Digital twin carbon data analysis system and method based on Internet of Things

Abstract

The invention discloses a digital twinning carbon data analysis system and method based on the Internet of Things, and relates to the technical field of digital twinning. The system comprises an Internet of Things data acquisition module, a digital twinborn analogue simulation module, an energy carbon data analysis module and a carbon market analysis module. The method comprises the following steps: acquiring Internet of Things equipment data, and transmitting the Internet of Things equipment data to a platform support layer according to a preset communication protocol; the data is deeply analyzed and preprocessed in the platform support layer and then is transmitted to the data medium station; the data is integrated, stored, calculated and shared in the data middle platform, and the data is transmitted to the application system layer; the data processed by the application system layer is sent to the comprehensive service layer for analysis, verification and processing; and the data is packaged into a format suitable for transmission in the comprehensive service layer and is transmitted to the user and terminal layer. According to the invention, fine management of the full life cycle of energy carbon data from data acquisition to data visual display to data analysis and deduction to report generation to investment scheme generation can be realized.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a digital twin carbon data analysis system and method based on the Internet of Things. Background Art

[0002] Digital twin technology has been widely applied and developed. It can construct a simulation model of physical objects through digital means, enabling understanding, analysis, and optimization of physical entities. Digital twin technology fully utilizes data from physical models, sensor updates, and operational history, integrating multidisciplinary, multi-physics, multi-scale, and multi-probability simulation processes to complete mapping in virtual space, thereby reflecting the entire lifecycle of the corresponding physical equipment.

[0003] However, enterprises lack the ability to directly connect and fine-tune management of various energy-consuming devices, such as campus lighting, water and electricity meters, and air conditioners, through digital twin technology. They also lack the ability to intelligently convert, visualize, and analyze energy consumption data, photovoltaic power generation data, voluntary carbon reduction data, and carbon emissions data. Furthermore, they lack the ability to accurately predict carbon reduction plans and accurately assess the carbon market. IoT-based digital twin carbon data analysis applications can effectively address these challenges faced by energy-consuming entities, such as enterprises and parks. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a digital twin carbon data analysis system and method based on the Internet of Things.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A digital twin carbon data analysis system based on the Internet of Things, including an Internet of Things data acquisition module, a digital twin simulation module, an energy-carbon data analysis module and a carbon market analysis module; the Internet of Things data acquisition module collects Internet of Things device data, wherein the Internet of Things device data includes building Internet of Things data, and the Internet of Things devices include building Internet of Things devices; the digital twin simulation module completes the visualization display of the real location of the building Internet of Things devices through analysis of the building Internet of Things data, realizes real-time control of the building Internet of Things devices, and displays energy-carbon data at the same time; the energy-carbon data analysis module analyzes the data collected by the Internet of Things devices, combines the digital twin model to perform virtual synchronous carbon reduction deduction on the data, and combines historical data and real-time data to predict carbon emissions within a certain period of time; combines energy consumption to form energy-saving reports and carbon emission reports; the carbon market analysis module obtains and analyzes real-time carbon prices in the carbon market to provide the best investment plan for the company's energy consumption and carbon emission situation.

[0007] Based on the above technical solution, further, the Internet of Things device includes at least a water and electricity meter device, an air conditioning device, a lighting device and a photovoltaic device; wherein the water and electricity meter device, the air conditioning device, the lighting device and the photovoltaic device respectively correspond to corresponding sensors.

[0008] Based on the above technical solution, further, the building IoT data at least includes access control status, air conditioning status, water and electricity meter status, photovoltaic equipment status and lighting status.

[0009] A digital twin carbon data analysis method based on the Internet of Things adopts a digital twin carbon data analysis system based on the Internet of Things, including the following steps: obtaining Internet of Things device data, processing the data, and transmitting it to the platform support layer according to a preset communication protocol; after the data is pre-processed at the platform support layer, it is transmitted to the data middle platform; after the data is integrated, stored, calculated and shared at the data middle platform, the data is transmitted to the application system layer; the data processed by the application system layer is sent to the comprehensive service layer for parsing, verification and processing; the data is encapsulated into a format suitable for transmission at the comprehensive service layer, and transmitted to the user and terminal layer.

[0010] Based on the above technical solution, further, the preprocessing includes data cleaning, data compression and data encryption.

[0011] Based on the above technical solution, further, the data middle platform includes a data acquisition platform and an energy and carbon data analysis platform.

[0012] Based on the above technical solution, further, according to the needs of the application system layer, relevant data is extracted to prepare for the next transmission; during data transmission, the data exchange needs between the data middle platform and the application system layer are met according to the transmission protocol.

[0013] Based on the above technical solution, the application system layer further obtains data according to actual needs, and the data is used for smart management, smart control and carbon reduction decision-making in the application system layer; among them, smart management includes access control management, conference room management, lighting management, equipment management, energy and carbon management and asset management; smart control includes lighting control, water saving, three-dimensional model simulation control and access control; carbon reduction decision-making includes carbon reduction reporting, carbon emission reporting and carbon market analysis.

[0014] Based on the above technical solution, data is further mined and processed in the comprehensive service layer. Data mining uses IoT devices to collect the park's energy consumption and carbon emission data in real time, cleans the collected raw data, removes duplicate, erroneous, and incomplete data, converts data in different formats and units into a unified format and unit, and integrates data from different data sources to form a complete energy-carbon data set. By constructing a multidimensional analysis model for energy consumption data with classification and sub-items, the energy-carbon data is deeply analyzed to understand the energy consumption and carbon emissions of the park in different time periods. Using preset statistical sub-items and energy-saving rules, key high-energy-consuming equipment is intelligently diagnosed, and energy efficiency diagnosis and optimization suggestions are proposed.

[0015] Based on the above technical solution, further, during the transmission process, the integrated service layer uses a hash algorithm to add a data integrity verification mechanism, ensuring the integrity and consistency of the data by calculating and comparing the hash value of the data. At the same time, it provides three flexible transmission methods: real-time transmission, batch transmission, and message queue, to adapt to the needs of different scenarios and make reasonable changes.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] (1) The digital twin carbon data analysis application based on the Internet of Things of the present invention can realize the refined management of the entire life cycle of energy and carbon data from data collection to data visualization display to data analysis and deduction to report generation to investment plan generation through the comprehensive management of the Internet of Things data acquisition module, digital twin simulation module, energy and carbon data analysis module, and carbon market analysis module.

[0018] (2) The system and method of the present invention break the singleness of application scenarios and can span different industries, such as energy, manufacturing, transportation and other vertical industries, and can realize adaptive switching of scenarios; support multiple data collection methods such as modbus and mqtt to ensure that the system has higher scalability; integrate data from different fields such as emission-controlled enterprises and parks, conduct all-round data integration, and improve the system visualization by integrating multiple aspects of data such as structured data, semi-structured data, and unstructured data in combination with digital twins; the database uses the domestically produced database Renmin University of China Golden Warehouse to improve the security of data storage; combine 24 industry standards to perform extremely small error calculations on the conversion of energy use into carbon emissions; obtain and analyze real-time carbon prices in the carbon market to ensure the authenticity of investment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the system of the present invention;

[0020] Figure 2 The figure is a simplified flow chart of the method of the present invention. DETAILED DESCRIPTION

[0021] It is worth noting that the raw materials used in the present invention are all common commercially available products, and their sources are not specifically limited.

[0022] The following raw material sources are illustrative:

[0023] Example 1

[0024] Combine Figure 1 As shown, this embodiment provides an IoT-based digital twin carbon data analysis system, comprising an IoT data acquisition module, a digital twin simulation module, an energy-carbon data analysis module, and a carbon market analysis module. These modules communicate with each other. The IoT-based digital twin carbon data analysis application, through comprehensive management of these modules, enables refined management of the entire energy-carbon data lifecycle, from data acquisition to data visualization, data analysis and deduction, report generation, and investment plan creation.

[0025] The IoT data acquisition module collects IoT device data, including building IoT data, and IoT devices including building IoT devices. These IoT devices include, but are not limited to, water and electricity meters, air conditioning units, lighting units, and photovoltaic units. Each of these meters, air conditioning units, lighting units, and photovoltaic units has corresponding built-in sensors, such as water and electricity sensors, capable of real-time monitoring of meter readings, water and electricity usage, and other information, providing data support for data mining, monitoring, and analysis. The building IoT data includes at least access control, air conditioning, and lighting conditions.

[0026] The digital twin simulation module analyzes building IoT data to visualize the actual location of equipment, achieve real-time control of the building, and display energy and carbon data. Specifically, it can visualize the actual location of various sensors such as water and electricity meters, and achieve real-time control of access control, air conditioning, and lighting.

[0027] The energy-carbon data analysis module analyzes the data collected by IoT devices, combines the digital twin model to perform virtual synchronous carbon reduction deduction on the data, and combines historical data and real-time data to predict carbon emissions within a certain period of time; energy-saving reports and carbon emission reports are formed in combination with energy consumption conditions; among them, the digital twin model is an existing means, and the digital twin model is a way to implement digital twin technology. It presents physical objects in virtual space in a digital way, that is, it creates virtual models for physical objects in a digital way to simulate their behavioral characteristics in the real environment. It is an integrated system of data, models and analysis tools applied to the entire product life cycle; it can integrate the manufacturing process in production and realize the digitalization of the entire process from basic materials, product design, process planning, production planning, manufacturing execution to use and maintenance. No more records will be made here. For example, different scenarios or plans are set, such as changing the energy structure, optimizing equipment operation, and improving process flows, and the set scenarios are simulated. By adjusting the parameters and conditions in the model, the carbon emissions of physical objects and changes in key indicators such as energy efficiency under different scenarios are observed. The simulation results are evaluated, and the emission reduction effects, economic benefits, and social benefits under different scenarios are analyzed. By comparing the advantages and disadvantages of different scenarios, the optimal emission reduction plan is selected. By accumulating and processing historical data, differential equations are established to describe system changes. The model parameters are solved using the least squares method. The model accuracy is verified through residual and posterior difference tests to predict carbon emissions within a certain period of time in the future.

[0028] The carbon market analysis module obtains and analyzes real-time carbon prices from the carbon market to provide optimal investment solutions based on a company's energy consumption and carbon emissions. The optimal approach is to comprehensively consider the accuracy of carbon price forecasts, accurate assessment of a company's carbon emissions, the economic viability of the investment solution, compliance with policies and regulations, technical feasibility and market potential, and social and environmental benefits.

[0029] Example 2

[0030] Based on the embodiment of a digital twin carbon data analysis system based on the Internet of Things, this embodiment provides a digital twin carbon data analysis method based on the Internet of Things. In the system of the digital twin carbon data analysis application of the Internet of Things, data transmission, that is, transmission from the Internet of Things device to the user and terminal layer, is divided into five transmission steps. Figure 2 As shown, Figure 2 The solid line in the figure represents the sequential relationship, and the dotted line represents the associative relationship. The specific process includes the following steps:

[0031] First, transmit from IoT devices to the platform support layer: obtain data from IoT devices such as lighting, air conditioning, water and electricity meters, access control, photovoltaic equipment, etc., collect and process data through built-in sensors, and then use wireless networks or wired connections to transmit data to the platform support layer of the IoT integration platform according to the preset communication protocol; among them, the IoT integration platform has the characteristics of unified identity authentication, consent authority management, compliance with unified standards, and a unified operation and maintenance system, ensuring the efficiency, security and convenience of data transmission; among them, data processing includes deleting duplicate and erroneous information to ensure data consistency.

[0032] Second, data is transferred from the platform support layer to the data middle platform. The platform support layer first undergoes in-depth analysis and preprocessing. This includes data type conversion, feature extraction, data cleaning, data compression, and data encryption. Data cleaning can utilize methods such as deletion, interpolation, and regression; data compression can utilize digital compression and lossless compression; and data encryption can utilize symmetric encryption and hash functions. The platform support layer then selects the appropriate communication protocol based on data transmission requirements and transmits the data to the data middle platform via appropriate network channels. The data middle platform comprises two core components: the data collection platform and the energy-carbon data analysis platform. These two platforms work together to achieve comprehensive data integration, secure storage, efficient computing, and flexible sharing, laying a solid foundation for subsequent data applications and decision support. Specifically, the data collection platform provides a real-time, accurate data source, while the energy-carbon data analysis platform utilizes carbon accounting algorithms to convert data into energy-carbon data and classify carbon emissions based on different energy consumption data.

[0033] Third, data is transferred from the data center to the application system layer. Data transferred to the application system layer is used for simulation and carbon reduction analysis. After data is integrated, stored, calculated, and shared on the data center, it is extracted as transmission-related data based on the needs of the application system layer for further transmission. The integration process involves using a schema integration approach to consolidate data views of various data sources into a global schema, which allows access to data from various data sources. The calculation process combines data transferred from the data center to the application system layer with application input data to perform relevant business calculations such as carbon emissions and energy consumption. The sharing process allows data from various modules in the application layer to be shared and shared across different systems. During data transfer, appropriate transmission protocols are considered to meet the data exchange requirements between the data center and the application system layer. These protocols include HTTP, RESTful API, and WebSocket, with specific selection criteria including real-time data transmission requirements, data volume, and system security requirements. As for transmission methods, there are two strategies: synchronous and asynchronous. Synchronous transmission involves transferring data directly to the application system layer upon extraction, which is suitable for scenarios with high real-time requirements. Asynchronous transmission, on the other hand, involves storing extracted data in an intermediate medium (such as a message queue), from which the application system layer retrieves data based on actual needs. This is more suitable for scenarios with lower real-time requirements. Within the application system layer, data will be used for smart management, smart control, and carbon reduction decision-making. Smart management includes access control, conference room management, lighting management, equipment management, energy and carbon management, and asset management. Smart control includes lighting control, water conservation, 3D model simulation control, and access control. Carbon reduction decision-making includes carbon reduction reporting, carbon emission reporting, and carbon market analysis.

[0034] In this embodiment, the simulation process involves using professional modeling software, such as Blender or 3D Max, to create a virtual model that simulates and restores real-world scenarios and integrates energy and carbon emission systems. This model encompasses all aspects of energy production, transmission, distribution, use, and carbon emissions, ensuring that it fully reflects the actual system's operation. IoT technology, sensors, and other devices are used to monitor energy usage and carbon emission data in real time. Energy consumption and carbon emission data are input into the virtual model in real time. By adjusting model parameters, energy and carbon emission conditions under different scenarios can be simulated. Real-time monitoring data is processed and analyzed to extract useful information. Based on the data analysis results and predicted trends, the system's operating status is automatically adjusted, and device parameters can also be manually controlled. Functions encompass multiple aspects, including energy usage, equipment operation and maintenance, and carbon emission monitoring. The model provides real-time monitoring, data analysis, forecasting and early warning, and decision support, supporting comprehensive management of energy and carbon data and equipment.

[0035] This carbon reduction analysis determines the park's baseline scenario—the carbon emissions without any additional emission reduction measures. Different technical conditions, policy scenarios, or management strategies are input into a hypothetical model. The simulation model is run to assess carbon emissions under different scenarios. By comparing carbon emissions under different scenarios, the effectiveness of various measures is evaluated. Based on the simulation results, the strategy with the best emission reduction effect is selected, and the strategy combination is optimized to achieve the greatest reduction. Based on the energy-carbon analysis, high-energy consumption, high-carbon emission processes, and waste points are identified. Based on the analysis results, targeted energy-saving measures are formulated. After implementing the energy-saving measures, energy consumption data is collected again to evaluate the energy-saving effects. The data before and after implementation are compared to quantify the energy-saving effects. The platform compiles the energy-saving measures, implementation process, and energy-saving effects into a standardized report for internal management and external regulatory purposes. Leveraging big data analysis techniques, historical carbon emission data is trend analyzed to identify the main drivers and changing trends of carbon emissions. Carbon emissions are then forecast under different scenarios using emission factor methods, gray prediction models, and the Environmental Kuznets Curve.

[0036] For example, if analysis reveals that a park's air conditioning system (particularly older central air conditioners) consumes extremely high energy and carbon emissions during summer operation, and its energy efficiency ratio is far below the industry average, the corresponding energy-saving measures include replacing the older central air conditioners with newer, more efficient systems, such as those using variable-frequency technology and heat recovery systems, to reduce energy consumption. It is also recommended to install an intelligent temperature control system that automatically adjusts the air conditioning temperature and air speed based on the indoor and outdoor temperature differences and human activity to avoid over-cooling or over-heating.

[0037] For example, if analysis reveals that certain production processes in a park suffer from unreasonable process flows, equipment idling, or low-load operation, resulting in significant energy waste, the corresponding energy-saving measures include streamlining and optimizing production processes, reducing unnecessary energy consumption, and improving production efficiency. At the same time, intelligent equipment scheduling is implemented to rationally arrange the operating hours and load rates of production equipment to avoid energy waste caused by idling or low-load operation.

[0038] Fourth, data transmission from the application system layer to the integrated services layer: Data is divided into independent modules within the application system layer. Therefore, before transmission, the data is consolidated to form a unified data view, and then formatted to ensure data consistency and compatibility. The data transmission method to the integrated services layer depends on the real-time requirements, data volume, system security requirements, and the integrated services layer's support. These methods include, but are not limited to, HTTP, RESTful APIs, WebSockets, and message queues (such as Kafka and RabbitMQ). Therefore, after selecting a transmission method based on specific protocols and specifications, the encrypted data is delivered to the integrated services layer for parsing, verification, and processing, including data cleansing, data mining, and data visualization, to meet the requirements of the integrated services layer. Finally, the data at the integrated services layer can be used for 3D visualization, smart device control, comprehensive carbon reduction report generation, and real-time carbon market analysis. In this embodiment, data mining utilizes Internet of Things (IoT) technology, sensors, smart meters and other equipment to collect real-time data on energy consumption and carbon emissions in the park, including the usage of various energy types such as water, electricity, gas, and heat. The collected raw data is cleaned to remove duplicate, erroneous, and incomplete data to ensure the accuracy and reliability of the data. Data in different formats and units are converted into a unified format and unit to facilitate subsequent analysis and processing. Data from different data sources are integrated to form a complete energy-carbon data set. By constructing a multi-dimensional data set of energy consumption data such as water, electricity, heat, and cooling, which is classified and itemized, the data is analyzed. An analytical model conducts in-depth analysis of energy-carbon data to understand the park's energy consumption and carbon emissions over different time periods. Specifically, analytical dimensions are defined (such as time, location, and energy consumption type), and key indicators reflecting energy consumption (such as total energy consumption and energy consumption per unit area) are designed. Subsequently, SQL queries, pivot tables, and other techniques are used to aggregate and conduct in-depth analysis of the integrated data. Energy consumption trends over different time periods are analyzed to understand the cyclical and seasonal characteristics of energy consumption. By comparing energy consumption data from different time periods, peak and low energy consumption periods can be identified, providing a basis for formulating energy-saving strategies. Pre-set statistical sub-items and energy-saving rules are used to conduct intelligent diagnosis of key high-energy-consuming equipment, providing energy efficiency diagnosis and optimization recommendations. Specifically, energy-carbon data is comprehensively analyzed using pre-set statistical sub-items (such as energy type, region, time, and equipment) to understand energy consumption and emissions over different time periods. Energy-saving rules are also formulated, such as setting energy consumption thresholds, optimizing equipment operation, promoting the use of clean energy, and advocating behavioral energy conservation, to guide the implementation of energy-saving measures. Among them, the diagnostic results may include peak and low periods of energy usage; the energy efficiency level, operating status, and load rate of the equipment; equipment failures and abnormal operating conditions, etc., and targeted plans may be given for equipment upgrades and replacements, energy management system optimization, energy-saving measures and strategies, and energy efficiency improvement plans.

[0039] Fifth, data transmission from the integrated service layer to the user and terminal layer: Before transmission, the integrated service layer encapsulates data into a suitable transmission format, such as JSON or XML. Necessary metadata is added during the encapsulation process to facilitate understanding and use of the data by the user and terminal layer. The data is then encrypted using encryption protocols such as HTTPS and TLS to prevent data theft or tampering during transmission. Finally, the identity and permissions of the user and terminal layer are verified to ensure that only legitimate users can access and receive data. During transmission, the integrated service layer implements data integrity verification using hash algorithms such as MD5 and SHA-256. This ensures data integrity and consistency by calculating and comparing hash values. Three flexible transmission modes are provided: real-time transmission, batch transmission, and message queues, allowing for flexible adaptation to different scenario requirements. Finally, after data parsing and verification at the user and terminal layer, the data is displayed. The display method can be customized and optimized based on specific application scenarios and needs.

[0040] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A digital twin carbon data analysis system based on the Internet of Things, characterized by: It includes IoT data acquisition module, digital twin simulation module, energy and carbon data analysis module and carbon market analysis module; The IoT data collection module collects IoT device data, wherein the IoT device data includes building IoT data, and the IoT devices include building IoT devices; The digital twin simulation module analyzes the building IoT data to visualize the real location of the building IoT equipment, realizes real-time control of the building IoT equipment, and displays energy and carbon data at the same time; The energy-carbon data analysis module analyzes the data collected by IoT devices, combines the digital twin model to perform virtual synchronous carbon reduction deduction on the data, and combines historical data and real-time data to predict carbon emissions within a set time; it also forms energy-saving reports and carbon emission reports based on energy consumption conditions; The carbon market analysis module obtains and analyzes real-time carbon prices in the carbon market and provides the best investment plan based on the company's energy consumption and carbon emissions.

2. According to the digital twin carbon data analysis system based on the Internet of Things according to claim 1, it is characterized by: The Internet of Things device at least includes a water and electricity meter device, an air conditioning device, a lighting device and a photovoltaic device; wherein the water and electricity meter device, the air conditioning device, the lighting device and the photovoltaic device are respectively provided with corresponding sensors.

3. According to the digital twin carbon data analysis system based on the Internet of Things according to claim 1, it is characterized by: The building IoT data at least includes access control status, air conditioning status, water and electricity meter status, photovoltaic equipment status and lighting status.

4. A digital twin carbon data analysis method based on the Internet of Things, characterized by: The digital twin carbon data analysis system based on the Internet of Things according to any one of claims 1 to 3 is adopted, comprising the following steps: Obtain IoT device data, process the data, and transmit it to the platform support layer according to the preset communication protocol; After the data is pre-processed in the platform support layer, it is transmitted to the data middle platform; After the data is integrated, stored, calculated and shared in the data center, it is transferred to the application system layer; The data processed by the application system layer is sent to the integrated service layer for analysis, verification and processing; The data is encapsulated into a format suitable for transmission at the integrated service layer and transmitted to the user and terminal layer.

5. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 4 is characterized in that: The preprocessing includes data cleaning, data compression and data encryption.

6. According to claim 4, a digital twin carbon data analysis method based on the Internet of Things is characterized by: The data middle platform includes a data collection platform and an energy and carbon data analysis platform.

7. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 4 is characterized in that: According to the needs of the application system layer, relevant data is extracted to prepare for the next step of transmission; during data transmission, the transmission protocol is used to meet the data exchange needs between the data center and the application system layer.

8. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 7 is characterized in that: The application system layer obtains data based on actual needs, and the data is used for smart management, smart control and carbon reduction decision-making in the application system layer; among them, smart management includes access control management, conference room management, lighting management, equipment management, energy and carbon management and asset management; smart control includes lighting control, water saving, three-dimensional model simulation control and access control; carbon reduction decision-making includes carbon reduction reports, carbon emission reports and carbon market analysis.

9. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 4 is characterized in that: The data is processed by data mining in the comprehensive service layer. Data mining is to use IoT devices to collect the park's energy consumption and carbon emission data in real time, clean the collected raw data, remove duplicate, erroneous, and incomplete data, convert data in different formats and units into a unified format and unit, and integrate data from different data sources to form a complete energy-carbon data set; and by building a multidimensional analysis model for energy consumption data with classification and sub-items, conduct in-depth analysis of energy-carbon data to understand the energy consumption and carbon emissions of the park in different time periods, use preset statistical sub-items and energy-saving rules to conduct intelligent diagnosis of key high-energy-consuming equipment, and put forward energy efficiency diagnosis and optimization suggestions.

10. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 4, characterized in that: During the transmission process, the integrated service layer uses a hash algorithm to add a data integrity verification mechanism to ensure the integrity and consistency of the data by calculating and comparing the hash value of the data. It also provides three transmission modes: real-time transmission, batch transmission, and message queue, to adapt to the needs of different scenarios and make reasonable changes.

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