Product Carbon Footprint Information Visualization System and Method

Abstract

The present application provides a product carbon footprint information visualization system and method, which relates to the field of information visualization. By collecting the basic information of a newly added product input by a user and extracting a set of carbon footprint accounting models from a database, and then introducing data processing and semantic understanding algorithms based on artificial intelligence and natural language understanding technologies at the backend for semantic analysis and feature query response, a carbon footprint accounting model matching the product information is returned as a recommended model. In this way, a carbon footprint accounting model with a higher relevance and better adaptation to the newly added product can be automatically queried, and the corresponding accounting boundary can be determined, so as to provide more accurate carbon emission data for the product production cycle of an enterprise, and then display these data on a screen for the user to view, providing a scientific basis for formulating 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:

[0007] 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;

[0008] A carbon footprint accounting model and accounting boundary determination module, which is used to build a 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;

[0009] A data filling module, which is used to input the full life cycle data of the product into the recommended carbon footprint accounting model based on the accounting boundary;

[0010] An accounting result determination module, 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.

[0011] According to another aspect of the present application, a method for visualizing product carbon footprint information is provided, which includes:

[0012] Obtain the basic information of the new product input by the user, where the basic information includes product strength, product name, quality, specifications, and manufacturer;

[0013] Build a 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;

[0014] Input the full life cycle data of the product into the recommended carbon footprint accounting model based on the accounting boundary;

[0015] 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.

[0016] Compared with the prior art, the product carbon footprint information visualization system and method provided by the present application first collect the basic information of the new product input by the user, extract the 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 technologies in the backend to perform semantic analysis and feature query response, so as to return a carbon footprint accounting model that matches the product information as a recommended model. In this way, a carbon footprint accounting model with a higher relevance and better adaptability to the new product can be automatically queried, and the corresponding accounting boundary can be determined, so as to provide more accurate carbon emission data for the product production cycle for enterprises, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures. Description of the Drawings

[0017] The above and other objects, features, and advantages of the present application will become more apparent by describing the embodiments of the present application in more detail with reference to the accompanying drawings. The 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 to the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 It is a system block diagram of a product carbon footprint information visualization system according to an embodiment of the present application.

[0019] Figure 2 It is a block diagram of a carbon footprint accounting model and accounting boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application.

[0020] Figure 3 It is a schematic diagram of data flow of a carbon footprint accounting model and accounting boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application.

[0021] Figure 4 It is a block diagram of a significant guidance query response unit between semantic features in a product carbon footprint information visualization system according to an embodiment of the present application.

[0022] Figure 5 It is a block diagram of a semantic encoding feature aggregation subunit of a carbon footprint accounting model text description in a product carbon footprint information visualization system according to an embodiment of the present application.

[0023] 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.

[0024] Figure 7 It is a flowchart of a product carbon footprint information visualization method according to an embodiment of the present application. Detailed implementation manners

[0025] Next, exemplary embodiments according to the present application will be described in detail with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein.

[0026] Carbon footprint accounting is the basis for evaluating the environmental impact of products. Through a scientific accounting model, the carbon emissions of products throughout their life cycle can be quantified, helping enterprises identify emission reduction potential, optimize production processes, and reduce costs. At the same time, accurate carbon footprint data is also an important basis for enterprises to fulfill their social responsibilities, meet regulatory requirements, and enhance brand image. In addition, the importance of visualization technology in information dissemination is self-evident. By displaying the carbon footprint accounting results on the screen, users can view them in a visual way to help them understand the carbon emissions of products throughout their life cycle, thus providing a scientific basis for formulating emission reduction measures.

[0027] To address the above technical problems, the present application proposes a product carbon footprint information visualization system. Figure 1 FIG. is a system block diagram of the product carbon footprint information visualization system according to an embodiment of the present application. As Figure 1 shown, the product carbon footprint information visualization system 100 according to an embodiment of the present application includes: a new product information entry module 110 for obtaining the basic information of a new product input by a user, where the basic information includes product type, product name, quality, specifications, and manufacturer; a carbon footprint accounting model and accounting boundary determination module 120 for modeling a carbon footprint accounting model based on the basic information of the new product to obtain a recommended carbon footprint accounting model and determining the accounting boundary; a data filling module 130 for inputting the full life cycle data of the product into the recommended carbon footprint accounting model based on the accounting boundary; and an accounting result determination module 140 for 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.

[0028] In the above product carbon footprint information visualization system 100, the new product information entry module 110 is used to obtain the basic information of the newly added product input by the user. The basic information includes product flammability, 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 carbon footprint calculation. For example, product flammability (such as flammable, corrosive) may affect its handling during production and transportation, while the product name and specifications help to clarify specific calculation standards and data sources. Quality information can be used to evaluate the material utilization efficiency and life cycle assessment, and then accurately predict carbon emissions at different stages. In addition, the information of the manufacturer can help to 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. For example, a form can be added to the enterprise's carbon footprint visualization system, requiring the user to fill in the required information. The form can use various forms such as drop-down menus, radio buttons, and text boxes, enabling the user to input relevant data conveniently and quickly. In addition, the system can also provide guiding texts and examples to help the user understand the importance of each piece of information and the filling method, so as to ensure the quality and consistency of the user input and further improve the reliability of the data.

[0029] In the above product carbon footprint information visualization system 100, the carbon footprint accounting model and accounting boundary determination module 120 is used to build 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. It should be understood that the production process, material use, and life cycle stage of each product are unique, providing key background data, enabling the model to accurately reflect the carbon emission characteristics of a specific product. By analyzing the basic information of the newly added product, a more practical accounting model can be established. During the process of model building, the accounting boundary must be clarified, that is, which stages and activities should be included when calculating the carbon footprint. For example, the main emission sources of some products may be concentrated in the extraction 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 product, these key emission sources can be identified and defined, ensuring that important emission sources are not omitted or irrelevant emission sources are overcalculated, thus helping the enterprise to better understand the carbon footprint of its products.

[0030] In the above product carbon footprint information visualization system 100, the data reporting module 130 is used to input the life cycle data of the product into the recommended carbon footprint accounting model based on the accounting boundary. It should be understood that the life cycle refers to the entire process of a product from raw material acquisition, design, production, transportation, use to final scrapping and disposal. It covers the environmental impacts and resource consumption at each stage, including the extraction of raw materials, energy use in the manufacturing process, energy consumption during the use stage of the product, and the recycling or treatment methods after use. Through the analysis of the life cycle, enterprises and decision-makers can identify the environmental impacts of products 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, etc. By clarifying the accounting boundary, all greenhouse gas emission sources within the product life cycle can be comprehensively captured, thus avoiding missing key links. Inputting the life cycle data into the recommended carbon footprint accounting model not only helps to form a systematic carbon management framework, but also helps enterprises to formulate more targeted emission reduction strategies. Specifically, first, it is necessary to collect various data related to the product life cycle. These data include the raw material sources of the product, production processes, transportation methods, use patterns, and final disposal methods. To effectively integrate these data, enterprises can establish a data collection system and use sensors, Internet of Things technology, and big data analysis tools to monitor and record the data at each link. Next, based on the defined accounting boundary, the collected 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 reading by the model. Among them, the recommended carbon footprint accounting model may include requirements for different data types, such as quantity, type, intensity. By inputting the data item by item into the model, 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, through the analysis of the calculation results, enterprises can identify the main carbon emission sources and high-emission links, and then formulate corresponding emission reduction measures.

[0031] In the above product carbon footprint information visualization system 100, the accounting result determination module 140 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. It should be understood that the carbon footprint accounting result can be visually presented in the form of charts, numbers, or graphics, which not only makes the data more intuitive and understandable 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 better understand the environmental impact of the products they purchase and thus make more sustainable consumption choices.

[0032] It should be understood that in the above product carbon footprint information visualization system, the step of building a carbon footprint accounting model and determining the accounting boundary based on the basic information of the new product is crucial because each product has its unique characteristics and production processes, which means that the carbon footprint accounting models for different products will also vary. By establishing a carbon footprint accounting model that conforms to the new product based on the basic information of the new product, it can ensure that the model is more in line with the actual situation of the specific product, thereby improving the accuracy and reliability of the accounting result. 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 not be the same. For example, most of the carbon emissions of some products may occur in the raw material extraction stage, while for others, they may mainly occur during manufacturing or transportation. Therefore, determining an appropriate accounting boundary is crucial for avoiding missing important emission sources or overcounting insignificant emission sources. This can help enterprises or other organizations better understand and manage the carbon emissions of their products throughout the life cycle.

[0033] Specifically, when building a carbon footprint accounting model based on the basic information of the new product, the technical concept of this application is to collect the basic information of the new product input by the user (including product type, product name, quality, specifications, and manufacturer), extract the 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 technologies in the backend to perform semantic analysis on the basic information of the new product and the text descriptions of these carbon footprint accounting models. Thus, using the text semantics of these carbon footprint accounting models as a prompt template to perform feature query response with the semantics of the basic information of the new product, so as to return a carbon footprint accounting model that matches the product information as a recommended model. In this way, a carbon footprint accounting model with a higher relevance and better adaptation to the new product can be automatically queried, and the corresponding accounting boundary can be determined, so as to provide more accurate product life cycle carbon emission data for enterprises, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0034] Figure 2 It is a block diagram of a carbon footprint accounting model and accounting boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application. Figure 3 It is a schematic diagram of data flow of a carbon footprint accounting model and accounting boundary determination module in a product carbon footprint information visualization system according to an embodiment of the present application. As Figure 2 and Figure 3 shown, the carbon footprint accounting model and accounting boundary determination module 120 includes: a carbon footprint accounting model set extraction unit 121, configured to extract a set of carbon footprint accounting models from a database; a carbon footprint accounting model text description unit 122, configured to extract text descriptions 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, configured to perform semantic encoding on 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 to obtain a new product basic information semantic encoding feature vector and a set of carbon footprint accounting model text description semantic encoding feature vectors; a significant guidance query response unit 124 between semantic features, configured to perform a significant guidance query response between semantic features on the new product basic information semantic encoding feature vector and the set of carbon footprint accounting model text description semantic encoding feature vectors to obtain a product-carbon footprint accounting model query response optimized encoding vector; a carbon footprint accounting model analysis unit 125, configured to generate the recommended carbon footprint accounting model based on the product-carbon footprint accounting model query response optimized encoding vector and determine the accounting boundary.

[0035] 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 descriptions of each carbon footprint accounting model in the set of carbon footprint accounting models are extracted to obtain a set of carbon footprint accounting model text descriptions. It should be understood that the database stores multiple verified carbon footprint accounting models, each model being for different product types, industries or life cycle stages, capable of meeting diverse calculation requirements. By extracting these models, the most suitable accounting method can be selected for a specific product, thus ensuring the accuracy and relevance of the accounting results. Further, by extracting the text descriptions of each carbon footprint accounting model in the set of carbon footprint accounting models, users can deeply understand the specific content of each model, including its scope of application, calculation method and key assumptions. This understanding helps enterprises select the most suitable model when conducting carbon emissions calculations, ensuring the accuracy and reliability of the accounting. Specifically, extracting the set of carbon footprint accounting models from the database needs to be completed by writing SQL query statements. This process includes accessing the database, executing queries to obtain relevant data of the carbon footprint accounting models, including information such as model ID, name, scope of application, etc. After extraction, the system can integrate these models into a set for subsequent processing. Next, extracting the text description of each model involves filtering out relevant text fields from the model set, usually including the definition of the model, applicable conditions, calculation method and key assumptions, etc. By writing corresponding queries and scripts, these text information can be obtained and summarized into a new set of text descriptions. The finally obtained set of carbon footprint accounting model text descriptions can be used as an important reference basis for enterprises to formulate carbon management strategies. It not only provides rich background information, but also helps enterprises quickly understand the applicable scenarios and calculation processes of various accounting models, and then select the best model to meet their specific carbon emissions calculation requirements.

[0036] Specifically, in the carbon footprint accounting model text description semantic encoding unit 123, semantic encoding is respectively performed on 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 to obtain a semantic encoding feature vector of the new product basic information and a set of semantic encoding feature vectors of the carbon footprint accounting model text descriptions.

[0037] Furthermore, in the process of semantic encoding of the basic information of the new product to obtain the semantic encoding feature vector of the new product basic information, it should be understood that the basic information of the product usually includes product name, strength, quality, specifications, manufacturer, etc. These information may have different meanings and importance in different contexts. Through semantic encoding, these information can be mapped to a high-dimensional space, so that the semantic relationship is retained in the feature vector, thus providing a basis for subsequent analysis and decision-making. Specifically, in the technical solution of this application, the Word Embedding technology is used to perform semantic encoding on the basic information of the new product. First, it is necessary to preprocess the basic information of the new product, including text cleaning and standardization, to remove irrelevant information or noise. Then, a pre-trained language model, such as Word2Vec, GloVe or BERT, is used to encode the product information, so as to represent each attribute of the product as a vector. 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; while attributes such as strength, quality and specifications can reflect their features in the entire feature space through corresponding encoding techniques. Finally, the vectors of each attribute are concatenated or weighted and summed to form a feature vector containing all the 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 can reflect the relationship between the features, making the entire feature vector richer and more meaningful.

[0038] Furthermore, in the set of semantic encoding feature vectors of carbon footprint accounting model text descriptions obtained by semantic encoding each carbon footprint accounting model text description in the set of carbon footprint accounting model text descriptions, 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, this text information can be transformed into high-dimensional vectors, which can not only preserve the semantics and context relationships of the text but also provide a basis for subsequent data mining and machine learning tasks. In the technical solution of this application, the Transformer model is used to perform semantic encoding on each carbon footprint accounting model text description in the set of carbon footprint accounting model text descriptions. Among them, the pre-trained model based on BERT (Bidirectional Encoder Representations from Transformers) in the Transformer model is used to capture the context information of each carbon footprint accounting model text description in the set of carbon footprint accounting model text descriptions to generate more accurate feature vectors. First, the text description of the carbon footprint accounting model needs to be preprocessed, including removing redundant punctuation marks, stop words, and converting to lowercase. Then, each text description is input into the selected Transformer model to generate a high-dimensional feature vector for the text, that is, the semantic encoding feature vector of the carbon footprint accounting model text description, to capture more complex context relationships through a multi-layer neural network. Next, by storing each semantic encoding feature vector of the carbon footprint accounting model text description in a unified data structure, the generated feature vectors are integrated into a set, that is, the set of semantic encoding feature vectors of the carbon footprint accounting model text description, for subsequent analysis and comparison. Finally, based on the obtained set of semantic encoding feature vectors of the carbon footprint accounting model text description, the understanding of different accounting models is deepened, and it can also promote the scientific and systematic carbon management, providing strong support for achieving the sustainable development goal.

[0039] Figure 4 Block diagram of the significant guidance query response unit between semantic features in the product carbon footprint information visualization system according to an embodiment of this application. As Figure 4As shown, the significant guidance query response unit 124 between semantic features includes: a carbon footprint accounting model text description semantic encoding feature aggregation subunit 1241, configured to perform feature aggregation processing based on linear embedding encoding on a set of carbon footprint accounting model text description semantic encoding feature vectors to obtain a prompt template; a cross-domain optimization query encoding subunit 1242, configured to perform cross-domain optimization query encoding on the new product basic information semantic encoding feature vector and the set of carbon footprint accounting model text description semantic encoding feature vectors based on the prompt template to obtain the product-carbon footprint accounting model query response optimized encoding vector.

[0040] Specifically, in the significant guidance query response unit 124 between semantic features, a significant guidance query response is performed between the new product basic information semantic encoding feature vector and the set of carbon footprint accounting model text description semantic encoding feature vectors to obtain the product-carbon footprint accounting model query response optimized encoding vector. It should be understood that the new product basic information semantic encoding feature vector and the set of carbon footprint accounting model text description semantic encoding feature vectors are respectively semantic encoding features extracted from the basic information of the new product and the text description of the carbon footprint accounting model. This enables the system to compare and match the similarities between the two at the semantic level, rather than simply based on keyword or surface form matching. Through semantic encoding and semantic feature query methods, the system can better understand the internal relationship between product characteristics and model descriptions, so as to find the most suitable accounting model. Based on this, in the technical solution of this application, the new product basic information semantic encoding feature vector and the set of carbon footprint accounting model text description semantic encoding feature vectors are further subjected to semantic feature query response optimization encoding to obtain the product-carbon footprint accounting model query response optimized encoding vector. By performing semantic feature query response optimization encoding on the new product basic information semantic encoding feature vector and the set of carbon footprint accounting model text description semantic encoding feature vectors, 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 for manually selecting a suitable model, and because it is based on semantic feature matching, the recommended results are usually more accurate.

[0041] Figure 5 It is a block diagram of the carbon footprint accounting model text description semantic encoding feature aggregation subunit in the product carbon footprint information visualization system according to an embodiment of the present application. As Figure 5As shown, the semantic encoding feature aggregation subunit 1241 of the carbon footprint accounting model text description includes: a semantic linear embedding encoding secondary subunit 1241-1 of the carbon footprint accounting model text description, which is used to perform linear embedding encoding on each carbon footprint accounting model text description semantic encoding feature vector in the set of carbon footprint accounting model text description semantic encoding feature vectors using a key embedding matrix to obtain a set of linearly transformed carbon footprint accounting model text description semantic encoding feature vectors; a text description semantic key matrix arrangement secondary subunit 1241-2, which is used to arrange the set of linearly transformed carbon footprint accounting model text description semantic encoding feature vectors into a matrix 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 linearly transformed carbon footprint accounting model text description semantic encoding feature vector 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 hint template.

[0042] Specifically, in the semantic linear embedding encoding secondary subunit 1241-1 of the carbon footprint accounting model text description, linear embedding encoding is performed on each carbon footprint accounting model text description semantic encoding feature vector in the set of carbon footprint accounting model text description semantic encoding feature vectors using a key embedding matrix to obtain a set of linearly transformed carbon footprint accounting model text description semantic encoding feature vectors. It should be understood that a linear transformation is performed on each element in the set of carbon footprint accounting model text description semantic encoding feature vectors using a key embedding matrix. This process maps the carbon footprint accounting model text semantic features in the original feature space to a new latent space, aiming to capture the relationships between different carbon footprint accounting model text semantics and be able to better represent the complex patterns in these text semantic data. After such linear embedding encoding, the set of linearly transformed carbon footprint accounting model text description semantic encoding feature vectors becomes more suitable for participating in subsequent attention mechanism calculations, improving the model's expression ability and enabling the model to more effectively identify the key feature representation information and feature association information related to subsequent matching tasks.

[0043] Specifically, the semantic linear embedding encoding secondary subunit 1241-1 of the carbon footprint accounting model text description includes: multiplying the carbon footprint accounting model text description semantic encoding feature vector by the key embedding matrix and then performing element-wise addition with the key embedding bias vector to obtain a linearly transformed carbon footprint accounting model text description semantic encoding feature vector.

[0044] Specifically, in the second-level subunit 1241-2 of the text description semantic key matrix arrangement, the set of text description semantic coding feature vectors of the carbon footprint accounting model after the linear transformation is arranged in a matrix to obtain the text description semantic key matrix of the carbon footprint accounting model. It should be understood that arranging these text description semantic coding feature vectors of the carbon footprint accounting model after the linear transformation in order constitutes a new data structure called the key matrix, which is convenient for parallel processing, thereby improving the calculation efficiency.

[0045] Specifically, in the second-level subunit 1241-3 of the text description semantic key matrix maximum value extraction, the maximum value of each text description semantic coding feature vector of the carbon footprint accounting model after the linear transformation in the text description semantic key matrix of the carbon footprint accounting model is extracted to obtain the significant feature vector of the text description semantic key matrix of the carbon footprint accounting model as the hint template. It should be understood that the maximum value of each column (i.e., each text description semantic coding feature vector of the carbon footprint accounting model after the linear transformation) is selected from the text description semantic key matrix of the carbon footprint accounting model to obtain the significant feature vector of the text description semantic key matrix of the carbon footprint accounting model. Furthermore, using the significant feature vector of the text description semantic key matrix of the carbon footprint accounting model as the hint template 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 text description semantic key matrix of the carbon footprint accounting model as an additional guiding signal helps reduce noise interference, strengthens the model's learning of important patterns and key text semantics of the carbon footprint accounting model, and also helps the attention mechanism to focus more on the key parts of the data, thereby improving the model's attention to the 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.

[0046] Figure 6 Block diagram of the cross-domain optimization query encoding subunit in the product carbon footprint information visualization system according to an embodiment of the present application. As Figure 6As shown, the cross-domain optimized query encoding subunit 1242 includes: a new product basic information semantic linear embedding encoding secondary subunit 1242-1, which is used to perform linear embedding encoding on the new product basic information semantic encoding feature vector using a query embedding matrix and a value embedding matrix to obtain a new product basic information semantic query vector and a new product basic information semantic value vector; a template hint optimized heterogeneous transformation encoding secondary subunit 1242-2, which is used to input the new product basic information semantic query vector, the new product basic information semantic value vector, each linearly transformed carbon footprint accounting model text description semantic encoding feature vector in the carbon footprint accounting model text description semantic key matrix, and the hint template into a heterogeneous transformer structure optimized based on the template hint to obtain a sequence of product-carbon footprint accounting model semantic cross-domain optimized query encoding vectors; a position-wise mean calculation secondary subunit 1242-3, which is used to calculate the position-wise mean vector of the sequence of 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.

[0047] Specifically, in the new product basic information semantic linear embedding encoding secondary subunit 1242-1, a query embedding matrix and a value embedding matrix are used to perform linear embedding encoding on the new product basic information semantic encoding feature vector to obtain a new product basic information semantic query vector and a new product basic information semantic value vector. It should be understood that the query embedding matrix and the value embedding matrix are respectively applied to the new product basic information semantic encoding 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 new product basic information semantics need to be concerned, while the value vector carries the content of the actual new product basic information semantic data. The combined use of the two can accurately select relevant information while maintaining the context, enhancing the generalization ability and accuracy of the model.

[0048] Specifically, in the template prompt optimization heterogeneous transformation encoding secondary subunit 1242-2, the newly added product basic information semantic query vector, the newly added product basic information semantic value vector, each linearly transformed carbon footprint accounting model text description semantic encoding feature vector in the carbon footprint accounting model text description semantic key matrix, and the prompt template are input into a heterogeneous transformer structure optimized based on the template prompt to obtain a sequence of product-carbon footprint accounting model semantic cross-domain optimized 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, each linearly transformed carbon footprint accounting model text description semantic encoding feature vector in the carbon footprint accounting model text description semantic key matrix, and the prompt template are jointly fed into a specially designed heterogeneous transformer structure optimized based on the template prompt. Such a structure usually includes heterogeneous multi-head attention mechanism components, which can automatically adjust the weight allocation strategy according to the provided prompt template to effectively optimize 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 currently input product basic information semantics 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.

[0049] Specifically, the template prompt optimization heterogeneous transformation encoding secondary subunit 1242-2 includes: after calculating the multiplication between the newly added product basic information semantic query vector and the transposed vector of the linearly transformed carbon footprint accounting model text description semantic encoding feature vector, dividing the obtained product-carbon footprint accounting model semantic association feature matrix by the two-norm of the significant feature vector of the carbon footprint accounting model text description semantic key matrix by position to obtain a product-carbon footprint accounting model semantic association representation matrix; inputting the product-carbon footprint accounting model semantic association representation matrix into a function for processing to obtain a product-carbon footprint accounting model semantic association weight matrix; multiplying the product-carbon footprint accounting model semantic association weight matrix by the significant feature vector of the carbon footprint accounting model text description semantic key matrix, and then performing a position-wise dot product of the obtained feature vector with the newly added product basic information semantic value vector to obtain a product-carbon footprint accounting model semantic cross-domain optimized query encoding vector.

[0050] Specifically, in the position-wise mean calculation secondary subunit 1242-3, the position-wise mean vector of the sequence of the product-carbon footprint accounting model semantic cross-domain optimization query encoding vectors is calculated to obtain the product-carbon footprint accounting model query response optimization encoding vector. It should be understood that the average value of the sequence of the product-carbon footprint accounting model semantic cross-domain optimization query encoding vectors is obtained 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, facilitating 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 the carbon footprint accounting model 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, thus helping enterprises and organizations to more effectively manage and reduce the carbon footprint of their products.

[0051] Specifically, semantic feature query response optimization encoding is performed on the set of the new product basic information semantic encoding feature vectors and the carbon footprint accounting model text description semantic encoding feature vectors according to the following feature response interaction formula to obtain the product-carbon footprint accounting model query response optimization encoding vector;

[0052] Among them, the feature response interaction formula is: ; where is the set of the carbon footprint accounting model text description semantic encoding feature vectors, are respectively the 1st, 2nd, and th carbon footprint accounting model text description semantic encoding feature vectors in the set of the carbon footprint accounting model text description semantic encoding feature vectors, is the th carbon footprint accounting model text description semantic encoding feature vector in the set of the carbon footprint accounting model text description semantic encoding feature vectors, and are respectively the key embedding matrix and the key embedding bias vector, is the th linearly transformed carbon footprint accounting model text description semantic encoding feature vector in the set of the linearly transformed carbon footprint accounting model text description semantic encoding feature vectors, is the carbon footprint accounting model text description semantic key matrix, is the maximum value extracted from the vector, is the carbon footprint accounting model text description semantic key matrix significant feature vector, is the new product basic information semantic encoding feature vector, and are respectively the query embedding matrix and the query bias vector, and are the value embedding matrix and the value bias vector respectively, is the semantic query vector of the basic information of the new product, is the semantic value vector of the basic information of the new product, is the two-norm of the vector, is a function, is matrix multiplication, is element-wise multiplication, is the -th product-carbon footprint accounting model semantic cross-domain optimization query coding vector in the sequence of product-carbon footprint accounting model semantic cross-domain optimization query coding vectors, is the number of vectors in the sequence of the product-carbon footprint accounting model semantic cross-domain optimization query coding vectors, is the product-carbon footprint accounting model query response optimization coding vector.

[0053] Specifically, the carbon footprint accounting model analysis unit 125 includes: inputting the optimized encoding vector of the product-carbon footprint accounting model query response into the intelligent recommendation module of the carbon footprint accounting model based on a classifier to obtain the recommended carbon footprint accounting model; determining the accounting boundary based on the recommended carbon footprint accounting model. That is to say, classification processing is performed by using the query response optimized encoding features between the semantic information of the new product and the text semantics of the carbon footprint accounting model, so as to return a carbon footprint accounting model that matches the product information as the recommended model. Furthermore, based on the recommended carbon footprint accounting model, the accounting boundary is determined. It is worth mentioning that in the technical solution of this application, the classifier is trained to learn how to determine the recommended carbon footprint accounting model according to input features (such as raw material type, production process, 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, through the cross-validation method, the performance of the model in different situations is evaluated to ensure its reliability. Next, the classifier that has been fully trained and successfully verified is applied to actual data analysis, and the recommended carbon footprint accounting model is identified based on the optimized encoding vector of the product-carbon footprint accounting model query response. This process not only improves the accounting efficiency but also reduces the errors that may be caused by human judgment. Finally, by combining the results of the classifier with the recommended carbon footprint accounting model, enterprises can clarify which activities are included in the accounting boundary and which can be ignored. This automated method based on the classifier enables enterprises to adjust the accounting boundary more flexibly and quickly to cope with the changes in different products and market demands. In this way, a carbon footprint accounting model with a higher relevance and better adaptation to the new product can be automatically queried, and the corresponding accounting boundary can be determined, so as to provide more accurate carbon emission data for the product life cycle of enterprises, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0054] Preferably, when the sets of the semantic encoding feature vectors of the new product basic information and the text description semantic encoding feature vectors of the carbon footprint accounting model respectively represent the semantic encoding features of the basic information of the new product and the set semantic encoding features of the text description of the carbon footprint accounting model, when performing query encoding optimization with the key matrix significant feature as the prompt template, the heterogeneous data encoding semantic distribution difference will lead to the difference in the template promptness based on the key matrix significant feature, resulting in the optimized encoding vector of the product-carbon footprint accounting model query response obtained by query encoding having a complex query aggregation space structure. Therefore, when inputting the optimized encoding vector of the product-carbon footprint accounting model query response into the intelligent recommendation module of the carbon footprint accounting model based on a classifier, it is expected to improve the classification regression convergence and generalization effect of the optimized encoding vector of the product-carbon footprint accounting model query response under the complex query aggregation space structure.

[0055] Therefore, the applicant of the present application considers optimizing the product - carbon footprint accounting model query response optimized coding vector when inputting it into the intelligent recommendation module of the carbon footprint accounting model based on a classifier. The optimization process includes: calculating the sum of the absolute values of each eigenvalue of the product - carbon footprint accounting model query response optimized coding vector to obtain the first product - carbon footprint accounting model query response optimized coding space structure value, and calculating the square root of the sum of the squares of each eigenvalue of the product - carbon footprint accounting model query response optimized coding vector to obtain the second product - carbon footprint accounting model query response optimized coding space structure value: ; where represents each eigenvalue of the product - carbon footprint accounting model query response optimized coding vector, , represents the summation function, represents the absolute value function, represents the set of real numbers, represents the first product - carbon footprint accounting model query response optimized coding space structure value, represents the second product - carbon footprint accounting model query response optimized coding space structure value, represents the set of real numbers, represents the length of the vector;

[0056] Multiply each eigenvalue of the optimized encoding vector of the product - carbon footprint accounting model query response by the first product - carbon footprint accounting model query response optimized encoding space structure value and the second product - carbon footprint accounting model query response optimized encoding space structure value respectively to obtain the first product - carbon footprint accounting model query response optimized encoding structure reference value and the second product - carbon footprint accounting model query response optimized encoding structure reference value corresponding to each eigenvalue; multiply each eigenvalue of the optimized encoding vector of the product - carbon footprint accounting model query response by the length of the optimized encoding vector of the product - carbon footprint accounting model query response and the square root of the length respectively to obtain the first product - carbon footprint accounting model query response optimized encoding scale transformation value and the second product - carbon footprint accounting model query response optimized encoding scale transformation value corresponding to each eigenvalue; divide the first product - carbon footprint accounting model query response optimized encoding structure reference value by the difference between the first product - carbon footprint accounting model query response optimized encoding space structure value and the first product - carbon footprint accounting model query response optimized encoding scale transformation value to obtain the first product - carbon footprint accounting model query response optimized encoding transformation adjustment value; divide the second product - carbon footprint accounting model query response optimized encoding structure reference value by the difference between the second product - carbon footprint accounting model query response optimized encoding space structure value and the second product - carbon footprint accounting model query response optimized encoding scale transformation value to obtain the second product - carbon footprint accounting model query response optimized encoding transformation adjustment value; calculate the weighted sum of the first product - carbon footprint accounting model query response optimized encoding transformation adjustment value and the second product - carbon footprint accounting model query response optimized encoding transformation adjustment value to obtain each eigenvalue of the optimized encoding vector of the product - carbon footprint accounting model query response.

[0057] Here, the optimized encoding vector of the product - carbon footprint accounting model query response is denoted as The optimization is expressed as: ;

[0058] In and cases, there is: ; where represents each eigenvalue of the optimized encoding vector of the product - carbon footprint accounting model query response, represents the first product - carbon footprint accounting model query response optimized encoding space structure value, represents the second product - carbon footprint accounting model query response optimized encoding space structure value, represents the first product - carbon footprint accounting model query response optimized encoding transformation adjustment value, represents the second product - carbon footprint accounting model query response optimized encoding transformation adjustment value, Denote the first product - carbon footprint accounting model query response optimized coding transformation adjustment feature vector obtained by vectorizing the arrangement of the multiple first product - carbon footprint accounting model query response optimized coding transformation adjustment values. Denote the second product - carbon footprint accounting model query response optimized coding transformation adjustment feature vector obtained by vectorizing the arrangement of the multiple second product - carbon footprint accounting model query response optimized coding transformation adjustment values. Denote the optimized product - carbon footprint accounting model query response optimized coding vector. Denote addition by position. Denote the coding transformation adjustment weighted value. Denote multiplication by position. Denote the set of real numbers. Denote the length of the vector.

[0059] That is, for 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, through taking the class - norm space structuring representation of the product - carbon footprint accounting model query response optimized coding vector as a reference window to perform scale - based box transformation on each eigenvalue of the product - carbon footprint accounting model query response optimized coding vector, and realizing the box attention weight adjustment of each eigenvalue of the product - carbon footprint accounting model query response optimized coding vector based on the spatial structure, to ensure the spatial transformation invariance of the product - carbon footprint accounting model query response optimized coding vector under the feature space interaction, thereby enhancing the convergence and generalization effects of the classification and regression of the feature set of the product - carbon footprint accounting model query response optimized coding vector under the complex spatial structure representation, and enhancing the accuracy of the recommended carbon footprint accounting model obtained by inputting it into the carbon footprint accounting model intelligent recommendation module based on the classifier. In this way, it can automatically query a carbon footprint accounting model with a higher relevance and better adaptation to the new product, and determine the corresponding accounting boundary, so as to provide more accurate product life - cycle carbon emission data for the enterprise, and then display these data on the screen for users to view, providing a scientific basis for formulating emission reduction measures.

[0060] In summary, the product carbon footprint information visualization system 100 according to the embodiments of the present application is elucidated. It collects the basic information of the newly added 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 technologies 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 the recommended model. In this way, it is possible to automatically query a more relevant and suitable carbon footprint accounting model for the newly added product, determine the corresponding accounting boundary, so as to provide more accurate carbon emission data for the product life cycle for the enterprise, and then display these data on the screen for the user to view, providing a scientific basis for formulating emission reduction measures.

[0061] As described above, the product carbon footprint information visualization system 100 according to the embodiments 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 program 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.

[0062] Alternatively, in another example, the product carbon footprint information visualization system 100 and the terminal device can also be separate devices, and the product carbon footprint information visualization system 100 can be connected to the terminal device through a wired and / or wireless network and transmit interaction information in accordance with a predefined data format.

[0063] Figure 7 FIG. is a flowchart of the product carbon footprint information visualization method according to the embodiments of the present application. As Figure 7 shown, the product carbon footprint information visualization method according to the embodiments of the present application includes: S110, obtaining the basic information of the newly added product input by the user, where the basic information includes product category, product name, quality, specification, and manufacturer; S120, performing carbon footprint accounting model modeling 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 full life cycle data of the product 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.

[0064] Here, those skilled in the art can understand that the specific operations of each step in the above product carbon footprint information visualization method have been described above with reference to Figures 1 to 6The description of the product carbon footprint information visualization system has been introduced in detail, and therefore, its repeated description will be omitted.

[0065] In summary, the product carbon footprint information visualization method based on the embodiments of the present application is elucidated. It collects the basic information of the newly added 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 technologies in the backend to perform semantic analysis and feature query response, so as to return the carbon footprint accounting model that matches the product information as the recommended model. In this way, it can automatically query a more relevant and suitable carbon footprint accounting model for the newly added product, determine the corresponding accounting boundary, so as to provide more accurate product life cycle carbon emission data for the enterprise, and then display these data on the screen for the user 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; A calculation result determination module, used to generate a carbon footprint calculation result based on the recommended carbon footprint calculation model, and transmit the carbon footprint calculation result to a screen for display; The carbon footprint accounting model and the 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.

2. The product carbon footprint information visualization system according to claim 1, 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.

3. The product carbon footprint information visualization system according to claim 2, 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.

4. The product carbon footprint information visualization system according to claim 3, 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.

5. The product carbon footprint information visualization system according to claim 4, characterized in that: The cross-domain optimization query encoding subunit includes: A new product basic information semantic linear embedding coding secondary subunit is used to use a query embedding matrix and a value embedding matrix to perform linear embedding coding on the new product basic information semantic coding feature vector 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.

6. The product carbon footprint information visualization system according to claim 5, 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.

7. The product carbon footprint information visualization system according to claim 6, 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.

8. 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; Generate a carbon footprint calculation result based on the recommended carbon footprint calculation model, and transmit the carbon footprint calculation result to a screen for display; Among them, 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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