Multi-dimensional carbon emission prediction method and system based on big data

Abstract

The invention relates to the field of carbon emission management, and discloses a multi-dimensional carbon emission prediction method and system based on big data, and the method comprises the steps: determining first carbon emission time sequence trend information of a plurality of subjects in a second time period; determining second carbon emission time sequence trend information of the plurality of subjects in a second time period according to the economic activity data of the plurality of subjects in the first time period; determining third carbon emission time sequence trend information of the plurality of main bodies in the second time period according to the technical upgrading data of the plurality of main bodies in the first time period; carbon emission time sequence trend correction information is determined according to the second carbon emission time sequence trend information and the third carbon emission time sequence trend information, and time sequence carbon emission predicted values of the multiple main bodies in the second time period are determined according to the time sequence carbon emission in the first time period, the first carbon emission time sequence trend information and the carbon emission time sequence trend correction information. According to the invention, the accuracy of carbon emission prediction under multiple influence factors under the big data condition can be improved.

Description

Technical Field

[0001] The present application relates to the field of carbon emission management technology, and more specifically, to a multi-dimensional carbon emission prediction method and system based on big data. Background Art

[0002] Carbon footprint refers to the total amount of greenhouse gas emissions generated directly or indirectly by an individual, organization, product or activity within a certain period of time, usually expressed in carbon dioxide equivalent. By statistically analyzing the carbon footprints of enterprises and institutions in the fields of manufacturing, transportation, construction, etc., carbon emissions can be predicted and managed. Accurate prediction and management of carbon emissions can help enterprises optimize their energy consumption structure and provide a scientific basis for government departments to formulate effective emission reduction policies, thereby achieving the goal of sustainable development.

[0003] Existing carbon emission prediction methods mainly rely on statistical models and empirical formulas. Although these methods can reflect the current emission trends to a certain extent, they often show the problem of insufficient carbon emission prediction accuracy when facing complex and changing industrial structures, technological progress and policy interventions. For example, the traditional linear regression model cannot effectively capture the nonlinear change characteristics of carbon emissions, resulting in a large deviation between the prediction results and the actual emissions. In addition, the dynamic changes of factors such as energy market fluctuations and technological innovation have also brought new uncertainties to carbon emission prediction. Therefore, it is of great theoretical significance and practical value to develop a more accurate carbon emission prediction method that can comprehensively consider multiple influencing factors. Summary of the invention

[0004] The purpose of this application is to provide a multi-dimensional carbon emission prediction method and system based on big data, which solves the technical problem of insufficient accuracy of carbon emission prediction under multiple influencing factors and achieves the technical effect of improving the accuracy of carbon emission prediction under multiple influencing factors.

[0005] An embodiment of the present application provides a multi-dimensional carbon emission prediction method based on big data, and the method includes: determining the first carbon emission time series trend information of multiple subjects in a second time period based on the time series carbon emissions of multiple subjects in a first time period; determining the second carbon emission time series trend information of multiple subjects in the second time period based on the economic activity data of multiple subjects in the first time period; determining the third carbon emission time series trend information of multiple subjects in the second time period based on the technical upgrade data of multiple subjects in the first time period; wherein the second time period is a subsequent time period of the first time period; determining the carbon emission time series trend correction information based on the second carbon emission time series trend information and the third carbon emission time series trend information, and determining the predicted value of the time series carbon emissions of multiple subjects in the second time period through a carbon emission prediction model based on the time series carbon emissions in the first time period, the first carbon emission time series trend information and the carbon emission time series trend correction information.

[0006] In one possible implementation, carbon emission timing trend correction information is determined based on the second carbon emission timing trend information and the third carbon emission timing trend information, including: when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are the same, the sum of the second carbon emission timing trend information and the third carbon emission timing trend information is used as the carbon emission timing trend correction information; when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are opposite, the difference between the second carbon emission timing trend information and the third carbon emission timing trend information is used as the carbon emission timing trend correction information.

[0007] In another possible implementation, the method also includes: when the second carbon emission timing trend information is a changing trend of decreasing carbon emissions, and when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are opposite, and when the carbon emission trend value of the second carbon emission timing trend information at any moment is greater than the preset second carbon emission trend value, the second carbon emission timing trend information is used as the carbon emission timing trend correction information.

[0008] In another possible implementation, the method also includes: when the second carbon emission timing trend information is a changing trend of increasing carbon emissions, and when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are the same, and when the carbon emission trend value of the third carbon emission timing trend information at any moment is greater than the preset third carbon emission trend value, the product of the second carbon emission timing trend information and the third carbon emission timing trend information at the corresponding moment is used as the carbon emission timing trend correction information.

[0009] In another possible implementation, the method also includes: obtaining a target fluctuation prediction model corresponding to the economic activity data of each subject in the first time period, and obtaining regional economic activity indicators corresponding to the prediction parameters in the fluctuation prediction model; adjusting the economic activity fluctuation threshold according to the regional economic activity indicators to obtain a corrected economic activity fluctuation threshold; wherein the regional economic activity indicators are economic activity indicators of the region where the subject is located; determining the economic activity data of each subject in the second time period according to the economic activity data of each subject in the first time period through the target fluctuation prediction model, and when the fluctuation value of the economic activity data of the first subject in the second time period is less than the corrected economic activity fluctuation threshold, determining the second carbon emission time series trend information of the first subject in the second time period according to the economic activity data of the first subject in the second time period.

[0010] In another possible implementation, the method also includes: when the fluctuation value of the economic activity data of the first subject in the second time period is greater than or equal to the modified economic activity fluctuation threshold, taking the product of the economic activity data of the first subject in the first time period and the regional economic activity index as the economic activity data of the first subject in the second time period.

[0011] In another possible implementation, the second carbon emission time series trend information of the first subject in the second time period is determined based on the economic activity data of the first subject in the second time period, including: determining the second carbon emission time series trend information of the first subject in the second time period based on the economic activity data of the first subject in the second time period through an economic activity data-carbon emission prediction model.

[0012] In another possible implementation, the economic activity fluctuation threshold is adjusted according to the regional economic activity index to obtain a modified economic activity fluctuation threshold, including: taking the product of the fluctuation value of the regional economic activity index and the adjustment factor as the modified economic activity fluctuation threshold; wherein the adjustment factor is determined according to the industry in which the subject is located.

[0013] An embodiment of the present application also provides a multi-dimensional carbon emission prediction system based on big data, including a unit for executing any of the methods described above.

[0014] An embodiment of the present application also provides a multi-dimensional carbon emission prediction system based on big data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0015] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0016] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of any of the methods described above when executed by a processor.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0018] The embodiment of the present application provides a multi-dimensional carbon emission prediction method based on big data, and the method includes: determining the first carbon emission time series trend information of multiple subjects in the second time period according to the time series carbon emissions of multiple subjects in the first time period; determining the second carbon emission time series trend information of multiple subjects in the second time period according to the economic activity data of multiple subjects in the first time period; determining the third carbon emission time series trend information of multiple subjects in the second time period according to the technology upgrade data of multiple subjects in the first time period; wherein the second time period is the subsequent time period of the first time period; determining the carbon emission time series trend correction information according to the second carbon emission time series trend information and the third carbon emission time series trend information, and determining the time series carbon emission prediction value of multiple subjects in the second time period according to the carbon emission prediction model, the first carbon emission time series trend information and the carbon emission time series trend correction information. The method in the embodiment of the present application can use the information of dimensions such as carbon emissions, economic activity intensity and technology upgrade information of carbon emission subjects in big data to predict carbon emissions and improve the prediction accuracy of carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 A schematic diagram of a process flow of a multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a workflow of a multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of a process flow of another multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the logical structure of a multi-dimensional carbon emission prediction system based on big data provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the physical structure of a multi-dimensional carbon emission prediction system based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0026] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0027] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0028] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0030] Existing carbon emission prediction methods often show the problem of insufficient carbon emission prediction accuracy when faced with complex and changeable conditions.

[0031] Based on the above reasons, the embodiment of the present application provides a multi-dimensional carbon emission prediction method based on big data, and the method includes: determining the first carbon emission time series trend information of multiple subjects in the second time period according to the time series carbon emissions of multiple subjects in the first time period; determining the second carbon emission time series trend information of multiple subjects in the second time period according to the economic activity data of multiple subjects in the first time period; determining the third carbon emission time series trend information of multiple subjects in the second time period according to the technology upgrade data of multiple subjects in the first time period; wherein the second time period is the subsequent time period of the first time period; determining the carbon emission time series trend correction information according to the second carbon emission time series trend information and the third carbon emission time series trend information, and determining the time series carbon emission prediction value of multiple subjects in the second time period according to the carbon emission prediction model, the first carbon emission time series trend information and the carbon emission time series trend correction information. The method in the embodiment of the present application can use the information of the carbon emission subject in the dimensions of carbon emissions, economic activity intensity and technology upgrade information in big data to predict carbon emissions and improve the prediction accuracy of carbon emissions.

[0032] In some scenarios, a multi-dimensional carbon emission prediction method based on big data in an embodiment of the present application can be applied to the prediction of carbon emissions. It can predict carbon emissions by combining information such as carbon emissions, economic activity intensity, and technology upgrade information of carbon emission entities in big data, thereby improving the prediction accuracy of carbon emissions.

[0033] The following is a specific example of a multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application.

[0034] Figure 1 A flowchart of a multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes S110 to S120, and S110 to S120 are described in detail below.

[0035] S110. Determine the first carbon emission time series trend information of the multiple subjects in the second time period based on the time series carbon emissions of the multiple subjects in the first time period. Determine the second carbon emission time series trend information of the multiple subjects in the second time period based on the economic activity data of the multiple subjects in the first time period. Determine the third carbon emission time series trend information of the multiple subjects in the second time period based on the technology upgrade data of the multiple subjects in the first time period. The second time period is a subsequent time period of the first time period.

[0036] Figure 2 A schematic diagram of a workflow of a multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application is shown in FIG. Figure 2As shown, when the method in the embodiment of the present application is working, the carbon emissions can be predicted by first using the information of the carbon emissions dimension. Specifically, the first carbon emissions time series trend information of the multiple subjects in the second time period can be determined based on the time series carbon emissions of the multiple subjects in the first time period. The first carbon emissions time series trend information is the carbon emissions change information corresponding to the carbon emissions dimension. Among them, the second time period is the subsequent time period of the first time period.

[0037] Exemplarily, the multiple entities may be enterprises and institutions of various types.

[0038] Exemplarily, the first time period may be the time-series carbon emissions within 7 days, 15 days or 30 days, and the first time period may be the time-series carbon emissions within 7 days, 15 days or 30 days following the first time period.

[0039] like Figure 2 As shown, when the method in the embodiment of the present application is working, it can also use the information of the subject in the economic activity data dimension to predict the carbon emissions, so as to predict the carbon emissions according to the economic activity status of the subject. Specifically, the second carbon emission time series trend information of the multiple subjects in the second time period can be determined based on the economic activity data of the multiple subjects in the first time period. The second carbon emission time series trend information is the carbon emission change information corresponding to the economic activity status dimension.

[0040] Exemplarily, the economic activity data-carbon emissions prediction model can be used to determine the second carbon emissions time series trend information of multiple subjects in a second time period based on the economic activity data of multiple subjects in a first time period. The economic activity carbon emissions prediction model can be trained on the LSTM model through economic activity data and carbon emissions time series trend information.

[0041] like Figure 2 As shown, when the method in the embodiment of the present application is working, it can also use the information of the technology upgrade dimension to predict carbon emissions. The technology upgrade information often affects the emission factor of carbon emissions, thereby improving the prediction accuracy of carbon emissions. Specifically, the third carbon emission time series trend information of multiple subjects in the second time period can be determined based on the technology upgrade data of multiple subjects in the first time period. The third carbon emission time series trend information is the carbon emission change information corresponding to the technology upgrade dimension.

[0042] Exemplarily, the technology upgrade data-carbon emissions prediction model can be used to determine the second carbon emissions time series trend information of multiple entities in a second time period based on the technology upgrade data of multiple entities in a first time period. The technology upgrade data-carbon emissions prediction model can be trained on the LSTM model through technology upgrade data and carbon emissions time series trend information.

[0043] S120. Determine the carbon emission time series trend correction information based on the second carbon emission time series trend information and the third carbon emission time series trend information, and determine the predicted values ​​of the time series carbon emissions of multiple entities in the second time period based on the time series carbon emissions in the first time period, the first carbon emission time series trend information and the carbon emission time series trend correction information through a carbon emission prediction model.

[0044] like Figure 2 As shown, during operation, the carbon emission time series trend correction information can be determined based on the second carbon emission time series trend information and the third carbon emission time series trend information. The carbon emission time series trend correction information represents the impact of integrating the data of economic activity dimension and the data of technology upgrade dimension on carbon emissions. Then, the accuracy of carbon emission prediction can be improved based on the carbon emission time series trend correction information.

[0045] Exemplarily, the carbon emission time series trend correction information can be obtained by combining the data of economic activity dimension and the data of technology upgrade dimension, which can correct the errors or mistakes in carbon emission prediction made by the data of economic activity dimension or the data of technology upgrade dimension alone.

[0046] like Figure 2 As shown, after obtaining the carbon emission time series trend correction information, the carbon emission prediction model can be used to determine the predicted values ​​of the time series carbon emissions of multiple subjects in the second time period according to the time series carbon emissions in the first time period, the first carbon emission time series trend information and the carbon emission time series trend correction information, thereby realizing the prediction of the carbon emissions in the second time period.

[0047] The beneficial effect of the above implementation method is that it combines the data of economic activity dimension, technology upgrade dimension and time series carbon emissions in big data to predict carbon emissions in subsequent time periods, thereby improving the prediction accuracy of carbon emissions.

[0048] The beneficial effect of the above-mentioned implementation method is that it can determine the carbon emission time series trend correction information based on the second carbon emission time series trend information and the third carbon emission time series trend information, realize the correction of carbon emission forecast data based on the data of economic activity dimension and the data of technology upgrade dimension, and further improve the accuracy of carbon emission forecast.

[0049] In some implementations, in the above S120, the carbon emission time series trend correction information is determined according to the second carbon emission time series trend information and the third carbon emission time series trend information, including: when the change trends of the second carbon emission time series trend information and the third carbon emission time series trend information are the same, the sum of the second carbon emission time series trend information and the third carbon emission time series trend information is used as the carbon emission time series trend correction information. When the change trends of the second carbon emission time series trend information and the third carbon emission time series trend information are opposite, the difference between the second carbon emission time series trend information and the third carbon emission time series trend information is used as the carbon emission time series trend correction information.

[0050] During prediction, when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are the same, it means that the carbon emission changing trends predicted from the economic activity data dimension and the carbon emission changing trends predicted from the technology upgrade data dimension are increasing or decreasing at the same time, that is, the carbon emission changing trends predicted from the economic activity data dimension and the carbon emission changing trends predicted from the technology upgrade data dimension have the same impact on the carbon emission prediction, and thus the sum of the second carbon emission time series trend information and the third carbon emission time series trend information can be used as the carbon emission time series trend correction information, so that the carbon emissions can be predicted.

[0051] During prediction, when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are opposite, it means that the carbon emission changing trends predicted from the economic activity data dimension and the carbon emission changing trends predicted from the technology upgrade data dimension have opposite effects on the carbon emission prediction. At this time, the difference between the second carbon emission time series trend information and the third carbon emission time series trend information can be used as the carbon emission time series trend correction information, which realizes the prediction of carbon emissions when the economic activity data and the technology upgrade data have opposite impacts on carbon emissions.

[0052] Exemplarily, when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are opposite, the second carbon emission time series trend information may be a plurality of carbon emission change values ​​in a time series indicating that the carbon emissions gradually increase, and the third carbon emission time series trend information may be a plurality of carbon emission change values ​​in a time series indicating that the carbon emissions gradually decrease, and the plurality of carbon emission change values ​​in the time series of the second carbon emission time series trend information and the third carbon emission time series trend information are all positive values.

[0053] The beneficial effect of the above-mentioned implementation method is that when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are the same, the sum of the second carbon emission time series trend information and the third carbon emission time series trend information is used as the carbon emission time series trend correction information to predict the carbon emissions.

[0054] The beneficial effect of the above-mentioned implementation method is that when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are opposite, the difference between the second carbon emission timing trend information and the third carbon emission timing trend information is used as the carbon emission timing trend correction information, so as to predict the carbon emissions.

[0055] In some implementations, the above method also includes: when the second carbon emission timing trend information shows a changing trend of decreasing carbon emissions, and when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are opposite, and when the carbon emission trend value of the second carbon emission timing trend information at any moment is greater than the preset second carbon emission trend value, the second carbon emission timing trend information is used as the carbon emission timing trend correction information.

[0056] During prediction, when the second carbon emission time series trend information shows a changing trend of decreasing carbon emissions, it means that the carbon emission forecast value obtained based on the subject's economic activity data is a gradual decrease in carbon emissions, that is, the subject may have a gradually decreasing carbon emission forecast value due to a decrease in the intensity of its economic activities.

[0057] During the prediction, when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are opposite, it means that the third carbon emission time series trend information shows an increase in carbon emissions, that is, the subject may increase carbon emissions due to changes in production technology.

[0058] During prediction, when the carbon emission trend value of the second carbon emission time series trend information at any moment is greater than the preset second carbon emission trend value, it means that the subject may have reduced the intensity of economic activities, resulting in too large a decrease in the predicted value of carbon emissions, that is, the subject may have significantly reduced the intensity of economic activities due to changes in the market environment. At this time, the second carbon emission time series trend information can be used as carbon emission time series trend correction information, so as to take the impact of carbon emissions brought by economic activity data as the main factor, thereby improving the prediction accuracy of carbon emissions.

[0059] The beneficial effect of the above implementation method is that when the subject's economic activity intensity may be significantly reduced due to changes in the market environment, the impact of carbon emissions brought about by economic activity data will be used as the main factor, thereby improving the accuracy of carbon emissions prediction.

[0060] The beneficial effect of the above-mentioned implementation method is that when the intensity of economic activities of the subject may be greatly reduced due to changes in the market environment, and when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are opposite, the impact of carbon emissions brought by economic activity data is used as the main factor, thereby improving the prediction accuracy of carbon emissions.

[0061] In some implementations, the above method also includes: when the second carbon emission timing trend information is a changing trend of increasing carbon emissions, and when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are the same, and when the carbon emission trend value of the third carbon emission timing trend information at any moment is greater than the preset third carbon emission trend value, the product of the second carbon emission timing trend information and the third carbon emission timing trend information at the corresponding moment is used as the carbon emission timing trend correction information.

[0062] During the prediction, when the second carbon emission time series trend information shows an increasing trend of carbon emissions, it means that the carbon emissions predicted by economic data are increasing, and when the changing trends of the second carbon emission time series trend information and the third carbon emission time series trend information are the same, it means that the carbon emissions predicted by technology upgrade data are also increasing, and when the carbon emission trend value of the third carbon emission time series trend information at any time is greater than the preset third carbon emission trend value, that is, the predicted value of carbon emissions caused by technology upgrading exceeds the predicted range, indicating that the economic activity intensity and technological changes of the subject at this time have led to a large increase in carbon emissions. At this time, the product of the second carbon emission time series trend information and the third carbon emission time series trend information at the corresponding time can be used as the carbon emission time series trend correction information to further increase the predicted increase in carbon emissions, so that the predicted increase in carbon emissions is more in line with the actual changes in carbon emissions, thereby improving the accuracy of the predicted carbon emissions.

[0063] The beneficial effect of the above-mentioned implementation method is that when the carbon emissions predicted by economic data show an increasing trend, and the carbon emissions predicted by technology upgrade data also show an increasing trend, in order to make the predicted increase in carbon emissions more in line with the actual changes in carbon emissions, the product of the second carbon emission time series trend information and the third carbon emission time series trend information at the corresponding moment is used as the carbon emission time series trend correction information, so that the predicted increase in carbon emissions is further increased, thereby improving the accuracy of the carbon emissions prediction value.

[0064] Figure 3 A flow chart of another multi-dimensional carbon emission prediction method based on big data provided in an embodiment of the present application is as follows: Figure 3 As shown, the above method further includes S210 to S220, and S210 to S220 are described in detail below.

[0065] S210: Obtain a target fluctuation prediction model corresponding to the economic activity data of each subject in the first time period, and obtain a regional economic activity index corresponding to the prediction parameter in the fluctuation prediction model. Adjust the economic activity fluctuation threshold according to the regional economic activity index to obtain a modified economic activity fluctuation threshold. The regional economic activity index is the economic activity index of the region where the subject is located.

[0066] In some cases, the intensity of economic activities of entities or markets may fluctuate due to the influence of the external economic environment, which may lead to poor accuracy in carbon emissions forecasts.

[0067] During the prediction, the target fluctuation prediction model corresponding to the economic activity data of each subject in the first time period can be obtained, and then the fluctuation of economic activities can be predicted based on the economic activity data of each subject in the first time period, and the prediction accuracy of carbon emissions can be improved based on the fluctuation of economic activities.

[0068] Exemplarily, there may be multiple fluctuation prediction models. When predicting the fluctuation of economic activity data, the fluctuation of economic activity can be predicted based on the target fluctuation prediction model that best matches the economic activity data in the first time period to improve the prediction accuracy of the economic activity data.

[0069] When constructing a fluctuation prediction model, the prediction parameters may also include regional economic activity indicators. The regional economic activity indicators are economic activity indicators of the region where the subject is located. The regional economic activity indicators are used to predict the economic activity data of the subject in the region.

[0070] During the forecasting, the regional economic activity indicators corresponding to the forecasting parameters in the fluctuation forecasting model can also be obtained, and the regional economic activity indicators can be used as the basis for subsequent economic activity forecasting.

[0071] During the forecast, the regional economic activity indicators reflect the fluctuations in the economic activities of the entities in the region, and the economic activity fluctuation thresholds can be adjusted according to the regional economic activity indicators to obtain the corrected economic activity fluctuation thresholds. The economic activity fluctuations of the entities can then be detected based on the corrected economic activity fluctuation thresholds to improve the accuracy of carbon emissions forecasts through the fluctuations in the economic activities of the entities.

[0072] S220. Determine the economic activity data of each subject in the second time period based on the economic activity data of each subject in the first time period through the target fluctuation prediction model; when the fluctuation value of the economic activity data of the first subject in the second time period is less than the corrected economic activity fluctuation threshold, determine the second carbon emission time series trend information of the first subject in the second time period based on the economic activity data of the first subject in the second time period.

[0073] When making predictions, the target fluctuation prediction model can be used to determine the economic activity data of each subject in the second time period based on the economic activity data of each subject in the first time period. The economic activity data in the second time period is the predicted economic activity data.

[0074] After obtaining the economic activity data in the second time period, when the fluctuation value of the economic activity data of the first subject in the second time period is less than the modified economic activity fluctuation threshold, it means that the predicted fluctuation value of the economic activity data of the first subject in the second time period is within the expected range, which can be said to be within a reasonable range, and thus the carbon emissions can be predicted based on the economic activity data of the first subject in the second time period.

[0075] When predicting carbon emissions, the second carbon emission time series trend information of the first subject in the second time period can be determined based on the economic activity data of the first subject in the second time period, and then the carbon emissions in the second time period can be predicted based on the second carbon emission time series trend information.

[0076] Exemplarily, the fluctuation value of the economic activity data of the first subject in the second time period may be calculated by taking the average of the fluctuation values ​​of a plurality of indicators of the economic activity data, and the fluctuation value of an indicator of the economic activity data may be the variance value of the indicator of the economic activity data.

[0077] The beneficial effect of the above implementation method is that a target fluctuation prediction model corresponding to the economic activity data of each subject in the first time period is obtained, thereby improving the accuracy of predicting the economic activity data through the target fluctuation prediction model.

[0078] The beneficial effect of the above-mentioned implementation method is that the economic activity fluctuation threshold is adjusted according to the regional economic activity indicators to obtain the corrected economic activity fluctuation threshold, and the economic activity fluctuation situation of the subject can be detected according to the corrected economic activity fluctuation threshold, thereby improving the scientific nature of the economic activity fluctuation threshold for detecting the fluctuation value of economic activity data.

[0079] The beneficial effect of the above implementation method is that when the fluctuation value of the economic activity data of the first subject in the second time period is less than the corrected economic activity fluctuation threshold, the carbon emissions are predicted based on the economic activity data of the first subject in the second time period, thereby improving the accuracy of the carbon emissions prediction.

[0080] In some implementations, the above method also includes: when the fluctuation value of the economic activity data of the first subject in the second time period is greater than or equal to the modified economic activity fluctuation threshold, taking the product of the economic activity data of the first subject in the first time period and the regional economic activity index as the economic activity data of the first subject in the second time period.

[0081] During the prediction, when the fluctuation value of the economic activity data of the first subject in the second time period is greater than or equal to the modified economic activity fluctuation threshold, it means that the fluctuation value of the economic activity data of the first subject in the second time period is too large, and the prediction of carbon emissions based on the economic activity data of the first subject in the second time period may be inaccurate. Therefore, the product of the economic activity data of the first subject at each time point in the first time period and the regional economic activity index can be used as the economic activity data of the first subject in the second time period, thereby improving the prediction accuracy of carbon emissions.

[0082] Exemplarily, the regional economic activity index can be a scalar, which characterizes the changing state of regional economic activities. Then, the product of the economic activity data of the first subject at each time point in the first time period and the regional economic activity index can be used as the economic activity data of the first subject in the second time period, thereby improving the accuracy of the prediction of the economic activity data of the first subject in the second time period.

[0083] Exemplarily, the regional economic activity index can be a time series vector, which characterizes the fluctuation state of regional economic activity. Then, the product of the economic activity data of the first subject at different time points in the first time period and the regional economic activity index at the corresponding time point can be used as the economic activity data of the first subject in the second time period, thereby improving the accuracy of the prediction of the economic activity data of the first subject in the second time period.

[0084] The beneficial effect of the above-mentioned implementation method is that when the fluctuation value of the economic activity data of the first subject in the second time period is too large, the product of the economic activity data of the first subject in the first time period and the regional economic activity index is used as the economic activity data of the first subject in the second time period, thereby improving the prediction accuracy of the economic activity data of the first subject in the second time period.

[0085] In some implementations, determining the second carbon emission time series trend information of the first subject in the second time period based on the economic activity data of the first subject in the second time period includes: determining the second carbon emission time series trend information of the first subject in the second time period based on the economic activity data of the first subject in the second time period through an economic activity data-carbon emission prediction model.

[0086] During prediction, the economic activity data-carbon emissions prediction model can be used to determine the second carbon emissions time series trend information of the first subject in the second time period based on the economic activity data of the first subject in the second time period, thereby realizing the prediction of the second carbon emissions time series trend information in the second time period.

[0087] Exemplarily, the economic activity data-carbon emissions prediction model can be trained by using the economic activity data and the second carbon emissions time series trend information within the same time period, thereby improving the prediction accuracy of the carbon emissions time series trend information based on the economic activity data.

[0088] The beneficial effect of the above implementation method is that, through the economic activity data-carbon emissions prediction model, accurate prediction of the second carbon emissions time series trend information in the second time period is achieved, thereby improving the prediction accuracy of carbon emissions.

[0089] In some implementations, the economic activity fluctuation threshold is adjusted according to the regional economic activity index to obtain a modified economic activity fluctuation threshold, including: taking the product of the fluctuation value of the regional economic activity index and the adjustment factor as the modified economic activity fluctuation threshold; wherein the adjustment factor is determined according to the industry in which the subject is located.

[0090] When making predictions, the product of the fluctuation value of the regional economic activity indicator and the adjustment factor can be used as the modified economic activity fluctuation threshold. The adjustment factor is determined based on the industry in which the subject is located. The adjustment factor represents the characteristics of different industries, thereby realizing the adjustment of the modified economic activity fluctuation threshold based on the characteristics of different industries and the fluctuation value of the regional economic activity indicator, thereby improving the scientific nature of the modified economic activity fluctuation threshold.

[0091] For example, the adjustment factor may be determined based on empirical values.

[0092] The beneficial effect of the above implementation method is that the revised economic activity fluctuation threshold is adjusted according to the characteristics of different industries and the fluctuation values ​​of regional economic activity indicators, thereby improving the scientific nature of the revised economic activity fluctuation threshold.

[0093] An embodiment of the present application also provides a multi-dimensional carbon emission prediction system based on big data, including a unit for executing any of the methods described above.

[0094] Figure 4 A logical structure diagram of a multi-dimensional carbon emission prediction system based on big data provided in an embodiment of the present application is shown in FIG. Figure 4As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12 and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12 and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects of the embodiment of the present application have been described in the above method and will not be repeated here.

[0095] An embodiment of the present application also provides a multi-dimensional carbon emission prediction system based on big data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0096] Figure 5 A schematic diagram of the physical structure of a multi-dimensional carbon emission prediction system based on big data provided in one embodiment of the present application is shown in FIG. Figure 5 As shown, the system 2 of this embodiment includes: at least one processor 20 ( Figure 5 Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, the steps in any of the above-mentioned method embodiments are implemented. The beneficial effects of the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0097] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0098] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0099] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0100] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0101] 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 present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0104] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, 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 devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-dimensional carbon emission prediction method based on big data, characterized in that: The method comprises: Determine the first carbon emission time series trend information of multiple subjects in the second time period based on the time series carbon emissions of multiple subjects in the first time period; determine the second carbon emission time series trend information of multiple subjects in the second time period based on the economic activity data of multiple subjects in the first time period; determine the third carbon emission time series trend information of multiple subjects in the second time period based on the technology upgrade data of multiple subjects in the first time period; wherein the second time period is a subsequent time period of the first time period; The carbon emission time series trend correction information is determined based on the second carbon emission time series trend information and the third carbon emission time series trend information, and the predicted values ​​of the time series carbon emissions of multiple subjects in the second time period are determined through the carbon emission prediction model based on the time series carbon emissions in the first time period, the first carbon emission time series trend information and the carbon emission time series trend correction information.

2. The method according to claim 1, characterized in that Determining carbon emission time series trend correction information according to the second carbon emission time series trend information and the third carbon emission time series trend information includes: When the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are the same, the sum of the second carbon emission timing trend information and the third carbon emission timing trend information is used as the carbon emission timing trend correction information; when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are opposite, the difference between the second carbon emission timing trend information and the third carbon emission timing trend information is used as the carbon emission timing trend correction information.

3. The method according to claim 2, characterized in that The method further comprises: When the second carbon emission timing trend information shows a changing trend of decreasing carbon emissions, and when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are opposite, and when the carbon emission trend value of the second carbon emission timing trend information at any moment is greater than the preset second carbon emission trend value, the second carbon emission timing trend information is used as the carbon emission timing trend correction information.

4. The method according to claim 3, characterized in that The method further comprises: When the second carbon emission timing trend information shows a changing trend of increasing carbon emissions, and when the changing trends of the second carbon emission timing trend information and the third carbon emission timing trend information are the same, and when the carbon emission trend value of the third carbon emission timing trend information at any moment is greater than the preset third carbon emission trend value, the product of the second carbon emission timing trend information and the third carbon emission timing trend information at the corresponding moment is used as the carbon emission timing trend correction information.

5. The method according to claim 4, characterized in that The method further comprises: Obtain a target fluctuation prediction model corresponding to the economic activity data of each subject in the first time period, and obtain a regional economic activity index corresponding to the prediction parameter in the fluctuation prediction model; adjust the economic activity fluctuation threshold according to the regional economic activity index to obtain a modified economic activity fluctuation threshold; wherein the regional economic activity index is the economic activity index of the region where the subject is located; Through the target fluctuation prediction model, the economic activity data of each subject in the second time period is determined according to the economic activity data of each subject in the first time period. When the fluctuation value of the economic activity data of the first subject in the second time period is less than the corrected economic activity fluctuation threshold, the second carbon emission time series trend information of the first subject in the second time period is determined according to the economic activity data of the first subject in the second time period.

6. The method according to claim 5, characterized in that The method further comprises: When the fluctuation value of the economic activity data of the first subject in the second time period is greater than or equal to the modified economic activity fluctuation threshold, the product of the economic activity data of the first subject in the first time period and the regional economic activity index is used as the economic activity data of the first subject in the second time period.

7. A multi-dimensional carbon emission prediction system based on big data, characterized in that: Comprising means for performing the method according to any one of claims 1 to 6.

8. A multi-dimensional carbon emission prediction system based on big data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images