Industrial carbon emission analysis method and electronic equipment

Abstract

The invention provides an industry carbon emission analysis method, which comprises the steps of determining an industry carbon emission influence factor analysis hierarchical structure and a correlation coefficient of hierarchical influence factors and industry carbon emission based on industry historical carbon emission data, the influence factor analysis hierarchical structure comprises industry carbon emission and a plurality of hierarchical influence factors; based on the influence factor analysis hierarchical structure and correlation coefficients of a plurality of hierarchical influence factors and the industry carbon emission, determining weights of the plurality of hierarchical influence factors during calculation of the industry carbon emission; and determining the carbon emission of the industry based on the hierarchical influence factors and the corresponding weights thereof. According to the method, industrial historical carbon emission data is taken as objective data support, accurate analysis of influence factors of each level of the industrial carbon emission is realized, and scientific basis and decision support are provided for prediction and calculation work of the industrial carbon emission.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and particularly relates to an industry carbon emission analysis method and an electronic device. BACKGROUND

[0002] The weight calculation of various influence factors for carbon emission prediction in the prior art is mostly performed by using the expert scoring method. However, the expert scoring method is highly subjective and lacks objective data support, and there are problems of contradictory assignment relationships between different influence factors and large calculation amount of consistency test. SUMMARY

[0003] Therefore, the present application aims to provide an industry carbon emission analysis method and an electronic device.

[0004] To achieve the above purpose, the present application provides an industry carbon emission analysis method, which comprises the following steps:

[0005] determining, based on industry historical carbon emission data, an influence factor analysis hierarchical structure of industry carbon emission and a correlation coefficient between hierarchical influence factors and the industry carbon emission, wherein the influence factor analysis hierarchical structure comprises industry carbon emission and a plurality of hierarchical influence factors;

[0006] determining, based on the influence factor analysis hierarchical structure and the correlation coefficient between the plurality of hierarchical influence factors and the industry carbon emission, weights of the plurality of hierarchical influence factors in calculating the industry carbon emission;

[0007] determining the industry carbon emission based on the hierarchical influence factors and the corresponding weights.

[0008] Further, the hierarchical influence factors comprise a plurality of middle-level influence factors and bottom-level influence factors, each of the middle-level influence factors corresponds to a plurality of bottom-level influence factors, and the industry carbon emission corresponds to a plurality of middle-level influence factors.

[0009] The step of determining, based on industry historical carbon emission data, an influence factor analysis hierarchical structure of industry carbon emission and a correlation coefficient between hierarchical influence factors and the industry carbon emission comprises the following steps:

[0010] determining, based on industry historical carbon emission data, a plurality of bottom-level influence factors and a correlation coefficient between the plurality of bottom-level influence factors and the industry carbon emission by using a Pearson correlation analysis method;

[0011] performing clustering processing on the plurality of bottom-level influence factors by using a clustering analysis method to obtain a plurality of middle-level influence factors, and each of the middle-level influence factors corresponds to a plurality of bottom-level influence factors.

[0012] determining the correlation coefficient between the middle layer influencing factor and the carbon emission of the industry based on the correlation coefficients between the multiple bottom layer influencing factors corresponding to the middle layer influencing factor and the carbon emission of the industry;

[0013] obtaining the industry carbon emission influencing factor analysis hierarchical structure based on the middle layer influencing factor and the bottom layer influencing factor.

[0014] Further, the Pearson correlation analysis method is used to determine the multiple bottom layer influencing factors and the correlation coefficients between the multiple bottom layer influencing factors and the carbon emission of the industry based on the historical carbon emission data of the industry, including:

[0015] The Pearson correlation analysis method is used to calculate the correlation coefficients between the multiple indexes and the carbon emission of the industry based on the historical carbon emission data of the industry;

[0016] Based on the correlation coefficients between the multiple indexes and the carbon emission of the industry, multiple bottom layer influencing factors are determined from the multiple indexes;

[0017] The correlation coefficient between the index corresponding to the bottom layer influencing factor and the carbon emission of the industry is determined as the correlation coefficient between the bottom layer influencing factor and the carbon emission of the industry.

[0018] Further, the weights of the multiple hierarchical influencing factors in calculating the carbon emission of the industry are determined based on the influencing factor analysis hierarchical structure and the correlation coefficients between the multiple hierarchical influencing factors and the carbon emission of the industry, including:

[0019] A judgment matrix is determined based on the influencing factor analysis hierarchical structure and the correlation coefficients between the multiple hierarchical influencing factors and the carbon emission of the industry;

[0020] The weights of the multiple hierarchical influencing factors in calculating the carbon emission of the industry are calculated based on the judgment matrix.

[0021] Further, the weights of the multiple hierarchical influencing factors in calculating the carbon emission of the industry are calculated based on the judgment matrix, including:

[0022] The product of each row element of the judgment matrix is calculated based on the judgment matrix;

[0023] A first vector is calculated based on the product of each row element;

[0024] A feature vector is obtained by normalizing the first vector, and the feature vector is the weight of the multiple hierarchical influencing factors in calculating the carbon emission of the industry.

[0025] Further, after the influence factor analysis hierarchy and the correlation coefficients between the multiple hierarchical influence factors and the industry carbon emissions are analyzed to determine the weights of the multiple hierarchical influence factors in calculating the industry carbon emissions, the method further includes:

[0026] performing consistency check on the judgment matrix corresponding to the multiple hierarchical influence factors based on the weights of the multiple hierarchical influence factors in calculating the industry carbon emissions;

[0027] in response to determining that the judgment matrix passes the consistency check, storing the weights of the hierarchical influence factors in calculating the industry carbon emissions;

[0028] in response to determining that the judgment matrix does not pass the consistency check, performing consistency adjustment on the judgment matrix to obtain an adjusted judgment matrix;

[0029] generating the weights of the multiple hierarchical influence factors in calculating the industry carbon emissions based on the adjusted judgment matrix, and repeating the above steps until the adjusted judgment matrix passes the consistency check.

[0030] Further, the consistency check on the judgment matrix corresponding to the multiple hierarchical influence factors based on the weights of the multiple hierarchical influence factors in calculating the industry carbon emissions includes:

[0031] calculating the maximum eigenvalue of the judgment matrix based on the weights of the multiple hierarchical influence factors in calculating the industry carbon emissions;

[0032] calculating a consistency check index based on the maximum eigenvalue;

[0033] determining an average random consistency index based on the judgment matrix;

[0034] determining whether the judgment matrix passes the consistency check based on the consistency check index and the average random consistency index.

[0035] Further, the consistency adjustment on the judgment matrix to obtain an adjusted judgment matrix includes:

[0036] determining the offset degree of each element in the judgment matrix to obtain an offset degree set;

[0037] determining a target offset degree in the offset degree set, and determining a target element in the judgment matrix corresponding to the target offset degree based on the target offset degree;

[0038] determining an adjusted target element based on the target element and the judgment matrix;

[0039] replacing the target element with the adjusted target element to obtain an adjusted judgment matrix.

[0040] Further, the judgment matrix is determined based on the influence factor analysis hierarchical structure and the correlation coefficients of the hierarchical influence factors and the industry carbon emission.

[0041] A plurality of target hierarchical influence factors are determined based on the influence factor analysis hierarchical structure.

[0042] A correlation parameter between any two target hierarchical influence factors is calculated based on the correlation coefficients of the target hierarchical influence factors and the industry carbon emission.

[0043] The correlation parameter is taken as an element of the judgment matrix to obtain the judgment matrix.

[0044] Based on the same inventive concept, the disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0045] Based on the same inventive concept, the disclosure further provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the method as described above.

[0046] As can be seen from the above, the industry carbon emission analysis method provided by the present application establishes an industry carbon emission influence factor analysis hierarchical structure and correlation coefficients of the hierarchical influence factors in the hierarchical structure and the industry carbon emission based on historical industry carbon emission data, which can improve the analysis accuracy of the industry carbon emission based on historical industry carbon emission data; and the weights of the hierarchical influence factors in the hierarchical structure in calculating the industry carbon emission are determined based on this, so as to realize batch calculation of the industry carbon emission. The present application takes historical industry carbon emission data as objective data support, realizes accurate analysis of the hierarchical influence factors of the industry carbon emission, and provides scientific basis and decision support for prediction and calculation of the industry carbon emission. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or related art, the drawings needed in the embodiments or related art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 A flow structure schematic diagram of an industry carbon emission analysis method according to an embodiment of the present application;

[0049] Figure 2 FIG. 1 is a structural schematic diagram of an industry carbon emission analysis device according to an embodiment of the present application;

[0050] Figure 3 FIG. 2 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments and the accompanying drawings.

[0052] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood as the common meanings understood by those skilled in the art to which the embodiments of the present application belong. The terms "first", "second" and similar terms used in the embodiments of the present application do not represent any order, number or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar terms mean that the components or objects before the terms encompass the components or objects listed after the terms and their equivalents, without excluding other components or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connection, but can include electrical connection, whether direct or indirect. The terms "upper", "lower", "left", "right" and the like only represent relative positional relationships, which can change accordingly when the absolute positions of the described objects change.

[0053] The existing analytic hierarchy process method for calculating the weight of influencing factors mainly uses the expert scoring method to set the judgment matrix, which is highly subjective and lacks objective data support, and may have problems such as contradictory assignment relationship between different influencing factors and large amount of calculation for consistency check.

[0054] Therefore, the present application proposes an industry carbon emission analysis method, which determines the weight of different influencing factors based on historical carbon emission data, so as to provide accurate analysis for industry carbon emission prediction and calculation, and provide scientific basis and decision support for energy saving and emission reduction work.

[0055] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0056] In some embodiments, an industry carbon emission analysis method, as shown in FIG. 1, includes the following steps: Figure 1

[0057] Step S100, determining an industry carbon emission influencing factor analysis hierarchical structure and a correlation coefficient between a hierarchical influencing factor and the industry carbon emission based on industry historical carbon emission data, wherein the influencing factor analysis hierarchical structure includes the industry carbon emission and a plurality of hierarchical influencing factors. ​

[0058] Specifically, the industry historical carbon emission data is the carbon emission amount recorded in the past of the industry, and the data of various indexes related to the carbon emission amount, the correlation coefficient of each index and the carbon emission amount is determined to quantify the correlation degree of each index and the carbon emission amount, and based on the correlation coefficient of each index and the carbon emission amount, a significance threshold (for example, p < 0.05) is set to filter out a plurality of indexes as the hierarchical influencing factors (i.e., the bottom layer influencing factors), and on this basis, the plurality of hierarchical influencing factors are clustered to form a plurality of hierarchical influencing factors higher than the bottom layer influencing factors (i.e., the middle layer influencing factors), and the hierarchical structure of the carbon emission amount influencing factors of the industry is obtained.

[0059] For example, based on the industry historical carbon emission data, 16 indexes related to the carbon emission amount of the industry are filtered out as the bottom layer influencing factors, and the correlation coefficient of the 16 indexes and the carbon emission amount of the industry is the correlation coefficient of the corresponding bottom layer influencing factor and the carbon emission amount of the industry, and on this basis, the 16 bottom layer influencing factors are clustered to obtain 4 middle layer influencing factors, and the correlation coefficient of the middle layer influencing factor and the carbon emission amount of the industry is obtained by multiplying the correlation coefficient of the corresponding bottom layer influencing factor and the carbon emission amount of the industry.

[0060] Step S200, based on the hierarchical structure of the influencing factors and the correlation coefficient of the plurality of hierarchical influencing factors and the carbon emission amount of the industry, the weight of the plurality of hierarchical influencing factors in calculating the carbon emission amount of the industry is determined;

[0061] Specifically, the relationship between the hierarchical influencing factors is determined based on the hierarchical structure of the influencing factors, for example: the bottom layer influencing factor belongs to a middle layer influencing factor, and based on the correlation coefficient of each hierarchical influencing factor and the carbon emission amount of the industry, a plurality of judgment matrices are established to respectively obtain the weight of each middle layer influencing factor in calculating the carbon emission amount of the industry, and the weight of each bottom layer influencing factor in calculating the value of the corresponding middle layer influencing factor, and the weight of the bottom layer influencing factor is multiplied by the weight of the corresponding middle layer influencing factor to obtain the weight of the bottom layer influencing factor in calculating the carbon emission amount of the industry.

[0062] Step S300, based on the hierarchical influencing factors and the corresponding weights, the carbon emission amount of the industry is determined.

[0063] Specifically, based on the weight of the hierarchical influencing factors in calculating the carbon emission amount of the industry, the carbon emission amount of the industry can be calculated according to the predicted value or actual value of the hierarchical influencing factors.

[0064] In the embodiment, the hierarchical structure of the industry carbon emission influencing factors is established based on the industry historical carbon emission data, and the correlation coefficients of the influencing factors at each level in the hierarchical structure and the industry carbon emission are determined, so that the analysis accuracy of the industry carbon emission is improved based on the industry historical carbon emission data; and the weights of the influencing factors at each level in the hierarchical structure in the calculation of the industry carbon emission are determined, so that the batch calculation of the industry carbon emission is realized. The application realizes the accurate analysis of the influencing factors at each level of the industry carbon emission based on the objective data of the industry historical carbon emission, and provides scientific basis and decision support for the prediction and calculation of the industry carbon emission.

[0065] In some embodiments, the hierarchical influencing factors include a plurality of middle-level influencing factors and bottom-level influencing factors, each of the middle-level influencing factors corresponds to a plurality of the bottom-level influencing factors, and the industry carbon emission corresponds to a plurality of the middle-level influencing factors.

[0066] In step S100, the hierarchical structure of the industry carbon emission influencing factors and the correlation coefficients of the influencing factors at each level and the industry carbon emission are determined based on the industry historical carbon emission data, including:

[0067] In step S101, the Pearson correlation analysis method is used to determine a plurality of bottom-level influencing factors and the correlation coefficients of the bottom-level influencing factors and the industry carbon emission based on the industry historical carbon emission data.

[0068] Specifically, the Pearson correlation analysis method is used to analyze the industry historical carbon emission data, obtain the correlation coefficients of a plurality of indexes and the industry carbon emission, select a plurality of indexes as the bottom-level influencing factors by setting a significance threshold (for example, p<0.05), and take the correlation coefficients of the indexes and the industry carbon emission as the correlation coefficients of the bottom-level influencing factors and the industry carbon emission.

[0069] In step S102, the clustering analysis method is used to cluster a plurality of bottom-level influencing factors, and a plurality of middle-level influencing factors are obtained, each of the middle-level influencing factors corresponding to a plurality of bottom-level influencing factors.

[0070] Specifically, the clustering analysis is performed on a plurality of the bottom-level influencing factors to classify the bottom-level influencing factors, the number of categories is the same as the number of the middle-level influencing factors, and the category names are the middle-level influencing factors.

[0071] In step S103, the correlation coefficients of the middle-level influencing factors and the industry carbon emission are determined based on the correlation coefficients of the bottom-level influencing factors corresponding to the middle-level influencing factors and the industry carbon emission.

[0072] Specifically, the correlation coefficient between the bottom-level influencing factor and the carbon emission of the industry is known, the correlation coefficient between the middle-level influencing factor and the carbon emission of the industry is calculated based on the correlation coefficient between the plurality of bottom-level influencing factors corresponding to the middle-level influencing factor and the carbon emission of the industry.

[0073] For example, the middle-level influencing factor corresponds to four bottom-level influencing factors, and the correlation coefficients between the four bottom-level influencing factors and the carbon emission of the industry are 0.96, 0.98, 0.93, and 0.97 respectively. Then, the correlation coefficient between the middle-level influencing factor and the carbon emission of the industry is 0.96*0.98*0.93*0.97=0.84869568.

[0074] Step S104: obtaining the industry carbon emission influencing factor analysis hierarchical structure based on the middle-level influencing factor and the bottom-level influencing factor.

[0075] Specifically, the industry carbon emission influencing factor analysis hierarchical structure is obtained according to the correspondence between the middle-level influencing factor and the bottom-level influencing factor. The top layer of the industry carbon emission influencing factor analysis hierarchical structure is the carbon emission of the industry, the middle layer is a plurality of middle-level influencing factors, and the bottom layer is a plurality of bottom-level influencing factors. The bottom-level influencing factor is connected to the corresponding middle-level influencing factor, and the middle-level influencing factor is connected to the carbon emission of the industry.

[0076] In the embodiment, the bottom-level influencing factor and the correlation coefficient between the bottom-level influencing factor and the carbon emission are determined based on the industry historical carbon emission data, and the clustering analysis is performed on the plurality of bottom-level influencing factors based on the correlation coefficient, to obtain the middle-level influencing factor and the correlation coefficient between the middle-level influencing factor and the carbon emission, obtain the industry carbon emission influencing factor analysis hierarchical structure, realize the hierarchical analysis of the carbon emission of the industry, and further obtain the weight of the middle-level influencing factor and the bottom-level influencing factor in the calculation of the carbon emission, so as to realize the accurate analysis of the influencing factors of each level of the carbon emission of the industry, and provide a scientific basis and decision support for the prediction and calculation of the carbon emission of the industry.

[0077] In some embodiments, step S101: the Pearson correlation analysis method is used to determine a plurality of bottom-level influencing factors and the correlation coefficient between the bottom-level influencing factors and the carbon emission of the industry based on the industry historical carbon emission data, including:

[0078] Step S101A: the Pearson correlation analysis method is used to calculate the correlation coefficient between a plurality of indexes and the carbon emission of the industry based on the industry historical carbon emission data.

[0079] Specifically, the industry historical carbon emission data includes the industry carbon emission amount and the numerical values of various indicators. Based on this, the Pearson correlation analysis method is used to calculate the correlation coefficients of various indicators and the industry carbon emission amount.

[0080] In step S101B, based on the correlation coefficients of the plurality of indicators and the industry carbon emission amount, a plurality of bottom-level influencing factors are determined from the plurality of indicators.

[0081] Specifically, by setting a significance threshold, indicators with correlation coefficients meeting the threshold are screened out from the plurality of indicators, and the indicators are determined as bottom-level influencing factors.

[0082] In step S101C, the correlation coefficient of the bottom-level influencing factor corresponding to the indicator and the industry carbon emission amount is determined as the correlation coefficient of the bottom-level influencing factor and the industry carbon emission amount.

[0083] Specifically, the correlation coefficient of the bottom-level influencing factor and the industry carbon emission amount is the same as the correlation coefficient of the indicator corresponding to the bottom-level influencing factor and the industry carbon emission amount.

[0084] In the embodiment, the bottom-level influencing factors and the correlation coefficients of the bottom-level influencing factors and the industry carbon emission amount are obtained by analyzing the industry historical carbon emission data, so that the established industry carbon emission influencing factor analysis hierarchical structure is supported by historical objective data, which is beneficial to improving the analysis accuracy of the method.

[0085] In some embodiments, step S200 includes:

[0086] In step S201, based on the influencing factor analysis hierarchical structure and the correlation coefficients of the plurality of hierarchical influencing factors and the industry carbon emission amount, a judgment matrix is determined.

[0087] Specifically, the hierarchical influencing factors include middle-level influencing factors and bottom-level influencing factors. Based on the correlation coefficients of the middle-level influencing factors and the industry carbon emission amount, a judgment matrix is determined to obtain the weights of the middle-level influencing factors in calculating the industry carbon emission amount. Based on the correlation coefficients of the bottom-level influencing factors corresponding to the middle-level influencing factors and the industry carbon emission amount, a judgment matrix is determined to obtain the weights of the plurality of bottom-level influencing factors in calculating the values of the middle-level influencing factors.

[0088] The elements in the judgment matrix are used to represent the comparison value of one influencing factor with another influencing factor in the same level. For example, the correlation coefficient of one middle-level influencing factor with the carbon emission of the industry is 0.85, and the correlation coefficient of another middle-level influencing factor with the carbon emission of the industry is 0.83, then 0.85 / 0.83 = 85 / 83 is an element in the judgment matrix.

[0089] For example, there are four middle-level influencing factors, and the serial numbers are 1, 2, 3 and 4, respectively. Then the judgment matrix A established based on this is:

[0090]

[0091] wherein a 11 is the correlation coefficient ratio of the middle-level influencing factor with serial number 1 and the middle-level influencing factor with serial number 1, a 12 is the correlation coefficient ratio of the middle-level influencing factor with serial number 1 and the middle-level influencing factor with serial number 2, and so on.

[0092] In step S202, the weights of the multiple level influencing factors in calculating the carbon emission of the industry are calculated based on the judgment matrix.

[0093] Specifically, the weights of the middle-level influencing factors in calculating the carbon emission of the industry and the weights of the bottom-level influencing factors in calculating the corresponding middle-level influencing factors can be obtained based on the judgment matrix. The weight of the bottom-level influencing factor in calculating the carbon emission of the industry is calculated based on the weight of the bottom-level influencing factor and the weight of the corresponding middle-level influencing factor.

[0094] For example, the weight of the middle-level influencing factor is 0.2, and the weight of one corresponding bottom-level influencing factor is 0.5. Then the weight of the bottom-level influencing factor in calculating the carbon emission of the industry is 0.2*0.5 = 0.1.

[0095] In this embodiment, based on the determination of the influencing factor analysis level structure and the correlation coefficient of the level influencing factor and the carbon emission of the industry, the weights of the level influencing factors in calculating the carbon emission of the industry are obtained by establishing the judgment matrix, so as to provide support for the prediction and calculation of the carbon emission of the industry.

[0096] In some embodiments, step S202: the weights of the multiple level influencing factors in calculating the carbon emission of the industry are calculated based on the judgment matrix, including:

[0097] In step S202A, the product of the elements in each row of the judgment matrix is calculated based on the judgment matrix.

[0098] Specifically, after obtaining the judgment matrix, the weights of the hierarchical influencing factors in calculating industry carbon emissions are calculated using an iterative method. The first step is to calculate the product of the elements in each row of the judgment matrix.

[0099] Step S202B: Calculate the first vector based on the product of the elements in each row;

[0100] Specifically, after obtaining the product of the elements in each row of the judgment matrix, the nth root of the product is calculated, where n is the same as the number of rows in the judgment matrix, and the resulting root is the value in the first vector.

[0101] Step S202C: Normalize the first vector to obtain a feature vector, where the feature vector represents the weights of the multiple hierarchical influencing factors in calculating industry carbon emissions.

[0102] Specifically, each value in the first vector is normalized to obtain a value in the feature vector, which is the weight of each level of influencing factor in calculating the industry's carbon emissions.

[0103] It should be noted that the weight calculation steps described in this embodiment are to calculate the weight of the middle-level influencing factor when calculating the industry carbon emissions. Based on this embodiment, a judgment matrix is ​​established based on the bottom-level influencing factors corresponding to the middle-level influencing factors. According to the method of this embodiment, the weight of the bottom-level influencing factor corresponding to the middle-level influencing factor when calculating the middle-level influencing factor can be calculated. Multiplying this value by the weight of the middle-level influencing factor when calculating the industry carbon emissions, the weight of the bottom-level influencing factor when calculating the industry carbon emissions can be obtained. Thus, the weight of each level influencing factor in the hierarchical structure of the influencing factor analysis when calculating the industry carbon emissions can be obtained.

[0104] For example, there are four mid-level influencing factors, and the judgment matrix A corresponding to these mid-level influencing factors is:

[0105]

[0106] To obtain the weight of each of the aforementioned mid-level influencing factors in calculating industry carbon emissions, the product M of the elements in each row of the judgment matrix is ​​first calculated. i

[0107]

[0108] M i They are 24, 3, 1 / 3, and 1 / 24 respectively;

[0109] Then calculate M i The nth root of W i

[0110]

[0111] W i 2.21, 1.32, 0.76, 0.45, and the first vector obtained is W = [W1, W2, W3...W n ] = [2.21, 1.32, 0.76, 0.45].

[0112] Finally, the first vector is normalized to obtain w i

[0113]

[0114] w i 0.466, 0.278, 0.160, 0.096, and the feature vector obtained is [0.466, 0.278, 0.160, 0.096], the weight of the middle layer influence element with the serial number 1 in calculating the carbon emission is 0.466, the weight of the middle layer influence element with the serial number 2 in calculating the carbon emission is 0.278, the weight of the middle layer influence element with the serial number 3 in calculating the carbon emission is 0.160, and the weight of the middle layer influence element with the serial number 4 in calculating the carbon emission is 0.096.

[0115] In the embodiment, the specific process of calculating the weight of the hierarchical influence factor in calculating the industry carbon emission by the judgment matrix is described, so that the process is clear and visual, and the accuracy of the analysis method is improved.

[0116] In some embodiments, after the weight of the hierarchical influence factor in calculating the industry carbon emission is determined based on the influence factor analysis hierarchical structure and the correlation coefficient between the hierarchical influence factor and the industry carbon emission in step S200, the method further comprises:

[0117] Step S400, consistency check is performed on the judgment matrix corresponding to the hierarchical influence factor based on the weight of the hierarchical influence factor in calculating the industry carbon emission.

[0118] Specifically, after the weight of the hierarchical influence factor is calculated, consistency check is performed on the judgment matrix corresponding to the weight based on the weight and the judgment matrix, to check the credibility of the weight value, and to determine whether the weight value is the final value.

[0119] It should be noted that, for the bottom layer influencing factor, after obtaining the weight of the bottom layer influencing factor relative to the corresponding middle layer influencing factor, the weight value is used to perform consistency check on the corresponding judgment matrix, and it is not necessary to convert the weight value to obtain the weight of the bottom layer influencing factor in calculating the industry carbon emission. That is, the weight value obtained based on the judgment matrix is used to perform consistency check on the judgment matrix, and after it is determined that the consistency check is passed, the weight value can be used for subsequent conversion to obtain the weight of the bottom layer influencing factor in calculating the industry carbon emission.

[0120] In step S500, in response to determining that the judgment matrix passes the consistency check, the weight of the hierarchical influencing factor in calculating the industry carbon emission is stored.

[0121] Specifically, after it is determined that the judgment matrix passes the consistency check, the weight of the hierarchical influencing factor in calculating the industry carbon emission calculated by the judgment matrix is stored as a final value.

[0122] In step S600, in response to determining that the judgment matrix does not pass the consistency check, the judgment matrix is adjusted for consistency to obtain an adjusted judgment matrix.

[0123] Specifically, after it is determined that the judgment matrix does not pass the consistency check, the judgment matrix is adjusted for consistency to obtain an adjusted judgment matrix.

[0124] In step S700, the weight of the plurality of hierarchical influencing factors in calculating the industry carbon emission is generated based on the adjusted judgment matrix, and the above steps are repeated until the adjusted judgment matrix passes the consistency check.

[0125] Specifically, the corresponding weight value is calculated based on the adjusted judgment matrix, and the consistency of the adjusted judgment matrix is checked based on the weight value. The process is repeated until the adjusted judgment matrix passes the consistency check, and the corresponding weight is stored as a final value.

[0126] In this embodiment, after the corresponding weight is calculated based on the judgment matrix, the consistency of the judgment matrix is checked based on the weight value to determine the consistency of the obtained weight, which is beneficial to improve the accuracy of the analysis method. After the judgment matrix does not pass the consistency check, the adjusted judgment matrix is obtained by adjusting the judgment matrix, and the steps of calculating the weight and consistency check are repeated until the adjusted judgment matrix passes the consistency check, which can improve the accuracy of the corresponding weight obtained by the analysis method and is beneficial to improve the practicality of the analysis method.

[0127] In some embodiments, step S400: the consistency test is performed on the judgment matrix corresponding to the weight of each of the plurality of hierarchical influencing factors in calculating the carbon emissions of the industry, comprising:

[0128] Step S401, calculating the maximum eigenvalue of the judgment matrix based on the weight of each of the plurality of hierarchical influencing factors in calculating the carbon emissions of the industry;

[0129] Specifically, the maximum eigenvalue of the judgment matrix is related to the weight calculated by the judgment matrix, and the maximum eigenvalue λ max The calculation formula is as follows:

[0130]

[0131] Wherein, A is the judgment matrix, and W is the value in the eigenvector, that is, the weight.

[0132] Step S402, calculating a consistency test index based on the maximum eigenvalue;

[0133] Specifically, the calculation formula of the consistency test index CI is as follows:

[0134]

[0135] Wherein, n is the number of values in the eigenvector.

[0136] Step S403, determining an average random consistency index based on the judgment matrix;

[0137] Specifically, the average random consistency index varies with the dimension n of the matrix.

[0138] Exemplarily, the value of the average random consistency index is obtained by looking up Table 1.

[0139] Table 1: average random consistency index RI corresponding value

[0140] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.45

[0141] Step S404, determining whether the judgment matrix passes the consistency test based on the consistency test index and the average random consistency index.

[0142] Specifically, a consistency ratio index is calculated based on the consistency test index and the average random consistency index to determine whether the judgment matrix passes the consistency test.

[0143] Exemplarily, the consistency ratio index CR is Wherein, CI is the consistency test index, and RI is the average random consistency index, when CR<0.1, the judgment matrix passes the consistency test.

[0144] In the embodiment, the process of consistency check of the judgment matrix is described to determine whether the judgment matrix passes the consistency check, which provides a check channel for obtaining corresponding weight values from the judgment matrix, and is beneficial to improve the practicability and accuracy of the analysis method.

[0145] In some embodiments, in step S600, the consistency adjustment of the judgment matrix is performed to obtain an adjusted judgment matrix, including:

[0146] In step S601, the offset degree of each element in the judgment matrix is determined to obtain an offset degree set.

[0147] Specifically, the offset degree β ij of each element a ij in the judgment matrix is calculated according to the following formula:

[0148]

[0149] wherein, β ij is the offset degree of each element in the judgment matrix, and β 12 is the offset degree of the target element. is the logical judgment information of the relative importance judgment of any two influence factors.

[0150]

[0151] wherein, k = 1, 2, …, n, and k ≠ i, j

[0152] In step S602, a target offset degree is determined in the offset degree set, and a target element in the judgment matrix corresponding to the target offset degree is determined based on the target offset degree.

[0153] Specifically, after the offset degree β ij of each element is calculated, the maximum value is determined as the target offset degree, and the element corresponding to the target offset degree is the target element.

[0154] For example, β 12 is the target offset degree, and the target element is a 12 .

[0155] In step S603, an adjusted target element is determined based on the target element and the judgment matrix.

[0156] Specifically, the calculation formula of the value of the adjusted target element is as follows:

[0157]

[0158] wherein, a ij is the adjusted target element.

[0159] Step S604, replacing the target element with the adjusted target element to obtain an adjusted judgment matrix.

[0160] Specifically, the target element is deleted from the judgment matrix, and the adjusted target element is supplemented into the judgment matrix to obtain the adjusted judgment matrix.

[0161] In the embodiment, by calculating the offset degree of each element, determining the target offset degree among the plurality of offset degrees, determining the target element based on the target offset degree, and adjusting the target element to obtain the adjusted target element, the target element in the judgment matrix is replaced to obtain the adjusted judgment matrix, and on this basis, the weight of the hierarchical influencing factor corresponding to the judgment matrix is obtained, which is beneficial to improve the accuracy of the weight value obtained by the analysis method.

[0162] In some embodiments, step S201: determining a judgment matrix based on the analysis of the hierarchical structure of the influencing factors and the correlation coefficients of the plurality of hierarchical influencing factors and the carbon emissions of the industry, comprising:

[0163] Step S201A, determining a plurality of target hierarchical influencing factors based on the analysis of the hierarchical structure of the influencing factors;

[0164] Specifically, in the hierarchical structure of the influencing factors, the bottom-level influencing factors belonging to the same middle-level influencing factor are determined as target hierarchical influencing factors, or the middle-level influencing factors are determined as target hierarchical influencing factors, to obtain different judgment matrices, and then obtain the weights of different hierarchical influencing factors relative to their upper-level influencing factors.

[0165] For example, the hierarchical structure of the influencing factors is shown in Table 2,

[0166] Table 2: Hierarchical structure of influencing factors for carbon emissions of the industry

[0167]

[0168]

[0169] When calculating the weight of the middle-level influencing factor relative to the carbon emissions of the industry, the four middle-level influencing factors are taken as target hierarchical influencing factors.

[0170] Step S201B, calculating the correlation parameter between any two target hierarchical influencing factors based on the correlation coefficients of the target hierarchical influencing factors and the carbon emissions of the industry;

[0171] Specifically, the correlation coefficient of the target level influencing factor and the carbon emission of the industry is known, and on this basis, the correlation parameters between the target level influencing factors are calculated, and the correlation parameters are taken as the elements of the judgment matrix, and then the judgment matrix is obtained.

[0172] In step S201C, the correlation parameters are taken as the elements of the judgment matrix to obtain the judgment matrix.

[0173] Specifically, the correlation parameters are taken as the elements of the judgment matrix, and then the judgment matrix is obtained.

[0174] For example, the correlation coefficient of energy activity in the middle level influencing factor is 0.96, the correlation coefficient of economic development is 0.48, the correlation coefficient of green certificate carbon market is 0.32, and the correlation coefficient of technological innovation is 0.24,

[0175]

[0176] Based on this, the elements in the judgment matrix are calculated, wherein p i is the correlation coefficient of a target level element.

[0177] Therefore, the judgment matrix A is obtained as follows:

[0178]

[0179] It should be noted that after obtaining each element in the judgment matrix, the elements in the judgment matrix are normalized to reflect the difference between different elements.

[0180]

[0181] wherein a' ij is the element after normalization, a ij is the element before normalization, so that the new judgment matrix obtained by replacing the element before normalization with the element after normalization is the final judgment matrix.

[0182] In this embodiment, the generation process of the judgment matrix is described to obtain the corresponding judgment matrix through the correlation coefficient of the level influencing factor and the carbon emission of the industry, and then the weight of the level influencing factor in calculating the carbon emission of the industry is obtained, which can improve the accuracy and practicability of the analysis method.

[0183] It should be noted that the method of the embodiments of the present application can be executed by a single device, such as a computer or a server, etc. The method of the embodiments of the present application can also be applied to a distributed scenario, and be completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present application, and the multiple devices can interact with each other to complete the method.

[0184] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

[0185] Based on the same inventive concept, the present application also provides an industry carbon emission analysis device corresponding to the method of any of the above embodiments.

[0186] Reference Figure 2 The device comprises:

[0187] The analysis module 100 is configured to determine an industry carbon emission impact factor analysis hierarchy based on industry historical carbon emission data, and a correlation coefficient between a hierarchical impact factor and the industry carbon emission, wherein the impact factor analysis hierarchy comprises the industry carbon emission and a plurality of hierarchical impact factors.

[0188] The calculation module 200 is configured to determine the weight of the plurality of hierarchical impact factors in calculating the industry carbon emission based on the impact factor analysis hierarchy and the correlation coefficient between the plurality of hierarchical impact factors and the industry carbon emission.

[0189] The application module 300 is configured to determine the industry carbon emission based on the hierarchical impact factors and the corresponding weights.

[0190] For the convenience of description, the above device is described in various modules based on functions. Of course, the functions of each module can be implemented in one or more software and / or hardware in the implementation of the present application.

[0191] The device of the above embodiments is used to implement the corresponding industry carbon emission analysis method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here again.

[0192] Corresponding to the method of any of the above embodiments based on the same inventive concept, the application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the industry carbon emission analysis method of any of the above embodiments.

[0193] Figure 3 A more specific hardware structure of an electronic device provided by the embodiment is shown, which can 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 for internal communication within the device.

[0194] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the embodiments of the present specification.

[0195] The memory 1020 can be implemented by a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 1020 and executed by the processor 1010.

[0196] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0197] The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0198] Bus 1050 includes a path for transferring information between the various components (e.g., processor 1010, memory 1020, input / output interface 1030, and communication interface 1040) of the device.

[0199] 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 can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary to implement the embodiments of the present specification, and does not have to contain all the components shown in the figure.

[0200] The electronic device of the above embodiment is used to implement the corresponding industry carbon emission analysis method in any of the preceding embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0201] Based on the same inventive concept, corresponding to any of the above embodiment methods, the present application also provides a non-transitory computer readable storage medium, which stores computer instructions for causing the computer to execute the industry carbon emission analysis method according to any of the above embodiments.

[0202] The computer readable medium of the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0203] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the industry carbon emission analysis method according to any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0204] Based on the same concept, the present application also provides a computer program product corresponding to the method of any of the above embodiments, comprising computer program instructions, when the computer program instructions run on a computer, make the computer execute the method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described here again.

[0205] It can be understood that, before using the technical solutions of various embodiments in the present disclosure, the type of personal information involved, the scope of use, the use scenario, etc. will be informed to the user in a proper manner, and the authorization of the user will be obtained.

[0206] For example, in response to receiving the active request of the user, prompt information is sent to the user to explicitly prompt the user that the operation requested to be executed will require the acquisition and use of personal information of the user. Thus, the user can choose whether to provide personal information to the software or hardware such as electronic devices, application programs, servers or storage media that execute the operation of the technical solutions of the present disclosure according to the prompt information.

[0207] As an optional but not limited implementation manner, in response to accepting the active request of the user, the manner of sending prompt information to the user may, for example, be a pop-up window manner, and the prompt information may be presented in the form of text in the pop-up window. In addition, the pop-up window may also carry selection controls for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0208] It can be understood that the above notification and user authorization process is only illustrative, and does not limit the implementation of the present disclosure, and other ways that meet the relevant laws and regulations can also be applied to the implementation of the present disclosure.

[0209] Those skilled in the art should understand that the above discussion of any of the embodiments is only exemplary and is not intended to imply that the scope of the present application is limited to these examples; under the idea of the present application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the embodiments of the present application as described above. In order to be brief, they are not provided in details.

[0210] Additionally, to simplify the description and discussion, and so as not to obscure the embodiments of the application being presented, the well-known functions or constructions of integrated circuit (IC) chips and other components can or can not be shown in the figures and will be omitted as not to unnecessarily obscure the embodiments of the application being presented. Moreover, the devices can be shown in block diagram form in order to avoid obscuring the embodiments of the application, and this also acknowledges the fact that the details in regard to the implementation of the block diagram devices are highly dependent on the platform within which the embodiments of the application are to be implemented (i.e., these details should be well within the purview of one of ordinary skill in the art). Where specific details are set forth in order to describe an illustrative embodiment of the application, it will be apparent to one of ordinary skill in the art that the embodiments of the application can be practiced without, or with variation of, these specific details. Thus, the description is to be considered as illustrative and not restrictive, and the scope of the application should be determined not with reference to the above description, but should be given to the appended claims.

[0211] While the application has been described in connection with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed.

[0212] Embodiments of the application are intended to cover all such alternatives, modifications and variations as falling within the scope of the broadest possible interpretation of the application as set forth in the appended claims. Accordingly, any and all such modifications, variations or equivalents that fall within the spirit and scope of the underlying principles should be intended to be embraced by the claims.

Claims

1. An industry carbon emission analysis method characterized by, The method comprises the following steps: determining an influence factor analysis hierarchical structure of the industry carbon emission and a correlation coefficient between the hierarchical influence factors and the industry carbon emission based on historical carbon emission data of the industry, wherein the influence factor analysis hierarchical structure comprises the industry carbon emission and a plurality of hierarchical influence factors; determining the weight of the plurality of hierarchical influence factors in calculating the industry carbon emission based on the influence factor analysis hierarchical structure and the correlation coefficient between the plurality of hierarchical influence factors and the industry carbon emission; determining the industry carbon emission based on the hierarchical influence factors and the corresponding weights.

2. The method of claim 1, wherein, The hierarchical influence factors comprise a plurality of middle-level influence factors and bottom-level influence factors, each middle-level influence factor corresponds to a plurality of bottom-level influence factors, and the industry carbon emission corresponds to a plurality of middle-level influence factors; The method for determining the influence factor analysis hierarchical structure of the industry carbon emission and the correlation coefficient between the hierarchical influence factors and the industry carbon emission based on historical carbon emission data of the industry comprises the following steps: determining a plurality of bottom-level influence factors and the correlation coefficient between the bottom-level influence factors and the industry carbon emission based on historical carbon emission data of the industry by using Pearson correlation analysis method; obtaining a plurality of middle-level influence factors by clustering the plurality of bottom-level influence factors by using clustering analysis method, each middle-level influence factor corresponds to a plurality of bottom-level influence factors; determining the correlation coefficient between the middle-level influence factors and the industry carbon emission based on the correlation coefficient between the plurality of bottom-level influence factors corresponding to the middle-level influence factors and the industry carbon emission; obtaining the influence factor analysis hierarchical structure of the industry carbon emission based on the middle-level influence factors and the bottom-level influence factors.

3. The method of claim 2, wherein, The method for determining a plurality of bottom-level influence factors and the correlation coefficient between the bottom-level influence factors and the industry carbon emission based on historical carbon emission data of the industry by using Pearson correlation analysis method comprises the following steps: calculating the correlation coefficient between a plurality of indexes and the industry carbon emission based on historical carbon emission data of the industry by using Pearson correlation analysis method; determining a plurality of bottom-level influence factors from the plurality of indexes based on the correlation coefficient between the plurality of indexes and the industry carbon emission; determining the correlation coefficient between the bottom-level influence factors and the industry carbon emission based on the correlation coefficient between the indexes corresponding to the bottom-level influence factors and the industry carbon emission.

4. The method of claim 1, wherein, The method for determining the weight of the plurality of hierarchical influence factors in calculating the industry carbon emission based on the influence factor analysis hierarchical structure and the correlation coefficient between the plurality of hierarchical influence factors and the industry carbon emission comprises the following steps: determining a judgment matrix based on the influence factor analysis hierarchical structure and the correlation coefficient between the plurality of hierarchical influence factors and the industry carbon emission; calculating the weight of the plurality of hierarchical influence factors in calculating the industry carbon emission based on the judgment matrix.

5. The method of claim 4, wherein, The method for calculating the weight of the plurality of hierarchical influence factors in calculating the industry carbon emission based on the judgment matrix comprises the following steps: calculating the product of each row element of the judgment matrix based on the judgment matrix; calculating a first vector based on the product of each row element; obtaining a feature vector by normalizing the first vector, wherein the feature vector is the weight of the plurality of hierarchical influence factors in calculating the industry carbon emission.

6. The method of claim 4, wherein, In the step of determining the weight of each of the plurality of hierarchical influence factors in calculating the carbon emission of the industry based on the hierarchical structure of the influence factor analysis and the correlation coefficient between each of the plurality of hierarchical influence factors and the carbon emission of the industry, the method further comprises: performing consistency check on the judgment matrix corresponding to each of the plurality of hierarchical influence factors based on the weight of each of the plurality of hierarchical influence factors in calculating the carbon emission of the industry; in response to determining that the judgment matrix passes the consistency check, storing the weight of each of the plurality of hierarchical influence factors in calculating the carbon emission of the industry; in response to determining that the judgment matrix fails to pass the consistency check, adjusting the judgment matrix to obtain an adjusted judgment matrix; generating the weight of each of the plurality of hierarchical influence factors in calculating the carbon emission of the industry based on the adjusted judgment matrix, and repeating the above steps until the adjusted judgment matrix passes the consistency check.

7. The method of claim 6, wherein, The step of performing consistency check on the judgment matrix corresponding to each of the plurality of hierarchical influence factors based on the weight of each of the plurality of hierarchical influence factors in calculating the carbon emission of the industry comprises: calculating the maximum eigenvalue of the judgment matrix based on the weight of each of the plurality of hierarchical influence factors in calculating the carbon emission of the industry; calculating a consistency check index based on the maximum eigenvalue; determining an average random consistency index based on the judgment matrix; determining whether the judgment matrix passes the consistency check based on the consistency check index and the average random consistency index.

8. The method of claim 6, wherein, The step of adjusting the judgment matrix to obtain an adjusted judgment matrix comprises: determining the offset degree of each element in the judgment matrix to obtain an offset degree set; determining a target offset degree in the offset degree set, and determining a target element in the judgment matrix corresponding to the target offset degree based on the target offset degree; determining an adjusted target element based on the target element and the judgment matrix; replacing the target element with the adjusted target element to obtain the adjusted judgment matrix.

9. The method of claim 4, wherein, The step of determining the judgment matrix based on the hierarchical structure of the influence factor analysis and the correlation coefficient between each of the plurality of hierarchical influence factors and the carbon emission of the industry comprises: determining a plurality of target hierarchical influence factors based on the hierarchical structure of the influence factor analysis; calculating a correlation parameter between any two of the target hierarchical influence factors based on the correlation coefficient between each of the target hierarchical influence factors and the carbon emission of the industry; obtaining the judgment matrix by taking the correlation parameter as an element of the judgment matrix.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor implements the method of any one of claims 1 to 9 when executing the program.

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