Driving factor analysis method and device based on LMDI and variational mode decomposition

Abstract

The invention provides a driving factor analysis method based on LMDI and variational mode decomposition, and the method comprises the steps: obtaining macroscopic data, which comprises a total production value, a total energy consumption amount, a three-production added value proportion, a three-production total energy consumption amount, and a three-production specific energy variety consumption amount; determining contribution values of macroscopic driving factors to consumption increase of a specific energy variety by using a logarithmic mean Dihell index (LMDI) method in combination with the macroscopic data, the macroscopic driving factors including economic development, industrial structure adjustment, technical energy consumption progress and fuel substitution; based on a variational mode decomposition method, the contribution value of each macroscopic driving factor to the consumption increase of the specific energy variety is decomposed into a long-term trend, medium-period fluctuation, short-period fluctuation and high-frequency disturbance, and similar frequency components of the macroscopic driving factors are combined; and reconstructing the long-term trend, the medium-period fluctuation, the short-period fluctuation and the high-frequency disturbance in the total growth. By the adoption of the scheme, driving factor analysis of energy consumption increase is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of decomposition of driving factors of energy consumption, and in particular to a driving factor analysis method and device based on LMDI and variational mode decomposition. Background Art

[0002] Climate change has had a long-term and profound impact on the development of human society. Since industrialization, greenhouse gas emissions caused by energy consumption have been the main cause of temperature rise. In order to achieve sustainable development, the global temperature rise needs to be limited to within 2°C, or even further limited to 1.5°C. However, to achieve this goal, global carbon emissions must be significantly reduced in the next few decades. The most important way to control carbon emissions is to control fossil energy emissions.

[0003] The growth of fossil energy consumption is driven by many macro factors and affected by external disturbances. The consumption growth of various energy varieties shows the characteristics of fluctuations. It is necessary to study the long-term trend of coal (or its fossil energy varieties) consumption growth, whether the internal trend change from positive to negative has been formed, and whether the recent rebound in coal consumption is a short-term anomaly. Answering these questions is not only crucial to the carbon peak target, but will also have a direct impact on the global temperature target.

[0004] The current decomposition methods usually focus on a single time scale for the peak year of coal consumption, and basically believe that coal consumption will no longer show an upward trend. There is a lack of multi-level and multi-period systematic analysis of the complex driving factors behind coal consumption. In fact, coal consumption has continued to grow at a high rate in recent years, and this objective fact contradicts the trend change found by many studies. Therefore, it is necessary to conduct research and analysis on whether the recent high-speed growth in coal consumption is a long-term trend or a short-term phenomenon, and further study the internal driving factors that cause this phenomenon. Summary of the invention

[0005] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the first purpose of this application is to propose a driving factor analysis method based on LMDI and variational mode decomposition, which can comprehensively reveal the complex driving forces and cyclical characteristics of the growth of specific energy types or total energy consumption, and provide a theoretical basis for the formulation of effective and feasible emission reduction policies. It has the advantages of multiple levels, multiple influencing factors, and multiple time scales.

[0007] The second purpose of this application is to propose a driving factor analysis device based on LMDI and variational mode decomposition.

[0008] The third objective of the present application is to provide a computer device.

[0009] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, the first embodiment of the present application proposes a driving factor analysis method based on LMDI and variational mode decomposition, including:

[0011] Obtain macro data, including gross domestic product, total energy consumption, proportion of tertiary industry value-added, total energy consumption of tertiary industry, and consumption of specific energy types of tertiary industry;

[0012] The Logarithmic Mean Divisia Index (LMDI) method is used in combination with macro data to determine the contribution of macro driving factors to the growth of consumption of specific energy varieties. Macro driving factors include economic development, industrial structure adjustment, technological energy consumption progress and fuel substitution.

[0013] Based on the variational mode decomposition method, the contribution of each macro-driving factor to the growth of consumption of specific energy varieties is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances. The similar frequency components of each macro-driving factor are merged to reconstruct the long-term trend, medium-term fluctuations, short-term fluctuations and high-frequency disturbances in the total growth.

[0014] Optionally, in one embodiment of the present application, the logarithmic mean Dimitrov index method is used in combination with macro data to determine the contribution value of macro driving factors to the growth of consumption of specific energy varieties, including:

[0015] Expressing the consumption of a specific energy type as the sum of the contributions of all macro drivers:

[0016]

[0017] Among them, C t represents the total consumption of a specific energy type in year t, G t represents the gross domestic product in year t, represents the proportion of the i-th industry in the gross domestic product in year t, represents the energy consumption per unit of GDP of the ith industry in year t, It represents the proportion of specific energy consumption of industry i in year t to the total consumption of the industry;

[0018] Based on the additive LMDI method, the consumption increment of specific energy varieties is expressed as the economic development effect. Industrial structure effect Technological progress effect Fuel substitution effect sum:

[0019]

[0020] in,

[0021]

[0022] in, is the total consumption of specific energy types in the ith industry in year t.

[0023] Optionally, in one embodiment of the present application, based on the variational mode decomposition method, the contribution value of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, including:

[0024] The signal is decomposed into K eigenmode functions {u k |k=1,2,3,...,K}, K is a predetermined parameter. In the intrinsic mode function, the contribution value is divided into long-term trend, medium-period fluctuation, short-period fluctuation and high-frequency disturbance according to the period from long to short. Each intrinsic mode function is an amplitude-frequency modulated signal. The intrinsic mode function u k With a center frequency of ω k ,{ω k |k=1,2,3,...,K}, the number of decomposition modes and the penalty factor value during decomposition are set manually.

[0025] Optionally, in one embodiment of the present application, based on the variational mode decomposition method, the contribution value of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, and also includes:

[0026] Perform Hilbert transform on each intrinsic mode function to construct an analytical signal and obtain its single-sided spectrum;

[0027] For each eigenmode function u k , by aliasing its center frequency ω k The exponential term modulates the spectrum of each mode to the corresponding baseband;

[0028] Using the H of the demodulated signal 1 Gaussian smoothing estimates the bandwidth length of each mode function and transforms the signal decomposition into a variational problem, which is expressed as:

[0029]

[0030] Among them, f is the original signal, δ is the Dirac distribution, and * is the convolution;

[0031] The introduction of quadratic penalty terms and Lagrange multipliers transforms the variational problem into an unconstrained optimization problem, which can be expressed as:

[0032]

[0033] Among them, α is the penalty factor;

[0034] The alternating direction multiplier method is used to solve the unconstrained optimization problem and obtain {u k},{ω k}.

[0035] To achieve the above-mentioned purpose, the second embodiment of the present invention proposes a driving factor analysis device based on LMDI and variational mode decomposition, comprising:

[0036] The data acquisition module is used to obtain macro data, wherein the macro data includes gross domestic product, total energy consumption, proportion of added value of the tertiary industry, total energy consumption of the tertiary industry, and consumption of specific energy varieties of the tertiary industry;

[0037] The driving force decomposition module is used to use the logarithmic mean Dirichlet index method combined with macro data to determine the contribution of macro driving factors to the growth of consumption of specific energy varieties, where macro driving factors include economic development, industrial structure adjustment, technological energy consumption progress and fuel substitution;

[0038] The multi-time scale decomposition module is used to decompose the contribution of each macro-driving factor to the growth of consumption of specific energy varieties into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances based on the variational mode decomposition method, merge the similar frequency components of each macro-driving factor, and reconstruct the long-term trend, medium-term fluctuations, short-term fluctuations and high-frequency disturbances in the total growth.

[0039] Optionally, in one embodiment of the present application, the logarithmic mean Dimitrov index method is used in combination with macro data to determine the contribution value of macro driving factors to the growth of consumption of specific energy varieties, including:

[0040] Expressing the consumption of a specific energy type as the sum of the contributions of all macro drivers:

[0041]

[0042] Among them, C t represents the total consumption of a specific energy type in year t, G t represents the gross domestic product in year t, represents the proportion of the i-th industry in the gross domestic product in year t, represents the energy consumption per unit of GDP of the ith industry in year t, It represents the proportion of specific energy consumption of industry i in year t to the total consumption of the industry;

[0043] Based on the additive LMDI method, the consumption increment of specific energy varieties is expressed as the economic development effect. Industrial structure effect Technological progress effect Fuel substitution effect sum:

[0044]

[0045] in,

[0046]

[0047] in, is the total consumption of specific energy types in the ith industry in year t.

[0048] Optionally, in one embodiment of the present application, based on the variational mode decomposition method, the contribution value of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, including:

[0049] The signal is decomposed into K eigenmode functions {u k |k=1,2,3,...,K}, K is a predetermined parameter. In the intrinsic mode function, the contribution value is divided into long-term trend, medium-period fluctuation, short-period fluctuation and high-frequency disturbance according to the period from long to short. Each intrinsic mode function is an amplitude-frequency modulated signal. The intrinsic mode function u k With a center frequency of ω k ,{ω k |k=1,2,3,...,K}, the number of decomposition modes and the penalty factor value during decomposition are set manually.

[0050] Optionally, in one embodiment of the present application, based on the variational mode decomposition method, the contribution value of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, and also includes:

[0051] Perform Hilbert transform on each intrinsic mode function to construct an analytical signal and obtain its single-sided spectrum;

[0052] For each eigenmode function u k , by aliasing its center frequency ω k The exponential term modulates the spectrum of each mode to the corresponding baseband;

[0053] Using the H of the demodulated signal 1 Gaussian smoothing estimates the bandwidth length of each mode function and transforms the signal decomposition into a variational problem, which is expressed as:

[0054]

[0055] Among them, f is the original signal, δ is the Dirac distribution, and * is the convolution;

[0056] The introduction of quadratic penalty terms and Lagrange multipliers transforms the variational problem into an unconstrained optimization problem, which can be expressed as:

[0057]

[0058] Among them, α is the penalty factor;

[0059] The alternating direction multiplier method is used to solve the unconstrained optimization problem and obtain {u k},{ω k}.

[0060] To achieve the above-mentioned purpose, the third aspect of the present invention proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned driving factor analysis method based on LMDI and variational mode decomposition is implemented.

[0061] In order to achieve the above-mentioned objectives, the fourth aspect of the present invention proposes a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor, can execute the above-mentioned driving factor analysis method based on LMDI and variational mode decomposition.

[0062] The driving factor analysis method based on LMDI and variational mode decomposition in the embodiment of this application is used to further analyze the driving force of the long-term trend in the increase in coal consumption and the intrinsic causes of the cyclical components by decomposing the growth of energy consumption into the sum of long-term trends and multiple cyclical components. Through this innovative analysis method, this application provides a new theoretical basis for the carbon peak strategy, helping decision makers to take into account the complex cyclical impacts behind the growth of fossil energy consumption when formulating emission reduction policies, thereby promoting the overall process of global climate governance, with the advantages of multiple levels, multiple influencing factors, and multiple time scales.

[0063] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0065] Figure 1 A flowchart of a driving factor analysis method based on LMDI and variational mode decomposition provided in Example 1 of the present application;

[0066] Figure 2This is a graph showing the logarithmic average Diehl index decomposition result of coal consumption growth within a preset time period in an embodiment of the present application;

[0067] Figure 3 A multi-time scale decomposition diagram of the logarithmic average Diehl index decomposition result of coal consumption growth within a preset time period in an embodiment of the present application;

[0068] Figure 4 A multi-time scale component reconstruction diagram of coal consumption growth within a preset time period in an embodiment of the present application;

[0069] Figure 5 A schematic diagram of the structure of a driving factor analysis device based on LMDI and variational mode decomposition provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0071] The following describes the driving factor analysis method and device based on LMDI and variational mode decomposition according to an embodiment of the present application with reference to the accompanying drawings.

[0072] Figure 1 A flowchart of a driving factor analysis method based on LMDI and variational mode decomposition provided in Example 1 of the present application.

[0073] like Figure 1 As shown, the driving factor analysis method based on LMDI and variational mode decomposition includes the following steps:

[0074] Step 101, obtaining macro data, wherein the macro data includes gross domestic product, total energy consumption, proportion of tertiary industry added value, total energy consumption of tertiary industry, and consumption of specific energy types of tertiary industry;

[0075] In this embodiment, the macro data obtained are macro data for each year within the studied time span.

[0076] In this embodiment, the national economy-related data are derived from the China Statistical Yearbook (CSY), and the GDP values ​​are converted into 2020 constant prices. The energy consumption-related data are derived from the China Energy Statistical Yearbook (CESY), and the energy consumption units are all in million tons of standard coal.

[0077] In this example, the economy is divided into three major industries, namely the primary industry, the secondary industry and the tertiary industry. The primary industry includes agriculture, forestry, animal husbandry, fishery and water conservancy; the secondary industry includes mining, manufacturing, electricity and heat, and construction; the rest are tertiary industries (transportation and commerce). The total coal consumption used in this example is the total coal consumption of the three major industries, excluding the coal consumption of residents.

[0078] Step 102, using the logarithmic mean Dirichlet index method LMDI combined with macro data to determine the contribution of macro driving factors to the growth of consumption of specific energy varieties, wherein the macro driving factors include economic development, industrial structure adjustment, technological energy consumption progress and fuel substitution;

[0079] In this embodiment, the total coal consumption in a preset time period is decomposed by the logarithmic mean Dirichlet index method. The annual consumption increment of a specific energy variety is expressed as the sum of the contribution values ​​of these four influencing factors. The annual consumption of a specific energy variety can be expressed as:

[0080]

[0081] Among them, C t represents the total coal consumption in year t, G t represents the gross national product in year t, represents the proportion of the ith industry in the total GDP in year t, represents the energy consumption per unit GDP of the ith industry in year t, It indicates the proportion of specific energy type consumption of industry i in year t to the total consumption of the industry.

[0082] In this embodiment, based on the additive LMDI method, the annual increase in coal consumption can be expressed as Industrial Structure Technological advancement Fuel substitution The sum of four influencing factors:

[0083]

[0084] The calculation formula for each item on the right side of the above formula is as follows:

[0085] Economic Development Effects:

[0086]

[0087] Industrial structure effect:

[0088]

[0089] Effects of technological progress:

[0090]

[0091] Fuel Substitution Effect:

[0092]

[0093] Thus, the contribution values ​​of the four macro-driving factors to the growth of coal consumption are obtained. The values ​​obtained are as follows: Figure 2 shown. Figure 2 In the four figures, the horizontal axis is time, and the vertical axis is the growth of coal consumption caused by influencing factors. Each column represents the part of the increase in coal consumption in that year compared with the previous year caused by the driving force of economic development. Figure 2 It can be seen intuitively that the increase in coal consumption caused by economic development has been positive for a long time and is significantly higher than the contribution of other factors; the contribution of the other three factors is negative for most of the time, among which the absolute value of the contribution of technological progress is relatively high. This shows that economic development has been the main driving force for the growth of coal consumption for a long time, and the reduction in energy intensity brought about by technological progress is the main driving force for reducing coal consumption.

[0094] Step 103, based on the variational mode decomposition method, decompose the contribution of each macro-driving factor to the growth of consumption of specific energy types into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, merge the similar frequency components of each macro-driving factor, and reconstruct the long-term trend, medium-term fluctuations, short-term fluctuations and high-frequency disturbances in the total growth.

[0095] In this embodiment, the contribution values ​​of the above four factors are decomposed respectively using variational mode decomposition. The contribution value sequence of each macro factor in a preset time period is used as the initial signal of variational mode decomposition, and the intrinsic mode functions of different center frequencies are decomposed. The decomposition process specifically includes:

[0096] 1) For each eigenmode function u k Perform Hilbert transform to construct an analytical signal and obtain its one-sided spectrum.

[0097] 2) For each eigenmode function u k , by aliasing its center frequency ω k The exponential term modulates the spectrum of each mode to the corresponding baseband.

[0098] 3) Using the H of the demodulated signal 1 Gaussian smoothing is used to estimate the bandwidth length of each mode function and transform the signal decomposition into a variational problem, where f is the original signal, δ is the Dirac distribution, and * is the convolution:

[0099]

[0100] 4) To solve the above variational problem, the quadratic penalty term and Lagrange multiplier λ are introduced to transform the problem into an unconstrained optimization problem, where α is the penalty factor:

[0101]

[0102] 5) Use the alternating direction method of multipliers (ADMM) to solve the above problem and obtain {u k},{ω k}.

[0103] Specifically, the variational mode decomposition results of the contribution values ​​of the four macro-driving factors are as follows: Figure 3 As shown. In the decomposition results of coal consumption, the IMF with a cycle of more than 15 years is considered a trend item, and the IMF with a cycle of less than 3 years is considered a disturbance item. Among them, IMF1 and IMF2 of economic development, IMF1 of industrial structure, IMF1 of technological progress, and IMF1 of fuel substitution are all regarded as trend items; the cycles of IMF2 of industrial structure, IMF2 of technological progress, and IMF2 of fuel substitution all fluctuate in 7-8 years, which can be considered as medium-cycle components; the cycles of IMF3 of industrial structure, IMF3 of technological progress, and IMF3 of fuel substitution all fluctuate in 3-4 years, which can be considered as short-cycle components; IMF4 of economic development, IMF4 of industrial structure, IMF4 of technological progress, and IMF4 of fuel substitution are all considered as disturbance items.

[0104] The trend terms of the four influencing factors are combined to reconstruct the trend term of the coal consumption increment. Similar operations are performed on the medium-term component, short-term component and disturbance term. The reconstructed trend term, medium-term component, short-term component and disturbance term of the coal consumption increment are as follows: Figure 4 As shown in the figure, the reconstructed medium-cycle component and short-cycle component of coal increment have periods of 7-8 years and 3-4 years respectively. The black solid line in each figure is the reconstructed trend quantity and cycle component.

[0105] The driving factor analysis method based on LMDI and variational mode decomposition in the embodiment of this application is used to further analyze the driving force of the long-term trend in the increase in coal consumption and the intrinsic causes of the cyclical components by decomposing the growth of energy consumption into the sum of long-term trends and multiple cyclical components. Through this innovative analysis method, this application provides a new theoretical basis for the carbon peak strategy, helping decision makers to take into account the complex cyclical impacts behind the growth of fossil energy consumption when formulating emission reduction policies, thereby promoting the overall process of global climate governance, with the advantages of multiple levels, multiple influencing factors, and multiple time scales.

[0106] In order to implement the above embodiments, the present application also proposes a driving factor analysis device based on LMDI and variational mode decomposition.

[0107] Figure 5 A schematic diagram of the structure of a driving factor analysis device based on LMDI and variational mode decomposition provided in an embodiment of the present application.

[0108] like Figure 5 As shown, the driving factor analysis device based on LMDI and variational mode decomposition includes:

[0109] The data acquisition module is used to obtain macro data, wherein the macro data includes gross domestic product, total energy consumption, proportion of added value of the tertiary industry, total energy consumption of the tertiary industry, and consumption of specific energy varieties of the tertiary industry;

[0110] The driving force decomposition module is used to use the logarithmic mean Dirichlet index method combined with macro data to determine the contribution of macro driving factors to the growth of consumption of specific energy varieties, where macro driving factors include economic development, industrial structure adjustment, technological energy consumption progress and fuel substitution;

[0111] The multi-time scale decomposition module is used to decompose the contribution of each macro-driving factor to the growth of consumption of specific energy varieties into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances based on the variational mode decomposition method, merge the similar frequency components of each macro-driving factor, and reconstruct the long-term trend, medium-term fluctuations, short-term fluctuations and high-frequency disturbances in the total growth.

[0112] Optionally, in one embodiment of the present application, the logarithmic mean Dimitrov index method is used in combination with macro data to determine the contribution value of macro driving factors to the growth of consumption of specific energy varieties, including:

[0113] Expressing the consumption of a specific energy type as the sum of the contributions of all macro drivers:

[0114]

[0115] Among them, C t represents the total consumption of a specific energy type in year t, G t represents the gross domestic product in year t, represents the proportion of the i-th industry in the gross domestic product in year t, represents the energy consumption per unit of GDP of the ith industry in year t, It represents the proportion of specific energy consumption of industry i in year t to the total consumption of the industry;

[0116] Based on the additive LMDI method, the consumption increment of specific energy varieties is expressed as the economic development effect. Industrial structure effect Technological progress effect Fuel substitution effect sum:

[0117]

[0118] in,

[0119]

[0120] in, is the total consumption of specific energy types in the ith industry in year t.

[0121] Optionally, in one embodiment of the present application, based on the variational mode decomposition method, the contribution value of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, including:

[0122] The signal is decomposed into K eigenmode functions {u k |k=1,2,3,...,K}, K is a predetermined parameter. In the intrinsic mode function, the contribution value is divided into long-term trend, medium-period fluctuation, short-period fluctuation and high-frequency disturbance according to the period from long to short. Each intrinsic mode function is an amplitude-frequency modulated signal. The intrinsic mode function u k With a center frequency of ω k ,{ω k |k=1,2,3,...,K}, the number of decomposition modes and the penalty factor value during decomposition are set manually.

[0123] Optionally, in one embodiment of the present application, based on the variational mode decomposition method, the contribution value of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, and also includes:

[0124] Perform Hilbert transform on each intrinsic mode function to construct an analytical signal and obtain its single-sided spectrum;

[0125] For each eigenmode function u k , by aliasing its center frequency ω k The exponential term modulates the spectrum of each mode to the corresponding baseband;

[0126] Using the H of the demodulated signal 1 Gaussian smoothing estimates the bandwidth length of each mode function and transforms the signal decomposition into a variational problem, which is expressed as:

[0127]

[0128] Among them, f is the original signal, δ is the Dirac distribution, and * is the convolution;

[0129] The introduction of quadratic penalty terms and Lagrange multipliers transforms the variational problem into an unconstrained optimization problem, which can be expressed as:

[0130]

[0131]

[0132] Among them, α is the penalty factor;

[0133] The alternating direction multiplier method is used to solve the unconstrained optimization problem and obtain {u k},{ω k}.

[0134] It should be noted that the aforementioned explanation of the embodiment of the driving factor analysis method based on LMDI and variational mode decomposition is also applicable to the driving factor analysis device based on LMDI and variational mode decomposition of this embodiment, and will not be repeated here.

[0135] In order to implement the above embodiments, the present invention further proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above embodiments is implemented.

[0136] In order to implement the above embodiments, the present invention further proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method of the above embodiments is implemented.

[0137] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0138] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0139] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0140] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied 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 the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can 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 combination with these instruction execution systems, devices or apparatuses. 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 box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be 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 and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present application 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 their combination: 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.

[0142] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0144] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A driving factor analysis method based on LMDI and variational mode decomposition, characterized in that: include: Obtaining macro data, wherein the macro data includes gross domestic product, total energy consumption, proportion of tertiary industry added value, total energy consumption of tertiary industry, and consumption of specific energy types of tertiary industry; The contribution of macro-driving factors to the growth of consumption of specific energy varieties is determined by using the logarithmic mean Dirichlet index method (LMDI) in combination with the macro data, wherein the macro-driving factors include economic development, industrial structure adjustment, technological energy consumption progress and fuel substitution; Based on the variational mode decomposition method, the contribution of each macro-driving factor to the growth of consumption of specific energy varieties is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances. The similar frequency components of each macro-driving factor are merged to reconstruct the long-term trend, medium-term fluctuations, short-term fluctuations and high-frequency disturbances in the total growth.

2. The method according to claim 1, characterized in that The logarithmic mean Dimitrov index method is combined with the macro data to determine the contribution of macro driving factors to the growth of consumption of specific energy varieties, including: Expressing the consumption of a specific energy type as the sum of the contributions of all macro drivers: Among them, C t represents the total consumption of a specific energy type in year t, G t represents the gross domestic product in year t, represents the proportion of the i-th industry in the gross domestic product in year t, represents the energy consumption per unit of GDP of the ith industry in year t, It represents the proportion of specific energy consumption of industry i in year t to the total consumption of the industry; Based on the additive LMDI method, the consumption increment of specific energy varieties is expressed as the economic development effect. Industrial structure effect Technological progress effect Fuel substitution effect sum: in, in, is the total consumption of specific energy types in the ith industry in year t.

3. The method according to claim 1, characterized in that Based on the variational mode decomposition method, the contribution of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, including: The signal is decomposed into K eigenmode functions {u k |k=1,2,3,...,K}, K is a predetermined parameter. In the intrinsic mode function, the contribution value is divided into long-term trend, medium-period fluctuation, short-period fluctuation and high-frequency disturbance according to the period from long to short. Each intrinsic mode function is an amplitude-frequency modulated signal. The intrinsic mode function u k With a center frequency of ω k ,{ω k |k=1,2,3,...,K}, the number of decomposition modes and the penalty factor value during decomposition are set manually.

4. The method according to claim 3, characterized in that The variational mode decomposition method decomposes the contribution of each macro-driving factor to the growth of consumption of a specific energy variety into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, and also includes: Perform Hilbert transform on each intrinsic mode function to construct an analytical signal and obtain its single-sided spectrum; For each eigenmode function u k , by aliasing its center frequency ω k The exponential term modulates the spectrum of each mode to the corresponding baseband; Using the H of the demodulated signal 1 Gaussian smoothing estimates the bandwidth length of each mode function and transforms the signal decomposition into a variational problem, which is expressed as: Among them, f is the original signal, δ is the Dirac distribution, and * is the convolution; The introduction of quadratic penalty terms and Lagrange multipliers transforms the variational problem into an unconstrained optimization problem, which can be expressed as: Among them, α is the penalty factor; The unconstrained optimization problem is solved using the alternating direction multiplier method, and we get {u k },{ω k }.

5. A driving factor analysis device based on LMDI and variational mode decomposition, characterized in that: include: A data acquisition module is used to acquire macro data, wherein the macro data includes gross domestic product, total energy consumption, proportion of added value of the tertiary industry, total energy consumption of the tertiary industry, and consumption of specific energy varieties of the tertiary industry; A driving force decomposition module, used to determine the contribution value of macro driving factors to the growth of consumption of specific energy varieties by using the logarithmic mean Dirichlet index method in combination with the macro data, wherein the macro driving factors include economic development, industrial structure adjustment, technological energy consumption progress and fuel substitution; The multi-time scale decomposition module is used to decompose the contribution of each macro-driving factor to the growth of consumption of specific energy varieties into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances based on the variational mode decomposition method, merge the similar frequency components of each macro-driving factor, and reconstruct the long-term trend, medium-term fluctuations, short-term fluctuations and high-frequency disturbances in the total growth.

6. The device according to claim 5, characterized in that The logarithmic mean Dimitrov index method is combined with the macro data to determine the contribution of macro driving factors to the growth of consumption of specific energy varieties, including: Expressing the consumption of a specific energy type as the sum of the contributions of all macro drivers: Among them, C t represents the total consumption of a specific energy type in year t, G t represents the gross domestic product in year t, represents the proportion of the i-th industry in the gross domestic product in year t, represents the energy consumption per unit of GDP of the ith industry in year t, It represents the proportion of specific energy consumption of industry i in year t to the total consumption of the industry; Based on the additive LMDI method, the consumption increment of specific energy varieties is expressed as the economic development effect. Industrial structure effect Technological progress effect Fuel substitution effect sum: in, in, is the total consumption of specific energy types in the ith industry in year t.

7. The device according to claim 5, characterized in that Based on the variational mode decomposition method, the contribution of each macro-driving factor to the growth of consumption of a specific energy variety is decomposed into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, including: The signal is decomposed into K eigenmode functions {u k |k=1,2,3,...,K}, K is a predetermined parameter. In the intrinsic mode function, the contribution value is divided into long-term trend, medium-period fluctuation, short-period fluctuation and high-frequency disturbance according to the period from long to short. Each intrinsic mode function is an amplitude-frequency modulated signal. The intrinsic mode function u k With a center frequency of ω k ,{ω k |k=1,2,3,...,K}, the number of decomposition modes and the penalty factor value during decomposition are set manually.

8. The device according to claim 7, characterized in that The variational mode decomposition method decomposes the contribution of each macro-driving factor to the growth of consumption of a specific energy variety into long-term trends, medium-term fluctuations, short-term fluctuations and high-frequency disturbances, and also includes: Perform Hilbert transform on each intrinsic mode function to construct an analytical signal and obtain its single-sided spectrum; For each eigenmode function u k , by aliasing its center frequency ω k The exponential term modulates the spectrum of each mode to the corresponding baseband; Using the H of the demodulated signal 1 Gaussian smoothing estimates the bandwidth length of each mode function and transforms the signal decomposition into a variational problem, which is expressed as: Among them, f is the original signal, δ is the Dirac distribution, and * is the convolution; The introduction of quadratic penalty terms and Lagrange multipliers transforms the variational problem into an unconstrained optimization problem, which can be expressed as: Among them, α is the penalty factor; The unconstrained optimization problem is solved using the alternating direction multiplier method, and we get {u k },{ω k }.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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