A carbon emission prediction method and system based on big data analysis

Abstract

The present application discloses a carbon emission prediction method and system based on big data analysis, which relates to the field of data prediction technology. The present application first determines the carbon emission data vector of each carbon source based on the carbon emission data of the target object in the past preset number of days, and then uses the improved time convolution network to determine the carbon emission feature matrix. Secondly, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention weighted feature matrix. Finally, the attention weighted feature matrix is ​​input into a pre-trained long short-term memory network to determine the carbon emission prediction data of each carbon source in the future preset number of days. In this way, the importance differences and fluctuation characteristics of different carbon sources are fully considered to improve the accuracy of carbon emission prediction, and the technical problem that the carbon emission prediction method in the prior art does not fully consider the importance differences and fluctuation characteristics of different carbon sources, and the simple merging of data leads to limited prediction accuracy.

Description

Technical Field

[0001] The present application relates to the field of data prediction technology, and in particular to a carbon emission prediction method and system based on big data analysis. Background Art

[0002] With the growing severity of global climate change, reducing greenhouse gas emissions has become a global concern. As a major component of greenhouse gases, accurate prediction of carbon emissions is crucial for developing effective emission reduction policies and measures. In recent years, the rapid development of big data analytics and artificial intelligence technologies has provided new methods and tools for carbon emission forecasting. Carbon emission forecasting is fundamental to evaluating and optimizing emission reduction strategies. By accurately predicting future carbon emission trends, businesses and research institutions can better set emission reduction targets, optimize resource allocation, and evaluate policy effectiveness. For example, businesses can use carbon emission forecasting to optimize production processes and reduce their carbon footprint.

[0003] At present, carbon emission prediction methods include correlation analysis algorithms and model estimation methods. The correlation analysis algorithm predicts the carbon emissions of the emission subject by analyzing the relationship between carbon emissions and strongly correlated factors (such as energy consumption, industrial production index, etc.); the model estimation method predicts carbon emissions by constructing a mathematical model (such as a regression model). For example, the publication number is CN113240155A, and the name is a method, device and terminal for predicting carbon emissions. The disclosed method includes: obtaining carbon emission historical data of multiple emission subjects, and the carbon emission historical data includes carbon emission influencing factor data and carbon emission result data of each emission subject; performing correlation analysis on the carbon emission influencing factor data and the carbon emission result data to select at least two strongly correlated influencing factors of the carbon emission result from the carbon emission influencing factor data; and predicting the carbon emissions of the emission subject based on at least two strongly correlated influencing factors.

[0004] For another example, the publication number is CN113657661A, and the name is a method, device, computer equipment and storage medium for predicting corporate carbon emissions. The disclosed method includes: obtaining data to be analyzed, the data to be analyzed including at least one of per capita GDP, energy intensity, added value of the industry to which the target enterprise belongs, total energy consumption of the industry to which the target enterprise belongs, main business income of the target enterprise, revenue and profit of the target enterprise, and electricity consumption of the target enterprise; performing empirical mode decomposition on the data to be analyzed to obtain multiple subsequences of different frequencies and a residual component sequence; inputting multiple sequences composed of each subsequence and residual component sequence into the carbon emission prediction model respectively to obtain sequence prediction results corresponding to each sequence; performing sequence reconstruction on the obtained sequence prediction results to obtain a reconstruction result, and determining the corporate carbon emission prediction result corresponding to the target enterprise based on the reconstruction result.

[0005] However, existing carbon emission prediction methods usually treat data from all carbon sources as a whole, ignoring the differences and interactions between different carbon sources, which seriously affects the accuracy of carbon emission prediction results. For example, the influencing factors of carbon sources such as industrial fuel combustion, electricity consumption and traffic emissions vary significantly. If they are simply combined into a single sequence model, key features will be lost, seriously affecting the accuracy of the prediction results. In addition, carbon emissions from different carbon sources may have different dynamic fluctuation characteristics (such as high-frequency mutations and low-frequency periodicity), and existing methods fail to fully consider these fluctuation differences.

[0006] Currently, no effective solution has been proposed to the problem that existing carbon emission prediction methods do not fully consider the differences in the importance and fluctuation characteristics of different carbon sources, and simply merge data, resulting in limited prediction accuracy. Summary of the Invention

[0007] The embodiments of the present disclosure provide a carbon emission prediction method and system based on big data analysis. This approach addresses the technical problem that existing carbon emission prediction methods fail to fully consider the differences in importance and fluctuation characteristics of different carbon sources, and simply combine data, resulting in limited prediction accuracy.

[0008] According to one aspect of an embodiment of the present disclosure, a carbon emission prediction method based on big data analysis is provided, comprising: determining a carbon emission data vector for each carbon source of the target object in the past preset days based on the carbon emission data of the target object in the past preset days; wherein the target object has at least two carbon sources; determining a carbon emission feature matrix of the target object based on the carbon emission data vectors of all carbon sources in the past preset days using an improved temporal convolutional network; wherein the improved temporal convolutional network assigns an independent convolution kernel and dilation coefficient to each carbon source, and the dilation coefficient is determined in real time based on the carbon emission fluctuation of the carbon source; the dimension of the carbon emission feature matrix is ​​n×m, wherein n is the number of carbon sources and m is the past preset days, and each element in the carbon emission feature matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past; and processing the carbon emission feature matrix using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix of the target object, and inputting the attention-weighted feature matrix into a pre-trained long short-term memory network to determine the carbon emission prediction data of each carbon source of the target object in the future preset days.

[0009] According to another aspect of the embodiments of the present disclosure, a storage medium is further provided. The storage medium includes a stored program, wherein the above method is executed by a processor when the program is running.

[0010] According to another aspect of the embodiment of the present disclosure, a carbon emission prediction system based on big data analysis is also provided, including: a first determination module for determining the carbon emission data vector of each carbon source of the target object in the past preset days based on the carbon emission data of the target object in the past preset days; wherein the target object has at least two carbon sources; a second determination module for determining the carbon emission feature matrix of the target object based on the carbon emission data vectors of all carbon sources in the past preset days using an improved temporal convolutional network; wherein the improved temporal convolutional network assigns an independent convolution kernel and dilation coefficient to each carbon source, and The expansion coefficient is determined in real time based on the carbon emission fluctuations of the carbon source; the dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the past preset number of days, and each element in the carbon emission feature matrix represents the carbon emission characteristic value of the corresponding carbon source on the jth day in the past; and a third determination module is used to use a pre-trained attention mechanism network to process the carbon emission feature matrix to obtain the attention weighted feature matrix of the target object, and input the attention weighted feature matrix into a pre-trained long short-term memory network to determine the carbon emission forecast data of each carbon source of the target object in the future preset number of days.

[0011] According to another aspect of the embodiment of the present disclosure, a carbon emission prediction system based on big data analysis is also provided, including a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: based on the carbon emission data of the target object in the past preset days, determining the carbon emission data vector of each carbon source of the target object in the past preset days; wherein the target object has at least two carbon sources; based on the carbon emission data vectors of all carbon sources in the past preset days, using an improved temporal convolutional network, determining the carbon emission feature matrix of the target object; wherein the improved temporal convolutional network assigns a unique feature matrix to each carbon source. The method comprises the following steps: first, a convolution kernel and a dilation coefficient are independently determined, and the dilation coefficient is determined in real time based on the carbon emission fluctuation of the carbon source; the dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the preset number of days in the past, and each element in the carbon emission feature matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past; and the carbon emission feature matrix is ​​processed by a pre-trained attention mechanism network to obtain an attention weighted feature matrix of the target object, and the attention weighted feature matrix is ​​input into a pre-trained long short-term memory network to determine the carbon emission forecast data of each carbon source of the target object in the preset number of days in the future.

[0012] This application first determines the carbon emission data vector of each carbon source based on the carbon emission data of the target object in the past preset number of days, so as to fully consider the differences and interactions between different carbon sources, and effectively avoid the problem of losing key features caused by the general treatment of all carbon source data. Then, the improved temporal convolutional network is used to determine the carbon emission feature matrix. The network assigns an independent convolution kernel and expansion coefficient to each carbon source, which can more accurately capture the carbon emission characteristics of each carbon source, and the expansion coefficient is determined in real time based on the carbon emission fluctuation of the carbon source, further improving the accuracy of the prediction. Secondly, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix. The attention mechanism can automatically learn and emphasize information that has an important impact on the prediction results, further improving the accuracy of the prediction. Finally, the attention-weighted feature matrix is ​​input into the pre-trained long-short-term memory network to determine the carbon emission forecast data for each carbon source in the next preset number of days. The long-short-term memory network has a strong time series processing capability and can capture long-term dependencies in carbon emission data to achieve accurate prediction of future carbon emissions. Therefore, this application, through the combination of refined data prediction, an improved temporal convolutional network, an attention mechanism network, and a long-short-term memory network, fully considers the differences in the importance and fluctuation characteristics of different carbon sources, significantly improving the accuracy of carbon emission prediction. This solves the technical problem that existing carbon emission prediction methods fail to fully consider the differences in the importance and fluctuation characteristics of different carbon sources and simply combine data, resulting in limited prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of this application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the drawings:

[0014] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to embodiment 1 of the present application;

[0015] Figure 2 This is a flow chart of the carbon emission prediction method based on big data analysis according to Example 1 of the present application;

[0016] Figure 3 is a schematic diagram of a carbon emission prediction system based on big data analysis according to Example 2 of the present application; and

[0017] Figure 4 This is a schematic diagram of the carbon emission prediction system based on big data analysis described in Example 3 of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present disclosure.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] Example 1

[0021] According to this embodiment, a method embodiment of a carbon emission prediction method based on big data analysis is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 The following is a hardware block diagram of a computing device for implementing a carbon emission prediction method based on big data analysis. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA, etc.), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, transmission device, and input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1More or fewer components than shown, or with Figure 1 Different configurations shown.

[0023] It should be noted that the one or more processors and / or other data prediction circuits described above may generally be referred to herein as "data prediction circuitry." The data prediction circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data prediction circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computing device. As described in the embodiments of the present disclosure, the data prediction circuitry acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0024] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the carbon emission prediction method based on big data analysis in the embodiment of the present disclosure. The processor executes various functional applications and data predictions by running the software programs and modules stored in the memory, that is, implementing the carbon emission prediction method based on big data analysis of the above-mentioned application. The memory may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranet, local area network, mobile communication network, and combinations thereof.

[0025] The transmission device is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by a communications provider of the computing device. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0026] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computing device.

[0027] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. Figure 1This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing device described above.

[0028] In the above operating environment, according to the first aspect of this embodiment, a carbon emission prediction method based on big data analysis is provided. Figure 2 A schematic diagram showing the process of the method is shown in FIG. Figure 2 As shown, the method includes:

[0029] S102: Based on the carbon emission data of the target object over the past preset number of days, determining the carbon emission data vector of each carbon source of the target object over the past preset number of days; wherein the target object has at least two carbon sources;

[0030] S104: Determine the carbon emission feature matrix of the target object using an improved temporal convolutional network based on the carbon emission data vectors of all carbon sources over the past preset number of days; wherein the improved temporal convolutional network assigns an independent convolution kernel and dilation coefficient to each carbon source, and the dilation coefficient is determined in real time based on the carbon emission fluctuation of the carbon source; the dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the past preset number of days, and each element in the carbon emission feature matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past; and

[0031] S106: Use a pre-trained attention mechanism network to process the carbon emission feature matrix to obtain an attention weighted feature matrix of the target object, and input the attention weighted feature matrix into a pre-trained long short-term memory network to determine the carbon emission forecast data of each carbon source of the target object in the future preset number of days.

[0032] In this embodiment, it has been found through research that carbon emission data has the characteristics of multi-source heterogeneity (such as industrial fuel combustion, electricity consumption, traffic emissions, etc.), strong time dependence (such as seasonal fluctuations, production cycle effects), and significant dynamic fluctuations (such as sudden changes in emissions caused by emergencies). For example, corporate carbon emissions may include both direct fuel combustion and indirect electricity consumption. The fluctuation patterns of different carbon sources (such as the suddenness of production process emissions and the periodicity of electricity consumption) are significantly different. If the multi-carbon source data are simply merged into a single sequence model, key features will be lost. In addition, existing deep learning models (such as standard TCN, LSTM) usually use unified network parameters (such as convolution kernel size, expansion coefficient) when processing multi-carbon source data, which makes it difficult to dynamically adapt to the fluctuation characteristics of different carbon sources. For example, industrial equipment carbon emissions may show high-frequency mutations, while building energy consumption carbon emissions have low-frequency periodicity. If a TCN with a fixed expansion coefficient is used, the time series characteristics of different frequencies cannot be effectively captured. Therefore, based on the multi-source heterogeneity, strong time dependence and significant dynamic fluctuations of carbon emission data, this application designs an independent feature extraction module for each carbon source, dynamically adapts to its fluctuation characteristics, and adjusts the convolution kernel parameters in real time based on the carbon source fluctuations to improve the ability to separate high-frequency and low-frequency features.

[0033] Specifically, in order to fully consider the differences and interactions between different carbon sources, the carbon emission data vector of each carbon source is first determined based on the carbon emission data of the target object in the past preset days (corresponding to step S102). Among them, refers to the entity whose carbon emissions need to be monitored and analyzed, such as enterprises, factories, cities, etc. Carbon source refers to the source of carbon emissions, such as the burning of fossil fuels, industrial processes, transportation, etc. The target object usually has multiple carbon sources. The past preset number of days refers to the historical time range used for analysis, and the selection of this range is usually set on demand based on the availability of data and the purpose of analysis. For each carbon source, its carbon emission data within the past preset number of days is organized into a carbon emission data vector to provide a basis for subsequent analysis and prediction. In this way, the problem of losing key features caused by the general treatment of all carbon source data is effectively avoided.

[0034] Temporal Convolutional Network (TCN) is a deep learning model for processing time series data. Although the standard TCN expands the receptive field through dilated convolution, it does not optimize the convolution kernel design in combination with the dynamic fluctuation characteristics of the carbon source, resulting in poor separation and extraction of high-frequency fluctuations and low-frequency trends. Based on this, this embodiment improves the temporal convolution network to adapt to the dynamic fluctuation characteristics of multi-source carbon emission data. Specifically, based on the carbon emission data vectors of all carbon sources in the past preset number of days, the improved temporal convolution network is used to determine the carbon emission feature matrix of the target object (corresponding to step S104). Among them, the improved temporal convolution network assigns an independent convolution kernel to each carbon source in order to capture the unique carbon emission pattern of each carbon source, and determines the corresponding expansion coefficient in real time according to the carbon emission fluctuation of the carbon source to better capture the short-term and long-term changes in the carbon emission of each carbon source. The dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the preset number of days in the past. Each element in the matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past. These eigenvalues ​​are obtained by processing raw carbon emission data using a temporal convolutional network, reflecting the complexity and dynamics of carbon emissions. This allows for more accurate capture of the carbon emission characteristics of each carbon source, providing more useful information for subsequent predictions.

[0035] Furthermore, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix of the target object, and the attention-weighted feature matrix is ​​input into a pre-trained long short-term memory network to determine the carbon emission forecast data for each carbon source of the target object in the preset number of days in the future (corresponding to step S106). Specifically, the pre-trained attention mechanism network is used to process the carbon emission feature matrix, assigning different weights to the features of each carbon source to highlight important carbon emission features. After the carbon emission feature matrix is ​​processed by the attention mechanism network, a corresponding attention-weighted feature matrix is ​​obtained, in which the weight of each element reflects its importance in the prediction. The pre-trained LSTM network is used to predict the carbon emission data for each carbon source of the target object in the preset number of days in the future based on the attention-weighted feature matrix. Thus, important carbon emission features can be highlighted through the attention mechanism, and the long-term dependencies of time series data can be captured using the LSTM network to achieve accurate prediction of carbon emissions for each carbon source of the target object in the preset number of days in the future.

[0036] As described in the background technology, existing carbon emission prediction methods usually process the data of all carbon sources as a whole, ignoring the differences and interactions between different carbon sources, which seriously affects the accuracy of carbon emission prediction results. For example, the influencing factors of carbon sources such as industrial fuel combustion, electricity consumption and traffic emissions vary significantly. If they are simply merged into a single sequence model, key features will be lost, which seriously affects the accuracy of the prediction results. In addition, carbon emissions from different carbon sources may have different dynamic fluctuation characteristics (such as high-frequency mutations and low-frequency periodicity), and existing methods fail to fully consider these fluctuation differences.

[0037] In view of this, the technical solution of the present application first determines the carbon emission data vector for each carbon source based on the carbon emission data of the target object over the past preset number of days, fully considering the differences and interactions between different carbon sources, effectively avoiding the problem of losing key features caused by general processing of all carbon source data. Then, an improved temporal convolutional network is used to determine the carbon emission feature matrix. This network assigns an independent convolution kernel and dilation coefficient to each carbon source, which can more accurately capture the carbon emission characteristics of each carbon source. The dilation coefficient is determined in real time based on the carbon emission fluctuations of the carbon source, further improving the accuracy of the prediction. Secondly, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix. The attention mechanism can automatically learn and emphasize information that has a significant impact on the prediction results, further improving the accuracy of the prediction. Finally, the attention-weighted feature matrix is ​​input into a pre-trained long-short-term memory network to determine the carbon emission forecast data for each carbon source over the next preset number of days. The long-short-term memory network has powerful time series processing capabilities and can capture long-term dependencies in carbon emission data to achieve accurate prediction of future carbon emissions. Therefore, this application, through the combination of refined data prediction, an improved temporal convolutional network, an attention mechanism network, and a long-short-term memory network, fully considers the differences in the importance and fluctuation characteristics of different carbon sources, significantly improving the accuracy of carbon emission prediction. This solves the technical problem that existing carbon emission prediction methods fail to fully consider the differences in the importance and fluctuation characteristics of different carbon sources and simply combine data, resulting in limited prediction accuracy.

[0038] Optionally, the carbon emission characteristic matrix of the target object is determined based on the carbon emission data vectors of all carbon sources in the past preset days using an improved temporal convolutional network, including: determining the carbon emission data matrix of the target object in the past preset days based on the carbon emission data vectors of all carbon sources in the past preset days; and inputting the carbon emission data matrix into the improved temporal convolutional network to output the carbon emission characteristic matrix of the target object.

[0039] Specifically, first obtain the carbon emission data C1~C1 of the target object in the past m days.m , determine the n carbon sources that generate carbon emissions from the target object, and analyze the carbon emission data C1~C m Perform analysis to determine the carbon emission data vector x for each carbon source i in the past m days i =[x i,1 ~x i,m ]; where i = 1 to n, j=1-m. Then, based on the carbon emission data vectors of all carbon sources in the past preset days, the carbon emission data matrix X of the target object in the past preset days is determined:

[0040]

[0041] Optionally, the improved temporal convolutional network includes a variance calculation unit, an expansion coefficient determination unit and a temporal convolutional layer; and the operation of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission characteristic matrix of the target object includes: inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object; wherein each element in the variance vector represents the variance calculation result of the carbon emission data vector of the corresponding carbon source in the past preset number of days; inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source, and transmitting the expansion coefficient to the temporal convolutional layer; and using the temporal convolutional layer, according to the corresponding expansion coefficient, performing expansion convolution on the carbon emission data vectors of all carbon sources in the past preset number of days in parallel to determine the carbon emission characteristic matrix of the target object.

[0042] Specifically, the improved temporal convolutional network includes a variance calculation unit, a dilation coefficient determination unit, and a temporal convolution layer. The variance function calculates the carbon emission fluctuations of each carbon source over the past m days, and the dilation coefficient determination unit dynamically adjusts the dilation coefficients of the dilated convolutions in the convolution layer based on the variance values. Therefore, the operations of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission feature matrix of the target object include:

[0043] (1) For each carbon source i, the carbon emission data vector x i =[x i,1 ~x i,m ], calculate its variance, and finally get a variance vector V=[Var(x i )~Var(x n )];

[0044] (2) Input the variance vector V into the expansion coefficient determination unit (e.g., composed of a mapping function) to generate the expansion coefficient d corresponding to each carbon source i i ;

[0045] (3) For each carbon source i, use its corresponding expansion coefficient d i Perform dilated convolution and output a feature vector y i =[y i,1 ~y i,m By performing dilated convolution on all carbon sources in parallel, the carbon emission feature matrix Y is finally output:

[0046]

[0047] In this way, the fluctuation characteristics of carbon emission data can be better captured, which is suitable for processing carbon source data with different fluctuation patterns.

[0048] In a specific embodiment, the operation of inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object includes: using the variance calculation unit to extract the carbon emission data vector of each carbon source i in the past preset number of days from the carbon emission data matrix; using the variance calculation unit to calculate the variance value of the carbon emission data vector of each carbon source in the past preset number of days according to the following formula:

[0049]

[0050] Among them, Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance value, x i =[x i,1 ~x i,m ]; m is the preset number of days in the past, x i,j is the carbon emission data of carbon source i on the past j day; μ i is x i the mean of ; and

[0051] Based on the variance values ​​of the carbon emission data vectors of all carbon sources in the past preset days, the variance vector V of the target object is determined; wherein V=[Var(x i )~Var(x n )], n is the number of carbon sources.

[0052] In a specific embodiment, the operation of inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source includes: using the expansion coefficient determination unit to calculate the expansion coefficient corresponding to each carbon source according to the following formula:

[0053] d i =a·Var(x i )+b;

[0054] Among them, d iis the expansion coefficient corresponding to carbon source i; Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance of ; a and b are constants.

[0055] In this way,

[0056] In a specific embodiment, the operation of using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object includes: using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient using the following formula to obtain the carbon emission feature value of each carbon source in the past preset days:

[0057]

[0058] Among them, y i,j is the carbon emission characteristic value of carbon source i on the jth day in the past; ω k is the kth weight of the convolution kernel; k max is the convolution kernel size; d i is the expansion coefficient corresponding to carbon source i; The carbon emission data vector x for carbon source i in the past preset days i In position jd i The value at k; k is the index of the convolution kernel, k = 0, 1, · · ·, k;

[0059] Determine the carbon emission characteristic matrix Y of the target object based on the carbon emission characteristic values ​​of all carbon sources in the past preset days;

[0060]

[0061] Among them, y n,m is the carbon emission characteristic value of the nth carbon source in the past mth day.

[0062] In another specific embodiment, the dilated convolution is performed on all carbon sources in parallel by treating the data of each carbon source as an independent channel, and then utilizing the characteristics of grouped convolution or depthwise separable convolution to assign an independent convolution kernel and dilation coefficient to each channel (carbon source), specifically:

[0063] (1) Convert the input data X from n×m to 1×n×m, that is, treat it as a multi-channel one-dimensional signal;

[0064] (2) Use grouped convolution to group the convolution kernels, with each group corresponding to a carbon source. The number of groups is set to n, meaning that each carbon source uses a separate convolution kernel;

[0065] (3) Assign an independent expansion coefficient d to each carbon source i , that is, in the convolution operation, the corresponding expansion coefficient is specified for each group.

[0066] (4) Use group convolution to perform parallel dilation convolution, and then concatenate the outputs of all carbon sources to obtain Y · , whose dimensions are 1×n×m;

[0067] (5) Y · The dimension of is converted to n×m, and we get Y.

[0068] In this way, all carbon sources can be processed efficiently in parallel while preserving the dimensionality of the time series.

[0069] Optionally, the operation of processing the carbon emission feature matrix using a pre-trained attention mechanism network to obtain the attention weighted feature matrix of the target object includes: using the attention mechanism network to obtain the carbon emission feature vector y of carbon source i from the carbon emission feature matrix i ; Among them, y i =[y i,1 ~y i,m ], y i,m is the carbon emission characteristic value of the i-th carbon source on the past m-th day, i is any one of 1 to n; based on the carbon emission characteristic vector y i , calculate the attention weight α of the carbon source i i ; Use attention weight α i For the carbon emission characteristic vector y i Perform weighted summation to obtain the attention weighted feature vector y of the carbon source i attn(i) ; and based on the attention weighted feature vectors of all carbon sources in the past preset number of days, determine the attention weighted feature matrix Y of the target object attn :

[0070]

[0071] Among them, y attn(n,m) is the attention-weighted feature value of carbon source n in the past m days.

[0072] Specifically, for each carbon source i, the carbon emission characteristic vector y i , calculate its attention weight α i ; The calculation of attention weight is usually achieved through the following steps:

[0073] (1) The eigenvector y i Mapped to a hidden representation through a fully connected layer (or linear transformation):

[0074] h i =W h y i +b h ;

[0075] Among them, W h is the weight matrix, b h is the bias term;

[0076] (2) Calculate the attention score e i :

[0077] e i =v T tanh(h i );

[0078] Among them, v is a learnable parameter vector;

[0079] (3) Use the softmax function to normalize the attention score to the weight α i :

[0080]

[0081] Among them, Y attn It is a weighted feature matrix, which represents the contribution of different carbon sources to the overall carbon emission prediction.

[0082] Then, using the attention weight α i For the carbon emission feature vector y i Perform weighted summation to get the output of the attention mechanism:

[0083]

[0084] Among them, y attn(i) The attention weighted feature vector for each carbon source i, y attn(i,m) The attention weighted feature value of each carbon source i in the past m days, y attn(i) =[y attn(i,1) ~y attn(i,m) ].

[0085] Finally, based on the attention weighted feature vectors of all carbon sources in the past preset number of days, the attention weighted feature matrix Y of the target object is determined. attn :

[0086]

[0087] Among them, y attn(n,m) is the attention-weighted feature value of carbon source n in the past m days.

[0088] In one embodiment, the attention weighted feature matrix Yattn Input the long short-term memory network to capture the long-term dependencies in the time series data, thereby outputting the carbon emission data of the target object in the next k days

[0089]

[0090] Among them, the carbon emission data of carbon source i in the next k days is y future(i,m+1) ~y future(i,m+k) .

[0091] In addition, according to this embodiment, a storage medium is also provided, which includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0092] This application first determines the carbon emission data vector of each carbon source based on the carbon emission data of the target object in the past preset number of days, so as to fully consider the differences and interactions between different carbon sources, and effectively avoid the problem of losing key features caused by the general treatment of all carbon source data. Then, the improved temporal convolutional network is used to determine the carbon emission feature matrix. The network assigns an independent convolution kernel and expansion coefficient to each carbon source, which can more accurately capture the carbon emission characteristics of each carbon source, and the expansion coefficient is determined in real time based on the carbon emission fluctuation of the carbon source, further improving the accuracy of the prediction. Secondly, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix. The attention mechanism can automatically learn and emphasize information that has an important impact on the prediction results, further improving the accuracy of the prediction. Finally, the attention-weighted feature matrix is ​​input into the pre-trained long-short-term memory network to determine the carbon emission forecast data for each carbon source in the next preset number of days. The long-short-term memory network has a strong time series processing capability and can capture long-term dependencies in carbon emission data to achieve accurate prediction of future carbon emissions. Therefore, this application, through the combination of refined data prediction, an improved temporal convolutional network, an attention mechanism network, and a long-short-term memory network, fully considers the differences in the importance and fluctuation characteristics of different carbon sources, significantly improving the accuracy of carbon emission prediction. This solves the technical problem that existing carbon emission prediction methods fail to fully consider the differences in the importance and fluctuation characteristics of different carbon sources and simply combine data, resulting in limited prediction accuracy.

[0093] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0094] Example 2

[0095] Figure 3 The schematic diagram of the structure of the carbon emission prediction system 300 based on big data analysis according to this embodiment is shown. The carbon emission prediction system 300 includes: a first determination module 310, which is used to determine the carbon emission data vector of each carbon source of the target object in the past preset days based on the carbon emission data of the target object in the past preset days; wherein the target object has at least two carbon sources; a second determination module 320, which is used to determine the carbon emission feature matrix of the target object based on the carbon emission data vectors of all carbon sources in the past preset days using an improved temporal convolutional network; wherein the improved temporal convolutional network assigns an independent convolution kernel and expansion coefficient to each carbon source, and the expansion coefficient is based on the carbon emission data vectors of the carbon source. The emission fluctuation situation is determined in real time; the dimension of the carbon emission characteristic matrix is ​​n×m, where n is the number of carbon sources, m is the preset number of days in the past, and each element in the carbon emission characteristic matrix represents the carbon emission characteristic value of the corresponding carbon source on the jth day in the past; and a third determination module 330 is used to process the carbon emission characteristic matrix using a pre-trained attention mechanism network to obtain the attention weighted feature matrix of the target object, and input the attention weighted feature matrix into a pre-trained long short-term memory network to determine the carbon emission forecast data of each carbon source of the target object in the preset number of days in the future.

[0096] Optionally, the carbon emission characteristic matrix of the target object is determined based on the carbon emission data vectors of all carbon sources in the past preset days using an improved temporal convolutional network, including: determining the carbon emission data matrix of the target object in the past preset days based on the carbon emission data vectors of all carbon sources in the past preset days; and inputting the carbon emission data matrix into the improved temporal convolutional network to output the carbon emission characteristic matrix of the target object.

[0097] Optionally, the improved temporal convolutional network includes a variance calculation unit, an expansion coefficient determination unit and a temporal convolutional layer; and the operation of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission characteristic matrix of the target object includes: inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object; wherein each element in the variance vector represents the variance calculation result of the carbon emission data vector of the corresponding carbon source in the past preset number of days; inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source, and transmitting the expansion coefficient to the temporal convolutional layer; and using the temporal convolutional layer, according to the corresponding expansion coefficient, performing expansion convolution on the carbon emission data vectors of all carbon sources in the past preset number of days in parallel to determine the carbon emission characteristic matrix of the target object.

[0098] Optionally, the operation of inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object includes: using the variance calculation unit to extract the carbon emission data vector of each carbon source in the past preset number of days from the carbon emission data matrix; and using the variance calculation unit to calculate the variance value of the carbon emission data vector of each carbon source in the past preset number of days according to the following formula:

[0099]

[0100] Among them, Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance value, x i =[x i,1 ~x i,m ]; m is the preset number of days in the past, x i,j is the carbon emission data of carbon source i on the past j day; μ i is x i the mean of ; and

[0101] Based on the variance values ​​of the carbon emission data vectors of all carbon sources in the past preset days, the variance vector V of the target object is determined; wherein V=[Var(x i )~Var(x n )], n is the number of carbon sources.

[0102] Optionally, the operation of inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source includes: using the expansion coefficient determination unit to calculate the expansion coefficient corresponding to each carbon source according to the following formula:

[0103] d i =a·Var(x i )+b;

[0104] Among them, d i is the expansion coefficient corresponding to carbon source i; Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance of ; a and b are constants.

[0105] Optionally, the operation of using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object includes: using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient using the following formula to obtain the carbon emission feature value of each carbon source in the past preset days:

[0106]

[0107] Among them, y i,j is the carbon emission characteristic value of carbon source i on the jth day in the past; ω k is the kth weight of the convolution kernel; k max is the convolution kernel size; d i is the expansion coefficient corresponding to carbon source i; The carbon emission data vector x for carbon source i in the past preset days i In position jd i The value at k; k is the index of the convolution kernel, k = 0, 1, · · ·, k;

[0108] Determine the carbon emission characteristic matrix Y of the target object based on the carbon emission characteristic values ​​of all carbon sources in the past preset days;

[0109]

[0110] Among them, y n,m is the carbon emission characteristic value of the nth carbon source in the past mth day.

[0111] Optionally, the operation of processing the carbon emission feature matrix using a pre-trained attention mechanism network to obtain the attention weighted feature matrix of the target object includes: using the attention mechanism network to obtain the carbon emission feature vector y of carbon source i from the carbon emission feature matrix i ; Among them, y i =[y i,1 ~y i,m ], y i,m is the carbon emission characteristic value of the i-th carbon source on the past m-th day, i is any one of 1 to n; based on the carbon emission characteristic vector y i, calculate the attention weight α of the carbon source i i ; Use attention weight α i For the carbon emission characteristic vector y i Perform weighted summation to obtain the attention weighted feature vector y of the carbon source i attn(i) ; and based on the attention weighted feature vectors of all carbon sources in the past preset number of days, determine the attention weighted feature matrix Y of the target object attn :

[0112]

[0113] Among them, y attn(n,m) is the attention-weighted feature value of carbon source n in the past m days.

[0114] Therefore, according to this embodiment, the carbon emission data vector for each carbon source is first determined based on the target object's carbon emission data for the past preset number of days. This fully accounts for the differences and interactions between different carbon sources, effectively avoiding the loss of key features that would result from lumping together data from all carbon sources. Next, an improved temporal convolutional network is used to determine the carbon emission feature matrix. This network assigns a separate convolution kernel and dilation coefficient to each carbon source, enabling more accurate capture of each carbon source's carbon emission characteristics. The dilation coefficient is determined in real time based on the source's carbon emission fluctuations, further improving prediction accuracy. Secondly, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix. The attention mechanism automatically learns and emphasizes information that has a significant impact on the prediction results, further improving prediction accuracy. Finally, the attention-weighted feature matrix is ​​input into a pre-trained long-short-term memory network to determine the carbon emission forecast data for each carbon source for the next preset number of days. Long-short-term memory networks have powerful time series processing capabilities and can capture long-term dependencies in carbon emission data, enabling accurate prediction of future carbon emissions. Therefore, this application, through the combination of refined data prediction, an improved temporal convolutional network, an attention mechanism network, and a long-short-term memory network, fully considers the differences in the importance and fluctuation characteristics of different carbon sources, significantly improving the accuracy of carbon emission prediction. This solves the technical problem that existing carbon emission prediction methods fail to fully consider the differences in the importance and fluctuation characteristics of different carbon sources and simply combine data, resulting in limited prediction accuracy.

[0115] Example 3

[0116] Figure 4The carbon emission prediction system 400 based on big data analysis according to the present embodiment is shown, comprising: a processor 410; and a memory 420, connected to the processor 410, for providing the processor 410 with instructions for processing the following processing steps: based on the carbon emission data of the target object in the past preset number of days, determining the carbon emission data vector of each carbon source of the target object in the past preset number of days; wherein the target object has at least two carbon sources; based on the carbon emission data vectors of all carbon sources in the past preset number of days, using the improved time convolution network, determining the carbon emission feature matrix of the target object; wherein the improved time convolution network is for each carbon The sources are assigned independent convolution kernels and expansion coefficients, and the expansion coefficients are determined in real time based on the carbon emission fluctuations of the carbon sources; the dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the preset number of days in the past, and each element in the carbon emission feature matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past; and the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix of the target object, and the attention-weighted feature matrix is ​​input into a pre-trained long short-term memory network to determine the carbon emission forecast data of each carbon source of the target object in the preset number of days in the future.

[0117] Optionally, the carbon emission characteristic matrix of the target object is determined based on the carbon emission data vectors of all carbon sources in the past preset days using an improved temporal convolutional network, including: determining the carbon emission data matrix of the target object in the past preset days based on the carbon emission data vectors of all carbon sources in the past preset days; and inputting the carbon emission data matrix into the improved temporal convolutional network to output the carbon emission characteristic matrix of the target object.

[0118] Optionally, the improved temporal convolutional network includes a variance calculation unit, an expansion coefficient determination unit and a temporal convolutional layer; and the operation of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission characteristic matrix of the target object includes: inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object; wherein each element in the variance vector represents the variance calculation result of the carbon emission data vector of the corresponding carbon source in the past preset number of days; inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source, and transmitting the expansion coefficient to the temporal convolutional layer; and using the temporal convolutional layer, according to the corresponding expansion coefficient, performing expansion convolution on the carbon emission data vectors of all carbon sources in the past preset number of days in parallel to determine the carbon emission characteristic matrix of the target object.

[0119] Optionally, the operation of inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object includes: using the variance calculation unit to extract the carbon emission data vector of each carbon source in the past preset number of days from the carbon emission data matrix; and using the variance calculation unit to calculate the variance value of the carbon emission data vector of each carbon source in the past preset number of days according to the following formula:

[0120]

[0121] Among them, Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance value, x i =[x i,1 ~x i,m ]; m is the preset number of days in the past, x i,j is the carbon emission data of carbon source i on the past j day; μ i is x i the mean of ; and

[0122] Based on the variance values ​​of the carbon emission data vectors of all carbon sources in the past preset days, the variance vector V of the target object is determined; wherein V=[Var(x i )~Var(x n )], n is the number of carbon sources.

[0123] Optionally, the operation of inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source includes: using the expansion coefficient determination unit to calculate the expansion coefficient corresponding to each carbon source according to the following formula:

[0124] d i =a·Var(x i )+b;

[0125] Among them, d i is the expansion coefficient corresponding to carbon source i; Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance of ; a and b are constants.

[0126] Optionally, the operation of using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object includes: using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient using the following formula to obtain the carbon emission feature value of each carbon source in the past preset days:

[0127]

[0128] Among them, y i,j is the carbon emission characteristic value of carbon source i on the jth day in the past; ω k is the kth weight of the convolution kernel; k max is the convolution kernel size; d i is the expansion coefficient corresponding to carbon source i; The carbon emission data vector x for carbon source i in the past preset days i In position jd i The value at k; k is the index of the convolution kernel, k = 0, 1, · · ·, k;

[0129] Determine the carbon emission characteristic matrix Y of the target object based on the carbon emission characteristic values ​​of all carbon sources in the past preset days;

[0130]

[0131] Among them, y n,m is the carbon emission characteristic value of the nth carbon source in the past mth day.

[0132] Optionally, the operation of processing the carbon emission feature matrix using a pre-trained attention mechanism network to obtain the attention weighted feature matrix of the target object includes: using the attention mechanism network to obtain the carbon emission feature vector y of carbon source i from the carbon emission feature matrix i ; Among them, y i =[y i,1 ~y i,m ], y i,m is the carbon emission characteristic value of the i-th carbon source on the past m-th day, i is any one of 1 to n; based on the carbon emission characteristic vector y i , calculate the attention weight α of the carbon source i i ; Use attention weight α i For the carbon emission characteristic vector y i Perform weighted summation to obtain the attention weighted feature vector y of the carbon source i attn(i) ; and based on the attention weighted feature vectors of all carbon sources in the past preset number of days, determine the attention weighted feature matrix Y of the target object attn :

[0133]

[0134] Among them, y attn(n,m) is the attention-weighted feature value of carbon source n in the past m days.

[0135] Therefore, according to this embodiment, the carbon emission data vector for each carbon source is first determined based on the target object's carbon emission data for the past preset number of days. This fully accounts for the differences and interactions between different carbon sources, effectively avoiding the loss of key features that would result from lumping together data from all carbon sources. Next, an improved temporal convolutional network is used to determine the carbon emission feature matrix. This network assigns a separate convolution kernel and dilation coefficient to each carbon source, enabling more accurate capture of each carbon source's carbon emission characteristics. The dilation coefficient is determined in real time based on the source's carbon emission fluctuations, further improving prediction accuracy. Secondly, the carbon emission feature matrix is ​​processed using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix. The attention mechanism automatically learns and emphasizes information that has a significant impact on the prediction results, further improving prediction accuracy. Finally, the attention-weighted feature matrix is ​​input into a pre-trained long-short-term memory network to determine the carbon emission forecast data for each carbon source for the next preset number of days. Long-short-term memory networks have powerful time series processing capabilities and can capture long-term dependencies in carbon emission data, enabling accurate prediction of future carbon emissions. Therefore, this application, through the combination of refined data prediction, an improved temporal convolutional network, an attention mechanism network, and a long-short-term memory network, fully considers the differences in the importance and fluctuation characteristics of different carbon sources, significantly improving the accuracy of carbon emission prediction. This solves the technical problem that existing carbon emission prediction methods fail to fully consider the differences in the importance and fluctuation characteristics of different carbon sources and simply combine data, resulting in limited prediction accuracy.

[0136] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0137] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0140] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0142] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A carbon emission prediction method based on big data analysis, characterized in that: include: Based on the carbon emission data of the target object in the past preset days, determining the carbon emission data vector of each carbon source of the target object in the past preset days; wherein the target object has at least two carbon sources; Determining a carbon emission feature matrix for the target object using an improved temporal convolutional network based on carbon emission data vectors for all carbon sources over the past preset number of days; wherein the improved temporal convolutional network assigns an independent convolution kernel and dilation coefficient to each carbon source, and the dilation coefficient is determined in real time based on the carbon emission fluctuations of the carbon source; the dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the past preset number of days, and each element in the carbon emission feature matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past; and Using a pre-trained attention mechanism network to process the carbon emission feature matrix to obtain an attention-weighted feature matrix of the target object, and inputting the attention-weighted feature matrix into a pre-trained long-short-term memory network to determine the carbon emission forecast data of each carbon source of the target object for a preset number of days in the future; The method of determining the carbon emission feature matrix of the target object using the improved temporal convolutional network based on the carbon emission data vectors of all carbon sources in the past preset days includes: Determining a carbon emission data matrix for the target object in the past preset days based on the carbon emission data vectors of all carbon sources in the past preset days; and Inputting the carbon emission data matrix into the improved temporal convolutional network, and outputting the carbon emission feature matrix of the target object; The improved temporal convolutional network includes a variance calculation unit, a dilation coefficient determination unit and a temporal convolution layer; Furthermore, the operation of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission feature matrix of the target object includes: Inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object; wherein each element in the variance vector represents the variance calculation result of the carbon emission data vector of the corresponding carbon source over the past preset number of days; Inputting the variance vector into the dilation coefficient determination unit to determine the dilation coefficient corresponding to each carbon source, and transmitting the dilation coefficient to the temporal convolution layer; and The temporal convolution layer is used to perform dilation convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object.

2. The method according to claim 1, characterized in that The operation of inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object includes: Utilizing the variance calculation unit, extracting the carbon emission data vectors of each carbon source for the past preset number of days from the carbon emission data matrix; The variance calculation unit is used to calculate the variance value of the carbon emission data vector of each carbon source in the past preset days according to the following formula: ； Among them, Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance value, x i =[x i,1 ~x i,m ]; m is the preset number of days in the past, is the carbon emission data of carbon source i on the past j day; is x i the mean of ; and Based on the variance values ​​of the carbon emission data vectors of all carbon sources in the past preset days, the variance vector V of the target object is determined; wherein V=[Var(x i )~Var(x n )], n is the number of carbon sources.

3. The method according to claim 1, characterized in that The operation of inputting the variance vector into the expansion coefficient determination unit to determine the expansion coefficient corresponding to each carbon source includes: The expansion coefficient determination unit is used to calculate the expansion coefficient corresponding to each carbon source according to the following formula: ； in, d i is the expansion coefficient corresponding to carbon source i; Var(x i ) is the carbon emission data vector x of carbon source i in the past preset days i The variance of ; a and b are constants.

4. The method according to claim 1, wherein The operation of using the temporal convolution layer to perform dilated convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object includes: Using the temporal convolution layer, according to the corresponding dilation coefficient, the carbon emission data vectors of all carbon sources in the past preset days are dilated and convolved in parallel using the following formula to obtain the carbon emission characteristic value of each carbon source in the past preset days: ； Among them, y i,j is the carbon emission characteristic value of carbon source i on the past j day; is the kth weight of the convolution kernel; k max is the maximum value of the convolution kernel; d i is the expansion coefficient corresponding to carbon source i; The carbon emission data vector x for carbon source i in the past preset days i Middle Carbon emission data at each location; k is the index of the convolution kernel, k=0,1,···,k max ; Determine the carbon emission characteristic matrix Y of the target object based on the carbon emission characteristic values ​​of all carbon sources in the past preset days; ； Among them, y n,m is the carbon emission characteristic value of the nth carbon source in the past mth day.

5. The method according to claim 1, wherein The operation of processing the carbon emission feature matrix using the pre-trained attention mechanism network to obtain the attention-weighted feature matrix of the target object includes: Using the attention mechanism network, the carbon emission feature vector y of carbon source i is obtained from the carbon emission feature matrix i ; Among them, y i =[y i,1 ~y i,m ], y i,m is the carbon emission characteristic value of the i-th carbon source on the past m-th day, where i is any item from 1 to n; Based on the carbon emission characteristic vector y i , calculate the attention weight of the carbon source i ; Using attention weights For the carbon emission characteristic vector y i Perform weighted summation to obtain the attention weighted feature vector y of the carbon source i attn(i) ;as well as Based on the attention weighted feature vectors of all carbon sources in the past preset number of days, determine the attention weighted feature matrix Y of the target object attn : ； Among them, y attn(n,m) is the attention-weighted feature value of carbon source n in the past m days.

6. A storage medium, characterized in that The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 5.

7. A carbon emission prediction system based on big data analysis, comprising: A first determining module is configured to determine a carbon emission data vector of each carbon source of the target object over the past preset number of days based on the carbon emission data of the target object over the past preset number of days; wherein the target object has at least two carbon sources; a second determination module, configured to determine a carbon emission characteristic matrix of the target object using an improved temporal convolutional network based on the carbon emission data vectors of all carbon sources over the past preset number of days; wherein the improved temporal convolutional network assigns an independent convolution kernel and dilation coefficient to each carbon source, and the dilation coefficient is determined in real time based on the carbon emission fluctuation of the carbon source; the dimension of the carbon emission characteristic matrix is ​​n×m, where n is the number of carbon sources and m is the past preset number of days, and each element in the carbon emission characteristic matrix represents the carbon emission characteristic value of the corresponding carbon source on the jth day in the past; and A third determination module is configured to process the carbon emission feature matrix using a pre-trained attention mechanism network to obtain an attention-weighted feature matrix of the target object, and input the attention-weighted feature matrix into a pre-trained long-short-term memory network to determine the carbon emission forecast data for each carbon source of the target object for a preset number of days in the future; The method of determining the carbon emission feature matrix of the target object using the improved temporal convolutional network based on the carbon emission data vectors of all carbon sources in the past preset days includes: Determining a carbon emission data matrix for the target object in the past preset days based on the carbon emission data vectors of all carbon sources in the past preset days; and Inputting the carbon emission data matrix into the improved temporal convolutional network, and outputting the carbon emission feature matrix of the target object; The improved temporal convolutional network includes a variance calculation unit, a dilation coefficient determination unit and a temporal convolution layer; Furthermore, the operation of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission feature matrix of the target object includes: Inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object; wherein each element in the variance vector represents the variance calculation result of the carbon emission data vector of the corresponding carbon source over the past preset number of days; Inputting the variance vector into the dilation coefficient determination unit to determine the dilation coefficient corresponding to each carbon source, and transmitting the dilation coefficient to the temporal convolution layer; and The temporal convolution layer is used to perform dilation convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object.

8. A carbon emission prediction system based on big data analysis, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Based on the carbon emission data of the target object in the past preset days, determining the carbon emission data vector of each carbon source of the target object in the past preset days; wherein the target object has at least two carbon sources; Determining a carbon emission feature matrix for the target object using an improved temporal convolutional network based on carbon emission data vectors for all carbon sources over the past preset number of days; wherein the improved temporal convolutional network assigns an independent convolution kernel and dilation coefficient to each carbon source, and the dilation coefficient is determined in real time based on the carbon emission fluctuations of the carbon source; the dimension of the carbon emission feature matrix is ​​n×m, where n is the number of carbon sources and m is the past preset number of days, and each element in the carbon emission feature matrix represents the carbon emission feature value of the corresponding carbon source on the jth day in the past; and Using a pre-trained attention mechanism network to process the carbon emission feature matrix to obtain an attention-weighted feature matrix of the target object, and inputting the attention-weighted feature matrix into a pre-trained long-short-term memory network to determine the carbon emission forecast data of each carbon source of the target object for a preset number of days in the future; The method of determining the carbon emission feature matrix of the target object using the improved temporal convolutional network based on the carbon emission data vectors of all carbon sources in the past preset days includes: Determining a carbon emission data matrix for the target object in the past preset days based on the carbon emission data vectors of all carbon sources in the past preset days; and Inputting the carbon emission data matrix into the improved temporal convolutional network, and outputting the carbon emission feature matrix of the target object; The improved temporal convolutional network includes a variance calculation unit, a dilation coefficient determination unit and a temporal convolution layer; Furthermore, the operation of inputting the carbon emission data matrix into the improved temporal convolutional network and outputting the carbon emission feature matrix of the target object includes: Inputting the carbon emission data matrix into the variance calculation unit to determine the variance vector of the target object; wherein each element in the variance vector represents the variance calculation result of the carbon emission data vector of the corresponding carbon source over the past preset number of days; Inputting the variance vector into the dilation coefficient determination unit to determine the dilation coefficient corresponding to each carbon source, and transmitting the dilation coefficient to the temporal convolution layer; and The temporal convolution layer is used to perform dilation convolution on the carbon emission data vectors of all carbon sources in the past preset days in parallel according to the corresponding dilation coefficient to determine the carbon emission feature matrix of the target object.

Images