carbon emission metering while adapting to the computing power and transmission capabilities of typical virtual terminals, providing reliable support for subsequent real-time data collection and metering.

[0088] S120: Upload local energy carbon emission measurement results to the cloud monitoring center, and construct a virtual mapping model based on digital twin technology and the topology information of the physical energy network;

[0089] In this embodiment of the application, in the scenario of digital energy metering, monitoring and tracking, in order to achieve accurate mapping between physical energy networks and virtual spaces, it is necessary to securely transmit local energy carbon emission metering results and construct a virtual mapping model to solve the problems of insufficient energy network visualization and unintuitive data association under the traditional static recording mode.

[0090] Specifically, a secure connection is first established between the energy metering terminal and the cloud monitoring center, and the local energy carbon emission metering results are encrypted using an encrypted transmission protocol to ensure that the data is not tampered with or leaked during transmission.

[0091] Meanwhile, after receiving the local energy carbon emission measurement results at the cloud monitoring center, the integrity and authenticity of the data are verified by checking the data signature and timestamp, thus completing the data reception and storage.

[0092] Furthermore, based on the topological information of the physical energy network, a virtual mapping model based on digital twins is created.

[0093] The topology information includes the location, connection relationships, and operating parameters of energy production facilities, transmission lines, distribution nodes, and end-user equipment. Through 3D modeling technology, the structure and layout of the physical energy network are recreated 1:1 in virtual space, enabling the virtual mapping model to possess the geometric characteristics and logical connections of the physical energy network.

[0094] Based on this, a mapping table is established between energy metering terminals, local energy carbon emission metering results, and a virtual mapping model, incorporating the topology information of the physical energy network. This mapping table clearly records the corresponding position of each energy metering terminal in the virtual mapping model, the association between its collected energy metering results and the corresponding node in the virtual mapping model.

[0095] This step, through secure transmission verification, digital twin modeling, and the establishment of virtual mapping relationships, achieves accurate mapping of physical energy networks to virtual space, providing virtual platform support for dynamic visualization tracking of energy flows and full-process carbon management, and enhancing the intuitiveness and relevance of energy metering and monitoring.

[0096] Step S120 in the method provided in this application embodiment includes:

[0097] Establish a secure connection between the energy metering terminal and the cloud monitoring center, and transmit and verify the local energy carbon emission metering results;

[0098] Based on the topology information of the physical energy network, a virtual mapping model of the physical energy network based on digital twin is created, wherein the virtual mapping model includes at least energy production facilities, transmission lines, distribution nodes, and end-user equipment.

[0099] Based on the topology information, a mapping table is established between the energy metering terminal, the local energy carbon emission metering results, and the virtual mapping model.

[0100] In this embodiment of the application, in order to achieve secure transmission of local energy carbon emission measurement results to the cloud and virtual mapping of physical energy networks, it is necessary to establish secure connections, construct virtual mapping models and associated mapping relationships, so as to lay the foundation for subsequent dynamic visualization of energy flow and carbon footprint tracking.

[0101] Specifically, a secure connection is first established between the energy metering terminal and the cloud monitoring center to transmit and verify the local energy carbon emission metering results.

[0102] The secure connection employs an end-to-end encryption mechanism, such as encrypting the metering data using the national cryptographic SM4 algorithm, and generating a unique digital signature (including terminal identifier, timestamp, and data digest) for each data item to ensure that the data is not tampered with or stolen during transmission.

[0103] Furthermore, after receiving the data, the cloud monitoring center first verifies the legitimacy of the terminal's identity through a digital certificate, then decrypts the data and verifies the signature information. If the signature matches the data digest and the timestamp is within the valid range, the metering data is determined to be complete and valid and stored in the database.

[0104] For example, a resident's terminal uploaded a local energy carbon emission measurement result of 85.3 kg CO2. After encrypted transmission, the cloud monitoring center decrypted and verified that the digital signature was correct, confirming that the measurement data came from the terminal numbered JM-203, and the collection time was 14:30 on June 15, 2025. Finally, the measurement data was stored in the historical record of the corresponding terminal.

[0105] Furthermore, based on the topology information of the physical energy network, a virtual mapping model of the physical energy network based on digital twins is created.

[0106] The topology information includes the geographical location, physical parameters, and connection relationships of energy production facilities, transmission lines, distribution nodes, and end-user equipment.

[0107] For example, energy production facilities can be coal-fired power plants, wind farms, photovoltaic power stations, etc.; transmission lines can be 110kV high-voltage transmission lines, natural gas main pipelines, etc.; distribution nodes can be regional substations, gas pressure regulating stations, etc.; end-user equipment can be industrial boilers, residential air conditioners, etc.; physical parameters can be line length, pipe diameter, rated power, etc.; connection relationships can include line connections between power plants and substations, pipeline branches between pressure regulating stations and users, etc.

[0108] In addition, the virtual mapping model uses existing 3D modeling engines to restore the physical energy network at a 1:1 scale. For example, it can accurately reproduce the spatial layout of two gas boiler rooms, three 10kV transmission lines, one regional substation, and 50 industrial energy-consuming devices in an industrial park in a virtual space, and give the virtual entities the same attribute parameters as the physical devices, such as the rated evaporation capacity of the boiler and the resistance value of the line.

[0109] Meanwhile, the virtual mapping model supports dynamic updates. When new transmission lines are added or user equipment is replaced in the physical energy network, the virtual mapping model can adjust its structure synchronously through topology information to ensure that the virtual space accurately replicates the physical energy network in real time.

[0110] Based on this, a mapping relationship table is established between energy metering terminals, local energy carbon emission metering results, and virtual mapping models by combining the topology information of the physical energy network.

[0111] Specifically, the mapping table records the relationship between the three in the form of structured data, including the unique identifier of the energy metering terminal, the three-dimensional coordinates of the terminal in the virtual mapping model, the ID of the corresponding virtual entity, the timestamp of the local energy carbon emission metering result, and the data index.

[0112] For example, an energy metering terminal numbered DL-042 is installed at the physical location of "Transmission Line-10 Segment". Its coordinates in the virtual mapping model are (X:3520.6, Y:1280.3, Z:50.2), and the corresponding virtual entity ID is "Line-10". The mapping relationship table will record "DL-042→Line-10→2024-06-15-15:00→92.7kgCO2→Data Storage Index ID:87621", realizing the precise binding of physical terminals, real-time metering data and virtual entities.

[0113] Ultimately, through the above steps, the local energy carbon emission measurement results are securely transmitted to the cloud monitoring center, the physical energy network is accurately mapped to the virtual space, and the three form an organic whole through a mapping relationship table. This process not only ensures the security and integrity of data transmission but also realizes the digital replication of the physical energy network, providing a reliable virtual carrier and data association foundation for subsequently synchronizing measurement data to the virtual model and conducting dynamic visualization analysis of energy flows.

[0114] For example, the physical energy network of an industrial park includes a photovoltaic power station, a 220kV transmission line, a regional substation, and 10 factory users. The topology information shows that the photovoltaic power station is connected to the substation through the transmission line, and then the substation branches the power to each factory.

[0115] Based on this virtual mapping model, the virtual entity of the photovoltaic power station has the same installed capacity (50MW) and land area (200 acres) as the physical power station, the virtual length of the transmission line (5km) is completely matched with the physical line, and the virtual energy-consuming equipment of each factory corresponds one-to-one with the actual model.

[0116] Meanwhile, the mapping table clearly records that the metering terminal “G C-001” installed at the photovoltaic power station outlet corresponds to the virtual entity “PV-Plant-Out”, and the 1200kgCO2 / h (equivalent carbon emission) data uploaded by it is associated with the real-time parameter panel of the virtual entity; the terminal “GY-012” of Factory B corresponds to the virtual entity “FactoryB-Boiler-2”, and its metering results are directly synchronized to the carbon emission index of the virtual boiler.

[0117] S130: Synchronize the local energy carbon emission metering to the virtual mapping model, and combine topology analysis and particle tracking technology to visualize and track the metered data, generating a dynamic visualization map of energy flow.

[0118] In this embodiment of the application, in the scenario of digital energy metering, monitoring and tracking, in order to achieve intuitive presentation and dynamic tracking of energy flow and carbon emission data, it is necessary to solve the problems of abstract energy flow process and unintuitive carbon footprint tracking in traditional metering through data mapping, path analysis and particle visualization.

[0119] Specifically, the standardized local energy carbon emission measurement results are first mapped to the virtual mapping model based on the established mapping relationship table.

[0120] Furthermore, topology analysis is performed based on the topology information of the physical energy network to determine the energy flow paths in the virtual mapping model. This topology analysis uses graph theory algorithms from existing technologies to analyze the connectivity relationships between entities in the virtual mapping model, identifying the complete energy link from production to consumption.

[0121] Based on this, and combined with the energy flow path, the energy flow of the physical energy network is particleized into the motion of energy particles in the virtual mapping model, and particle tracking is performed. The particle tracking results are recorded as a dynamic visualization map of energy flow.

[0122] The particleization process abstracts each unit of energy into a virtual particle with attributes, assigning it parameters such as carbon emission value and source identifier. Particle tracking simulates the movement of particles along the energy flow path in real time using an animation engine.

[0123] In addition, the dynamic visualization map uses a time axis as a reference to display the particle trajectory, the number of particles passing through each node per unit time, and the cumulative carbon emission value in real time, so that users can intuitively observe the spatiotemporal distribution of energy flow and changes in carbon emissions.

[0124] This step achieves linkage between virtual and real data through data mapping, clarifies energy flow paths through topology analysis, and transforms the abstract energy flow process into an intuitive dynamic visualization map through particle tracking. This provides a visual analytical basis for subsequent carbon footprint tracing and digital measurement, and enhances the intuitiveness and operability of energy monitoring.

[0125] Step S130 in the method provided in this application embodiment includes:

[0126] Based on the mapping table, the standardized local energy carbon emission measurement results are mapped to the virtual mapping model;

[0127] Based on the topology information, topology analysis is performed to determine the energy flow path in the virtual mapping model;

[0128] By combining the energy flow path, the energy flow of the physical energy network is particleized into the motion of energy particles in the virtual mapping model, and particle tracking is performed. The particle tracking results are recorded as a dynamic visualization map of energy flow.

[0129] In this embodiment of the application, in order to achieve dynamic and visual tracking of energy flow and carbon emission data, it is necessary to transform the abstract energy flow process into an intuitive virtual scene through data mapping, path analysis and particle simulation, so as to provide visual support for carbon footprint tracing and full-process carbon emission measurement.

[0130] Specifically, the standardized local energy carbon emission measurement results are first mapped to the virtual mapping model based on the constructed mapping table.

[0131] The standardization process includes unifying the data format, that is, unifying the energy consumption unit of different terminals to kWh and the carbon emission unit to kgCO2; calibrating the timestamp to ensure that the data is synchronized with the time dimension of the virtual model; and removing outliers that exceed the reasonable range to avoid abnormal data interfering with the accuracy of the virtual mapping model.

[0132] For example, the local energy carbon emission metering result uploaded by an industrial terminal to the cloud monitoring center is "Update time 2025-07-01-09:00, current energy consumption 200kWh, cumulative carbon emission 157kgCO2". After standardization, it is matched to the virtual entity "Factory-C-MainLine" in the virtual mapping model through the mapping relationship table, and the carbon emission parameter panel of the entity is updated in real time to ensure that the energy consumption and carbon emission data of this node in the virtual mapping model are completely synchronized with the metering results of the physical terminal, so as to provide accurate basic data for subsequent energy flow path analysis and particle tracking.

[0133] Furthermore, topology analysis is performed based on the topology information of the physical energy network to determine the energy flow paths in the virtual mapping model.

[0134] Among them, topology analysis uses the shortest path algorithm and connectivity analysis method in existing graph theory to analyze the connection relationship between entities in the virtual mapping model, such as the line connection between energy production facilities and distribution nodes, and the branch relationship between distribution nodes and end users, so as to identify the complete link of energy from production to consumption.

[0135] For example, starting from "WindFarm-01" (virtual entity of wind farm), by analyzing its line connection attributes with "Substation-03" (virtual entity of substation), including line capacity, whether it is connected, etc., and then tracing the power distribution lines between the substation and "ResidentialArea-B" (virtual entity of residential area), the main energy flow path of "wind farm → substation → residential area" is finally determined, and the transmission efficiency and carbon emission coefficient of each node in the path are marked, such as line transmission loss rate of 3% and wind farm equivalent carbon emission coefficient of 0.05kgCO2 / kWh, etc.

[0136] Based on this, by combining the energy flow path, the energy flow particles of the physical energy network are transformed into the motion of energy particles in the virtual mapping model, the particles are tracked, and the recorded particle tracking results are transformed into a dynamic visualization map of energy flow.

[0137] The particle-based processing involves abstracting each unit of energy into a virtual particle carrying attribute information, including energy type, carbon emission value, source identifier, and timestamp. Particle tracking then uses existing animation rendering engines to simulate the particle's motion along the energy flow path in real time. For example, 1 kWh of electricity is assigned a carbon emission value of 0.785 kg CO2, and a source identifier of "coal-fired power plant #5" is set.

[0138] For example, the particles representing wind power start from the virtual entity "WindFarm-01" and move along the virtual entity of the transmission line to "Substation-03" at a speed matching the actual transmission rate. After arriving at the substation, the particles split into multiple groups according to the power distribution ratio and flow to different residential user nodes.

[0139] Meanwhile, the particle color changes dynamically with the carbon emission value, with low-carbon-emission particles appearing blue and high-carbon-emission particles appearing orange, and the motion trajectory will be displayed in real time in the virtual mapping model.

[0140] In addition, the dynamic visualization map of energy flow is based on the time axis and integrates particle tracking results to form a dynamic picture that includes energy flow path, particle flow rate (the number of particles passing through per unit time), and real-time carbon emission value.

[0141] The dynamic visualization map of energy flow supports multi-dimensional interaction. Users can zoom in to view the particle distribution details of local nodes, such as the cumulative particle amount at the entrance of a residential building corresponding to 500kWh of energy consumption. Users can use the timeline slider to trace back the energy flow status at historical moments, such as viewing the peak particle movement in the factory area at 08:00. Users can use the filtering function to focus on the particle trajectory of a specific energy type, such as displaying the transmission path of thermal power particles separately.

[0142] Meanwhile, real-time statistical data is annotated next to each virtual entity in the dynamic visualization map of energy flow, such as the particle throughput rate (97%) and cumulative carbon emissions (1200kgCO2) of "Substation-03", to intuitively reflect the relationship between energy flow and carbon emissions.

[0143] For example, during a certain period of time, “CoalPlant-02” (coal-fired power plant) transmits electrical energy to “IndustrialParkA” (industrial park). In the virtual mapping model, orange particles (representing high-carbon emission coal-fired power plants) continuously flow out from the virtual entity of the power plant and move towards the park along the transmission line. “Transformer-05” (transformer virtual entity) on the path shows the particle loss rate (2%) and the corresponding carbon emission loss (5kgCO2 / minute).

[0144] Meanwhile, in the dynamic visualization map of energy flow, the particle density of the virtual area of ​​the industrial park increases over time. By 10:00, the cumulative particle amount corresponds to 8000kWh of energy consumption, and the total carbon emissions are marked as 6280kgCO2, which is consistent with the sum of the metering results of each terminal in the park during this period.

[0145] Ultimately, through the above steps, the local energy carbon emission measurement results are accurately mapped to the virtual mapping model. The energy flow process is transformed into an intuitive dynamic visualization map of energy flow through particle tracking, realizing the visual correlation between energy flow and carbon emissions, and providing a traceable virtual scenario for subsequent carbon footprint tracing.

[0146] S140: Track carbon emission footprints based on dynamic visualization maps of energy flows, and realize digital energy metering based on the tracking results.

[0147] In this embodiment of the application, in the scenario of digital energy metering, monitoring and tracking, in order to achieve accurate traceability and quantitative measurement of the entire carbon emission process, it is necessary to transform the dynamic information of the dynamic visualization map of energy flow into quantifiable carbon emission data by traversing the nodes of the metering object, tracking the carbon footprint and statistical analysis, so as to solve the problems of incomplete traceability, data fragmentation and inaccurate cost accounting in traditional carbon emission measurement.

[0148] Specifically, the process first traverses multiple measurement object nodes and then traces the carbon emission footprint based on the dynamic visualization map of energy flow.

[0149] The metering target nodes include corresponding virtual entities in the virtual mapping model, such as energy production facilities, transmission lines, distribution nodes, and end-user equipment. By tracing the time axis and particle trajectory of the dynamic visualization map of energy flow, each node is traversed in reverse or forward along the energy flow path to trace the source and propagation path of carbon emissions.

[0150] Furthermore, based on the source tracing results, carbon footprint extraction and full-process carbon emission quantification statistics are performed to measure the digital carbon emission cost.

[0151] Among them, carbon footprint extraction is the extraction of carbon emission data from each measurement object node from the source tracing results, including initial carbon emissions in the energy production stage, carbon emissions lost during transmission, and carbon emissions from use in the end consumption stage.

[0152] In addition, carbon emission quantification statistics calculate the total carbon emissions of the entire process by summing up the carbon emissions of each measurement object, and calculate the digital carbon emission cost by combining the carbon emission unit price (unit: yuan / kgCO2).

[0153] This step uses carbon footprint tracing to clarify the source and destination of carbon emissions. Combined with quantitative statistics, it achieves accurate measurement of carbon emissions and costs throughout the entire process, transforming visualized dynamic information into structured digital results. This provides a reliable digital basis for energy management, carbon emission cost accounting, and emission reduction decisions, and improves the closed-loop management of energy metering and monitoring.

[0154] Step S140 in the method provided in this application embodiment includes:

[0155] Traverse multiple metering object nodes and trace carbon emission footprints based on dynamic visualization maps of energy flow.

[0156] Based on the source tracing results, carbon footprint is extracted and carbon emissions are quantified and statistically analyzed throughout the entire process to measure the digital cost of carbon emissions.

[0157] In this embodiment of the application, in order to achieve precise control over the entire process of carbon emissions of the objects to be metered and billed, it is necessary to carry out carbon footprint tracing and quantitative statistics for the nodes of the metered objects, and transform the dynamic information in the visualization map into digital data that can be used for billing, so as to solve the problems of vague carbon emission responsibility definition and inaccurate billing basis in traditional metering.

[0158] Specifically, the process first traverses multiple measurement object nodes and then traces the carbon emission footprint based on the dynamic visualization map of energy flow.

[0159] Among them, the metering object node refers to the object in the physical energy network that needs to be metered and billed, such as end users such as industrial enterprises, commercial complexes, and residential communities, as well as some energy distribution nodes that need to be accounted for separately, such as regional substations.

[0160] In specific source tracing, the particle trajectory backtracking capability of the dynamic visualization map of energy flow is used to trace the energy source and corresponding carbon emission path from the node being measured.

[0161] For example, for the metering object node "Mall-B" (commercial complex), the source of energy particles converging to this node can be viewed through the dynamic visualization map of energy flow: 30% comes from "Sol arFarm-04" (photovoltaic power plant) and 70% comes from "CoalPlant-03" (coal-fired power plant).

[0162] At the same time, it can track the particles as they pass through “Substation-08” (substation) and “Line-22” (transmission line) during transmission to clarify the carbon emission contribution at each stage, such as the initial carbon emissions from coal-fired power plants and the carbon emissions from transmission line losses.

[0163] Furthermore, based on the source tracing results, carbon footprint is extracted and carbon emissions are quantified and statistically analyzed throughout the entire process to measure the digital carbon emission costs.

[0164] The extraction of carbon footprint requires distinguishing between direct carbon emissions (carbon emissions generated by the operation of its own energy-consuming equipment) and indirect carbon emissions (carbon emissions from energy production and transmission) of the measurement object node, and determining the portion of carbon emissions included in the measurement scope according to the billing rules.

[0165] In addition, quantitative statistics calculate the digital carbon emission cost by accumulating the carbon emissions at each stage of the traceability path and combining it with a preset carbon emission unit price.

[0166] For example, the traceability results of the metering object node "Factory-E" show that the direct carbon emission is 200kgCO2, the carbon emission allocated to the energy production process is 500kgCO2, and the carbon emission allocated to the transmission process is 50kgCO2. If, according to the relevant agreement, the billing rules cover all processes and the carbon emission unit price is 0.15 yuan / kgCO2, then its digital carbon emission cost is (200+500+50)×0.15=112.5 yuan.

[0167] In another example, if the source tracing of the metered object node "Residential-A" (residential community) shows that all of its energy comes from "WindFarm-06" (wind farm) and "GasSupply-02" (gas supply station).

[0168] The carbon footprint path for wind power is “WindFarm-06→Substation-10→Residential-A-Elec”, corresponding to an equivalent carbon emission of 80 kg CO2 (wind power has a low carbon emission coefficient); the carbon footprint path for gas is “GasSupply-02→Pipeline-15→Residential-A-Gas”, corresponding to a carbon emission of 300 kg CO2.

[0169] Based on this, according to the billing rules for residential electricity and gas, carbon emission costs are included in energy costs. Calculated at 0.12 yuan / kgCO2, the digital carbon emission cost of this residential community is (80+300)×0.12=45.6 yuan, which will be settled together with energy costs.

[0170] Meanwhile, during the quantitative statistical process, the system will automatically associate the identity information, traceability time range, and carbon emission details of the measurement object nodes, and generate a digital measurement report.

[0171] The identity information of the measurement object node includes user ID, address and other information; the traceability time range can be divided into monthly, quarterly and annual; the digital measurement report includes carbon footprint path chart, carbon emission ratio of each link, total carbon emission and cost calculation basis, so as to facilitate user query and audit in the later stage.

[0172] For example, the quarterly carbon emission metering report of "Factory-F" shows that its total carbon emissions are 12,000 kg CO2, of which 70% comes from coal-fired power and 30% from natural gas, with a total cost of 1,800 yuan. The digital metering report can be directly connected to the billing platform to complete the fee deduction.

[0173] Ultimately, through the above steps of carbon footprint tracing, carbon emission extraction, quantitative statistics, and cost accounting, a closed-loop management system from carbon footprint tracing to cost measurement has been achieved, ensuring accurate and transparent carbon emission billing at the measurement target nodes, and providing a scientific quantitative basis and decision-making basis for energy carbon management.

[0174] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects:

[0175] This application proposes a method for digital energy metering, monitoring, and tracking. First, the metering characteristics of each energy metering terminal are acquired, and based on this, the terminals are clustered and typical virtual terminals are defined. Then, a differentiated carbon emission calculation model library is constructed for these typical virtual terminals, incorporating pre-configured rules, regression analysis, and lightweight machine learning algorithms. After performance verification and optimization, carbon emission calculation components are adapted for each individual terminal, which then collects energy consumption data and performs local energy carbon emission metering. The metering results are then uploaded to a cloud monitoring center, ensuring reliable data transmission through a secure connection. Simultaneously, based on the physical energy network topology information, a virtual mapping model is constructed using digital twin technology, establishing a mapping relationship table between terminals, metering results, and the virtual mapping model. Next, the metering results are synchronized to the virtual mapping model, and the energy flow path is determined through topology analysis. Particle tracking technology is used to particleize the energy flow, generating a dynamic visualization map of the energy flow to intuitively present the correlation between energy flow and carbon emissions. Finally, carbon footprint tracing is performed by traversing the metering object nodes according to the map, extracting carbon emission data from each stage and performing quantitative statistics to accurately measure the digital carbon emission cost, thereby achieving digital metering, monitoring, and tracking of the entire energy process.

[0176] The method provided in this application, through the technical solution of "local energy carbon emission metering - virtual space mapping - dynamic visualization tracking - full-process traceability metering", solves the problems of metering deviation caused by differences in terminal characteristics, abstract and unintuitive energy flow process, and incomplete carbon footprint traceability in traditional energy metering. It realizes full-process digital management from energy consumption collection to carbon emission cost accounting, and improves the accuracy, intuitiveness and completeness of energy metering and monitoring.

[0177] Example 2, as shown in the appendix Figure 2 As shown, based on the inventive concept of the energy digital metering, monitoring and tracking method provided in Embodiment 1, this application also provides an energy digital metering, monitoring and tracking system, specifically including:

[0178] The local energy carbon emission metering module 01 is used to collect energy consumption data in real time through multiple energy metering terminals deployed in the physical energy network, and to perform local energy carbon emission metering through the carbon emission calculation component embedded in the energy metering terminal.

[0179] The cloud-based virtual mapping modeling module 02 is used to upload local energy carbon emission measurement results to the cloud monitoring center and construct a virtual mapping model based on digital twin technology and combined with the topology information of the physical energy network.

[0180] The energy flow dynamic visualization module 03 is used to synchronize the local energy carbon emission metering to the virtual mapping model, and combine topology analysis and particle tracking technology to visualize and track the metered data, generating an energy flow dynamic visualization map.

[0181] The carbon footprint tracing module 04 is used to trace carbon emission footprints based on dynamic visualization maps of energy flows and to achieve digital energy metering based on the tracing results.

[0182] In one embodiment, the local energy carbon emission metering module 01 is also used for:

[0183] Obtain the metering characteristics of the energy metering terminal at each energy metering point, wherein the metering characteristics include at least sampling frequency, sampling accuracy, and data transmission capability;

[0184] Based on the metering characteristics, multiple energy metering terminals are clustered, and multiple typical virtual terminals are defined accordingly.

[0185] Differentiated carbon emission calculation models are constructed for multiple typical virtual terminals, forming a differentiated carbon emission calculation model library;

[0186] The adaptability of each differentiated carbon emission calculation model in the differentiated carbon emission calculation model library to the corresponding energy metering terminal under the typical virtual terminal is evaluated, and the typical virtual terminal is adaptively adjusted by combining the threshold discrimination method to obtain multiple carbon emission calculation components.

[0187] In one embodiment, the cloud-based virtual mapping modeling module 02 is also used for:

[0188] Establish a secure connection between the energy metering terminal and the cloud monitoring center, and transmit and verify the local energy carbon emission metering results;

[0189] Based on the topology information of the physical energy network, a virtual mapping model of the physical energy network based on digital twin is created, wherein the virtual mapping model includes at least energy production facilities, transmission lines, distribution nodes, and end-user equipment.

[0190] Based on the topology information, a mapping table is established between the energy metering terminal, the local energy carbon emission metering results, and the virtual mapping model.

[0191] In one embodiment, the energy flow dynamic visualization module 03 is also used for:

[0192] Based on the mapping table, the standardized local energy carbon emission measurement results are mapped to the virtual mapping model;

[0193] Based on the topology information, topology analysis is performed to determine the energy flow path in the virtual mapping model;

[0194] By combining the energy flow path, the energy flow of the physical energy network is particleized into the motion of energy particles in the virtual mapping model, and particle tracking is performed. The particle tracking results are recorded as a dynamic visualization map of energy flow.

[0195] In one embodiment, the carbon footprint tracing module 04 is also used for:

[0196] Traverse multiple metering object nodes and trace carbon emission footprints based on dynamic visualization maps of energy flow.

[0197] Based on the source tracing results, carbon footprint is extracted and carbon emissions are quantified and statistically analyzed throughout the entire process to measure the digital cost of carbon emissions.

[0198] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0199] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0200] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for digital energy metering, monitoring, and tracking, characterized in that, include: Energy consumption data is collected in real time through multiple energy metering terminals deployed in the physical energy network, and local energy carbon emission metering is performed through the carbon emission calculation component embedded in the energy metering terminal. Upload local energy carbon emission measurement results to the cloud monitoring center, and construct a virtual mapping model based on digital twin technology and the topology information of the physical energy network; The local energy carbon emission metering is synchronized to the virtual mapping model, and the metered data is visualized and tracked using topology analysis and particle tracking technology to generate a dynamic visualization map of energy flow. Carbon emission footprints are traced and tracked based on dynamic visualization maps of energy flows, and digital energy metering is achieved based on the traceability results; Energy consumption data is collected in real time through multiple energy metering terminals deployed in the physical energy network, and local energy carbon emission metering is performed through the carbon emission calculation component embedded in the energy metering terminal. Prior to this, the following steps were taken: Obtain the metering characteristics of the energy metering terminal at each energy metering point, wherein the metering characteristics include at least sampling frequency, sampling accuracy, and data transmission capability; Based on the metering characteristics, multiple energy metering terminals are clustered, and multiple typical virtual terminals are defined accordingly. Differentiated carbon emission calculation models are constructed for multiple typical virtual terminals, forming a differentiated carbon emission calculation model library; The adaptability of each differentiated carbon emission calculation model in the differentiated carbon emission calculation model library to the corresponding energy metering terminal under the typical virtual terminal is evaluated, and the typical virtual terminal is adaptively adjusted by combining the threshold discrimination method to obtain multiple carbon emission calculation components. Upload local energy carbon emission measurement results to the cloud monitoring center, and based on digital twin technology, combine the topology information of the physical energy network to construct a virtual mapping model, including: Establish a secure connection between the energy metering terminal and the cloud monitoring center, and transmit and verify the local energy carbon emission metering results; Based on the topology information of the physical energy network, a virtual mapping model of the physical energy network based on digital twin is created, wherein the virtual mapping model includes at least energy production facilities, transmission lines, distribution nodes, and end-user equipment. Based on the topology information, a mapping table is established between the energy metering terminal, the local energy carbon emission metering results, and the virtual mapping model.

2. The energy digital metering, monitoring, and tracking method as described in claim 1, characterized in that, Differentiated carbon emission calculation models are constructed for multiple typical virtual terminals, forming a differentiated carbon emission calculation model library, including: Based on historical prior knowledge, define the initial model category mapping relationship; Multiple differentiated carbon emission calculation models are constructed based on the initial model category mapping relationship, and model performance is verified. The multiple differentiated carbon emission calculation models include at least: Computational models based on pre-configured rules; computational models based on regression analysis; computational models based on lightweight machine learning algorithms; Based on the model performance verification results, multiple differentiated carbon emission calculation models are expanded and constructed, and model performance verification is repeated. The differentiated carbon emission calculation model library is selected and output with the optimal model performance verification results as the goal.

3. The energy digital metering, monitoring, and tracking method as described in claim 2, characterized in that, The adaptability of each differentiated carbon emission calculation model in the differentiated carbon emission calculation model library to the corresponding energy metering terminal under the typical virtual terminal is evaluated, and the typical virtual terminal is adaptively adjusted using a threshold discrimination method to obtain multiple carbon emission calculation components, including: A validation dataset is constructed, and the model performance is validated by combining the individual configuration information of the energy metering terminals, including computational accuracy and computational timeliness. If the calculation accuracy does not meet the preset threshold, then corresponding data is collected, and reinforcement learning is performed on the differentiated carbon emission calculation model to obtain the carbon emission calculation component; If the timeliness of the calculation does not meet the preset threshold, the differentiated carbon emission calculation model is compressed based on knowledge distillation to obtain the carbon emission calculation component.

4. The energy digital metering, monitoring, and tracking method as described in claim 1, characterized in that, The local energy carbon emission metering is synchronized to the virtual mapping model, and the metered data is visualized and tracked using topology analysis and particle tracking technology to generate a dynamic visualization map of energy flow, including: Based on the mapping table, the standardized local energy carbon emission measurement results are mapped to the virtual mapping model; Based on the topology information, topology analysis is performed to determine the energy flow path in the virtual mapping model; By combining the energy flow path, the energy flow of the physical energy network is particleized into the motion of energy particles in the virtual mapping model, and particle tracking is performed. The particle tracking results are recorded as a dynamic visualization map of energy flow.

5. The energy digital metering, monitoring, and tracking method as described in claim 4, characterized in that, Carbon emission footprints are traced and tracked based on dynamic visualization maps of energy flows, and digital energy metering is achieved based on the traceability results, including: Traverse multiple metering object nodes and trace carbon emission footprints based on dynamic visualization maps of energy flow. Based on the source tracing results, carbon footprint is extracted and carbon emissions are quantified and statistically analyzed throughout the entire process to measure the digital cost of carbon emissions.

6. An energy digital metering, monitoring, and tracking system, characterized in that, The system is used to execute the energy digital metering, monitoring and tracking method according to any one of claims 1-5, the system comprising: The local energy carbon emission metering module is used to collect energy consumption data in real time through multiple energy metering terminals deployed in the physical energy network, and to perform local energy carbon emission metering through the carbon emission calculation component embedded in the energy metering terminal. The cloud-based virtual mapping modeling module is used to upload local energy carbon emission measurement results to the cloud monitoring center and construct a virtual mapping model based on digital twin technology and the topology information of the physical energy network. The energy flow dynamic visualization module is used to synchronize the local energy carbon emission measurement to the virtual mapping model, and combine topology analysis and particle tracking technology to visualize and track the data obtained by measurement, and generate a dynamic visualization map of energy flow. The carbon footprint traceability module is used to trace carbon emission footprints based on dynamic visualization maps of energy flows, and to achieve digital energy metering based on the traceability results.

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