Carbon footprint prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a carbon footprint prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a background data set of a target object, and determining a data source corresponding to each type of background data in the background data set; acquiring sampling information of each type of background data, and generating a data acquisition task according to the data source and the sampling information; determining access information of the data source, and sending a data acquisition task to the data source based on the access information; receiving background data sent by the data source, and processing the background data to obtain model input data; determining a target carbon footprint prediction model from the candidate carbon footprint prediction models according to the target object; and inputting the model input data into the target carbon footprint prediction model to output predicted carbon emission data of the target object. Therefore, according to the scheme, the carbon emission data of different objects can be efficiently and accurately predicted, and data support is provided for calculation of the carbon footprint.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission data processing, and in particular to a carbon footprint prediction method, device, electronic device and storage medium. Background Art

[0002] As the global climate change problem becomes increasingly serious, all walks of life are paying more and more attention to carbon emissions. Carbon footprint, as an important indicator for measuring carbon emissions, can help companies and governments understand the impact of their activities or products on the environment. The calculation of carbon footprint usually relies on a large amount of background data, such as energy consumption, transportation, production processes, etc. However, existing technologies are difficult to meet the needs of carbon footprint analysis. Summary of the invention

[0003] The purpose of this application is to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first purpose of the present application is to propose a carbon footprint prediction method to achieve efficient and accurate prediction of carbon emission data of different objects.

[0005] The second objective of this application is to provide a carbon footprint prediction device.

[0006] The third objective of the present application is to provide an electronic device.

[0007] A fourth objective of the present application is to provide a computer-readable storage medium.

[0008] To achieve the above-mentioned purpose, the first aspect of the present application proposes a carbon footprint prediction method, including: determining a background data set that needs to be collected for a target object, and determining the data source corresponding to each type of background data in the background data set; obtaining sampling information for each type of background data, and generating a data collection task for the background data based on the data source and sampling information corresponding to the background data; determining access information for the data source, and sending the data collection task to the data source based on the access information; receiving background data sent by each data source, and processing the received background data to obtain model input data; determining a target carbon footprint prediction model to be called from candidate carbon footprint prediction models according to the target object; inputting the model input data into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs the predicted carbon emission data of the target object based on the model input data.

[0009] To achieve the above-mentioned purpose, the second aspect of the present application proposes a carbon footprint prediction device, including: a first determination module, used to determine the background data set that needs to be collected for the target object, and determine the data source corresponding to each type of background data in the background data set; a generation module, used to obtain sampling information of each type of background data, and generate a data collection task for the background data according to the data source and sampling information corresponding to the background data; a sending module, used to determine the access information of the data source, and based on the access information, send the data collection task to the data source; a processing module, used to receive the background data sent by each data source, and process the received background data to obtain model input data; a second determination module, used to determine the target carbon footprint prediction model to be called from the candidate carbon footprint prediction models according to the target object; a prediction module, used to input the model input data into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs the predicted carbon emission data of the target object according to the model input data.

[0010] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: a processor; and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, so that the processor can execute the carbon footprint prediction method described in the first aspect embodiment above.

[0011] To achieve the above-mentioned purpose, the fourth aspect of the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer instructions are used to enable the computer to execute the carbon footprint prediction method described in the first aspect of the embodiment.

[0012] The carbon footprint prediction method, device, electronic device and storage medium provided in the present application determine the background data set that needs to be collected by the target object, and obtain the data source of each type of background data in the background data set, and further determine the data collection task of the background data, so as to collect the background data according to the data source and the data collection task, so as to determine the model input data corresponding to the background data. By determining the target carbon footprint prediction model from the candidate carbon footprint prediction models, and inputting the model input data into the target carbon footprint prediction model, the predicted carbon emission data of the target object can be obtained, and the carbon emission data of different objects can be predicted efficiently and accurately, which provides data support for the calculation of carbon footprint and the management of carbon emissions, thereby improving the reliability and adaptability of carbon footprint prediction.

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

[0014] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic diagram of a process flow of a carbon footprint prediction method provided in an embodiment of the present application; Figure 2 A schematic flow chart of a process of determining a target carbon footprint prediction model in a carbon footprint prediction method provided in an embodiment of the present application; Figure 3 A schematic flow chart of another carbon footprint prediction method provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a carbon footprint prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] Embodiments of the present application are described in detail below, and examples of the embodiments 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 application, and should not be construed as limiting the present application.

[0016] The carbon footprint prediction method and device according to the embodiments of the present application are described below with reference to the accompanying drawings.

[0017] Figure 1 is a flow chart of a carbon footprint prediction method provided in an embodiment of the present application, such as Figure 1 As shown, the carbon footprint prediction method of the embodiment of the present application includes but is not limited to the following steps: S101, determining a background data set that needs to be collected for a target object, and determining a data source corresponding to each type of background data in the background data set.

[0018] It should be noted that the execution subject of the carbon footprint prediction method provided in the embodiment of the present application is an electronic device, which may be a terminal device. Optionally, the terminal device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a personal computer (PC), a television, etc. The embodiment of the present application does not make specific limitations.

[0019] In some implementations, the object to be subjected to carbon footprint prediction can be used as the target object, and then the background data set to be collected for the target object can be determined according to the requirements of carbon footprint prediction. For example, a region, industry, enterprise, or specific project or activity can be used as the target object.

[0020] Optionally, the background data set includes but is not limited to: energy consumption data, traffic data, industrial production data, weather data, etc. That is, at least one of energy consumption data, traffic data, industrial production data and weather data can be selected to form the background data set of the target object.

[0021] In some implementations, for each type of background data in the background data set, the data source corresponding to the background data can be determined by identifying the data source of the background data. Optionally, for any background data, if it is identified that any background data contains multiple data sources, the multiple data sources can be evaluated for quality to determine the data source with the best quality, and then the background data can be collected from the data source.

[0022] S102, acquiring sampling information of each type of background data, and generating a data collection task for the background data according to the data source and sampling information corresponding to the background data.

[0023] In some implementations, sampling information of background data may be generated based on the carbon footprint prediction indication of the target object. Alternatively, sampling information of each type of background data may be generated based on identification information of each type of background data and in combination with the carbon footprint prediction indication.

[0024] Furthermore, according to the data source corresponding to the background data and the sampling information, a data collection task for each type of background data can be generated.

[0025] For example, if the background data is energy consumption data, its identifier is A, and the corresponding data source is data source 1, then a data collection task for energy consumption data can be generated based on identifier A and data source 1 to collect energy consumption data in data source 1.

[0026] S103, determining access information of the data source, and sending a data collection task to the data source based on the access information.

[0027] In some implementations, the address information of the data source may be obtained, and the access information of the data source may be generated according to the address information, so as to send the data collection task to the data source according to the access information. Optionally, the data source may verify the access information, and receive the data collection task after the verification is passed, so as to collect the background data according to the data collection task.

[0028] Optionally, the data source may authenticate the access information, for example, by verifying the identity of the access information based on an identity token carried in the access information.

[0029] S104, receiving background data sent by each data source, and processing the received background data to obtain model input data.

[0030] In some implementations, the background data is processed by receiving the background data sent by each data source, and then performing operations such as removing noise, processing missing values, and formatting the background data to obtain the processed background data.

[0031] Furthermore, key features are extracted from the processed background data, and the extracted key features are used as model input data.

[0032] In some implementations, after obtaining the background data, the candidate carbon footprint prediction model can also be updated based on the background data to improve the accuracy and real-time performance of the prediction model. Optionally, sample data for model training can be generated based on the background data, and the candidate carbon footprint prediction model can be trained using the sample data to update the parameters of the prediction model.

[0033] S105, determining a target carbon footprint prediction model to be called from candidate carbon footprint prediction models according to the target object.

[0034] Optionally, the candidate carbon footprint prediction model may be a prediction model trained using historical data based on a machine learning or deep learning algorithm.

[0035] In some implementations, the target carbon footprint prediction model to be called can be determined from the candidate carbon footprint prediction models according to the matching degree between the target object and multiple candidate carbon footprint prediction models. In other words, the matching value between the target object and each candidate carbon footprint prediction model can be calculated, and the candidate carbon footprint prediction model with the largest matching value can be used as the target carbon footprint prediction model.

[0036] S106, inputting the model input data into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs predicted carbon emission data of the target object according to the model input data.

[0037] In some implementations, by inputting model input data into a target carbon footprint prediction model, the target carbon footprint prediction model can calculate the carbon emission data corresponding to the model input data based on its own machine learning or deep learning algorithm, and output the carbon emission data as the predicted carbon emission data of the target object.

[0038] In the carbon footprint prediction method provided in the embodiment of the present application, by determining the background data set that needs to be collected for the target object, and obtaining the data source of each type of background data in the background data set, further, determining the data collection task of the background data, so as to collect the background data according to the data source and the data collection task, the model input data corresponding to the background data can be determined. By determining the target carbon footprint prediction model from the candidate carbon footprint prediction models, and inputting the model input data into the target carbon footprint prediction model, the predicted carbon emission data of the target object can be obtained, and the carbon emission data of different objects can be predicted efficiently and accurately, providing data support for the calculation of carbon footprint and the management of carbon emissions, thereby improving the reliability and adaptability of carbon footprint prediction.

[0039] Based on the above embodiments, the present application embodiment can explain the process of determining the target carbon footprint prediction model, such as Figure 2 As shown, the process of determining the target carbon footprint prediction model provided in the embodiment of the present application includes but is not limited to the following steps: S201, obtaining object description information of a target object.

[0040] Optionally, the object description information of the target object may be determined based on the type, attribute and feature of the target object. That is, at least one of the type, attribute and feature of the target object may be used as the object description information.

[0041] S202: Obtain model description information of the candidate carbon footprint prediction model.

[0042] Optionally, the model description information of the candidate carbon footprint prediction model can be determined based on information such as the version information of the model, the algorithm used by the model, and the performance of the model. In other words, at least one of the version information, the algorithm used, and the performance of the candidate carbon footprint prediction model can be used as the model description information.

[0043] S203, performing matching according to the object description information and the model description information to obtain a matching degree between the target object and the candidate carbon footprint prediction model.

[0044] In some implementations, the similarity of the vectors can be calculated based on the object description information and the model description information, and the calculated similarity value is used as the matching degree between the target object and the candidate carbon footprint prediction model. That is, by vectorizing the object description information and the model description information, a first vector corresponding to the object description information and a second vector corresponding to the model description information are obtained.

[0045] Further, the similarity between the first vector and the second vector is calculated, and the similarity value is obtained as the matching degree between the target object and the candidate carbon footprint prediction model. Optionally, the cosine similarity between the first vector and the second vector can be calculated.

[0046] S204: Determine a target carbon footprint prediction model from the candidate carbon footprint prediction models according to the matching degree.

[0047] In some implementations, a threshold value of the matching degree can be set, and a comparison result can be obtained by comparing the matching degree with the set threshold value, and a target carbon footprint prediction model can be determined from the candidate carbon footprint prediction models according to the comparison result. Optionally, the candidate carbon footprint prediction model with the highest matching degree can be determined as the target carbon footprint prediction model, so that the prediction model can more accurately reflect the carbon emission characteristics of the target object, so as to improve the accuracy of the carbon footprint prediction.

[0048] In some implementations, the target carbon footprint prediction model may also be determined based on the available resources of the model. Optionally, a first candidate carbon footprint prediction model with a matching degree greater than a set threshold may be selected from the candidate carbon footprint prediction models, and the available resources of the first candidate carbon footprint prediction model may be obtained.

[0049] Furthermore, by comparing the available resources of the first candidate carbon footprint prediction model, the second candidate carbon footprint prediction model with the best available resources is selected from the first candidate carbon footprint prediction model as the target carbon footprint prediction model. Optionally, the first candidate carbon footprint prediction model with the largest available resources can be selected as the second candidate carbon footprint prediction model with the best resources.

[0050] In the carbon footprint prediction method provided in the embodiment of the present application, by obtaining object description information of the target object and model description information of the candidate carbon footprint prediction models, a candidate carbon footprint prediction model with the highest matching degree with the target object is obtained from the candidate carbon footprint prediction models according to the object description information and the model description information, and the candidate carbon footprint prediction model is used as the target carbon footprint prediction model, so that the prediction model can more accurately reflect the carbon emission characteristics of the target object to improve the accuracy of the carbon footprint prediction.

[0051] Figure 3 is a flow chart of a carbon footprint prediction method provided in an embodiment of the present application, such as Figure 3 As shown, the carbon footprint prediction method of the embodiment of the present application includes but is not limited to the following steps: S301, determining a background data set that needs to be collected for a target object, and determining a data source corresponding to each type of background data in the background data set.

[0052] Optionally, the background data set includes at least one background data selected from the group consisting of energy consumption data, traffic data, industrial production data and weather data.

[0053] In some implementations, the background data required by the target object can be determined from a variety of different background data according to the object identification of the target object to form a background data set. Optionally, the background data set corresponding to the target object can be determined by obtaining the object identification of the target object and determining the background data set according to the object identification.

[0054] That is to say, the correspondence between different objects and background data sets can be pre-established according to the object identification, and the background data set of the target object can be determined from the correspondence by determining the object identification of the target object.

[0055] In the embodiment of the present application, the method for determining the data source in step S301 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0056] S302, acquiring sampling information of each type of background data, and generating a data collection task for the background data according to the data source and sampling information corresponding to the background data.

[0057] In the embodiment of the present application, the implementation method of step S302 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0058] S303: Determine access information of the data source.

[0059] In the embodiment of the present application, the implementation method of step S303 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0060] S304: Send a request to the data source based on the access information, where the request carries the authorization information to be verified.

[0061] In some implementations, a request for a data source may be generated based on the access information and the authorization information to be verified, and the request may be sent to the data source, wherein the authorization information to be verified is used to verify whether the access information has access rights.

[0062] Optionally, after receiving the request, the data source parses the request to obtain the authorization information to be verified, verifies the authorization information according to the set authorization policy or rule, and sends a verification success response after the verification succeeds.

[0063] S305, receiving a verification passed response sent by the data source, and sending a data collection task to the data source.

[0064] In some implementations, when a verification response is received from the data source, it indicates that the access information verification is passed, and the background data can be obtained from the data source. The data collection task is sent to the data source to obtain the background data from the data source according to the data collection task.

[0065] S306, receiving background data sent by each data source, and processing the received background data to obtain model input data.

[0066] In the embodiment of the present application, the implementation method of step S306 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0067] S307: Determine a target carbon footprint prediction model to be called from candidate carbon footprint prediction models according to the target object.

[0068] In the embodiment of the present application, the implementation method of step S307 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0069] S308, inputting the model input data into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs the predicted carbon emission data of the target object according to the model input data.

[0070] In the embodiment of the present application, the implementation method of step S308 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0071] In some implementations, after obtaining the predicted carbon emission data of the target object, the carbon emission behavior data of the target object can be obtained based on the predicted carbon emission data, so as to adjust the production activities of the target object according to the behavior data to achieve energy conservation and emission reduction.

[0072] Optionally, it is possible to determine whether there is an abnormal event in which the target object's carbon emissions exceed a set amount at a future time based on the predicted carbon emission data of the target object. If the predicted carbon emission data is greater than the set amount of carbon emissions, it is determined that an abnormal event exists.

[0073] Optionally, in response to the existence of an abnormal event, first carbon emission data of the target object at a historical moment is obtained, and second carbon emission data that does not exceed a set amount is obtained from the first carbon emission data. Further, behavior data of the target object corresponding to the second carbon emission data is obtained, and energy-saving and emission reduction information of the target object is generated based on the behavior data of the target object, so as to adjust the carbon emission behavior of the target object according to the energy-saving and emission reduction information.

[0074] In the carbon footprint prediction method provided in the embodiment of the present application, by determining the background data set that needs to be collected by the target object, and obtaining the data source of each type of background data in the background data set, further, determining the data collection task of the background data, and sending the data collection task to the data source according to the access information of the data source to collect the background data, the model input data corresponding to the background data can be determined. By determining the target carbon footprint prediction model from the candidate carbon footprint prediction models, and inputting the model input data into the target carbon footprint prediction model, the predicted carbon emission data of the target object can be obtained, and the carbon emission data of different objects can be predicted efficiently and accurately, which provides data support for the calculation of carbon footprint and the management of carbon emissions, thereby improving the reliability and adaptability of carbon footprint prediction.

[0075] Corresponding to the carbon footprint prediction methods proposed in the above-mentioned embodiments, an embodiment of the present application further proposes a carbon footprint prediction device. Since the carbon footprint prediction device proposed in the embodiment of the present application corresponds to the carbon footprint prediction methods proposed in the above-mentioned embodiments, the implementation method of the above-mentioned carbon footprint prediction method is also applicable to the carbon footprint prediction device proposed in the embodiment of the present application, and will not be described in detail in the following embodiments.

[0076] In order to implement the above embodiment, the present application also proposes a carbon footprint prediction device.

[0077] Figure 4 A schematic diagram of the structure of a carbon footprint prediction device provided in an embodiment of the present application.

[0078] like Figure 4 As shown, the carbon footprint prediction device 400 includes: The first determination module 401 is used to determine the background data set that needs to be collected by the target object, and determine the data source corresponding to each type of background data in the background data set; A generating module 402 is used to obtain sampling information of each type of background data, and generate a data collection task for the background data according to the data source and sampling information corresponding to the background data; The sending module 403 is used to determine the access information of the data source and send the data collection task to the data source based on the access information; The processing module 404 is used to receive the background data sent by each data source, and process the received background data to obtain model input data; The second determination module 405 is used to determine the target carbon footprint prediction model to be called from the candidate carbon footprint prediction models according to the target object; The prediction module 406 is used to input the model input data into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs the predicted carbon emission data of the target object according to the model input data.

[0079] In a possible implementation manner of the embodiment of the present application, the first determination module 401 is further used to: obtain an object identifier of the target object, and determine a background data set corresponding to the target object according to the object identifier.

[0080] In a possible implementation of an embodiment of the present application, the second determination module 405 is also used to: obtain object description information of the target object; obtain model description information of the candidate carbon footprint prediction model; match the object description information and the model description information to obtain the matching degree between the target object and the candidate carbon footprint prediction model; and determine the target carbon footprint prediction model from the candidate carbon footprint prediction models based on the matching degree.

[0081] In a possible implementation of an embodiment of the present application, the second determination module 405 is also used to: select a first candidate carbon footprint prediction model whose matching degree is greater than a set threshold from the candidate carbon footprint prediction models; obtain available resources of the first candidate carbon footprint prediction model; and select a second candidate carbon footprint prediction model with the best resources among the available resources from the first candidate carbon footprint prediction model as the target carbon footprint prediction model.

[0082] In a possible implementation of an embodiment of the present application, the sending module 403 is also used to: send a request to the data source based on the access information, the request carrying the authorization information to be verified; receive a verification response sent by the data source, and send a data collection task to the data source.

[0083] In a possible implementation manner of the embodiment of the present application, the background data set includes: at least one background data of energy consumption data, traffic data, industrial production data and weather data.

[0084] In a possible implementation method of an embodiment of the present application, the device also includes: judging whether there will be an abnormal event in which the carbon emissions of the target object exceed a set amount at a future moment based on the predicted carbon emission data of the target object; in response to the existence of an abnormal event, acquiring the first carbon emission data of the target object at a historical moment, and acquiring the second carbon emission data that does not exceed the set amount from the first carbon emission data; acquiring the behavior data of the target object corresponding to the second carbon emission data, and generating energy-saving and emission reduction information for the target object based on the behavior data of the target object.

[0085] In the carbon footprint prediction device provided in the embodiment of the present application, by determining the background data set that needs to be collected for the target object, and obtaining the data source of each type of background data in the background data set, further, determining the data collection task of the background data, so as to collect the background data according to the data source and the data collection task, the model input data corresponding to the background data can be determined. By determining the target carbon footprint prediction model from the candidate carbon footprint prediction models, and inputting the model input data into the target carbon footprint prediction model, the predicted carbon emission data of the target object can be obtained, and the carbon emission data of different objects can be predicted efficiently and accurately, providing data support for the calculation of carbon footprint and the management of carbon emissions, thereby improving the reliability and adaptability of carbon footprint prediction.

[0086] It should be noted that the above explanations of the embodiment of the carbon footprint prediction method are also applicable to the carbon footprint prediction device of this embodiment, and will not be repeated here.

[0087] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments. In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0088] The collection, storage, use, processing, transmission, provision and application of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0089] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0090] This application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, this application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, risks can be minimized by limiting data collection and deleting data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0091] In the description of the aforementioned embodiments, 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 application. 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, unless they contradict each other.

[0092] 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 this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0093] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment 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 application 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 the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0094] 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.

[0095] It should be understood that the various parts of the present application 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 a combination thereof: 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.

[0096] 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.

[0097] In addition, each functional unit in each embodiment of the present application 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 a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0098] 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 application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for predicting carbon footprint, characterized in that: The method comprises: Determine a background data set that needs to be collected for the target object, and determine a data source corresponding to each type of background data in the background data set; Acquire sampling information of each type of background data, and generate a data collection task for the background data according to the data source and sampling information corresponding to the background data; Determining access information of the data source, and sending the data collection task to the data source based on the access information; Receiving background data sent by each data source, and processing the received background data to obtain model input data; According to the target object, determining a target carbon footprint prediction model to be called from candidate carbon footprint prediction models; The model input data is input into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs the predicted carbon emission data of the target object according to the model input data.

2. The method according to claim 1, characterized in that The background data set that needs to be collected for the target object is determined, including: An object identifier of the target object is obtained, and the background data set corresponding to the target object is determined according to the object identifier.

3. The method according to claim 1, characterized in that The step of determining a target carbon footprint prediction model to be called from candidate carbon footprint prediction models according to the target object includes: Obtaining object description information of the target object; Obtaining model description information of the candidate carbon footprint prediction model; Matching is performed according to the object description information and the model description information to obtain a matching degree between the target object and the candidate carbon footprint prediction model; According to the matching degree, the target carbon footprint prediction model is determined from the candidate carbon footprint prediction models.

4. The method according to claim 3, characterized in that Determining the target carbon footprint prediction model from the candidate carbon footprint prediction models according to the matching degree includes: From the candidate carbon footprint prediction models, select a first candidate carbon footprint prediction model whose matching degree is greater than a set threshold; Acquiring available resources of the first candidate carbon footprint prediction model; A second candidate carbon footprint prediction model with the best resources among the available resources is selected from the first candidate carbon footprint prediction models as the target carbon footprint prediction model.

5. The method according to any one of claims 1 to 4, characterized in that The sending the data collection task to the data source based on the access information includes: Based on the access information, sending a request to the data source, the request carrying the authorization information to be verified; A verification pass response sent by the data source is received, and the data collection task is sent to the data source.

6. The method according to any one of claims 1 to 4, characterized in that The background data set includes at least one background data selected from the group consisting of energy consumption data, traffic data, industrial production data and weather data.

7. The method according to any one of claims 1 to 4, characterized in that After outputting the predicted carbon emission data of the target object, the method further includes: According to the predicted carbon emission data of the target object, determining whether there is an abnormal event in which the carbon emission of the target object exceeds a set amount at a future time; In response to the abnormal event, first carbon emission data of the target object at a historical moment is acquired, and second carbon emission data that does not exceed the set amount is acquired from the first carbon emission data; Behavior data of the target object corresponding to the second carbon emission data is acquired, and energy-saving and emission reduction information of the target object is generated according to the behavior data of the target object.

8. A carbon footprint prediction device, characterized in that: The device comprises: A first determination module is used to determine a background data set that needs to be collected by the target object, and to determine a data source corresponding to each type of background data in the background data set; A generation module, used to obtain sampling information of each type of background data, and generate a data collection task for the background data according to the data source and sampling information corresponding to the background data; A sending module, used to determine the access information of the data source, and send the data collection task to the data source based on the access information; A processing module, used for receiving background data sent by each data source, and processing the received background data to obtain model input data; A second determination module is used to determine a target carbon footprint prediction model to be called from candidate carbon footprint prediction models according to the target object; The prediction module is used to input the model input data into the target carbon footprint prediction model, and the target carbon footprint prediction model outputs the predicted carbon emission data of the target object according to the model input data.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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