Carbon emission data monitoring method, device and computer program product

Abstract

The application discloses a carbon emission data monitoring method and device and a computer program product, relates to the technical field of data monitoring, and comprises the following steps: collecting metering data through a pre-deployed carbon monitoring terminal, and acquiring artificial supplementary data uploaded by a user through a structured input interface; calculating theoretical carbon emission based on the metering data and the artificial supplementary data; cross-checking the theoretical carbon emission and pre-acquired actual carbon emission to identify carbon data anomalies; labeling abnormal equipment corresponding to the carbon data anomalies in a pre-constructed three-dimensional digital twin power plant model, and correcting data of the abnormal equipment. Through cross-checking, data correction is realized by relying on the three-dimensional digital twin power plant model, thereby realizing accurate monitoring and correction of carbon emission data.

Description

Technical Field

[0001] This application relates to the field of data monitoring technology, and in particular to carbon emission data monitoring methods, equipment and computer program products. Background Technology

[0002] Currently, carbon emission monitoring in thermal power plants mainly relies on two technical approaches: one is the automatic monitoring method based on a Continuous Flue Gas Monitoring System (CEMS), which uses sensors installed on the chimney to collect data such as carbon dioxide concentration and flue gas flow rate in real time to calculate actual carbon emissions; the other is manual calculation based on material consumption, which manually calculates theoretical carbon emissions by statistically analyzing data such as coal consumption and coal carbon content. However, CEMS systems are susceptible to factors such as sensor drift, flue gas operating conditions, and equipment aging, leading to deviations or even anomalies in monitoring data. Furthermore, some small power plants or older units have not fully deployed CEMS systems, resulting in incomplete data coverage. The manual calculation method relies on manually entering coal data and coal quality test reports, which is inefficient, time-consuming, and prone to data errors due to human mistakes, making accurate monitoring and timely correction impossible.

[0003] Therefore, how to accurately monitor and correct carbon emission data has become a technical problem that this application urgently needs to solve. Summary of the Invention

[0004] The main purpose of this application is to provide a carbon emission data monitoring method, device and computer program product, which aims to solve the technical problem of how to accurately monitor and correct carbon emission data.

[0005] To achieve the above objectives, this application proposes a carbon emission data monitoring method, the method comprising:

[0006] Measurement data is collected through pre-deployed carbon monitoring terminals, and supplementary data uploaded by users through a structured input interface is also obtained.

[0007] The theoretical carbon emissions are calculated based on the measured data and the artificially supplemented data.

[0008] Cross-check the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies;

[0009] In a pre-constructed three-dimensional digital twin power plant model, the abnormal equipment corresponding to the carbon data anomaly is marked, and the data of the abnormal equipment is corrected.

[0010] In one embodiment, the step of calculating the theoretical carbon emissions based on the metered data and the artificially supplemented data includes:

[0011] The measurement data and the manually supplemented data are standardized and preprocessed to obtain a standardized dataset.

[0012] A theoretical carbon emission calculation model was constructed based on the law of carbon conservation.

[0013] The standardized dataset is input into the carbon emission calculation model to obtain the theoretical carbon emissions.

[0014] In one embodiment, the step of constructing a theoretical carbon emission calculation model based on the carbon conservation law includes:

[0015] Determine carbon inputs and carbon retentions based on the aforementioned carbon conservation law;

[0016] A basic balance formula is constructed based on the carbon input and carbon retention terms, and a theoretical carbon emission calculation model is derived based on the basic balance formula and a preset molar mass ratio.

[0017] In one embodiment, the step of cross-checking the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies includes:

[0018] Obtain actual carbon emissions;

[0019] The actual carbon emissions are compared with the theoretical carbon emissions, and the deviation is calculated.

[0020] If the deviation exceeds a preset threshold, the corresponding measurement data of the calculated theoretical carbon emissions are cross-compared to identify carbon data anomalies.

[0021] In one embodiment, before the step of marking the anomalous equipment corresponding to the carbon data anomaly in a pre-constructed three-dimensional digital twin power plant model and correcting the data of the anomalous equipment, the method further includes:

[0022] Obtain the factory area asset plan input by the user, and sort out the core change elements of the factory area over time based on the factory area asset plan;

[0023] A dynamic data asset catalog is established based on the core change elements of the factory area over time, and model update rules are defined in the dynamic data asset catalog.

[0024] Ground-based laser point cloud scanning was used to acquire 3D point cloud data of the factory area, and UAV oblique photography was used to acquire topographic data of the factory area.

[0025] The three-dimensional point cloud data and the topographic data of the plant area are spatiotemporally aligned, and a three-dimensional digital twin power plant model is constructed.

[0026] The three-dimensional digital twin power plant model is updated based on the dynamic data asset catalog and the model update rules.

[0027] In one embodiment, the step of marking the anomalous equipment corresponding to the carbon data anomaly in a pre-constructed three-dimensional digital twin power plant model and correcting the data of the anomalous equipment includes:

[0028] Identify the data nodes where the carbon data is abnormal;

[0029] Based on the pre-built ternary association library, reverse matching is performed on the physical devices corresponding to the data nodes, and the physical devices are marked as abnormal devices.

[0030] The abnormal equipment is marked in the three-dimensional digital twin power plant model;

[0031] The system retrieves the device operation data of the malfunctioning device within a preset time range and generates a parameter curve based on the device operation data.

[0032] The parameter curves were corrected using the same operating condition interpolation method.

[0033] In one embodiment, the step of reverse matching based on a pre-built ternary association library with the physical device corresponding to the data node and marking the physical device as an abnormal device further includes:

[0034] Collect basic information about each physical device, and map the basic information to the three-dimensional digital twin power plant model to obtain a first mapping relationship;

[0035] The carbon monitoring terminal is mapped to the three-dimensional digital twin power plant model to obtain a second mapping relationship;

[0036] A ternary association library is established based on the first mapping relationship and the second mapping relationship.

[0037] In one embodiment, the step of correcting the parameter curve using the same operating condition interpolation method includes:

[0038] The data nodes are marked on the parameter curves, and the operating characteristics of the data nodes are determined.

[0039] Retrieve valid operating data that matches the described operating condition characteristics, and calculate correction values ​​based on the valid operating data;

[0040] The data node in the parameter curve is replaced with the correction value to perform parameter curve correction.

[0041] In addition, to achieve the above objectives, this application also proposes a carbon emission data monitoring device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the carbon emission data monitoring method described above.

[0042] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the carbon emission data monitoring method described above.

[0043] One or more technical solutions proposed in this application have at least the following technical effects:

[0044] First, by combining automatic data collection from carbon monitoring terminals with manually supplemented structured data entry, the problem of single data sources and large errors in manual data entry is solved, providing comprehensive and quality-controllable basic data for accurate monitoring. Second, a theoretical carbon emission calculation model is constructed based on the law of carbon conservation. Standardized multi-source data is substituted into the model for calculation, establishing a scientific and unified carbon emission accounting benchmark to ensure the theoretical authority and traceability of monitoring results. Furthermore, by cross-checking theoretical and actual carbon emissions, the limitation of judging anomalies by a single threshold is overcome, enabling accurate differentiation between "data collection anomalies" and "real carbon emission fluctuations," achieving accurate identification of abnormal data. Finally, by relying on a three-dimensional digital twin power plant model to associate abnormal equipment and using interpolation under the same operating conditions to correct data, the pain points of difficult abnormal equipment location and strong subjectivity in data correction in traditional monitoring are solved. Through visual annotation and scientific correction, a closed-loop management is formed, ultimately achieving precise control of carbon emission data throughout the entire chain from collection, accounting, anomaly identification to correction, effectively achieving the technical goals of accurate monitoring and correction. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the first embodiment of the carbon emission data monitoring method of this application.

[0048] Figure 2This is a flowchart illustrating the fourth embodiment of the carbon emission data monitoring method of this application.

[0049] Figure 3 This is a flowchart illustrating the sixth embodiment of the carbon emission data monitoring method of this application.

[0050] Figure 4 This is a schematic diagram of the module structure of the carbon emission data monitoring device according to an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the carbon emission data monitoring method in this application embodiment.

[0052] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0054] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of this application embodiment is as follows: collect metering data through pre-deployed carbon monitoring terminals and obtain manually supplemented data uploaded by users through a structured input interface; calculate the theoretical carbon emissions based on the metering data and the manually supplemented data; cross-check the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies; mark the abnormal equipment corresponding to the carbon data anomalies in a pre-constructed three-dimensional digital twin power plant model, and correct the data of the abnormal equipment.

[0056] In this embodiment, for ease of description, the carbon emission data monitoring system will be used as the implementing entity for the following description.

[0057] This embodiment takes into account that: carbon emission monitoring in thermal power plants mainly relies on two technical approaches: one is the automatic monitoring method based on a continuous flue gas monitoring system (CEMS), which uses sensors installed on the chimney to collect data such as carbon dioxide concentration and flue gas flow rate in real time to calculate the actual carbon emissions; the other is manual calculation based on material consumption, which manually calculates the theoretical carbon emissions by statistically analyzing data such as coal consumption and coal carbon content. However, CEMS systems are susceptible to factors such as sensor drift, flue gas operating conditions, and equipment aging, leading to deviations or even anomalies in monitoring data. Furthermore, some small power plants or older units have not fully deployed CEMS systems, resulting in incomplete data coverage. The manual calculation method relies on manually entering coal data, coal quality test reports, and other information, which is inefficient, time-consuming, and prone to data errors due to human mistakes, making accurate monitoring and timely correction impossible.

[0058] Therefore, this application provides a solution. First, by combining automatic collection of measurement data from carbon monitoring terminals with structured data entry and manual supplementation, it addresses the problems of single data sources and large errors in manual data entry in existing monitoring systems, providing comprehensive and quality-controllable basic data for accurate monitoring. Second, based on the law of carbon conservation, a theoretical carbon emission calculation model is constructed. Standardized multi-source data is substituted into the model for calculation, establishing a scientific and unified carbon emission accounting benchmark to ensure the theoretical authority and traceability of monitoring results. Furthermore, by cross-checking theoretical and actual carbon emissions, the limitations of judging anomalies by a single threshold are overcome, enabling accurate differentiation between "data collection anomalies" and "real carbon emission fluctuations," achieving accurate identification of abnormal data. Finally, by relying on a three-dimensional digital twin power plant model to associate abnormal equipment and using the same operating condition interpolation method to correct data, it solves the pain points of difficult abnormal equipment location and strong subjectivity in data correction in traditional monitoring. Through visual annotation and scientific correction, a closed-loop management is formed, ultimately achieving precise control of carbon emission data throughout the entire chain from collection, accounting, anomaly identification to correction, effectively achieving the technical goals of accurate monitoring and correction.

[0059] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a carbon emission data monitoring system. The following description uses a carbon emission data monitoring system as an example to illustrate this embodiment and the subsequent embodiments.

[0060] Based on this, embodiments of this application provide a method for monitoring carbon emission data, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the carbon emission data monitoring method of this application.

[0061] In this embodiment, the carbon emission data monitoring method includes steps S10 to S40:

[0062] Step S10: Collect measurement data through pre-deployed carbon monitoring terminals and obtain manually supplemented data uploaded by users through a structured input interface;

[0063] It should be noted that carbon monitoring terminals refer to dedicated monitoring equipment deployed at key carbon emission nodes in power plants, including flue gas flow sensors, online coal quality analyzers, ash and slag carbon content detectors, and coal conveyor scales; metering data refers to real-time operational data related to carbon emissions automatically collected by carbon monitoring terminals, such as boiler flue gas flow and coal weight.

[0064] The structured data entry interface refers to the standardized data entry interface or data upload channel provided by the system, which supports batch import or manual entry of data in preset formats; manually supplemented data refers to carbon emission-related data that cannot be obtained through automatic monitoring and needs to be entered by operation and maintenance personnel or management personnel, such as offline coal quality test reports, ash and slag transportation records, fuel consumption, etc.

[0065] Specifically, the system first deploys carbon monitoring terminals at key locations such as boilers, turbines, dust collectors, and ash storage facilities based on the distribution of carbon emission nodes in the power plant. The terminals transmit the collected metering data to the system server in real time via wired or wireless communication modules. At the same time, the system provides a structured data entry interface with preset data entry templates. The templates include mandatory fields such as coal quality based carbon content, ash carbon content, and fuel oil calorific value. Users can submit supplementary data manually by filling in the data or uploading an Excel file. The system automatically verifies the format of the entered data to ensure that the data conforms to the preset specifications.

[0066] In one possible implementation, the carbon monitoring terminal has edge computing capabilities, which can preprocess the collected raw data locally (such as filtering and noise reduction, outlier removal) before transmitting it to the server, effectively reducing the bandwidth usage of data transmission; the structured input interface supports data docking with power plant ERP systems and coal quality management systems, realizing automatic synchronization of manually supplemented data and reducing the workload of manual data entry.

[0067] Step S20: Calculate the theoretical carbon emissions based on the measured data and the artificially supplemented data;

[0068] It should be noted that theoretical carbon emissions refer to the carbon emissions obtained by calculating the difference between carbon emission inputs and retentions according to the law of carbon conservation, reflecting the carbon emission level of a power plant under ideal operating conditions.

[0069] Specifically, the system first performs standardized preprocessing on the metered data and manually supplemented data, including standardizing the data format and converting units, such as converting coal consumption from tons to kilograms.

[0070] Furthermore, a theoretical carbon emission calculation model is constructed based on the law of carbon conservation. The law of carbon conservation states that in a closed system, the total amount of carbon remains constant, that is, the total amount of carbon input to the system equals the total amount of carbon output to the system (including emitted carbon dioxide, carbon retained in ash, etc.). The basic balance formula of the model is: Theoretical carbon emissions = (coal-fired carbon input + fuel oil carbon input + other fuel carbon input) - (ash carbon retention + fly ash carbon retention + other carbon sequestration retention). Each input and retention item is calculated through measurement data and manually supplemented data. For example, coal-fired carbon input = coal consumption × coal quality as received carbon content ÷ 100. Finally, the standardized dataset is input into the calculation model, and the system automatically executes the formula to calculate the theoretical carbon emissions and generates a corresponding accounting report.

[0071] Step S30: Cross-check the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies;

[0072] Actual carbon emissions refer to the carbon emissions calculated from the carbon dioxide concentration and flow rate in flue gas collected by a continuous emission monitoring system (CEMS). It represents the total amount of carbon dioxide actually emitted by the power plant. Cross-checking refers to the process of comparing and analyzing theoretical calculations with actual monitoring values ​​to determine whether the data is abnormal based on the deviation. Carbon data anomalies refer to deviations between theoretical and actual carbon emissions that exceed a reasonable range, or problems such as sensor malfunctions or data transmission errors in the data acquisition chain.

[0073] The system first acquires the pre-stored actual carbon emissions, which are collected in real time by the CEMS system and transmitted to the server. Then, it calculates the deviation between the theoretical carbon emissions and the actual carbon emissions, where the deviation is calculated as |theoretical value - actual value| ÷ theoretical value × 100%. If the deviation exceeds a preset threshold (e.g., ±5%), the system initiates an anomaly tracing process, cross-referencing the measurement data used to calculate the theoretical carbon emissions with manually supplemented data. This includes verifying the operating status of the carbon monitoring terminal, the accuracy of the manually supplemented data, and the calibration records of the CEMS system, thereby identifying the type of carbon data anomaly (e.g., abnormal data acquisition, abnormal operating condition fluctuations, or abnormal actual emissions).

[0074] Step S40: Mark the abnormal equipment corresponding to the carbon data anomaly in the pre-constructed three-dimensional digital twin power plant model, and correct the data of the abnormal equipment.

[0075] Additionally, it should be noted that a 3D digital twin power plant model refers to a digital virtual model built based on the actual physical scene of a power plant, which includes the 3D structure of all equipment, pipelines, buildings, etc., and is associated with real-time operating data; abnormal equipment refers to physical equipment directly related to abnormal carbon data, such as malfunctioning carbon monitoring terminals, leaking boiler components, etc.; data correction refers to the process of supplementing or correcting abnormal carbon emission data using scientific methods.

[0076] Specifically, the system first uses a pre-built ternary association database to reverse match the physical devices corresponding to carbon data anomalies. This database stores the mapping relationships between carbon monitoring terminals, physical devices, and digital twin model nodes. Then, in the 3D digital twin power plant model, abnormal devices are marked by highlighting, flashing, and labeling abnormal information. Maintenance personnel can visually view the location and status of abnormal devices through the model. Next, the system retrieves the device operation data of the abnormal device within a preset time range (such as the past 72 hours) to generate parameter curves (such as flue gas flow curves and coal consumption curves). Finally, the system uses the same operating condition interpolation method to correct the parameter curves. This method involves retrieving historical valid data that matches the operating condition characteristics (such as boiler load, coal quality parameters, and flue gas oxygen content) of the abnormal data node, calculating the correction value through weighted averaging, and replacing the abnormal data node.

[0077] This embodiment provides a carbon emission data monitoring method. First, by combining automatic collection of measurement data from carbon monitoring terminals with structured data entry and manual supplementation, it addresses the problems of single data sources and large errors in manual data entry in existing monitoring methods, providing comprehensive and quality-controllable basic data for accurate monitoring. Second, based on the law of carbon conservation, a theoretical carbon emission calculation model is constructed. Standardized multi-source data is substituted into the model for calculation, establishing a scientific and unified carbon emission accounting benchmark to ensure the theoretical authority and traceability of monitoring results. Furthermore, by cross-checking theoretical carbon emissions with actual carbon emissions, the limitation of judging anomalies by a single threshold is overcome, enabling accurate differentiation between "data collection anomalies" and "real carbon emission fluctuations," achieving accurate identification of abnormal data. Finally, by relying on a three-dimensional digital twin power plant model to associate abnormal equipment and using the same operating condition interpolation method to correct data, it solves the pain points of difficult abnormal equipment location and strong subjectivity in data correction in traditional monitoring. Through visual annotation and scientific correction, a closed-loop management is formed, ultimately achieving precise control of carbon emission data throughout the entire chain from collection, accounting, anomaly identification to correction, effectively achieving the technical goals of accurate monitoring and correction.

[0078] Based on the first embodiment of this application, a second embodiment of this application is proposed. In the second embodiment of this application, content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter.

[0079] In this embodiment, step S20, which calculates the theoretical carbon emissions based on the measured data and the artificially supplemented data, may include steps S21 to S23:

[0080] Step S21: Perform standardization preprocessing on the measurement data and the manually supplemented data to obtain a standardized dataset;

[0081] Standardization preprocessing refers to the process of performing operations such as format conversion, unit calibration, and outlier removal on data from different sources and in different formats; a standardized dataset refers to a dataset that has been preprocessed and has a unified format, consistent units, and meets the data quality requirements.

[0082] Specifically, the first step is to standardize the format of the measurement data and manually supplemented data by converting all data into a preset JSON format for storage. Then, unit calibration is performed to convert all physical quantities into international standard units. Next, the 3σ principle is used to identify and remove abnormal data that deviates from the normal range. Finally, the processed data is integrated into a standardized dataset and stored in a specified database table for subsequent calculations.

[0083] Step S22: Construct a theoretical carbon emission calculation model based on the carbon conservation law;

[0084] The system first outlines the carbon flow path of the power plant, clarifying the entire process of carbon from fuel input to final emission. Then, it determines the specific application boundary of the carbon conservation law by combining the power plant's fuel type (such as coal, gas, oil, etc.) and production process. Next, based on the carbon flow path and application boundary, it constructs a theoretical carbon emission calculation model framework applicable to the power plant. The model framework includes three core parts: input items, calculation logic, and output items.

[0085] In one feasible implementation, step S22 may include steps S221 to S222:

[0086] Step S221: Determine the carbon input items and carbon retention items according to the carbon conservation law;

[0087] Carbon inputs refer to the total amount of all carbon-containing substances input into the power plant system during the power plant's production process, including fuel inputs and auxiliary material inputs; carbon retentions refer to the total amount of carbon elements that are not emitted into the atmosphere during the power plant's production process but remain in solid waste, liquid waste, or products.

[0088] Specifically, by analyzing the power plant's production process flow chart and material balance sheet, all input and retention paths of carbon-containing substances are identified; then, based on the law of carbon conservation, carbon-containing substances in the input paths are defined as carbon input items, and carbon-containing substances in the retention paths are defined as carbon retention items.

[0089] Step S222: Construct a basic balance formula based on the carbon input item and the carbon retention item, and derive a theoretical carbon emission calculation model based on the basic balance formula and the preset molar mass ratio.

[0090] The preset molar mass ratio refers to the ratio of the molar mass of carbon to carbon dioxide (12:44), which is used to convert carbon emissions into carbon dioxide emissions.

[0091] First, a basic balance formula is constructed based on carbon inputs and carbon retentions: Total carbon inputs = Total carbon retentions + Carbon emissions. Then, the basic balance formula is transformed to obtain Carbon emissions = Total carbon inputs - Total carbon retentions. Next, a preset molar mass ratio is introduced to convert carbon emissions into carbon dioxide emissions, i.e., theoretical carbon emissions = (Total carbon inputs - Total carbon retentions) × (44 / 12). Finally, the specific calculation methods of each carbon input and carbon retention are substituted into the formula to form a complete theoretical carbon emissions calculation model.

[0092] Step S23: Input the standardized dataset into the carbon emission calculation model for calculation to obtain the theoretical carbon emissions.

[0093] Specifically, the system reads the standardized dataset and calculates each data item sequentially according to the preset logic of the carbon emission calculation model: first, it calculates the total amount of each carbon input item, then it calculates the total amount of each carbon retention item, and finally it subtracts the total amount of retention items from the total amount of input items to obtain the carbon element emission amount, and then combines the carbon to carbon dioxide molar mass ratio to convert it into carbon dioxide emission amount, i.e., theoretical carbon emission amount; after the calculation is completed, the system automatically verifies the results, and if the results exceed the preset reasonable range, it triggers the recalculation process.

[0094] In this embodiment, standardized preprocessing eliminates differences in format, units, and quality between metered data and manually supplemented data; a calculation model based on the carbon conservation law ensures the rigor of the accounting logic; and automated calculation achieves accurate quantification of theoretical carbon emissions, significantly improving accounting efficiency while reducing errors caused by human intervention. The synergistic effect of these three elements ultimately generates a theoretical carbon emission figure with high accuracy and traceability, providing a reliable benchmark for subsequent cross-verification with actual carbon emissions, and providing core data support for the refined management, anomaly identification, and compliance control of power plant carbon emissions.

[0095] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed. In this third embodiment, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0096] In this embodiment, the step S30, which involves cross-checking the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies, may include steps S31 to S33:

[0097] Step S31: Obtain the actual carbon emissions;

[0098] Specifically, the system's actual data acquisition module establishes a real-time communication connection with the power plant's pre-deployed continuous flue gas monitoring system. Through a standardized data interface, it acquires real-time flue gas flow data and carbon dioxide concentration data in the flue gas collected by the continuous flue gas monitoring system. The actual carbon emissions are then calculated according to a preset formula (actual carbon emissions = flue gas flow rate × carbon dioxide concentration × conversion coefficient).

[0099] Step S32: Compare the actual carbon emissions with the theoretical carbon emissions and calculate the deviation range;

[0100] Specifically, the system's deviation calculation module first reads the values ​​of actual carbon emissions and theoretical carbon emissions, calculates the absolute difference between the two, then divides the absolute difference by the absolute value of theoretical carbon emissions, and finally multiplies it by 100% to obtain the percentage value of the deviation range. After the calculation is completed, the system automatically retains two decimal places for the deviation range value, which is convenient for subsequent threshold comparison.

[0101] In one possible implementation, the system can dynamically adjust the calculation logic of the deviation amplitude according to the operating conditions of the power plant. During the changing operating conditions such as unit start-up and shutdown and large load fluctuations, the relative deviation (compared with historical data under the same operating conditions) is used instead of the absolute deviation amplitude calculation to avoid misjudgment caused by changes in operating conditions.

[0102] Step S33: When the deviation exceeds a preset threshold, cross-compare the corresponding measurement data of the calculated theoretical carbon emissions to identify carbon data anomalies.

[0103] Cross-comparison refers to the process of comparing and verifying measurement data with multiple sources of data, such as manually supplemented data, historical data under the same operating conditions, and equipment operating status data; carbon data anomalies refer to situations where carbon data deviates from the true value due to data acquisition errors, equipment failures, or other reasons.

[0104] Specifically, the system's anomaly identification module first determines whether the deviation exceeds a preset threshold (e.g., ±5%). If it does, a cross-comparison process is initiated: first, the integrity of the measurement data is checked to confirm whether there are any missing or abrupt data; second, the measurement data is cross-validated with manually supplemented data, such as comparing the coal consumption data with the coal consumption in the manually entered coal quality test report; finally, the measurement data is compared with historical measurement data under the same operating conditions. If a certain measurement data deviates significantly from the historical data, it is identified as carbon data anomaly, and the monitoring equipment corresponding to the anomaly data is marked.

[0105] In this embodiment, by obtaining actual carbon emissions to provide a real emission reference for comparison with theoretical values, the deviation range is calculated to objectively quantify the degree of difference between the two data dimensions, breaking through the subjective limitations of traditional qualitative judgment. Furthermore, by cross-comparing multi-source data, the root causes of carbon data anomalies are accurately identified, effectively distinguishing between "abnormal data collection" and "fluctuations in actual carbon emissions," significantly improving the accuracy, objectivity, and pertinence of carbon data anomaly identification.

[0106] Based on the above embodiments of this application, a fourth embodiment of this application is proposed. In this fourth embodiment, content that is the same as or similar to that in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0107] Based on this, refer to Figure 2 , Figure 2 This is a schematic flowchart of the fourth embodiment of this application, as shown below. Figure 2 As shown, before step S40, which involves marking the abnormal equipment corresponding to the carbon data anomaly in a pre-constructed three-dimensional digital twin power plant model and correcting the data for the abnormal equipment, steps S341 to S345 are included:

[0108] Step S341: Obtain the factory area asset plan input by the user, and sort out the core change elements of the factory area in the time dimension based on the factory area asset plan;

[0109] It should be noted that, in this embodiment of the application, the plant asset planning refers to the user's planning scheme for the addition, renovation, demolition, relocation, etc. of various assets such as production equipment, building facilities, monitoring terminals, and pipeline systems within the plant area in the future.

[0110] The core change elements in the time dimension refer to key information such as the time nodes of asset changes, the type of change (new addition / renovation / demolition), the scope of impact, and related data interfaces involved in the plant area asset planning.

[0111] Specifically, the system first receives the plant asset planning documents uploaded by users through a visual interactive interface (supporting formats such as PDF, Word, and Excel). It then uses natural language processing technology to perform structured parsing of the document content, identifying information such as asset names, planned implementation time, and changes. Next, it categorizes and filters the identified information, extracting core change elements in the time dimension, such as "addition of flue gas monitoring terminal for Boiler No. 3 in June 2026" and "renovation of coal conveyor belt system in September 2026." These elements are then stored in a time series database to form a traceable change record.

[0112] Step S342: Establish a dynamic data asset catalog based on the core change elements of the factory area in the time dimension, and define model update rules in the dynamic data asset catalog;

[0113] The dynamic data asset catalog refers to a structured catalog system that records various data assets (such as monitoring data, equipment parameters, asset ledgers, spatial coordinates, etc.) in the plant area and can be automatically updated as the plant area assets change; the model update rules refer to a set of rules that define the triggering conditions, update content, update priority, and update frequency for updating the three-dimensional digital twin power plant model.

[0114] Specifically, the system first reads the core change elements in the time dimension and constructs a dynamic data asset catalog framework based on the asset change type. The catalog includes fields such as data asset ID, asset association ID, data type, update timestamp, and data storage path. Then, it defines model update rules for different asset change scenarios, such as "when a new monitoring terminal is added, trigger the terminal model addition operation at the corresponding location of the model and synchronize the associated data collection interface" and "when an asset is dismantled, delete the corresponding component and associated data link in the model". Finally, the dynamic data asset catalog and model update rules are stored in the configuration database, and the real-time monitoring triggers of the rules are set.

[0115] Step S343: Use ground laser point cloud scanning to obtain three-dimensional point cloud data of the factory area, and use UAV oblique photography to obtain topographic data of the factory area.

[0116] The system first plans ground laser scanning routes and sites to ensure coverage of all core production areas and key facilities within the factory. Then, it uses a ground laser scanner to scan each site to acquire 3D point cloud data of equipment and buildings within the factory. Simultaneously, it plans drone flight paths, setting flight altitudes of 100-200 meters and image overlap rates of 80%. The drone uses a five-lens tilting camera to capture images of the factory area, and reconstructs 3D topographic data of the factory area using photogrammetry. Finally, both types of data are stored on a 3D spatial data server for preliminary noise removal and data compression.

[0117] In one possible implementation, the system can supplement the large-scale terrain and landforms around the factory area by combining satellite remote sensing data. For obstructed areas within the factory area (such as the bottom of equipment and gaps between buildings), a handheld laser scanning device can be used for supplementary scanning to further improve the integrity and coverage of the data.

[0118] Step S344: Spatiotemporally align the three-dimensional point cloud data of the plant area and the topographic data of the plant area, and construct a three-dimensional digital twin power plant model.

[0119] First, the 3D point cloud data and topographic data of the factory area are converted into a unified WGS-84 geodetic coordinate system. The coordinates are registered by identifying landmark buildings (such as chimneys and main buildings) in the factory area as feature control points. Then, the data is synchronized to the same reference time node according to the data acquisition timestamp to eliminate spatial deviation caused by time difference.

[0120] Furthermore, the 3D point cloud data is fused with the terrain data to remove redundant data and fill in missing data in occluded areas. Finally, a 3D digital twin power plant model is constructed based on the fused data, and material textures, equipment parameter labels, and data association interfaces are added to the model to obtain the 3D digital twin power plant model.

[0121] Step S345: Update the three-dimensional digital twin power plant model based on the dynamic data asset catalog and the model update rules.

[0122] The system monitors changes in the dynamic data asset catalog in real time. When a triggering event that matches the model update rules is detected (such as the completion of a new asset integration or the completion of a renovation project), it automatically retrieves the corresponding updated data (such as the asset's 3D model, parameter configuration, and data interface information) from the dynamic data asset catalog. Then, it determines the update method according to the model update rules. For example, for new assets, it incrementally adds model components; for asset renovation, it replaces model components; and for asset removal, it deletes model components. Next, it uses a 3D model incremental update algorithm to perform a partial update of the model without rebuilding the entire model. Finally, it synchronizes the updated model to the display terminal, supporting real-time viewing and interactive operations by users.

[0123] In this embodiment, by sorting out the core changing elements of the factory area over time, a precise basis is provided for model iteration. A dynamic data asset catalog and update rules are established to clarify the model update standards. High-precision multi-source spatial data is obtained by combining ground laser point cloud scanning and UAV oblique photography. A high-fidelity three-dimensional digital twin model is constructed through spatiotemporal alignment. Based on dynamic rules, the model is automatically updated incrementally. This effectively solves the pain points of traditional static digital twin models being unable to adapt to the dynamic changes of factory assets and the disconnect between data and physical scenes, and significantly improves the timeliness, accuracy and practicality of the model.

[0124] Based on the above embodiments of this application, a fifth embodiment of this application is proposed. In this fifth embodiment, content that is the same as or similar to that in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0125] In this embodiment, step S40, which involves marking the abnormal equipment corresponding to the carbon data anomaly in a pre-constructed three-dimensional digital twin power plant model and correcting the data of the abnormal equipment, may include steps S41 to S45:

[0126] Step S41: Identify the data nodes where the carbon data is abnormal;

[0127] Specifically, key information such as timestamps, data types, and acquisition channel codes of abnormal data are extracted from the carbon data anomaly identification results. The original acquisition terminal ID and data storage node ID corresponding to the abnormal data are queried in reverse using a data link tracing algorithm. The abnormal data node is marked as a carbon data anomaly, and the anomaly type of the node (such as data jump or data missing) is recorded synchronously.

[0128] Step S42: Based on the pre-built ternary association library, perform reverse matching with the physical devices corresponding to the data nodes, and mark the physical devices as abnormal devices;

[0129] It should be noted that the ternary association database refers to a structured database that stores the correspondence between data node ID, physical device ID, and data acquisition channel ID; data node refers to the acquisition or storage node that has been marked as having carbon data anomalies; physical device refers to the hardware equipment in the plant area used for carbon data acquisition and transmission, such as flue gas flow sensors and carbon dioxide concentration monitors; and abnormal device refers to the physical device that has been determined to be associated with carbon data anomalies.

[0130] The system calls the ternary association library, inputs the abnormal data node ID, and queries the corresponding physical device ID, device name, installation location and other information through the reverse index. The physical device is marked as an abnormal device, and the device status is updated to "abnormal" in the device management database. At the same time, the marking time and associated abnormal data information are recorded.

[0131] Step S43: Mark the abnormal equipment in the three-dimensional digital twin power plant model;

[0132] Specifically, the 3D model components corresponding to abnormal equipment are located in the 3D digital twin power plant model and visualized by highlighting, flashing, and color differentiation (such as flashing red). At the same time, a floating window pops up in the model to display the basic information of the abnormal equipment, abnormal data details, and the time of the abnormality. The annotation information can be linked with the equipment management system, and clicking the floating window will jump to the equipment details page.

[0133] Step S44: Retrieve the device operation data of the abnormal device within a preset time range, and generate a parameter curve based on the device operation data;

[0134] Based on the abnormal device ID, retrieve all operating parameter data within a preset time range from the device operation database. After cleaning the data (removing duplicate and invalid values), generate parameter curves by sorting by timestamps. Supports superimposed comparison and display of multiple parameter curves.

[0135] For example, in one specific implementation, the system retrieves the flue gas flow data from the No. 3 boiler flue gas flow sensor from 14:00 on February 14, 2026 to 15:00 on August 15, 2026, and after cleaning, generates a "time-flue gas flow" parameter curve. The curve clearly shows that the data at 14:00 jumps from 100,000 cubic meters / hour to 150,000 cubic meters / hour.

[0136] Step S45: Correct the parameter curve using the same working condition interpolation method.

[0137] Same-condition interpolation refers to the method of obtaining the correction value by retrieving valid operating data that is consistent with the operating condition characteristics of abnormal data nodes and using interpolation calculation; parameter curve refers to the visualized curve of the operating parameters of abnormal equipment changing over time.

[0138] Specifically, the operating condition characteristics corresponding to the abnormal data nodes are first determined, then valid operating data under the same operating conditions are retrieved, and the correction values ​​are calculated by weighted averaging or linear interpolation. Finally, the abnormal data nodes in the parameter curve are replaced with the correction values ​​to regenerate a continuous parameter curve.

[0139] In this embodiment, by accurately locating the data nodes with carbon data anomalies, the corresponding physical devices are back-linked and visualized in the three-dimensional digital twin power plant model. Combined with the equipment operation data, parameter curves are generated. Finally, the abnormal data is scientifically corrected using the same operating condition interpolation method. This effectively solves the pain points of vague root cause location, low investigation efficiency, and lack of scientific basis for data correction in traditional carbon data anomaly processing, and significantly improves the accuracy and reliability of the data.

[0140] In one feasible implementation, step S45 may include steps S451 to S453:

[0141] Step S451: Mark the data nodes on the parameter curve and determine the operating characteristics of the data nodes;

[0142] Data nodes refer to the abnormal data points in the parameter curves; operating condition characteristics refer to the operating status parameters of the power plant when abnormal data nodes occur, such as boiler load, calorific value of coal, ambient temperature, and oxygen content in flue gas.

[0143] Specifically, abnormal data nodes are marked on the parameter curves, and all operating condition characteristic parameters corresponding to the node are extracted by linking to the real-time database of the power plant to form an operating condition characteristic set, which provides matching conditions for subsequent data retrieval.

[0144] Step S452: Retrieve valid operating data that matches the operating condition characteristics, and calculate the correction value based on the valid operating data;

[0145] Based on the set of operating conditions, retrieve valid operating data under the same or similar operating conditions from the historical operating database (set the operating condition similarity threshold ≥90%), and calculate the weighted average of the retrieved valid data (the weight is determined according to the time interval between the data collection time and the abnormal node, the closer the interval, the higher the weight) to obtain the correction value.

[0146] Step S453: Replace the data node in the parameter curve with the correction value to perform parameter curve correction.

[0147] The calculated correction values ​​replace the abnormal data nodes in the parameter curve, and the continuous parameter curve is redrawn. At the same time, a correction log is recorded, including information on abnormal data nodes, correction values, correction time, and operating conditions, to support subsequent source tracing and query.

[0148] Based on the above embodiments of this application, a sixth embodiment of this application is proposed. In the sixth embodiment of this application, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0149] Based on this, please refer to Figure 3 , Figure 3 This is a flowchart illustrating the sixth embodiment of this application, as shown below. Figure 3 As shown, before step S42, which involves reverse matching of the physical devices corresponding to the data nodes based on a pre-built ternary association library and marking the physical devices as abnormal devices, steps S01 to S03 are also included:

[0150] Step S01: Collect basic information of each physical device, and map the basic information to the three-dimensional digital twin power plant model to obtain the first mapping relationship;

[0151] It should be noted that physical equipment refers to various hardware devices used in power plants for production operation, environmental monitoring, and maintenance support, such as boilers, steam turbines, flue gas flow sensors, and coal conveyor belt systems; basic information refers to the core attribute data of physical equipment, such as unique identifier ID, equipment name, model and specifications, installation location coordinates, manufacturer, commissioning time, and rated parameters.

[0152] The first mapping relationship refers to the unique association record between physical equipment and the corresponding virtual components in the three-dimensional digital twin power plant model.

[0153] Specifically, basic information is collected through a multi-channel integration approach: it supports manual entry of standardized forms, automatic identification of equipment IDs and models using RFID tags, and batch import of equipment spatial coordinates from BIM models; it uses a spatial coordinate matching algorithm to accurately align the installation location of physical equipment with the spatial coordinates of virtual components in the three-dimensional digital twin power plant model, generating a first mapping relationship containing physical equipment ID, model component ID, and installation location coordinates, which is then stored in a relational database and indexed.

[0154] Step S02: Map the carbon monitoring terminal to the three-dimensional digital twin power plant model to obtain a second mapping relationship;

[0155] Specifically, the system collects basic information about carbon monitoring terminals through wireless communication, narrowband IoT, etc.: terminal ID, monitoring parameter type, installation location coordinates, communication protocol, sampling frequency, etc.; and uses spatial interpolation algorithms to accurately match the installation location of carbon monitoring terminals with the coordinates of virtual monitoring points in the three-dimensional digital twin power plant model, generating a second mapping relationship containing carbon monitoring terminal ID, model monitoring point ID, and monitoring parameter type, which is stored in a time-series database to support rapid querying of high-frequency data.

[0156] Step S03: Establish a ternary association library based on the first mapping relationship and the second mapping relationship.

[0157] Specifically, the first and second mapping relationships are read respectively. Using the spatial topology relationship algorithm of the three-dimensional digital twin power plant model, the physical equipment corresponding to the carbon monitoring terminal (such as the flue gas sensor installed on the boiler corresponding to the boiler equipment) is identified. The three core fields of physical equipment ID, carbon monitoring terminal ID, and model component ID are extracted, a ternary association table is constructed and stored in the graph database, and the characteristics of the graph database are used to realize fast association query of any node.

[0158] In this embodiment, by mapping the basic information of physical equipment and carbon monitoring terminals to digital twin models to form a dual mapping relationship, a ternary association library covering equipment-terminal-model is established, providing accurate basic association support for carbon data anomaly location, equipment fault tracing, and carbon emission visualization management.

[0159] This application also provides a carbon emission data monitoring device; please refer to... Figure 4 The carbon emission data monitoring device includes:

[0160] The data acquisition module 10 is used to collect measurement data through a pre-deployed carbon monitoring terminal and to acquire manually supplemented data uploaded by users through a structured input interface.

[0161] Theoretical calculation module 20 is used to calculate theoretical carbon emissions based on the measured data and the artificially supplemented data;

[0162] The cross-verification module 30 is used to cross-verify the theoretical carbon emissions with the pre-acquired actual carbon emissions to identify carbon data anomalies.

[0163] The data correction module 40 is used to mark the abnormal equipment corresponding to the carbon data anomaly in the pre-constructed three-dimensional digital twin power plant model, and to correct the data of the abnormal equipment.

[0164] The carbon emission data monitoring device provided in this application, employing the carbon emission data monitoring method described in the above embodiments, can solve the technical problem of carbon emission data monitoring. Compared with the prior art, the beneficial effects of the carbon emission data monitoring device provided in this application are the same as those of the carbon emission data monitoring method provided in the above embodiments, and other technical features in the carbon emission data monitoring device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0165] This application provides a carbon emission data monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the carbon emission data monitoring method in the first embodiment described above.

[0166] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a carbon emission data monitoring device suitable for implementing embodiments of this application. The carbon emission data monitoring device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The carbon emission data monitoring device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0167] like Figure 5As shown, the carbon emission data monitoring device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the carbon emission data monitoring device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the carbon emission data monitoring equipment to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows carbon emission data monitoring equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0168] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0169] The carbon emission data monitoring device provided in this application, employing the carbon emission data monitoring method described in the above embodiments, can solve the technical problem of carbon emission data monitoring. Compared with the prior art, the beneficial effects of the carbon emission data monitoring device provided in this application are the same as those of the carbon emission data monitoring method provided in the above embodiments, and other technical features of this carbon emission data monitoring device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0170] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0171] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0172] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the carbon emission data monitoring method in the above embodiments.

[0173] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0174] The aforementioned computer-readable storage medium may be included in the carbon emission data monitoring device; or it may exist independently and not be assembled into the carbon emission data monitoring device.

[0175] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the carbon emission data monitoring device, the carbon emission data monitoring device performs the following actions: collects measurement data through pre-deployed carbon monitoring terminals and obtains manually supplemented data uploaded by users through a structured input interface; calculates theoretical carbon emissions based on the measurement data and the manually supplemented data; cross-checks the theoretical carbon emissions with the pre-acquired actual carbon emissions to identify carbon data anomalies; and marks the abnormal devices corresponding to the carbon data anomalies in a pre-constructed three-dimensional digital twin power plant model and corrects the data of the abnormal devices.

[0176] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0178] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0179] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described carbon emission data monitoring method, and is capable of solving the technical problem of carbon emission data monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the carbon emission data monitoring method provided in the above embodiments, and will not be repeated here.

[0180] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the carbon emission data monitoring method described above.

[0181] The computer program product provided in this application can solve the technical problem of carbon emission data monitoring. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the carbon emission data monitoring method provided in the above embodiments, and will not be repeated here.

[0182] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for monitoring carbon emission data, characterized in that, The carbon emission data monitoring method includes: Measurement data is collected through pre-deployed carbon monitoring terminals, and supplementary data uploaded by users through a structured input interface is also obtained. The theoretical carbon emissions are calculated based on the measured data and the artificially supplemented data. Cross-check the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies; Obtain the factory area asset plan input by the user, and sort out the core change elements of the factory area over time based on the factory area asset plan; A dynamic data asset catalog is established based on the core change elements of the factory area over time, and model update rules are defined in the dynamic data asset catalog. Three-dimensional point cloud data of the factory area was obtained by ground laser point cloud scanning, and topographic data of the factory area was obtained by UAV oblique photography. The three-dimensional point cloud data and the topographic data of the plant area are spatiotemporally aligned, and a three-dimensional digital twin power plant model is constructed. The three-dimensional digital twin power plant model is updated based on the dynamic data asset catalog and the model update rules; In a pre-constructed three-dimensional digital twin power plant model, the abnormal equipment corresponding to the carbon data anomaly is marked, and the data of the abnormal equipment is corrected; this step specifically includes: determining the data node of the carbon data anomaly; Collect basic information about each physical device, and map the basic information to the three-dimensional digital twin power plant model to obtain the first mapping relationship; The carbon monitoring terminal is mapped to the three-dimensional digital twin power plant model to obtain a second mapping relationship; A ternary association library is established based on the first mapping relationship and the second mapping relationship; Based on the pre-built ternary association library, reverse matching is performed on the physical devices corresponding to the data nodes, and the physical devices are marked as abnormal devices. The abnormal equipment is marked in the three-dimensional digital twin power plant model; The system retrieves the device operation data of the malfunctioning device within a preset time range and generates a parameter curve based on the device operation data. The parameter curve is corrected using the same working condition interpolation method. This step specifically includes: marking the data nodes on the parameter curve and determining the working condition characteristics of the data nodes. Retrieve valid operating data that matches the described operating condition characteristics, and calculate correction values ​​based on the valid operating data; The data node in the parameter curve is replaced with the correction value to perform parameter curve correction.

2. The carbon emission data monitoring method as described in claim 1, characterized in that, The step of calculating the theoretical carbon emissions based on the measured data and the artificially supplemented data includes: The measurement data and the manually supplemented data are standardized and preprocessed to obtain a standardized dataset. A theoretical carbon emission calculation model was constructed based on the law of carbon conservation. The standardized dataset is input into the carbon emission calculation model to obtain the theoretical carbon emissions.

3. The carbon emission data monitoring method as described in claim 2, characterized in that, The steps for constructing a theoretical carbon emission calculation model based on the carbon conservation law include: Determine carbon inputs and carbon retentions based on the aforementioned carbon conservation law; A basic balance formula is constructed based on the carbon input and carbon retention terms, and a theoretical carbon emission calculation model is derived based on the basic balance formula and a preset molar mass ratio.

4. The carbon emission data monitoring method as described in claim 1, characterized in that, The step of cross-checking the theoretical carbon emissions with the pre-obtained actual carbon emissions to identify carbon data anomalies includes: Obtain actual carbon emissions; The actual carbon emissions are compared with the theoretical carbon emissions, and the deviation is calculated. If the deviation exceeds a preset threshold, the corresponding measurement data of the calculated theoretical carbon emissions are cross-compared to identify carbon data anomalies.

5. A carbon emission data monitoring device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the carbon emission data monitoring method as described in any one of claims 1 to 4.

6. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the carbon emission data monitoring method as described in any one of claims 1 to 4.

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