Data analysis method and system based on dual-carbon target and storage medium

Abstract

The invention provides a data analysis method and system based on a dual-carbon target and a storage medium, and the optimization method comprises the steps: obtaining a traffic mode of a current city, analyzing a city traffic travel mode and a corresponding energy type according to the traffic mode, and constructing a carbon emission calculation model based on the energy type corresponding to the travel mode; identifying a plurality of influence factors of carbon emission in urban traffic according to the carbon emission calculation model, performing sensitivity analysis on the plurality of influence factors, and determining key influence factors; constructing a carbon emission prediction simulation model based on the determined key influence factors, and predicting the current urban traffic carbon emission trend and the urban traffic carbon peak reaching and carbon neutralization time according to the carbon emission prediction simulation model; the traffic structure of the current city is optimized and adjusted on the basis of the predicted carbon peak reaching time and the predicted carbon neutralization time, and the data analysis method based on the dual-carbon target is comprehensive in analysis and high in pertinence.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and specifically to a data analysis method, system and storage medium based on dual carbon goals. Background Art

[0002] With the accelerated expansion of urbanization and the rapid development of the economy, residents' personalized travel needs are also increasing. The continuous growth of the number of motor vehicles and the large amount of exhaust gas emitted by vehicles during use have made the transportation industry a major contributor to urban carbon emissions. Different modes of transportation have large differences in carbon emissions, and the optimization of travel structure has become an important factor affecting transportation carbon emissions. At present, most of the analysis and research on urban dual carbon data focuses on population, wealth, and environment, focusing on the individual contribution and impact of each factor. However, the carbon emission path is affected by multiple factors. Only considering the individual impact of factors is difficult to provide an accurate reference for urban transportation emission reduction management, which affects the accuracy of carbon emission analysis. Summary of the invention

[0003] Based on this, the purpose of the present invention is to provide a data analysis method, system, and storage medium based on the dual carbon goals to solve the technical problems existing in the prior art.

[0004] The present invention proposes a data analysis method based on the dual carbon goals, including: Obtain the current traffic mode of the city, analyze the city's traffic travel mode and the corresponding energy type according to the traffic mode, and build a carbon emission calculation model based on the energy type corresponding to the travel mode; Identify several influencing factors of carbon emissions in urban transportation according to the carbon emissions calculation model, conduct sensitivity analysis on several of the influencing factors, and determine key influencing factors; Based on the determined key influencing factors, a carbon emission prediction simulation model is constructed, and the carbon emission trend of current urban transportation in the next few years and the time when urban transportation carbon emissions peak and carbon neutrality are reached are predicted according to the carbon emission prediction simulation model; Optimize and adjust the current urban transportation structure based on the predicted carbon peak and carbon neutrality time.

[0005] Preferably, the expression of the carbon emission calculation model is:

[0006] In the formula, For the Carbon emissions from burning energy For the The amount of energy consumed, For the The average low calorific value of this energy source is For the Carbon content per unit calorific value of energy, For the The carbon oxidation rate of the energy combustion, For the The energy conversion coefficient of standard coal. Preferably, the step of identifying several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, performing sensitivity analysis on several of the influencing factors, and determining key influencing factors comprises: Obtain the original data of several influencing factors, standardize the original data, and establish a factor standardized correlation matrix based on the standardized data; Obtaining several eigenvalues ​​and corresponding eigenvectors of the factor standardized correlation matrix, taking variables with eigenvalues ​​greater than a preset value as analysis variables, and constructing a load coefficient matrix according to the weight coefficients of each analysis variable to each influencing factor; Establishing a corresponding relationship expression between the analysis variable and the plurality of influencing factors according to the load coefficient matrix, and obtaining the influence score of the analysis variable in the plurality of influencing factors according to the corresponding relationship expression and the original data of the plurality of influencing factors; The impact weights of several influencing factors on carbon emissions are obtained based on the impact scores, and sensitivity analysis is performed on the several influencing factors according to the impact weights to determine the key influencing factors.

[0007] Preferably, the step of performing sensitivity analysis on the plurality of influencing factors according to the influencing weights to determine key influencing factors comprises: Construct a carbon emission analysis model based on several influencing factors and corresponding impact weights; According to the carbon emission analysis model, based on the incremental decomposition method, quantify the sensitive effect of each influencing factor on carbon emissions; The influence of various influencing factors on urban transportation carbon emissions is analyzed based on the quantitative sensitive effects, and the key influencing factors are determined according to the influence size.

[0008] Preferably, the carbon emission analysis model is expressed as:

[0009] In the formula, For the Carbon emissions from various modes of travel, For the The influence weight of each influencing factor is For the The first mode of travel The impact of each factor on carbon emissions is is the number of influencing factors; The expression of the incremental decomposition method is:

[0010] In the formula, For the The quantitative sensitivity of the influencing factors is For the The weight of each travel mode in urban transportation travel modes, To measure time, For the Within the measurement time The first mode of travel The impact of each factor on carbon emissions is For the Within the measurement time The first mode of travel The impact of each influencing factor on carbon emissions; ; In the formula, For the Within the measurement time Carbon emissions from various modes of travel, For the Within the measurement time Carbon emissions from different modes of travel.

[0011] Preferably, the step of constructing a carbon emission prediction simulation model based on the determined key influencing factors includes: Taking the current year as the base year, obtain the original carbon emission data for several years before the base year; Building an original carbon emission prediction simulation model based on the determined key influencing factors, performing a root test on the time series of the original carbon emission prediction simulation model, and determining whether the time series values ​​of each key influencing factor are all less than a preset time series value; If they are all less than the time series value, it means that the time series values ​​of each key influencing factor have reached a stable state; if the time series value of a key influencing factor is greater than the preset time series value, the time series value is sequentially subjected to multi-order differences and then root tests and judgments are performed until it is less than the preset time series value; Regression processing is performed on the carbon emission prediction simulation model whose time series values ​​have reached a stable state to determine the standardized coefficient and key value corresponding to each key influencing factor, wherein the standardized coefficient is used to reflect the impact of the key influencing factor on carbon emissions, and the key value is used to reflect the impact of the key influencing factor on the change of carbon emissions; Predicting carbon emission forecast data for several years before the base year based on the carbon emission forecast simulation model after regression processing, and comparing the carbon emission forecast data with the original carbon emission data; If the average error rate of the comparison results is less than the preset error rate, it is determined that the carbon emission prediction simulation model after regression processing meets the requirements and is used as the target carbon emission prediction simulation model.

[0012] Preferably, the step of performing regression processing on the carbon emission prediction simulation model whose time series values ​​have all reached a stable state comprises: Constructing an independent variable sample matrix based on each key influencing factor, constructing a dependent variable sample matrix based on the predicted value of the carbon emission prediction simulation model, and standardizing the independent variable sample matrix and the dependent variable sample matrix; Extracting the principal components from the independent variable sample matrix, and establishing regression equations of the independent variable sample matrix and the dependent variable sample matrix on the principal components; Based on the cross-validation method, the appropriate number of components in the regression equation is extracted to obtain a target regression equation of energy consumption per ton of standard coal for the main component that meets the accuracy requirements, wherein the cross-validation reflects the prediction ability of the main component for energy consumption per ton of standard coal; According to the inverse standard process, the target regression equation is reduced to the regression equation of the carbon emission prediction simulation model for each key influencing factor to complete the regression processing of the carbon emission prediction simulation model.

[0013] The present invention also proposes a data analysis system based on the dual carbon goals, including: A construction module is used to obtain the current traffic mode of the city, analyze the city's traffic travel mode and the corresponding energy type according to the traffic mode, and construct a carbon emission calculation model based on the energy type corresponding to the travel mode; An analysis module, used to identify several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, perform sensitivity analysis on several of the influencing factors, and determine key influencing factors; A prediction module is used to construct a carbon emission prediction simulation model based on the determined key influencing factors, and predict the carbon emission trend of current urban transportation in the next few years and the time when urban transportation carbon emissions will peak and reach carbon neutrality according to the carbon emission prediction simulation model; The adjustment module is used to optimize the current city’s traffic structure based on the predicted carbon peak and carbon neutrality time.

[0014] The present invention also proposes a storage medium on which a computer program is stored, and when the program is executed by a processor, the data analysis method based on the dual carbon goals is implemented.

[0015] The present invention also proposes a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data analysis method based on the dual carbon targets when executing the computer program.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the data analysis method based on the dual carbon targets provided by the present application first obtains the current city's traffic pattern and constructs a carbon emission calculation model, identifies several influencing factors of carbon emissions in urban traffic according to the carbon emission calculation model, performs sensitivity analysis on several of the influencing factors, and determines the key influencing factors; after determining the key influencing factors and then analyzing them, the amount of analysis of subsequent data can be reduced, a carbon emission prediction simulation model can be constructed, and the carbon emission trend of the current urban traffic in the next few years and the time of carbon peak and carbon neutrality of urban traffic can be predicted according to the carbon emission prediction simulation model; the current city's traffic structure is optimized and adjusted based on the predicted carbon peak and carbon neutrality time. The present application identifies multiple carbon emission influencing factors through a carbon emission calculation model, constructs a carbon emission prediction simulation model after determining the key influencing factors, comprehensively considers the influence of multiple factors, improves the accuracy of data analysis, and is suitable for large-scale promotion.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a data analysis method based on the dual carbon goals in the first embodiment of the present invention; Figure 2 FIG. 4 is a structural block diagram of a computer in a fourth embodiment of the present invention.

[0019] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0020] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] Embodiment 1 See also Figure 1 , shown is a data analysis method based on dual carbon targets in the first embodiment of the present invention, which is used to analyze carbon emission data in the field of urban transportation, and optimize and adjust the transportation structure based on the carbon emission data analysis. Specifically, the data analysis method based on dual carbon targets includes steps S10 to S40: S10, obtaining the current traffic mode of the city, analyzing the city's traffic travel mode and the corresponding energy type according to the traffic mode, and building a carbon emission calculation model based on the energy type corresponding to the travel mode; Optionally, the urban transportation system includes the urban passenger transportation system and the freight transportation system. The urban transportation system is the main carrier to assist residents and goods in spatial conversion, and plays a vital role in the development of the city. Different urban passenger transportation modes have obvious differences in functions and target groups. Urban passenger transportation modes include private cars, taxis, buses, rail transit, urban rapid transit, etc.; freight transportation modes include vans, etc.; energy driving modes of transportation vehicles include non-renewable fossil energy such as gasoline, diesel, kerosene, natural gas, and also renewable energy such as electricity; in Chinese cities, passenger transportation has gradually occupied a dominant position in urban transportation; data such as the city's passenger transportation mode and the overall energy structure of the transportation industry can be obtained through the city's transportation development report, statistical yearbook, statistical bulletin and other materials; the characteristics of various transportation modes and energy consumption can also be obtained through databases such as the China Energy Statistical Yearbook, and are not limited here.

[0023] Optionally, the expression of the carbon emission calculation model is:

[0024] In the formula, For the Carbon emissions from burning energy For the The amount of energy consumed, For the The average low calorific value of this energy source is For the Carbon content per unit calorific value of energy, For the The carbon oxidation rate of the energy combustion, For the The energy conversion coefficient of standard coal. It is understandable that the above formula mainly considers the direct carbon emissions from the combustion of fossil energy; if the carbon emission impact of new energy such as electricity is considered, the expression of the carbon emission calculation model can be:

[0025] In the formula, Indirect carbon emissions from electricity, is the amount of electricity used, is the grid emission factor, is the electricity standard coal conversion coefficient, and other parameters are the same as the above formula. S20, identifying several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, performing sensitivity analysis on several of the influencing factors, and determining key influencing factors; Optionally, by studying and analyzing the factors affecting carbon emissions in urban transportation, targeted emission reduction measures can be specified, thereby effectively improving the efficiency of travel structure adjustment; optionally, the factors affecting urban transportation carbon emissions mainly include economic development level, energy type, travel intensity, infrastructure construction, urban population, green coverage rate, transportation structure, etc.; it can be understood that the level of economic development can include factors such as per capita GDP and urbanization level; infrastructure construction can include factors such as per capita road area and facility investment; the transportation structure can include the share rate of each transportation mode, car ownership, and car energy supply type.

[0026] Optionally, in this embodiment, the step of identifying several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, performing sensitivity analysis on the several influencing factors, and determining key influencing factors includes: Obtain the original data of several influencing factors, standardize the original data, and establish a factor standardized correlation matrix based on the standardized data; Obtaining several eigenvalues ​​and corresponding eigenvectors of the factor standardized correlation matrix, taking variables with eigenvalues ​​greater than a preset value as analysis variables, and constructing a load coefficient matrix according to the weight coefficients of each analysis variable to each influencing factor; Establishing a corresponding relationship expression between the analysis variable and the plurality of influencing factors according to the load coefficient matrix, and obtaining the influence score of the analysis variable in the plurality of influencing factors according to the corresponding relationship expression and the original data of the plurality of influencing factors; The impact weights of several influencing factors on carbon emissions are obtained based on the impact scores, and sensitivity analysis is performed on the several influencing factors according to the impact weights to determine the key influencing factors.

[0027] Optionally, the carbon emissions of urban transportation systems are composed of multiple factors, and there may be certain correlations between the factors. Therefore, when analyzing carbon emissions data, it is necessary to comprehensively consider the influence of multiple factors to improve the accuracy of data analysis; in this embodiment, the same group of variables with possible correlations can be converted into a group of uncorrelated analysis variables with fewer elements through orthogonal transformation, and a large number of correlated initial variables can be replaced by a group of fewer uncorrelated analysis variables. While retaining the initial variable information as much as possible, the complexity of the analysis is reduced and the analysis efficiency is improved; further, before the analysis, the acquired raw data is first standardized so that the dimensions and index levels of each raw data are consistent, and then a factor standardized correlation matrix is ​​established; several eigenvalues ​​and corresponding eigenvectors of the factor standardized correlation matrix are calculated, and variables with eigenvalues ​​greater than preset values ​​are used as analysis variables. Optionally, in this embodiment, the preset value can be 1.

[0028] Furthermore, the contribution rate and cumulative contribution rate of each analysis variable are calculated according to the eigenvalue, and the load coefficient of each analysis variable is obtained according to the eigenvalue and eigenvector; a load coefficient matrix is ​​constructed according to the weight coefficient of each analysis variable to each influencing factor; and then the corresponding relationship expression between the analysis variable and several influencing factors is established.

[0029] Optionally, in this embodiment, the expression for calculating the contribution rate is:

[0030] In the formula, For the The contribution rate of the analyzed variables, For the A feature value greater than a preset value, For the eigenvalues, is the number of eigenvalues.

[0031] Optionally, in this embodiment, the step of performing sensitivity analysis on the plurality of influencing factors according to the influencing weights to determine key influencing factors includes: Construct a carbon emission analysis model based on several influencing factors and corresponding impact weights; According to the carbon emission analysis model, based on the incremental decomposition method, quantify the sensitive effect of each influencing factor on carbon emissions; The influence of various influencing factors on urban transportation carbon emissions is analyzed based on the quantitative sensitive effects, and the key influencing factors are determined according to the influence size.

[0032] The expression of the carbon emission analysis model is:

[0033] In the formula, For the Carbon emissions from various modes of travel, For the The influence weight of each influencing factor is For the The first mode of travel The impact of each factor on carbon emissions is is the number of influencing factors; The expression of the incremental decomposition method is:

[0034] In the formula, For the The quantitative sensitivity of the influencing factors is For the The weight of each travel mode in urban transportation travel modes, To measure time, For the Within the measurement time The first mode of travel The impact of each factor on carbon emissions is For the Within the measurement time The first mode of travel The impact of each influencing factor on carbon emissions; ; In the formula, For the Within the measurement time Carbon emissions from various modes of travel, For the Within the measurement time Carbon emissions from different modes of travel.

[0035] In this embodiment, the effect of each influencing factor on urban traffic can be obtained through the above calculation process, and the key influencing factors among several influencing factors can be obtained by sorting the influencing effects; optionally, in this embodiment, the first five influencing factors can be selected as key influencing factors.

[0036] S30, based on the determined key influencing factors, construct a carbon emission prediction simulation model, and predict the carbon emission trend of the current urban transportation in the next few years and the time for the urban transportation to reach carbon peak and carbon neutrality according to the carbon emission prediction simulation model; The steps of constructing a carbon emission prediction simulation model based on the determined key influencing factors include: Taking the current year as the base year, obtain the original carbon emission data for several years before the base year; Building an original carbon emission prediction simulation model based on the determined key influencing factors, performing a root test on the time series of the original carbon emission prediction simulation model, and determining whether the time series values ​​of each key influencing factor are all less than a preset time series value; If they are all less than the time series value, it means that the time series values ​​of each key influencing factor have reached a stable state; if the time series value of a key influencing factor is greater than the preset time series value, the time series value is sequentially subjected to multi-order differences and then root tests and judgments are performed until it is less than the preset time series value; Regression processing is performed on the carbon emission prediction simulation model whose time series values ​​have reached a stable state to determine the standardized coefficient and key value corresponding to each key influencing factor, wherein the standardized coefficient is used to reflect the impact of the key influencing factor on carbon emissions, and the key value is used to reflect the impact of the key influencing factor on the change of carbon emissions; Predicting carbon emission forecast data for several years before the base year based on the carbon emission forecast simulation model after regression processing, and comparing the carbon emission forecast data with the original carbon emission data; If the average error rate of the comparison results is less than the preset error rate, it is determined that the carbon emission prediction simulation model after regression processing meets the requirements and is used as the target carbon emission prediction simulation model.

[0037] Optionally, the present application can use a random environmental impact assessment model as the original carbon emission prediction simulation model; the environmental impact assessment model can be expanded according to the number of key factors and has strong applicability; the application of the expandable environmental impact assessment model needs to be based on a stable time series. If the time series is unstable, it may cause regression of parameters that do not have a correlation relationship, affecting the results. Therefore, before analysis, the original model needs to be root-tested, and the time series values ​​of each key factor obtained are compared with the preset time series values; if they are all less than the preset time series values, a stable state is reached. If there is a time series value greater than the preset time series value, the time series value is subjected to first-order difference processing and then root testing and judgment is performed; if it is still not satisfied, a second-order difference processing is performed, and so on, until it is less than the preset time series value.

[0038] Optionally, in this embodiment, the step of performing a root test on the time series of the original carbon emission prediction simulation model includes: Obtaining the likelihood function of the original carbon emission prediction simulation model, and constructing a conventional Bayesian model and a structural point mutation Bayesian model based on the Bayesian theorem and the likelihood function; respectively obtaining a first priori probability and a first posterior probability of the conventional Bayesian model and a second priori probability and a second posterior probability of the structural point mutation Bayesian model, Obtaining a Bayesian factor between the conventional Bayesian model and the structural point mutation Bayesian model based on the first prior probability, the first posterior probability, the second prior probability, and the second posterior probability, and determining a structural mutation point of the time series in the original carbon emission prediction simulation model based on the value of the Bayesian factor; Based on the credible interval method, a root test is performed on the time series of the carbon emission prediction simulation model with determined structural mutation points to determine whether it has a unit root.

[0039] Optionally, in this embodiment, when a conventional time series unit root test method is used for root test, since the model may contain a time series with structural mutation points, the conventional root test method is likely to misjudge stationarity, and the test efficiency is low, resulting in the problem of "pseudo-testing". This embodiment is based on Bayesian theoretical research, first detects the position of the structural mutation point in the time series, and then uses a credible interval method to test the time series when the position of the structural mutation point is known to determine whether it has a unit root, thereby effectively reducing the frequency of misjudgment and improving the efficiency of root testing.

[0040] Furthermore, the step of performing regression processing on the carbon emission prediction simulation model whose time series values ​​have all reached a stable state includes: Constructing an independent variable sample matrix based on each key influencing factor, constructing a dependent variable sample matrix based on the predicted value of the carbon emission prediction simulation model, and standardizing the independent variable sample matrix and the dependent variable sample matrix; Extracting the principal components from the independent variable sample matrix, and establishing regression equations of the independent variable sample matrix and the dependent variable sample matrix on the principal components; Based on the cross-validation method, the appropriate number of components in the regression equation is extracted to obtain a target regression equation of energy consumption per ton of standard coal for the main component that meets the accuracy requirements, wherein the cross-validation reflects the prediction ability of the main component for energy consumption per ton of standard coal; According to the inverse standard process, the target regression equation is reduced to the regression equation of the carbon emission prediction simulation model for each key influencing factor to complete the regression processing of the carbon emission prediction simulation model.

[0041] Through the above regression processing, the collinearity problem that occurs in the regression process of the environmental impact assessment model can be effectively solved, and the errors of the standardized coefficients and key values ​​of each key influencing factor can be reduced; because the errors of the standardized coefficients and key values ​​are too large, the prediction results will be biased due to slight changes in the data, resulting in the environmental impact assessment model being unable to accurately predict the carbon emission results.

[0042] In some optional embodiments, regression processing can also be performed by stepwise regression method. After the stepwise regression processing, the standardized coefficient and key value errors obtained are small, and the prediction effect of the carbon emission prediction simulation model is accurate; optionally, the step of performing regression processing on the carbon emission prediction simulation model whose time series values ​​have reached a stable state includes: Performing univariate linear regression on several key influencing factors in the carbon emission prediction simulation model to obtain the regression square sum corresponding to the univariate linear regression of several key influencing factors; Obtain the reference key influencing factor corresponding to the maximum value of the regression square sum, and based on the reference key influencing factor, add other key influencing factors in batches to perform stepwise regression analysis, so that the regression square sum of all key influencing factors after linear regression is greater than the preset regression square sum; The corresponding target residual sum of squares is calculated based on the target regression sum of squares after regression of several key influencing factors, and the target regression sum of squares is used as the standardized coefficient corresponding to the key influencing factors, and the target residual sum of squares is used as the key value corresponding to the key influencing factors.

[0043] S40, optimize and adjust the current urban transportation structure based on the predicted carbon peak and carbon neutrality time.

[0044] Optionally, in this embodiment, meeting the travel needs of residents is the primary goal, and the optimization and adjustment of the transportation structure comprehensively considers factors such as urban population, economic development, and technological breakthroughs, adjusts the travel proportions of various modes of transportation, optimizes the structure of energy, and optimizes the structure of transportation, maximizes transportation efficiency, considers transportation energy intensity, industrial service capabilities, total turnover of the transportation industry, transportation employment population, urbanization level, etc., and increases the proportion of travel by new energy transportation and public transportation.

[0045] In summary, the data analysis method based on the dual carbon goals provided by this application first obtains the current city's traffic pattern and constructs a carbon emission calculation model. According to the carbon emission calculation model, several influencing factors of carbon emissions in urban transportation are identified, and sensitivity analysis is performed on several of the influencing factors to determine the key influencing factors; after determining the key influencing factors and conducting analysis, the amount of analysis of subsequent data can be reduced, and a carbon emission prediction simulation model can be constructed. According to the carbon emission prediction simulation model, the carbon emission trend of the current urban transportation in the next few years and the time of carbon peak and carbon neutrality of urban transportation are predicted; based on the predicted carbon peak and carbon neutrality time, the current city's traffic structure is optimized and adjusted. This application identifies a variety of carbon emission influencing factors through a carbon emission calculation model, and constructs a carbon emission prediction simulation model after determining the key influencing factors. The impact of multiple factors is comprehensively considered to improve the accuracy of data analysis, which is suitable for large-scale promotion.

[0046] Embodiment 2 This embodiment provides a data analysis system based on the dual carbon goals, including: A construction module is used to obtain the current traffic mode of the city, analyze the city's traffic travel mode and the corresponding energy type according to the traffic mode, and construct a carbon emission calculation model based on the energy type corresponding to the travel mode; An analysis module, used to identify several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, perform sensitivity analysis on several of the influencing factors, and determine key influencing factors; A prediction module is used to construct a carbon emission prediction simulation model based on the determined key influencing factors, and predict the carbon emission trend of current urban transportation in the next few years and the time when urban transportation carbon emissions will peak and reach carbon neutrality according to the carbon emission prediction simulation model; The adjustment module is used to optimize the current city’s traffic structure based on the predicted carbon peak and carbon neutrality time.

[0047] Among them, the expression of the carbon emission calculation model is:

[0048] In the formula, For the Carbon emissions from burning energy For the The amount of energy consumed, For the The average low calorific value of this energy source is For the Carbon content per unit calorific value of energy, For the The carbon oxidation rate of the energy combustion, For the The energy conversion coefficient of standard coal. The analysis module is specifically used for: Obtain the original data of several influencing factors, standardize the original data, and establish a factor standardized correlation matrix based on the standardized data; Obtaining several eigenvalues ​​and corresponding eigenvectors of the factor standardized correlation matrix, taking variables with eigenvalues ​​greater than a preset value as analysis variables, and constructing a load coefficient matrix according to the weight coefficients of each analysis variable to each influencing factor; Establishing a corresponding relationship expression between the analysis variable and the plurality of influencing factors according to the load coefficient matrix, and obtaining the influence score of the analysis variable in the plurality of influencing factors according to the corresponding relationship expression and the original data of the plurality of influencing factors; The impact weights of several influencing factors on carbon emissions are obtained based on the impact scores, and sensitivity analysis is performed on the several influencing factors according to the impact weights to determine the key influencing factors.

[0049] The step of performing sensitivity analysis on the influencing factors according to the influencing weights to determine the key influencing factors comprises: Construct a carbon emission analysis model based on several influencing factors and corresponding impact weights; According to the carbon emission analysis model, based on the incremental decomposition method, quantify the sensitive effect of each influencing factor on carbon emissions; The influence of various influencing factors on urban transportation carbon emissions is analyzed based on the quantitative sensitive effects, and the key influencing factors are determined according to the influence size.

[0050] The expression of the carbon emission analysis model is:

[0051] In the formula, For the Carbon emissions from various modes of travel, For the The influence weight of each influencing factor is For the The first mode of travel The impact of each factor on carbon emissions is is the number of influencing factors; The expression of the incremental decomposition method is:

[0052] In the formula, For the The quantitative sensitivity of the influencing factors is For the The weight of each travel mode in urban transportation travel modes, To measure time, For the Within the measurement time The first mode of travel The impact of each factor on carbon emissions is For the Within the measurement time The first mode of travel The impact of each influencing factor on carbon emissions; ; In the formula, For the Within the measurement time Carbon emissions from various modes of travel, For the Within the measurement time Carbon emissions from different modes of travel.

[0053] The prediction module is specifically used for: Taking the current year as the base year, obtain the original carbon emission data for several years before the base year; Building an original carbon emission prediction simulation model based on the determined key influencing factors, performing a root test on the time series of the original carbon emission prediction simulation model, and determining whether the time series values ​​of each key influencing factor are all less than a preset time series value; If they are all less than the time series value, it means that the time series values ​​of each key influencing factor have reached a stable state; if the time series value of a key influencing factor is greater than the preset time series value, the time series value is sequentially subjected to multi-order differences and then root tests and judgments are performed until it is less than the preset time series value; Regression processing is performed on the carbon emission prediction simulation model whose time series values ​​have reached a stable state to determine the standardized coefficient and key value corresponding to each key influencing factor, wherein the standardized coefficient is used to reflect the impact of the key influencing factor on carbon emissions, and the key value is used to reflect the impact of the key influencing factor on the change of carbon emissions; Predicting carbon emission forecast data for several years before the base year based on the carbon emission forecast simulation model after regression processing, and comparing the carbon emission forecast data with the original carbon emission data; If the average error rate of the comparison results is less than the preset error rate, it is determined that the carbon emission prediction simulation model after regression processing meets the requirements and is used as the target carbon emission prediction simulation model.

[0054] The step of performing regression processing on the carbon emission prediction simulation model whose time series values ​​have all reached a stable state comprises: Constructing an independent variable sample matrix based on each key influencing factor, constructing a dependent variable sample matrix based on the predicted value of the carbon emission prediction simulation model, and standardizing the independent variable sample matrix and the dependent variable sample matrix; Extracting the principal components from the independent variable sample matrix, and establishing regression equations of the independent variable sample matrix and the dependent variable sample matrix on the principal components; Based on the cross-validation method, the appropriate number of components in the regression equation is extracted to obtain a target regression equation of energy consumption per ton of standard coal for the main component that meets the accuracy requirements, wherein the cross-validation reflects the prediction ability of the main component for energy consumption per ton of standard coal; According to the inverse standard process, the target regression equation is reduced to the regression equation of the carbon emission prediction simulation model for each key influencing factor to complete the regression processing of the carbon emission prediction simulation model.

[0055] Embodiment 3 This embodiment proposes a storage medium on which a computer program is stored. When the program is executed by a processor, the data analysis method based on the dual carbon goals as described above is implemented.

[0056] Embodiment 4 The present invention also provides a computer, see Figure 2 , shown is a computer in an embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned data analysis method based on the dual carbon goals is implemented.

[0057] The memory 10 includes at least one type of storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as a hard disk of the computer. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer, but also to temporarily store data that has been output or is to be output.

[0058] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0059] It should be pointed out that Figure 2 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0060] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0061] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0062] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0063] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A data analysis method based on the dual carbon goals, characterized in that: include: Obtain the current traffic mode of the city, analyze the city's traffic travel mode and the corresponding energy type according to the traffic mode, and build a carbon emission calculation model based on the energy type corresponding to the travel mode; Identify several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, conduct sensitivity analysis on several of the influencing factors, and determine key influencing factors; Based on the determined key influencing factors, a carbon emission prediction simulation model is constructed, and the carbon emission trend of current urban transportation in the next few years and the time for carbon peak and carbon neutrality of urban transportation are predicted according to the carbon emission prediction simulation model; Optimize and adjust the current urban transportation structure based on the predicted carbon peak and carbon neutrality time.

2. The data analysis method based on the dual carbon goals according to claim 1 is characterized in that: The expression of the carbon emission calculation model is: In the formula, For the Carbon emissions from burning energy For the The amount of energy consumed, For the The average low calorific value of this energy source is For the Carbon content per unit calorific value of energy, For the The carbon oxidation rate of the energy combustion, For the The energy conversion coefficient of standard coal.

3. The data analysis method based on the dual carbon goals according to claim 1 is characterized in that: The steps of identifying several influencing factors of carbon emissions in urban transportation according to the carbon emissions calculation model, performing sensitivity analysis on several of the influencing factors, and determining key influencing factors include: Obtain the original data of several influencing factors, standardize the original data, and establish a factor standardized correlation matrix based on the standardized data; Obtaining several eigenvalues ​​and corresponding eigenvectors of the factor standardized correlation matrix, taking variables with eigenvalues ​​greater than a preset value as analysis variables, and constructing a load coefficient matrix according to the weight coefficients of each analysis variable to each influencing factor; Establishing a corresponding relationship expression between the analysis variable and the plurality of influencing factors according to the load coefficient matrix, and obtaining the influence score of the analysis variable in the plurality of influencing factors according to the corresponding relationship expression and the original data of the plurality of influencing factors; The impact weights of several influencing factors on carbon emissions are obtained based on the impact scores, and sensitivity analysis is performed on the several influencing factors according to the impact weights to determine the key influencing factors.

4. The data analysis method based on the dual carbon goals according to claim 3 is characterized in that: The step of performing sensitivity analysis on the influencing factors according to the influencing weights to determine the key influencing factors comprises: Construct a carbon emission analysis model based on several influencing factors and corresponding impact weights; According to the carbon emission analysis model, based on the incremental decomposition method, quantify the sensitive effect of each influencing factor on carbon emissions; The influence of various influencing factors on urban transportation carbon emissions is analyzed based on the quantitative sensitive effects, and the key influencing factors are determined according to the influence size.

5. The data analysis method based on the dual carbon goals according to claim 4 is characterized in that: The expression of the carbon emission analysis model is: In the formula, For the Carbon emissions from various modes of travel, For the The influence weight of each influencing factor is For the The first mode of travel The impact of each factor on carbon emissions is is the number of influencing factors; The expression of the incremental decomposition method is: In the formula, For the The quantitative sensitivity of the influencing factors is For the The weight of each travel mode in urban transportation travel modes, To measure time, For the Within the measurement time The first mode of travel The impact of each factor on carbon emissions is For the Within the measurement time The first mode of travel The impact of each influencing factor on carbon emissions; ； In the formula, For the Within the measurement time Carbon emissions from various modes of travel, For the Within the measurement time Carbon emissions from different modes of travel.

6. The data analysis method based on the dual carbon goals according to claim 1 is characterized in that: The steps of constructing a carbon emission prediction simulation model based on the determined key influencing factors include: Taking the current year as the base year, obtain the original carbon emission data for several years before the base year; Building an original carbon emission prediction simulation model based on the determined key influencing factors, performing a root test on the time series of the original carbon emission prediction simulation model, and determining whether the time series values ​​of each key influencing factor are all less than a preset time series value; If they are all less than the time series value, it means that the time series values ​​of each key influencing factor have reached a stable state; if the time series value of a key influencing factor is greater than the preset time series value, the time series value is sequentially subjected to multi-order differences and then root tests and judgments are performed until it is less than the preset time series value; Regression processing is performed on the carbon emission prediction simulation model whose time series values ​​have reached a stable state to determine the standardized coefficient and key value corresponding to each key influencing factor, wherein the standardized coefficient is used to reflect the impact of the key influencing factor on carbon emissions, and the key value is used to reflect the impact of the key influencing factor on the change of carbon emissions; Predicting carbon emission forecast data for several years before the base year based on the carbon emission forecast simulation model after regression processing, and comparing the carbon emission forecast data with the original carbon emission data; If the average error rate of the comparison results is less than the preset error rate, it is determined that the carbon emission prediction simulation model after regression processing meets the requirements and is used as the target carbon emission prediction simulation model.

7. The data analysis method based on the dual carbon goals according to claim 6 is characterized in that: The step of performing regression processing on the carbon emission prediction simulation model whose time series values ​​have all reached a stable state comprises: Constructing an independent variable sample matrix based on each key influencing factor, constructing a dependent variable sample matrix based on the predicted value of the carbon emission prediction simulation model, and standardizing the independent variable sample matrix and the dependent variable sample matrix; Extracting the principal components from the independent variable sample matrix, and establishing regression equations of the independent variable sample matrix and the dependent variable sample matrix on the principal components; Based on the cross-validation method, the appropriate number of components in the regression equation is extracted to obtain a target regression equation of energy consumption per ton of standard coal for the main component that meets the accuracy requirements, wherein the cross-validation reflects the prediction ability of the main component for energy consumption per ton of standard coal; According to the inverse standard process, the target regression equation is reduced to the regression equation of the carbon emission prediction simulation model for each key influencing factor to complete the regression processing of the carbon emission prediction simulation model.

8. A data analysis system based on the dual carbon goals, characterized in that: include; A construction module is used to obtain the current traffic mode of the city, analyze the city's traffic travel mode and the corresponding energy type according to the traffic mode, and construct a carbon emission calculation model based on the energy type corresponding to the travel mode; An analysis module, used to identify several influencing factors of carbon emissions in urban transportation according to the carbon emission calculation model, perform sensitivity analysis on several of the influencing factors, and determine key influencing factors; A prediction module is used to construct a carbon emission prediction simulation model based on the determined key influencing factors, and predict the carbon emission trend of current urban transportation in the next few years and the time when urban transportation carbon emissions will peak and reach carbon neutrality according to the carbon emission prediction simulation model; The adjustment module is used to optimize the current city’s traffic structure based on the predicted carbon peak and carbon neutrality time.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data analysis method based on the dual carbon goals as described in any one of claims 1 to 7 is implemented.

10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data analysis method based on the dual carbon goals is implemented as described in any one of claims 1 to 7.

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