CN117474567A - Carbon footprint analysis method and system based on generated model and mobile collaborative signature - Google Patents
Carbon footprint analysis method and system based on generated model and mobile collaborative signature Download PDFInfo
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Abstract
The invention discloses a carbon footprint analysis method and a system based on a generated model and a mobile collaborative signature, wherein the method comprises the following steps: acquiring key data of a product life cycle of a target enterprise, wherein the product life cycle at least comprises various stages of production, transportation, maintenance and waste treatment and recovery of a product; inquiring carbon emission factors matched in each stage through a carbon factor database, and calculating carbon emission data in each stage based on the key data and the carbon emission factors; inputting the carbon emission data of each stage and the product category of the target enterprise into a generating model to obtain a target stage needing improvement and optimization and an improvement and optimization mode; the improved optimization mode comprises technical improvement and enterprise cooperation improvement, and the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content; and generating a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage needing to be improved and optimized and the improved optimization mode.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a carbon footprint analysis method and system based on a generated model and a mobile collaborative signature.
Background
With the accelerated implementation of international carbon tariff policies and regulations, represented by the european union carbon boundary regulation mechanism (CBAM), the heat and demand for low-carbon and green development product lines to check for greenhouse gases continues to rise. The product carbon footprint authentication is a scientific evaluation of the influence of the product on climate change, helps enterprises and consumers to make more environment-friendly decisions and important basis, and has important significance and effect on environmental protection and sustainable development.
At present, the whole processes of data collection, verification, authentication, analysis and the like of the carbon footprint authentication business are mostly completed manually off line, and the accuracy, reliability and traceability of the data collection and analysis process cannot be guaranteed. Furthermore, how to give optimization suggestions for carbon emissions for enterprises is also of great importance.
Disclosure of Invention
In view of the above-mentioned drawbacks or shortcomings in the prior art, the present invention provides a carbon footprint analysis method and system based on a generated model and a mobile collaborative signature, which can solve the above-mentioned technical problems.
The invention provides a carbon footprint analysis method based on a generated model and a mobile collaborative signature, which comprises the following steps:
acquiring key data of a product life cycle of a target enterprise, wherein the product life cycle at least comprises production, transportation, maintenance and waste treatment and recovery stages of the product, inquiring carbon emission factors matched with the stages through a carbon factor database, and calculating carbon emission data of the stages based on the key data and the carbon emission factors; inputting the carbon emission data of each stage and the product category of the target enterprise into a generated model to obtain a target stage needing improvement and optimization and an improvement and optimization mode; the improved optimization mode comprises technical improvement and enterprise cooperation improvement, and the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content; generating a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage needing improvement optimization and the improvement optimization mode; invoking an electronic seal to sign the carbon footprint analysis report and issuing a carbon footprint authentication certificate; after checking and authenticating the carbon emission data in the carbon footprint analysis report, carrying out hash operation, signing and timestamping to realize the verification and the storage of the right, calling a mobile collaborative signature to sign a data open source protocol, and carrying out the publicity of the data to be open source.
The invention provides a carbon footprint analysis system based on a generated model and a mobile collaborative signature, which comprises the following components: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring key data of a product life cycle of a target enterprise, and the product life cycle at least comprises various stages of production, transportation, maintenance and waste treatment and recovery of a product; the calculation module is used for inquiring the carbon emission factors matched with each stage through the carbon factor database and calculating carbon emission data of each stage based on the key data and the carbon emission factors; the model processing module is used for inputting the carbon emission data of each stage and the product category of the target enterprise into a generated model to obtain a target stage needing improvement and optimization and an improvement and optimization mode; the improved optimization mode comprises technical improvement and enterprise cooperation improvement, and the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content; the generation module is used for generating a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage needing improvement and optimization and the improvement and optimization mode; the report processing module is used for calling the electronic seal to sign the carbon footprint analysis report and issuing a carbon footprint authentication certificate; after checking and authenticating the carbon emission data in the carbon footprint analysis report, carrying out hash operation, signing and timestamping to realize the verification and the storage of the right, calling a mobile collaborative signature to sign a data open source protocol, and carrying out the publicity of the data to be open source.
According to the carbon footprint analysis method and system based on the generated model and the mobile collaborative signature, a localized professional carbon footprint analysis optimization platform is provided for enterprises, carbon emission data is analyzed through the generated model, the phase of an optimized product can be improved through high-efficiency generation, a specific improved optimization scheme is adopted, and traceability of data analysis of the carbon footprint analysis enterprises and availability and accuracy of analysis results are ensured.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is a flow chart of a method for carbon footprint analysis based on a generative model, provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a method for carbon footprint analysis based on a generative model according to yet another embodiment of the present invention;
FIG. 3 is a flow chart of a method for carbon footprint analysis based on a generative model, provided in accordance with yet another embodiment of the present invention;
FIG. 4 is a block diagram of a carbon footprint analysis system based on a generative model according to yet another embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Referring to fig. 1, an embodiment of the present invention proposes a carbon footprint analysis method based on a generative model, including:
step S110, obtaining key data of a product life cycle of a target enterprise, wherein the product life cycle at least comprises production, transportation, maintenance and waste treatment and recovery stages of a product.
Specifically, the target enterprise is an enterprise having an authentication requirement for the carbon footprint. The production of the product comprises the production, extraction, manufacture and processing of raw materials; the transportation of the product comprises the transportation in the production process of the product and the transportation after the product is manufactured; the maintenance of the product comprises the use and maintenance of the product itself and the maintenance of the equipment for producing the product; waste treatment and recovery includes treatment and recovery of waste during product production, transportation, and maintenance. The key data comprise raw and auxiliary material consumption, production energy consumption and packaging material consumption in a production stage, main raw and auxiliary material suppliers and the transportation distance of the main raw and auxiliary material suppliers to target enterprises in a transportation stage, energy consumption and greenhouse gas consumption of product use and maintenance in a maintenance stage, energy consumption and waste treatment capacity in a waste treatment and recovery stage and the like.
And step S120, inquiring the carbon emission factors matched in each stage through a carbon factor database, and calculating carbon emission data of each stage based on the key data and the carbon emission factors.
Specifically, the carbon factor database comprises carbon factor data corresponding to various product types, corresponding target product types are determined according to product types of target enterprises, then carbon emission factors matched in each stage are queried based on the target product types, and carbon emission data in each stage are calculated according to a data correlation algorithm.
And step S130, inputting the carbon emission data of each stage and the product category of the target enterprise into a generative model to obtain a target stage needing improvement and optimization and an improvement and optimization mode.
Since carbon footprint certification is concerned with greenhouse gas emissions generated throughout the life cycle of the enterprise, not just the carbon emissions of the end product. Therefore, the main purpose of carbon footprint analysis is to help target enterprises find out target stages with carbon emission exceeding the standard in the life cycle of products, namely target stages needing improvement and optimization; in addition, specific improved optimization schemes can be provided for the target stage, so that green development is realized. In order to reduce the improvement optimization cost, the improvement optimization mode comprises a technical improvement scheme of a target enterprise in a target stage and a cooperation improvement scheme of the target enterprise in the target stage and other enterprises, and the generated model can determine which improvement optimization mode to adopt according to cost accounting.
In this step, the generated model refers to a model obtained by training with a decoder structure of a transducer, and the training data includes key data of product life cycles of each qualified enterprise under each product category and a technical scheme of each stage. Here, qualified enterprises refer to that the greenhouse gas emission amount generated in the whole production process of the enterprises meets the carbon footprint authentication requirement, and the carbon emission amount of the final product also meets the carbon footprint authentication requirement.
Further, the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content. The prompt information is used for enabling the generated model to recall training data learned in the training process, so that the prompt information comprises input information and required elements for outputting contents of the generated model, and masking (i.e., [ MASK ]) is performed at a position where the generated contents of the model are required to excite the generating capacity of the model. Taking the movie comment emotion classification task as an example, the model needs to generate movie emotion classification summary according to the input sentence. The input information is' special effect is very cool and dazzling, i like. The prompt message may be "the input content is 'the special effect is very cool and dazzling, i like it', please output this is a [ MASK ] movie"; the prompt message may also be "the input content is 'the special effect is very cool and dazzling, i likes' please output the movie [ MASK ].
Specifically, the store can complete this step based on the hint information template by:
filling the prompt information template based on the carbon emission data of each stage and the product category of the target enterprise to obtain prompt information; and inputting the prompt information into a generated model, and determining the target stage as a target stage needing improvement aiming at the target stage of the carbon emission data of each stage and the output carbon emission data exceeding the average carbon emission data under the product category.
Here, the prompt message template includes a requirement element for the output content of the generated model, for example, "determine a target stage, where carbon emission data of the target stage exceeds average carbon emission data under the product category, please output the target stage [ MASK ], and give an improved optimization mode [ MASK ] on the premise of minimum improved optimization cost"; also included is input information "carbon emission data for each of the stages and product category for the target business".
And step S140, generating a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage needing improvement optimization and the improvement optimization mode.
Specifically, the carbon footprint analysis report of the target enterprise firstly analyzes carbon footprint data from each dimension, including but not limited to carbon emission and duty ratio conditions of each stage of the full life cycle, energy or resource emission and duty ratio conditions of each stage, emission ranking list of each stage and the like, and the analysis result of each dimension is clearly shown through a chart; secondly, related auxiliary information can be acquired based on an improved optimization mode, if the improved optimization mode is a technical improvement scheme, a corresponding technical improvement case is acquired based on the technical improvement scheme; if the improvement optimization mode is the cooperation improvement scheme, the contact information of the enterprise matched with the cooperation improvement scheme and the product or service information related to the cooperation improvement scheme are acquired. Thus, through the carbon footprint analysis report, the target enterprise can not only know the carbon emission condition of the whole life cycle, but also clearly know the target stage of the improvement optimization and how to improve.
The carbon footprint analysis method based on the generation type model provides a localization professional carbon footprint analysis optimization platform for enterprises, analyzes carbon emission data through the generation type model, can improve the stage of optimized products and a specific improved optimization scheme in high efficiency generation, and ensures the traceability of data analysis of the carbon footprint analysis enterprises and the availability and accuracy of analysis results.
Because the generated model cannot accurately answer the results outside the training data set (different from the newly added data of the verification set and the test set, such as real-time news, unpublished enterprise information, and the like), in order to solve the above problems, the generated model plug-in is generated at the same time, so that the cost caused by fine tuning or retraining is avoided. By combining the powerful content generation capability and context understanding capability of the generative model with the data and specific functions provided by the plug-in, the credibility of the generated result of the generative model is increased.
As some optional implementations of the embodiments of the present invention, as shown in fig. 2, the generating model includes a search engine plug-in, and the inputting the carbon emission data of each stage and the product category of the target enterprise into the generating model, to obtain an improved optimization mode includes:
step S210, the generative model outputs first improvement optimizing information of the target stage;
step S220, the search engine plug-in obtains matched candidate enterprises and key data related to the candidate enterprises and the improved optimization information based on the product category and the first improved optimization information;
step S230, inputting the key data of the candidate enterprises into the generated model, and outputting the target enterprises to be cooperated and the carbon emission data reduced after cooperation.
Specifically, the first improvement optimization information is an enterprise cooperation improvement scheme, since the latest enterprise data cannot be obtained by the generated model, key information of the target stage requiring cooperation improvement optimization can be determined according to the first improvement optimization information, then the candidate enterprise matched with the search information and key data related to the improvement optimization information of the candidate enterprise are obtained by taking the product category of the target enterprise and the key information as the search information through the search engine plug-in, wherein the key data comprises carbon emission data of the candidate enterprise for completing the target stage, carbon emission data generated by the candidate enterprise and the target enterprise due to the transportation distance, and the cooperation price of the candidate enterprise. For example, the target enterprise is an aluminum product manufacturing enterprise, the carbon emission of the enterprise in the aluminum product waste treatment and recovery stage exceeds the standard, and the first improvement optimization information is determined to be an enterprise for green treatment of aluminum product waste for cooperation treatment through cost comparison. Based on the above, the key information is green treatment of waste, the product category of "aluminum products" and the key information of "green treatment of waste" are used as search information to obtain matched candidate enterprises, carbon emission data generated by the candidate enterprises and target enterprises due to the transportation distance, carbon emission data for performing green treatment process, and the cooperative price of the candidate enterprises.
And finally, inputting the key data of each candidate enterprise into the generated model, calculating the generated model according to the key data of each candidate enterprise, comparing the key data with the carbon emission from two dimensions of cost and carbon emission, and outputting target enterprises to be cooperated and carbon emission data reduced after cooperation.
As another alternative implementation manner of the embodiment of the present invention, as shown in fig. 3, the generative model includes a paper database plugin, and the inputting the carbon emission data of each stage and the product category of the target enterprise into the generative model, to obtain an improved optimization manner includes:
step S310, the generative model outputs second improvement optimizing information of the target stage;
step S320, the paper database plug-in obtains a matched target paper based on a pre-established paper index and the second improved optimization information;
step S330, inputting the target paper into the generated model, and outputting the structural technical improvement scheme and the carbon emission data reduced after reconstruction.
Specifically, the second improvement optimization information is a technical improvement scheme, and since the generated model cannot accurately generate the improvement technical scheme, the technical direction of the cooperative improvement optimization required in the target stage can be determined according to the first improvement optimization information, and then the matched target paper is acquired based on the pre-established paper index and the second improvement optimization information through the paper database plug-in. Specifically, the paper index includes keywords that characterize the improvement technology of each paper, and the matched target paper can be obtained by finding the keywords of the matched improvement technology in the paper index through the technical direction of the optimization to be improved in the second improvement optimization information. Further, the target paper is input into the generating model, and the generating model learns and understands the paper content through strong understanding and generating capability of the generating model, and outputs a structural technical improvement scheme and carbon emission data reduced after reconstruction. The technical improvement of the structuring comprises an improvement of equipment dimension, an improvement of process dimension and an improvement of raw material dimension.
In order to make the carbon footprint analysis report more visual and easy to understand, as some optional implementations of the embodiments of the present invention, the generating the carbon footprint analysis report of the target enterprise according to the carbon emission data of each of the stages, the target stage requiring improvement optimization, and the improvement optimization mode includes:
determining an image to be supplemented by the carbon footprint analysis report according to the target stage and the improved optimization mode; and generating a carbon footprint analysis report of the target enterprise according to the image.
Specifically, the image to be supplemented can be used for expressing materials and equipment to be prepared for the technical improvement scheme, and specific implementation steps of the improvement scheme; the method can also be used for visually describing a cooperation scheme of enterprise cooperation, namely a circulation scheme of products, equipment, materials and the like based on a target stage of cooperation and an upstream stage and a downstream stage of the target stage, and can also be used for representing the comparison of carbon emission data of each stage.
Optionally, the image to be supplemented is acquired by the following method: determining whether the image to be supplemented belongs to the image related to the key data or the image related to the improved optimization mode; if the images belong to the images related to the key data, determining a stage corresponding to the images, and acquiring the images to be supplemented according to the stage; if the image belongs to the image related to the improved optimization mode, generating image prompt information based on the improved optimization mode, and inputting the image prompt information into an image generation model to obtain the image to be supplemented.
Specifically, it is first determined whether an image is directly available or needs to be generated by a generative model. Generally, the images related to the key data of each stage of the life cycle of the target enterprise product can be directly obtained by photographing or inquiring a database; improving the optimization mode related images requires synthesis by an image generation model.
Specifically, it is possible to determine which phases require the supplementary image from the key data of each phase. For example, the carbon emissions at a stage are much smaller than the average carbon emissions, indicating that the process recipe at that stage is worth highlighting by image; the carbon emission amount at a certain stage is far greater than the average carbon emission amount, and then the factors influencing the carbon emission amount can be determined according to the improved optimization mode of the generated model output, and the images corresponding to the factors are highlighted.
Further, aiming at the improved optimization mode, the distinguishing point of the improved scheme and the original scheme and the specific cooperation point of the enterprise cooperation scheme can be realized through the image generation technology. Therefore, the image prompt information can be generated based on the improved optimization mode, wherein each image element can be expressed by a plurality of words (for example, the smile can be expressed by laugh, smile, haha and the like), but a target word corresponding to each image element can be determined through repeated experiments, the target word is input into the image generation model, the output image quality is highest, and therefore, the association relation between the image element and the target word can be built in advance. In this scheme, the image prompt information includes a plurality of image elements forming an image, a target word for expressing each image element may be determined based on the above-mentioned association relation, the image prompt information is optimized by using each target word, and the optimized image prompt information is input into an image generation model, so as to obtain the image to be supplemented.
To ensure the true integrity and confidentiality of the data of the target enterprise during the carbon footprint analysis process, as some optional implementations of embodiments of the invention, the method further includes:
invoking an electronic seal to sign the carbon footprint analysis report and issuing a carbon footprint authentication certificate; after checking and authenticating the carbon emission data in the carbon footprint analysis report, carrying out hash operation, signing and timestamping to realize the verification and the storage of the right, calling a mobile collaborative signature to sign a data open source protocol, and carrying out the publicity of the data to be open source.
Specifically, after carbon emission data is received, calculating a HASH value of the carbon emission data by adopting an MD5 algorithm, signing and stamping a timestamp on the HASH value to realize a right-keeping certificate of the right of the data, ensuring that the data is complete and not tampered, and providing evidence for data use right disputes possibly occurring in the later period; when the enterprise decides to open the source of the data, the mobile collaborative signature component is invoked, and the mobile collaborative signature component is used to sign the data open source protocol with the same legal effectiveness as the paper protocol.
After data validation and protocol signing are completed, the carbon emission data is incorporated into a carbon factor database; the carbon-carbon factor database can set the authority of factor data according to the requirement, and further ensures the safety of data access.
According to the carbon footprint analysis method based on the generated model, through the standardization and unification of data and flow, the whole flow line of the carbon footprint authentication analysis business is realized, the automatic and digital flow is simplified, the efficiency of carbon footprint analysis is improved, the reality, the completeness and the confidentiality of the data of the carbon footprint authentication enterprise are ensured, the verification and authentication process is convenient and quick, traceable, and the authentication result is real and reliable.
In one embodiment, referring to FIG. 4, a schematic structural diagram of a generative model-based carbon footprint analysis system is provided. The apparatus may be used to perform the generative model-based carbon footprint analysis method shown in FIGS. 1-3, the apparatus comprising: an acquisition module 410, a calculation module 420, a model processing module 430, and a generation module 440; wherein,
an obtaining module 410, configured to obtain key data of a product life cycle of a target enterprise, where the product life cycle includes at least stages of production, transportation, maintenance, and waste treatment and recovery of a product; a calculation module 420, configured to query the carbon factor database for the carbon emission factors matched in each stage, and calculate carbon emission data of each stage based on the key data and the carbon emission factors; the model processing module 430 is configured to input the carbon emission data of each stage and the product category of the target enterprise into a generative model, so as to obtain a target stage requiring improvement and optimization and an improved optimization mode; the improved optimization mode comprises technical improvement and enterprise cooperation improvement, and the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content; the generating module 440 is configured to generate a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage requiring improvement optimization, and the improvement optimization mode.
Further, the model processing module 430 is further configured to input the prompt information into a generative model, and determine, for each target stage of the carbon emission data of the stages, where the output carbon emission data exceeds the average carbon emission data under the product category, the target stage as a target stage requiring improvement.
Further, the generated model includes a search engine plug-in, and the model processing module 430 is further configured to output first improvement optimization information of the target phase; the search engine plug-in acquires matched candidate enterprises and key data related to the candidate enterprises and the improved optimization information based on the product category and the first improved optimization information; and inputting the key data of the candidate enterprises into the generated model, and outputting target enterprises to be cooperated and carbon emission data reduced after cooperation.
Further, the generative model includes a paper database plug-in, and the model processing module 430 is further configured to output second improved optimization information of the target phase; the paper database plug-in obtains a matched target paper based on a pre-established paper index and the second improvement optimization information; and inputting the target paper into the generated model, and outputting a structural technical improvement scheme and reduced carbon emission data after reconstruction.
Further, the generating module 440 is further configured to determine an image that the carbon footprint analysis report needs to be supplemented according to the target stage and the improved optimization; and generating a carbon footprint analysis report of the target enterprise according to the image.
Further, the generating module 440 is further configured to determine whether the image to be supplemented belongs to the image related to the key data or the image related to the improved optimization mode; if the images belong to the images related to the key data, determining a stage corresponding to the images, and acquiring the images to be supplemented according to the stage; if the image belongs to the image related to the improved optimization mode, generating image prompt information based on the improved optimization mode, and inputting the image prompt information into an image generation model to obtain the image to be supplemented.
Further, the system also includes an issuing module and an encryption module (not shown in fig. 4), wherein the issuing module is configured to invoke the electronic seal to sign the carbon footprint analysis report, issuing a carbon footprint authentication certificate; and the encryption module is used for carrying out hash operation, signature and time stamp marking on the carbon emission data in the carbon footprint analysis report after verification and authentication, so as to realize the verification and the storage of the weight, calling a mobile collaborative signature to sign a data open source protocol, and carrying out the public display on the data to be open source.
The carbon footprint analysis system based on the generation type model provides a localization professional carbon footprint analysis optimization platform for enterprises, analyzes carbon emission data through the generation type model, can efficiently generate an improved optimized product stage and a specific improved optimization scheme, and ensures traceability of data analysis of the carbon footprint analysis enterprises and availability and accuracy of analysis results.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. Where the names of the units do not constitute a limitation of the module itself in some cases.
The above description is only illustrative of the preferred embodiments of the present invention and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present invention is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present invention (but not limited to) having similar functions are replaced with each other.
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (10)
1. A carbon footprint analysis method based on a generative model and a mobile collaborative signature, comprising:
acquiring key data of a product life cycle of a target enterprise, wherein the product life cycle at least comprises production, transportation, maintenance and waste treatment and recovery stages of a product;
inquiring the carbon emission factors matched with each stage through a carbon factor database, and calculating carbon emission data of each stage based on the key data and the carbon emission factors;
inputting the carbon emission data of each stage and the product category of the target enterprise into a generated model to obtain a target stage needing improvement and optimization and an improvement and optimization mode; the improved optimization mode comprises technical improvement and enterprise cooperation improvement, and the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content;
generating a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage needing improvement optimization and the improvement optimization mode;
invoking an electronic seal to sign the carbon footprint analysis report and issuing a carbon footprint authentication certificate; after checking and authenticating the carbon emission data in the carbon footprint analysis report, carrying out hash operation, signing and timestamping to realize the verification and the storage of the rights, calling the mobile collaborative signature to sign a data open source protocol, and carrying out the publicity of the data to be opened.
2. The method of claim 1, wherein inputting the carbon emission data for each of the stages and the product categories of the target business into a generative model results in a target stage requiring improved optimization, comprising:
filling the prompt information template based on the carbon emission data of each stage and the product category of the target enterprise to obtain prompt information;
and inputting the prompt information into a generated model, and determining the target stage as a target stage needing improvement aiming at the target stage of the carbon emission data of each stage and the output carbon emission data exceeding the average carbon emission data under the product category.
3. The method of claim 1 or 2, wherein the generative model comprises a search engine plug-in, and the inputting the carbon emission data for each of the stages and the product categories of the target enterprise into the generative model results in an improved optimization, comprising:
the generating model outputs first improvement optimizing information of the target stage;
the search engine plug-in acquires matched candidate enterprises and key data related to the candidate enterprises and the improved optimization information based on the product category and the first improved optimization information;
and inputting the key data of the candidate enterprises into the generated model, and outputting target enterprises to be cooperated and carbon emission data reduced after cooperation.
4. The method of claim 1 or 2, wherein the generative model comprises a paper database plug-in, and the inputting the carbon emission data for each of the stages and the product categories of the target enterprise into the generative model results in an improved optimization, comprising:
the generated model outputs second improved optimization information of the target stage;
the paper database plug-in obtains a matched target paper based on a pre-established paper index and the second improvement optimization information;
and inputting the target paper into the generated model, and outputting a structural technical improvement scheme and reduced carbon emission data after reconstruction.
5. The method of claim 1, wherein generating the carbon footprint analysis report of the target business based on the carbon emission data for each of the stages, the target stage requiring improved optimization, and an improved optimization scheme comprises:
determining an image to be supplemented by the carbon footprint analysis report according to the target stage and the improved optimization mode;
and generating a carbon footprint analysis report of the target enterprise according to the image.
6. The method of claim 5, wherein the image to be supplemented is obtained by:
determining whether the image to be supplemented belongs to the image related to the key data or the image related to the improved optimization mode;
if the images belong to the images related to the key data, determining a stage corresponding to the images, and acquiring the images to be supplemented according to the stage;
if the image belongs to the image related to the improved optimization mode, generating image prompt information based on the improved optimization mode, and inputting the image prompt information into an image generation model to obtain the image to be supplemented.
7. A carbon footprint analysis system based on a generative model and a mobile collaborative signature, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring key data of a product life cycle of a target enterprise, and the product life cycle at least comprises various stages of production, transportation, maintenance and waste treatment and recovery of a product;
the calculation module is used for inquiring the carbon emission factors matched with each stage through the carbon factor database and calculating carbon emission data of each stage based on the key data and the carbon emission factors;
the model processing module is used for inputting the carbon emission data of each stage and the product category of the target enterprise into a generated model to obtain a target stage needing improvement and optimization and an improvement and optimization mode; the improved optimization mode comprises technical improvement and enterprise cooperation improvement, and the generated model corresponds to a prompt information template outputting carbon footprint improvement optimization content;
the generation module is used for generating a carbon footprint analysis report of the target enterprise according to the carbon emission data of each stage, the target stage needing improvement and optimization and the improvement and optimization mode;
the report processing module is used for calling the electronic seal to sign the carbon footprint analysis report and issuing a carbon footprint authentication certificate; after checking and authenticating the carbon emission data in the carbon footprint analysis report, carrying out hash operation, signing and timestamping to realize the verification and the storage of the rights, calling the mobile collaborative signature to sign a data open source protocol, and carrying out the publicity of the data to be opened.
8. The system of claim 7, wherein the generative model comprises a search engine plug-in, the model processing module further configured to,
the generating model outputs first improvement optimizing information of the target stage; the search engine plug-in acquires matched candidate enterprises and key data related to the candidate enterprises and the improved optimization information based on the product category and the first improved optimization information; and inputting the key data of the candidate enterprises into the generated model, and outputting target enterprises to be cooperated and carbon emission data reduced after cooperation.
9. The system of claim 7, wherein the generative model comprises a paper database plug-in, the model processing module further configured to,
the generated model outputs second improved optimization information of the target stage; the paper database plug-in obtains a matched target paper based on a pre-established paper index and the second improvement optimization information; and inputting the target paper into the generated model, and outputting a structural technical improvement scheme and reduced carbon emission data after reconstruction.
10. The system of claim 7, wherein the generation module is further configured to:
determining whether the image to be supplemented by the carbon footprint analysis report belongs to the image related to the key data or the image related to the improved optimization mode; if the images belong to the images related to the key data, determining a target stage corresponding to the images, and acquiring the images to be supplemented according to the target stage; if the image belongs to the image related to the improved optimization mode, generating image prompt information based on the improved optimization mode, and inputting the image prompt information into an image generation model to obtain the image to be supplemented; and generating a carbon footprint analysis report of the target enterprise according to the image to be supplemented.
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