An Internet of Things-based digital twin carbon data analysis system and method

Abstract

The present invention discloses an Internet of Things-based digital twin carbon data analysis system and method, which relates to the field of digital twin technology. The system includes an Internet of Things data acquisition module, a digital twin simulation module, an energy and carbon data analysis module, and a carbon market analysis module. The method includes obtaining Internet of Things device data and transmitting it to the platform support layer according to a preset communication protocol; after the data is deeply analyzed and preprocessed in the platform support layer, it is transmitted to the data middle platform; 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; the data is encapsulated into a format suitable for transmission in the comprehensive service layer and transmitted to the user and terminal layer. The present invention can achieve refined management of the entire life cycle of energy and carbon data from data acquisition to data visualization display, data analysis and deduction, report generation, and investment plan generation.

Description

Technical Field

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

[0002] At present, digital twin technology has been widely applied and developed. It can construct a simulation model for an object in the physical world through digital means to achieve the understanding, analysis, and optimization of the physical entity. Digital twin technology makes full use of data such as physical models, sensor updates, and operation history, integrates the simulation processes of multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities, and completes the mapping in the virtual space to reflect the entire life cycle process of the corresponding physical equipment.

[0003] However, for enterprises, there is a lack of a direct connection and refined management mode for various energy-consuming devices such as park lighting, water and electricity meters, and air conditioners through digital twin technology, a lack of intelligent conversion, three-dimensional visualization display, and data analysis capabilities for energy consumption data, photovoltaic power generation data, voluntary carbon emission reduction data, and carbon emission data, a lack of the ability to accurately deduce carbon reduction plans, and a lack of the ability to accurately evaluate the carbon market. The digital twin carbon data analysis application based on the Internet of Things can effectively solve the problems of energy-consuming units such as enterprises and parks. Summary of the Invention

[0004] In view of 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 solutions adopted by the present invention are as follows:

[0006] A digital twin carbon data analysis system based on the Internet of Things includes an Internet of Things data collection module, a digital twin simulation module, an energy and carbon data analysis module, and a carbon market analysis module; the Internet of Things data collection module collects Internet of Things device data, where 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 positions of the building Internet of Things devices through the analysis of the building Internet of Things data, realizes the real-time control of the building Internet of Things devices, and simultaneously performs energy and carbon data display; the energy and 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 the carbon emissions within a certain period of time; forms an energy-saving report and a carbon emission report in combination with the energy consumption situation; the carbon market analysis module provides an optimal investment plan for the enterprise's energy consumption and carbon emission situation through the acquisition and analysis of the real-time carbon price in the carbon market.

[0007] Based on the above technical solution, further, the Internet of Things devices at least include water and electricity meter devices, air conditioning devices, lighting devices, and photovoltaic devices; among them, the water and electricity meter devices, air conditioning devices, lighting devices, and photovoltaic devices are respectively corresponding to corresponding sensors.

[0008] Based on the above technical solution, further, the building Internet of Things 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 uses a digital twin carbon data analysis system based on the Internet of Things, including the following steps: obtaining Internet of Things device data, and performing data processing, and transmitting it to the platform support layer according to a preset communication protocol; after the data is preprocessed 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 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 in 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 requirements of the application system layer, relevant data for transmission is extracted for the next step of transmission; when data is transmitted, the data exchange requirements between the data middle platform and the application system layer are met according to the transmission protocol.

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

[0014] Based on the above technical solution, further, data is mined and processed in the integrated service layer. Among them, data mining uses Internet of Things devices to collect energy consumption and carbon emission data of the park in real time, cleans the collected raw data, removes duplicate, incorrect, and incomplete data, converts data in different formats and units into a unified format and unit, integrates data from different data sources to form a complete energy and carbon dataset; and through the construction of a multi-dimensional analysis model for energy consumption data classified by item, deeply analyzes the energy and carbon data, understands the energy consumption and carbon emission conditions of the park at different time periods, uses preset statistical items and energy-saving rules to conduct intelligent diagnosis on key high-energy-consuming equipment, and puts forward energy efficiency diagnosis and optimization suggestions.

[0015] Based on the above technical solution, further, during the transmission process, the integrated service layer uses the hash algorithm to add a data integrity verification mechanism, ensures the integrity and consistency of the data by calculating and comparing the hash value of the data, and at the same time provides three flexible transmission methods: real-time transmission, batch transmission, and message queue, so as to reasonably transform according to different scenario requirements.

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

[0017] (1) Through the comprehensive management of the Internet of Things data collection module, digital twin simulation module, energy and carbon data analysis module, and carbon market analysis module, 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, data analysis and deduction, report generation, and investment plan generation.

[0018] (2) The system and method of the present invention break the singularity of application scenarios, can span different industries, such as vertical industries such as energy, manufacturing, transportation, etc., and can realize scene adaptive switching; support multiple data collections such as modbus and mqtt, ensuring that the system has higher scalability; integrate data from different fields such as emission control enterprises and parks, conduct comprehensive data integration, and through the integration of structured data, semi-structured data, unstructured data, etc., combined with digital twins to improve the visualization degree of the system; the database uses the domestic database KingbaseES to improve the security of data storage; combined with 24 industry standards, extremely small error calculations are carried out for energy use conversion carbon emissions; through the acquisition and analysis of the real-time carbon price in the carbon market, the authenticity of the investment plan is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system of the present invention;

[0020] Figure 2 It is a simple flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] It should be noted that the raw materials used in the present invention are all ordinary commercially available products, and no specific limitations are imposed on their sources.

[0022] The following raw material sources are for exemplary illustration:

[0023] Example 1

[0024] Combined with Figure 1 As shown, this embodiment provides 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. Each module communicates and transmits with each other. The digital twin carbon data analysis application based on the Internet of Things can achieve refined management of the entire life cycle of energy-carbon data from data acquisition to data visualization display, data analysis and deduction, report generation, and investment plan generation through the comprehensive management of the Internet of Things data acquisition module, digital twin simulation module, energy-carbon data analysis module, and carbon market analysis module.

[0025] The Internet of Things data acquisition module acquires Internet of Things device data. Among them, 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 Internet of Things devices include, but are not limited to, water and electricity meter devices, air conditioning devices, lighting devices, and photovoltaic devices; among them, the water and electricity meter devices, air conditioning devices, lighting devices, and photovoltaic devices are respectively equipped with various corresponding built-in sensors, such as water and electricity sensors, etc. Various sensors can real-time monitor information such as the readings of water and electricity meters, water consumption, and electricity consumption; providing data support for data mining, monitoring, and analysis. The building Internet of Things data at least includes access control situations, air conditioning situations, and lighting situations.

[0026] The digital twin simulation module completes the visual display of the actual positions of the devices through the analysis of the building Internet of Things data, realizes the real-time control of the building, and at the same time displays the energy-carbon data for data display; specifically, it can complete the visual display of the actual positions of various sensors such as water and electricity meters, and realize the real-time control of access control, air conditioning, and lighting.

[0027] The energy and carbon data analysis module analyzes the data collected by Internet of Things devices, conducts virtual synchronous carbon emission reduction deduction on the data in combination with the digital twin model, and predicts the carbon emissions within a certain period of time in combination with historical data and real-time data; forms an energy-saving report and a carbon emission report in combination with the energy consumption situation; among them, the digital twin model is an existing method, and the digital twin model is the implementation method of digital twin technology. It presents physical objects in the virtual space in a digital way, that is, creates a virtual model for physical objects in a digital way and simulates 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 processes in production and realize the whole-process digitization from basic materials, product design, process planning, production planning, manufacturing execution to use and maintenance, and will not be described in detail here. Exemplarily, different scenarios or solutions are set, such as changing the energy structure, optimizing equipment operation, improving the process flow, etc., and the set scenarios are simulated; by adjusting the parameters and conditions in the model, observing the changes in key indicators such as the carbon emissions and energy utilization efficiency of physical objects under different scenarios, evaluating the simulation results, analyzing the emission reduction effects, economic benefits and social benefits under different scenarios, and selecting the optimal emission reduction solution by comparing the advantages and disadvantages of different scenarios. By accumulating and generating historical data, establishing differential equations to describe system changes, using the least squares method to solve model parameters, and verifying the model accuracy through residual tests and posterior difference tests, the carbon emissions within a certain period in the future are predicted.

[0028] The carbon market analysis module provides the optimal investment plan for the enterprise's energy consumption and carbon emissions situation through the acquisition and analysis of the real-time carbon price in the carbon market. Among them, the idea of the optimal judgment is: comprehensively consider the accuracy of carbon price prediction, the accurate assessment of the enterprise's carbon emissions situation, the economy of the investment plan, the compliance with policies and regulations, the technical feasibility and market potential, and the social and environmental benefits of the investment plan.

[0029] Embodiment 2

[0030] Based on a digital twin carbon data analysis system based on the Internet of Things provided in the embodiment, this embodiment provides a digital twin carbon data analysis method based on the Internet of Things. In the system to which the digital twin carbon data analysis of the Internet of Things is applied, the data transmission from the Internet of Things device to the user and terminal layer is divided into five transmission steps. As shown in Figure 2 shown, among them, Figure 2 the solid lines represent sequential relationships, and the dotted lines represent association relationships; the specific process includes the following steps:

[0031] First, from the IoT devices to the platform support layer: Obtain the data of IoT devices such as lighting, air conditioners, water and electricity meters, access control, and photovoltaic devices. Collect data through built-in sensors and process the data. Then, use wireless networks or wired connections, etc., and transmit it 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, unified permission management, following unified standards, and having a unified operation and maintenance system, ensuring the efficiency, security, and convenience of data transmission. Among them, data processing includes deleting duplicate and error information to ensure data consistency.

[0032] Second, from the platform support layer to the data middle platform: The data is first deeply analyzed and preprocessed in the platform support layer. The deep analysis and preprocessing process includes data type conversion, feature extraction, data cleaning, data compression, and data encryption. Among them, data cleaning can be carried out in ways such as deletion method, interpolation method, and regression method. Data compression can be carried out in ways such as digital compression and lossless compression. Data encryption can be carried out in ways such as symmetric encryption and hash function. Then, the platform support layer selects an appropriate communication protocol according to the data transmission requirements and transmits it to the data middle platform through an appropriate network channel. The data middle platform contains two core components, namely the data collection platform and the energy and carbon data analysis platform. These two platforms work together to achieve comprehensive integration, secure storage, efficient calculation, and flexible sharing of data, laying a solid foundation for subsequent data applications and decision support. Specifically, the data collection platform provides real-time and accurate data sources, while the energy and carbon data analysis platform uses carbon accounting algorithms to convert the data into energy and carbon data and classify the carbon emissions of different energy consumption data.

[0033] Third, it is transmitted from the data middle platform to the application system layer. Among them, the data transmitted to the application system layer is used for simulation and carbon emission reduction analysis: After the data is integrated, stored, calculated, and shared in the data middle platform, according to the requirements of the application system layer, it is extracted as transmission-related data for the next step of transmission; among them, the integration process is: using the schema integration method, the data views of various types of data are integrated into a global schema, and the data of each data source can be accessed according to the global schema; the calculation process is: the data transmitted from the data middle platform to the application system layer is combined with the data input by the application for relevant business calculations such as carbon emissions and energy consumption; the sharing process is: the data of each module in the application layer can jointly call the data of other modules as needed to achieve data transmission and sharing between different systems. When transmitting data, appropriate transmission protocols will be considered to meet the data exchange requirements between the data middle platform and the application system layer. These protocols cover HTTP, RESTful API, WebSocket, etc. The specific selection basis includes the real-time requirements of data transmission, the size of the data volume, and the system security requirements. As for the transmission method, there are two strategies: synchronous transmission, that is, once the data is extracted, it is immediately directly transmitted to the application system layer, which is suitable for scenarios with high real-time requirements; asynchronous transmission is to store the extracted data in an intermediate medium (such as a message queue), and the application system layer pulls the data according to actual needs, which is more suitable for scenarios with low real-time requirements. The data in the application system layer will be used for intelligent management, intelligent control, and carbon emission reduction decision-making. Intelligent management includes access control management, meeting room management, lighting management, equipment management, energy and carbon management, and asset management. Intelligent control includes lighting control, water conservation, three-dimensional model simulation control, and access control. Carbon emission reduction decision-making includes carbon emission reduction reports, carbon emission reports, and carbon market analysis.

[0034] In this embodiment, the simulation process is as follows: Use professional modeling software, such as blender, 3Dmax, etc., to establish a virtual model that simulates and restores the real scene and combines the energy and carbon emission systems, including all aspects such as energy production, transmission, distribution, use, and carbon emissions, to ensure that the operation of the actual system can be comprehensively reflected. Use Internet of Things technology, sensors and other devices to monitor the energy usage and carbon emission data in real time, input data such as energy consumption and carbon emissions into the virtual model in real time, and simulate the energy and carbon emission situations under different scenarios by adjusting the model parameters. Process and analyze the real-time monitoring data, extract useful information, and automatically adjust the system operation status according to the data analysis results and prediction trends, or manually control and adjust the equipment parameters. The functions cover multiple aspects such as energy use, equipment operation and maintenance, and carbon emission monitoring. The model has functions such as real-time monitoring, data analysis, prediction and early warning, and decision support, and supports the comprehensive management of energy and carbon data and equipment.

[0035] The carbon reduction analysis: Determine the baseline scenario of the park, i.e., the carbon emission situation without taking any additional emission reduction measures. Take different technical conditions, policy scenarios, or management strategies as input variables and input them into the virtual model. Run the simulation model to evaluate the carbon emission situations under different scenarios. By comparing the carbon emissions under different scenarios, evaluate the emission reduction effects of various measures. According to the simulation results, select the strategy with the best emission reduction effect, optimize the strategy combination to achieve the maximum emission reduction effect. Based on the energy-carbon analysis, identify high-energy-consuming and high-carbon-emitting links and waste points. According to the analysis results, formulate targeted energy-saving measures. After implementing the energy-saving measures, collect energy consumption data again to evaluate the energy-saving effect. Compare the data before and after implementation to quantify the energy-saving effect. The platform will organize the energy-saving measures, implementation process, and energy-saving effect into a standard report for internal management and external supervision of the enterprise. Use big data analysis technology to conduct trend analysis on historical carbon emission data, identify the main driving factors and change trends of carbon emissions, and use methods such as the emission factor method, grey prediction model, and environmental Kuznets curve to predict the carbon emissions under different scenarios.

[0036] Exemplarily, when it is analyzed that the energy consumption and carbon emissions of the air-conditioning system in the park (especially old central air conditioners) are extremely high during the summer operation, and the energy efficiency ratio is much lower than the industry average, the corresponding energy-saving measures are: Replace the old central air conditioner with a new air-conditioning system with higher energy efficiency, such as using variable frequency technology, heat recovery system, etc., to reduce energy consumption. At the same time, it is recommended to install an intelligent temperature control system to automatically adjust the air-conditioning temperature and wind speed according to the indoor and outdoor temperature difference and personnel activities to avoid excessive cooling or heating.

[0037] Exemplarily, when it is analyzed that in some production links in the park, there are phenomena such as unreasonable process flow, equipment idling, or low-load operation, resulting in serious energy waste, the corresponding energy-saving measures are: Sort out and optimize the production process, reduce unnecessary energy-consuming links, and improve production efficiency. At the same time, conduct intelligent scheduling of equipment, reasonably arrange the operation time and load rate of production equipment, and avoid energy waste caused by equipment idling or low-load operation.

[0038] Fourth, transfer from the application system layer to the integrated service layer: The data is divided into independent modules in the application system layer. Therefore, before transmission, the data will be integrated to form a unified data view, and then the format will be converted to ensure data consistency and compatibility. The transmission method of the data to the integrated service layer depends on the real-time requirements of the data, the amount of data, the security requirements of the system, and the support of the integrated service layer, including but not limited to HTTP, RESTful API, WebSocket, message queues (such as Kafka, RabbitMQ), etc. Therefore, after selecting the transmission method according to certain protocols and specifications, the encrypted data will be sent to the integrated service layer for parsing, verification, and processing, including data cleaning, data mining, data visualization, etc., to meet the requirements of the integrated service layer. Finally, the data in the integrated service layer can be used for three-dimensional visualization display, intelligent device control, integrated carbon reduction report generation, and real-time carbon market analysis. In this embodiment, data mining uses devices such as Internet of Things (IoT) technology, sensors, and intelligent meters to collect the energy consumption and carbon emission data of the park in real time, including the usage of various energy types such as water, electricity, gas, and heat; clean the collected raw data to remove duplicate, incorrect, and incomplete data to ensure the accuracy and reliability of the data, convert data in different formats and units into a unified format and unit for subsequent analysis and processing, integrate data from different data sources to form a complete energy-carbon dataset, and conduct in-depth analysis of the energy-carbon data by constructing a multi-dimensional analysis model for energy consumption data such as classified water, electricity, heat, and cold to understand the energy consumption and carbon emission situation of the park at different times; specifically, define the analysis dimensions (such as time, location, energy consumption type, etc.), and then design key indicators reflecting the energy consumption status (such as total energy consumption, energy consumption per unit area, etc.); subsequently, use technologies such as SQL queries and pivot tables to aggregate and deeply analyze the integrated data; analyze the energy consumption trends at different times to understand the periodic changes and seasonal characteristics of energy consumption; by comparing the energy consumption data at different times, the peak and trough periods of energy consumption can be identified, providing a basis for formulating energy-saving strategies. And use the preset statistical items and energy-saving rules to conduct intelligent diagnosis on key high-energy-consuming equipment and put forward energy efficiency diagnosis and optimization suggestions; specifically, comprehensively analyze the energy-carbon data through preset statistical items (such as energy type, area, time, equipment, etc.) to understand the energy consumption and emission situation at each time period; at the same time, formulate energy-saving rules, such as setting energy consumption thresholds, optimizing equipment operation, promoting the use of clean energy, and advocating behavioral energy conservation, etc., to guide the implementation of energy-saving measures. Among them, the diagnosis results may include the peak and trough periods of energy use; the energy efficiency level, operation status, and load rate of the equipment; the faults and abnormal operation conditions of the equipment, etc. Specifically, give suggestions on equipment upgrade and replacement, energy management system optimization, energy-saving measures and strategies, and energy efficiency improvement plans.

[0039] Fifth, transmit from the integrated service layer to the user and terminal layer: Before transmission, the data will be encapsulated into a format suitable for transmission in the integrated service layer, such as JSON, XML, etc. Necessary metadata will be added during the encapsulation process to enable the user and terminal layer to better understand and use the data. Then, encryption protocols such as HTTPS and TLS will be used to encrypt the data to prevent the data from being stolen or tampered with during transmission. Finally, the identity and permissions of the user and terminal layer will be verified to ensure that only legitimate users can access and receive the data. During transmission, the integrated service layer will apply hash algorithms such as MD5 and SHA-256 to add a data integrity verification mechanism. By calculating and comparing the hash values of the data, the integrity and consistency of the data are ensured. At the same time, three flexible transmission methods, namely real-time transmission, batch transmission, and message queue, are provided to reasonably transform according to different scenario requirements. Finally, the data is displayed after data parsing and verification in the user and terminal layer, and the display method can be customized and optimized according to specific application scenarios and requirements.

[0040] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than a limitation on the protection scope of the present invention. Any simple modification or equivalent replacement of the technical solution of the present invention by those of ordinary skill in the art shall not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A digital twin carbon data analysis method based on the Internet of Things, characterized by: A digital twin carbon data analysis system based on the Internet of Things is used to achieve Among them, the digital twin carbon data analysis system includes the Internet of Things data acquisition module, the digital twin simulation module, the energy carbon data analysis module and the 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 to provide the best investment plan based on the company's energy consumption and carbon emissions. The digital twin carbon data analysis method includes 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; the data middle platform includes two core components: the data collection platform and the energy-carbon data analysis platform. The data collection platform provides a real-time and accurate data source, while the energy-carbon data analysis platform uses the carbon accounting algorithm to convert the data into energy-carbon data and classify the carbon emissions of different energy consumption data; After the data is integrated, stored, calculated and shared in the data center, the data is transmitted to the application system layer; the integration process is: using the pattern integration method, the data views of various types of data are integrated into a global pattern, and the data of each data source is accessed according to the global pattern; the calculation process is: the data transmitted from the data center to the application system layer is combined with the data input by the application to perform carbon emission and energy consumption related business calculations; the sharing process is: the data of each module in the application layer is called together through the data of other modules as needed to realize data transmission and sharing between different systems; The data processed by the application system layer is sent to the integrated service layer for analysis, verification and processing; Data is encapsulated into a format suitable for transmission at the integrated service layer and transmitted to the user and terminal layer; Among them, when the data is transmitted from the data center to the application system layer, the data transmitted to the application system layer is used for simulation and carbon reduction analysis; The carbon reduction analysis: determine the park's baseline scenario, the carbon emissions without any additional emission reduction measures, input different technical conditions, policy scenarios or management strategies as input variables into the virtual model, run the simulation model, evaluate the carbon emissions under different scenarios, and evaluate the emission reduction effects of various measures by comparing the carbon emissions under different scenarios; 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.

2. According to the method for analyzing digital twin carbon data based on the Internet of Things in claim 1, it is characterized by: The preprocessing includes data cleaning, data compression and data encryption.

3. According to the method for analyzing digital twin carbon data based on the Internet of Things in claim 1, it is characterized by: The data middle platform includes a data collection platform and an energy and carbon data analysis platform.

4. According to the method for analyzing digital twin carbon data based on the Internet of Things in claim 1, it is characterized by: 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.

5. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 4 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.

6. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 1 is 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.

7. The method for analyzing digital twin carbon data based on the Internet of Things according to claim 1 is characterized in that: The IoT devices include at least a water and electricity meter device, an air conditioning device, a lighting device, and a photovoltaic device; Among them, the water and electricity meter devices, air conditioning devices, lighting devices and photovoltaic devices are respectively equipped with corresponding sensors.

8. The digital twin carbon data analysis method based on the Internet of Things according to claim 1 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.

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