Carbon footprint calculation method and device considering green evidence and green electricity transaction

Abstract

The invention provides a carbon footprint calculation method and device considering green evidence and green electricity transactions, and relates to the technical field of carbon footprint calculation, and the method comprises the steps: calculating the total carbon emission of a user through the basic power consumption data of the user, the data of the green electricity transaction in which the user participates, and the information of the green evidence purchased by the user; calculating the time length of the carbon footprint according to the timeliness of the green electricity transaction and the time length of the green certificate; training a carbon footprint prediction model through basic power consumption data, green power transaction data and green certificate information of a plurality of users and historical carbon footprints corresponding to total carbon emission within a time length; and predicting the target basic electricity consumption data, the target green electricity transaction data and the target green certificate information of any target user through the carbon footprint prediction model so as to predict a target carbon footprint of the target time duration. Therefore, through the reckoning of the green electricity transaction data and the green certificate information, the contribution of the user in the aspect of green energy is reflected more accurately, and the accuracy of carbon footprint statistics is improved.

Description

Technical Field

[0001] The present invention relates to the field of carbon footprint calculation technology, and in particular to a carbon footprint calculation method, device, electronic device and storage medium taking into account green certificate and green electricity transactions. Background Art

[0002] As the background technology of global climate issues urgently needs the attention of the government, carbon emission management has become an area of ​​focus for enterprises and governments. Traditional carbon footprint calculation methods are usually based on direct and indirect emissions of enterprises, but in the context of the gradual introduction of green electricity (green electricity) and green environmental protection power certificates (green certificates), the existing carbon footprint calculation methods cannot effectively count the impact of these factors, and it is difficult to check and reflect the efforts of enterprises or individuals in carbon footprint. Therefore, it is of great significance to formulate a plan and a carbon footprint calculation method for green certificate and green electricity trading. Summary of the invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of the present invention is to propose a carbon footprint calculation method that takes into account green certificate and green electricity transactions. By incorporating green electricity transaction data and green certificate information, it can more accurately reflect the user's contribution to green energy and improve the accuracy of carbon footprint statistics.

[0005] The second object of the present invention is to provide a carbon footprint calculation device that takes into account green certificate and green electricity transactions.

[0006] A third objective of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium storing computer instructions.

[0008] To achieve the above-mentioned purpose, the first embodiment of the present invention proposes a carbon footprint calculation method taking into account green certificate and green electricity trading, the method comprising: Obtain users’ basic electricity consumption data, green electricity transaction data in which users participate, and information on green certificates purchased by users; Calculate the user's total carbon emissions based on basic electricity consumption data, green electricity trading data and green certificate information; According to the timeliness of green electricity transactions and the duration of green certificates, statistics are taken within the final time of green certificates or green electricity to calculate the duration of the carbon footprint; The machine learning model is trained by using the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length, so as to train a carbon footprint prediction model; The carbon footprint prediction model is used to predict the target basic electricity consumption data, target green electricity transaction data and target green certificate information of any target user to predict the target carbon footprint of the target time length.

[0009] To achieve the above-mentioned purpose, the second embodiment of the present invention proposes a carbon footprint calculation device taking into account green certificate and green electricity trading, the device comprising: The acquisition module is used to obtain the user's basic electricity consumption data, the green electricity transaction data in which the user participates, and the green certificate information purchased by the user; The first calculation module is used to calculate the user's total carbon emissions based on basic electricity consumption data, green electricity transaction data and green certificate information; The second calculation module is used to calculate the time length of the carbon footprint according to the timeliness of green electricity transactions and the time length of green certificates within the final time of green certificates or green electricity; A training module, used to train a machine learning model through the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, and the historical carbon footprint corresponding to the total carbon emissions within the time length, so as to train a carbon footprint prediction model; The prediction module is used to predict the target basic electricity consumption data, target green electricity transaction data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length.

[0010] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0011] In order to achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0012] The carbon footprint calculation method, device, electronic device and storage medium taking into account green certificate and green electricity trading provided by the embodiment of the present invention calculate the total carbon emissions of the user through the basic electricity consumption data of the user, the green electricity trading data in which the user participates and the green certificate information purchased by the user; calculate the time length of the carbon footprint according to the timeliness of the green electricity trading and the time length of the green certificate; train a carbon footprint prediction model through the basic electricity consumption data, green electricity trading data and green certificate information of multiple users, and the historical carbon footprint corresponding to the total carbon emissions within the time length; predict the target basic electricity consumption data, target green electricity trading data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length. Therefore, by taking into account the green electricity trading data and green certificate information, the user's contribution to green energy can be more accurately reflected, and the accuracy of carbon footprint statistics can be improved.

[0013] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flow chart of a method for calculating the carbon footprint of green certificates and green electricity transactions provided by an embodiment of the present invention; Figure 2 A structural schematic diagram of a carbon footprint calculation device taking into account green certificate and green electricity transactions provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0016] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of relevant laws and regulations.

[0017] The following describes the carbon footprint calculation method, device, electronic device and storage medium taking into account green certificate and green electricity trading according to an embodiment of the present invention with reference to the accompanying drawings.

[0018] Figure 1 A flowchart of a carbon footprint calculation method taking into account green certificate and green electricity trading provided in an embodiment of the present invention.

[0019] like Figure 1As shown, the method comprises the following steps: Step 101, obtaining the user's basic electricity consumption data, the user's participation in green electricity transaction data, and the user's purchased green certificate information.

[0020] In some embodiments, an implementation method for obtaining a user's basic electricity usage data, green electricity transaction data in which the user participates, and green certificate information purchased by the user may be to obtain the user's electricity usage data, and based on the user's electricity usage data, divide the user's corresponding traditional electricity and green electricity; obtain the basic electricity usage data corresponding to the user's traditional electricity, green electricity transaction data corresponding to the green electricity in which the user participates, and green certificate information purchased by the user.

[0021] Among them, the basic electricity consumption data corresponding to traditional electricity includes but is not limited to electricity consumption, electricity source structure (such as coal power, natural gas power generation, wind power, solar energy, etc.) and regional distribution, and this embodiment does not make specific limitations on this.

[0022] The green electricity transaction data corresponding to green electricity includes but is not limited to the amount of green electricity purchased by the user through the power grid or market platform, the source of green electricity (such as wind power, solar energy), transaction platform information, etc., and this embodiment does not make specific limitations on this.

[0023] The green certificate information includes but is not limited to the type, quantity, certification platform, and power range of the green certificate, etc. This embodiment does not make any specific limitations on this.

[0024] Step 102, calculate the user's total carbon emissions based on basic electricity consumption data, green electricity transaction data and green certificate information.

[0025] In some embodiments, one implementation method of calculating the total carbon emissions of a user based on basic electricity consumption data, green electricity transaction data, and green certificate information can be: according to the fuel characteristics and methods of traditional electricity in the basic electricity consumption data, the first carbon emissions of traditional electricity are calculated; by introducing the green electricity zero consumption coefficient and the green electricity transaction data, the second carbon emissions that green electricity can accommodate are calculated; by the type of green certificates purchased by the user in the green certificate information, the number of green certificates, and the electricity covered by the green certificates, the third carbon emissions that the green certificate can accommodate are calculated; the difference between the first carbon emissions and the second and third carbon emissions is used as the total carbon emissions of the user. In this way, more reasonable and accurate carbon footprint data can be obtained.

[0026] Furthermore, in the process of carbon footprint calculation, green certificates are regarded as the basis for judging the carbon emissions of traditional electricity. The corresponding carbon emissions are judged according to the type and quantity of green certificates purchased by users and the electricity covered by the green certificates.

[0027] Among them, for different types of green certificates (such as renewable energy certificates and international green certificates), the present invention adjusts the calculation method of carbon footprint according to their identification standards and adjustment ratios. For example, the carbon emission ratio corresponding to the green certificate may vary according to the certification body and regional standards.

[0028] Therefore, by combining the green electricity trading data and the overall green certificate information, and analyzing the user's green coverage certificate ratio, the carbon emissions to be deducted (the second carbon emissions and the third carbon emissions) are calculated, making the carbon footprint calculation more accurate.

[0029] Step 103, based on the timeliness of green electricity transactions and the duration of green certificates, statistics are performed within the final time of green certificates or green electricity to calculate the duration of the carbon footprint.

[0030] In some embodiments, the user calculates the second carbon emissions and the third carbon emissions that can be accommodated through the green electricity and green certificate transactions, and reduces the corresponding carbon emissions. Through accurate calculations, the user obtains the final carbon footprint after comprehensively considering the green certificate and green electricity transactions, which is their true low-carbon efforts.

[0031] Among them, if the user's green certificate or green electricity is no longer valid, it will no longer be used as a carbon emission deduction standard to ensure the accuracy and timeliness of carbon footprint calculation.

[0032] Step 104, training the machine learning model through the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within a certain period of time, so as to train a carbon footprint prediction model.

[0033] In some embodiments, a machine learning model is trained through the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within a time length, so as to train a carbon footprint prediction model. One implementation method can be to divide the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within a time length into a training set, a test set and a validation set; the basic electricity consumption data, green electricity transaction data and green certificate information in the training set are used as the input of the machine learning model, and the historical carbon footprint corresponding to the total carbon emissions within the time length is used as the output of the machine learning model to obtain an initial carbon footprint prediction model; the validation set and the test set are cross-validated to confuse and verify the initial carbon footprint prediction model until the accuracy and recall rate of the initial carbon footprint prediction model are less than the preset threshold, and the carbon footprint prediction model is obtained. In this way, accurate prediction of any target user is achieved, and the carbon footprint of the target user can be more accurately evaluated.

[0034] Step 105, predicting the target basic electricity consumption data, target green electricity transaction data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length.

[0035] In some embodiments, a carbon footprint statistical report chart is generated based on the target basic electricity consumption data, target green electricity transaction data, target green certificate information, and target time length of the target carbon footprint; and a carbon emission management strategy for the target user is formulated based on the carbon footprint statistical report chart. Thus, the user's efforts in low-carbon electricity are displayed through a visualized carbon footprint statistical report chart, providing data support for the decision-making of enterprises and individuals in carbon management and carbon first, which helps enterprises to formulate reasonable carbon emission management strategies.

[0036] In summary, the trading of green electricity and green certificates introduces a unique relative reduction factor for carbon footprint calculation, which not only reduces direct carbon emissions, but also realizes the calculation of indirect carbon footprint. In practical applications, the effectiveness of green certificates and the effective zero emission of green electricity form the basis for carbon footprint deduction. By incorporating green electricity and green certificate trading information, carbon footprint calculation has shifted from the traditional direct calculation method based on total consumption to a composite calculation model involving multiple factors, thereby more accurately reflecting the user's contribution to green energy and improving the accuracy of carbon footprint statistics.

[0037] The carbon footprint calculation method taking into account green certificate and green electricity trading of the embodiment of the present invention calculates the total carbon emissions of the user through the basic electricity consumption data of the user, the green electricity trading data in which the user participates, and the green certificate information purchased by the user; calculates the time length of the carbon footprint according to the timeliness of the green electricity trading and the time length of the green certificate; trains a carbon footprint prediction model through the basic electricity consumption data, green electricity trading data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length; predicts the target basic electricity consumption data, target green electricity trading data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length. Therefore, by taking into account the green electricity trading data and green certificate information, the user's contribution to green energy can be more accurately reflected, and the accuracy of carbon footprint statistics can be improved.

[0038] In order to implement the above-mentioned embodiment, the present invention also proposes a carbon footprint calculation device taking into account green certificate and green electricity transactions.

[0039] Figure 2 A structural schematic diagram of a carbon footprint calculation device taking into account green certificate and green electricity transactions provided in an embodiment of the present invention.

[0040] like Figure 2As shown, the carbon footprint calculation device 20 taking into account green certificate and green electricity transactions includes: an acquisition module 21, a first calculation module 22, a second calculation module 23, a training module 24 and a prediction module 25.

[0041] The acquisition module 21 is used to acquire the user's basic electricity consumption data, the green electricity transaction data in which the user participates, and the green certificate information purchased by the user; The first calculation module 22 is used to calculate the total carbon emissions of the user based on the basic electricity consumption data, green electricity transaction data and green certificate information; The second calculation module 23 is used to calculate the time length of the carbon footprint according to the timeliness of green electricity transactions and the time length of the green certificate within the final time of the green certificate or green electricity; The training module 24 is used to train the machine learning model through the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, and the historical carbon footprint corresponding to the total carbon emissions within the time length, so as to train a carbon footprint prediction model; The prediction module 25 is used to predict the target basic electricity consumption data, target green electricity transaction data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length.

[0042] Furthermore, in a possible implementation of the embodiment of the present invention, the acquisition module 21 is specifically configured to: Acquire user power consumption data, and divide the user's corresponding traditional power and green power based on the user's power consumption data; Obtain the basic electricity consumption data corresponding to the user's traditional electricity, the green electricity transaction data corresponding to the green electricity in which the user participates, and the green certificate information purchased by the user.

[0043] Further, in a possible implementation of the embodiment of the present invention, the first calculation module 22 is specifically configured to: According to the fuel characteristics and methods of traditional electricity in the basic electricity consumption data, the first carbon emissions of traditional electricity are calculated; By introducing the green electricity zero consumption coefficient and the green electricity trading data, the second carbon emission that green electricity can accommodate is calculated; The third carbon emissions that the green certificate can accommodate are calculated based on the type of green certificate purchased by the user, the number of green certificates, and the electricity covered by the green certificate in the green certificate information; The difference between the first carbon emission and the second and third carbon emissions is taken as the total carbon emission of the user.

[0044] Furthermore, in a possible implementation of the embodiment of the present invention, the training module 24 is specifically used to: Divide the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length into a training set, a test set and a validation set; The basic electricity consumption data, green electricity transaction data and green certificate information in the training set are used as the input of the machine learning model, and the historical carbon footprint of the total carbon emissions within the time length is used as the output of the machine learning model to obtain the initial carbon footprint prediction model. The verification set and the test set are cross-validated to perform confusion verification on the initial carbon footprint prediction model until the accuracy and recall rate of the initial carbon footprint prediction model are less than a preset threshold, thereby obtaining a carbon footprint prediction model.

[0045] Furthermore, in a possible implementation of the embodiment of the present invention, the device further includes: A generation module, for generating a carbon footprint statistical report chart based on the target basic electricity consumption data, the target green electricity transaction data, the target green certificate information and the target carbon footprint of the target time length; The formulation module is used to formulate a carbon emission management strategy for the target user based on the carbon footprint statistical report chart.

[0046] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment and will not be repeated here.

[0047] The carbon footprint calculation device taking into account green certificate and green electricity trading of the embodiment of the present invention calculates the total carbon emissions of the user through the basic electricity consumption data of the user, the green electricity trading data in which the user participates, and the green certificate information purchased by the user; calculates the time length of the carbon footprint according to the timeliness of the green electricity trading and the time length of the green certificate; trains a carbon footprint prediction model through the basic electricity consumption data, green electricity trading data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length; predicts the target basic electricity consumption data, target green electricity trading data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length. Therefore, by taking into account the green electricity trading data and green certificate information, the user's contribution to green energy can be more accurately reflected, and the accuracy of carbon footprint statistics can be improved.

[0048] In order to implement the above embodiment, the present invention further provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the aforementioned method.

[0049] In order to implement the above embodiment, the present invention further proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the above method.

[0050] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0051] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0052] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0053] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0054] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0055] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0056] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules. If the integrated module is implemented in the form of software functional modules and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0057] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A carbon footprint calculation method taking into account green certificate and green electricity trading, characterized in that: The method comprises: Obtain users’ basic electricity consumption data, green electricity transaction data in which users participate, and information on green certificates purchased by users; Calculate the user's total carbon emissions based on basic electricity consumption data, green electricity trading data and green certificate information; According to the timeliness of green electricity transactions and the duration of green certificates, statistics are taken within the final time of green certificates or green electricity to calculate the duration of the carbon footprint; The machine learning model is trained by using the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length, so as to train a carbon footprint prediction model; The carbon footprint prediction model is used to predict the target basic electricity consumption data, target green electricity transaction data and target green certificate information of any target user to predict the target carbon footprint of the target time length.

2. The method according to claim 1, characterized in that The acquisition of the user's basic electricity consumption data, the green electricity transaction data in which the user participates, and the green certificate information purchased by the user includes: Acquire user power consumption data, and divide the user's corresponding traditional power and green power based on the user's power consumption data; Obtain the basic electricity consumption data corresponding to the user's traditional electricity, the green electricity transaction data corresponding to the green electricity in which the user participates, and the green certificate information purchased by the user.

3. The method according to claim 1, characterized in that The total carbon emissions of users are calculated based on basic electricity consumption data, green electricity transaction data and green certificate information, including: According to the fuel characteristics and methods of traditional electricity in the basic electricity consumption data, the first carbon emissions of traditional electricity are calculated; By introducing the green electricity zero consumption coefficient and the green electricity trading data, the second carbon emission that green electricity can accommodate is calculated; The third carbon emissions that the green certificate can accommodate are calculated based on the type of green certificate purchased by the user, the number of green certificates, and the electricity covered by the green certificate in the green certificate information; The difference between the first carbon emission and the second and third carbon emissions is taken as the total carbon emission of the user.

4. The method according to claim 1, characterized in that: The method of training a machine learning model based on the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, and the historical carbon footprint corresponding to the total carbon emissions within the time length to train a carbon footprint prediction model includes: Divide the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length into a training set, a test set and a validation set; The basic electricity consumption data, green electricity transaction data and green certificate information in the training set are used as the input of the machine learning model, and the historical carbon footprint of the total carbon emissions within the time length is used as the output of the machine learning model to obtain the initial carbon footprint prediction model. The verification set and the test set are cross-validated to perform confusion verification on the initial carbon footprint prediction model until the accuracy and recall rate of the initial carbon footprint prediction model are less than a preset threshold, thereby obtaining a carbon footprint prediction model.

5. The method according to claim 1, characterized in that The method further comprises: Generate a carbon footprint statistical report chart based on the target basic electricity consumption data, target green electricity transaction data, target green certificate information and target carbon footprint of the target time length; Formulate carbon emission management strategies for target users based on the carbon footprint statistical report chart.

6. A carbon footprint calculation device taking into account green certificate and green electricity trading, characterized in that: The device comprises: The acquisition module is used to obtain the user's basic electricity consumption data, the green electricity transaction data in which the user participates, and the green certificate information purchased by the user; The first calculation module is used to calculate the user's total carbon emissions based on basic electricity consumption data, green electricity transaction data and green certificate information; The second calculation module is used to calculate the time length of the carbon footprint according to the timeliness of green electricity transactions and the time length of green certificates within the final time of green certificates or green electricity; A training module, used to train a machine learning model through the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, and the historical carbon footprint corresponding to the total carbon emissions within the time length, so as to train a carbon footprint prediction model; The prediction module is used to predict the target basic electricity consumption data, target green electricity transaction data and target green certificate information of any target user through the carbon footprint prediction model to predict the target carbon footprint of the target time length.

7. The device according to claim 6, characterized in that The acquisition module is specifically used for: Acquire user power consumption data, and divide the user's corresponding traditional power and green power based on the user's power consumption data; Obtain the basic electricity consumption data corresponding to the user's traditional electricity, the green electricity transaction data corresponding to the green electricity in which the user participates, and the green certificate information purchased by the user.

8. The device according to claim 6, characterized in that The first calculation module is specifically used for: According to the fuel characteristics and methods of traditional electricity in the basic electricity consumption data, the first carbon emissions of traditional electricity are calculated; By introducing the green electricity zero consumption coefficient and the green electricity trading data, the second carbon emission that green electricity can accommodate is calculated; The third carbon emissions that the green certificate can accommodate are calculated based on the type of green certificate purchased by the user, the number of green certificates, and the electricity covered by the green certificate in the green certificate information; The difference between the first carbon emission and the second and third carbon emissions is taken as the total carbon emission of the user.

9. The device according to claim 6, characterized in that The training module is specifically used for: Divide the basic electricity consumption data, green electricity transaction data and green certificate information of multiple users, as well as the historical carbon footprint corresponding to the total carbon emissions within the time length into a training set, a test set and a validation set; The basic electricity consumption data, green electricity transaction data and green certificate information in the training set are used as the input of the machine learning model, and the historical carbon footprint of the total carbon emissions within the time length is used as the output of the machine learning model to obtain the initial carbon footprint prediction model. The verification set and the test set are cross-validated to perform confusion verification on the initial carbon footprint prediction model until the accuracy and recall rate of the initial carbon footprint prediction model are less than a preset threshold, thereby obtaining a carbon footprint prediction model.

10. The device according to claim 6, characterized in that The device further comprises: A generation module, for generating a carbon footprint statistical report chart based on the target basic electricity consumption data, the target green electricity transaction data, the target green certificate information and the target carbon footprint of the target time length; The formulation module is used to formulate a carbon emission management strategy for the target user based on the carbon footprint statistical report chart.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

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