Carbon emission reduction project information prediction method and device, medium, equipment and product

Abstract

The invention relates to a carbon emission reduction project information prediction method and device, a medium, equipment and a product. The method comprises the following steps: in response to received demand information of a target carbon emission reduction project, obtaining characteristic elements of the target carbon emission reduction project; inputting the feature elements into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project; and outputting the project information of the target carbon emission reduction project. And inputting the feature elements of the target carbon emission reduction project into a pre-trained carbon emission reduction project information analysis model to obtain project information of the project predicted by the carbon emission reduction project information analysis model, so that a user can formulate a project file of the target carbon emission reduction project. As the analysis model based on big data training is used for predicting the project information, the accuracy of the project information and the matching property with user requirements are remarkably improved, the influence of human subjective judgment is reduced, and the accuracy and feasibility of the target carbon emission reduction project are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of carbon emission technology, and in particular, to a method, device, medium, equipment and product for predicting carbon emission reduction project information. Background Art

[0002] Carbon reduction refers to reducing the emission of greenhouse gases such as carbon dioxide, thereby alleviating the climate crisis. In related technologies, due to the complex methodological rules for designing carbon reduction projects and the large number of parameters involved, some of which, such as emission parameters and additionality parameters, will change with the level and number of regional production facilities. As a result, in the process of designing carbon reduction projects, staff are usually required to subjectively estimate and make decisions on carbon reduction based on their work experience, which affects the accuracy and feasibility of carbon reduction projects. Summary of the invention

[0003] The main purpose of the present disclosure is to provide a carbon emission reduction project information prediction method, device, medium, equipment and product, aiming to solve the above-mentioned technical problems.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for predicting carbon emission reduction project information is provided, comprising: In response to receiving demand information of a target carbon emission reduction project, acquiring characteristic elements of the target carbon emission reduction project; Inputting the characteristic element into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project; wherein the carbon emission reduction project information analysis model is used to predict the project information according to the characteristic element, and the project information includes the carbon emission reduction amount; The project information of the target carbon emission reduction project is output.

[0005] Optionally, the carbon emission reduction project information analysis model includes a carbon emission reduction prediction module; The carbon emission reduction prediction module is used to generate the carbon emission reduction according to the characteristic elements.

[0006] Optionally, the carbon emission reduction project information analysis model further includes an additionality prediction module, and the project information further includes additionality results; The additionality prediction module is used to generate the additionality result according to the additionality characteristic elements in the characteristic elements; The outputting the project information of the target carbon emission reduction project includes: The carbon emission reduction and the additionality result are outputted.

[0007] Optionally, the training method of the carbon emission reduction prediction module includes: Obtaining a sample carbon emission reduction project set; wherein the sample carbon emission reduction project set includes sample carbon emission reduction projects marked with actual carbon emission reduction amounts; Inputting the sample carbon emission reduction project set into the carbon emission reduction amount prediction module, so that the carbon emission reduction amount prediction module generates a predicted carbon emission reduction amount according to the actual characteristic elements of the sample carbon emission reduction project; According to the actual carbon emission reduction and the predicted carbon emission reduction, the network parameters of the carbon emission reduction prediction module are adjusted.

[0008] Optionally, the training method of the carbon emission reduction prediction module further includes: Obtaining the emission reduction correlation results corresponding to each of the actual characteristic elements in the sample carbon emission reduction project set; The actual characteristic elements corresponding to the emission reduction correlation results that meet the preset emission reduction screening conditions are determined as sample emission reduction characteristic elements; The carbon emission reduction prediction module generates a predicted carbon emission reduction according to the actual characteristic elements of the sample carbon emission reduction project, including: The carbon emission reduction prediction module generates the predicted carbon emission reduction according to the sample emission reduction characteristic element.

[0009] Optionally, the training method of the additionality prediction module comprises: Obtaining a sample carbon emission reduction project set, wherein the sample carbon emission reduction project set includes sample carbon emission reduction projects marked with actual additionality labels; Inputting the sample carbon emission reduction project set into the additionality prediction module, so that the additionality prediction module generates a predicted additionality result according to the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction project; According to the actual additionality label and the predicted additionality result, the network parameters of the additionality prediction module are adjusted.

[0010] Optionally, the training method of the additionality prediction module further includes: Obtain additionality association results corresponding to each of the actual additionality characteristic elements in the sample carbon emission reduction project set; The actual additionality characteristic element corresponding to the additionality association result that meets the preset additionality screening condition is determined as the sample additionality characteristic element; The additionality prediction module generates a predicted additionality result according to the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction project, including: The carbon emission reduction prediction module generates the predicted additionality result according to the sample additionality characteristic element.

[0011] According to a second aspect of an embodiment of the present disclosure, there is provided a carbon emission reduction project information prediction device, comprising: An acquisition module, configured to acquire characteristic elements of a target carbon emission reduction project in response to receiving demand information of the target carbon emission reduction project; An input module, used to input the characteristic element into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project; wherein the carbon emission reduction project information analysis model is used to predict the project information according to the characteristic element, and the project information includes a predicted amount of carbon emission reduction; An output module is used to output the project information of the target carbon emission reduction project.

[0012] According to a third aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the carbon emission reduction project information prediction method provided in the first aspect of the present disclosure are implemented.

[0013] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the carbon emission reduction project information prediction method provided in the first aspect of the present disclosure.

[0014] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the carbon emission reduction project information prediction method provided in the first aspect of the present disclosure.

[0015] The above technical solution proposed in the embodiment of the present disclosure obtains characteristic elements of the target carbon emission reduction project through the demand information of the target carbon emission reduction project, and then inputs these characteristic elements into the pre-trained carbon emission reduction project information analysis model, and then receives the project information of the project predicted by the carbon emission reduction project information analysis model, and finally outputs the project information for the user to formulate a project file for the target carbon emission reduction project. Since the project information is predicted using an analysis model based on big data training, the accuracy of the project information and its matching with user needs are significantly improved, the influence of human subjective judgment is reduced, and the accuracy and feasibility of the target carbon emission reduction project are improved.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The present invention is a flowchart of a carbon emission reduction project information prediction method according to an exemplary embodiment.

[0018] Figure 2 It is a block diagram of a device for predicting carbon emission reduction project information according to an exemplary embodiment.

[0019] Figure 3 The present invention is a block diagram of a device for predicting carbon emission reduction project information according to an exemplary embodiment.

[0020] Figure 4 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0021] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0022] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the location and with the authorization given by the owner of the corresponding device.

[0023] The present invention provides a carbon emission reduction project information prediction method, device, medium, equipment and product. The specific implementation methods of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 FIG. 1 is a flow chart of a method for predicting carbon emission reduction project information according to an exemplary embodiment. Figure 1 As shown, the carbon emission reduction project information prediction method can be executed by a terminal, including the following S101-S103.

[0025] In S101 , in response to receiving demand information of a target carbon emission reduction project, characteristic elements of the target carbon emission reduction project are acquired.

[0026] In an embodiment of the present disclosure, the demand information of the target carbon emission reduction project can be determined and input by the user, and after receiving the demand information, the demand information is parsed to obtain the characteristic elements of the target carbon emission reduction project. For example, the characteristic element types may include numerical values, keywords, and pictures.

[0027] In S102, the characteristic elements are input into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project.

[0028] In one embodiment of the present disclosure, a carbon emission reduction project information analysis model is trained based on characteristic elements of multiple disclosed carbon emission reduction projects and their corresponding project information. The carbon emission reduction project information analysis model is used to predict the project information of the target carbon emission reduction project based on the characteristic elements parsed from the demand information for the target carbon emission reduction project; wherein the project information may include the carbon emission reduction amount; in some embodiments, the project information may also include additionality results, confidence intervals, etc.

[0029] In S103, project information of the target carbon emission reduction project is output.

[0030] In one embodiment of the present disclosure, the predicted project information is outputted so that the user can formulate a project document for the target carbon emission reduction project.

[0031] The technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: obtaining characteristic elements of the target carbon emission reduction project through the demand information of the target carbon emission reduction project, then inputting these characteristic elements into the pre-trained carbon emission reduction project information analysis model, then receiving the project information of the project predicted by the carbon emission reduction project information analysis model, and finally outputting the project information for the user to formulate a project file for the target carbon emission reduction project. Since the project information is predicted using an analysis model based on big data training, the accuracy of the project information and its matching with user needs are significantly improved, the influence of human subjective judgment is reduced, and the accuracy and feasibility of the target carbon emission reduction project are improved.

[0032] As an optional implementation, the carbon emission reduction project information analysis model includes a carbon emission reduction prediction module.

[0033] In one embodiment of the present disclosure, the carbon emission reduction prediction module is used to generate carbon emission reduction according to the characteristic elements of the above-mentioned target carbon emission reduction project. Specifically, carbon emission reduction is usually measured in tons and refers to the amount of carbon dioxide or other greenhouse gas emissions reduced through various measures, which may include energy efficiency improvement, renewable energy utilization, industrial process improvement, forest protection and restoration, etc.

[0034] As an optional implementation, a training method for a carbon emission reduction prediction module includes: obtaining a sample carbon emission reduction project set; inputting the sample carbon emission reduction project set into the carbon emission reduction prediction module, so that the carbon emission reduction prediction module generates a predicted carbon emission reduction based on actual characteristic elements of the sample carbon emission reduction projects; and adjusting the network parameters of the carbon emission reduction prediction module based on the actual carbon emission reduction and the predicted carbon emission reduction.

[0035] In one embodiment of the present disclosure, a sample carbon emission reduction project set refers to a sample set consisting of a large number of publicly disclosed or implemented carbon emission reduction projects as sample carbon emission reduction projects, wherein each sample carbon emission reduction project is marked with its actual project information, such as the actual carbon emission reduction amount.

[0036] Based on the machine learning method, the sample carbon emission reduction project set is input into the carbon emission reduction prediction module for training. The carbon emission reduction prediction module will generate the corresponding predicted carbon emission reduction according to the actual characteristic elements of the sample carbon emission reduction project, and then compare the difference between the actual carbon emission reduction of the same sample carbon emission reduction project and the corresponding predicted carbon emission reduction, and use the optimization algorithm to adjust the network parameters of the carbon emission reduction prediction module to continuously reduce the prediction error. The parameter adjustment process can be repeated iteratively until the prediction accuracy of the carbon emission reduction prediction module reaches the expected mark, and then the training process ends.

[0037] As an optional implementation, the training method of the carbon emission reduction prediction module also includes: obtaining emission reduction correlation results corresponding to each actual characteristic element in the sample carbon emission reduction project set; and determining the actual characteristic elements corresponding to the emission reduction correlation results that meet the preset emission reduction screening conditions as sample emission reduction characteristic elements.

[0038] In one embodiment of the present disclosure, the sample carbon emission reduction project set may be analyzed to filter out the sample emission reduction characteristic elements from various actual characteristic elements for use in the subsequent training of the carbon emission reduction prediction module.

[0039] The screening method of the sample emission reduction characteristic elements can be: performing correlation analysis through statistical methods, regression analysis methods or machine learning methods to determine the correlation between each actual characteristic element and the emission reduction amount, that is, obtaining the emission reduction amount correlation result corresponding to each actual characteristic element; then, according to the preset emission reduction screening conditions, obtaining one or more actual characteristic elements whose emission reduction amount correlation results meet the screening conditions, that is, screening out the sample emission reduction characteristic elements with a high correlation with the emission reduction amount. Among them, the emission reduction screening condition can be a specific emission reduction amount correlation threshold.

[0040] In some embodiments, the method for screening the sample emission reduction characteristic elements may also be: using one or more actual characteristic elements with the highest availability as the sample emission reduction characteristic elements.

[0041] At this time, the carbon emission reduction prediction module generates the predicted carbon emission reduction according to the actual characteristic elements of the sample carbon emission reduction project, including: the carbon emission reduction prediction module generates the predicted carbon emission reduction according to the sample emission reduction characteristic elements.

[0042] It is easy to understand that the above-screened sample emission reduction characteristic elements are input into the carbon emission reduction prediction module, so that the carbon emission reduction prediction module generates a predicted carbon emission reduction according to the sample emission reduction characteristic elements for use in the subsequent network parameter adjustment step.

[0043] As an optional implementation, the carbon emission reduction project information analysis model also includes an additionality prediction module, and the project information also includes additionality results.

[0044] In one embodiment of the present disclosure, additionality means that the emission reductions generated by a carbon emission reduction project are additional to the baseline, that is, if the implementation of the project is only possible due to carbon market or other policy incentives, the project is considered to be additional. To ensure the authenticity and effectiveness of carbon emission reduction projects, carbon credits are only considered valid if the emission reductions of the project are additional. Therefore, in the process of designing carbon emission reduction projects, it is also necessary to ensure that the target carbon emission reduction project is additional.

[0045] Specifically, the additionality prediction module is used to generate additionality results based on the additionality characteristic elements of the above-mentioned target carbon emission reduction project. The categories of additionality characteristic elements may include financial data, legal and policy background, technology usage, and market barriers. The additionality result is additionality or no additionality.

[0046] At this time, the project information of the target carbon emission reduction project is output, including: output carbon emission reduction amount and additionality results.

[0047] It is understandable that after the carbon emission reduction prediction module generates the carbon emission reduction, and the additionality prediction module generates the additionality result, the carbon emission reduction and the additionality result are output for the user to formulate a project document for the target carbon emission reduction project.

[0048] As an optional implementation, a training method for an additionality prediction module includes: obtaining a sample carbon emission reduction project set, inputting the sample carbon emission reduction project set into the additionality prediction module, so that the additionality prediction module generates a predicted additionality result based on the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction projects; and adjusting the network parameters of the additionality prediction module based on the actual additionality label and the predicted additionality result.

[0049] In one embodiment of the present disclosure, each sample carbon emission reduction project in the sample carbon emission reduction project set is marked with an actual additionality label, that is, whether the project has additionality.

[0050] Based on the machine learning method, the sample carbon emission reduction project set is input into the additionality prediction module for training. The additionality prediction module will generate the corresponding predicted additionality results according to the actual additionality characteristic elements of the sample carbon emission reduction project. Then, the difference between the actual additionality label and the corresponding predicted additionality result of the same sample carbon emission reduction project is compared, and the network parameters of the additionality prediction module are adjusted using the optimization algorithm to continuously reduce the prediction error. The parameter adjustment process can be repeated iteratively until the prediction accuracy of the additionality prediction module reaches the expected mark, and then the training process ends.

[0051] As an optional implementation, the training method of the additionality prediction module also includes: obtaining additionality association results corresponding to each actual additionality characteristic element in the sample carbon emission reduction project set; and determining the actual additionality characteristic element corresponding to the additionality association result that meets the preset additionality screening conditions as the sample additionality characteristic element.

[0052] In one embodiment of the present disclosure, a sample carbon emission reduction project set may be analyzed to filter out sample additionality characteristic elements from various actual additionality characteristic elements for subsequent training of additionality prediction modules.

[0053] The screening method of sample additionality characteristic elements can be: performing correlation analysis through statistical methods, regression analysis methods or machine learning methods to determine the correlation between each actual additionality characteristic element and the additionality result, that is, obtaining the additionality correlation result corresponding to each actual additionality characteristic element; then, according to the pre-set additionality screening conditions, obtaining one or more actual additionality characteristic elements whose additionality correlation results meet the screening conditions, that is, screening out sample additionality characteristic elements with a high correlation with the additionality result. Among them, the additionality screening conditions can be specific additionality results, for example, having additionality; or other standards related to project additionality.

[0054] In some embodiments, the method for screening sample additional characteristic elements may also be: taking one or more actual additional characteristic elements that are at the top of the list in terms of availability as sample additional characteristic elements.

[0055] At this time, the above-mentioned additionality prediction module generates a predicted additionality result according to the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction project, including: the carbon emission reduction amount prediction module generates a predicted additionality result according to the sample additionality characteristic elements.

[0056] It is easy to understand that the above-screened sample additionality characteristic elements are input into the additionality prediction module, so that the additionality prediction module generates a predicted additionality result according to the sample additionality characteristic elements for use in the subsequent network parameter adjustment step.

[0057] Figure 2 FIG. 1 is a block diagram of a device for predicting carbon emission reduction project information according to an exemplary embodiment. Figure 2 As shown, the device for predicting carbon emission reduction project information may include: a public emission reduction project information collection and registration module, an emission reduction project measurement and registration module, a data storage module, a carbon emission reduction project information analysis model training module and a rapid prediction I / O module; wherein, the carbon emission reduction project information analysis model training module may include: an emission reduction characteristic element screening submodule, a carbon emission reduction amount prediction submodule, an additionality characteristic element screening submodule and an additionality prediction submodule.

[0058] The public emission reduction project information collection and registration module is used to collect and format the project information of public and verified carbon emission reduction projects through robotic process automation (RPA) or other means.

[0059] For example, project information may include, but is not limited to, the project's emission reduction mechanism, technical solutions, design parameters, time nodes, geographical location, economic feasibility, as well as monitoring data, test data, regional, national, and global industry status parameters; for carbon sink emission reduction projects, it may also include forest land, grassland, soil test data, tree statistics, small patch maps, remote sensing satellite images, etc.

[0060] The emission reduction project calculation and registration module is used to parse the characteristic elements in the project information according to the emission reduction project methodology and relevant policy regulations; it can also be used for methodological applicability judgment, project emission reduction calculation, project additionality demonstration and characteristic element formatting.

[0061] For example, the above-mentioned emission reduction project methodologies and related policy provisions include but are not limited to the emission reduction project development methodologies and emission factor libraries promulgated in the Clean Development Mechanism (CDM), the Verified Carbon Standard (VCS), and the Gold Standard (GS).

[0062] The data storage module is used to store the project information output by the above-mentioned public emission reduction project information collection and registration module and the characteristic elements output by the above-mentioned emission reduction project calculation and registration module, so as to be used by the subsequent carbon emission reduction project information analysis model training module.

[0063] The carbon emission reduction project information analysis model training module is used to train the carbon emission reduction project information analysis model through the information stored in the above-mentioned data storage module, using the emission reduction characteristic element screening submodule, the carbon emission reduction amount prediction submodule, the additionality characteristic element screening submodule, and the additionality prediction submodule. The trained model can predict the project information of the target project and output the predicted project information to the fast prediction I / O module.

[0064] The emission reduction characteristic element screening submodule is used to screen out characteristic elements with a high correlation with the emission reduction calculation results from the registered full amount of characteristic elements through machine learning methods, and push them to the carbon emission reduction prediction submodule.

[0065] The carbon emission reduction prediction submodule is used to use the screened characteristic elements and their corresponding emission reductions as training samples to train the carbon emission reduction prediction module in the carbon emission reduction project information analysis model.

[0066] The additionality characteristic element screening submodule is used to screen out additionality characteristic elements with a high correlation with the additionality judgment results from the full amount of registered characteristic elements through machine learning methods, and push them to the additionality prediction submodule.

[0067] The additionality prediction submodule is used to train the model by taking the screened additionality characteristic elements and their corresponding additionality labels as training samples.

[0068] The rapid prediction I / O module is used to receive the accuracy evaluation parameters for the target project input by the user. If the evaluation parameters are higher than a certain threshold, the project information of the target project can be fed back to the carbon emission reduction project information analysis model training module, that is, the target project is used as part of the training set to further optimize the carbon emission reduction project information analysis model. In addition, the rapid prediction I / O module can also be used to establish an audit process to receive the audit parameters input by the user to assist in verifying the operation of each module in the device for carbon emission reduction project information prediction.

[0069] Figure 3 FIG. 1 is a block diagram of a carbon emission reduction project information prediction device according to an exemplary embodiment. Figure 3 As shown, the carbon emission reduction project information prediction device 300 may include an acquisition module 301 , an input module 302 and an output module 303 .

[0070] The acquisition module 301 is used to acquire characteristic elements of the target carbon emission reduction project in response to receiving demand information of the target carbon emission reduction project.

[0071] The input module 302 is used to input the characteristic elements into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project; wherein the carbon emission reduction project information analysis model is used to predict the project information based on the characteristic elements, and the project information includes the predicted amount of carbon emission reduction.

[0072] The output module 303 is used to output the project information of the target carbon emission reduction project.

[0073] The technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: obtaining characteristic elements of the target carbon emission reduction project through the demand information of the target carbon emission reduction project, then inputting these characteristic elements into the pre-trained carbon emission reduction project information analysis model, then receiving the project information of the project predicted by the carbon emission reduction project information analysis model, and finally outputting the project information for the user to formulate a project file for the target carbon emission reduction project. Since the project information is predicted using an analysis model based on big data training, the accuracy of the project information and its matching with user needs are significantly improved, the influence of human subjective judgment is reduced, and the accuracy and feasibility of the target carbon emission reduction project are improved.

[0074] Optionally, the carbon emission reduction project information analysis model includes a carbon emission reduction prediction module; The carbon emission reduction prediction module is used to generate the carbon emission reduction according to the characteristic elements.

[0075] Optionally, the carbon emission reduction project information analysis model further includes an additionality prediction module, and the project information further includes additionality results; The additionality prediction module is used to generate the additionality result according to the additionality characteristic elements in the characteristic elements; The output module 303 is used to output the carbon emission reduction amount and the additionality result.

[0076] Optionally, the carbon emission reduction project information prediction device 300 further includes a first training module; The first training module includes: The first acquisition submodule is used to acquire a sample carbon emission reduction project set; wherein the sample carbon emission reduction project set includes sample carbon emission reduction projects marked with actual carbon emission reduction amounts; A first input submodule, configured to input the sample carbon emission reduction project set into the carbon emission reduction amount prediction module, so that the carbon emission reduction amount prediction module generates a predicted carbon emission reduction amount according to the actual characteristic elements of the sample carbon emission reduction project; The first adjustment submodule is used to adjust the network parameters of the carbon emission reduction prediction module according to the actual carbon emission reduction and the predicted carbon emission reduction.

[0077] Optionally, the first training module further includes: The second acquisition submodule is used to obtain the emission reduction amount correlation result corresponding to each of the actual characteristic elements in the sample carbon emission reduction project set; The first screening submodule is used to determine the actual characteristic elements corresponding to the emission reduction association results that meet the preset emission reduction screening conditions as sample emission reduction characteristic elements; At this time, the first input submodule is further used to input the sample carbon emission reduction project set into the carbon emission reduction prediction module, so that the carbon emission reduction prediction module generates the predicted carbon emission reduction according to the sample emission reduction characteristic elements.

[0078] Optionally, the carbon emission reduction project information prediction device 300 further includes a second training module; The second training module includes: A third acquisition submodule is used to acquire a sample carbon emission reduction project set, wherein the sample carbon emission reduction project set includes sample carbon emission reduction projects marked with actual additionality labels; A second input submodule, for inputting the sample carbon emission reduction project set into the additionality prediction module, so that the additionality prediction module generates a predicted additionality result according to the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction project; The second adjustment submodule is used to adjust the network parameters of the additionality prediction module according to the actual additionality label and the predicted additionality result.

[0079] Optionally, the second training module further includes: A fourth acquisition submodule is used to obtain the additionality association results corresponding to each of the actual additionality characteristic elements in the sample carbon emission reduction project set; A second screening submodule is used to determine the actual additionality characteristic element corresponding to the additionality association result that meets the preset additionality screening condition as the sample additionality characteristic element; At this time, the second input submodule is further used to input the sample carbon emission reduction project set into the additionality prediction module, so that the carbon emission reduction prediction module generates the predicted additionality result according to the sample additionality characteristic element.

[0080] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0081] The present disclosure also provides a non-temporary computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned carbon emission reduction project information prediction method provided by the present disclosure.

[0082] Figure 4 FIG. 4 is a block diagram of an electronic device 400 according to an exemplary embodiment. Figure 4 As shown, the electronic device 400 may include: a processor 401 , a memory 402 . The electronic device 400 may also include one or more of a multimedia component 403 , an input / output (I / O) interface 404 , and a communication component 405 .

[0083] The processor 401 is used to control the overall operation of the electronic device 400 to complete all or part of the steps in the above-mentioned carbon emission reduction project information prediction method. The memory 402 is used to store various types of data to support the operation of the electronic device 400, and these data may include instructions for any application or method used to operate on the electronic device 400, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 403 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 402 or sent through the communication component 405. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 404 provides an interface between the processor 401 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 4G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 405 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0084] In some embodiments, the electronic device 400 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned carbon emission reduction project information prediction method.

[0085] In addition, in one embodiment, the embodiment of the present disclosure further provides a computer-readable storage medium including program instructions, which implements the steps of the above-mentioned carbon emission reduction project information prediction method when the program instructions are executed by the processor. For example, the computer-readable storage medium can be the above-mentioned memory 402 including program instructions, and the above-mentioned program instructions can be executed by the processor 401 of the electronic device 400 to complete the above-mentioned carbon emission reduction project information prediction method.

[0086] In addition, in one embodiment, an embodiment of the present disclosure also provides a computer program product, which is a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned carbon emission reduction project information prediction method when executed by the programmable device.

[0087] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0088] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0089] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A carbon emission reduction project information prediction method, characterized in that: include: In response to receiving demand information of a target carbon emission reduction project, acquiring characteristic elements of the target carbon emission reduction project; Inputting the characteristic element into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project; wherein the carbon emission reduction project information analysis model is used to predict the project information according to the characteristic element, and the project information includes the carbon emission reduction amount; The project information of the target carbon emission reduction project is output.

2. The method according to claim 1, characterized in that The carbon emission reduction project information analysis model includes a carbon emission reduction prediction module; The carbon emission reduction prediction module is used to generate the carbon emission reduction according to the characteristic elements.

3. The method according to claim 2, characterized in that The carbon emission reduction project information analysis model also includes an additionality prediction module, and the project information also includes additionality results; The additionality prediction module is used to generate the additionality result according to the additionality characteristic elements in the characteristic elements; The outputting the project information of the target carbon emission reduction project includes: The carbon emission reduction and the additionality result are outputted.

4. The method according to claim 2, characterized in that: The training method of the carbon emission reduction prediction module comprises: Obtaining a sample carbon emission reduction project set; wherein the sample carbon emission reduction project set includes sample carbon emission reduction projects marked with actual carbon emission reduction amounts; Inputting the sample carbon emission reduction project set into the carbon emission reduction amount prediction module, so that the carbon emission reduction amount prediction module generates a predicted carbon emission reduction amount according to the actual characteristic elements of the sample carbon emission reduction project; According to the actual carbon emission reduction and the predicted carbon emission reduction, the network parameters of the carbon emission reduction prediction module are adjusted.

5. The method according to claim 4, characterized in that The training method of the carbon emission reduction prediction module further includes: Obtaining the emission reduction correlation results corresponding to each of the actual characteristic elements in the sample carbon emission reduction project set; The actual characteristic elements corresponding to the emission reduction correlation results that meet the preset emission reduction screening conditions are determined as sample emission reduction characteristic elements; The carbon emission reduction prediction module generates a predicted carbon emission reduction according to the actual characteristic elements of the sample carbon emission reduction project, including: The carbon emission reduction prediction module generates the predicted carbon emission reduction according to the sample emission reduction characteristic element.

6. The method according to claim 3, characterized in that The training method of the additionality prediction module comprises: Obtaining a sample carbon emission reduction project set, wherein the sample carbon emission reduction project set includes sample carbon emission reduction projects marked with actual additionality labels; Inputting the sample carbon emission reduction project set into the additionality prediction module, so that the additionality prediction module generates a predicted additionality result according to the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction project; According to the actual additionality label and the predicted additionality result, the network parameters of the additionality prediction module are adjusted.

7. The method according to claim 6, characterized in that The training method of the additionality prediction module further includes: Obtain additionality association results corresponding to each of the actual additionality characteristic elements in the sample carbon emission reduction project set; The actual additionality characteristic element corresponding to the additionality association result that meets the preset additionality screening condition is determined as the sample additionality characteristic element; The additionality prediction module generates a predicted additionality result according to the actual additionality characteristic elements in the actual characteristic elements of the sample carbon emission reduction project, including: The carbon emission reduction prediction module generates the predicted additionality result according to the sample additionality characteristic element.

8. A carbon emission reduction project information prediction device, characterized in that: include: An acquisition module, configured to acquire characteristic elements of a target carbon emission reduction project in response to receiving demand information of the target carbon emission reduction project; An input module, used to input the characteristic element into a pre-trained carbon emission reduction project information analysis model to obtain project information corresponding to the target carbon emission reduction project; wherein the carbon emission reduction project information analysis model is used to predict the project information according to the characteristic element, and the project information includes a predicted amount of carbon emission reduction; An output module is used to output the project information of the target carbon emission reduction project.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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