Calculation method for monthly carbon emission of park and related equipment

Abstract

The invention provides a method for calculating monthly carbon emission of a park and related equipment. The method comprises the following steps: acquiring various carbon emission influence factors in a historical period and an influence value of each carbon emission influence factor; based on a plurality of influence values, screening the plurality of carbon emission influence factors to obtain a plurality of target carbon emission influence factors; calculating the annual carbon emission of the park based on each target carbon emission influence factor; obtaining annual energy consumption data, seasonal energy consumption data and monthly energy consumption data of the historical time period, and determining target monthly energy consumption data based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data; based on the annual carbon emission and the target monthly energy consumption data, the monthly carbon emission of each month in the historical time period is determined, the technical problem that the monthly carbon emission is determined inaccurately in the prior art is solved, and the purpose of accurately determining the monthly carbon emission is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for calculating the monthly carbon emissions of a park and related equipment. Background Art

[0002] Since the 21st century, global warming has become an increasingly serious problem, and the biggest reason is the increasing carbon dioxide emissions. Therefore, reducing carbon emissions is a global concern, and how to accurately calculate carbon emissions has also become a concern for all countries. As the occupancy rate of enterprises in industrial parks gradually increases, the contribution rate of carbon emissions in the park to the country will continue to rise. The carbon emission accounting method directly affects the assessment of the park's carbon emission level and is the basis for formulating an effective carbon emission reduction strategy. Therefore, it is very important and necessary to study the carbon emission accounting model for the park and implement effective emission reduction measures.

[0003] At present, relevant standards and guidelines have been issued for carbon emission calculations at the city, industry, and enterprise levels at home and abroad, but there are no relevant standards or guidelines for the calculation of carbon emissions at the park level. The traditional method of calculating carbon emissions in a park mostly adopts the carbon emission factor method, that is, according to the carbon emission inventory, the activity data and emission factor of each emission source are used, and the product of the activity data and the emission factor is used as the carbon emissions. It is mainly used in carbon emission accounting for objects of different dimensions such as countries, regions, and industries. The advantages are wide application range and low cost, and the disadvantages are that it relies on a large amount of statistical data, and the timeliness and resolution are not high. For the calculation of carbon emissions in the park, the activity data required by the carbon emission factor method is difficult to count, and the data lags a lot, making it difficult to effectively achieve accurate and timely calculation of monthly carbon emissions. Summary of the invention

[0004] In view of this, the purpose of this application is to propose a method for calculating the monthly carbon emissions of a park and related equipment to overcome all or part of the deficiencies in the prior art.

[0005] Based on the above purpose, the present application provides a method for calculating the monthly carbon emissions of a park, including: obtaining multiple carbon emission influencing factors in a historical period and the influence value of each carbon emission influencing factor; based on multiple influence values, screening the multiple carbon emission influencing factors to obtain multiple target carbon emission influencing factors; based on each target carbon emission influencing factor, calculating the annual carbon emissions of the park; obtaining the annual energy consumption data, quarterly energy consumption data and monthly energy consumption data of the historical period, and determining the target monthly energy consumption data based on the annual energy consumption data, the quarterly energy consumption data and the monthly energy consumption data; determining the monthly carbon emissions of each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data.

[0006] Optionally, based on multiple influence values, the multiple carbon emission influencing factors are screened to obtain multiple target carbon emission influencing factors, including: for each carbon emission influencing factor, based on the influence value of the carbon emission influencing factor, performing a linear analysis on the carbon emission influencing factor to obtain a linear coefficient; based on the influence value of the carbon emission influencing factor, performing a nonlinear analysis on the carbon emission influencing factor to obtain a nonlinear coefficient; calculating the absolute value of the product of the linear coefficient and the nonlinear coefficient to obtain a product value; sorting the product values ​​of all carbon emission influencing factors in descending order, and determining the multiple carbon emission influencing factors corresponding to the first N product values ​​as the multiple target carbon emission influencing factors, wherein N is a positive integer.

[0007] Optionally, the calculating the annual carbon emissions of the park based on each target carbon emission influencing factor includes: determining the annual carbon emissions by the following formula: Among them, y t is the annual carbon emissions, t is the historical year, a o is the carbon emission corresponding to the basic carbon emission influencing factors, φ i is the influence coefficient, y t-i is the annual carbon emissions in year ti, is the first asymmetric distributed lag parameter, is the second asymmetric distributed lag parameter, is the third asymmetric distributed lag parameter, is the fourth asymmetric distributed lag parameter, is x t-i The forward decomposition of It is z t-i The forward decomposition of is x t-i The negative decomposition of It is z t-i Negative decomposition, x t-i is the electricity consumption data of the park in year ti, z t-i is the weighted value corresponding to all target carbon emission influencing factors in year ti, u t,γ is the random error term, p is the first lag order of the dependent variable and the lag order of the explanatory variable, q is the second lag order of the dependent variable and the lag order of the explanatory variable, s is the third lag order of the dependent variable and the lag order of the explanatory variable, and i is a positive integer.

[0008] Optionally, determining the target monthly energy consumption data based on the annual energy consumption data, the quarterly energy consumption data and the monthly energy consumption data includes: based on the annual energy consumption data, using the Denton proportion method to filter the target quarterly energy consumption data from the quarterly energy consumption data; based on the target quarterly energy consumption data, using the Denton proportion method to filter the target monthly energy consumption data from the monthly energy consumption data.

[0009] Optionally, determining the monthly carbon emissions for each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data includes: determining the monthly carbon emissions by the following formula: Among them, y l,γ is the monthly carbon emissions in month 1, Q l,γ is the target monthly energy consumption data for the first month, A is the predetermined matrix, T represents the transposition operation, argmin represents the value of the variable when the following formula reaches the minimum value, y t is the annual carbon emissions.

[0010] Optionally, performing a linear analysis on the carbon emission influencing factor based on the influence value of the carbon emission influencing factor to obtain a linear coefficient includes: determining the linear coefficient by the following formula: Wherein, r is the linear coefficient, X is the impact value of the carbon emission influencing factor, and Y is the historical annual carbon emission in the non-historical period. is the average impact value of the carbon emission influencing factors, is the average annual carbon emissions in the historical period other than the historical period mentioned, XX is the sum of squares of the deviations from the mean of X, l YY is the sum of squares of the deviations from the mean of Y, l XY is the sum of squared deviations of X and Y from the mean.

[0011] Optionally, performing nonlinear analysis on the carbon emission influencing factors based on the influence values ​​of the carbon emission influencing factors to obtain nonlinear coefficients includes: determining the nonlinear coefficients by the following formula: Wherein, MIC(X,Y) is the nonlinear coefficient, X is the impact value of the carbon emission influencing factor, Y is the expected annual carbon emission, and n x and n y Represents the number of grids on the horizontal and vertical axes respectively, G is the nth grid in (X,Y) x ×n y grid, I G (X, Y) is the mutual information value of the grid G, B(n, α) is the function of n B(n, α) = n α , where n α (0<α<1) is the upper limit of the number of grid divisions, R is a real number, log2 min(n x ,ny ) indicates the normalization operation to ensure that the MIC value is between 0 and 1.

[0012] Based on the same inventive concept, the present application also provides a device for calculating the monthly carbon emissions of a park, including: an acquisition module, configured to obtain multiple carbon emission influencing factors in a historical period and the influence value of each carbon emission influencing factor; a screening module, configured to screen the multiple carbon emission influencing factors based on multiple influence values, and obtain multiple target carbon emission influencing factors; a calculation module, configured to calculate the annual carbon emissions of the park based on each target carbon emission influencing factor; a first determination module, configured to obtain the annual energy consumption data, quarterly energy consumption data and monthly energy consumption data of the historical period, and determine the target monthly energy consumption data based on the annual energy consumption data, the quarterly energy consumption data and the monthly energy consumption data; a second determination module, configured to determine the monthly carbon emissions of each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data.

[0013] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0014] Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method as described above.

[0015] From the above, it can be seen that the method and related equipment for calculating the monthly carbon emissions of the park provided in the present application include obtaining a variety of carbon emission influencing factors in the historical period and the influence value of each carbon emission influencing factor. Based on multiple influence values, the multiple carbon emission influencing factors are screened to obtain a variety of target carbon emission influencing factors, so as to achieve the purpose of accurately identifying the factors affecting carbon emissions. Based on each target carbon emission influencing factor, the annual carbon emissions of the park are calculated, which has the function of accurately calculating the annual carbon emissions. The annual energy consumption data, seasonal energy consumption data and monthly energy consumption data of the historical period are obtained, and the target monthly energy consumption data is determined based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data, eliminating the interference of irrelevant data, so that the determined target monthly energy consumption data is more accurate. Based on the annual carbon emissions and the target monthly energy consumption data, the monthly carbon emissions of each month in the historical period are determined, so as to achieve the purpose of accurately determining the monthly carbon emissions in the historical time period. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flow chart of a method for calculating monthly carbon emissions of a park according to an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the structure of a device for calculating monthly carbon emissions of a park according to an embodiment of the present application;

[0019] Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0021] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] As mentioned in the background technology section, global warming has become an increasingly serious problem since the 21st century, and the biggest reason is the increasing emissions of carbon dioxide. Since the industrial revolution, greenhouse gas emissions generated by human production and life have caused global warming of about 1.5°C, and the global temperature has risen by 1.3°C in the 21st century alone. Carbon dioxide contributes more than 78% of the world's known climate influencing factors. Therefore, reducing carbon emissions is a global concern, and how to accurately calculate carbon emissions has also become a concern for all countries.

[0023] As the occupancy rate of enterprises in industrial parks gradually increases, the contribution rate of carbon emissions in the park to the country will continue to rise. As of 2021, my country's industrial parks have contributed about 30% of the country's carbon emissions. As an important platform for promoting innovation and development and the gathering of various industrial enterprises, as the main battlefield of a strong manufacturing country and an important source of greenhouse gas emissions, the industrial park relies on the characteristics of different regions and different leading industries, accurately focuses on relevant factor resources, and is expected to become a new breakthrough point for achieving pollution reduction and carbon reduction in the industrial park through differentiated and precise carbon emission accounting model research. The carbon emission accounting method directly affects the assessment of the carbon emission level of the industrial park and is the basis for formulating an effective carbon emission reduction strategy. Therefore, it is very important and necessary to study the carbon emission accounting model of the industrial park and promote effective emission reduction measures.

[0024] At present, relevant standards and guidelines have been issued for carbon emission calculation at the city, industry and enterprise levels at home and abroad, but there are no relevant standards or guidelines for carbon emission calculation methods at the park level, let alone carbon emission prediction methods. Carbon emissions generated by energy consumption are the core component of the park's total carbon emissions, and carbon emissions generated by electricity account for about half of the carbon emissions generated by energy consumption in the park.

[0025] Traditional methods for calculating carbon emissions in industrial parks mostly use the carbon emission factor method, that is, according to the carbon emission inventory, for each emission source's activity data and emission factor, the product of activity data and emission factor is used as carbon emissions. It is mainly used in carbon emission accounting for different dimensions such as countries, regions, and industries. Its advantages are wide application range and low cost, and its disadvantages are that it relies on a large amount of statistical data, and its timeliness and resolution are not high. For the calculation of industrial park carbon emissions, the activity data required by the carbon emission factor method is difficult to calculate, and the data lags behind, making it difficult to effectively and accurately calculate monthly carbon emissions in real time.

[0026] In view of this, the present application embodiment proposes a method for calculating the monthly carbon emissions of a park, referring to Figure 1 , including the following steps:

[0027] Step 101: Acquire multiple carbon emission influencing factors in a historical period and the impact value of each carbon emission influencing factor.

[0028] In this step, the park refers to an office area or industrial area of ​​an enterprise with a certain area and functional area. The park contributes carbon emissions every month. Accurately and timely determining the monthly carbon emissions is very necessary for formulating strategies to reduce carbon emissions. It should be noted that this application can not only calculate the monthly carbon emissions of the current month in a timely and accurate manner, but also accurately calculate the monthly carbon emissions of historical years, which has a corrective effect on the monthly carbon emissions of historical years. The historical period can be a historical year or the current year. For example, the historical period can be a historical year before the current year, or it can be from January to the present of the current year. If you want to calculate the carbon emissions of a historical period, you must first obtain the various carbon emission influencing factors corresponding to the park in the historical period and the impact value of each carbon emission influencing factor. The above-mentioned carbon emission influencing factors and impact values ​​are obtained through report statistics or monitoring equipment. Carbon emission influencing factors include, but are not limited to, factors such as comprehensive energy consumption of the park, total income of the park, output of major products in the park, and other factors, and form five categories of economic / energy indicators, product output indicators, production index indicators, and other indicators. Of course, other factors can also be considered, and there is no restriction on this.

[0029] Step 102: Screen the multiple carbon emission influencing factors based on the multiple influencing values ​​to obtain multiple target carbon emission influencing factors.

[0030] In this step, there are multiple carbon emission influencing factors in the park, but not every carbon emission influencing factor has a decisive impact on carbon emissions. If all carbon emission influencing factors are used in the subsequent calculation of carbon emissions, on the one hand, the calculation efficiency will be reduced. On the other hand, carbon emission influencing factors that have no decisive impact on the calculation of carbon emissions are mixed in, and there are redundant carbon emission influencing factors, which will make the calculation results of carbon emissions inaccurate. Therefore, it is necessary to screen multiple carbon emission factors based on multiple influencing values ​​to obtain multiple target carbon emission influencing factors. It should be noted that the threshold can be set according to actual needs to control the number of target carbon emission influencing factors obtained. By screening the carbon emission influencing factors, the carbon emission influencing factors with low decisiveness are excluded, and the carbon emission influencing factors with strong decisiveness are screened out, so as to achieve the purpose of accurately identifying the factors affecting carbon emissions.

[0031] Step 103, calculating the annual carbon emissions of the park based on each target carbon emission influencing factor.

[0032] In this step, the historical carbon emission data of the park and other supplementary variable data are mostly in annual units due to statistical methods, while the power consumption data and output data as core explanatory variables are mostly in monthly units. Therefore, it is necessary to first calculate the annual carbon emissions of the park based on each target carbon emission influencing factor, so that the monthly carbon emissions can be calculated later through the annual carbon emissions.

[0033] It should be noted that this application uses the NARDL model to construct the calculation formula for annual carbon emissions. The NARDL model is an autoregressive distributed lag model for analyzing asymmetric effects. It allows asymmetry to be considered in the model, that is, positive and negative shocks may have different effects on variables. The application of the NARDL model involves the following steps: (1) Data preparation: First, the relevant data set needs to be prepared; (2) Model setting: Setting the order and form of the NARDL model, which includes selecting an appropriate lag order and determining the relationship between variables in the model; (3) Parameter estimation: Parameter estimation is performed using statistical software, which usually involves maximum likelihood estimation or other optimization algorithms; (4) Model testing: Diagnostic testing of the model to ensure the applicability and effectiveness of the model; (5) This may include checking the residuals of the model, performing heteroscedasticity tests, etc. Result interpretation: Interpret the output results of the model and understand the impact of the variables in the model and their interactions. Pay special attention to the existence and significance of asymmetric effects; (6) Prediction and application: Use the model for application analysis. The NARDL model is used to calculate the annual carbon emissions of the park and the formula for calculating the annual carbon emissions is accurately determined, which has the effect of accurately calculating the annual carbon emissions.

[0034] Step 104, obtaining the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data of the historical period, and determining the target monthly energy consumption data based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data.

[0035] In this step, to achieve the "dual carbon" goal, energy is the main battlefield and electricity is the main force. Therefore, the present invention explores the construction of a park carbon emission calculation model with the park electricity consumption as the main explanatory variable, in order to accurately understand the park's carbon emissions and provide data support for more targeted formulation of the park's carbon reduction and carbon reduction plan. Energy consumption data can reflect the amount of electricity consumed, not just the amount of electricity consumed by one device. Therefore, energy consumption data is a data sequence. Annual energy consumption data, seasonal energy consumption data, and monthly energy consumption data for historical time periods are obtained. The annual energy consumption data contains seasonal energy consumption data, and the seasonal energy consumption data contains monthly energy consumption data. There is a correlation between the above three types of energy consumption data. In order to make the subsequent use of monthly energy consumption data more accurate, it is necessary to use the correlation between the above three types of energy consumption data to screen and classify the data sequence in the monthly energy consumption data, determine the target monthly energy consumption data, eliminate the interference of irrelevant data, and make the determined target monthly energy consumption data more accurate.

[0036] Step 105: Determine the monthly carbon emissions for each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data.

[0037] In this step, there is a direct proportional relationship between monthly carbon emissions and monthly energy consumption data, and an increase in monthly energy consumption data will usually lead to an increase in monthly carbon emissions. Annual carbon emissions have constraints on monthly carbon emissions, that is, monthly carbon emissions cannot be greater than annual carbon emissions. Therefore, in order to accurately calculate the monthly carbon emissions for each month in the historical time period, it is necessary to use accurate annual carbon emissions and target monthly energy consumption data to determine the monthly carbon emissions for each month in the historical time period, so as to achieve the purpose of accurately determining the monthly carbon emissions in the historical time period, eliminating the problem of large calculation errors in traditional carbon emission calculation methods, and better reflecting the monthly carbon emissions of the park. It should be noted that when the monthly carbon emissions in the historical period are the monthly carbon emissions of the current month, the monthly carbon emissions are calculated in a timely manner, which effectively solves the problem of strong lag in traditional carbon emission calculation methods.

[0038] Through the above scheme, multiple carbon emission influencing factors and the influence value of each carbon emission influencing factor in the historical period are obtained. Based on multiple influence values, the multiple carbon emission influencing factors are screened to obtain multiple target carbon emission influencing factors, so as to achieve the purpose of accurately identifying the factors affecting carbon emissions. Based on each target carbon emission influencing factor, the annual carbon emissions of the park are calculated, which has the function of accurately calculating the annual carbon emissions. The annual energy consumption data, seasonal energy consumption data and monthly energy consumption data of the historical period are obtained, and the target monthly energy consumption data is determined based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data, eliminating the interference of irrelevant data, so that the determined target monthly energy consumption data is more accurate. Based on the annual carbon emissions and the target monthly energy consumption data, the monthly carbon emissions of each month in the historical period are determined, so as to achieve the purpose of accurately determining the monthly carbon emissions in the historical time period.

[0039] In some embodiments, based on multiple influence values, the multiple carbon emission influencing factors are screened to obtain multiple target carbon emission influencing factors, including: for each carbon emission influencing factor, based on the influence value of the carbon emission influencing factor, performing a linear analysis on the carbon emission influencing factor to obtain a linear coefficient; based on the influence value of the carbon emission influencing factor, performing a nonlinear analysis on the carbon emission influencing factor to obtain a nonlinear coefficient; calculating the absolute value of the product of the linear coefficient and the nonlinear coefficient to obtain a product value; sorting the product values ​​of all carbon emission influencing factors in order from large to small, and determining the multiple carbon emission influencing factors corresponding to the first N product values ​​as the multiple target carbon emission influencing factors, wherein N is a positive integer.

[0040] In this embodiment, there are many factors affecting carbon emissions, and the correlation with carbon emissions is relatively complex. There may be a linear relationship or a nonlinear relationship. Therefore, this embodiment adopts a comprehensive correlation analysis method to analyze the linear correlation and nonlinear correlation between the historical annual carbon emissions in the non-historical time period and its influencing factors, and effectively identify the influencing factors and the degree of influence that affect carbon emissions. Based on the influence value of the carbon emission influencing factor, the carbon emission influencing factor is linearly analyzed to obtain the linear coefficient; based on the influence value of the carbon emission influencing factor, the carbon emission influencing factor is nonlinearly analyzed to obtain the nonlinear coefficient. Based on the linear coefficient and the nonlinear coefficient, the comprehensive correlation coefficient of each influencing factor is calculated, and the comprehensive correlation coefficient of carbon emissions and its influencing factors is defined as the absolute value product of the linear coefficient and the nonlinear coefficient, that is: comprehensive correlation coefficient (product value) = | linear coefficient | × | nonlinear coefficient |. The product values ​​are sorted in order from large to small, and the influencing factors with strong correlation with carbon emissions are analyzed and screened, and the multiple carbon emission influencing factors corresponding to the first N product values ​​are determined as multiple target carbon emission influencing factors, where N can be set according to demand. For example, based on the calculation results, the top five influencing factors with the comprehensive correlation coefficient are selected as supplementary variables for the park carbon emission model. By selecting carbon emission influencing factors and using a comprehensive correlation analysis method to quantify the correlation coefficients between these influencing factors and carbon emissions, the supplementary variables for the input model are screened, the influence of carbon emission influencing factors with low correlation is eliminated, and the purpose of accurately determining carbon emission influencing factors with strong correlation with carbon emissions is achieved.

[0041] In some embodiments, the calculating the annual carbon emissions of the park based on each target carbon emission influencing factor includes: determining the annual carbon emissions by the following formula: Among them, y t is the annual carbon emissions, t is the historical year, a o is the carbon emission corresponding to the basic carbon emission influencing factors, φ i is the influence coefficient, y t-i is the annual carbon emissions in year ti, is the first asymmetric distributed lag parameter, is the second asymmetric distributed lag parameter, is the third asymmetric distributed lag parameter, is the fourth asymmetric distributed lag parameter, is x t-i The forward decomposition of It is z t-i The forward decomposition of is x t-i The negative decomposition of It is z t-i Negative decomposition, xt-i is the electricity consumption data of the park in year ti, z t-i is the weighted value corresponding to all target carbon emission influencing factors in year ti, u t,γ is the random error term, p is the first lag order of the dependent variable and the lag order of the explanatory variable, q is the second lag order of the dependent variable and the lag order of the explanatory variable, s is the third lag order of the dependent variable and the lag order of the explanatory variable, and i is a positive integer.

[0042] In this embodiment, the relationship between the selected carbon emission influencing factors and the carbon emissions is not all linear. Based on this, the NARDL model is adopted to construct a park carbon emission calculation model. The NARDL model is a new method improved on ARDL. Under the premise of retaining the flexible selection of the variable order of the ARDL model, it allows the combination of nonlinear asymmetry and cointegration relationship of a small sample influencing factor in the formula. By decomposing the explanatory variable and the supplementary variable into two parts of positive change and negative change, the impact of these two parts on the dependent variable is analyzed, thereby obtaining reliable results. At present, the NARDL method is widely used in environmental problem analysis, so this embodiment applies the NARDL model to the carbon emission calculation problem. The NARDL model breaks the limitation that the traditional ARDL model can only assume linear conditions, and can more scientifically reflect the nonlinear relationship between carbon emissions and its influencing factors, so that the calculation conclusion is more accurate. In addition, the NARDL model will not be affected by the endogeneity problem between the explanatory variable and the supplementary variable, and does not require all variables to be first-order single integral sequences. Even in the case of small sample capacity, the calculation result still has a high accuracy. Therefore, the NARDL model is adopted to calculate the annual carbon emissions.

[0043] In the formula for calculating annual carbon emissions, y tis the annual carbon emissions, t is the historical year. It should be noted that the current year includes the time that has passed, so the current year can also be regarded as a historical year. In addition to the target carbon emission influencing factors, there are also basic carbon emission influencing factors. For the historical period, the carbon emissions corresponding to the basic carbon emission influencing factors are fixed, so this part of carbon emissions can be directly added. There are guiding annual carbon emissions before the historical period, and the data before the historical period can be used to derive the annual carbon emissions of the historical period. When calculating the carbon emissions of the historical period, the annual carbon emissions before the historical period must also be considered. Based on the data corresponding to the annual carbon emissions before the historical period, the annual carbon emissions of the historical period can be derived. Therefore, the electricity consumption data corresponding to the annual carbon emissions before the historical period must also be used. The impact of each target carbon emission influencing factor on carbon emissions is also different. Therefore, the predetermined weight corresponding to each target carbon emission influencing factor is obtained, where the predetermined weight is determined according to the historical data. The product value of the impact value corresponding to each carbon emission influencing factor and the predetermined weight corresponding to the carbon emission influencing factor is calculated respectively, and all the product values ​​are added to obtain the weighted value. Through the calculation formula of annual carbon emissions, the annual carbon emissions of the historical period are quantified and the annual carbon emissions of the historical period are accurately determined.

[0044] In some embodiments, determining the target monthly energy consumption data based on the annual energy consumption data, the quarterly energy consumption data and the monthly energy consumption data includes: based on the annual energy consumption data, using the Denton proportion method to filter the target quarterly energy consumption data from the quarterly energy consumption data; based on the target quarterly energy consumption data, using the Denton proportion method to filter the target monthly energy consumption data from the monthly energy consumption data.

[0045] In this embodiment, in order to more accurately calculate the monthly carbon emission data of the park, this embodiment selects the Denton proportion method for data conversion. The basic principle of the Denton proportion method is to use the least squares method. Under the total amount constraint of the annual benchmark, by minimizing the relative adjustment differences between adjacent quarters, the changes in the connected quarterly sequence are made as proportional as possible to the changes in the original quarterly sequence, thereby retaining the short-term motion characteristics of the original sequence while avoiding the cross-year ladder problem of the connected sequence. Splitting annual carbon emission data based on the Denton proportion method effectively solves the problem that traditional carbon emission calculation methods cannot calculate high-frequency carbon emission data. The Denton proportion method is a commonly used data mining algorithm that can be used for classification, clustering and other problems. Its principle is to find the k sample points with the highest similarity in the training set, form them into a new set, and then perform label prediction on the set. Repeat this iteration until convergence. The advantage of this method is that the most relevant part can be found quickly.

[0046] Its basic mathematical form is:

[0047]

[0048] In the above formula, X t is the original sequence (i.e. annual energy consumption data), Y t is the connected sequence (i.e. the data after the connection of annual energy consumption data and seasonal energy consumption data), which is also the sequence required in the optimization problem, X t-1 With X t The closest sequence, Y t-1 With Y t The closest concatenated sequence, A m is the annual GDP constraint (annual benchmark value), T represents the quarterly series length (the last quarter for which raw quarterly data are available), and β represents the number of years (the last year for which annual benchmark values ​​are available).

[0049] Similarly, the formula for connecting quarterly data with monthly data is:

[0050]

[0051] In the above formula, P l is the original sequence (i.e., seasonal energy consumption data), Q l is the connected sequence (i.e. the data after the connection of the quarterly energy consumption data and the monthly energy consumption data), which is also the sequence required in the optimization problem, Q l-1 For Q l The closest connected sequence, P l-1 For P l The closest sequence, O t is the quarterly GDP constraint (quarterly benchmark value), L represents the monthly series length (the last month for which the original quarterly data can be obtained), and T represents the number of quarters (the last quarter for which the quarterly benchmark value can be obtained).

[0052] In some embodiments, determining the monthly carbon emissions for each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data includes: determining the monthly carbon emissions by the following formula: Among them, y l,γ is the monthly carbon emissions in month 1, Q l,γ is the target monthly energy consumption data for the first month, A is the predetermined matrix, T represents the transposition operation, argmin represents the value of the variable when the following formula reaches the minimum value, y t is the annual carbon emissions.

[0053] In this embodiment, the target monthly energy consumption data is a sequence composed of multiple data, and the target monthly energy consumption data can be connected into a curve, and the target monthly energy consumption data corresponding to each month has curvature. The target monthly energy consumption data has a positive correlation with the monthly carbon emissions. The larger the target monthly energy consumption data, the larger the monthly carbon emissions. Therefore, based on the target monthly energy consumption data, the curve of monthly carbon emissions that is closest to the curve corresponding to the target monthly energy consumption data is determined, that is, the curvature of the above two curves in the same month is closest. In addition, the sum of all monthly carbon emissions must not be greater than the annual carbon emissions, and the annual carbon emissions have a constraining effect on all monthly carbon emissions obtained. The formula for determining the monthly carbon emissions is solved by mathematical programming or a commercial solver to achieve the purpose of accurately determining the monthly carbon emissions.

[0054] In some embodiments, performing a linear analysis on the carbon emission influencing factor based on the impact value of the carbon emission influencing factor to obtain a linear coefficient includes: determining the linear coefficient by the following formula: Wherein, r is the linear coefficient, X is the impact value of the carbon emission influencing factor, and Y is the historical annual carbon emission in the non-historical period. is the average impact value of the carbon emission influencing factors, is the average annual carbon emissions in the historical period other than the historical period mentioned, XX is the sum of squares of the deviations from the mean of X, l YY is the sum of squares of the deviations from the mean of Y, l XY is the sum of squared deviations of X and Y from the mean.

[0055] In this embodiment, Pearson correlation analysis is used to study the correlation between carbon emissions and its influencing factors. The Pearson correlation coefficient (i.e., the linear coefficient in this embodiment), also known as the product difference correlation coefficient, is a statistical indicator that expresses the degree and direction of linear correlation between two variables. The correlation coefficient of the sample is represented by the symbol r. The historical annual carbon emissions that are not in the historical period are representative historical annual carbon emissions. Therefore, the above historical annual carbon emissions are selected as the known annual carbon emissions for correlation calculation. Among them:

[0056] represents the sum of squares of the deviations from the mean of X;

[0057] represents the sum of squares of the deviations from the mean of Y;

[0058] It represents the sum of squared deviations of X from the mean.

[0059] Pearson correlation analysis is a statistical method used to measure the degree of linear correlation between two continuous variables. This method quantifies the linear relationship between two variables by calculating the covariance of the two variables and their respective standard deviations. The value of the Pearson correlation coefficient is between -1 and 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no linear relationship. This method is applicable to the case where two continuous variables are linearly related. When the data satisfies the normal distribution, the Pearson correlation coefficient can give a more accurate correlation assessment. The correlation coefficient is a dimensionless statistical indicator with a value range of -1≤r≤1. A correlation coefficient less than 0 indicates a negative correlation, greater than 0 indicates a positive correlation, and equal to 0 indicates no correlation. The larger the absolute value of the correlation coefficient, the closer the correlation between the two variables. To determine whether there is a linear relationship, you need to look at the scatter plot of the two variables. If the scatter plot is roughly a straight line, it means there is a linear relationship. If it is not a straight line, there is no linear relationship. Linear regression analysis is to find a best-fitting straight line so that the sum of the distances of all sample points to this straight line is minimized. The least squares method is generally used to solve this problem. In the linear regression model, both the independent variable and the dependent variable are continuous numerical variables. Therefore, the original data needs to be preprocessed, such as standardization and denoising. The correlation between carbon emissions and its influencing factors is quantified to accurately determine the linear coefficient.

[0060] In some embodiments, the nonlinear analysis of the carbon emission influencing factor based on the influence value of the carbon emission influencing factor to obtain the nonlinear coefficient includes: determining the nonlinear coefficient by the following formula: Wherein, MIC(X,Y) is the nonlinear coefficient, X is the impact value of the carbon emission influencing factor, Y is the expected annual carbon emission, and n x and n y Represents the number of grids on the horizontal and vertical axes respectively, G is the nth grid in (X,Y) x ×n y grid, I G (X, Y) is the mutual information value of the grid G, B(n, α) is the function of n B(n, α) = n α , where n α (0<α<1) is the upper limit of the number of grid divisions, R is a real number, log2 min(n x ,n y ) indicates the normalization operation to ensure that the MIC value is between 0 and 1.

[0061] In this embodiment, the maximum mutual information coefficient method (Maximal Information Coefficient, MIC) is used to analyze the nonlinear correlation between carbon emissions and influencing factors. The maximum mutual information coefficient is mainly used to quantify the nonlinear correlation between two variables. Compared with mutual information (MI), the maximum mutual information coefficient has higher accuracy. The main idea is: assuming that there is a certain correlation between the two variables, you can draw this set of variables on the coordinate axis, and use a set of grids to divide these two-dimensional scattered points to form a scatter plot. Then, based on the approximate probability density distribution of the two variables in the grid, the mutual information of the two variables is obtained, and the correlation between the two variables is obtained by regularization quantification.

[0062] The maximum mutual information coefficient method is a method used to measure the degree of association between two variables. It can capture linear or nonlinear relationships. Compared with traditional mutual information, MIC has higher accuracy. The basic principle of MIC is to grid the scatter plot of the variables, calculate the maximum mutual information value under different gridding schemes, and then normalize it, and finally select the maximum value of the mutual information at different scales as the MIC value. This process involves three main steps: given i and j, grid the scatter plot composed of X and Y with i columns and j rows; find the maximum mutual information value; normalize the maximum mutual information value, and select the maximum value of the mutual information at different scales as the MIC value. For the joint variable (X, Y), X∈R n , Y∈R n . The correlation between carbon emissions and its influencing factors is quantified to achieve the purpose of accurately determining the nonlinear coefficient.

[0063] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0064] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a device for calculating the monthly carbon emissions of a park.

[0066] refer to Figure 2 , the calculation device of the monthly carbon emissions of the park includes:

[0067] The acquisition module 10 is configured to acquire multiple carbon emission influencing factors in a historical period and an impact value of each carbon emission influencing factor.

[0068] The screening module 20 is configured to screen the multiple carbon emission influencing factors based on multiple influencing values ​​to obtain multiple target carbon emission influencing factors.

[0069] The calculation module 30 is configured to calculate the annual carbon emissions of the park based on each target carbon emission influencing factor.

[0070] The first determination module 40 is configured to obtain the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data of the historical period, and determine the target monthly energy consumption data based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data.

[0071] The second determination module 50 is configured to determine the monthly carbon emissions of each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data.

[0072] Through the above-mentioned device, a variety of carbon emission influencing factors and the influence value of each carbon emission influencing factor in the historical period are obtained. Based on the multiple influence values, the multiple carbon emission influencing factors are screened to obtain multiple target carbon emission influencing factors, so as to achieve the purpose of accurately identifying the factors affecting carbon emissions. Based on each target carbon emission influencing factor, the annual carbon emissions of the park are calculated, which has the function of accurately calculating the annual carbon emissions. The annual energy consumption data, seasonal energy consumption data and monthly energy consumption data of the historical period are obtained, and the target monthly energy consumption data is determined based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data, eliminating the interference of irrelevant data, so that the determined target monthly energy consumption data is more accurate. Based on the annual carbon emissions and the target monthly energy consumption data, the monthly carbon emissions of each month in the historical period are determined, so as to achieve the purpose of accurately determining the monthly carbon emissions in the historical time period.

[0073] In some embodiments, the screening module 20 is further configured to, for each carbon emission influencing factor, perform a linear analysis on the carbon emission influencing factor based on the influence value of the carbon emission influencing factor to obtain a linear coefficient; perform a nonlinear analysis on the carbon emission influencing factor based on the influence value of the carbon emission influencing factor to obtain a nonlinear coefficient; calculate the absolute value of the product of the linear coefficient and the nonlinear coefficient to obtain a product value; sort the product values ​​of all carbon emission influencing factors in order from large to small, and determine the multiple carbon emission influencing factors corresponding to the first N product values ​​as the multiple target carbon emission influencing factors, where N is a positive integer.

[0074] In some embodiments, the calculation module 30 is further configured to determine the annual carbon emissions by the following formula: Among them, y t is the annual carbon emissions, t is the historical year, a o is the carbon emission corresponding to the basic carbon emission influencing factors, φ i is the influence coefficient, y t-i is the annual carbon emissions in year ti, is the first asymmetric distributed lag parameter, is the second asymmetric distributed lag parameter, is the third asymmetric distributed lag parameter, is the fourth asymmetric distributed lag parameter, is x t-i The forward decomposition of It is z t-i The forward decomposition of is x t-i The negative decomposition of It is z t-i Negative decomposition, x t-i is the electricity consumption data of the park in year ti, z t-i is the weighted value corresponding to all target carbon emission influencing factors in year ti, u t,γ is the random error term, p is the first lag order of the dependent variable and the lag order of the explanatory variable, q is the second lag order of the dependent variable and the lag order of the explanatory variable, s is the third lag order of the dependent variable and the lag order of the explanatory variable, and i is a positive integer.

[0075] In some embodiments, the first determination module 40 is further configured to filter target seasonal energy consumption data from seasonal energy consumption data using the Denton ratio method based on the annual energy consumption data; and filter target monthly energy consumption data from monthly energy consumption data using the Denton ratio method based on the target seasonal energy consumption data.

[0076] In some embodiments, the second determination module 50 is further configured to determine the monthly carbon emissions by the following formula: Among them, y l,γ is the monthly carbon emissions in month 1, Q l,γ is the target monthly energy consumption data for the first month, A is the predetermined matrix, T represents the transposition operation, argmin represents the value of the variable when the following formula reaches the minimum value, y t is the annual carbon emissions.

[0077] In some embodiments, the screening module 20 is further configured to determine the linear coefficient by the following formula: Wherein, r is the linear coefficient, X is the impact value of the carbon emission influencing factor, and Y is the historical annual carbon emission in the non-historical period. is the average impact value of the carbon emission influencing factors, is the average annual carbon emissions in the historical period other than the historical period mentioned, XX is the sum of squares of the deviations from the mean of X, l YY is the sum of squares of the deviations from the mean of Y, l XY is the sum of squared deviations of X and Y from the mean.

[0078] In some embodiments, the screening module 20 is further configured to determine the nonlinear coefficient by the following formula: Wherein, MIC(X,Y) is the nonlinear coefficient, X is the impact value of the carbon emission influencing factor, Y is the expected annual carbon emission, and n x and n y Represents the number of grids on the horizontal and vertical axes respectively, G is the nth grid in (X,Y) x ×n y grid, I G (X, Y) is the mutual information value of the grid G, B(n, α) is the function of n B(n, α) = n α , where n α (0<α<1) is the upper limit of the number of grid divisions, R is a real number, log2 min(n x ,n y ) indicates the normalization operation to ensure that the MIC value is between 0 and 1.

[0079] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0080] The device of the above embodiment is used to implement the calculation method of the monthly carbon emissions of the corresponding park in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0081] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for calculating the monthly carbon emissions of the park as described in any of the above embodiments is implemented.

[0082] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0083] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0084] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0085] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0086] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0087] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0088] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0089] The electronic device of the above embodiment is used to implement the calculation method of the monthly carbon emissions of the corresponding park in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0090] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for calculating the monthly carbon emissions of the park as described in any of the above embodiments.

[0091] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0092] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for calculating the monthly carbon emissions of the park as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0093] Based on the same concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer executes the method for calculating the monthly carbon emissions of the park as described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0094] It should be noted that the embodiments of the present application can be further described in the following manner:

[0095] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0096] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0097] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0098] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0099] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0100] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0101] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0102] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A method for calculating the monthly carbon emissions of a park, characterized in that: include: Obtain various carbon emission influencing factors in historical periods and the impact value of each carbon emission influencing factor; Based on the multiple impact values, the multiple carbon emission impact factors are screened to obtain multiple target carbon emission impact factors; Calculate the annual carbon emissions of the park based on each target carbon emission influencing factor; Obtaining annual energy consumption data, seasonal energy consumption data, and monthly energy consumption data for the historical period, and determining target monthly energy consumption data based on the annual energy consumption data, the seasonal energy consumption data, and the monthly energy consumption data; Based on the annual carbon emissions and the target monthly energy consumption data, the monthly carbon emissions for each month in the historical period are determined.

2. The method according to claim 1, characterized in that Based on the multiple impact values, the multiple carbon emission impact factors are screened to obtain multiple target carbon emission impact factors, including: For each carbon emission influencing factor, based on the influence value of the carbon emission influencing factor, a linear analysis is performed on the carbon emission influencing factor to obtain a linear coefficient; based on the influence value of the carbon emission influencing factor, a nonlinear analysis is performed on the carbon emission influencing factor to obtain a nonlinear coefficient; the absolute value of the product of the linear coefficient and the nonlinear coefficient is calculated to obtain a product value; The product values ​​of all carbon emission influencing factors are sorted in descending order, and the multiple carbon emission influencing factors corresponding to the first N product values ​​are determined as the multiple target carbon emission influencing factors, where N is a positive integer.

3. The method according to claim 1, characterized in that The annual carbon emissions of the park are calculated based on each target carbon emission influencing factor, including: The annual carbon emissions are determined by the following formula: Among them, y t is the annual carbon emissions, t is the historical year, a o is the carbon emission corresponding to the basic carbon emission influencing factors, φ i is the influence coefficient, y t-i is the annual carbon emissions in year ti, is the first asymmetric distributed lag parameter, is the second asymmetric distributed lag parameter, is the third asymmetric distributed lag parameter, is the fourth asymmetric distributed lag parameter, is x t-i The forward decomposition of It is z t-i The forward decomposition of is x t-i The negative decomposition of It is z t-i Negative decomposition, x t-i is the electricity consumption data of the park in year ti, z t-i is the weighted value corresponding to all target carbon emission influencing factors in year ti, u t,γ is the random error term, p is the first lag order of the dependent variable and the lag order of the explanatory variable, q is the second lag order of the dependent variable and the lag order of the explanatory variable, s is the third lag order of the dependent variable and the lag order of the explanatory variable, and i is a positive integer.

4. The method according to claim 1, characterized in that: The determining target monthly energy consumption data based on the annual energy consumption data, the quarterly energy consumption data and the monthly energy consumption data comprises: Based on the annual energy consumption data, the target seasonal energy consumption data is selected from the seasonal energy consumption data using the Denton ratio method; Based on the target quarterly energy consumption data, the target monthly energy consumption data is screened from the monthly energy consumption data using the Denton ratio method.

5. The method according to claim 1, characterized in that The determining of the monthly carbon emissions for each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data includes: The monthly carbon emissions are determined by the following formula: Among them, y l,γ is the monthly carbon emissions in month 1, Q l,γ is the target monthly energy consumption data for the first month, A is the predetermined matrix, T represents the transposition operation, argmin represents the value of the variable when the following formula reaches the minimum value, y t is the annual carbon emissions.

6. The method according to claim 2, characterized in that The linear analysis of the carbon emission influencing factors based on the influence values ​​of the carbon emission influencing factors to obtain linear coefficients includes: The linear coefficient is determined by the following formula: Wherein, r is the linear coefficient, X is the impact value of the carbon emission influencing factor, and Y is the historical annual carbon emission in the non-historical period. is the average impact value of the carbon emission influencing factors, is the average annual carbon emissions in the historical period other than the historical period mentioned, XX is the sum of squares of the deviations from the mean of X, l YY is the sum of squares of the deviations from the mean of Y, l XY is the sum of squared deviations of X and Y from the mean.

7. The method according to claim 2, characterized in that The nonlinear analysis of the carbon emission influencing factors based on the influence values ​​of the carbon emission influencing factors to obtain nonlinear coefficients includes: The nonlinear coefficient is determined by the following formula: Wherein, MIC(X,Y) is the nonlinear coefficient, X is the impact value of the carbon emission influencing factor, Y is the expected annual carbon emission, and n x and n y Represents the number of grids on the horizontal and vertical axes respectively, G is the nth grid in (X,Y) x ×n y grid, I G (X, Y) is the mutual information value of the grid G, B(n, α) is the function of n B(n, α) = n α , where n α (0<α<1) is the upper limit of the number of grid divisions, R is a real number, log2 min(n x ,n y ) indicates the normalization operation to ensure that the MIC value is between 0 and 1.

8. A device for calculating monthly carbon emissions of a park, characterized in that: include: An acquisition module is configured to acquire multiple carbon emission influencing factors in a historical period and an impact value of each carbon emission influencing factor; A screening module is configured to screen the multiple carbon emission influencing factors based on multiple influencing values ​​to obtain multiple target carbon emission influencing factors; A calculation module is configured to calculate the annual carbon emissions of the park based on each target carbon emission influencing factor; A first determination module is configured to obtain the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data of the historical period, and determine the target monthly energy consumption data based on the annual energy consumption data, the seasonal energy consumption data and the monthly energy consumption data; The second determination module is configured to determine the monthly carbon emissions of each month in the historical period based on the annual carbon emissions and the target monthly energy consumption data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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