Carbon quota surplus / deficit forecasting methods, devices, electronic equipment, and storage media

Abstract

This application provides a carbon allowance surplus / deficit prediction method, device, electronic equipment, and storage medium, relating to the field of data processing technology. The carbon allowance surplus / deficit prediction method includes: first, obtaining a baseline value of carbon emission intensity for each carbon emission source in at least one preset operating condition interval; each preset operating condition interval corresponds to a parameter range of a preset operating condition variable; then, obtaining the production plan of each carbon emission source in a preset future period and the prediction parameters of the preset operating condition variable; determining a target operating condition interval from at least one preset operating condition interval based on the prediction parameters of the preset operating condition variable; finally, predicting the carbon allowance surplus / deficit for the carbon emission source based on the carbon emission baseline value of the target operating condition interval and the production plan, obtaining the predicted carbon allowance surplus / deficit amount for the carbon emission source in the future period. This application can predict the carbon allowance surplus / deficit situation of a carbon emission source through historical data and production plans, helping enterprises to accurately fulfill their carbon allowance obligations.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method, device, electronic equipment, and storage medium for predicting carbon quota surpluses and shortages. Background Technology

[0002] In recent years, the country has been increasing its control over emissions and has introduced tools such as carbon trading to support companies in fulfilling their obligations and to reward their emission reduction actions. If companies want to achieve accurate compliance or increase value through trading, they must improve their carbon emission management capabilities.

[0003] Existing technologies for optimizing corporate carbon emission management are mostly fragmented or too macro-level. Fragmented technologies only focus on helping continuous production companies establish carbon emission databases and improve carbon emission accounting. Although they improve the efficiency of carbon emission indicator accounting, they still cannot empower companies to deepen the application of carbon emission data, and naturally cannot help companies gain benefits. Relatively macro-level technologies use big data processing methodologies from other fields. These methodologies are mostly applicable to regions or governments, and are extremely difficult to implement at the level of enterprises, factories, and specific emission sources. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a carbon quota surplus / deficit prediction method, device, electronic equipment, and storage medium, so as to help continuous production enterprises deepen the application of carbon emission data and achieve accurate prediction of carbon quota surplus / deficit.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a method for predicting carbon quota surpluses and shortages, the method comprising:

[0007] Obtain the baseline value of carbon emission intensity for each carbon emission source in the subject of carbon emission to be predicted in at least one preset operating condition interval; each preset operating condition interval corresponds to a parameter range of a preset operating condition variable;

[0008] Obtain the production plan and prediction parameters of preset operating condition variables for each carbon emission source in a preset future time period;

[0009] Based on the prediction parameters of the preset working condition variables, a target working condition interval is determined from the at least one preset working condition interval;

[0010] Based on the carbon emission baseline value for the target operating condition range and the production plan, the carbon emission entity is predicted to have a carbon quota surplus or deficit, and the predicted carbon quota surplus or deficit for the carbon emission entity in the future period is obtained.

[0011] Optionally, obtaining the baseline value of carbon emission intensity for each carbon emission source in the carbon emission subject to be predicted within at least one preset operating condition range includes:

[0012] Based on the historical carbon emission data of each carbon emission source in each preset operating condition interval, calculate the carbon emission intensity benchmark value for each preset operating condition interval.

[0013] Optionally, before obtaining the carbon emission intensity benchmark value for each carbon emission source in the carbon emission subject to be predicted in at least one preset operating condition range, the method further includes:

[0014] Based on the historical carbon emission data of each carbon emission source within a preset historical period, the actual parameters of the preset operating condition variables of each carbon emission source within the preset historical period are classified to obtain at least one preset operating condition interval.

[0015] Optionally, before classifying the actual parameters of the preset operating condition variables for each carbon emission source within the preset historical period based on the historical carbon emission data of each carbon emission source within the preset historical period to obtain the at least one preset operating condition interval, the method further includes:

[0016] A correlation analysis is performed on the historical carbon emission data and the actual parameters of at least one operating condition variable within the preset historical period to obtain the carbon emission correlation of the at least one operating condition variable.

[0017] Based on the carbon emission correlation of the at least one operating condition variable, the operating condition variable with the highest carbon emission correlation among the at least one operating condition variable is determined as the preset operating condition variable.

[0018] Optionally, the prediction parameters include: production load rate; the prediction parameters for obtaining the preset operating condition variables of each carbon emission source in a preset future time period include:

[0019] Based on the production plan, determine the production load rate for the preset future time period.

[0020] Optionally, the prediction parameters further include: production process state parameters; the prediction parameters for obtaining the preset operating condition variables of each carbon emission source within a preset future time period include:

[0021] The production process status parameters for the preset future period are obtained from the historical data of each carbon emission source or from the prediction database for the preset future period.

[0022] Optionally, the step of forecasting the carbon allowance surplus / deficit of the carbon-emitting entity based on the carbon emission baseline value of the target operating condition range and the production plan, to obtain the predicted carbon allowance surplus / deficit amount of the carbon-emitting entity in the future period, includes:

[0023] Based on the carbon emission baseline value of the target operating condition range and the production plan, predict the carbon emission forecast of each carbon emission source in the future period.

[0024] Based on the predicted carbon emissions of each carbon emission source in the carbon emission subject, determine the predicted carbon emissions of the carbon emission subject in the future period.

[0025] Based on the carbon emission forecast of the carbon emission subject and the carbon emission allowance of the carbon emission subject, determine the carbon allowance surplus or deficit forecast of the carbon emission subject in the future period.

[0026] Secondly, this application also provides a carbon quota surplus / deficit prediction device, including: a carbon emission intensity benchmark value acquisition module, a prediction parameter acquisition module, a determination module, and a prediction module;

[0027] The carbon emission intensity benchmark value acquisition module is used to acquire the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset operating condition interval; each preset operating condition interval corresponds to a parameter range of a preset operating condition variable;

[0028] The prediction parameter acquisition module is used to acquire the production plan and prediction parameters of the preset operating condition variables for each carbon emission source in a preset future time period.

[0029] The determining module is used to determine a target working condition interval from the at least one preset working condition interval based on the prediction parameters of the preset working condition variables.

[0030] The prediction module is used to predict the carbon quota surplus or deficit of the carbon emission entity based on the carbon emission baseline value of the target operating condition range and the production plan, so as to obtain the predicted carbon quota surplus or deficit of the carbon emission entity in the future period.

[0031] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores program instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the carbon quota surplus / deficit prediction method as described in any of the first aspects.

[0032] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the carbon quota surplus / deficit prediction method as described in any of the first aspects.

[0033] The beneficial effects of this application are as follows: This application provides a method for predicting carbon quota surplus and deficit. First, the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted is obtained in at least one preset operating condition interval; each preset operating condition interval corresponds to a parameter range of a preset operating condition variable; then, the production plan of each carbon emission source in a preset future period and the prediction parameters of the preset operating condition variable are obtained; based on the prediction parameters of the preset operating condition variable, a target operating condition interval is determined from at least one preset operating condition interval; finally, based on the carbon emission benchmark value of the target operating condition interval and the production plan, the carbon quota surplus and deficit of the carbon emission subject is predicted to obtain the predicted amount of carbon quota surplus and deficit of the carbon emission subject in the future period. Since there is a strong correlation between production conditions and emissions during continuous production, and these conditions are related to the carbon emitter's production plan and other objective factors, they are traceable and predictable. Therefore, this application, combining the production characteristics of continuous production enterprises and addressing the shortcomings of existing technologies, provides a carbon allowance surplus / deficit prediction method. This method can predict the carbon allowance surplus / deficit of carbon emitters through historical data, actual production conditions, and production plans, thereby optimizing the carbon trading strategies of carbon emitters and helping them to accurately fulfill their carbon obligations. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating a carbon quota surplus / deficit prediction method provided in an embodiment of this application;

[0036] Figure 2 A schematic diagram of the carbon emission source accounting boundary is provided as an embodiment of this application;

[0037] Figure 3 A schematic diagram of a carbon emission model architecture provided in an embodiment of this application;

[0038] Figure 4 A schematic diagram of a carbon emission model architecture is provided for yet another embodiment of this application;

[0039] Figure 5A flowchart of a carbon quota surplus / deficit prediction method provided in another embodiment of this application;

[0040] Figure 6 A graph showing the relationship between carbon emission intensity and operating condition variables of a carbon emission source, provided as an embodiment of this application;

[0041] Figure 7 A flowchart of a carbon quota surplus / deficit prediction method provided in another embodiment of this application;

[0042] Figure 8 A schematic diagram of a carbon quota surplus / deficit prediction device provided in an embodiment of this application;

[0043] Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0045] In this application, unless otherwise expressly specified and limited, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one feature. In the description of this invention, "a plurality of" means at least two, such as two or three, unless otherwise expressly specified. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0046] This invention provides a method and system for quantitatively predicting carbon allowance surpluses and deficits for continuous production enterprises, helping them to deepen the application of carbon emission data, optimize carbon trading strategies, and achieve accurate compliance and asset appreciation.

[0047] Currently, continuous production enterprises manage carbon emissions in a relatively rudimentary manner, relying mainly on manual reporting. This results in poor data timeliness, and the lack of suitable data analysis methods and tools leads to the concealment and oversight of a large amount of forward-looking and instructive information. In the past, enterprises only needed to use carbon emission data during annual compliance checks, and even if compliance was not met, the penalties were minimal. However, in recent years, the government has increasingly tightened its control over emissions and introduced tools such as carbon trading to support enterprises in fulfilling their compliance obligations and to reward their emission reduction efforts. If enterprises want to achieve accurate compliance or added value through trading, they must improve their carbon emission management capabilities.

[0048] Existing technologies for corporate carbon emission management are fragmented or too macro-level. Fragmented technologies only focus on helping continuous production companies establish carbon emission databases and improve carbon emission accounting, but they cannot empower companies to deepen the application of carbon emission data or help them gain benefits. On the other hand, macro-level technologies adopt big data processing methodologies from other fields, which are extremely difficult to implement at the enterprise, factory, and specific emission source levels.

[0049] To address the problems in the prior art, this application provides various possible implementations to achieve accurate prediction of carbon quota surpluses and deficits for continuous production enterprises. The following explanations, in conjunction with the accompanying drawings, use multiple examples to illustrate these methods. Figure 1 This is a flowchart illustrating a carbon quota surplus / deficit prediction method according to an embodiment of this application. This method can be implemented by an electronic device running the aforementioned carbon quota surplus / deficit prediction method. This electronic device can be, for example, a terminal device or a server. Figure 1 As shown, the method includes:

[0050] Step 101: Obtain the baseline value of carbon emission intensity for each carbon emission source in the carbon emission subject to be predicted in at least one preset operating condition interval; each preset operating condition interval corresponds to a parameter range of a preset operating condition variable.

[0051] It should be noted that the carbon emission subject is the subject of the carbon allowance surplus / deficit forecast in this application, which may be, for example, a continuous production enterprise or factory, and this application does not limit it.

[0052] Each carbon emission entity may include one or more carbon emission sources. In step 101 of this application, the carbon emission intensity benchmark value obtained for each carbon emission source in at least one preset operating condition range may be obtained; alternatively, the carbon emission sources in the carbon emission entity may be screened, and for at least one screened carbon emission source, the carbon emission intensity benchmark value obtained for each carbon emission source in at least one preset operating condition range may be obtained.

[0053] In one specific implementation approach, carbon emission sources that can be included in the carbon emission entity's accounting scope can be selected based on the specific industry in which the carbon emission entity operates and the corresponding accounting standards. Figure 2A schematic diagram of the carbon emission source accounting boundary is provided as an embodiment of this application, such as... Figure 2 As shown, this carbon emission entity has three carbon emission sources: emission source A, emission source B, and emission source C. Since carbon emission source C is not included in the accounting scope of the industry in which this carbon emission entity operates (for ease of understanding, in...), Figure 2 The calculation boundary is defined by a dashed line (the carbon emission sources within the dashed line are included in the calculation scope). Then, when performing step 101, the carbon emission intensity benchmark values ​​of emission source A and emission source B in at least one preset operating condition range can be obtained respectively.

[0054] In another specific implementation, modeling software can be used to define the carbon emission model boundary of each carbon emission subject, the emission index of each carbon emission source, and configure the calculation formula, thereby establishing the carbon emission model of that carbon emission subject. Figure 3 A schematic diagram of a carbon emission model architecture provided in an embodiment of this application; Figure 4 A schematic diagram of a carbon emission model architecture is provided for another embodiment of this application; as shown Figure 3 , Figure 4 As shown, within each carbon emitting entity, the carbon emission model boundary can be determined for each carbon emitting entity. Figure 3 (The boundary is defined by a dashed line), emission targets for each carbon emission source, configuration calculation formulas, etc. (e.g.) Figure 4 Based on this model, a benchmark value for the carbon emission intensity of each carbon emission source can be obtained in at least one preset operating condition range.

[0055] It should also be noted that the preset operating condition interval is an interval composed of the parameter ranges of preset operating condition variables. The preset operating condition variables can be single or multiple variables; this application does not limit the specific number of preset operating condition variables. If the preset operating condition variable is a single variable, such as temperature, then the operating condition interval can be the interval corresponding to the temperature range (for example, 0 to 5 degrees Celsius can be one preset operating condition interval, 10 to 20 degrees Celsius can be another preset operating condition interval, etc.). If the preset operating condition variables are multiple variables, such as temperature and load rate, then the operating condition interval can be the interval jointly defined by the temperature range and the load rate range (for example, a temperature of 0 to 5 degrees Celsius and a load rate of 50% to 60% can be one preset operating condition interval, a temperature of 10 to 20 degrees Celsius and a load rate of 50% to 60% can be another preset operating condition interval, etc.). Similarly, when there are more than two preset operating condition variables, the parameter ranges of each preset operating condition variable can also be used to form the interval.

[0056] Furthermore, the selection of preset operating condition variables can be chosen by engineers or obtained by filtering based on preset algorithms; this application does not limit this.

[0057] The above is merely an example. In actual implementation, there may be other ways to set the preset operating condition range, and this application does not limit this.

[0058] Step 102: Obtain the production plan for each carbon emission source in the preset future time period and the prediction parameters of preset operating condition variables.

[0059] To accurately predict carbon allowance surpluses and deficits within a predetermined future period, it is necessary to obtain the production plans for each carbon emission source and the predicted parameters for predetermined operating condition variables within that period. Production plans may include, for example, total planned output and production arrangements for each statistical period. Depending on the specific form of the predetermined operating condition variable, its predicted parameters can be obtained from historical data and relevant forecast data. For instance, if the predetermined operating condition variable is temperature, its predicted parameters can be obtained from weather forecast data; similarly, if the predetermined operating condition variable is pressure, its predicted parameters can be obtained from historical pressure values ​​for the same period (e.g., parameters from the same period in previous years).

[0060] The above is merely an example. In actual implementation, there may be other ways to obtain prediction parameters, and this application does not limit them.

[0061] Step 103: Determine the target working condition interval from at least one preset working condition interval based on the prediction parameters of the preset working condition variables.

[0062] It should be noted that, based on the prediction parameters of the preset working condition variables, one or more target working condition intervals are determined from at least one preset working condition interval. This application does not limit the number of target working condition intervals determined.

[0063] In one possible implementation, if the preset future time period is the next two months, and each day corresponds to a preset operating condition interval, then a target operating condition interval can be determined for each day based on the prediction parameters of the preset operating condition variables. The above is merely an illustrative example; in actual implementation, the time period corresponding to each preset operating condition interval can also be one hour, or several hours, etc. This application does not limit this. When obtaining the target operating condition space, the target operating condition interval corresponding to each time period at the current precision can be determined based on the precision of the specific operating condition interval.

[0064] Step 104: Based on the carbon emission baseline value of the target operating condition range and the production plan, predict the carbon quota surplus or deficit of the carbon emission entity to obtain the predicted carbon quota surplus or deficit of the carbon emission entity in the future period.

[0065] In one possible implementation, based on the carbon emission baseline value for the target operating period and the production plan, the predicted carbon emissions of the carbon emission source in the future period can be obtained. By summing the predicted carbon emissions of all carbon emission sources (or carbon emission sources within the accounting scope) of the carbon emission entity, the total predicted carbon emissions of the carbon emission entity in the future period can be obtained. Based on the carbon quota of the carbon emission entity in the future period and the total predicted carbon emissions of the carbon emission entity in the future period, a carbon quota surplus / deficit forecast is performed for the carbon emission entity, yielding the predicted carbon quota surplus / deficit amount for the future period.

[0066] In summary, this application provides a method for predicting carbon allowance surplus / deficit. First, it obtains the baseline carbon emission intensity value for each carbon emission source in at least one preset operating condition interval within the carbon emission entity to be predicted. Each preset operating condition interval corresponds to a parameter range of a preset operating condition variable. Then, it obtains the production plan for each carbon emission source within a preset future time period and the prediction parameters of the preset operating condition variable. Based on the prediction parameters of the preset operating condition variable, a target operating condition interval is determined from at least one preset operating condition interval. Finally, based on the baseline carbon emission value of the target operating condition interval and the production plan, the carbon allowance surplus / deficit of the carbon emission entity is predicted to obtain the predicted carbon allowance surplus / deficit amount for the carbon emission entity in the future time period. Since there is a strong correlation between production conditions and emissions during continuous production, and these conditions are related to the carbon emitter's production plan and other objective factors, they are traceable and predictable. Therefore, this application, combining the production characteristics of continuous production enterprises and addressing the shortcomings of existing technologies, provides a carbon allowance surplus / deficit prediction method. This method can predict the carbon allowance surplus / deficit of carbon emitters through historical data, actual production conditions, and production plans, thereby optimizing the carbon trading strategies of carbon emitters and helping them to accurately fulfill their carbon obligations.

[0067] Optionally, in the above Figure 1 Based on this, this application also provides a possible implementation of the carbon quota surplus / deficit prediction method, which obtains a baseline value of carbon emission intensity for each carbon emission source in the carbon emission subject to be predicted within at least one preset operating condition range, including:

[0068] Based on the historical carbon emission data of each carbon emission source in each preset operating condition range, calculate the benchmark value of carbon emission intensity for each preset operating condition range.

[0069] In one possible implementation, each carbon emission source can collect at least one historical carbon emission data point for each preset operating condition interval during actual use. Based on the historical carbon emission data of each carbon emission source in each preset operating condition interval, a benchmark value for carbon emission intensity for each preset operating condition interval can be calculated using methods such as arithmetic mean, median, and clustering algorithms.

[0070] The above is merely an illustrative example. In actual implementation, the benchmark value of carbon emission intensity can be calculated in other ways, and this application does not limit this.

[0071] In one specific implementation, before calculating the carbon emission intensity benchmark value for each preset operating condition interval based on the historical carbon emission data of each carbon emission source for each preset operating condition interval, the method further includes:

[0072] Historical carbon emission data for each carbon emission source is categorized, and each historical carbon emission data is automatically categorized into a defined operating condition range.

[0073] In one specific implementation, before calculating the carbon emission intensity benchmark value for each preset operating condition interval based on the historical carbon emission data of each carbon emission source for each preset operating condition interval, the method further includes:

[0074] If there is at least one target operating condition interval with a historical carbon emission data volume less than a preset data volume threshold, the target operating condition interval is merged with the preset operating condition interval with the least historical carbon emission data volume among the adjacent preset operating condition intervals to obtain a new preset operating condition interval.

[0075] The above is merely an example; in actual implementation, there may be other implementation methods, which this application does not limit.

[0076] Optionally, in the above Figure 1 Based on this, this application also provides a possible implementation of a carbon quota surplus / deficit prediction method. Before obtaining the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset operating condition range, the method further includes:

[0077] Based on the historical carbon emission data of each carbon emission source within a preset historical period, the actual parameters of the preset operating condition variables of each carbon emission source within the preset historical period are classified to obtain at least one preset operating condition interval.

[0078] In one possible implementation, historical carbon emission data for each carbon emission source within a preset historical period can be obtained. Based on the amount of historical carbon emission data obtained for each carbon emission source within the preset historical period, or based on the amount of historical carbon emission data, the actual parameters of preset operating condition variables for each carbon emission source within the preset historical period can be classified. This results in each preset operating condition interval after classification including a similar amount of historical carbon emission data, making the classification of preset operating condition intervals more uniform, thereby further making the predicted carbon quota surplus / deficit more accurate.

[0079] In another possible implementation, when classifying the actual parameters of the preset operating condition variables for each carbon emission source, the upper and lower limits of the actual parameters of the preset operating condition variables (or the theoretical parameters of the preset operating condition variables of the carbon emission source) within a preset historical period can be used to divide the space between the upper and lower limits according to a preset number of divisions, thereby dividing the space into a uniform preset number of preset operating condition intervals.

[0080] In one specific implementation, the preset operating condition variables can be, for example, temperature and load rate. The divided preset operating condition intervals can be as shown in Table 1, which is a preset operating condition interval division table provided in an embodiment of this application:

[0081] Table 1. A preset working condition interval division table provided in an embodiment of this application.

[0082]

[0083] The above is merely an example; in actual implementation, there may be other implementation methods, which this application does not limit.

[0084] Optionally, based on the above embodiments, this application also provides a possible implementation of the carbon quota surplus / deficit prediction method. Figure 5 A flowchart illustrating a carbon quota surplus / deficit prediction method is provided as another embodiment of this application; as shown Figure 5 As shown, before classifying the actual parameters of preset operating condition variables for each carbon emission source within a preset historical period based on historical carbon emission data for each carbon emission source, and obtaining at least one preset operating condition interval, the method further includes:

[0085] Step 501: Perform correlation analysis on historical carbon emission data and the actual parameters of at least one operating condition variable within a preset historical period to obtain the carbon emission correlation of at least one operating condition variable.

[0086] In one possible implementation, correlation analysis is performed on historical carbon emission data and the actual parameters of at least one operating condition variable within a preset historical period to obtain the carbon emission correlation of at least one operating condition variable. The operating condition variable may include, for example, temperature, pressure, load rate, and operating time; this application does not limit this, and engineers can expand upon it based on specific carbon emission sources.

[0087] Furthermore, this application does not limit the specific calculation method for carbon emission correlation. Users can determine the calculation method according to actual use, such as correlation algorithm, graphical method, etc.

[0088] Step 502: Based on the carbon emission correlation of at least one operating condition variable, determine the operating condition variable with the highest carbon emission correlation from at least one operating condition variable as the preset operating condition variable.

[0089] In one possible implementation, carbon emission correlation of multiple operating condition variables is calculated in step 501, and the operating condition variable with the highest carbon emission correlation is selected as the preset operating condition variable. Alternatively, a preset number (greater than or equal to one) of operating condition variables with the highest carbon emission correlation can be selected as the preset operating condition variable. Or, at least one operating condition variable that is correlated (or strongly correlated) with carbon emission intensity can be determined from at least one operating condition variable as the preset operating condition variable.

[0090] The above is merely an example; in actual implementation, there may be other implementation methods, which this application does not limit.

[0091] In one specific implementation method Figure 6 A graph showing the relationship between carbon emission intensity and operating condition variables of a carbon emission source is provided as an embodiment of this application; for example... Figure 6 As shown in the graph, the relationship between the carbon emission intensity of emission source A and operating condition variables such as temperature, load rate, pressure, and operating time shows a significant correlation between the carbon emission intensity of emission source A and temperature and load rate. Therefore, temperature and load rate can be selected as preset operating condition variables. It should be noted that the above graph visually illustrates the correlation (engineers can use this graph to judge the accuracy of the selected preset operating condition variables), but this graph may not be specifically generated when the computer program selects preset operating condition variables.

[0092] Optionally, in the above Figure 1 Based on this, this application also provides a possible implementation of the carbon quota surplus / deficit prediction method, wherein the prediction parameters include: production load rate; and prediction parameters for preset operating condition variables of each carbon emission source in a preset future time period, including:

[0093] Based on the production plan, determine the production load rate for the preset future period.

[0094] In one possible implementation, the production load rate Fh for a preset future period can be determined based on the production plan:

[0095] ;in, This refers to the planned production volume (i.e., planned product output) of carbon emission sources within a predetermined future time period.

[0096] In one specific implementation, if the production plan does not include a specific product output arrangement, it can be achieved through the production plan of carbon emission sources. Product output can be determined within the forecast period. Then through product output Production load rate during the forecast period The above is merely an illustrative example. In actual implementation, there may be other implementation methods, which are not limited in this application.

[0097] Optionally, in the above Figure 1 Based on this, this application also provides a possible implementation of the carbon quota surplus / deficit prediction method, wherein the prediction parameters further include: production process state parameters; and prediction parameters for obtaining preset operating condition variables for each carbon emission source within a preset future time period, including:

[0098] Obtain production process status parameters for the preset future period from historical data of each carbon emission source or from a forecast database for the preset future period.

[0099] In one possible implementation, production process status parameters for a preset future period can be obtained from historical data (such as historical runtime data, pressure data, etc.) or a prediction database for a preset future period (such as a temperature prediction database provided by weather forecasts). For example, runtime data and pressure data for a preset future period can be obtained from historical runtime data, pressure data, etc.; or, for another example, temperature for a preset future period can be obtained from a temperature prediction database provided by weather forecasts.

[0100] The above is merely an example; in actual implementation, there may be other prediction methods, which are not limited in this application.

[0101] Optionally, in the above Figure 1 Based on this, this application also provides a possible implementation of the carbon quota surplus / deficit prediction method. Figure 7 A flowchart illustrating a carbon quota surplus / deficit prediction method provided in another embodiment of this application; as shown Figure 7 As shown, based on the carbon emission baseline value for the target operating condition range and the production plan, carbon allowance surplus / deficit forecasts are performed for the carbon emission entities to obtain the predicted carbon allowance surplus / deficit amounts for the carbon emission entities in the future period, including:

[0102] Step 301: Based on the carbon emission baseline value of the target operating condition range and the production plan, predict the carbon emission forecast of each carbon emission source in the future period.

[0103] Step 302: Determine the predicted carbon emissions of each carbon emission source in the future period based on the predicted carbon emissions of the main carbon emission entities.

[0104] Step 303: Based on the carbon emission forecasts and carbon emission allowances of the carbon emission entities, determine the carbon allowance surplus or deficit forecasts for the carbon emission entities in the future period.

[0105] In one possible implementation, the predicted carbon emissions for each carbon emission source (or each carbon emission source within the accounting scope) of the carbon emission subject in the future period are: The predicted carbon emissions of the main carbon emitters in the future period (i.e., the total predicted carbon emissions of the main carbon emitters in the future period). It can be calculated in the following way:

[0106] ;

[0107] Based on the carbon emission allowances (carbon allowances) of the carbon emission entity in the future period. Compared with the total predicted carbon emissions of this carbon emitter in the future period Calculate the predicted carbon allowance surplus or deficit for carbon emitters in future periods:

[0108] ;

[0109] It should be noted that carbon quotas For example, it can be calculated using any of the following methods, and this application does not limit this method:

[0110] First, the historical emissions method can directly calculate the annual carbon allowance D of a carbon emitter using historical average allowance amounts. The carbon allowance for any carbon emission source among the main carbon emitters:

[0111] Where i is greater than or equal to 1, the reduction coefficient can be set according to the reduction coefficient issued by the local or national authorities.

[0112] The carbon allowance for any carbon emission source (or any carbon emission source within the accounting scope) of each carbon emitter can be obtained using the above method. Then, by summing the carbon allowances of all carbon emission sources for each carbon emitter, the total annual carbon allowance D for that carbon emitter can be obtained.

[0113] Where N is the number of carbon emission sources (or any carbon emission source within the accounting scope).

[0114] Second, the historical intensity decline method calculates the carbon allowance D for the main carbon emitters during the forecast period by using the product output Q within the forecast period. The carbon allowance for any carbon emission source among the main carbon emitters:

[0115] Where i is greater than or equal to 1, the reduction coefficient can be set according to the reduction coefficient published by the local or national authorities;

[0116] The output of the product corresponding to the carbon emission source can be obtained through the production plan of the carbon emission source.

[0117] The historical average of the carbon emission intensity corresponding to this carbon emission source:

[0118] ;in, The historical carbon emissions for the preset historical time period corresponding to this carbon emission source; The product output for the preset historical time period corresponding to this carbon emission source;

[0119] It can be calculated in the following way:

[0120] ;

[0121] in, Emissions from fuel combustion are carbon emission sources;

[0122] Emissions from industrial processes that are sources of carbon emissions;

[0123] Net electrical and thermal emissions from carbon emission sources;

[0124] This refers to the amount of CO2 recovered and utilized from carbon emission sources.

[0125] Third, the baseline method calculates the enterprise's carbon quota D for the forecast period by using the product output Q within the forecast period. The carbon allowance for any carbon emission source among the main carbon emitters:

[0126] The benchmark value can be set based on benchmark values ​​published by local authorities or the state.

[0127] The above is merely an example. In actual implementation, there may be other methods for calculating carbon quotas, which this application does not limit.

[0128] Therefore, this application provides a method for predicting carbon allowance surplus and deficit, from carbon emission modeling and accounting to in-depth data application.

[0129] The carbon quota surplus / deficit prediction device, electronic equipment, and storage medium used to implement the carbon quota surplus / deficit prediction device, electronic equipment, and storage medium provided in this application are described below. The specific implementation process and technical effects are described above and will not be repeated below.

[0130] This application provides a possible implementation example of a carbon quota surplus / deficit prediction device, which can execute the carbon quota surplus / deficit prediction method provided in the above embodiments. Figure 8This is a schematic diagram of a carbon quota surplus / deficit prediction device provided in one embodiment of this application. Figure 8 As shown, the carbon quota surplus / deficit prediction device 100 includes: a carbon emission intensity benchmark value acquisition module 81, a prediction parameter acquisition module 83, a determination module 85, and a prediction module 87.

[0131] The carbon emission intensity benchmark value acquisition module 81 is used to acquire the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset operating condition interval; each preset operating condition interval corresponds to a parameter range of a preset operating condition variable;

[0132] The prediction parameter acquisition module 83 is used to acquire the production plan and prediction parameters of preset operating condition variables for each carbon emission source in a preset future time period.

[0133] The determination module 85 is used to determine the target working condition interval from at least one preset working condition interval based on the prediction parameters of the preset working condition variables.

[0134] The prediction module 87 is used to predict the carbon quota surplus or deficit of the carbon emission entity based on the carbon emission baseline value of the target operating condition range and the production plan, and to obtain the predicted carbon quota surplus or deficit of the carbon emission entity in the future period.

[0135] Optionally, the carbon emission intensity benchmark value acquisition module 81 is used to calculate the carbon emission intensity benchmark value for each preset operating condition interval based on the historical carbon emission data of each carbon emission source in each preset operating condition interval.

[0136] Optionally, the carbon quota surplus / deficit prediction device 100 further includes: a calculation module, used to classify the actual parameters of the preset operating condition variables of each carbon emission source within the preset historical period based on the historical carbon emission data of each carbon emission source within the preset historical period, and obtain at least one preset operating condition interval.

[0137] Optionally, a calculation module is used to perform correlation analysis on historical carbon emission data and the actual parameters of at least one operating condition variable within a preset historical period to obtain the carbon emission correlation of at least one operating condition variable; based on the carbon emission correlation of at least one operating condition variable, the operating condition variable with the highest carbon emission correlation is determined from at least one operating condition variable as the preset operating condition variable.

[0138] Optionally, the prediction parameters include: production load rate; prediction module 87, used to determine the production load rate for a preset future period based on the production plan.

[0139] Optionally, the prediction parameters also include: production process status parameters; prediction module 87 is used to obtain production process status parameters for a preset future period from historical data of each carbon emission source or from a prediction database for a preset future period.

[0140] Optionally, the prediction module 87 is used to predict the carbon emission forecast of each carbon emission source in the future period based on the carbon emission baseline value of the target operating condition range and the production plan; determine the carbon emission forecast of the carbon emission subject in the future period based on the carbon emission forecast of each carbon emission source in the carbon emission subject; and determine the carbon quota surplus / deficit forecast of the carbon emission subject in the future period based on the carbon emission forecast of the carbon emission subject and the carbon emission quota of the carbon emission subject.

[0141] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0142] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0143] This application provides a possible implementation example of an electronic device capable of executing the carbon quota surplus / deficit prediction method provided in the above embodiments. Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of this application. The device can be integrated into a terminal device or a chip of a terminal device. The terminal can be a computing device with data processing capabilities.

[0144] The electronic device includes a processor 901, a storage medium 902, and a bus. The storage medium stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the carbon quota surplus / deficit prediction method described above. The specific implementation and technical effects are similar and will not be described in detail here.

[0145] This application provides a possible implementation example of a computer-readable storage medium capable of executing the carbon quota surplus / deficit prediction method provided in the above embodiments. The storage medium stores a computer program, which is executed by a processor to perform the steps of the carbon quota surplus / deficit prediction method.

[0146] A computer program stored in a storage medium may include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0148] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0150] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0151] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A carbon credit balance forecasting method, characterized by, The method comprises: obtaining a carbon emission intensity benchmark value of each carbon emission source in a carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to a parameter range of a preset working condition variable; obtaining a production plan of each carbon emission source in a preset future period and a predicted parameter of a preset working condition variable; determining a target working condition interval from the at least one preset working condition interval according to the predicted parameter of the preset working condition variable; performing carbon quota surplus / deficit prediction on the carbon emission subject according to the carbon emission benchmark value of the target working condition interval and the production plan, to obtain a carbon quota surplus / deficit prediction value of the carbon emission subject in the future period; the method further comprises: obtaining a carbon emission intensity benchmark value of each carbon emission source in a carbon emission subject to be predicted in at least one preset working condition interval, comprising: calculating the carbon emission intensity benchmark value of each preset working condition interval according to historical carbon emission data of each carbon emission source in each preset working condition interval; the method further comprises: before the step of obtaining a carbon emission intensity benchmark value of each carbon emission source in a carbon emission subject to be predicted in at least one preset working condition interval, the method further comprises: classifying actual parameters of the preset working condition variable of each carbon emission source in a preset historical period according to historical carbon emission data of each carbon emission source in the preset historical period, to obtain the at least one preset working condition interval; 2. The method of claim 1, wherein, the predicted parameter comprises: production load rate; the step of obtaining a predicted parameter of a preset working condition variable of each carbon emission source in a preset future period, comprises: determining a production load rate of the preset future period according to the production plan. the method further comprises:

3. The method of claim 1, wherein, before the step of classifying actual parameters of the preset working condition variable of each carbon emission source in a preset historical period according to historical carbon emission data of each carbon emission source in the preset historical period, to obtain the at least one preset working condition interval, the method further comprises: performing correlation analysis on the historical carbon emission data and actual parameters of at least one working condition variable in the preset historical period, to obtain a carbon emission correlation degree of the at least one working condition variable; 4. The method of claim 1, wherein, determining a working condition variable with the highest carbon emission correlation degree from the at least one working condition variable as the preset working condition variable according to the carbon emission correlation degree of the at least one working condition variable. the predicted parameter further comprises: production process state parameter; the step of obtaining a predicted parameter of a preset working condition variable of each carbon emission source in a preset future period, comprises: obtaining the production process state parameter in the preset future period from historical contemporaneous data of each carbon emission source or a prediction database of the preset future period. the step of performing carbon quota surplus / deficit prediction on the carbon emission subject according to the carbon emission benchmark value of the target working condition interval and the production plan, to obtain a carbon quota surplus / deficit prediction value of the carbon emission subject in the future period, comprises: predicting a carbon emission prediction value of each carbon emission source in the future period according to the carbon emission benchmark value of the target working condition interval and the production plan; According to the carbon emission prediction quantity of each carbon emission source in the carbon emission body, a carbon emission prediction quantity of the carbon emission body in the future period is determined; According to the carbon emission prediction quantity of the carbon emission body and the carbon emission quota quantity of the carbon emission body, a carbon quota surplus or deficiency prediction quantity of the carbon emission body in the future period is determined.

5. A carbon credit balance forecasting device, characterized by, Comprise: A carbon emission intensity reference value acquisition module, a prediction parameter acquisition module, a determination module, and a prediction module; The carbon emission intensity reference value acquisition module is configured to acquire a carbon emission intensity reference value of each carbon emission source in the carbon emission body in at least one preset working condition interval; Each preset working condition interval corresponds to a parameter range of a preset working condition variable; The prediction parameter acquisition module is configured to acquire a production plan of each carbon emission source in a preset future period and a prediction parameter of a preset working condition variable; The determination module is configured to determine a target working condition interval from the at least one preset working condition interval according to the prediction parameter of the preset working condition variable; The prediction module is configured to perform carbon quota surplus or deficiency prediction on the carbon emission body according to the carbon emission reference value of the target working condition interval and the production plan, to obtain a carbon quota surplus or deficiency prediction quantity of the carbon emission body in the future period; The carbon emission intensity reference value acquisition module is further configured to calculate the carbon emission intensity reference value of each preset working condition interval according to historical carbon emission data of each carbon emission source in each preset working condition interval; The device further comprises a calculation module configured to classify actual parameters of the preset working condition variable of each carbon emission source in a preset historical period according to historical carbon emission data of each carbon emission source in the preset historical period, to obtain the at least one preset working condition interval; The prediction parameter comprises a production load rate, and the prediction module is further configured to determine a production load rate of the preset future period according to the production plan.

6. An electronic device, comprising: Comprise: A processor, a storage medium, and a bus, the storage medium stores program instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, the processor executes the program instructions to execute the steps of the carbon quota surplus or deficiency prediction method in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to execute the steps of the carbon quota surplus or deficiency prediction method in any one of claims 1 to 4.

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