A regional carbon emissions accounting system

Abstract

The present invention relates to the field of environmental science and engineering technology. The present invention discloses a regional carbon emission accounting system. The system uses multiple sensors and intelligent devices to collect carbon emission data in real time and transmit the data through wireless communication. The data integration module cleans and standardizes the carbon emission data, and evaluates the reliability of the data source based on fuzzy rules to form an accurate comprehensive data set. The dynamic evaluation module uses a machine learning algorithm to analyze the comprehensive data set to generate evaluation feedback, and feeds it back to the intelligent monitoring module to optimize the monitoring strategy and data collection frequency. Through the visualization display module, the analysis results are presented in the form of charts, supporting user interactive data query and retrieval, and improving the scientificity and real-time nature of carbon emission management. While improving the accuracy of carbon emission accounting, the system provides an important basis for policy formulation and effective emission reduction, helping to cope with global climate change.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and engineering technology, and in particular to a regional carbon emission accounting system. Background Art

[0002] As the global climate change problem becomes more and more serious, the monitoring and accounting of carbon emissions has gradually become an important part of environmental management and policy making. In recent years, relevant technologies for regional carbon emission accounting have continued to develop. Early carbon emission accounting relied on censuses and estimates. These methods are usually based on static models and are difficult to reflect actual dynamic changes. With the rise of sensor technology, the Internet of Things (IoT), big data, and artificial intelligence, researchers have gradually realized real-time monitoring and analysis of carbon emissions. Many existing systems use network sensors to collect data and combine traditional accounting models for analysis to identify and manage carbon emission sources. However, these systems still have significant deficiencies in real-time data collection capabilities, data reliability assessment, and dynamic adjustment strategies.

[0003] There are many limitations in existing carbon emission accounting technologies, which limit their effectiveness in practical applications. First, many existing systems lack efficient data processing capabilities and are often unable to clean up outliers and fill missing values ​​in a timely manner, resulting in low data quality, which in turn affects the reliability of the analysis results. Secondly, existing technologies mostly adopt static monitoring strategies, fail to dynamically adjust the monitoring frequency and strategy based on actual data feedback, and fail to accurately capture the key factors affecting carbon emissions. In addition, the current monitoring and evaluation methods are relatively simple, lack in-depth research on multi-source data fusion, and fail to effectively integrate information between different data sources, resulting in an inability to comprehensively evaluate carbon emissions in the region. Therefore, in response to these shortcomings, the regional carbon emission accounting system proposed in this paper provides a solution through comprehensive data collection, real-time dynamic evaluation and visual display, which significantly improves the accuracy and real-time nature of data collection, thereby promoting the improvement of carbon emission management efficiency. Summary of the invention

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

[0005] Therefore, the present invention provides a regional carbon emission accounting system, which can improve the timeliness and accuracy of regional carbon emission accounting to address the limitations of existing technologies in data collection, integration and evaluation.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a regional carbon emission accounting system, including a data acquisition module, a data integration module, a dynamic evaluation module, an intelligent monitoring module and a visual display module;

[0007] The data acquisition module collects carbon emission data in real time and transmits the collected carbon emission data to the data integration module;

[0008] The data integration module integrates and processes the collected carbon emission data to form a comprehensive data set, and transmits it to the dynamic evaluation module;

[0009] The integration process includes: the data integration module receives the carbon emission data from the data acquisition module, cleans the received carbon emission data, removes invalid or abnormal data, fills in missing values, and standardizes all carbon emission data.

[0010] Construct fuzzy rules to judge the reliability and importance of each data source, evaluate the impact of each data source according to the defined fuzzy rules, and convert them into fuzzy values , construct fuzzy sets :

[0011] ;

[0012] Normalize the fuzzy values ​​and calculate the weight of each data source :

[0013] ;

[0014] Before carbon emission data is integrated, a dynamic weight adjustment model is used to adjust the and reliability factor Adjust weights:

[0015] ;

[0016] The carbon emission values ​​from various data sources are integrated using the weighted average method to form a comprehensive data set :

[0017] ;

[0018] in, The raw carbon emission values ​​calculated for each data source, n is the number of data sources; the resulting comprehensive data set will contain each data source at time Contribution and dynamic weight;

[0019] The dynamic assessment module uses a machine learning algorithm to analyze the comprehensive data set to generate assessment feedback, and transmits the assessment feedback results to the intelligent monitoring module;

[0020] The use of the machine learning algorithm includes: the dynamic assessment module obtains the latest comprehensive data set from the data integration module, uses the deep learning model to analyze the comprehensive data set, identifies the main factors affecting carbon emissions, and trains the model to predict future carbon emissions;

[0021] Using the improved convolutional neural network, the input data is set to , the output is the carbon emission value predicted by the model , loss function Defined as:

[0022] ;

[0023] in, is the true value, is the predicted value, is the number of samples;

[0024] Update model parameters through back-propagation algorithm :

[0025] ;

[0026] in, is the learning rate, is the gradient of the loss function;

[0027] The intelligent monitoring module continuously monitors energy consumption and greenhouse gas emissions, and updates the comprehensive data set in real time;

[0028] The visualization display module displays the carbon emission situation and intuitively presents the evaluation feedback results obtained from the dynamic evaluation module, forming a complete carbon emission accounting process.

[0029] The real-time collection of carbon emission data includes: a data acquisition module collects carbon emission data in real time by combining multiple sensors and smart devices;

[0030] The sensors include gas sensors, energy consumption sensors, and traffic flow monitoring sensors;

[0031] The smart device includes a smart terminal and a wireless communication submodule;

[0032] The system starts triggering carbon emission data collection at a scheduled time, and locally integrates and preliminarily analyzes data from different sensors. By setting a dynamic sampling rate, it conducts time series analysis on carbon emission data within a certain period of time to determine the key data that needs to be collected first.

[0033] Through the wireless communication submodule, the processed carbon emission data is transmitted to the data integration module in real time. During the carbon emission data upload process, the system will automatically adjust the data upload frequency according to the network stability, perform fast and large-capacity transmission when the network conditions are good, and transmit a small amount of key data when the network conditions are poor. After the data transmission is successful, the data integration module will generate a confirmation receipt and transmit it back to the data collection module.

[0034] The evaluation feedback results include: the adjusted deep learning model will generate evaluation feedback, and the intelligent monitoring module will adjust the monitoring strategy after receiving the feedback; based on the feedback, the intelligent monitoring module will optimize the monitoring frequency and data collection method;

[0035] The output of the dynamic assessment module will include the contribution of each data source to carbon emissions and the importance score, forming feedback:

[0036] ;

[0037] in, is the feedback rating, is the average carbon emission value of the comprehensive data set, is the standard deviation.

[0038] The updating of the comprehensive dataset includes: ,The intelligent monitoring module adjusts the monitoring strategy and increases the ,frequency of monitoring when the monitoring is a high contribution data source;

[0039] Update monitoring frequency:

[0040] ;

[0041] When monitoring is a low-contribution data source, reduce the monitoring frequency;

[0042] Update monitoring frequency:

[0043] ;

[0044] in, is the adjustment step size, is the updated monitoring frequency, is the current monitoring frequency.

[0045] As a preferred solution of the regional carbon emission accounting system described in the present invention, wherein: the display of carbon emission conditions includes: the visual display module establishes a data connection with the dynamic evaluation module and the intelligent monitoring module through the API interface, monitors data updates in real time, and when the dynamic evaluation module completes the analysis or the intelligent monitoring module obtains new data, the display module can immediately receive the information;

[0046] After receiving the evaluation feedback results from the dynamic evaluation module and the real-time data from the intelligent monitoring module, the display module organizes the data through the cache mechanism and uses tags to associate timestamps to ensure the time consistency of data from different sources;

[0047] The visualization display module generates display content using dynamic templates based on the processed comprehensive data set; the display content includes: real-time monitoring charts, carbon emission analysis results and trend forecasts;

[0048] Provide users with interactive functions, users can choose to view data in different time periods, or filter according to specific types of energy consumption and carbon emissions; the visualization module sets a timed update mechanism to automatically refresh the data view every 5 minutes.

[0049] As a preferred solution of the regional carbon emission accounting method described in the present invention, the system is activated to start data collection, and carbon emission related data is obtained in real time through multiple sensors and smart devices, and the data is initially integrated and analyzed locally to reduce the amount of data that needs to be transmitted;

[0050] The processed data is transmitted to the data integration platform in real time via wireless communication;

[0051] The received data will be cleaned, invalid or outliers will be removed, missing values ​​will be filled to ensure data quality, fuzzy rules will be used to evaluate the reliability of data sources, weights will be adjusted dynamically, and ultimately an accurate comprehensive data set will be formed for subsequent analysis;

[0052] The comprehensive dataset is analyzed by a deep learning model to identify the main influencing factors and adjust the analysis parameters based on the feedback. The generated evaluation results will be used to optimize the monitoring strategy and data collection frequency.

[0053] The results are displayed through visualization means, including real-time data monitoring and trend analysis. The system regularly evaluates based on user feedback and data, continuously adjusts strategies and parameters, improves system efficiency and updates carbon reduction strategies.

[0054] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a regional carbon emission accounting system when executing the computer program.

[0055] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a regional carbon emission accounting system.

[0056] Beneficial effects of the present invention: The present invention realizes efficient collection of real-time data by innovatively integrating multiple sensor networks and intelligent devices; adopts fuzzy rules and dynamic weight adjustment mechanism to improve the accuracy of data integration; applies deep learning model for dynamic evaluation to effectively capture the main influencing factors of carbon emission changes. Through the above innovations, the present invention can significantly improve the accuracy and real-time performance of carbon emission accounting, provide more scientific decision-making basis for policy makers and enterprises in the formulation and implementation of carbon emission reduction measures, and ultimately promote the realization of regional sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0058] Figure 1 A schematic diagram of working modules of a regional carbon emission accounting system provided for one embodiment of the present invention.

[0059] Figure 2 A schematic flow chart of a method for calculating regional carbon emissions provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and which provides a regional carbon emission accounting system, including: a data acquisition module, a data integration module, a dynamic evaluation module, an intelligent monitoring module, and a visual display module;

[0067] The data acquisition module collects carbon emission data in real time and transmits the collected carbon emission data to the data integration module; through the integration of multiple sensors, the system can capture the carbon emission dynamics in the region in real time, ensuring timely monitoring and response to carbon emission changes, and providing a solid foundation for emission reduction decisions.

[0068] The data integration module integrates the collected carbon emission data to form a comprehensive data set, which is then passed to the dynamic assessment module. Through a strict data processing process, such as removing invalid data and filling missing values, the quality of the data can be significantly improved, providing a reliable basis for further analysis and enhancing the effectiveness of the system in policy making and environmental management.

[0069] The dynamic assessment module uses machine learning algorithms to analyze comprehensive data sets to generate assessment feedback, and transmits the assessment feedback results to the intelligent monitoring module; using advanced machine learning and deep learning algorithms, the system can identify carbon emission trends, provide decision makers with data-based insights, and closely integrate scientific research with policy making.

[0070] The intelligent monitoring module continuously monitors energy consumption and greenhouse gas emissions, and updates the comprehensive data set in real time; it dynamically adjusts the monitoring frequency based on feedback, allowing the system to flexibly switch between high-contribution and low-contribution data sources, optimize resource usage, ensure efficient data collection, and promptly improve monitoring accuracy when needed, reducing unnecessary waste of resources.

[0071] The visualization module displays carbon emissions and intuitively presents the evaluation feedback results obtained from the dynamic evaluation module, forming a complete carbon emissions accounting process. Through the display of charts and dynamic templates, users can easily obtain the required information and conduct detailed analysis. Real-time updates and interactive functions enable users to understand the dynamics of carbon emissions while making flexible responses based on data.

[0072] Furthermore, the data collection module collects carbon emission data in real time through a combination of multiple sensors and smart devices;

[0073] The sensors include gas sensors, energy consumption sensors, and traffic flow monitoring sensors;

[0074] The smart device includes a smart terminal and a wireless communication submodule;

[0075] Furthermore, the system starts triggering data collection at a fixed time, fuses and preliminarily analyzes data from different sensors locally, and reduces the amount of data transmission; by setting a dynamic sampling rate, it conducts time series analysis on data within a certain period of time, determines the key data that needs to be collected first, and realizes effective data integration;

[0076] Through the wireless communication submodule, the processed data is transmitted to the data integration module in real time. During the data upload process, the system will automatically adjust the data upload frequency according to the network stability, perform fast and large-capacity transmission when the network conditions are good, and transmit a small amount of key data when the network conditions are poor. After the data transmission is successful, the data integration module will generate a confirmation receipt and transmit it back to the data acquisition module to ensure that the data has been successfully received.

[0077] Furthermore, the data integration module receives carbon emission data from the data acquisition module, which is multi-source data, cleans the received data, removes invalid or abnormal data, fills in missing values, and standardizes all data;

[0078] It should be noted that multi-source data specifically includes:

[0079] Traffic data: vehicle flow, speed, fuel type, travel routes.

[0080] Industrial emissions data: CO from factories 2 、SO 2 Gas emissions and energy consumption reports.

[0081] Agricultural data: fertilizers used in agricultural production, fuel consumption, and corresponding greenhouse gas emissions.

[0082] Household energy data: household electricity, water, gas and other energy usage and corresponding emission data.

[0083] Furthermore, fuzzy rules are constructed to judge the reliability and importance of each data source. The influence of each data source is evaluated according to the defined fuzzy rules and converted into fuzzy values. , construct fuzzy sets :

[0084] ;

[0085] Normalize the fuzzy values ​​and calculate the weight of each data source :

[0086] ;

[0087] Before data integration, a dynamic weight adjustment model is used to adjust the and reliability factor Adjust weights:

[0088] ;

[0089] The carbon emission values ​​from various data sources are integrated using the weighted average method to form a comprehensive data set :

[0090] ;

[0091] in, The raw carbon emission values ​​calculated for each data source, n is the number of data sources; the resulting comprehensive data set will contain each data source at time contribution and dynamic weight.

[0092] Furthermore, the dynamic evaluation module obtains the latest comprehensive data set from the data integration module, uses the deep learning model to analyze the comprehensive data set, identifies the main factors affecting carbon emissions, and trains the model to predict future carbon emissions; during the training process, the model automatically adjusts the evaluation parameters;

[0093] Using an improved convolutional neural network, the network structure is as follows:

[0094] Input layer: Receives multiple feature inputs, such as traffic flow, industrial emission data, weather conditions, etc.

[0095] Convolutional layer: extract features and use multiple convolution kernels to obtain feature maps.

[0096] Activation layer: Use activation functions to enhance nonlinear features.

[0097] Set the input data to , the output is the carbon emission value predicted by the model , loss function Defined as:

[0098] ;

[0099] in, is the true value, is the predicted value, is the number of samples;

[0100] Update model parameters through back-propagation algorithm :

[0101] ;

[0102] in, is the learning rate, is the gradient of the loss function.

[0103] Furthermore, the adjusted deep learning model will generate evaluation feedback. After receiving the feedback, the intelligent monitoring module will adjust the monitoring strategy. Based on the feedback, the intelligent monitoring module will optimize the monitoring frequency and data collection method.

[0104] The output of the dynamic assessment module will include the contribution of each data source to carbon emissions and the importance score, forming feedback:

[0105] ;

[0106] in, is the feedback rating, is the average carbon emission value of the comprehensive data set, is the standard deviation.

[0107] Furthermore, based on the feedback rating ,The intelligent monitoring module adjusts the monitoring strategy and increases the ,frequency of monitoring and improves the accuracy of data collection when monitoring ,is a high contribution data source;

[0108] Update monitoring frequency:

[0109] ;

[0110] When monitoring is a low-contribution data source, reduce the monitoring frequency;

[0111] Update monitoring frequency:

[0112] ;

[0113] in, is the adjustment step size, is the updated monitoring frequency, is the current monitoring frequency.

[0114] Furthermore, the visualization module establishes data connection with the dynamic evaluation module and the intelligent monitoring module through the API interface, and monitors data updates in real time. When the dynamic evaluation module completes the analysis or the intelligent monitoring module obtains new data, the display module can immediately receive the information;

[0115] After receiving the evaluation feedback results from the dynamic evaluation module and the real-time data from the intelligent monitoring module, the display module organizes the data through the cache mechanism and uses tags to associate timestamps to ensure the time consistency of data from different sources;

[0116] The visualization display module generates display content using dynamic templates based on the processed comprehensive data set; the display content includes: real-time monitoring charts, carbon emission analysis results and trend forecasts;

[0117] Provide users with interactive functions, users can choose to view data in different time periods, or filter according to specific types of energy consumption and carbon emissions; the visualization module sets a timed update mechanism to automatically refresh the data view every 5 minutes.

[0118] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0119] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0121] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0122] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0123] Example 4, reference Figure 2 , which is an embodiment of the present invention, provides a regional carbon emission accounting method, including the system being activated to start data collection, obtaining carbon emission data in real time through multiple sensors and smart devices, and preliminarily fusing and analyzing the carbon emission data locally to reduce the amount of data that needs to be transmitted;

[0124] The processed carbon emission data is transmitted in real time via wireless communication;

[0125] The received carbon emission data will be cleaned, invalid or outliers will be removed, missing values ​​will be filled, fuzzy rules will be used to evaluate the reliability of the data source, and weights will be adjusted dynamically to form an accurate comprehensive data set for subsequent analysis;

[0126] The comprehensive dataset is analyzed by a deep learning model to identify the main influencing factors and adjust the analysis parameters based on the feedback. The generated evaluation results will be used to optimize the monitoring strategy and data collection frequency.

[0127] The results are displayed through visualization means, including real-time data monitoring and trend analysis. The system regularly evaluates based on user feedback and data, continuously adjusts strategies and parameters, enhances system efficiency and updates carbon reduction strategies.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A regional carbon emission accounting system, characterized by: It includes data collection module, data integration module, dynamic evaluation module, intelligent monitoring module and visual display module; The data acquisition module collects carbon emission data in real time and transmits the collected carbon emission data to the data integration module; The data integration module integrates and processes the collected carbon emission data to form a comprehensive data set, and transmits it to the dynamic evaluation module; The integration process includes: the data integration module receives the carbon emission data from the data acquisition module, cleans the received carbon emission data, removes invalid or abnormal data, fills in missing values, and standardizes all carbon emission data. Construct fuzzy rules to judge the reliability and importance of each data source, evaluate the impact of each data source according to the defined fuzzy rules, and convert them into fuzzy values , construct fuzzy sets : ； Normalize the fuzzy values ​​and calculate the weight of each data source : ； Before carbon emission data is integrated, a dynamic weight adjustment model is used to adjust the and reliability factor Adjust weights: ； The carbon emission values ​​from various data sources are integrated using the weighted average method to form a comprehensive data set : ； in, The raw carbon emission values ​​calculated for each data source, n is the number of data sources; the resulting comprehensive data set will contain each data source at time Contribution and dynamic weight; The dynamic assessment module uses a machine learning algorithm to analyze the comprehensive data set to generate assessment feedback, and transmits the assessment feedback results to the intelligent monitoring module; The evaluation feedback results include: the adjusted deep learning model will generate evaluation feedback, and the intelligent monitoring module will adjust the monitoring strategy after receiving the feedback; based on the feedback, the intelligent monitoring module will optimize the monitoring frequency and data collection method; The output of the dynamic assessment module will include the contribution of each data source to carbon emissions and the importance score, forming feedback: ； in, is the feedback rating, is the average carbon emission value of the comprehensive data set, is the standard deviation; The use of the machine learning algorithm includes: the dynamic assessment module obtains the latest comprehensive data set from the data integration module, uses the deep learning model to analyze the comprehensive data set, identifies the main factors affecting carbon emissions, and trains the model to predict future carbon emissions; Using the improved convolutional neural network, the input data is set to , the output is the carbon emission value predicted by the model , loss function Defined as: ； in, is the true value, is the predicted value, is the number of samples; Update model parameters through back-propagation algorithm : ； in, is the learning rate, is the gradient of the loss function; The intelligent monitoring module continuously monitors energy consumption and greenhouse gas emissions, and updates the comprehensive data set in real time; The updating of the comprehensive dataset includes: ,The intelligent monitoring module adjusts the monitoring strategy and increases the ,frequency of monitoring when the monitoring is a high contribution data source; Update monitoring frequency: ； When monitoring is a low-contribution data source, reduce the monitoring frequency; Update monitoring frequency: ； in, is the adjustment step size, is the updated monitoring frequency, is the current monitoring frequency; The visualization display module displays the carbon emission situation and intuitively presents the evaluation feedback results obtained from the dynamic evaluation module, forming a complete carbon emission accounting process.

2. A regional carbon emission accounting system as claimed in claim 1, characterized in that: The real-time collection of carbon emission data includes: a data acquisition module collects carbon emission data in real time by combining multiple sensors and smart devices; The sensors include gas sensors, energy consumption sensors, and traffic flow monitoring sensors; The smart device includes a smart terminal and a wireless communication submodule; The system starts triggering carbon emission data collection at a scheduled time, and locally integrates and preliminarily analyzes data from different sensors. By setting a dynamic sampling rate, it conducts time series analysis on carbon emission data within a certain period of time to determine the key data that needs to be collected first. Through the wireless communication submodule, the processed carbon emission data is transmitted to the data integration module in real time. During the carbon emission data upload process, the system will automatically adjust the data upload frequency according to the network stability, perform fast and large-capacity transmission when the network conditions are good, and transmit a small amount of key data when the network conditions are poor. After the data transmission is successful, the data integration module will generate a confirmation receipt and transmit it back to the data collection module.

3. A regional carbon emission accounting system as claimed in claim 2, characterized in that: The display of carbon emissions includes: the visual display module establishes a data connection with the dynamic assessment module and the intelligent monitoring module through the API interface, monitors data updates in real time, and when the dynamic assessment module completes the analysis or the intelligent monitoring module obtains new data, the display module can immediately receive the information; After receiving the analysis results from the dynamic assessment module and the real-time data from the intelligent monitoring module, the display module organizes the data through the cache mechanism and uses tags to associate timestamps to make the time of data from different sources consistent; The visualization display module uses dynamic templates to generate display content based on the processed comprehensive data set; The display content includes: real-time monitoring charts, carbon emission analysis results and trend forecasts; Provide users with interactive functions, users can choose to view data in different time periods, or filter according to energy consumption and carbon emissions; the visualization module sets a timed update mechanism to automatically refresh the data view every 5 minutes.

4. A regional carbon emission accounting method, applied to a regional carbon emission accounting system as claimed in any one of claims 1 to 3, characterized in that: include, The system is activated to start data collection, acquiring carbon emission-related data in real time through multiple sensors and smart devices. The data is initially integrated and analyzed locally to reduce the amount of data that needs to be transmitted; The processed data is transmitted to the data integration platform in real time via wireless communication; The received data will be cleaned, invalid or outliers will be removed, missing values ​​will be filled to ensure data quality, fuzzy rules will be used to evaluate the reliability of data sources, weights will be adjusted dynamically, and ultimately an accurate comprehensive data set will be formed for subsequent analysis; The comprehensive dataset is analyzed by a deep learning model to identify the main influencing factors and adjust the analysis parameters based on the feedback. The generated evaluation results will be used to optimize the monitoring strategy and data collection frequency. The results are displayed through visualization means, including real-time data monitoring and trend analysis. The system regularly evaluates based on user feedback and data, continuously adjusts strategies and parameters, improves system efficiency and updates carbon reduction strategies.

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