Carbon emission data intelligent analysis method and system

Abstract

The embodiment of the invention provides a carbon emission data intelligent analysis method and system, and the method comprises the steps: obtaining initial carbon emission data, carrying out the verification based on the initial carbon emission data, determining the verification carbon emission data, and carrying out the abnormal data detection based on the verification carbon emission data; establishing a carbon emission prediction model, performing carbon emission related prediction based on the carbon emission related data and the carbon emission prediction model, and determining carbon emission prediction data; establishing a unit energy consumption calculation model, and calculating unit carbon emission and production energy consumption data based on the related data of the production working condition and the unit energy consumption calculation model; and a carbon emission knowledge technology file library is established, question data of the user is obtained, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge technology file library. Carbon checking data, carbon checking data and power plant real-time operation data are accessed, and the functions of data checking, automatic report generation, real-time prediction, multi-dimensional analysis, intelligent question and answer and the like are achieved.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of carbon emission technology, and in particular to a method for intelligent analysis of carbon emission data. Background Art

[0002] In July 2021, the carbon emission rights trading market started trading, and the power generation industry was included in the first batch. By collecting reports and statements on key indicator parameters such as fossil fuel consumption, elemental carbon content, and online power of the controlled and emission-controlled enterprises, and after on-site confirmation, the material balance method was used to determine CO2 emissions and emission intensity. Controlled and emission-controlled enterprises generally have problems such as non-compliance with procedures, inadequate performance of duties, errors in parameter selection and statistical calculations, and outstanding data quality issues. Since the continuous monitoring technology of CO2 emissions from flue gas in thermal power plants is not yet mature, at this stage, it is urgent to use information technology to standardize the management and control of the entire carbon accounting process, conduct calibration and verification of monitoring equipment, direct collection of original data, supervision of the performance of personnel, and standardized management of the accounting process, so as to avoid human interference in data and ensure that carbon emission data is true, complete, and accurate.

[0003] The current carbon emission trading market uses an accounting method that requires regulated companies to report data on the national carbon market management platform, and to conduct monthly evidence storage and annual verification. At present, there is a general lack of refined monthly management, and the management and control system is more of a "post-event" verification and confirmation of carbon emission data: for example, if you need to know the carbon emissions of a company in 2023, general personnel need to collect, organize and confirm the full-year information provided by the company in January 2024. Reviewing and verifying the accuracy of carbon emission data, including the full visualization and comparative analysis of historical carbon emission indicators of the unit to avoid error superposition and increase compliance costs, is also an important part of carbon emission work. Therefore, a better solution is urgently needed. Summary of the invention

[0004] In view of this, the embodiments of this specification provide a carbon emission data intelligent analysis method. One or more embodiments of this specification also involve a carbon emission data intelligent analysis system, a computing device, a computer-readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0005] According to a first aspect of an embodiment of this specification, a method for intelligent analysis of carbon emission data is provided, comprising: Acquire initial carbon emission data, perform verification based on the initial carbon emission data to determine verified carbon emission data, perform abnormal data detection based on the verified carbon emission data, determine abnormal data detection results, and generate a carbon emission report based on the abnormal data detection results and the verified carbon emission data; Obtain coal quality test data, and determine the carbon content of furnace coal based on the coal quality test data; establish a carbon emission prediction model, perform carbon emission related predictions based on carbon emission related data and the carbon emission prediction model, and determine carbon emission prediction data; Establish a unit energy consumption calculation model, calculate the unit carbon emissions and production energy consumption data based on the relevant data of production conditions and the unit energy consumption calculation model, compare and analyze the unit carbon emissions and production energy consumption data with the carbon emission data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system, and determine the data difference; Based on carbon market policies and interpretations, the carbon market Q&A knowledge base and carbon market trends, a carbon emissions knowledge and technology document library is established. Based on the carbon emissions knowledge and technology document library, a carbon Q&A assistant module is created to obtain user question data. The carbon Q&A assistant module answers the question data based on the carbon emissions knowledge and technology document library.

[0006] In a possible implementation, verifying and determining verified carbon emission data based on the initial carbon emission data includes: Obtain historical data of the power plant, and calibrate the initial carbon emission data based on the confidence interval and historical data to determine the calibrated carbon emission data; Acquire real-time data from power plants, establish a carbon emission data indicator prediction model, and verify and determine the carbon emission data based on the real-time data and the carbon emission data indicator prediction model.

[0007] In a possible implementation, abnormal data detection is performed based on the verified carbon emission data, and abnormal data detection results are determined, including: Abnormal data detection is performed based on the verified carbon emission data and the abnormal threshold value to determine the abnormal data detection result; wherein the abnormal data detection result includes an alarm result, a concern result and a normal result.

[0008] In a possible implementation, obtaining coal quality test data, and determining the elemental carbon content data of furnace coal based on the coal quality test data, includes: Obtain element carbon related data; wherein, element carbon related data include air-dried basis ash, air-dried basis sulfur, air-dried basis volatile matter, air-dried basis moisture, total moisture and air-dried basis lower calorific value, and use correlation coefficient model and linear regression analysis model to verify the correlation between element carbon related data and element carbon; The coal quality data is fitted through historical coal quality test data and daily test data, and the elemental carbon content data of the furnace coal is determined by combining the correlation.

[0009] In a possible implementation, the method further includes: Obtain real-time data of the unit, and generate performance monitoring diagrams based on the real-time data of the unit and the process flow chart of a typical thermal power enterprise; Perform trend analysis and indicator statistics based on performance monitoring graphs.

[0010] In a possible implementation, the method further includes: Conduct carbon emission intensity analysis based on power generation, heat supply, heat supply ratio, and carbon emission data, determine the emission intensity analysis results, and display historical trend changes based on the emission intensity analysis results.

[0011] In a possible implementation, a carbon question-answering assistant module is created based on the carbon emission knowledge and technology document library to obtain user question data. The carbon question-answering assistant module answers the question data based on the carbon emission knowledge and technology document library, including: Create a carbon question-and-answer assistant module based on the carbon emission knowledge and technical document library through a large language model; The carbon question and answer assistant module obtains the user's question data and answers the question data by optimizing the question classification and answer retrieval algorithm based on the carbon emission knowledge and technical document library.

[0012] According to a second aspect of an embodiment of this specification, a carbon emission data intelligent analysis system is provided, including: A data verification module is configured to obtain initial carbon emission data, verify and determine verified carbon emission data based on the initial carbon emission data, perform abnormal data detection based on the verified carbon emission data, determine abnormal data detection results, and generate a carbon emission report based on the abnormal data detection results and the verified carbon emission data; The data prediction module is configured to obtain coal quality test data, determine the carbon content data of furnace coal based on the coal quality test data; establish a carbon emission prediction model, perform carbon emission related prediction based on the carbon emission related data and the carbon emission prediction model, and determine the carbon emission prediction data; The data analysis module is configured to establish a unit energy consumption calculation model, calculate the unit carbon emissions and production energy consumption data based on relevant data of production conditions and the unit energy consumption calculation model, compare and analyze the unit carbon emissions and production energy consumption data with the carbon emission data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system, and determine the data difference; The knowledge question and answer module is configured to establish a carbon emission knowledge and technology document library based on carbon market policies and interpretations, the carbon market Q&A knowledge base and carbon market trends, create a carbon question and answer assistant module based on the carbon emission knowledge and technology document library, obtain user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library.

[0013] According to a third aspect of an embodiment of this specification, a computing device is provided, including: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned carbon emission data intelligent analysis method are implemented.

[0014] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned carbon emission data intelligent analysis method are implemented.

[0015] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned carbon emission data intelligent analysis method.

[0016] The embodiments of this specification provide a method and system for intelligent analysis of carbon emission data, which includes: obtaining initial carbon emission data, verifying and determining verified carbon emission data based on the initial carbon emission data, and detecting abnormal data based on verified carbon emission data; establishing a carbon emission prediction model, performing carbon emission related prediction based on carbon emission related data and the carbon emission prediction model, and determining carbon emission prediction data; establishing a unit energy consumption calculation model, calculating the unit carbon emission and production energy consumption data based on relevant data of production conditions and the unit energy consumption calculation model; establishing a carbon emission knowledge and technology document library, obtaining user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library. Access to carbon inventory data, carbon verification data and real-time operation data of power plants to realize functions such as data verification, automatic report generation, real-time prediction, multi-dimensional analysis and intelligent question and answer. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 2 It is a schematic diagram of abnormal data level classification of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 3 This is a schematic diagram of a report template for a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 4 This is a schematic diagram of enterprise basic information data association of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 5 It is a schematic diagram of low-level thermal data analysis of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 6 This is a schematic diagram of production data uploading of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 7 It is a schematic diagram of an indicator trend analysis of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Figure 8 It is a dot-line graph of carbon emission intensity and carbon emission amount of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Fig. 9 It is a schematic diagram of carbon emission intensity trend of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Fig.10 It is a schematic diagram of unit benchmarking analysis of a carbon emission data intelligent analysis method provided by an embodiment of this specification; Fig.11 It is a structural schematic diagram of a carbon emission data intelligent analysis system provided by an embodiment of this specification; Fig.12 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0018] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0019] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0020] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0021] In this specification, a carbon emission data intelligent analysis method is provided. This specification also relates to a carbon emission data intelligent analysis system, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0022] See also Figure 1 , Figure 1 A flow chart of a carbon emission data intelligent analysis method provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0023] Step 101: Acquire initial carbon emission data, perform calibration based on the initial carbon emission data to determine the calibrated carbon emission data, perform abnormal data detection based on the calibrated carbon emission data, determine the abnormal data detection result, and generate a carbon emission report based on the abnormal data detection result and the calibrated carbon emission data.

[0024] The initial carbon emission data refers to the data transmitted through the carbon emission control system, for example, the carbon emission control system within the enterprise. The verified carbon emission data refers to the data after the initial carbon emission data is verified by the verification rules.

[0025] In practical applications, to establish carbon emission data verification rules, the verification methods that need to be established include threshold verification, historical data verification, real-time carbon emission measurement verification, and intelligent data verification. The system automatically gives early warnings for abnormal data, promptly discovers possible data quality problems, and supervises data quality management. The data of each power plant is trained in the dimension of an independent unit to form a verification capability for each plant. This solution supports administrators to customize the configuration of data verification rules, such as modifying the parameter range of the verification rules, early warning reminder methods, etc. Finally, the causes of errors in the reported data are statistically tracked, and a deviation analysis of the reported data is formed.

[0026] In one possible implementation, the carbon emission data is calibrated based on the initial carbon emission data, including: obtaining historical data of the power plant, and calibrating the initial carbon emission data based on the confidence interval and the historical data to determine the calibrated carbon emission data; obtaining real-time data of the power plant, establishing a carbon emission data indicator prediction model, and calibrating the carbon emission data based on the real-time data and the carbon emission data indicator prediction model.

[0027] In actual applications, this solution should support multiple verification mechanisms, including but not limited to threshold verification, historical data verification, real-time carbon emission measurement verification, and intelligent data verification.

[0028] Data is checked according to preset thresholds. When the data reported by the control system is not within the threshold range, an error message is given, and detailed information on the data range required for reporting is indicated. At the same time, instructions for reporting abnormal data are designed for the personnel in charge of checking to record or clarify data issues. The first entry of relevant explanations for the description item can be automatically brought out as an alternative when the same threshold problem occurs later. The threshold range uses the system's built-in threshold settings by default, and supports manual personalized adjustments by system administrators for the entire group and unit level.

[0029] The threshold verification of 13 types of carbon emission parameters for the entire group is shown in the following table: Table 1

[0030] The unit-level data thresholds are based on the low calorific value of the coal entering the furnace, total water, internal water, and total sulfur on an air-dry basis. The received carbon content, air-dry basis hydrogen, internal water, and air-dry basis total sulfur are comprehensively sampled. Historical data is collected (optional, from the previous year, two years, three years, four years, and five years) and the maximum and minimum values ​​are found. The values ​​are magnified and reduced by a certain proportion (adjustable proportion) by %, respectively, as the calibration range of the key indicator parameters of a single unit.

[0031] For example, based on the element carbon content analysis index in No. 2 in Table 1, the specific judgment formula is as follows: ) / measurement of air-dry basis high heat -1|<10%; Among them, Cad refers to the elemental carbon content of the integrated sample on an air-dry basis; NCVgr,ad: refers to the high calorific value on an air-dry basis; the measured high calorific value on an air-dry basis refers to the high calorific value on an air-dry basis of the integrated sample; C ad is the amount of carbon, H ad is the amount of hydrogen. The coefficients of carbon and hydrogen as well as the constant 0.0335 are obtained through a large number of fittings of previous data.

[0032] During the verification process, the high calorific value on an air-dry basis can be judged based on the above formula to determine whether it is within the normal range.

[0033] Furthermore, combined with the historical data of the power plant (optional, the previous year, two years, three years, four years, five years), the confidence interval is introduced as the rationality range of the reported data, so as to dynamically adjust the data verification range according to the historical data and form the one-plant-one-policy capability of data verification on the power plant side. The confidence level evaluation index is used as the basis for judging the accuracy of the reported data, and the administrator can adjust the confidence level.

[0034] ① Set the confidence level. The system provides three confidence levels: 68%, 95%, and 99.7%. The default is 95%. ② Calculate the confidence interval. The confidence interval = [Historical data mean - t*SE, historical data mean + t*SE]. The historical data mean uses the verified monthly production and emission data in the carbon report. t is obtained by querying the t distribution table in combination with the sample quantity. SE is the sample standard error. ③ Support unified configuration of confidence levels, generate confidence intervals by unit and indicator, and remind when the reported data is outside the interval.

[0035] Furthermore, real-time data is used to calculate and verify carbon emissions. The real-time data of the company's data center is connected, and based on the big data model, a prediction model for carbon emission data indicators such as emissions, quotas, power generation coal consumption, and heating coal consumption is established to form monthly carbon emission forecast data, which is compared with the monthly carbon emission data of the reported data to give a carbon emission difference value. If the deviation exceeds a certain ratio (the ratio is adjustable), an alarm will be issued.

[0036] It can also include carbon emission data verification: including but not limited to providing emission and quota calculation methods according to the latest accounting guidelines, providing inventory data, real-time calculation data, forecast data, threshold verification, historical value verification and other data cross-checking solutions, forming historical data comparison for the same unit, detecting data deviations from the same coal source, analyzing emission differences of similar units, and multi-source mutual verification.

[0037] Furthermore, administrators can enable, disable, modify or configure data verification rules according to actual needs: for threshold verification, administrators can adjust the threshold verification range of each indicator; for historical data verification, administrators can adjust the confidence interval and historical data selection range; for real-time carbon emission measurement verification, administrators can adjust the abnormal reminder value range; for fitted prediction data verification, administrators can make personalized adjustments to the data abnormality reminder range for the entire group and power plant.

[0038] In one possible implementation, abnormal data detection is performed based on the verified carbon emission data to determine the abnormal data detection result, including: abnormal data detection is performed based on the verified carbon emission data and an abnormal threshold to determine the abnormal data detection result; wherein the abnormal data detection result includes an alarm result, a concern result and a normal result.

[0039] In practical applications, see Figure 2 This solution has the function of automatically detecting abnormal data and issuing warnings. The abnormal data levels are divided into three categories: alarm (red mark), attention (yellow mark), and normal (green mark). The number of abnormal data can be counted by category / level / abnormal overall situation. The system supports the generation of an "Abnormal Data List" after abnormal data appears, and supports file list download, forwarding, and sharing functions.

[0040] Furthermore, the system can count all abnormal data and abnormal reasons, generate abnormal data records, and display the problem data to users in the form of reports or graphics to help improve data quality, and support export in PDF, word, etc. Abnormal data summary refers to forming an initial problem list and recording the results of each processing.

[0041] This solution can also generate carbon emission reports, compile templates that meet the group company's standards, and ensure the security and compliance of documents through permission control and version management. The system supports the configuration of data indicators associated with the indicator library, and generates reports with unified format and accurate content through indicator data collection, and has periodic automatic generation and push functions to improve the efficiency of report preparation.

[0042] See also Figure 3 In this solution, a template that meets the standards of the group company is established, carbon emission indicators are collected from the data center and carbon asset trading operation platform system, and a complete carbon emission report is formed. At the same time, administrators can create, update, and customize the design report template according to specific needs. This solution controls the operation of templates based on user roles and permissions. Specific permissions are required to create, change, delete, and upgrade templates to ensure the security and compliance of documents.

[0043] See also Figure 4 , supports the configuration of data indicators associated with the indicator library, realizes the generation of report with unified format and accurate content through indicator data acquisition, and has the function of periodic automatic generation and push to improve the efficiency of report preparation.

[0044] Furthermore, this solution can customize the report generation cycle according to the provided template, and generate the annual emission analysis report of the controlled enterprises, the annual total report of the subsidiaries and the group company on schedule. It can count the power plants that have generated reports and the power plants that have not started the report submission work in real time, display the number of enterprises that have (not) completed the report submission, and click to view the specific list of enterprises. For overdue reports, this solution has a reminder function to help understand the generation of carbon emission reports of power plants and grasp the progress of report upload data.

[0045] Step 102: Obtain coal quality test data, and determine the elemental carbon content data of furnace coal based on the coal quality test data; establish a carbon emission prediction model, perform carbon emission related predictions based on the carbon emission related data and the carbon emission prediction model, and determine the carbon emission prediction data.

[0046] Among them, the coal quality test data is the relevant data obtained after testing the coal, which can be obtained through calorific value measurement or industrial analysis. There can be multiple carbon emission prediction models, which are used to predict different types of data. The carbon emission related data can be determined according to the corresponding carbon emission prediction model. For example, for a carbon emission prediction model used to predict carbon emissions, the corresponding carbon emission related data can be the amount of coal entering the furnace and the lower heating value of the coal entering the furnace. Accordingly, the carbon emission related prediction and carbon emission prediction data are the predictions and data corresponding to the carbon emission prediction model.

[0047] In actual applications, a unified carbon emission prediction general model is built on the platform to ensure the consistency and authority of the model. According to the specific characteristics of different units, the carbon emission prediction model is applied and optimized, and these parameters are bound to the actual data collection point codes of each unit in each power plant in the data center to achieve real-time data entry, calculation, and analysis. Combined with the data reported by the carbon inventory digital management and control system, timely warnings are issued for situations that exceed the forecast range, and the reasons for exceeding the limit are analyzed.

[0048] Furthermore, the overall technical path of this plan is to use the basic carbon emission data and the production plan data provided by the emission-controlled enterprises to predict indicators such as unit emissions / quotas, carbon content, coal consumption for power generation / heating, and carbon emission intensity for power generation / heating.

[0049] In one possible implementation, coal quality test data is obtained, and elemental carbon content data of furnace coal is determined based on the coal quality test data, including: obtaining elemental carbon related data; wherein the elemental carbon related data includes air-dried basis ash, air-dried basis sulfur, air-dried basis volatile matter, air-dried basis moisture, total moisture and air-dried basis lower calorific value, and using a correlation coefficient model and a linear regression analysis model to verify the correlation between the elemental carbon related data and elemental carbon; fitting the coal quality data through historical coal quality test data and daily test data, and determining the elemental carbon content data of furnace coal in combination with the correlation.

[0050] In practical applications, see Figure 5 The daily industrial analysis of the power plant can obtain coal quality data such as air-dried ash, air-dried sulfur, air-dried volatile matter, air-dried moisture, total moisture and air-dried lower calorific value. The correlation coefficient model and linear regression analysis model are used to verify the correlation between the above parameters and elemental carbon. Coal quality parameters are affected by coal type and blending. The coal quality data are fitted through historical coal quality test data and daily test data to predict the elemental carbon content data of the coal entering the furnace.

[0051] For further information, see Figure 6 , relying on machine learning technology, predict the enterprise's emission data based on the coal consumption and low calorific value, or fit the predicted data to form carbon emission / quota prediction data based on the production data provided by the enterprise. This solution needs to have batch import and export functions.

[0052] Specifically, in this solution, 8 models are set to predict different data, as described below, wherein the input parameters are carbon emission related data, and the prediction parameters are carbon emission prediction data.

[0053] (1) Carbon emissions (coal burning 1) prediction model: Prediction parameters: Carbon emissions (coal burning 1); Input parameters: Amount of coal fed into the furnace (unit: tons); Lower calorific value of coal fed into the furnace (unit: GJ / t); (2) Carbon emissions (coal burning 2) prediction model: Prediction parameters: Carbon emissions (coal burning 2); Input parameters: Electricity generation (unit: MWh); Heating capacity (unit: GJ); Unit capacity (unit: MW); Operating hours (unit: hours); (3) Power generation emission intensity prediction model: Prediction parameters: Power generation emission intensity; Input parameters: Heating ratio (unit: %); Unit capacity (unit: MW); Unit type; Load (output) coefficient (unit: %); (4) Power generation coal consumption prediction model: Prediction parameters: coal consumption for power generation; Input parameters: Heating ratio (unit: %); Unit capacity (unit: MW); Unit type; Load (output) coefficient (unit: %); (5) Comprehensive sample received basic element carbon content prediction model: Prediction parameters: Comprehensive sample received basic element carbon content; Input parameters: Low calorific value of coal entering the furnace (unit: GJ / t); Air-dry basis moisture of coal entering the furnace (unit: %); Basic sulfur content of coal fed into the furnace (unit: %); (6) Comprehensive sample air dry basis element carbon content prediction model: Prediction parameters: Comprehensive sample air dry basis elemental carbon content; Input parameters: Volatile matter of the integrated sample on an air-dried basis (unit: %); Comprehensive sample air dry basis ash (unit: %); Comprehensive sample dry basis moisture (unit: %); Comprehensive sample air dry basis high calorific value (unit: GJ / t); Total sulfur of the composite sample on an air-dry basis (unit: %); (7) Carbon emissions (gas) prediction model: Prediction parameters: Carbon emissions (gas); Input parameters: Gas consumption (unit: 10,000 cubic meters); Gas lower calorific value (unit: GJ / t); (8) Power generation consumption prediction model: Prediction parameters: Power generation consumption; Input parameters: Load rate (unit: %); Heating ratio (unit: %); Unit capacity (unit: MW); Unit type; In the above prediction model, the key steps to optimize the carbon emission prediction model include data preprocessing, feature engineering and model fusion. Data preprocessing improves data quality through cleaning, normalization and other methods; feature engineering enhances model sensitivity by extracting and selecting features; model fusion combines multiple models such as random forests and gradient boosting to improve prediction performance by combining the advantages of different models. In the model training phase, historical data and pre-set algorithms are used to adjust model parameters to optimize prediction performance. After training, the model needs to be tested with a validation data set to evaluate the accuracy and reliability of its predictions. Common validation methods include cross-validation and leave-one-out validation to ensure that the model has good generalization capabilities.

[0054] In this scheme, a neural network can be used to estimate time entropy (NNetEn). By training the neural network, features that reflect its intrinsic structure and dynamic characteristics can be extracted from the time series, thereby estimating the time entropy. This method can effectively process nonlinear and non-stationary time series data, providing a new perspective to understand and analyze the complexity of time series. When implementing a neural network to estimate time entropy, first prepare an appropriate time series data set. Then, select a suitable neural network structure, such as a multilayer perceptron or a recurrent neural network, and train it so that the network can learn the patterns and characteristics of the time series. During the training process, the prediction error is minimized and the generalization ability of the model is improved by adjusting the network parameters and optimizing the loss function. Finally, the entropy value of the time series is estimated using the trained network.

[0055] Furthermore, in the process of using neural network for prediction, it is necessary to first perform fuzzy clustering on the input data, and the initial clustering conditions of the fuzzy clustering algorithm improved by genetic algorithm are used in the clustering algorithm to solve the problem of local convergence.

[0056] It should be noted that other models can also be used for prediction, such as singular spectrum decomposition (SSD), variational mode decomposition (VMD), kernel extreme learning machine (KELM), least squares support vector machine (LSSVM), etc., to improve the prediction accuracy through error correction and induced ordered weighted average (IOWA) operator. This method is particularly suitable for processing highly complex carbon emission data.

[0057] In a possible implementation, the method further includes: acquiring real-time data of the unit, generating a performance monitoring diagram based on the real-time data of the unit and a process flow diagram of a typical thermal power enterprise; and performing trend analysis and indicator statistics based on the performance monitoring diagram.

[0058] In actual applications, the system can view real-time data such as the unit's power generation / heating coal consumption, boiler forward / reverse balance efficiency, etc. The system needs to include a typical thermal power enterprise process flow chart, on which you can view real-time data on indicators such as the thermal system, boiler performance, steam turbine performance, etc., such as the coal consumption, operating hours, and load rate of the system. On the process flow chart, the indicator data is displayed in three rows, the top row is real-time data, the middle row is data calculated from carbon emissions, and the bottom row is thermal system design data. The three rows of data are marked with different colors. See Figure 7 The system supports trend analysis of indicators. Click the data in the above figure to enter the data bar history, support time period selection, to query indicator data in different time periods. You can also select the start time and end time to query the indicator statistics of different time periods.

[0059] Step 103: Establish a unit energy consumption calculation model, calculate the unit carbon emissions and production energy consumption data based on the relevant data of the production conditions and the unit energy consumption calculation model, compare and analyze the unit carbon emissions and production energy consumption data with the carbon emission data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system, and determine the data difference.

[0060] In actual applications, the energy consumption indicators, emission intensity indicators, unit boiler efficiency, carbon content per unit calorific value of coal and other indicators of each unit in the enterprise under multiple time dimensions are displayed in a graphical form, with the unit as the dimension, forming a ranking comparison of units or companies, and displaying the maximum and minimum values ​​of indicators under a fixed time scale. It supports the analysis of carbon emissions and energy consumption at the unit level, the analysis of carbon emission intensity, and the analysis of carbon emission trends.

[0061] Establish a unit energy consumption calculation model. The system can obtain relevant data on production conditions from the data center or carbon asset trading operation platform system, and calculate the unit carbon emissions and production energy consumption data in combination with the power generation data. Compare and analyze the carbon emissions data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system to find the data difference. Support data drilling and deep mining, and mine and analyze the changes in key parameters such as load rate, standard coal consumption for power generation, low calorific value of fossil fuels, heat supply ratio, carbon oxidation rate, and carbon content per unit calorific value to find the reasons for the data difference.

[0062] For carbon emission indicators, monthly year-on-year and month-on-month analysis can be set from aspects such as unit type, cooling method, fuel type, etc., and you can choose January to December; quarterly year-on-year and month-on-month analysis can choose the first quarter, second quarter, third quarter, and fourth quarter; annual year-on-year and month-on-month analysis can choose the year. The analysis results can be displayed in different forms (such as a combination of graphs, tables, and icons).

[0063] In a possible implementation, it also includes: performing carbon emission intensity analysis based on power generation, heat supply, heat supply ratio, and carbon emission data, determining emission intensity analysis results, and displaying historical trend changes based on the emission intensity analysis results.

[0064] In practical applications, see Figure 8 and Fig. 9 , calculate the carbon emission intensity analysis of power generation / heating based on the data of power generation, heat supply, heat supply ratio and carbon emission, and display the historical trend changes. Display the carbon emission intensity parameters in real time, and visualize the healthy interval of carbon emission intensity in combination with the quota value, so as to assist in the analysis of the carbon emission status of the unit.

[0065] Furthermore, there are monthly year-on-year and month-on-month analysis, which can be selected from January to December; quarterly year-on-year and month-on-month analysis, which can be selected from the first quarter, second quarter, third quarter, and fourth quarter; annual year-on-year and month-on-month analysis, which can be selected from the year. The analysis results can be displayed in different forms (combination of graphs, tables, and icons).

[0066] Furthermore, the unit load rate data is obtained to generate the coal consumption, carbon emission intensity and unit load rate fluctuation operation curve. The unit load rate fluctuation is tracked, and trend analysis is performed on each unit according to a certain period. The operation safety status of each unit is monitored to see if it deteriorates or changes, and the rationality of load changes, operation duration and carbon emission changes is verified.

[0067] See also Fig.10, visualize the carbon emission source data. According to the emission source trend analysis, the total carbon emissions of enterprises are divided into specific emission sources, and the time series change analysis is carried out according to the emission sources, such as fossil fuel combustion, external electricity emission trends, power generation, heating carbon emission trends, etc.

[0068] Step 104: Based on carbon market policies and interpretations, the carbon market Q&A knowledge base and carbon market trends, a carbon emission knowledge and technology document library is established, and a carbon Q&A assistant module is created based on the carbon emission knowledge and technology document library to obtain user question data. The carbon Q&A assistant module answers the question data based on the carbon emission knowledge and technology document library.

[0069] In actual application, we collect carbon market policies and interpretations, carbon market Q&A knowledge base, carbon market trends and other contents, establish a carbon emission knowledge and technology document library, upload all materials to the knowledge platform, and create a carbon Q&A assistant module. In the form of Q&A / voice interaction, users input questions, and the system module can answer them in a targeted manner based on the built-in knowledge and technology document library, making it convenient for practitioners to quickly obtain the specific requirements of industry standards and technical indicators, understand industry trends and interpret policies and regulations. We have added years of work experience of carbon asset companies, such as common carbon emission indicator thresholds, post-dispute handling methods, and other unique modules to effectively provide knowledge to carbon inventory business personnel quickly and accurately.

[0070] In one possible implementation, a carbon question and answer assistant module is created based on the carbon emission knowledge and technology document library to obtain user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library, including: creating a carbon question and answer assistant module based on the carbon emission knowledge and technology document library through a language big model; obtaining user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library by optimizing question classification and answer retrieval algorithms.

[0071] In practical applications, data enhancement technology is used to improve the performance of the search engine when processing the knowledge base, and to achieve deeper information mining and knowledge discovery. The language big model is introduced, which has the ability of natural language processing, including semantic understanding, sentiment analysis, etc., to improve the system's ability to accurately parse user queries. By optimizing the question classification and answer retrieval algorithms, users' questions can be responded to quickly and accurately, and real-time and accurate information feedback can be provided. The dialogue management is implemented, which can maintain the dialogue state, understand the context, and provide a smooth multi-round dialogue experience.

[0072] Furthermore, a historical conversation storage and retrieval mechanism is designed to enable users to review and utilize previous conversation content. Backend management tools are provided to allow administrators to customize sensitive word lists and data desensitization rules, implement sensitive content detection and filtering mechanisms, ensure the compliance of conversation content, and protect user privacy. A permission control mechanism can also be designed to set the scope of knowledge base access according to different positions and roles. Users can also decide to use Internet-based Q&A or knowledge base-based Q&A mode.

[0073] The embodiments of this specification provide a method and system for intelligent analysis of carbon emission data, which includes: obtaining initial carbon emission data, verifying and determining verified carbon emission data based on the initial carbon emission data, and detecting abnormal data based on verified carbon emission data; establishing a carbon emission prediction model, performing carbon emission related prediction based on carbon emission related data and the carbon emission prediction model, and determining carbon emission prediction data; establishing a unit energy consumption calculation model, calculating the unit carbon emission and production energy consumption data based on relevant data of production conditions and the unit energy consumption calculation model; establishing a carbon emission knowledge and technology document library, obtaining user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library. Access to carbon inventory data, carbon verification data and real-time operation data of power plants to realize functions such as data verification, automatic report generation, real-time prediction, multi-dimensional analysis and intelligent question and answer.

[0074] Corresponding to the above method embodiment, this specification also provides a carbon emission data intelligent analysis system embodiment, Fig.11 FIG. 1 is a schematic diagram showing a structure of a carbon emission data intelligent analysis system provided by an embodiment of the present specification. Fig.11 As shown, the device comprises: The data verification module 1101 is configured to obtain initial carbon emission data, verify and determine verified carbon emission data based on the initial carbon emission data, perform abnormal data detection based on the verified carbon emission data, determine abnormal data detection results, and generate a carbon emission report based on the abnormal data detection results and the verified carbon emission data; The data prediction module 1102 is configured to obtain coal quality test data, determine the element carbon content data of the furnace coal based on the coal quality test data; establish a carbon emission prediction model, perform carbon emission related prediction based on the carbon emission related data and the carbon emission prediction model, and determine the carbon emission prediction data; The data analysis module 1103 is configured to establish a unit energy consumption calculation model, calculate the unit carbon emissions and production energy consumption data based on the relevant data of the production conditions and the unit energy consumption calculation model, compare and analyze the unit carbon emissions and production energy consumption data with the carbon emission data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system, and determine the data difference; The knowledge question and answer module 1104 is configured to establish a carbon emission knowledge and technology document library based on carbon market policies and interpretations, the carbon market Q&A knowledge base and carbon market trends, create a carbon question and answer assistant module based on the carbon emission knowledge and technology document library, obtain user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library.

[0075] In a possible implementation, verifying and determining verified carbon emission data based on the initial carbon emission data includes: Obtain historical data of the power plant, and calibrate the initial carbon emission data based on the confidence interval and historical data to determine the calibrated carbon emission data; Acquire real-time data from power plants, establish a carbon emission data indicator prediction model, and verify and determine the carbon emission data based on the real-time data and the carbon emission data indicator prediction model.

[0076] In a possible implementation, abnormal data detection is performed based on the verified carbon emission data, and abnormal data detection results are determined, including: Abnormal data detection is performed based on the verified carbon emission data and the abnormal threshold value to determine the abnormal data detection result; wherein the abnormal data detection result includes an alarm result, a concern result and a normal result.

[0077] In a possible implementation, obtaining coal quality test data, and determining the elemental carbon content data of furnace coal based on the coal quality test data, includes: Obtain element carbon related data; wherein, element carbon related data include air-dried basis ash, air-dried basis sulfur, air-dried basis volatile matter, air-dried basis moisture, total moisture and air-dried basis lower calorific value, and use correlation coefficient model and linear regression analysis model to verify the correlation between element carbon related data and element carbon; The coal quality data is fitted through historical coal quality test data and daily test data, and the elemental carbon content data of the furnace coal is determined by combining the correlation.

[0078] In a possible implementation, the method further includes: Obtain real-time data of the unit, and generate performance monitoring diagrams based on the real-time data of the unit and the process flow chart of a typical thermal power enterprise; Perform trend analysis and indicator statistics based on performance monitoring graphs.

[0079] In a possible implementation, the method further includes: Conduct carbon emission intensity analysis based on power generation, heat supply, heat supply ratio, and carbon emission data, determine the emission intensity analysis results, and display historical trend changes based on the emission intensity analysis results.

[0080] In a possible implementation, a carbon question-answering assistant module is created based on the carbon emission knowledge and technology document library to obtain user question data. The carbon question-answering assistant module answers the question data based on the carbon emission knowledge and technology document library, including: Create a carbon question-and-answer assistant module based on the carbon emission knowledge and technical document library through a large language model; The carbon question and answer assistant module obtains the user's question data and answers the question data by optimizing the question classification and answer retrieval algorithm based on the carbon emission knowledge and technical document library.

[0081] The embodiments of this specification provide a method and system for intelligent analysis of carbon emission data. The intelligent analysis system for carbon emission data includes: obtaining initial carbon emission data, verifying and determining verified carbon emission data based on the initial carbon emission data, and detecting abnormal data based on verified carbon emission data; establishing a carbon emission prediction model, performing carbon emission related prediction based on carbon emission related data and the carbon emission prediction model, and determining carbon emission prediction data; establishing a unit energy consumption calculation model, and calculating the unit carbon emission and production energy consumption data based on relevant data of production conditions and the unit energy consumption calculation model; establishing a carbon emission knowledge and technology document library, obtaining user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library. Access to carbon inventory data, carbon verification data and real-time operation data of power plants to realize functions such as data verification, automatic report generation, real-time prediction, multi-dimensional analysis and intelligent question and answer.

[0082] The above is a schematic scheme of a carbon emission data intelligent analysis system of this embodiment. It should be noted that the technical scheme of the carbon emission data intelligent analysis system and the technical scheme of the above-mentioned carbon emission data intelligent analysis method belong to the same concept, and the details not described in detail in the technical scheme of the carbon emission data intelligent analysis system can be found in the description of the technical scheme of the above-mentioned carbon emission data intelligent analysis method.

[0083] Fig.12 The block diagram of a computing device 1200 according to one embodiment of the present specification is shown. The components of the computing device 1200 include but are not limited to a memory 1210 and a processor 1220. The processor 1220 is connected to the memory 1210 via a bus 1230, and the database 1250 is used to store data.

[0084] The computing device 1200 also includes an access device 1240 that enables the computing device 1200 to communicate via one or more networks 1260. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 1240 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0085] In one embodiment of the present specification, the above components of the computing device 1200 and Fig.12 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Fig.12 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0086] The computing device 1200 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 1200 may also be a mobile or stationary server.

[0087] Among them, the processor 1220 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned carbon emission data intelligent analysis method. The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned carbon emission data intelligent analysis method belong to the same concept. For details not described in detail in the technical scheme of the computing device, please refer to the description of the technical scheme of the above-mentioned carbon emission data intelligent analysis method.

[0088] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned carbon emission data intelligent analysis method.

[0089] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned carbon emission data intelligent analysis method belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the above-mentioned carbon emission data intelligent analysis method.

[0090] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned carbon emission data intelligent analysis method.

[0091] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the above-mentioned carbon emission data intelligent analysis method belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the above-mentioned carbon emission data intelligent analysis method.

[0092] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0094] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A carbon emission data intelligent analysis method, characterized in that: include: Acquire initial carbon emission data, perform verification based on the initial carbon emission data to determine verified carbon emission data, perform abnormal data detection based on the verified carbon emission data to determine abnormal data detection results, and generate a carbon emission report based on the abnormal data detection results and the verified carbon emission data; Acquire coal quality test data, and determine the elemental carbon content data of furnace coal based on the coal quality test data; Establishing a carbon emission prediction model, performing carbon emission related prediction based on carbon emission related data and the carbon emission prediction model, and determining carbon emission prediction data; Establish a unit energy consumption calculation model, calculate the unit carbon emissions and production energy consumption data based on relevant data of production conditions and the unit energy consumption calculation model, compare and analyze the unit carbon emissions and production energy consumption data with the carbon emission data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system, and determine the data difference; Based on carbon market policies and interpretations, the carbon market Q&A knowledge base and carbon market trends, a carbon emissions knowledge and technology document library is established, and a carbon Q&A assistant module is created based on the carbon emissions knowledge and technology document library to obtain user question data. The carbon Q&A assistant module answers the question data based on the carbon emissions knowledge and technology document library.

2. The method according to claim 1, characterized in that: The step of verifying and determining the verified carbon emission data based on the initial carbon emission data includes: Acquire historical data of the power plant, and calibrate the initial carbon emission data based on the confidence interval and the historical data to determine the calibrated carbon emission data; Real-time data of the power plant is acquired, a carbon emission data index prediction model is established, and the carbon emission data is verified and determined based on the real-time data and the carbon emission data index prediction model.

3. The method according to claim 1, characterized in that The performing abnormal data detection based on the verified carbon emission data and determining the abnormal data detection result includes: Abnormal data detection is performed based on the verified carbon emission data and the abnormal threshold value to determine the abnormal data detection result; wherein the abnormal data detection result includes an alarm result, a concern result and a normal result.

4. The method according to claim 1, characterized in that: The step of obtaining coal quality test data and determining the elemental carbon content data of furnace coal based on the coal quality test data includes: Obtaining element carbon related data; wherein the element carbon related data includes air-dried basis ash, air-dried basis sulfur, air-dried basis volatile matter, air-dried basis moisture, total moisture and air-dried basis lower calorific value, and using a correlation coefficient model and a linear regression analysis model to verify the correlation between the element carbon related data and the element carbon; The coal quality data is fitted by using the historical coal quality test data and the daily test data, and the data of the elemental carbon content of the furnace coal is determined by combining the above correlation.

5. The method according to claim 1, characterized in that Also includes: Acquire real-time data of the unit, and generate a performance monitoring diagram based on the real-time data of the unit and a process flow diagram of a typical thermal power enterprise; Trend analysis and indicator statistics are performed based on the performance monitoring graph.

6. The method according to claim 1, characterized in that Also includes: Carbon emission intensity analysis is performed based on power generation, heat supply, heat supply ratio, and carbon emission data to determine emission intensity analysis results, and historical trend changes are displayed based on the emission intensity analysis results.

7. The method according to claim 1, characterized in that The step of creating a carbon question and answer assistant module based on the carbon emission knowledge and technology document library, obtaining user question data, and the carbon question and answer assistant module answering the question data based on the carbon emission knowledge and technology document library includes: Based on the carbon emission knowledge and technical document library, a carbon question-answering assistant module is created through a language macro model; The user's question data is obtained, and the carbon question-answering assistant module answers the question data based on the carbon emission knowledge and technical document library by optimizing question classification and answer retrieval algorithms.

8. A carbon emission data intelligent analysis system, characterized in that: include: a data verification module configured to obtain initial carbon emission data, verify and determine verified carbon emission data based on the initial carbon emission data, perform abnormal data detection based on the verified carbon emission data, determine abnormal data detection results, and generate a carbon emission report based on the abnormal data detection results and the verified carbon emission data; A data prediction module is configured to obtain coal quality test data and determine the elemental carbon content data of furnace coal based on the coal quality test data; Establishing a carbon emission prediction model, performing carbon emission related prediction based on carbon emission related data and the carbon emission prediction model, and determining carbon emission prediction data; The data analysis module is configured to establish a unit energy consumption calculation model, calculate the unit carbon emissions and production energy consumption data based on relevant data of production conditions and the unit energy consumption calculation model, compare and analyze the unit carbon emissions and production energy consumption data with the carbon emission data and energy consumption data in the carbon inventory digital management and control system and the carbon asset trading operation platform system, and determine the data difference; The knowledge question and answer module is configured to establish a carbon emission knowledge and technology document library based on carbon market policies and interpretations, a carbon market Q&A knowledge base and carbon market trends, create a carbon question and answer assistant module based on the carbon emission knowledge and technology document library, obtain user question data, and the carbon question and answer assistant module answers the question data based on the carbon emission knowledge and technology document library.

9. A computing device, characterized in that include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the carbon emission data intelligent analysis method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the carbon emission data intelligent analysis method according to any one of claims 1 to 7.

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