A carbon emission accounting method for big data centers

Abstract

The present invention relates to the field of big data analysis technology, and in particular to a carbon emission accounting method for a big data center. The method comprises the following steps: obtaining energy consumption data of a big data center and resident population data of the area where the big data center is located and calculating the greenhouse carbon emissions of the center; obtaining refrigeration equipment data of the big data center and calculating the fugitive carbon emissions of the center; calculating the greenhouse gas emissions of the big data center based on the greenhouse carbon emissions data of the center and the fugitive carbon emissions data of the center; constructing a carbon emission accounting model based on the greenhouse gas emissions data of the big data center; obtaining fuel transportation data of the big data center and wastewater treatment data of the big data center and calculating the indirect combustion of the center; using the indirect combustion data of the center to correct the carbon emission accounting model, obtain an optimized carbon emission accounting model and upload it to the big data center cloud platform. The present invention uses big data analysis technology to realize the accounting of carbon emissions of the big data center.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a carbon emission accounting method for a big data center. Background Art

[0002] With the rapid development of information technology, big data centers play an important role in supporting the operation and development of modern society. However, the high energy consumption and carbon emissions of big data centers have become environmental issues of global concern. According to statistics, the energy consumption and carbon emissions of big data centers account for a considerable proportion of the world's total energy consumption and carbon emissions.

[0003] However, there are some problems with the current carbon emission accounting methods for big data centers. First, the existing methods usually lack adaptability to the characteristics of big data centers and cannot accurately and comprehensively calculate the carbon emissions of big data centers. Second, the existing methods do not adequately consider the impact of environmental factors and cannot accurately assess the carbon emission levels of big data centers. In addition, the existing methods are also insufficient in comparing and assessing the carbon emission levels of big data centers and cannot accurately determine the position and level of big data centers in the industry.

[0004] Therefore, there is an urgent need for a method that can accurately calculate and evaluate the carbon emissions of big data centers, which can take into account the characteristics of big data centers and the impact of environmental factors, and can provide comparison and evaluation strategies to help big data centers reduce carbon emissions and improve energy efficiency. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a carbon emission accounting method for a big data center to solve at least one of the above technical problems.

[0006] This application provides a carbon emissions accounting method for a big data center, including the following steps:

[0007] Step S1: Obtain energy consumption data of a big data center and data on permanent population in the area where the big data center is located, and perform carbon emission analysis on the energy consumption data of the big data center and data on permanent population in the area where the big data center is located, so as to obtain greenhouse carbon emission data of the center;

[0008] Step S2: Acquire the refrigeration equipment data of the big data center, and perform carbon emission analysis on the refrigeration equipment data of the big data center, so as to obtain the fugitive carbon emission data of the center;

[0009] Step S3: Calculate the greenhouse carbon emission data of the center and the fugitive carbon emission data of the center by using the greenhouse gas emission calculation formula of the big data center, so as to obtain the greenhouse gas emission data of the big data center, wherein the greenhouse gas emission calculation formula of the big data center is specifically:

[0010]

[0011] Where E is the carbon emissions of big data centers, Burning carbon dioxide emissions for big data centers, Burning methane emissions for big data centers, is the global warming potential of methane, Burning nitrous oxide emissions for big data centers, is the global warming potential of nitrous oxide, E es It is the emission of greenhouse gas after combustion caused by the escape of refrigerant substances in the big data center;

[0012] Step S4: construct a carbon emission accounting model based on the greenhouse gas emission data of the big data center;

[0013] Step S5: Obtain the fuel transportation data of the big data center and the wastewater treatment data of the big data center, and perform indirect carbon emission analysis on the fuel transportation data of the big data center and the wastewater treatment data of the big data center, so as to obtain the indirect combustion data of the center;

[0014] Step S6: Use the central indirect combustion data to correct the carbon emission accounting model to obtain an optimized carbon emission accounting model, and upload the optimized carbon emission accounting model to the big data center cloud platform to perform the carbon emission accounting task.

[0015] In the present invention, the energy consumption data of the big data center and the resident population data of the area where the big data center is located are obtained through the big data center cloud platform, and the carbon emission analysis is performed on the energy consumption data of the big data center and the resident population data of the area where the big data center is located, so as to obtain the greenhouse carbon emission data of the center; by combining the energy consumption data of the big data center with the resident population data for analysis, the carbon emission of the big data center can be estimated more accurately. The traditional carbon emission calculation method may ignore the impact of regional population factors on carbon emissions, and combining population data can more accurately reflect the actual situation. Carbon emission analysis can quantify the environmental impact of the big data center to evaluate its contribution to climate change. This can help big data center managers and relevant stakeholders better understand their environmental benefits and take measures to reduce carbon emissions. The refrigeration equipment data of the big data center is obtained through the big data center cloud platform, and the carbon emission analysis is performed on the refrigeration equipment data of the big data center, so as to obtain the fugitive carbon emission data of the center; the refrigeration equipment of the big data center usually generates fugitive carbon emissions during operation. By obtaining and analyzing the energy consumption data of the refrigeration equipment, the fugitive carbon emissions of the big data center can be accurately evaluated. This helps to fully understand the carbon footprint of the data center. By analyzing the refrigeration equipment data, opportunities for energy efficiency improvement can be found. Refrigeration equipment is usually one of the parts with higher energy consumption in a big data center. By understanding the carbon emissions of refrigeration equipment, we can find equipment or operation modes with low energy efficiency and make corresponding improvement suggestions to reduce fugitive carbon emissions and improve overall energy efficiency. The greenhouse carbon emissions data of the center and the fugitive carbon emissions data of the center are calculated by the greenhouse gas emissions calculation formula of the big data center to obtain the greenhouse gas emissions data of the big data center. The big data center can use greenhouse gas emission data to evaluate the effectiveness of emission reduction measures and track emission trends. This can monitor and quantify emission reduction results, adjust and improve emission reduction strategies in a timely manner, and provide a basis and guidance for achieving carbon neutrality and sustainable development goals. A carbon emission accounting model is constructed based on the greenhouse gas emission data of the big data center; by constructing a carbon emission accounting model, the carbon emission hotspots of the big data center can be understood. This will help determine which activities play a key role in greenhouse gas emissions, such as energy-intensive operations, energy efficiency performance of specific equipment, etc. By accurately identifying carbon emission hotspots, targeted measures can be taken to reduce carbon footprints. The big data center fuel transportation data and big data center wastewater treatment data are obtained through the big data center cloud platform, and indirect carbon emissions are analyzed on the big data center fuel transportation data and big data center wastewater treatment data to obtain the center's indirect combustion data; by analyzing the indirect combustion data, it is possible to reveal sources of carbon emissions that may be overlooked. Fuel transportation and wastewater treatment may involve combustion processes, which will produce additional carbon emissions. Through meticulous data analysis, these hidden sources of carbon emissions can be identified and quantified, and appropriate measures can be taken to reduce these emissions.The carbon emission accounting model is modified using the indirect combustion data of the center to obtain an optimized carbon emission accounting model, and the optimized carbon emission accounting model is uploaded to the cloud platform of the big data center to perform the carbon emission accounting task. By modifying the carbon emission accounting model and taking into account the impact of indirect combustion, the accuracy and precision of the accounting results can be improved. Traditional carbon emission accounting often only considers direct emissions and ignores the contribution of indirect combustion. By optimizing the model and uploading it to the cloud platform to perform tasks, the center can calculate and report carbon emissions more accurately and provide more reliable data support. After uploading the optimized carbon emission accounting model to the cloud platform of the big data center, automated and centralized carbon emission accounting can be achieved. The cloud platform has powerful computing and data processing capabilities and can handle large amounts of data, model iterations, and computing tasks. By taking advantage of the cloud platform, the center can efficiently perform carbon emission accounting, save time and labor costs, and ensure data consistency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0017] Figure 1 A flowchart of the steps of the carbon emission accounting method of a big data center of the present invention;

[0018] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0019] Figure 3 Detailed step flow diagram of step S12 in the present invention;

[0020] Figure 4 Detailed step flow diagram of step S122 in the present invention;

[0021] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0025] See also Figures 1 to 4 The present invention provides a carbon emission accounting method for a big data center, wherein the method comprises the following steps:

[0026] Step S1: Obtain energy consumption data of a big data center and data on permanent population in the area where the big data center is located, and perform carbon emission analysis on the energy consumption data of the big data center and data on permanent population in the area where the big data center is located, so as to obtain greenhouse carbon emission data of the center;

[0027] In this embodiment, the big data center energy consumption data and the permanent population data of the area where the big data center is located are obtained through the big data center cloud platform, and the carbon emissions are calculated for the big data center energy consumption data and the permanent population data of the area where the big data center is located, so as to obtain the center's greenhouse carbon emissions data.

[0028] Step S2: Acquire the refrigeration equipment data of the big data center, and perform carbon emission analysis on the refrigeration equipment data of the big data center, so as to obtain the fugitive carbon emission data of the center;

[0029] In this embodiment, the big data center refrigeration equipment data is obtained through the big data center cloud platform, and the carbon emissions of the big data center refrigeration equipment data are calculated to obtain the center's fugitive carbon emissions data.

[0030] Step S3: Calculate the greenhouse carbon emission data of the center and the fugitive carbon emission data of the center by using the greenhouse gas emission calculation formula of the big data center, so as to obtain the greenhouse gas emission data of the big data center, wherein the greenhouse gas emission calculation formula of the big data center is specifically:

[0031]

[0032] Where E is the carbon emissions of big data centers, Burning carbon dioxide emissions for big data centers, Burning methane emissions for big data centers, is the global warming potential of methane, Burning nitrous oxide emissions for big data centers, is the global warming potential of nitrous oxide, E es It is the emission of greenhouse gas after combustion caused by the escape of refrigerant substances in the big data center;

[0033] In this embodiment, the greenhouse carbon emission data of the center and the fugitive carbon emission data of the center are calculated by the greenhouse gas emission calculation formula of the big data center, so as to obtain the greenhouse gas emission data of the big data center, wherein the greenhouse gas emission calculation formula of the big data center fully considers the carbon dioxide emission of the big data center combustion that affects the carbon emission E of the big data center. Methane emissions from burning in big data centers Global Warming Potential of Methane Nitrous oxide emissions from burning in big data centers Global Warming Potential of Nitrous Oxide Emissions of greenhouse gases from combustion caused by the escape of refrigerant substances in big data centers es , forming a functional relationship:

[0034]

[0035] This formula takes into account the emissions of multiple greenhouse gases, including carbon dioxide, methane, and nitrous oxide. Compared with the traditional formula that only considers carbon dioxide emissions, this formula comprehensively considers the impact of more greenhouse gases and more accurately evaluates carbon emissions. The global warming potential coefficients of methane and nitrous oxide are introduced into the formula. These coefficients reflect the contribution of different greenhouse gases to global warming and help quantify the impact of different gases. This can more accurately determine the carbon emissions of big data centers. The formula also includes the carbon emissions after greenhouse gas emissions caused by the escape of refrigerant substances. This is a factor that is not considered in traditional formulas. However, the escape of refrigerant substances is also one of the important sources of carbon emissions in big data centers. The introduction of this formula makes the calculation of carbon emissions more comprehensive and accurate.

[0036] Step S4: construct a carbon emission accounting model based on the greenhouse gas emission data of the big data center;

[0037] In this embodiment, the greenhouse gas emission data of the big data center is divided into a greenhouse training set and a greenhouse test set according to a division ratio of 8:2, and the greenhouse training set and the greenhouse test set are modeled based on the random forest algorithm to obtain a carbon emission accounting model.

[0038] Step S5: Obtain the fuel transportation data of the big data center and the wastewater treatment data of the big data center, and perform indirect carbon emission analysis on the fuel transportation data of the big data center and the wastewater treatment data of the big data center, so as to obtain the indirect combustion data of the center;

[0039] In this embodiment, the big data center fuel transportation data and the big data center wastewater treatment data are obtained through the big data center cloud platform, and indirect carbon emissions are calculated for the big data center fuel transportation data and the big data center wastewater treatment data, so as to obtain the center's indirect combustion data.

[0040] Step S6: Use the central indirect combustion data to correct the carbon emission accounting model to obtain an optimized carbon emission accounting model, and upload the optimized carbon emission accounting model to the big data center cloud platform to perform the carbon emission accounting task.

[0041] In this embodiment, the central indirect combustion data is converted into input variables, and the input variables are input into the carbon emission accounting model to obtain an optimized carbon emission accounting model, and the optimized carbon emission accounting model is uploaded to the big data center cloud platform to perform the carbon emission accounting task.

[0042] In the present invention, the energy consumption data of the big data center and the resident population data of the area where the big data center is located are obtained through the big data center cloud platform, and the carbon emission analysis is performed on the energy consumption data of the big data center and the resident population data of the area where the big data center is located, so as to obtain the greenhouse carbon emission data of the center; by combining the energy consumption data of the big data center with the resident population data for analysis, the carbon emission of the big data center can be estimated more accurately. The traditional carbon emission calculation method may ignore the impact of regional population factors on carbon emissions, and combining population data can more accurately reflect the actual situation. Carbon emission analysis can quantify the environmental impact of the big data center to evaluate its contribution to climate change. This can help big data center managers and relevant stakeholders better understand their environmental benefits and take measures to reduce carbon emissions. The refrigeration equipment data of the big data center is obtained through the big data center cloud platform, and the carbon emission analysis is performed on the refrigeration equipment data of the big data center, so as to obtain the fugitive carbon emission data of the center; the refrigeration equipment of the big data center usually generates fugitive carbon emissions during operation. By obtaining and analyzing the energy consumption data of the refrigeration equipment, the fugitive carbon emissions of the big data center can be accurately evaluated. This helps to fully understand the carbon footprint of the data center. By analyzing the refrigeration equipment data, opportunities for energy efficiency improvement can be found. Refrigeration equipment is usually one of the parts with higher energy consumption in a big data center. By understanding the carbon emissions of refrigeration equipment, we can find equipment or operation modes with low energy efficiency and make corresponding improvement suggestions to reduce fugitive carbon emissions and improve overall energy efficiency. The greenhouse carbon emissions data of the center and the fugitive carbon emissions data of the center are calculated by the greenhouse gas emissions calculation formula of the big data center to obtain the greenhouse gas emissions data of the big data center. The big data center can use greenhouse gas emission data to evaluate the effectiveness of emission reduction measures and track emission trends. This can monitor and quantify emission reduction results, adjust and improve emission reduction strategies in a timely manner, and provide a basis and guidance for achieving carbon neutrality and sustainable development goals. A carbon emission accounting model is constructed based on the greenhouse gas emission data of the big data center; by constructing a carbon emission accounting model, the carbon emission hotspots of the big data center can be understood. This will help determine which activities play a key role in greenhouse gas emissions, such as energy-intensive operations, energy efficiency performance of specific equipment, etc. By accurately identifying carbon emission hotspots, targeted measures can be taken to reduce carbon footprints. The big data center fuel transportation data and big data center wastewater treatment data are obtained through the big data center cloud platform, and indirect carbon emissions are analyzed on the big data center fuel transportation data and big data center wastewater treatment data to obtain the center's indirect combustion data; by analyzing the indirect combustion data, it is possible to reveal sources of carbon emissions that may be overlooked. Fuel transportation and wastewater treatment may involve combustion processes, which will produce additional carbon emissions. Through meticulous data analysis, these hidden sources of carbon emissions can be identified and quantified, and appropriate measures can be taken to reduce these emissions.The carbon emission accounting model is modified using the indirect combustion data of the center to obtain an optimized carbon emission accounting model, and the optimized carbon emission accounting model is uploaded to the cloud platform of the big data center to perform the carbon emission accounting task. By modifying the carbon emission accounting model and taking into account the impact of indirect combustion, the accuracy and precision of the accounting results can be improved. Traditional carbon emission accounting often only considers direct emissions and ignores the contribution of indirect combustion. By optimizing the model and uploading it to the cloud platform to perform tasks, the center can calculate and report carbon emissions more accurately and provide more reliable data support. After uploading the optimized carbon emission accounting model to the cloud platform of the big data center, automated and centralized carbon emission accounting can be achieved. The cloud platform has powerful computing and data processing capabilities and can handle large amounts of data, model iterations, and computing tasks. By taking advantage of the cloud platform, the center can efficiently perform carbon emission accounting, save time and labor costs, and ensure data consistency and accuracy.

[0043] Optionally, step S1 includes the following steps:

[0044] Step S11: extracting the usage of the energy consumption data of the big data center, thereby obtaining the electricity consumption data of the big data center and the fossil fuel usage data of the big data center;

[0045] In this embodiment, data extraction is performed on the energy consumption data of the big data center to obtain the electricity consumption data of the big data center and the fossil fuel usage data of the big data center.

[0046] Step S12: Calculate the carbon dioxide emissions based on the electricity consumption data of the big data center and the resident population data of the area where the big data center is located, so as to obtain the carbon dioxide emissions data of the center;

[0047] In this embodiment, the carbon emissions of the electricity consumption data of the big data center are calculated to obtain the electric and heat data of the big data center; the population carbon emissions are calculated based on the resident population data of the area where the big data center is located to obtain the center population carbon emissions data; the big data center electric and heat data and the center population carbon emissions data are then merged to obtain the center carbon dioxide emissions data.

[0048] Step S13: Calculate the methane emissions of the fossil fuel usage data of the big data center to obtain the methane emissions data of the center;

[0049] In this embodiment, the methane emissions are calculated for the fossil fuel usage data of the big data center using the methane emissions formula, thereby obtaining the center's methane emissions data.

[0050] Step S14: Calculate the nitrous oxide emissions based on the fossil fuel usage data of the big data center, thereby obtaining the nitrous oxide emissions data of the center;

[0051] In this embodiment, the nitrous oxide emissions are calculated for the fossil fuel usage data of the big data center using the nitrous oxide emissions formula, thereby obtaining the center's nitrous oxide emissions data.

[0052] Step S15: Merging the central methane emission data, the central carbon dioxide emission data and the central nitrous oxide emission data to obtain the central greenhouse carbon emission data.

[0053] In this embodiment, the central methane emission data, the central carbon dioxide emission data and the central nitrous oxide emission data are merged according to the associated fields between the data, so as to obtain the central greenhouse carbon emission data.

[0054] In the present invention, the energy consumption data of the big data center is extracted to obtain the electricity consumption data of the big data center and the fossil fuel usage data of the big data center; by extracting the electricity consumption data and fossil fuel usage data of the big data center, energy management and monitoring can be achieved. Carbon dioxide emissions are calculated for the electricity consumption data of the big data center and the resident population data of the area where the big data center is located, so as to obtain the carbon dioxide emissions data of the center; calculating carbon dioxide emissions can help the big data center evaluate the efficiency of energy use and find potential room for improvement. By analyzing the emission data, it can be determined whether there are energy waste or inefficiency problems, and corresponding measures can be taken, such as improving equipment efficiency, optimizing energy management, and adopting renewable energy, so as to improve energy utilization efficiency and reduce carbon dioxide emissions. Methane emissions are calculated for the fossil fuel usage data of the big data center to obtain the methane emissions data of the center; by obtaining the methane emissions data of the center, the source and scale of methane emissions can be more accurately understood. Such information is crucial for formulating effective emission reduction strategies. The center can analyze methane emission data, identify the main emission sources and key influencing factors, and take corresponding measures to reduce methane emissions. The nitrous oxide emissions of the big data center fossil fuel usage data are calculated to obtain the center's nitrous oxide emissions data; nitrous oxide is one of the pollutants in the atmosphere and is harmful to air quality and the environment. By calculating the nitrous oxide emissions of the data center, the impact on the surrounding environment can be evaluated and whether it complies with environmental regulations and standards can be determined. At the same time, the emission data can be analyzed to identify the main emission sources and potential influencing factors, and corresponding measures can be taken to reduce nitrous oxide emissions. The center's methane emission data, the center's carbon dioxide emission data, and the center's nitrous oxide emission data are merged to obtain the center's greenhouse carbon emission data. By comprehensively considering the emission data of gases such as methane, carbon dioxide, and nitrous oxide, the contribution of data centers to carbon emissions and their environmental impact can be more comprehensively evaluated. Methane and carbon dioxide are the main greenhouse gases, while nitrous oxide is an atmospheric pollutant. The merging of emission data including these three gases can provide a comprehensive carbon emission situation, providing a more accurate basis for evaluating the environmental sustainability of data centers and formulating emission reduction strategies.

[0055] Optionally, step S12 is specifically:

[0056] Step S121: Calculate the carbon emissions of the electricity consumption data of the big data center, so as to obtain the electric and heat data of the big data center;

[0057] In this embodiment, the carbon emissions of the electricity consumption data of the big data center are calculated using the electric heat carbon emissions calculation formula, thereby obtaining the electric heat data of the big data center.

[0058] Step S122: Calculate the population carbon emissions based on the resident population data of the area where the big data center is located, so as to obtain the center population carbon emissions data;

[0059] In this embodiment, the population carbon emission calculation formula is used to calculate the population carbon emission of the resident population data in the area where the big data center is located, thereby obtaining the center population carbon emission data;

[0060] Step S123: Merge the central electricity and heat carbon emission data and the central population carbon emission data to obtain the central carbon dioxide emission data.

[0061] In this embodiment, the central electric and thermal carbon emission data and the central population carbon emission data are merged according to the associated fields between the data, so as to obtain the central carbon dioxide emission data.

[0062] The present invention calculates the carbon emissions of the electricity consumption data of the big data center, thereby obtaining the electric and heat data of the big data center; understanding the electric and heat data of the big data center is helpful to optimize energy management. By analyzing the electric and heat data, it can be determined whether there is a problem of energy waste or heat leakage, and corresponding measures can be taken to adjust and improve it. The population carbon emissions of the resident population data in the area where the big data center is located are calculated to obtain the carbon emissions data of the center population; population is one of the main sources of carbon emissions. By calculating the population carbon emissions in the area where the center is located, the carbon footprint and environmental impact of the area can be evaluated. Understanding the population carbon emissions in the area where the center is located helps to identify the main emission sources, determine the key emission reduction areas, and provide guidance for formulating emission reduction strategies. Calculating the population carbon emissions in the area where the center is located is an important step in achieving sustainable development goals. Understanding the carbon emissions of the population can quantitatively measure and monitor the progress of carbon emission reduction in the region to achieve carbon emission reduction. The center electric and heat carbon emissions data and the center population carbon emissions data are merged to obtain the center carbon dioxide emissions data. By merging the electric and heat carbon emissions data and the population carbon emissions data, a comprehensive assessment of the overall carbon emissions of the center can be achieved. This will help to obtain more accurate carbon footprint data and provide a more comprehensive environmental impact assessment.

[0063] Optionally, step S121 is specifically:

[0064] Step S1211: extracting the power consumption data of the big data center, thereby obtaining the center's outsourced power data, the center's outsourced heat data, the center's self-generated green power data, and the center's outsourced green power data;

[0065] In this embodiment, power data and thermal data are extracted from the power consumption data of the big data center, so as to obtain the center's outsourced power data, the center's outsourced thermal data, the center's self-generated green electricity data, and the center's outsourced green electricity data.

[0066] Step S1212: Classify and calculate the center's outsourced power data, the center's outsourced heat data, the center's self-generated green electricity data, and the center's outsourced green electricity data using a preset electric and heat classification algorithm, thereby obtaining the big data center's electric and heat data;

[0067] In this embodiment, an electric and thermal classification algorithm is constructed by combining relevant parameters such as logistic regression related parameters, big data center electricity related data, and big data center heat related data. The electric and thermal classification algorithm is used to classify and calculate the center's purchased electricity data, the center's purchased heat data, the center's spontaneous green electricity data, and the center's purchased green electricity data, thereby obtaining the big data center's electric and thermal data.

[0068] The function formula of the electrothermal classification algorithm is as follows:

[0069]

[0070] Where CF is the electric heat consumption coefficient, d is the time interval, t is the time, i is the time point, P i is the purchased electricity at the i-th time point, H i is the purchased heat at the i-th time point, G i is the spontaneous green electricity at the i-th time point, R i is the purchased green electricity at the i-th time point, π is the pi, and n is the number of time points.

[0071] The present invention constructs a function formula of an electric heat classification algorithm, which is used to classify and calculate the electric heat consumption coefficient obtained by calculating the center's outsourced power data, the center's outsourced heat data, the center's self-generated green electricity data, and the center's outsourced green electricity data, thereby playing a role in quickly and accurately classifying various types of power data, heat data, and green electricity data, so as to achieve a more accurate assessment of the energy consumption of the big data center. This formula fully considers the time interval d, time t, time point i, and the outsourced power H at the i-th time point that affect the electric heat consumption coefficient CF. i , the spontaneous green electricity G at the i-th time point i , the purchased green electricity R at the i-th time point i , forming a functional relationship:

[0072]

[0073] Where P i =AD i ×EF a is the net purchased electricity AD of the big data center at the i-th time point i The annual average power supply emission factor EF of the national power supply in the big data center area aMultiplied together, this calculation can reveal the energy efficiency of a large data center. If a large data center has a high carbon emission from purchased electricity compared to the average emission factor of the country where it is supplied, this may indicate energy waste or inefficient energy use. i =AP i ×EF b The carbon emission calculation result of purchased heat can reveal the energy efficiency of the big data center. If the carbon emission of purchased heat is high, relative to the regional average annual heating emission factor, there may be energy waste or inefficient energy use. By analyzing the carbon emission calculation results, the big data center can identify opportunities and directions for improving energy efficiency, thereby reducing energy costs and environmental impact. i +H i -G i -R i It can help evaluate the greenhouse gas emissions of big data centers. The energy composition of purchased electricity and purchased heat determines its carbon emissions, while self-generated green electricity and purchased green electricity help reduce greenhouse gas emissions. By analyzing the values ​​of each item in the formula, the carbon emissions of the big data center can be estimated, and it can also be used to evaluate the energy costs of the big data center. Purchased electricity and purchased heat will increase energy costs, while self-generated green electricity and purchased green electricity can reduce costs. By calculating the values ​​and proportions of each item in the formula, the energy cost structure of the big data center can be determined and potential opportunities for cost reduction can be found. Through this algorithm function formula, the algorithm can be made more refined and explainable.

[0074] Step S1213: Calculate the electric heat data of the big data center using the electric heat carbon emission calculation formula to obtain the electric heat carbon emission data of the center, wherein the electric heat carbon emission calculation formula is specifically:

[0075]

[0076] Where E v is the carbon emission of electric heating, P elec is the annual electricity consumption of the big data center, CF is the electric heat consumption coefficient, ER is the global unified equivalent emission coefficient, EF is the efficiency of the purchased power plant, LHV is the lower calorific value of standard coal, T out is the outlet temperature of the big data center, T in is the inlet temperature of the big data center, and WU is the comprehensive energy efficiency of the big data center.

[0077] The present invention constructs a calculation formula for electric heating carbon emissions, which is used to calculate the electric heating data of the big data center to obtain the electric heating carbon emissions of the big data center, revealing the efficiency and optimization potential of energy utilization in the big data center. The calculation formula for electric heating carbon emissions fully considers the factors that affect the electric heating carbon emissions E v The annual power consumption of the big data center is P elec , electric heat consumption coefficient CF, global unified equivalent emission coefficient ER, efficiency of purchased power plants EF, lower calorific value LHV of standard coal, outlet temperature T of the big data center out , the inlet temperature T of the big data center in , the comprehensive energy efficiency WU of the big data center forms a functional relationship:

[0078]

[0079] P elec ×CF×ER calculates the global unified equivalent emissions of big data centers. This indicator is used to measure the contribution of big data centers to greenhouse gas emissions. By monitoring and comparing the global unified equivalent emissions, emission reduction strategies and sustainable development goals can be formulated to promote the development of data centers in a more environmentally friendly and low-carbon direction. EF×LHV can obtain the actual power generation efficiency of the power plant. This indicator is used to measure the efficiency level of the power plant in converting fuel into electrical energy. This value indicates the energy utilization effect of the power plant when using fuel to generate electricity. The higher the actual power generation efficiency, the more effectively the power plant can use fuel, reduce energy waste, and thus generate more electricity in the power generation process. Therefore What is obtained is the actual amount of energy consumed by the big data center. The efficiency level of the data center in terms of energy utilization can be evaluated. This value can provide information about energy consumption, help the data center understand its own energy utilization effect, and evaluate and improve its energy management strategy. What is obtained is the heat recovery efficiency of the data center. The heat recovery efficiency measures the degree to which the data center recovers and reuses heat energy during the energy utilization process. By calculating the temperature difference between the outlet temperature and the inlet temperature and dividing it by the comprehensive energy efficiency, a ratio can be obtained to indicate how much heat energy the data center successfully recovered and utilized. The formula for obtaining electric heat carbon emissions improves the accuracy and authenticity of subsequent calculations of greenhouse gas emissions of large data centers.

[0080] In the present invention, data extraction is performed on the electricity consumption data of the big data center to obtain the center's outsourced electricity data, the center's outsourced heat data, the center's self-generated green electricity data, and the center's outsourced green electricity data; the center's outsourced electricity data, the center's outsourced heat data, the center's self-generated green electricity data, and the center's outsourced green electricity data are classified and calculated by a preset electric and thermal classification algorithm to obtain the electric and thermal data of the big data center; by classifying and calculating various types of power data, thermal data, and green electricity data, the energy consumption of the big data center can be more accurately evaluated. This helps to understand the distribution and use of electricity and heat, and helps to find bottlenecks and potential improvement points for energy consumption. At the same time, different energy sources of the big data center and their proportions in the power and heat supply can be identified. This helps to optimize energy utilization strategies, such as by increasing the proportion of self-generated green electricity or improving the heat supply system, reducing dependence on traditional energy, thereby reducing carbon emissions and energy costs. The electric and thermal data of the big data center are calculated by the electric and thermal carbon emissions calculation formula to obtain the center's electric and thermal carbon emissions data; by obtaining the electric and thermal carbon emissions data, the efficiency and optimization potential of energy utilization in the big data center can be revealed. By analyzing emissions data, key links with high carbon emissions can be identified.

[0081] Optionally, step S122 is specifically:

[0082] Step S1221: screening the carbon emission characteristics of the resident population data in the area where the big data center is located by principal component analysis, thereby obtaining the carbon emission characteristics of the population;

[0083] In this embodiment, the principal component analysis method is used to calculate the covariance matrix of the resident population data in the area where the big data center is located, so as to obtain the correlation of population variables, extract the eigenvalues ​​and eigenvectors of the covariance matrix, select the first few principal components with the largest explained variance according to the size of the eigenvalues, and map the resident population data in the area where the big data center is located to the selected principal component space to obtain the reduced-dimensional data. By performing statistical analysis on the reduced-dimensional data, the carbon emission characteristics of the population are obtained. Principal component analysis can help discover the main variation patterns in the data to determine which variables are most important for explaining the variance of population carbon emissions.

[0084] Step S1222: Standardizing the population carbon emission characteristics to obtain population carbon emission standardized data;

[0085] In this embodiment, the population carbon emission characteristics are standardized by a zero-mean standardization method, thereby obtaining population carbon emission standardized data.

[0086] Step S1223: performing cluster analysis on the population carbon emission standardization data according to a preset clustering algorithm, thereby obtaining a carbon emission population data set;

[0087] In this implementation, the K-means clustering algorithm is used to perform cluster analysis on the standardized data of population carbon emissions, thereby obtaining a carbon emission population dataset.

[0088] Step S1224: Calculate the population carbon emissions for the carbon emission population dataset, thereby obtaining the central population carbon emissions data.

[0089] In this embodiment, the population carbon emission calculation formula is used to calculate the population carbon emission of the carbon emission population data set, thereby obtaining the central population carbon emission data.

[0090] The present invention uses principal component analysis to screen the carbon emission characteristics of the resident population data in the area where the big data center is located, thereby obtaining the population carbon emission characteristics; principal component analysis can reduce complex high-dimensional data to lower dimensions. By retaining the main components, the carbon emission characteristics of the population data can be better understood and explained. At the same time, as much information as possible is retained to capture the variation of the data. The population carbon emission characteristics are standardized to obtain the population carbon emission standardized data; different carbon emission characteristics often have different measurement units and dimensions, such as the population size in people and energy consumption in gigajoules or kilowatt-hours. Standardization will eliminate these dimensional differences and convert all features into a unified dimensionless representation, so that different features can be analyzed in a comparable manner. According to a preset clustering algorithm, the population carbon emission standardized data is clustered and analyzed to obtain a carbon emission population data set; the cluster analysis results can provide a reference for the formulation of carbon emission reduction policies. By understanding the carbon emission characteristics and behavior patterns of different populations, carbon emission reduction measures can be designed and implemented in a targeted manner to meet the needs and challenges of different populations. The population carbon emission is calculated for the carbon emission population data set to obtain the central population carbon emission data. By regularly calculating the carbon emissions of the central population, the effectiveness of carbon reduction policies can be monitored and evaluated in real time. By analyzing the carbon emissions data of the central population, the differences in carbon emissions between different groups of people can be identified. This provides a basis for formulating differentiated carbon reduction policies.

[0091] Optionally, the calculation of population carbon emissions in step S1224 is specifically as follows:

[0092] Get the location information of the big data center;

[0093] In this embodiment, the big data center location information is obtained through the big data center cloud platform.

[0094] The carbon emission population data set and the location information of the big data center are calculated through the population carbon emission calculation formula to obtain the center population carbon emission data. The population carbon emission calculation formula is as follows:

[0095]

[0096] Where E p is the population carbon emission of the big data center, N is the number of people working in the big data center, f is the population category coefficient, and n f is the population size of population f, c f is the carbon emission of population f, ρ is the average air density in the big data center area, m is the average molecular weight of air, V d is the volume of the big data center, v x,y is the average wind speed at the two-dimensional spatial location of the big data center, t d is the total time of use of the big data center, A is the altitude harmonization parameter, h s is the average altitude of the area surrounding the big data center, h d is the average altitude of the big data center, B is the atmospheric pressure emphasis and parameter, p is the atmospheric pressure in the big data center area, and p0 is 101 kPa atmospheric pressure.

[0097] The present invention constructs a population carbon emission calculation formula, which is used to quickly and accurately calculate the population carbon emission of the big data center using the carbon emission population data set and the location information of the big data center. The population carbon emission calculation formula fully considers the population carbon emission E that affects the big data center. p The number of people working in the big data center is N, the population category coefficient is f, and the population of f people is n f , the carbon emissions of population c f , the average air density ρ in the big data center area, the average molecular weight of air m, and the volume V of the big data center d , the average wind speed v at the two-dimensional spatial location of the big data center x,y , the total time t used by the big data center d , altitude harmonization parameter A, the average altitude h of the area surrounding the big data center s , the average altitude of the big data center is h d , atmospheric pressure stress and parameter B, atmospheric pressure p in the big data center area, 101kPa atmospheric pressure p0, form a functional relationship:

[0098]

[0099] in, Refers to the total carbon emissions of each group of people in the big data center, ρmV d (v x,y )t dThe air quality of a large data center can be obtained to ensure the normal operation of the big data center and maintain good environmental conditions. The big data center requires an efficient cooling system to maintain the normal operating temperature of the equipment. Taking into account parameters such as air density, molecular weight, volume, wind speed, etc. can help optimize the design and operation of the cooling system and improve energy efficiency and the overall efficiency of the system. It represents the ratio between the altitude difference and the air density. This ratio can reflect the effect of altitude on air density to a certain extent. It can be used to estimate the effect of the altitude difference between the area surrounding the big data center and the data center itself on the air density. The standard atmospheric pressure of 101kPa is usually used to represent the reference value of atmospheric pressure. Therefore, by comparing the atmospheric pressure in the big data center area with the reference value, we can understand the difference between the atmospheric pressure in the big data center area and the standard atmospheric pressure and the effect of this difference on the carbon emissions of the population. The population carbon emissions of the big data center obtained by this formula improve the accuracy and authenticity of the subsequent calculation of the carbon dioxide emissions of the big data center.

[0100] The present invention obtains the location information of the big data center through the big data center cloud platform. The carbon emission population data set and the location information of the big data center are calculated by the population carbon emission calculation formula to obtain the carbon emission data of the center population; the carbon emission data of the center population can be used to evaluate the effects of carbon emission reduction policies and measures. By combining the carbon emission population data set and the location information of the big data center, the carbon emission characteristics of different regions and populations can be deeply understood.

[0101] Optionally, step S13 is specifically:

[0102] Obtain methane emission factors by searching public information from environmental organizations;

[0103] In this embodiment, the methane emission factor is obtained by searching for public information on the official website of the Environmental Protection Agency.

[0104] The methane emission factor and the fossil fuel usage data of the big data center are calculated through the methane emission formula to obtain the central methane emission data. The methane emission formula is as follows:

[0105]

[0106] in is the methane emission from burning in big data centers, k is the total number of fuel types, a is the type of fossil fuel, AD a is the methane activity coefficient of a type of fossil fuel, EF is the methane emission factor, v is the volume of the fuel cube, p is the atmospheric pressure, T is the temperature in the combustion chamber, and F a is the fuel consumption of the burner of type a fossil fuel, and V is the operating time of the combustion equipment.

[0107] The present invention constructs a methane emission formula, which is used to calculate the methane emissions from burning in big data centers by using the methane emission factor and the fossil fuel usage data of big data centers. The methane emission formula fully considers the factors that affect the methane emissions from burning in big data centers. The total number of fuel types k, the type of fossil fuel a, and the methane activity coefficient AD of a fossil fuel type a , methane emission factor EF, fuel cube volume v, atmospheric pressure p, combustion chamber temperature T, fuel consumption F of a burner of a fossil fuel type a , the operating time V of the combustion equipment forms a functional relationship:

[0108]

[0109] By calculating the methane activity coefficient of each fossil fuel type, it is possible to distinguish and measure the methane emissions of different types of fossil fuels. This helps to accurately estimate the methane emissions caused by fuel consumption and to keep abreast of the emissions of different fuel types. It can express the methane emissions per unit volume of fuel. This result can be used to estimate the methane emissions from a fuel source or activity and help monitor and manage greenhouse gas emissions to develop emission reduction strategies and implement environmental protection measures. The methane emissions from the combustion of a big data center obtained by this formula improves the accuracy and authenticity of the subsequent calculation of greenhouse gas emissions from big data centers.

[0110] The present invention obtains methane emission factors by retrieving public information from environmental organizations; calculates the methane emission factors and the fossil fuel usage data of the big data center through the methane emission formula to obtain the center's methane emission data; by calculating the center's methane emission data, the greenhouse gas emission reduction potential of the big data center can be evaluated. By understanding the main sources of methane emissions, operations, procurement, and technology can be improved in a targeted manner to reduce methane emissions, improve energy efficiency, promote the use of clean energy, and coordinate with renewable energy transformation and carbon neutrality goals.

[0111] Optionally, step S14 is specifically:

[0112] Obtain nitrous oxide emission factors by searching public information from environmental organizations;

[0113] In this embodiment, the nitrous oxide emission factor is obtained by searching for public information on the official website of the Environmental Protection Agency.

[0114] The nitrous oxide emission factor and the fossil fuel usage data of the big data center are calculated through the nitrous oxide emission formula to obtain the central nitrous oxide emission data. The nitrous oxide emission formula is as follows:

[0115]

[0116] in is the nitrous oxide emission from burning in big data centers, k is the total number of fuel types, a is the type of fossil fuel, BC a is the nitrous oxide activity coefficient of a type of fossil fuel, GH is the nitrous oxide emission factor, v is the volume of the fuel cube, p is the atmospheric pressure, T is the temperature in the combustion chamber, F a is the fuel consumption of type a fossil fuel, and V is the operating time of the combustion equipment.

[0117] The present invention constructs a formula for nitrous oxide emissions, which is used to calculate the nitrous oxide emissions from burning in big data centers using the nitrous oxide emission factor and the fossil fuel usage data of big data centers. The formula fully considers the factors that affect the nitrous oxide emissions from burning in big data centers. The total number of fuel types k, the type of fossil fuel a, the nitrous oxide activity coefficient BD of a type of fossil fuel a , nitrous oxide emission factor GH, fuel cube volume v, atmospheric pressure p, combustion chamber temperature T, fuel consumption F of a fossil fuel type a , the operating time V of the combustion equipment forms a functional relationship:

[0118]

[0119] By calculating the activity coefficient of nitrous oxide of different fossil fuel types, it is possible to distinguish and measure the nitrous oxide emissions of different types of fossil fuels. This helps to accurately estimate the nitrous oxide emissions caused by fuel consumption and to keep abreast of the emissions of different fuel types. It can express the nitrous oxide emissions per unit volume of fuel. This result can be used to estimate the nitrous oxide emissions generated by a certain fuel source or activity, and help monitor and manage greenhouse gas emissions to develop emission reduction strategies and implement environmental protection measures. The nitrous oxide emissions from burning in big data centers obtained by this formula improve the accuracy and authenticity of subsequent calculations of greenhouse gas emissions from big data centers.

[0120] In the present invention, the nitrous oxide emission factor is obtained by retrieving public information from environmental organizations; the nitrous oxide emission factor and the fossil fuel usage data of the big data center are calculated through the nitrous oxide emission formula to obtain the nitrous oxide emission data of the center; the nitrous oxide emission data of the center can help monitor and evaluate the impact of the big data center on air quality. Nitrous oxide is an important component that causes air pollution and acid rain, which is harmful to human health and the ecosystem. By accurately measuring and mastering the nitrous oxide emissions of the center, managers can take corresponding emission control measures to reduce environmental pollution risks and improve air quality. Measures to reduce nitrous oxide emissions, such as optimizing combustion equipment, improving combustion processes, and adding emission control devices, can reduce greenhouse gas emissions and reduce the negative impact of climate change.

[0121] Optionally, step S2 specifically includes:

[0122] Obtain refrigeration equipment data for big data centers;

[0123] In this embodiment, the refrigeration equipment data of the big data center is obtained through the big data center cloud platform.

[0124] Extracting intact refrigeration equipment data and scrapped refrigeration equipment data from the refrigeration equipment data of the big data center, thereby obtaining intact refrigeration equipment data and scrapped refrigeration equipment data;

[0125] In this embodiment, data extraction is performed on the refrigeration equipment data of the big data center to obtain data on intact refrigeration equipment and data on scrapped refrigeration equipment.

[0126] Extract refrigerant data from intact refrigeration equipment data and scrapped refrigeration equipment data to obtain a refrigerant data set;

[0127] In this embodiment, data extraction is performed on intact refrigeration equipment data and scrapped refrigeration equipment data to obtain a refrigerant data set;

[0128] Obtain data on global warming trends of refrigerants by searching public information from environmental organizations;

[0129] In this embodiment, the global warming trend data of refrigerants is obtained by searching for public information on the official website of the Environmental Protection Agency.

[0130] The fugitive emissions formula is used to calculate the refrigerant data set and the refrigerant global warming trend data to obtain the central fugitive carbon emissions data. The fugitive emissions formula is as follows:

[0131]

[0132] Where E esE is the greenhouse gas emissions from the combustion of refrigerant substances in the big data center. run E is the emission of substances emitted by the operating refrigeration equipment, scrap is the emission of substances emitted by scrapped refrigeration equipment, l is the type of refrigerant, B l is the charge mass of the first refrigerant, GWP l is the global warming potential coefficient of the first refrigerant, M l is the mass of the remaining refrigerant type l when the refrigeration equipment is scrapped, η rec,l is the recovery rate of the lth type of refrigerant remaining when the refrigeration equipment is scrapped, and U is the total number of refrigerant types.

[0133] The present invention constructs a fugitive emissions formula for calculating the post-combustion greenhouse gas emissions generated by the fugitive refrigerant substances in a big data center using a refrigerant data set and a refrigerant global warming trend data set. The fugitive emissions formula combined with relevant data sets can more accurately estimate the post-combustion greenhouse gas emissions generated by the fugitive refrigerant substances in a big data center. This helps to understand the specific contribution and impact of data centers on greenhouse gas emissions, and provides a specific data basis for reducing emissions. The formula fully considers the factors that affect the post-combustion greenhouse gas emissions E generated by the fugitive refrigerant substances in a big data center. es Emissions of substances emitted from operating refrigeration equipment E rum and the emission of substances emitted by scrapped refrigeration equipment E scrap , forming E es =E run +E scrap Functional relationship. l ×GWP l By multiplying the charge mass of the refrigerant by the global warming potential coefficient, the greenhouse gas emissions of the refrigerant can be obtained. This calculation method can provide a more accurate estimate of emissions and help understand the actual contribution of big data centers to greenhouse gas emissions. This refers to the proportion of the first refrigerant that can be recovered and reused after the refrigeration equipment is scrapped. The recovery rate is divided by 100 to convert the percentage into a decimal. By multiplying these three factors, the greenhouse gas emissions of the first refrigerant at the end of life of the refrigeration equipment can be calculated. Calculating the refrigerant emissions at the end of life of the refrigeration equipment can remind us of the importance of recycling and reuse. By increasing the recycling rate and reducing refrigerant emissions, it helps to reduce the impact of climate change and reduce dependence on limited resources.

[0134] The present invention obtains the refrigeration equipment data of the big data center through the big data center cloud platform. The intact refrigeration equipment data and the scrapped refrigeration equipment data of the big data center are extracted to obtain the intact refrigeration equipment data and the scrapped refrigeration equipment data; by obtaining the scrapped refrigeration equipment data, the equipment treatment and waste management can be understood. This is helpful to formulate a suitable waste treatment strategy. Refrigerant data is extracted from the intact refrigeration equipment data and the scrapped refrigeration equipment data to obtain a refrigerant data set; obtaining the refrigerant data set can help the big data center understand the types and quantities of different refrigerants used in the refrigeration system, so as to evaluate their potential impact on the environment. Some refrigerants have high greenhouse gas potential GWP and play an important role in global warming. By understanding the use of refrigerants, managers can take corresponding measures, such as using low GWP refrigerants and improving refrigerant recovery rates. Refrigerant global warming trend data is obtained by retrieving public information from environmental organizations; these data can be used to evaluate the greenhouse gas potential (GWP) of different refrigerants, compare the relevant costs and benefits of refrigerant selection, and evaluate the effects of different policy measures. Based on these data, governments and decision makers can formulate more targeted and feasible policies to reduce greenhouse gas emissions and address climate change. The central fugitive carbon emissions data is calculated by the fugitive emissions formula for the refrigerant dataset and the refrigerant global warming trend data; the fugitive carbon emissions data provides the specific emissions of refrigerants during use and treatment. By calculating the refrigerant dataset and the global warming trend data, the greenhouse gas emission level of the refrigeration system can be evaluated. This helps to understand the contribution of refrigerants to greenhouse gas emissions and the overall environmental impact of refrigeration equipment.

[0135] Optionally, step S5 specifically includes:

[0136] Obtain big data center fuel transportation data and big data center wastewater treatment data;

[0137] In this embodiment, the big data center fuel transportation data and the big data center wastewater treatment data are obtained through the big data center cloud platform.

[0138] The indirect carbon emissions calculation formula is used to calculate the fuel transportation data of the big data center and the wastewater treatment data of the big data center to obtain the indirect combustion data of the center. The indirect carbon emissions calculation formula is as follows:

[0139]

[0140] Where E jj is the carbon emissions generated by indirect contact of big data centers, E ys Carbon emissions from fossil fuel transportation, E hsis the carbon emissions from fossil fuel activities, E fs is the carbon emission generated by anaerobic treatment of domestic sewage generated by the big data center during the production process, D is the transportation distance of fossil fuels, T is the transportation weight of fossil fuels, HF c is the carbon footprint factor coefficient, w is the total number of fossil fuel types, z is the fossil fuel type coefficient, AD z is the activity level of the zth fossil fuel, CF Z is the carbon footprint factor coefficient of the zth fossil fuel, is the methane emissions from the anaerobic wastewater treatment process, is the global warming potential of methane, W is the amount of wastewater generated during the anaerobic treatment process, COD in is the chemical oxygen demand concentration in the wastewater inlet of the anaerobic treatment system, COD out is the chemical oxygen demand concentration in the wastewater at the outlet of the anaerobic treatment system, S is the total amount of organic matter removed in the form of sludge, EF is the methane emission factor, and R is the amount of methane recovered.

[0141] The present invention constructs an indirect carbon emission calculation formula, which is used to calculate the carbon emissions generated by the indirect contact of the big data center by the fuel transportation data and the wastewater treatment data of the big data center. The indirect carbon emission calculation formula fully considers the factors affecting the carbon emissions generated by the indirect contact of the big data center E jj Carbon emissions from fossil fuel transportation ys , carbon emissions from fossil fuel activities E hs , the carbon emissions generated by the anaerobic treatment of domestic sewage generated by the big data center during the production process E fs , forming E jj =E ys +E hs +E fs Functional relationship. Where D×T×HF c Used to calculate the carbon emissions generated by the transportation of fossil fuels. By calculating the carbon emissions from the transportation of fossil fuels, the carbon footprint of the fuel transportation process can be estimated. This helps to understand and evaluate the carbon emissions of data centers or other facilities. Represents the carbon emissions generated by each fossil fuel due to its carbon footprint. This calculation formula can be used to compare the carbon emission levels between different types of fossil fuels. Different fuels may have different carbon footprint factor coefficients, resulting in different carbon emissions. By estimating carbon emissions, carbon management and emission reduction strategies can be initiated. This formula can be used to obtain the methane emissions during wastewater treatment Among them, the methane emissions generated by the anaerobic wastewater treatment process represent the amount of methane gas produced by anaerobic treatment in the wastewater treatment system. The global warming potential of methane is a factor used to measure the contribution of methane gas to climate change, which is usually calculated in terms of CO2 equivalent. -3 is a constant for converting units, adjusting the units to the appropriate form. in -COD out )-S]Wherein, the wastewater volume represents the wastewater flow in the treatment system, and the inlet chemical oxygen demand concentration and outlet chemical oxygen demand concentration represent the chemical oxygen demand concentration of the wastewater at the time of entry and exit, respectively. The total amount of organic matter removed by sludge represents the total amount of organic matter removed from the wastewater through the sludge treatment process. By calculating the total amount of organic matter removed, the effect of organic matter removal during anaerobic treatment can be evaluated. This helps to understand the performance and treatment capacity of the treatment system. [W×(COD in -COD out )-S]×EF-R Among them, by calculating the net methane emissions, the net emission of methane gas in the anaerobic treatment process can be estimated. This helps to understand the actual situation of methane emissions in the wastewater treatment system and provide a basis for carbon emission assessment.

[0142] The present invention obtains the fuel transportation data of the big data center and the wastewater treatment data of the big data center through the big data center cloud platform. The fuel transportation data of the big data center and the wastewater treatment data of the big data center are calculated by the indirect carbon emission calculation formula to obtain the indirect combustion data of the center; calculating the indirect carbon emissions of the big data center can help understand the carbon emissions generated in the indirect energy consumption link. This helps to comprehensively evaluate the carbon footprint of the data center, including the environmental impacts related to the energy supply chain and wastewater treatment. By understanding the carbon emissions in the transportation link, the data center can choose a more environmentally friendly transportation method, improve or reduce the transportation distance, and thus reduce carbon emissions and energy costs.

[0143] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0144] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A carbon emission accounting method for a big data center, characterized in that: The following steps are involved: Step S1: Obtain the energy consumption data of the big data center and the resident population data of the area where the big data center is located, and perform carbon emission analysis on the energy consumption data of the big data center and the resident population data of the area where the big data center is located, so as to obtain the greenhouse carbon emission data of the center; Step S1 is specifically as follows: Step S11: extracting the usage of the energy consumption data of the big data center, thereby obtaining the electricity consumption data of the big data center and the fossil fuel usage data of the big data center; Step S12: Calculate the carbon dioxide emissions based on the electricity consumption data of the big data center and the resident population data of the area where the big data center is located, so as to obtain the carbon dioxide emissions data of the center; Step S13: Calculate the methane emissions of the fossil fuel usage data of the big data center to obtain the methane emissions data of the center; Step S14: Calculate the nitrous oxide emissions based on the fossil fuel usage data of the big data center, thereby obtaining the nitrous oxide emissions data of the center; Step S15: merging the central methane emission data, the central carbon dioxide emission data and the central nitrous oxide emission data to obtain the central greenhouse carbon emission data; Step S2: Acquire the refrigeration equipment data of the big data center, perform carbon emission analysis on the refrigeration equipment data of the big data center, and thus obtain the center's fugitive carbon emission data; Step S2 is specifically as follows: Obtain refrigeration equipment data for big data centers; Extracting intact refrigeration equipment data and scrapped refrigeration equipment data from the refrigeration equipment data of the big data center, thereby obtaining intact refrigeration equipment data and scrapped refrigeration equipment data; Extract refrigerant data from intact refrigeration equipment data and scrapped refrigeration equipment data to obtain a refrigerant data set; Obtain data on global warming trends of refrigerants by searching public information from environmental organizations; The fugitive emissions formula is used to calculate the refrigerant data set and the refrigerant global warming trend data to obtain the central fugitive carbon emissions data. The fugitive emissions formula is as follows: Where E es E is the greenhouse gas emissions from the combustion of refrigerant substances in the big data center. run E is the emission of substances emitted by the operating refrigeration equipment, scrap is the emission of substances emitted by scrapped refrigeration equipment, l is the type of refrigerant, B l is the charge mass of the first refrigerant, GWP l is the global warming potential coefficient of the first refrigerant, M l is the mass of the remaining refrigerant type l when the refrigeration equipment is scrapped, η rec,l is the recovery rate of the lth type of refrigerant remaining when the refrigeration equipment is scrapped, and U is the total number of refrigerant types; Step S3: Calculate the greenhouse carbon emission data of the center and the fugitive carbon emission data of the center by using the greenhouse gas emission calculation formula of the big data center, so as to obtain the greenhouse gas emission data of the big data center, wherein the greenhouse gas emission calculation formula of the big data center is specifically: Where E is the carbon emissions of big data centers, Burning carbon dioxide emissions for big data centers, Burning methane emissions for big data centers, is the global warming potential of methane, Burning nitrous oxide emissions for big data centers, is the global warming potential of nitrous oxide, E es It is the emission of greenhouse gas after combustion caused by the escape of refrigerant substances in the big data center; Step S4: construct a carbon emission accounting model based on the greenhouse gas emission data of the big data center; Step S5: Obtain the fuel transportation data and wastewater treatment data of the big data center, and perform indirect carbon emission analysis on the fuel transportation data and wastewater treatment data of the big data center to obtain the indirect combustion data of the center; Step S5 is specifically as follows: Obtain big data center fuel transportation data and big data center wastewater treatment data; The indirect carbon emissions calculation formula is used to calculate the fuel transportation data of the big data center and the wastewater treatment data of the big data center to obtain the indirect combustion data of the center. The indirect carbon emissions calculation formula is as follows: Where E jj is the carbon emissions generated by indirect contact of big data centers, E ys Carbon emissions from fossil fuel transportation, E hs is the carbon emissions from fossil fuel activities, E fs is the carbon emission generated by anaerobic treatment of domestic sewage generated by the big data center during the production process, D is the transportation distance of fossil fuels, T is the transportation weight of fossil fuels, and HF c is the carbon footprint factor coefficient, w is the total number of fossil fuel types, z is the fossil fuel type coefficient, AD z is the activity level of the zth fossil fuel, CF Z is the carbon footprint factor coefficient of the zth fossil fuel, is the methane emissions from the anaerobic wastewater treatment process, is the global warming potential of methane, W is the amount of wastewater generated during the anaerobic treatment process, COD in is the chemical oxygen demand concentration in the wastewater inlet of the anaerobic treatment system, COD out is the chemical oxygen demand concentration in the wastewater at the outlet of the anaerobic treatment system, S is the total amount of organic matter removed in the form of sludge, EF is the methane emission factor, and R is the amount of methane recovered; Step S6: Use the central indirect combustion data to correct the carbon emission accounting model to obtain an optimized carbon emission accounting model, and upload the optimized carbon emission accounting model to the big data center cloud platform to perform the carbon emission accounting task.

2. The method according to claim 1, characterized in that: Step S12 is specifically as follows: Step S121: Calculate the carbon emissions of the electricity consumption data of the big data center, so as to obtain the electric and heat data of the big data center; Step S122: Calculate the population carbon emissions based on the resident population data of the area where the big data center is located, so as to obtain the center population carbon emissions data; Step S123: Merge the central electricity and heat carbon emission data and the central population carbon emission data to obtain the central carbon dioxide emission data.

3. The method according to claim 2, characterized in that Step S121 is specifically as follows: Step S1211: extracting the power consumption data of the big data center, thereby obtaining the center's outsourced power data, the center's outsourced heat data, the center's self-generated green power data, and the center's outsourced green power data; Step S1212: Classify and calculate the center's outsourced power data, the center's outsourced heat data, the center's self-generated green electricity data, and the center's outsourced green electricity data using a preset electric and heat classification algorithm, thereby obtaining the big data center's electric and heat data; The function formula of the electrothermal classification algorithm is as follows: Where CF is the electric heat consumption coefficient, d is the time interval, t is the time, i is the time point, P i is the purchased electricity at the i-th time point, H i is the purchased heat at the i-th time point, G i is the spontaneous green electricity at the i-th time point, R i is the purchased green electricity at the i-th time point, π is the pi, and n is the number of time points; Step S1213: Calculate the electric heat data of the big data center using the electric heat carbon emission calculation formula to obtain the electric heat carbon emission data of the center, wherein the electric heat carbon emission calculation formula is specifically: Where E v is the carbon emission of electric heating, P elec is the annual electricity consumption of the big data center, CF is the electric heat consumption coefficient, ER is the global unified equivalent emission coefficient, EF is the efficiency of the purchased power plant, LHV is the lower calorific value of standard coal, T out is the outlet temperature of the big data center, T in is the inlet temperature of the big data center, and WU is the comprehensive energy efficiency of the big data center.

4. The method according to claim 2, characterized in that: Step S122 is specifically as follows: Step S1221: screening the carbon emission characteristics of the resident population data in the area where the big data center is located by principal component analysis, thereby obtaining the carbon emission characteristics of the population; Step S1222: Standardizing the population carbon emission characteristics to obtain population carbon emission standardized data; Step S1223: performing cluster analysis on the population carbon emission standardization data according to a preset clustering algorithm, thereby obtaining a carbon emission population data set; Step S1224: Calculate the population carbon emissions for the carbon emission population dataset, thereby obtaining the central population carbon emissions data.

5. The method according to claim 4, characterized in that The specific calculation of population carbon emissions in step S1224 is: Get the location information of the big data center; The carbon emission population data set and the location information of the big data center are calculated through the population carbon emission calculation formula to obtain the center population carbon emission data. The population carbon emission calculation formula is as follows: Where E p is the population carbon emissions of the big data center, N is the number of people working in the big data center, f is the population category coefficient, and n f is the population size of population f, c f is the carbon emission of population f, ρ is the average air density in the big data center area, m is the average molecular weight of air, V d is the volume of the big data center, v x，y is the average wind speed at the two-dimensional spatial location of the big data center, t d is the total time of use of the big data center, A is the altitude harmonization parameter, h s is the average altitude of the area surrounding the big data center, h d is the average altitude of the big data center, B is the atmospheric pressure emphasis and parameter, p is the atmospheric pressure in the big data center area, and p0 is 101 kPa atmospheric pressure.

6. The method according to claim 1, characterized in that Step S13 is specifically as follows: Obtain methane emission factors by searching public information from environmental organizations; The methane emission factor and the fossil fuel usage data of the big data center are calculated through the methane emission formula to obtain the central methane emission data. The methane emission formula is as follows: in is the methane emission from burning in big data centers, k is the total number of fuel types, a is the type of fossil fuel, AD a is the methane activity coefficient of a type of fossil fuel, EF is the methane emission factor, v is the volume of the fuel cube, p is the atmospheric pressure, T is the temperature in the combustion chamber, and F a is the fuel consumption of the burner of type a fossil fuel, and V is the operating time of the combustion equipment.

7. The method according to claim 1, characterized in that Step S14 is specifically as follows: Obtain nitrous oxide emission factors by searching public information from environmental organizations; The nitrous oxide emission factor and the fossil fuel usage data of the big data center are calculated through the nitrous oxide emission formula to obtain the central nitrous oxide emission data. The nitrous oxide emission formula is as follows: in is the nitrous oxide emission from burning in big data centers, k is the total number of fuel types, a is the type of fossil fuel, BD a is the nitrous oxide activity coefficient of a type of fossil fuel, GH is the nitrous oxide emission factor, v is the volume of the fuel cube, p is the atmospheric pressure, T is the temperature in the combustion chamber, F a is the fuel consumption of type a fossil fuel, and V is the operating time of the combustion equipment.

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