Carbon emission operation and maintenance data calling and analysis method

Abstract

The application relates to the field of carbon emission operation and maintenance analysis, and specifically discloses a carbon emission operation and maintenance data calling and analysis method, which comprises the following steps: obtaining carbon emission intensity of a power plant in an accounting period and comparing the carbon emission intensity with historical same period and industry average level to identify abnormal power plants; determining an abnormal reason based on fuel carbon content deviation as fuel quality fluctuation or unit operation abnormality; if the reason is fuel problem, correcting an emission factor through a machine learning model and recalculating the carbon emission intensity; if the reason is unit fault, drawing a load-emission intensity curve to identify an inefficient unit with emission sudden increase and obtaining an abnormal period and abnormal degree of the unit; determining whether the inefficient unit is single to determine whether a source point is a unit equipment or a shared auxiliary equipment, and then formulating a reasonable troubleshooting order in combination with historical fault records; the method solves the problems of difficult carbon emission abnormality attribution of the power plant, slow operation and maintenance response, realizes dynamic correction of the emission factor, accurate positioning of a fault source point and optimization of a troubleshooting path, and improves carbon management efficiency.

Description

A method for retrieving and analyzing carbon emission operation and maintenance data Technical Field

[0001] This invention relates to the field of carbon emission operation and maintenance analysis, and specifically to a method for retrieving and analyzing carbon emission operation and maintenance data. Background Technology

[0002] Against the backdrop of addressing global climate change and promoting low-carbon development, carbon emission management has become a key focus for various industries. Currently, data management and analysis technologies for carbon emissions are constantly evolving, but existing technologies still have many shortcomings, particularly in the area of ​​refined operation and maintenance management of carbon emissions in industrial sectors such as power plants.

[0003] For example, Chinese patent CN115098577A discloses a cloud center system for building carbon emission data management. This system primarily measures carbon emissions during the building's operation and maintenance phase, acquiring, transmitting, and processing energy consumption monitoring data through data, transmission, and processing modules. However, this system has the following significant shortcomings in practical applications: 1. Lack of root cause analysis: It can only perform basic data processing and transmission, lacking the ability to systematically analyze abnormal carbon emission situations. It cannot delve into the specific causes of these anomalies, such as fuel quality fluctuations or unit malfunctions, thus failing to meet the needs of problem localization in actual operation and maintenance, resulting in delayed operation and maintenance response.

[0004] 2. Lack of dynamic adaptability: When processing carbon emission data, the impact of factors such as fuel quality fluctuations on carbon emission calculations was not considered, and no corresponding dynamic correction model was established. This may affect the accuracy of carbon emission intensity calculation results and fail to provide more accurate data support for carbon quota management.

[0005] 3. Weak operation and maintenance guidance capabilities: For identified carbon emission anomalies, the system cannot analyze the abnormal time period and severity in detail, nor can it determine the specific source of the anomaly and the reasonable order of investigation. In practical applications, it is difficult to efficiently guide relevant personnel to investigate and resolve problems, which is not conducive to achieving refined carbon emission management.

[0006] In summary, existing technologies have significant gaps in carbon emission operation and maintenance management, especially in the industrial sector and power plants. There is an urgent need for a carbon emission analysis method that can achieve multi-dimensional anomaly identification, accurate cause location, dynamic data correction, and scientific operation and maintenance guidance, in order to improve the scientific nature and effectiveness of carbon emission management. Summary of the Invention

[0007] To address the above problems, this invention proposes a method for retrieving and analyzing carbon emission operation and maintenance data. The specific technical solution is as follows: A method for retrieving and analyzing carbon emission operation and maintenance data includes the following steps: S1, obtaining the carbon emission intensity of each power plant within the accounting period and comparing it with the historical average carbon emission intensity and the industry average carbon emission intensity, identifying power plants with abnormal carbon emissions and recording them as abnormal power plants.

[0008] S2. Based on the carbon content of the fuel used in the accounting cycle of the abnormal power plant, determine whether the cause of the abnormal carbon emissions is fuel quality fluctuation or abnormal unit operation. If it is the former, execute S3; if it is the latter, execute S4.

[0009] S3. Correct the fuel emission factor based on the carbon content of the fuel and recalculate the carbon emission intensity of abnormal power plants based on the corrected results, and provide feedback.

[0010] S4. Draw the load-emission intensity curves of each unit in the abnormal power plant, identify the unit with a sudden increase in emissions as an inefficient unit, obtain the abnormal information and investigation information of the inefficient unit and provide feedback. The abnormal information includes the abnormal time period and the degree of abnormality, and the investigation information includes the investigation source and the investigation order.

[0011] Compared with existing technologies, the carbon emission operation and maintenance data retrieval and analysis method described in this invention has the following beneficial effects: 1. Accurately locate the cause of anomalies: Based on the analysis of the carbon content of fuel in abnormal power plants, this invention can clearly determine whether the carbon emission anomaly is caused by fuel quality fluctuations or unit operation anomalies, providing a basis for taking targeted measures in the future, avoiding blind investigation, and improving work efficiency.

[0012] 2. Dynamically corrected emission factors: This invention uses machine learning algorithms to construct a fuel emission factor correction model, which dynamically corrects the emission factors based on changes in the carbon content of the fuel, making the calculation of carbon emission intensity more accurate and providing reliable data support for carbon emission management.

[0013] 3. In-depth analysis of inefficient units: By plotting load-emission intensity curves and analyzing the slope of the tangents, this invention can accurately identify inefficient units with sudden increases in emissions, and at the same time obtain detailed information such as the abnormal period and degree of abnormality, providing strong support for the optimized operation and maintenance of the units.

[0014] 4. Scientifically formulate troubleshooting strategies: This invention determines the source of troubleshooting based on whether the inefficient unit is solely due to its own equipment or shared auxiliary equipment. It then combines historical fault records to formulate a reasonable troubleshooting sequence, making the troubleshooting work more scientific and targeted, enabling the rapid location of the root cause of the problem and reducing troubleshooting time and costs. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 is a schematic diagram of the method flow of the present invention.

[0017] Figure 2 is a schematic diagram of the workflow of the present invention.

[0018] Figure 3 is a structural block diagram of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figures 1, 2 and 3. The carbon emission operation and maintenance data retrieval and analysis method provided by the present invention includes the following steps: S1, obtain the carbon emission intensity of each power plant within the accounting period and compare it with the historical same period and the industry average carbon emission intensity, identify power plants with abnormal carbon emissions and record them as abnormal power plants.

[0021] As a preferred option, the specific analysis process of step S1 is as follows: retrieve carbon emission operation and maintenance data of each power plant in the target area from the information platform.

[0022] Set the duration of the accounting period, obtain the fuel consumption and power generation of each power plant in the target area within the accounting period, and calculate the carbon emission intensity of each power plant within the accounting period by combining the fuel emission factors stored in the database.

[0023] The carbon emission intensity of each power plant in each historical year within the accounting period is obtained and the average value is calculated to obtain the carbon emission intensity of each power plant in the same historical period.

[0024] Obtain the industry's average carbon emission intensity during the accounting period.

[0025] A power plant is considered to have abnormal carbon emissions if it meets at least one of the following conditions; otherwise, its carbon emissions are considered normal.

[0026] (1) The carbon emission intensity during the accounting period is greater than the historical carbon emission intensity during the same period than the set first threshold for emission increase.

[0027] (2) The carbon emission intensity during the accounting period is greater than the industry average carbon emission intensity. The emission increase is greater than the second threshold set for the emission increase.

[0028] Each power plant with abnormal carbon emissions is recorded as an abnormal power plant.

[0029] It should be noted that the carbon emission operation and maintenance data of each power plant in the target area are uploaded to the information platform for monitoring. The carbon emission operation and maintenance data includes, but is not limited to, fuel consumption data, power generation data, emission-related data, and equipment operation data.

[0030] It should be noted that the fuel emission factors stored in the database are set according to industry standards and are dynamically updated.

[0031] It should be noted that the ratio of the product of fuel consumption and fuel emission factor to electricity generation is used as carbon emission intensity.

[0032] It should be noted that the historical years mentioned are those close to the current year, which are more relevant for reference.

[0033] It should be noted that judging whether a power plant's carbon emissions are abnormal based on the increase in carbon emission intensity compared to the same period in history is a longitudinal comparison based on the power plant's own historical carbon emission data, which can reflect the changing trend of its carbon emission intensity. Specifically, it uses the power plant's average carbon emission intensity over a certain period in the past as a benchmark, sets a reasonable upper limit for the increase, and thus judges whether its current carbon emissions are fluctuating abnormally.

[0034] It should be noted that the first threshold for emission increase is set with reference to the fluctuation range of historical data and the expected reduction of emissions due to technological improvements. In one specific embodiment, the first threshold for emission increase is 5%.

[0035] It should be noted that judging whether a power plant's carbon emissions are abnormal based on the increase in carbon emission intensity relative to the industry average is a horizontal comparison based on the average carbon emission intensity data of similar power plants within the industry. This reflects the company's relative emission level within the industry. Specifically, a threshold for the increase is set based on the industry average carbon emission intensity to determine whether the company's carbon emissions significantly deviate from industry standards.

[0036] It should be noted that the second threshold for emission increase is set with reference to industry emission reduction targets, emission values ​​corresponding to mainstream technology levels, etc. In one specific embodiment, the second threshold for emission increase is 8%.

[0037] In this embodiment, the present invention compares the carbon emission intensity of a power plant during its accounting period with historical averages and industry averages, thereby accurately identifying power plants with abnormal carbon emissions from both vertical and horizontal dimensions, improving the comprehensiveness and accuracy of anomaly identification.

[0038] S2. Based on the carbon content of the fuel used in the accounting cycle of the abnormal power plant, determine whether the cause of the abnormal carbon emissions is fuel quality fluctuation or abnormal unit operation. If it is the former, execute S3; if it is the latter, execute S4.

[0039] As a preferred option, the specific analysis process of step S2 is as follows: retrieve the test reports of the fuel used by the abnormal power plant within the accounting period to obtain the carbon content of the fuel used.

[0040] The carbon content of the fuel used is compared with the set reference carbon content of the fuel. The amount by which the carbon content of the fuel used exceeds the reference carbon content is recorded as the carbon content deviation of the fuel used.

[0041] The carbon content deviation of the fuel used is compared with the preset carbon content deviation threshold. If the carbon content deviation of the fuel used is greater than the deviation threshold, the cause of abnormal carbon emissions is fuel quality fluctuation; otherwise, the cause of abnormal carbon emissions is abnormal unit operation.

[0042] It should be noted that the reference carbon content of the fuel is set according to industry standards and updated dynamically, and is compatible with the fuel emission factors stored in the database.

[0043] It should be noted that, all other things being equal, the higher the carbon content of the fuel, the greater the carbon emissions during normal combustion.

[0044] It should be noted that when investigating the causes of abnormal carbon emissions from power plants, the carbon content of the fuel is determined based on the fuel test report. If the carbon content remains unchanged, fuel problems can be ruled out. If the carbon content changes, the abnormal carbon emissions can be attributed to fluctuations in fuel quality.

[0045] In this embodiment, the present invention, based on the analysis of the carbon content of fuel in abnormal power plants, can clearly determine whether the abnormal carbon emissions are caused by fuel quality fluctuations or abnormal unit operation, providing a basis for taking targeted measures, avoiding blind investigation, and improving work efficiency.

[0046] S3. Correct the fuel emission factor based on the carbon content of the fuel and recalculate the carbon emission intensity of abnormal power plants based on the corrected results, and provide feedback.

[0047] As a preferred embodiment, the specific analysis process of step S3 includes: acquiring multiple sets of data on fuel carbon content and corresponding fuel emission factors, and constructing a training set.

[0048] Based on the training set, a model is constructed using machine learning algorithms, with fuel carbon content as input and fuel emission factor as output, which is denoted as the fuel emission factor correction model.

[0049] The carbon content deviation of the fuel used is input into the fuel emission factor correction model to obtain the correction amount of the fuel emission factor of the fuel used.

[0050] The fuel emission factor is corrected based on the correction amount of the fuel emission factor to obtain the corrected fuel emission factor.

[0051] The carbon emission intensity of the abnormal power plant was recalculated based on the revised fuel emission factor.

[0052] It should be noted that multiple sets of data on fuel carbon content and corresponding fuel emission factors were obtained through historical practical experience.

[0053] It should be noted that the corrected fuel emission factor is obtained by summing the fuel emission factor stored in the database with the correction amount of the fuel emission factor of the fuel used.

[0054] It should be noted that the process of recalculating the carbon emission intensity of abnormal power plants is based on the same principle as the process of calculating the carbon emission intensity of each power plant within the accounting period.

[0055] In this embodiment, the present invention utilizes machine learning algorithms to construct a fuel emission factor correction model, dynamically correcting the emission factor based on changes in fuel carbon content, thereby making the calculation of carbon emission intensity more accurate and providing reliable data support for carbon emission management.

[0056] S4. Draw the load-emission intensity curves of each unit in the abnormal power plant, identify the unit with a sudden increase in emissions as an inefficient unit, obtain the abnormal information and investigation information of the inefficient unit and provide feedback. The abnormal information includes the abnormal time period and the degree of abnormality, and the investigation information includes the investigation source and the investigation order.

[0057] As a preferred option, the specific analysis process for plotting the load-emission intensity curve in step S4 is as follows: retrieve the operating logs of each unit in the abnormal power plant within the accounting period.

[0058] The range of operating load of each unit within the accounting period is obtained, and the sub-intervals of operating load of each unit are obtained by dividing the intervals into equal lengths according to the set rules.

[0059] Obtain the carbon emission intensity of the unit within the operating time period corresponding to the operating load sub-interval, and record it as the carbon emission intensity corresponding to the operating load sub-interval. Statistically obtain the carbon emission intensity corresponding to each sub-interval of the operating load of each unit.

[0060] By binding the median of the operating load sub-interval with the carbon emission intensity corresponding to the operating load sub-interval, a set of data relating operating load and carbon emission intensity is obtained. Based on the carbon emission intensity corresponding to each sub-interval of the operating load of each unit, a set of data relating operating load and carbon emission intensity for each unit is obtained.

[0061] A coordinate system is established with operating load as the abscissa and carbon emission intensity as the ordinate. Based on the data on the relationship between operating load and carbon emission intensity of each unit, the corresponding data points are marked on the coordinate system. Using the mathematical modeling method, the curve of carbon emission intensity of each unit changing with operating load is plotted and recorded as the load-emission intensity curve of each unit.

[0062] It should be noted that the operating load of power plant units may fluctuate within the accounting period. The range of the operating load of the units within the accounting period can be obtained by using the maximum and minimum values ​​of the operating load of the power plant units within the accounting period.

[0063] It should be noted that the carbon emission intensity of the unit within the corresponding operating time period of the operating load sub-interval is obtained through the emission factor method. The specific calculation process is the same as the calculation process of the carbon emission intensity of each power plant within the accounting period.

[0064] As a preferred embodiment, the specific analysis process for identifying units with sudden emission increases in step S4 is as follows: obtain the load-emission intensity curves of each unit during the historical normal operation of the abnormal power plant and analyze the range of the tangent slope of each curve. Compare the range of the tangent slope of the load-emission intensity curves of each unit during the historical normal operation and record it as the reference range of the tangent slope of the load-emission intensity curves of each unit.

[0065] Obtain the tangent slope of each point on the load-emission intensity curve of each unit in the abnormal power plant. If the tangent slope of a certain point on the load-emission intensity curve of a certain unit exceeds its reference range, the unit's emissions will suddenly increase. Statistically identify the units with sudden emission increases and record them as inefficient units.

[0066] As a preferred embodiment, the specific analysis process for obtaining the abnormal time period of the inefficient unit in step S4 is as follows: obtain the load-emission intensity curve of the inefficient unit, screen out each data point whose tangent slope on the load-emission intensity curve exceeds the reference range, and record them as each abnormal data point.

[0067] The operating time periods corresponding to the operating load sub-intervals of each abnormal data point are obtained and spliced ​​together to obtain the abnormal time periods of inefficient units.

[0068] As a preferred embodiment, the specific analysis process for obtaining the degree of abnormality of the inefficient unit in step S4 is as follows: obtain the tangent slope of each abnormal data point on the load-emission intensity curve of the inefficient unit and compare them with each other, and obtain the abnormal data point with the largest tangent slope, and record it as the marked abnormal data point.

[0069] The amount by which the slope of the tangent line for the marked abnormal data points exceeds the upper limit of its parameter range is recorded as the overshoot of the tangent slope.

[0070] Based on the established quantitative mapping relationship between tangent slope overshoot and anomaly degree, the anomaly degree of inefficient units is matched and obtained.

[0071] In this embodiment, by plotting load-emission intensity curves and analyzing the tangent slope, the present invention can accurately identify inefficient units with sudden increases in emissions, and at the same time obtain detailed information such as the abnormal period and degree of abnormality, providing strong support for the optimized operation and maintenance of the units.

[0072] As a preferred embodiment, the specific analysis process for obtaining the source of inefficient unit investigation in step S4 is as follows: count the number of inefficient units in the abnormal power plant. If it is a single inefficient unit, the source of investigation for the inefficient unit is the unit's own equipment; otherwise, the source of investigation for the inefficient unit is the shared auxiliary equipment of the inefficient unit.

[0073] As a preferred embodiment, the specific analysis process for obtaining the troubleshooting sequence of inefficient units in step S4 is as follows: obtain the historical fault records of the inefficient units, and obtain the historical fault counts of each piece of equipment and each shared auxiliary piece of equipment of the inefficient unit.

[0074] When the source of the problem is the inefficient unit's own equipment, sort the equipment of the inefficient unit in descending order of its historical failure count to obtain the troubleshooting order for the inefficient unit's own equipment.

[0075] When the source of the problem is the shared auxiliary equipment of the inefficient unit, sort the shared auxiliary equipment of the inefficient unit in descending order of its historical failure count to obtain the troubleshooting order of the shared auxiliary equipment of the inefficient unit.

[0076] In this embodiment, the present invention determines the source of the problem based on whether the inefficient unit is solely its own equipment or shared auxiliary equipment, and then formulates a reasonable troubleshooting sequence based on historical fault records, making the troubleshooting work more scientific and targeted, enabling the rapid location of the root cause of the problem and reducing troubleshooting time and costs.

[0077] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0078] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0079] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0080] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0081] 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.

[0082] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for retrieving and analyzing carbon emission operation and maintenance data, characterized in that, The process includes the following steps: S1. Obtain the carbon emission intensity of each power plant in the target area within the accounting period, and compare it with the historical average carbon emission intensity and the preset industry average carbon emission intensity to identify power plants with abnormal carbon emissions and record them as abnormal power plants; The specific analysis process of step S1 includes: retrieving carbon emission operation and maintenance data of each power plant in the target area from the information platform; setting the duration of the accounting period, obtaining the fuel consumption and power generation of each power plant in the target area within the accounting period, and calculating the carbon emission intensity of each power plant within the accounting period by combining the fuel emission factors stored in the database; obtaining the carbon emission intensity of each power plant within the accounting period in each historical year and calculating the average to obtain the carbon emission intensity of each power plant in the same historical period. S1. Obtain the industry average carbon emission intensity within the accounting period; S2. Based on the carbon content of the fuel used by the abnormal power plant within the accounting period, determine whether the cause of the abnormal carbon emission is fuel quality fluctuation or unit operation abnormality. If it is the former, proceed to S3; otherwise, proceed to S4; S3. Correct the fuel emission factor according to the fuel carbon content and recalculate the carbon emission intensity of the abnormal power plant based on the corrected result, and provide feedback; S4. Plot the load-emission intensity curve of each unit in the abnormal power plant to identify units with sudden emission increases, mark them as inefficient units, obtain the abnormal information and investigation information of inefficient units and provide feedback. The abnormal information includes the abnormal time period and the degree of abnormality, and the investigation information includes the investigation source and the investigation order; the steps are as follows. The specific analysis process for obtaining the source point of inefficient units in step S4 is as follows: Count the number of inefficient units within the abnormal power plant. If it is a single inefficient unit, the source point is the unit's own equipment; otherwise, the source point is the shared auxiliary equipment. The specific analysis process for obtaining the order of inefficient unit investigation in step S4 is as follows: Obtain the historical fault records of the inefficient units to get the historical fault counts of each piece of equipment and each piece of shared auxiliary equipment within the inefficient unit. When the source point is the unit's own equipment, sort the equipment in the inefficient unit according to its historical fault count from highest to lowest to obtain the investigation order of the unit's own equipment. When the source point is... When sharing auxiliary equipment with inefficient units, the shared auxiliary equipment is sorted in descending order of its historical failure count to obtain the troubleshooting order for the shared auxiliary equipment of inefficient units. The specific analysis process for obtaining the degree of abnormality of inefficient units in step S4 is as follows: the tangent slope of each abnormal data point on the load-emission intensity curve of the inefficient unit is obtained and compared with each other, and the abnormal data point with the largest tangent slope is recorded as the marked abnormal data point; the amount by which the tangent slope of the marked abnormal data point exceeds the upper limit of its parameter range is obtained and recorded as the overshoot of the tangent slope; the degree of abnormality of the inefficient unit is matched according to the set quantitative mapping relationship between the tangent slope overshoot and the degree of abnormality.

2. The carbon emission operation and maintenance data retrieval and analysis method according to claim 1, characterized in that: The specific analysis process of step S1 also includes: if a power plant meets at least one of the following conditions, the power plant's carbon emissions are abnormal; otherwise, the power plant's carbon emissions are normal; (1) the carbon emission intensity during the accounting period is greater than the historical carbon emission intensity during the same period by a set first threshold for emission increase; (2) the carbon emission intensity during the accounting period is greater than the industry average carbon emission intensity by a set second threshold for emission increase; count each power plant with abnormal carbon emissions and record them as each abnormal power plant.

3. The method for retrieving and analyzing carbon emission operation and maintenance data according to claim 1, characterized in that: The specific analysis process of step S2 is as follows: retrieve the test report of the fuel used in the accounting period of the abnormal power plant to obtain the carbon content of the fuel used; compare the carbon content of the fuel used with the set reference carbon content of the fuel to obtain the amount by which the carbon content of the fuel used exceeds the reference carbon content, and record it as the carbon content deviation of the fuel used; compare the carbon content deviation of the fuel used with the preset carbon content deviation threshold. If the carbon content deviation of the fuel used is greater than the deviation threshold, the cause of the abnormal carbon emission is the fluctuation of fuel quality; otherwise, the cause of the abnormal carbon emission is the abnormal operation of the unit.

4. The carbon emission operation and maintenance data retrieval and analysis method according to claim 3, characterized in that: The specific analysis process of step S3 includes: acquiring multiple sets of data on fuel carbon content and corresponding fuel emission factors, and constructing a training set; based on the training set, constructing a model with fuel carbon content as input and fuel emission factors as output based on a machine learning algorithm, which is denoted as the fuel emission factor correction model; inputting the carbon content deviation of the used fuel into the fuel emission factor correction model to obtain the correction amount of the fuel emission factor of the used fuel; correcting the fuel emission factor according to the correction amount of the fuel emission factor to obtain the corrected fuel emission factor; and recalculating the carbon emission intensity of the abnormal power plant based on the corrected fuel emission factor.

5. The method for retrieving and analyzing carbon emission operation and maintenance data according to claim 1, characterized in that: The specific analysis process for plotting the load-emission intensity curve in step S4 is as follows: Retrieve the operating logs of each unit in the abnormal power plant within the accounting period; obtain the range of operating load for each unit within the accounting period and divide it into sub-intervals of operating load according to set rules; obtain the carbon emission intensity of the unit within the corresponding operating time period of the operating load sub-interval, and record it as the carbon emission intensity corresponding to the operating load sub-interval; statistically obtain the carbon emission intensity corresponding to each sub-interval of operating load for each unit; bind the median of the operating load sub-interval with the carbon emission intensity corresponding to the operating load sub-interval to obtain a set of data relating operating load and carbon emission intensity; based on the carbon emission intensity corresponding to each sub-interval of operating load for each unit, obtain each set of data relating operating load and carbon emission intensity for each unit; establish a coordinate system with operating load as the abscissa and carbon emission intensity as the ordinate; mark the corresponding data points in the coordinate system based on each set of data relating operating load and carbon emission intensity for each unit; and plot the curve of carbon emission intensity changing with operating load for each unit using a mathematical model, recording it as the load-emission intensity curve for each unit.

6. The method for retrieving and analyzing carbon emission operation and maintenance data according to claim 1, characterized in that: The specific analysis process for identifying units with sudden emission increases in step S4 is as follows: Obtain the load-emission intensity curves of each unit in the abnormal power plant during its historical normal operation and analyze the range of the tangent slope of each curve. Compare the range of the tangent slope of the load-emission intensity curves of each unit during its historical normal operation and record it as the reference range of the tangent slope of the load-emission intensity curves of each unit. Obtain the tangent slope of each point on the load-emission intensity curves of each unit in the abnormal power plant. If the tangent slope of a certain point on the load-emission intensity curve of a certain unit exceeds its reference range, then the unit's emissions have suddenly increased. Statistically identify the units with sudden emission increases and record them as inefficient units.

7. The carbon emission operation and maintenance data retrieval and analysis method according to claim 6, characterized in that: The specific analysis process for obtaining the abnormal time period of the inefficient unit in step S4 is as follows: obtain the load-emission intensity curve of the inefficient unit, screen out each data point whose tangent slope on the load-emission intensity curve exceeds the reference range and record it as an abnormal data point; obtain the operating time period corresponding to the operating load sub-interval to which each abnormal data point belongs and splice them together to obtain the abnormal time period of the inefficient unit.

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