Product carbon footprint information visualization system and method

Abstract

The invention provides a product carbon footprint information visualization system and method, and relates to the field of information visualization, and the method comprises the steps: collecting the basic information of a new product input by a user, and extracting a set of carbon footprint accounting models from a database; and then a data processing and semantic understanding algorithm based on artificial intelligence and a natural language understanding technology is introduced at a back end to carry out semantic analysis and feature query response, so that a carbon footprint accounting model matched with the product information is returned as a recommendation model. Therefore, the more adaptive carbon footprint accounting model with higher relevancy with the newly added product can be automatically inquired, and the corresponding accounting boundary is determined, so that more accurate product production cycle carbon emission data can be provided for enterprises, the data are displayed on a screen for users to check, and a scientific basis is provided for making emission reduction measures.

Description

Technical Field

[0001] The present application relates to the field of information visualization, and more specifically, to a product carbon footprint information visualization system and method. Background Art

[0002] As global attention to climate change grows, companies and organizations are increasingly focusing on reducing the impact of their operations and products on the environment. Therefore, accurately measuring the carbon emissions generated by products at various stages of their life cycle has become a key requirement. As an important indicator to measure the environmental impact of products during their life cycle, carbon footprint not only reflects the greenhouse gas emissions generated by products at various stages such as production, transportation, use and disposal, but also directly affects the company's sustainable development strategy and market competitiveness.

[0003] However, traditional carbon footprint accounting methods usually rely on manual recording and calculation, which is not only time-consuming but also prone to human errors. In addition, the traditional carbon footprint accounting method does not have a unified accounting standard and methodology, resulting in poor comparability between carbon footprint data, which makes it difficult to compare carbon emissions between different companies and is not conducive to setting industry-wide emission reduction targets. Not only that, under the traditional method, the transparency of carbon footprint data is often not high, which is not conducive to consumers, investors and other stakeholders to understand the actual environmental performance of the company, which in turn affects trust and brand image.

[0004] Therefore, an optimized product carbon footprint information visualization solution is desired. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a product carbon footprint information visualization system and method, which first collects the basic information of the new product input by the user, and extracts a set of carbon footprint accounting models from the database, and then introduces data processing and semantic understanding algorithms based on artificial intelligence and natural language understanding technology in the back end to perform semantic analysis and feature query response, so as to return the carbon footprint accounting model that matches the product information as a recommended model. In this way, it is possible to automatically query a carbon footprint accounting model that is more relevant and more suitable for the new product, and determine the corresponding accounting boundaries, so as to provide enterprises with more accurate product production cycle carbon emission data, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0006] According to one aspect of the present application, a product carbon footprint information visualization system is provided, comprising: A new product information entry module is used to obtain basic information of new products input by users, including product strength, product name, quality, specifications and manufacturer; A carbon footprint accounting model and accounting boundary determination module, which is used to model a carbon footprint accounting model based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model and determine the accounting boundary; A data reporting module, used to input the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary; The calculation result determination module is used to generate a carbon footprint calculation result based on the recommended carbon footprint calculation model, and transmit the carbon footprint calculation result to the screen for display.

[0007] According to another aspect of the present application, a method for visualizing product carbon footprint information is provided, which includes: Obtaining basic information of the newly added product input by the user, the basic information including product strength, product name, quality, specifications and manufacturer; Modeling a carbon footprint accounting model based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model and determine the accounting boundary; Inputting the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary; A carbon footprint calculation result is generated based on the recommended carbon footprint calculation model, and the carbon footprint calculation result is transmitted to a screen for display.

[0008] Compared with the prior art, the product carbon footprint information visualization system and method provided by the present application first collects the basic information of the new product input by the user, extracts a set of carbon footprint accounting models from the database, and then introduces data processing and semantic understanding algorithms based on artificial intelligence and natural language understanding technology at the back end to perform semantic analysis and feature query response, so as to return the carbon footprint accounting model that matches the product information as a recommended model. In this way, it is possible to automatically query the carbon footprint accounting model that is more relevant and more suitable for the new product, and determine the corresponding accounting boundary, so as to provide enterprises with more accurate carbon emission data of the product production cycle, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 4 is a system block diagram of a product carbon footprint information visualization system according to an embodiment of the present application.

[0011] Figure 2 It is a block diagram of a carbon footprint calculation model and a calculation boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application.

[0012] Figure 3 This is a data flow diagram of a carbon footprint calculation model and a calculation boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application.

[0013] Figure 4 It is a block diagram of a query response unit for significant guidance between semantic features in a product carbon footprint information visualization system according to an embodiment of the present application.

[0014] Figure 5 A block diagram of a text description semantic encoding feature aggregation subunit of a carbon footprint accounting model in a product carbon footprint information visualization system according to an embodiment of the present application.

[0015] Figure 6 It is a block diagram of a cross-domain optimization query encoding subunit in a product carbon footprint information visualization system according to an embodiment of the present application.

[0016] Figure 7 Flow chart of a method for visualizing product carbon footprint information according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0018] Carbon footprint accounting is the basis for evaluating the environmental impact of products. Through scientific accounting models, the carbon emissions of products throughout their life cycle can be quantified, helping companies identify emission reduction potential, optimize production processes, and reduce costs. At the same time, accurate carbon footprint data is also an important basis for companies to fulfill their social responsibilities, meet regulatory requirements, and enhance their brand image. In addition, the importance of visualization technology in information communication is self-evident. By displaying the carbon footprint accounting results on the screen, users can view them in a visual way, helping them understand the carbon emissions of products throughout their life cycle, thereby providing a scientific basis for formulating emission reduction measures.

[0019] In response to the above technical problems, this application proposes a product carbon footprint information visualization system. Figure 1 FIG. 1 is a system block diagram of a product carbon footprint information visualization system according to an embodiment of the present application. Figure 1As shown, according to the product carbon footprint information visualization system 100 of the embodiment of the present application, it includes: a new product information entry module 110, which is used to obtain the basic information of the new product input by the user, and the basic information includes product strength, product name, quality, specifications and manufacturer; a carbon footprint accounting model and accounting boundary determination module 120, which is used to model the carbon footprint accounting model based on the basic information of the new product to obtain a recommended carbon footprint accounting model, and determine the accounting boundary; a data reporting module 130, which is used to input the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary; an accounting result determination module 140, which is used to generate a carbon footprint accounting result based on the recommended carbon footprint accounting model, and transmit the carbon footprint accounting result to the screen for display.

[0020] In the above-mentioned product carbon footprint information visualization system 100, the newly added product information entry module 110 is used to obtain the basic information of the newly added product input by the user, and the basic information includes product strength, product name, quality, specifications and manufacturer. It should be understood that the basic information of the newly added product input by the user not only helps to identify the unique characteristics of the product, but also ensures that the accounting model is tailored for a specific product, thereby improving the accuracy and reliability of the carbon footprint calculation. For example, the strength of the product (such as flammability, corrosiveness) may affect its handling method during production and transportation, while the product name and specifications help to clarify the specific calculation standards and data sources. Quality information can be used to evaluate the utilization efficiency and life cycle assessment of materials, and then accurately predict carbon emissions at different stages. In addition, the manufacturer's information can help trace the source of raw materials and the emission characteristics of the production process, so as to more comprehensively consider the carbon emissions of the product throughout its life cycle. Specifically, in order to obtain this basic information, a user-friendly input interface can be designed, such as adding a form in the carbon footprint visualization system of the enterprise, requiring the user to fill in the required information. The form can use a variety of forms such as drop-down menus, radio buttons and text boxes, so that users can easily and quickly enter relevant data. In addition, the system can also provide guiding texts and examples to help users understand the importance and filling-in method of each piece of information to ensure the quality and consistency of user input and further improve the reliability of the data.

[0021] In the above-mentioned product carbon footprint information visualization system 100, the carbon footprint accounting model and accounting boundary determination module 120 is used to model the carbon footprint accounting model based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model and determine the accounting boundary. It should be understood that the production process, material use and life cycle stage of each product are unique, providing key background data so that the model can accurately reflect the carbon emission characteristics of a specific product. By analyzing the basic information of the newly added products, a more practical accounting model can be established. In the process of modeling, it is necessary to clarify the accounting boundary, that is, which stages and activities should be included in the calculation of the carbon footprint. For example, the main emission sources of some products may be concentrated in the mining and transportation of raw materials, while other products may mainly generate higher carbon emissions during the manufacturing process. Therefore, based on the basic information of the newly added products, these key emission sources can be identified and defined to ensure that important emission sources are not missed or insignificant emission sources are over-calculated, thereby helping companies to better understand the carbon footprint of their products.

[0022] In the above-mentioned product carbon footprint information visualization system 100, the data reporting module 130 is used to input the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary. It should be understood that the full life cycle refers to the entire process of a product from the acquisition of raw materials, design, production, transportation, use to final scrapping and disposal. It covers the environmental impact and resource consumption at each stage, including the mining of raw materials, energy use in the manufacturing process, energy consumption of products in the use stage, and recycling or treatment methods after use. Through the analysis of the full life cycle, enterprises and decision makers can identify the impact of products on the environment at different stages, so as to formulate more sustainable design and management strategies, reduce resource waste and carbon emissions, and promote sustainable development. In the technical solution of this application, the accounting boundary defines the various stages and activities that need to be considered in the carbon footprint assessment, including the extraction of raw materials, production process, transportation, use and waste treatment. By clarifying the accounting boundary, all greenhouse gas emission sources in the product life cycle can be fully captured, thereby avoiding missing key links. Inputting the full life cycle data into the recommended carbon footprint accounting model will not only help form a systematic carbon management framework, but also help companies develop more targeted emission reduction strategies. Specifically, first of all, it is necessary to collect various data related to the entire life cycle of the product. These data include the source of raw materials, production process, transportation method, usage mode and final disposal method of the product. In order to effectively integrate these data, companies can establish a data collection system and use sensors, Internet of Things technology and big data analysis tools to monitor and record data from each link. Next, based on the defined accounting boundary, the collected full life cycle data is systematically input into the recommended carbon footprint accounting model. Specifically, Excel tables or databases can be used to organize the data into a structured format for easy model reading. Among them. The recommended carbon footprint accounting model may include requirements for different data types, such as quantity, type, and intensity. By inputting the data into the model item by item, the carbon emissions at different life cycle stages can be accurately calculated, and finally the total carbon footprint of the entire product life cycle can be obtained. Finally, by analyzing the calculation results, companies can identify the main sources of carbon emissions and high-emission links, and then formulate corresponding emission reduction measures.

[0023] In the above-mentioned product carbon footprint information visualization system 100, the calculation result determination module 140 is used to generate a carbon footprint calculation result based on the recommended carbon footprint calculation model, and transmit the carbon footprint calculation result to the screen for display. It should be understood that the carbon footprint calculation results can be visualized in the form of charts, numbers or graphics, which not only makes the data more intuitive and easy to understand, but also promotes corporate transparency and social responsibility. In this way, decision makers can quickly identify key issues and formulate corresponding emission reduction measures. At the same time, consumers can also better understand the environmental impact of the products they purchase, so as to make more sustainable consumption choices.

[0024] It should be understood that in the above-mentioned product carbon footprint information visualization system, the steps of modeling the carbon footprint accounting model based on the basic information of the newly added product and determining the accounting boundary are crucial, because each product has its own unique characteristics and production process, which means that the carbon footprint accounting models of different products will also be different. By establishing a carbon footprint accounting model that conforms to the newly added product based on the basic information of the newly added product, it can be ensured that the model is more in line with the actual situation of the specific product, thereby improving the accuracy and reliability of the accounting results. At the same time, the accounting boundary defines which stages or activities should be included in the carbon footprint calculation. For different products, their key emission sources may be different. For example, most of the carbon emissions of some products may occur in the raw material mining stage, while for other products, they may mainly occur in the manufacturing or transportation process. Therefore, determining the appropriate accounting boundary is crucial to avoid missing important emission sources or over-calculating insignificant emission sources. This can help companies or other organizations better understand and manage the carbon emissions of their products throughout their life cycle.

[0025] Specifically, when modeling the carbon footprint accounting model based on the basic information of the newly added product, the technical concept of the present application is to collect the basic information of the newly added product (including product strength, product name, quality, specifications and manufacturer) input by the user, and extract a set of carbon footprint accounting models from the database, and then introduce data processing and semantic understanding algorithms based on artificial intelligence and natural language understanding technology in the back end to perform semantic analysis on the basic information of the newly added product and the text description of these carbon footprint accounting models, so as to use the semantics of the text of these carbon footprint accounting models as prompt templates to use the semantics of the basic information of the newly added product to perform feature query responses, so as to return the carbon footprint accounting model that matches the product information as a recommended model. In this way, it is possible to automatically query a carbon footprint accounting model that is more relevant and more suitable for the newly added product, and determine the corresponding accounting boundary, so as to provide enterprises with more accurate carbon emission data for the product production cycle, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0026] Figure 2 It is a block diagram of a carbon footprint calculation model and a calculation boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application. Figure 3 This is a data flow diagram of a carbon footprint calculation model and a calculation boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application. Figure 2 and Figure 3 As shown, the carbon footprint accounting model and accounting boundary determination module 120 includes: a carbon footprint accounting model set extraction unit 121, which is used to extract a set of carbon footprint accounting models from a database; a carbon footprint accounting model text description unit 122, which is used to extract the text description of each carbon footprint accounting model in the set of carbon footprint accounting models to obtain a set of carbon footprint accounting model text descriptions; a carbon footprint accounting model text description semantic encoding unit 123, which is used to semantically encode the basic information of the newly added product and the text description of each carbon footprint accounting model in the set of carbon footprint accounting model text descriptions to obtain a new product. A set of semantic coding feature vectors of added product basic information and semantic coding feature vectors of text description of carbon footprint accounting model; a semantic feature significant guidance query response unit 124, used to perform semantic feature significant guidance query response on the set of added product basic information semantic coding feature vectors and text description of carbon footprint accounting model to obtain a product-carbon footprint accounting model query response optimized coding vector; a carbon footprint accounting model analysis unit 125, used to generate the recommended carbon footprint accounting model based on the product-carbon footprint accounting model query response optimized coding vector, and determine the accounting boundary.

[0027] Specifically, in the carbon footprint accounting model set extraction unit 121 and the carbon footprint accounting model text description unit 122, a set of carbon footprint accounting models is extracted from the database, and the text description of each carbon footprint accounting model in the set of carbon footprint accounting models is extracted to obtain a set of carbon footprint accounting model text descriptions. It should be understood that a plurality of verified carbon footprint accounting models are stored in the database, each of which is targeted at different product types, industries or life cycle stages and can meet diverse calculation needs. By extracting these models, the most appropriate accounting method can be selected for a specific product, thereby ensuring the accuracy and relevance of the accounting results. Further, by extracting the text description of each carbon footprint accounting model in the set of carbon footprint accounting models, the user can deeply understand the specific content of each model, including its scope of application, calculation method and key assumptions. This understanding helps enterprises to select the most appropriate model when performing carbon emission calculations to ensure the accuracy and reliability of the accounting. Specifically, extracting a set of carbon footprint accounting models from the database needs to be completed by writing SQL query statements. This process involves accessing the database and executing queries to obtain relevant data about the carbon footprint accounting model, including information such as model ID, name, and scope of application. After extraction, the system can integrate these models into a collection for subsequent processing. Next, extracting the text description of each model involves screening out relevant text fields from the model collection, which usually includes the model's definition, applicable conditions, calculation method, and key assumptions. By writing corresponding queries and scripts, these text information can be obtained and summarized into a new text description collection. The resulting carbon footprint accounting model text description collection can serve as an important reference for companies to formulate carbon management strategies. It not only provides rich background information, but also helps companies quickly understand the applicable scenarios and calculation processes of various accounting models, and then select the best model to meet their specific carbon emission calculation needs.

[0028] Specifically, in the carbon footprint accounting model text description semantic encoding unit 123, the basic information of the new product and each carbon footprint accounting model text description in the set of carbon footprint accounting model text descriptions are semantically encoded to obtain a set of semantic encoding feature vectors of the basic information of the new product and semantic encoding feature vectors of the carbon footprint accounting model text descriptions.

[0029] Further, in the semantic encoding of the basic information of the new product to obtain the semantic encoding feature vector of the basic information of the new product, it should be understood that the basic information of the product generally includes the product name, strength, quality, specifications and manufacturer, etc., and these information may have different meanings and importance in different contexts. Through semantic encoding, this information can be mapped to a high-dimensional space so that the semantic relationship is retained in the feature vector, thereby providing a basis for subsequent analysis and decision-making. Specifically, in the technical solution of the present application, the basic information of the new product is semantically encoded using word embedding technology. First, the basic information of the new product needs to be preprocessed, including text cleaning and standardization, and irrelevant information or noise is removed. Then, a pre-trained language model such as Word2Vec, GloVe or BERT is used to encode the product information to represent each attribute of the product as a vector, and the dimension and direction of the vector will reflect its position and relationship in the semantic space. Among them, the product name may be encoded as a feature vector, which represents the similarity with other product names; and the attributes such as strength, quality and specifications can be reflected in the entire feature space through corresponding encoding techniques. Finally, the vectors of each attribute are concatenated or weighted to form a feature vector containing all basic information, that is, the semantic encoding feature vector of the new product basic information. The semantic encoding feature vector of the new product basic information not only retains the independent information of each attribute, but also reflects the relationship between the features, making the entire feature vector richer and more meaningful.

[0030] Furthermore, in the process of semantically encoding each carbon footprint accounting model text description in the set of the carbon footprint accounting model text description to obtain a set of semantically encoded feature vectors of the carbon footprint accounting model text description, it should be understood that the text description of each model may contain complex terms, methodologies and assumptions, and this information needs to be converted into a machine-readable form when processed and analyzed. Through semantic encoding, these text information can be converted into high-dimensional vectors, which can not only maintain the semantics and contextual relationship of the text, but also provide a basis for subsequent data mining and machine learning tasks. In the technical solution of the present application, the Transformer model is used to semantically encode each carbon footprint accounting model text description in the set of the carbon footprint accounting model text description. Among them, a pre-trained model based on BERT (Bidirectional Encoder Representations from Transformers) in the Transformer model is used to capture the contextual information of each carbon footprint accounting model text description in the set of carbon footprint accounting model text descriptions to generate a more accurate feature vector. First, the text description of the carbon footprint accounting model needs to be preprocessed, including removing redundant punctuation, stop words, and lowercasing. Then, each text description is input into the selected Transformer model to generate a high-dimensional feature vector for the text, namely, the semantic encoding feature vector of the carbon footprint accounting model text description, so as to capture more complex contextual relationships through a multi-layer neural network. Next, by storing each carbon footprint accounting model text description semantic encoding feature vector in a unified data structure, the generated feature vectors are integrated into a set, namely, the set of carbon footprint accounting model text description semantic encoding feature vectors, for subsequent analysis and comparison. Finally, based on the obtained set of carbon footprint accounting model text description semantic encoding feature vectors, we can deepen our understanding of different accounting models, promote the scientific and systematic carbon management, and provide strong support for achieving sustainable development goals.

[0031] Figure 4 FIG. 1 is a block diagram of a query response unit for significant guidance between semantic features in a product carbon footprint information visualization system according to an embodiment of the present application. Figure 4As shown, the semantic feature significant guidance query response unit 124 includes: a carbon footprint accounting model text description semantic coding feature aggregation subunit 1241, which is used to perform feature aggregation processing based on linear embedding coding on the set of semantic coding feature vectors of the carbon footprint accounting model text description to obtain a prompt template; a cross-domain optimized query coding subunit 1242, which is used to perform cross-domain optimized query coding on the set of the semantic coding feature vectors of the new product basic information and the semantic coding feature vectors of the carbon footprint accounting model text description based on the prompt template to obtain the product-carbon footprint accounting model query response optimized coding vector.

[0032] Specifically, in the semantic feature inter-significant guidance query response unit 124, the set of semantic encoding feature vectors of the new product basic information and the set of semantic encoding feature vectors of the text description of the carbon footprint accounting model are subjected to semantic feature inter-significant guidance query response to obtain the product-carbon footprint accounting model query response optimization encoding vector. It should be understood that the set of semantic encoding feature vectors of the new product basic information and the set of semantic encoding feature vectors of the text description of the carbon footprint accounting model are semantic encoding features extracted from the basic information of the new product and the text description of the carbon footprint accounting model, respectively. This enables the system to compare and match the similarity between the two at the semantic level, rather than simply matching based on keywords or surface forms. Through the method of semantic encoding and semantic feature query, the system can better understand the intrinsic connection between product characteristics and model descriptions, so as to find the most suitable accounting model. Based on this, in the technical solution of the present application, the set of semantic encoding feature vectors of the new product basic information and the set of semantic encoding feature vectors of the text description of the carbon footprint accounting model are further subjected to semantic feature inter-significant guidance query response optimization encoding to obtain the product-carbon footprint accounting model query response optimization encoding vector. By optimizing the query response encoding between semantic features of the semantic encoding feature vector of the basic product information and the semantic encoding feature vector set of the accounting model text description, the carbon footprint accounting model that best matches the characteristics of the new product can be identified. This intelligent recommendation reduces the time and labor intensity required to manually select a suitable model, and because it is based on semantic feature matching, the recommendation results are usually more accurate.

[0033] Figure 5 The block diagram is a subunit for the text description semantic encoding feature aggregation of the carbon footprint calculation model in the product carbon footprint information visualization system according to an embodiment of the present application. Figure 5As shown, the carbon footprint accounting model text description semantic coding feature aggregation subunit 1241 includes: a carbon footprint accounting model text description semantic linear embedding coding secondary subunit 1241-1, which is used to use a key embedding matrix to perform linear embedding coding on each carbon footprint accounting model text description semantic coding feature vector in the set of the carbon footprint accounting model text description semantic coding feature vector to obtain a set of carbon footprint accounting model text description semantic coding feature vectors after linear transformation; a text description semantic key matrix arrangement secondary subunit 1241-2, which is used to perform matrix arrangement on the set of carbon footprint accounting model text description semantic coding feature vectors after linear transformation to obtain a carbon footprint accounting model text description semantic key matrix; a text description semantic key matrix maximum value extraction secondary subunit 1241-3, which is used to extract the maximum value of each carbon footprint accounting model text description semantic coding feature vector after linear transformation in the carbon footprint accounting model text description semantic key matrix to obtain a carbon footprint accounting model text description semantic key matrix significant feature vector as the prompt template.

[0034] Specifically, in the carbon footprint accounting model text description semantic linear embedding coding secondary subunit 1241-1, the key embedding matrix is ​​used to perform linear embedding coding on each carbon footprint accounting model text description semantic coding feature vector in the set of the carbon footprint accounting model text description semantic coding feature vector to obtain a set of carbon footprint accounting model text description semantic coding feature vectors after linear transformation. It should be understood that the key embedding matrix is ​​used to perform a linear transformation on each element in the set of the carbon footprint accounting model text description semantic coding feature vector. This process maps the carbon footprint accounting model text semantic features in the original feature space to a new latent space, in order to capture the relationship between different carbon footprint accounting model text semantics and better represent the complex patterns in these text semantic data. After such linear embedding coding, the set of carbon footprint accounting model text description semantic coding feature vectors after linear transformation becomes more suitable for participating in the subsequent attention mechanism calculation, which improves the model's expression ability and enables the model to more effectively identify key feature representation information and feature association information related to subsequent matching tasks.

[0035] Specifically, the carbon footprint accounting model text description semantic linear embedding coding secondary sub-unit 1241-1 includes: multiplying the carbon footprint accounting model text description semantic coding feature vector by the key embedding matrix and then adding it to the key embedding bias vector by position to obtain the carbon footprint accounting model text description semantic coding feature vector after linear transformation.

[0036] Specifically, in the text description semantic key matrix arrangement secondary subunit 1241-2, the set of the linearly transformed carbon footprint accounting model text description semantic encoding feature vectors is matrix-arranged to obtain the carbon footprint accounting model text description semantic key matrix. It should be understood that the linearly transformed carbon footprint accounting model text description semantic encoding feature vectors are arranged in order to form a new data structure called a key matrix, so as to facilitate parallel processing, thereby improving calculation efficiency.

[0037] Specifically, in the text description semantic key matrix maximum value extraction secondary subunit 1241-3, the maximum value of each linearly transformed carbon footprint accounting model text description semantic encoding feature vector in the carbon footprint accounting model text description semantic key matrix is ​​extracted to obtain the carbon footprint accounting model text description semantic key matrix significant feature vector as the prompt template. It should be understood that the maximum value of each column (i.e., each linearly transformed carbon footprint accounting model text description semantic encoding feature vector) is selected from the carbon footprint accounting model text description semantic key matrix to obtain the carbon footprint accounting model text description semantic key matrix significant feature vector. Furthermore, the significant feature vector of the semantic key matrix of the text description of the carbon footprint accounting model is used as a prompt template, which represents the most prominent or representative part of the semantic information in the set of text semantics of the carbon footprint accounting model. Introducing the significant feature vector of the semantic key matrix of the text description of the carbon footprint accounting model as an additional guidance signal helps to reduce noise interference and strengthen the model's learning of important patterns and key carbon footprint accounting model text semantics. At the same time, it can also help the attention mechanism focus more on the key parts of the data, thereby improving the model's attention to important detailed semantics of the carbon footprint accounting model, and providing a basis for subsequent semantic feature matching and carbon footprint accounting model recommendation tasks.

[0038] Figure 6 FIG. 1 is a block diagram of a cross-domain optimization query encoding subunit in a product carbon footprint information visualization system according to an embodiment of the present application. Figure 6As shown, the cross-domain optimized query encoding subunit 1242 includes: a newly added product basic information semantic linear embedding encoding secondary subunit 1242-1, which is used to use the query embedding matrix and the value embedding matrix to perform linear embedding encoding on the newly added product basic information semantic encoding feature vector to obtain the newly added product basic information semantic query vector and the newly added product basic information semantic value vector; a template prompt optimization heterogeneous conversion encoding secondary subunit 1242-2, which is used to input the newly added product basic information semantic query vector, the newly added product basic information semantic value vector, the carbon footprint accounting model text description semantic encoding feature vectors after each linear transformation in the carbon footprint accounting model text description semantic key matrix, and the prompt template into a template prompt optimized heterogeneous converter structure to obtain a sequence of product-carbon footprint accounting model semantic cross-domain optimized query encoding vectors; a position mean calculation secondary subunit 1242-3, which is used to calculate the position mean vector of the sequence of the product-carbon footprint accounting model semantic cross-domain optimized query encoding vectors to obtain the product-carbon footprint accounting model query response optimized encoding vector.

[0039] Specifically, in the newly added product basic information semantic linear embedding coding secondary subunit 1242-1, the newly added product basic information semantic coding feature vector is linearly embedded using the query embedding matrix and the value embedding matrix to obtain the newly added product basic information semantic query vector and the newly added product basic information semantic value vector. It should be understood that the query embedding matrix and the value embedding matrix are respectively used to act on the newly added product basic information semantic coding feature vector to generate the corresponding query vector and value vector. These two vectors each play different but complementary roles: the query vector is used to determine which newly added product basic information semantics need to be paid attention to, while the value vector carries the actual content of the newly added product basic information semantic data. The combination of the two can accurately select relevant information while maintaining the context, enhancing the generalization ability and accuracy of the model.

[0040] Specifically, in the template prompt optimization heterogeneous conversion encoding secondary subunit 1242-2, the newly added product basic information semantic query vector, the newly added product basic information semantic value vector, the carbon footprint accounting model text description semantic encoding feature vectors after each linear transformation in the carbon footprint accounting model text description semantic key matrix, and the prompt template are input into the heterogeneous converter structure based on template prompt optimization to obtain a sequence of product-carbon footprint accounting model semantic cross-domain optimization query encoding vectors. It should be understood that the newly added product basic information semantic query vector, the newly added product basic information semantic value vector, the carbon footprint accounting model text description semantic encoding feature vectors after each linear transformation in the carbon footprint accounting model text description semantic key matrix, and the prompt template are jointly sent to a specially designed heterogeneous converter structure based on template prompt optimization, which usually includes a heterogeneous multi-head attention mechanism component, which can automatically adjust the weight allocation strategy according to the provided prompt template, and realize the effective optimization of the cross-domain query response encoding of the product basic information semantics and multiple carbon footprint accounting model semantics. This not only helps the model respond according to the semantics of the current input product basic information, but also takes into account the overall characteristics of the carbon footprint accounting model semantic set, thereby matching and expressing more accurate and relevant features, and enhancing the ability to handle complex product-carbon footprint accounting model query response and matching tasks.

[0041] Specifically, the template prompt optimization heterogeneous conversion encoding secondary subunit 1242-2 includes: calculating the multiplication between the semantic query vector of the new product basic information and the transposed vector of the semantic encoding feature vector of the carbon footprint accounting model text description after the linear transformation, and then dividing the obtained product-carbon footprint accounting model semantic association feature matrix by the bi-norm of the significant feature vector of the carbon footprint accounting model text description semantic key matrix by position to obtain the product-carbon footprint accounting model semantic association representation matrix; inputting the product-carbon footprint accounting model semantic association representation matrix The function is processed to obtain the semantic association weight matrix of the product-carbon footprint accounting model; after multiplying the semantic association weight matrix of the product-carbon footprint accounting model with the significant feature vector of the carbon footprint accounting model text description semantic key matrix, the obtained feature vector is multiplied by the position point with the semantic value vector of the new product basic information to obtain the product-carbon footprint accounting model semantic cross-domain optimization query encoding vector.

[0042] Specifically, in the position-based mean calculation secondary subunit 1242-3, the position-based mean vector of the sequence of the product-carbon footprint accounting model semantic cross-domain optimization query encoding vector is calculated to obtain the product-carbon footprint accounting model query response optimization encoding vector. It should be understood that the sequence of the product-carbon footprint accounting model semantic cross-domain optimization query encoding vector is averaged according to the position, and finally a single product-carbon footprint accounting model query response optimization encoding vector is obtained. This operation not only simplifies the result form and facilitates further carbon footprint accounting model recommendation classification tasks, but also retains the key features of multiple carbon footprint accounting model semantics, thereby realizing the intelligent recommendation of carbon footprint accounting models for new products through feature query response and matching. This method not only improves the accuracy of model recommendation, but also makes the operation of the entire system more efficient and reliable, thereby helping enterprises and organizations to more effectively manage and reduce the carbon footprint of their products.

[0043] Specifically, the semantic feature inter-query response optimization encoding is performed on the set of the semantic encoding feature vector of the new product basic information and the semantic encoding feature vector of the carbon footprint accounting model text description using the following feature response interaction formula to obtain the product-carbon footprint accounting model query response optimization encoding vector; Among them, the characteristic response interaction formula is: ;in, is a set of semantically encoded feature vectors of the text description of the carbon footprint accounting model, are the first, second and third in the set of semantic encoding feature vectors of the text description of the carbon footprint accounting model. A semantic encoding feature vector of the text description of the carbon footprint accounting model, The first one in the set of semantic encoding feature vectors for the text description of the carbon footprint accounting model A semantic encoding feature vector of the text description of the carbon footprint accounting model, and are the key embedding matrix and key embedding bias vector respectively, is the first in the set of semantic encoding feature vectors of the text description of the carbon footprint accounting model after linear transformation. The semantic encoding feature vector of the text description of the carbon footprint accounting model after linear transformation, The semantic key matrix for the carbon footprint accounting model text description, To extract the maximum value in a vector, is the significant feature vector of the semantic key matrix of the text description of the carbon footprint accounting model, A semantic encoding feature vector for the basic information of the newly added product, and are the query embedding matrix and the query bias vector respectively, and are the value embedding matrix and value bias vector respectively, To add a semantic query vector for basic product information, is the semantic value vector of the new product basic information, is the bi-norm of the vector, for function, is matrix multiplication, For point-by-position multiplication, The first in the sequence of query encoding vectors for semantic cross-domain optimization of product-carbon footprint accounting model Product-carbon footprint accounting model semantic cross-domain optimization query encoding vector, the number of vectors in the sequence of query encoding vectors for the semantic cross-domain optimization of the product-carbon footprint accounting model, An encoding vector is optimized for the product-carbon footprint accounting model query response.

[0044] Specifically, the carbon footprint accounting model analysis unit 125 includes: inputting the product-carbon footprint accounting model query response optimization coding vector into the carbon footprint accounting model intelligent recommendation module based on the classifier to obtain the recommended carbon footprint accounting model; based on the recommended carbon footprint accounting model, determining the accounting boundary. That is to say, the query response optimization coding features between the newly added product basic information semantics and the carbon footprint accounting model text semantics are used for classification processing, so as to return the carbon footprint accounting model matching the product information as the recommended model. Then, based on the recommended carbon footprint accounting model, the accounting boundary is determined. It is worth mentioning that in the technical solution of the present application, the classifier is trained to learn how to determine the recommended carbon footprint accounting model based on input features (such as raw material type, production process, and transportation method). Specifically, during the training process, the data set needs to be divided into a training set and a test set to verify the performance and accuracy of the classifier. Then, the cross-validation method is used to evaluate the performance of the model in different situations to ensure its reliability. Next, the fully trained and successfully verified classifier is applied to the actual data analysis, and the recommended carbon footprint accounting model is identified based on the product-carbon footprint accounting model query response optimization encoding vector. This process not only improves the accounting efficiency, but also reduces the errors that may be caused by human judgment. Finally, the results of the classifier are combined with the recommended carbon footprint accounting model, so that the company can clearly know which activities are included in the accounting boundary and which can be ignored. This classifier-based automated method enables companies to adjust the accounting boundaries more flexibly and quickly to cope with changes in different products and market demands. In this way, it is possible to automatically query the carbon footprint accounting model that is more relevant and more suitable for the new products, and determine the corresponding accounting boundaries, so as to provide companies with more accurate product production cycle carbon emission data, and then display these data on the screen for users to view, providing a scientific basis for the formulation of emission reduction measures.

[0045] Preferably, when the set of the semantic coding feature vector of the basic information of the newly added product and the set of the semantic coding feature vector of the text description of the carbon footprint accounting model respectively represent the semantic coding features of the basic information of the newly added product and the set semantic coding features of the text description of the carbon footprint accounting model, when performing query coding optimization using the key matrix significant features as the prompt template, the difference in the semantic distribution of heterogeneous data encoding will lead to the difference in template prompting based on the key matrix significant features, causing the product-carbon footprint accounting model query response optimization coding vector obtained by the query coding to have a complex query aggregation spatial structure. Therefore, when the product-carbon footprint accounting model query response optimization coding vector is input into the classifier-based carbon footprint accounting model intelligent recommendation module, it is expected to improve the classification regression convergence and generalization effect of the product-carbon footprint accounting model query response optimization coding vector under the complex query aggregation spatial structure.

[0046] Therefore, the applicant of the present application considers optimizing the product-carbon footprint accounting model query response optimized coding vector when inputting the product-carbon footprint accounting model query response optimized coding vector into the classifier-based carbon footprint accounting model intelligent recommendation module. The optimization process includes: calculating the absolute value sum of each eigenvalue of the product-carbon footprint accounting model query response optimized coding vector to obtain a first product-carbon footprint accounting model query response optimized coding space structure value, and calculating the square root of the sum of squares of each eigenvalue of the product-carbon footprint accounting model query response optimized coding vector to obtain a second product-carbon footprint accounting model query response optimized coding space structure value: ;in, Represents each eigenvalue of the product-carbon footprint accounting model query response optimized encoding vector, , represents the summation function, represents the absolute value function, represents the set of real numbers, Indicates the first product-carbon footprint accounting model query response optimization encoding space structure value, Indicates the second product-carbon footprint accounting model query response optimization encoding space structure value, represents the set of real numbers, Indicates the length of a vector; Each eigenvalue of the product-carbon footprint accounting model query response optimized coding vector is multiplied by the first product-carbon footprint accounting model query response optimized coding space structure value and the second product-carbon footprint accounting model query response optimized coding space structure value to obtain the first product-carbon footprint accounting model query response optimized coding structure reference value and the second product-carbon footprint accounting model query response optimized coding structure reference value corresponding to each eigenvalue; each eigenvalue of the product-carbon footprint accounting model query response optimized coding vector is multiplied by the length of the product-carbon footprint accounting model query response optimized coding vector and the square root of the length to obtain the first product-carbon footprint accounting model query response optimized coding scale transformation value and the second product-carbon footprint accounting model query response optimized coding scale transformation value corresponding to each eigenvalue; the first product-carbon footprint accounting model query response optimized coding vector is multiplied by the length of the product-carbon footprint accounting model query response optimized coding vector and the square root of the length to obtain the first product-carbon footprint accounting model query response optimized coding scale transformation value and the second product-carbon footprint accounting model query response optimized coding scale transformation value corresponding to each eigenvalue The structural reference value is divided by the difference between the first product-carbon footprint accounting model query response optimized coding space structure value and the first product-carbon footprint accounting model query response optimized coding scale transformation value to obtain the first product-carbon footprint accounting model query response optimized coding transformation adjustment value; the second product-carbon footprint accounting model query response optimized coding structural reference value is divided by the difference between the second product-carbon footprint accounting model query response optimized coding space structure value and the second product-carbon footprint accounting model query response optimized coding scale transformation value to obtain the second product-carbon footprint accounting model query response optimized coding transformation adjustment value; the weighted sum of the first product-carbon footprint accounting model query response optimized coding transformation adjustment value and the second product-carbon footprint accounting model query response optimized coding transformation adjustment value is calculated to obtain each eigenvalue of the optimized product-carbon footprint accounting model query response optimized coding vector.

[0047] Here, the product-carbon footprint accounting model query response optimization encoding vector is recorded as The optimization expression is: ; exist ,and In the case of: ;in, Represents each eigenvalue of the product-carbon footprint accounting model query response optimized encoding vector, Indicates the first product-carbon footprint accounting model query response optimization encoding space structure value, Indicates the second product-carbon footprint accounting model query response optimization encoding space structure value, Indicates the first product-carbon footprint accounting model query response optimization code transformation adjustment value, Indicates the second product - carbon footprint accounting model query response optimization code transformation adjustment value, represents a first product-carbon footprint accounting model query response optimized coding transformation adjustment feature vector obtained by vectorizing the plurality of first product-carbon footprint accounting model query response optimized coding transformation adjustment values, represents a second product-carbon footprint accounting model query response optimized coding transformation adjustment feature vector obtained by vectorizing a plurality of second product-carbon footprint accounting model query response optimized coding transformation adjustment values, represents the optimized product-carbon footprint accounting model query response optimized encoding vector, It means adding by position. represents the coding transformation adjustment weight value, represents positional multiplication, represents the set of real numbers, Indicates the length of a vector.

[0048] That is, with respect to the spatial structure information of the feature set of the product-carbon footprint accounting model query response optimized coding vector in the high-dimensional space, the scale-based frame transformation of each eigenvalue of the product-carbon footprint accounting model query response optimized coding vector is performed by taking the class norm space structured representation of the product-carbon footprint accounting model query response optimized coding vector as a reference window, and the spatial structure-based frame attention weight adjustment of each eigenvalue of the product-carbon footprint accounting model query response optimized coding vector is implemented to ensure the spatial transformation invariance of the product-carbon footprint accounting model query response optimized coding vector under the interaction of feature space, thereby improving the convergence and generalization effect of classification regression of the feature set of the product-carbon footprint accounting model query response optimized coding vector under the complex spatial structure representation, and improving the accuracy of the recommended carbon footprint accounting model obtained by inputting it into the classifier-based carbon footprint accounting model intelligent recommendation module. In this way, it is possible to automatically query a carbon footprint accounting model that is more relevant and more suitable for the new products, and determine the corresponding accounting boundaries, so as to provide enterprises with more accurate carbon emission data for the product production cycle. These data will then be displayed on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0049] In summary, the product carbon footprint information visualization system 100 based on the embodiment of the present application is explained, which collects the basic information of the newly added products input by the user, extracts the set of carbon footprint accounting models from the database, and then introduces data processing and semantic understanding algorithms based on artificial intelligence and natural language understanding technology in the back end to perform semantic analysis and feature query response, so as to return the carbon footprint accounting model that matches the product information as a recommended model. In this way, it is possible to automatically query the carbon footprint accounting model that is more relevant and more suitable for the newly added products, and determine the corresponding accounting boundaries, so as to provide enterprises with more accurate product production cycle carbon emission data, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0050] As described above, the product carbon footprint information visualization system 100 according to the embodiment of the present application can be implemented in various terminal devices. In one example, the product carbon footprint information visualization system 100 can be integrated into the terminal device as a software module and / or a hardware module. For example, the product carbon footprint information visualization system 100 can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the product carbon footprint information visualization system 100 can also be one of the many hardware modules of the terminal device.

[0051] Alternatively, in another example, the product carbon footprint information visualization system 100 and the terminal device may also be separate devices, and the product carbon footprint information visualization system 100 may be connected to the terminal device via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0052] Figure 7 FIG. 1 is a flow chart of a method for visualizing product carbon footprint information according to an embodiment of the present application. Figure 7 As shown, according to the product carbon footprint information visualization method of the embodiment of the present application, it includes: S110, obtaining the basic information of the newly added product input by the user, the basic information including product strength, product name, quality, specifications and manufacturer; S120, modeling a carbon footprint accounting model based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model, and determining the accounting boundary; S130, inputting the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary; S140, generating a carbon footprint accounting result based on the recommended carbon footprint accounting model, and transmitting the carbon footprint accounting result to the screen for display.

[0053] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned product carbon footprint information visualization method have been referred to above. Figures 1 to 6The product carbon footprint information visualization system has been described in detail, and therefore, its repeated description will be omitted.

[0054] In summary, the product carbon footprint information visualization method based on the embodiment of the present application is explained, which collects the basic information of the new product input by the user, extracts the set of carbon footprint accounting models from the database, and then introduces data processing and semantic understanding algorithms based on artificial intelligence and natural language understanding technology in the back end to perform semantic analysis and feature query response, so as to return the carbon footprint accounting model that matches the product information as a recommended model. In this way, it is possible to automatically query the carbon footprint accounting model that is more relevant and more suitable for the new product, and determine the corresponding accounting boundaries, so as to provide enterprises with more accurate product production cycle carbon emission data, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

Claims

1. A product carbon footprint information visualization system, characterized in that: include: A new product information entry module is used to obtain basic information of new products input by users, including product strength, product name, quality, specifications and manufacturer; A carbon footprint accounting model and accounting boundary determination module, which is used to model a carbon footprint accounting model based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model and determine the accounting boundary; A data reporting module, used to input the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary; The calculation result determination module is used to generate a carbon footprint calculation result based on the recommended carbon footprint calculation model, and transmit the carbon footprint calculation result to the screen for display.

2. The product carbon footprint information visualization system according to claim 1, characterized in that: The carbon footprint accounting model and accounting boundary determination module include: A carbon footprint accounting model set extraction unit, used to extract a set of carbon footprint accounting models from a database; A carbon footprint accounting model text description unit, used to extract the text description of each carbon footprint accounting model in the set of carbon footprint accounting models to obtain a set of carbon footprint accounting model text descriptions; A carbon footprint accounting model text description semantic encoding unit, used to semantically encode each carbon footprint accounting model text description in the set of basic information of the newly added product and the carbon footprint accounting model text description to obtain a set of semantic encoding feature vectors of the basic information of the newly added product and semantic encoding feature vectors of the carbon footprint accounting model text description; A semantic feature significant guidance query response unit, used for performing a semantic feature significant guidance query response on a set of the semantic coding feature vector of the new product basic information and the semantic coding feature vector of the carbon footprint accounting model text description to obtain a product-carbon footprint accounting model query response optimized coding vector; The carbon footprint accounting model analysis unit is used to optimize the encoding vector based on the product-carbon footprint accounting model query response, generate the recommended carbon footprint accounting model, and determine the accounting boundary.

3. The product carbon footprint information visualization system according to claim 2, characterized in that: The semantic feature inter-significant guidance query response unit includes: A carbon footprint accounting model text description semantic coding feature aggregation subunit, used for performing feature aggregation processing based on linear embedding coding on a set of semantic coding feature vectors of the carbon footprint accounting model text description to obtain a prompt template; The cross-domain optimized query encoding subunit is used to perform cross-domain optimized query encoding on the set of the semantic encoding feature vector of the basic information of the newly added product and the semantic encoding feature vector of the text description of the carbon footprint accounting model based on the prompt template to obtain the product-carbon footprint accounting model query response optimized encoding vector.

4. The product carbon footprint information visualization system according to claim 3, characterized in that: The carbon footprint accounting model text description semantic encoding feature aggregation subunit includes: A carbon footprint accounting model text description semantic linear embedding coding secondary subunit is used to use a key embedding matrix to perform linear embedding coding on each carbon footprint accounting model text description semantic coding feature vector in the set of carbon footprint accounting model text description semantic coding feature vectors to obtain a set of carbon footprint accounting model text description semantic coding feature vectors after linear transformation; A text description semantic key matrix arrangement secondary subunit is used to perform matrix arrangement on the set of semantic encoding feature vectors of the text description of the carbon footprint accounting model after the linear transformation to obtain a text description semantic key matrix of the carbon footprint accounting model; The text description semantic key matrix maximum value extraction secondary sub-unit is used to extract the maximum value of the carbon footprint accounting model text description semantic encoding feature vector after each linear transformation in the carbon footprint accounting model text description semantic key matrix to obtain the carbon footprint accounting model text description semantic key matrix significant feature vector as the prompt template.

5. The product carbon footprint information visualization system according to claim 4, characterized in that: The carbon footprint accounting model text description semantic linear embedding encoding secondary sub-unit includes: The semantic encoding feature vector of the text description of the carbon footprint accounting model is multiplied by the key embedding matrix and then added to the key embedding bias vector by position to obtain the semantic encoding feature vector of the text description of the carbon footprint accounting model after linear transformation.

6. The product carbon footprint information visualization system according to claim 5, characterized in that: The cross-domain optimization query encoding subunit includes: A new product basic information semantic linear embedding coding secondary sub-unit is used to use a query embedding matrix and a value embedding matrix to perform linear embedding coding on the semantic coding feature vector of the new product basic information to obtain a new product basic information semantic query vector and a new product basic information semantic value vector; The template prompt optimization heterogeneous conversion encoding secondary subunit is used to input the newly added product basic information semantic query vector, the newly added product basic information semantic value vector, the carbon footprint accounting model text description semantic encoding feature vector after each linear transformation in the carbon footprint accounting model text description semantic key matrix, and the prompt template into a heterogeneous converter structure optimized based on the template prompt to obtain a sequence of product-carbon footprint accounting model semantic cross-domain optimized query encoding vectors; The secondary subunit for calculating the positional mean is used to calculate the positional mean vector of the sequence of the product-carbon footprint accounting model semantic cross-domain optimized query encoding vector to obtain the product-carbon footprint accounting model query response optimized encoding vector.

7. The product carbon footprint information visualization system according to claim 6, characterized in that: The template prompts the optimization of heterogeneous conversion encoding secondary subunits, including: After calculating the multiplication between the semantic query vector of the newly added product basic information and the transposed vector of the semantic encoding feature vector of the text description of the carbon footprint accounting model after the linear transformation, the obtained product-carbon footprint accounting model semantic association feature matrix is ​​divided by the bi-norm of the significant feature vector of the text description semantic key matrix of the carbon footprint accounting model by position to obtain the product-carbon footprint accounting model semantic association representation matrix; Input the semantic association matrix of the product-carbon footprint accounting model The function is processed to obtain the semantic association weight matrix of the product-carbon footprint accounting model; After multiplying the product-carbon footprint accounting model semantic association weight matrix with the significant feature vector of the carbon footprint accounting model text description semantic key matrix, the obtained feature vector is point-multiplied with the new product basic information semantic value vector to obtain the product-carbon footprint accounting model semantic cross-domain optimized query encoding vector.

8. The product carbon footprint information visualization system according to claim 7, characterized in that: The carbon footprint accounting model analysis unit includes: Inputting the product-carbon footprint accounting model query response optimized encoding vector into a classifier-based carbon footprint accounting model intelligent recommendation module to obtain the recommended carbon footprint accounting model; Based on the recommended carbon footprint accounting model, the accounting boundary is determined.

9. A method for visualizing product carbon footprint information, characterized in that: include: Obtaining basic information of the newly added product input by the user, the basic information including product strength, product name, quality, specifications and manufacturer; Modeling a carbon footprint accounting model based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model and determine the accounting boundary; Inputting the product's full life cycle data into the recommended carbon footprint accounting model based on the accounting boundary; A carbon footprint calculation result is generated based on the recommended carbon footprint calculation model, and the carbon footprint calculation result is transmitted to a screen for display.

10. The product carbon footprint information visualization method according to claim 9, characterized in that: A carbon footprint accounting model is built based on the basic information of the newly added product to obtain a recommended carbon footprint accounting model, and the accounting boundary is determined, including: Extracting a collection of carbon footprint accounting models from a database; Extracting the text description of each carbon footprint accounting model in the set of carbon footprint accounting models to obtain a set of carbon footprint accounting model text descriptions; Semantically encoding the basic information of the newly added product and each carbon footprint accounting model text description in the set of carbon footprint accounting model text descriptions to obtain a set of semantic encoding feature vectors of the basic information of the newly added product and semantic encoding feature vectors of the carbon footprint accounting model text descriptions; Perform semantic feature inter-significant guidance query response on the set of the semantic coding feature vector of the new product basic information and the semantic coding feature vector of the carbon footprint accounting model text description to obtain a product-carbon footprint accounting model query response optimized coding vector; Based on the product-carbon footprint accounting model query response, the encoding vector is optimized, the recommended carbon footprint accounting model is generated, and the accounting boundary is determined.

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