Product life cycle carbon footprint data collection and analysis system and method

Abstract

The present application provides a system and method for collecting and analyzing carbon footprint data throughout the entire life cycle of a product, which relates to the field of data collection and analysis. It introduces artificial intelligence data processing technology to perform data analysis and semantic understanding on product data throughout the entire life cycle, and calculates the initial total carbon emissions of the product at each stage. Then, based on the semantic information of each stage and the connection between them, a carbon emission correction factor is obtained to make corresponding corrections to the initial total carbon emissions, thereby more accurately reflecting the differences in each processing process, better adapting to the dynamic changes of different product processes and external conditions, and obtaining a total carbon emission value of the product throughout its life cycle that is closer to the actual value. In this way, the real environmental impact of the product can be reflected more accurately, providing enterprises with more reliable product carbon footprint data support throughout its life cycle, and formulating effective emission reduction measures.

Description

Technical Field

[0001] The present application relates to the field of data collection and analysis, and more specifically, to a system and method for collecting and analyzing carbon footprint data of a product throughout its life cycle. Background Art

[0002] Against the backdrop of increasingly severe global climate change, assessing and managing the carbon footprint of products throughout their lifecycle has become increasingly important. Products generate carbon emissions at every stage of their lifecycle, from raw material acquisition, manufacturing, transportation, use, to final recycling or disposal. The accumulation of these emissions forms the product's carbon footprint, which is crucial for assessing its environmental performance. Assessing a product's carbon footprint throughout its lifecycle not only helps identify and reduce carbon emissions but also enhances a company's image as a sustainable company and meets consumer expectations for environmental responsibility.

[0003] However, traditional product carbon footprint data collection and calculation methods usually obtain the total carbon emissions value by directly adding up the carbon emissions of each stage of the product. This carbon emissions accumulation method is usually based on static data, does not take into account the temporal characteristics of the product throughout its entire life cycle, and ignores the interactions and influences between different stages. For example, waste generated during the raw material mining process may affect carbon emissions during the transportation stage, but traditional calculation methods may not fully consider these interdependent relationships. In addition, the production batches, usage conditions, recycling methods, etc. of each product may be different, which will result in different carbon emissions for each treatment even in the same type of production process. Therefore, a simple accumulation method is difficult to adapt to this variability.

[0004] Therefore, an optimized solution for collecting and analyzing carbon footprint data of products throughout their life cycle 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 full life cycle carbon footprint data collection and analysis system and method, which introduces more sophisticated artificial intelligence data processing technology to perform data analysis and semantic understanding on the product full life cycle data, and calculates the initial total carbon emissions of the product at each stage. Then, based on the semantic information of each stage and the connection between each other, the carbon emission correction factor is obtained to make corresponding corrections to the initial total carbon emission value, so as to more accurately reflect the differences in each processing process, better adapt to the dynamic changes of different product processes and external conditions, and obtain a total carbon emission value of the product full life cycle that is closer to the actual value. In this way, the real environmental impact of the product can be reflected more accurately, and more reliable product full life cycle carbon footprint data support can be provided to enterprises to formulate effective emission reduction measures.

[0006] According to one aspect of the present application, a method for collecting and analyzing carbon footprint data of a product throughout its life cycle is provided, comprising:

[0007] Acquire product lifecycle data, including data from the raw material acquisition phase, manufacturing phase, transportation phase, usage phase, and recycling and settlement phase;

[0008] Calculating an initial total carbon emission value based on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data, and the recycling and settlement stage data;

[0009] After semantic encoding of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data and the recycling and settlement phase data, the semantic encoding features of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data and the recycling and settlement phase data are subjected to time series information propagation processing based on the decay of the principal component of the node feature to obtain the principal component time series propagation aggregation feature of the product life cycle data, including: principal component extraction of the semantic encoding features of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data and the recycling and settlement phase data to obtain a sequence of principal component representations of the semantic encoding features of the current phase data and principal component representations of the semantic encoding features of the historical phase data; time series decay dynamic aggregation of the sequences of principal component representations of the semantic encoding features of the current phase data and principal component representations of the semantic encoding features of the historical phase data to obtain the principal component time series propagation aggregation feature of the product life cycle data;

[0010] Based on the principal component time series propagation aggregation characteristics of the product's full life cycle data, a carbon emission correction factor is generated, and the total value of the product's full life cycle carbon emissions is calculated.

[0011] According to another aspect of the present application, a system for collecting and analyzing carbon footprint data of a product throughout its life cycle is provided, comprising:

[0012] A product life cycle data acquisition module is used to acquire product life cycle data, including raw material acquisition stage data, manufacturing stage data, transportation stage data, usage stage data, and recycling and settlement stage data;

[0013] a carbon emission initial total value calculation module, configured to calculate the carbon emission initial total value based on the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data, and the recycling and settlement phase data;

[0014] A node feature principal component decay time series information propagation processing module is used to perform semantic encoding on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data, and then perform time series information propagation processing based on node feature principal component decay on the semantic encoding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain the principal component time series propagation aggregation features of the product life cycle data, including: principal component extraction on the semantic encoding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain a sequence of principal component representations of the semantic encoding features of the current stage data and principal component representations of the semantic encoding features of the historical stage data; and time series decay dynamic aggregation on the sequence of principal component representations of the semantic encoding features of the current stage data and principal component representations of the semantic encoding features of the historical stage data to obtain the principal component time series propagation aggregation features of the product life cycle data;

[0015] The module for calculating the total carbon emissions of a product over its entire life cycle is used to generate a carbon emission correction factor based on the principal component time series propagation aggregation characteristics of the product's entire life cycle data, and calculate the total carbon emissions of the product over its entire life cycle.

[0016] Compared with the existing technology, the present application provides a product life cycle carbon footprint data collection and analysis system and method, which introduces more sophisticated artificial intelligence data processing technology to perform data analysis and semantic understanding of product life cycle data, and calculates the initial total carbon emissions of each stage of the product. Then, based on the semantic information of each stage and the connection between them, the carbon emission correction factor is obtained to make corresponding corrections to the initial total carbon emission value, thereby more accurately reflecting the differences in each processing process, better adapting to the dynamic changes of different product processes and external conditions, and obtaining a total carbon emission value of the product life cycle that is closer to the actual value. In this way, the real environmental impact of the product can be reflected more accurately, providing enterprises with more reliable product life cycle carbon footprint data support, and formulating effective emission reduction measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended 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 drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 Flowchart of a method for collecting and analyzing carbon footprint data of a product throughout its life cycle according to an embodiment of the present application.

[0019] Figure 2 In the product life cycle carbon footprint data collection and analysis method according to an embodiment of the present application, after semantic encoding of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data, the semantic encoding features of the raw material acquisition stage data, the semantic encoding features of the manufacturing stage data, the semantic encoding features of the transportation stage data, the semantic encoding features of the usage stage data and the semantic encoding features of the recycling and settlement stage data are subjected to time series information propagation processing based on the attenuation of the principal component of the node features to obtain a flowchart of the time series propagation aggregation features of the principal component of the product life cycle data.

[0020] Figure 3 A flowchart of the method for collecting and analyzing carbon footprint data for the entire life cycle of a product according to an embodiment of the present application is provided for performing temporal information propagation processing based on the attenuation of principal components of node features on the semantic coding features of data in the raw material acquisition stage, the manufacturing stage, the transportation stage, the usage stage, and the recycling and settlement stage to obtain the principal component temporal propagation aggregation features of data for the entire life cycle of the product.

[0021] Figure 4 In the product life cycle carbon footprint data collection and analysis method according to an embodiment of the present application, the semantic coding features of the data in the raw material acquisition stage, the semantic coding features of the data in the manufacturing stage, the semantic coding features of the data in the transportation stage, the semantic coding features of the data in the use stage, and the semantic coding features of the data in the recycling and settlement stage are processed with time-series information propagation based on the attenuation of the principal components of node features to obtain a data flow diagram of the time-series propagation aggregation features of the principal components of the product life cycle data.

[0022] Figure 5 This is a system block diagram of a product full life cycle carbon footprint data collection and analysis system according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] 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 herein.

[0024] The life cycle carbon footprint refers to the total carbon emissions generated by a product throughout its entire life cycle, from raw material acquisition, manufacturing, transportation, use, to final recycling or disposal. This concept covers the environmental impact of every stage of a product and emphasizes that carbon emissions from all links must be considered when evaluating and managing product sustainability.

[0025] Traditional carbon footprint calculation methods typically use a direct accumulation of carbon emissions from each product phase to arrive at a total emissions value. This static data-based accumulation method fails to fully consider the temporal nature of a product's life cycle and overlooks the interactions between different phases.

[0026] In response to the above technical problems, this application proposes a method for collecting and analyzing carbon footprint data of a product throughout its life cycle. Figure 1 Flowchart of the method for collecting and analyzing carbon footprint data of a product throughout its life cycle according to an embodiment of the present application. Figure 1 As shown, the method for collecting and analyzing carbon footprint data of a product throughout its life cycle according to an embodiment of the present application includes: S110, acquiring product life cycle data, wherein the product life cycle data includes raw material acquisition stage data, manufacturing stage data, transportation stage data, usage stage data and recycling and settlement stage data; S120, calculating the initial total value of carbon emissions based on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data; S130, after semantically encoding the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data, the semantic encoding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data are subjected to time series information propagation processing based on the principal component attenuation of node features to obtain the principal component time series propagation aggregation features of the product life cycle data; S140, generating a carbon emission correction factor based on the principal component time series propagation aggregation features of the product life cycle data, and calculating the total value of carbon emissions of the product throughout its life cycle.

[0027] In the above-mentioned product lifecycle carbon footprint data collection and analysis method, step S110 acquires product lifecycle data, which includes data from the raw material acquisition phase, manufacturing phase, transportation phase, usage phase, and recycling and settlement phase. It should be understood that acquiring product lifecycle data enables a comprehensive assessment of the environmental impact of a product throughout its entire lifecycle, including raw material acquisition, manufacturing, transportation, usage, and recycling phases. This data not only helps companies identify and optimize resource use and emissions in their production processes, but also ensures regulatory compliance, meets consumer expectations for sustainability, and provides a scientific basis for strategic decision-making.

[0028] In the above-described product lifecycle carbon footprint data collection and analysis method, step S120 calculates an initial total carbon emissions value based on the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data, and the recycling and settlement phase data. It should be understood that by calculating and aggregating carbon emissions at each phase, a comprehensive understanding of carbon emissions at all important phases of the product lifecycle can be achieved. This allows calculation of an initial total carbon emissions value for each phase of the product, thereby providing a comprehensive environmental impact assessment for enterprises.

[0029] Specifically, in step S120, based on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the use stage data, and the recycling and settlement stage data, the initial total carbon emissions value is calculated, including: determining the carbon emissions value of the raw material acquisition stage based on the raw material acquisition stage data; determining the carbon emissions value of the manufacturing stage based on the manufacturing stage data; determining the carbon emissions value of the transportation stage based on the transportation stage data; determining the carbon emissions value of the use stage based on the use stage data; determining the carbon emissions value of the recycling and settlement stage based on the recycling and settlement stage data; and calculating the sum of the carbon emissions value of the raw material acquisition stage, the carbon emissions value of the manufacturing stage, the carbon emissions value of the transportation stage, the carbon emissions value of the use stage, and the carbon emissions value of the recycling and settlement stage as the initial total carbon emissions value. It is worth mentioning that in the raw material acquisition stage, the life cycle assessment (LCA) method needs to be used to collect the type, quantity, acquisition method and environmental impact of the raw materials used and their supply chain, and then determine the carbon emissions value of the raw material acquisition stage based on energy consumption and related carbon emission factors. Next, during the manufacturing phase, it's necessary to track information such as the types of energy used, production efficiency, waste emissions, and their treatment methods to determine the carbon emissions of the manufacturing phase. During the transportation phase, data such as transportation distance, transportation method, load, and fuel usage are collected to calculate the carbon emissions of the product during transportation. Furthermore, during the use phase, data such as user usage frequency, time, and conditions are collected to determine the carbon emissions of the use phase. Finally, during the recycling and settlement phase, it's necessary to record the product's recycling method, treatment process, and any possible reuse or resource recovery to determine the carbon emissions of the recycling phase.

[0030] In the above-mentioned product lifecycle carbon footprint data collection and analysis method, step S130, after semantically encoding the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data, and the recycling and settlement phase data, performs time-series information propagation processing based on the decay of node feature principal components on the semantically encoded features of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data, and the recycling and settlement phase data to obtain the principal component time-series propagation aggregation features of the product lifecycle data. It should be understood that the semantic encoding process involves labeling and classifying data, allowing data from different sources and types to be uniformly understood and processed. This process not only improves data readability but also promotes the correlation between data from different phases. After the data semantic encoding is completed, semantic encoding features can be extracted from the data at each phase. These features include key indicators and attributes of each phase. These extracted features not only capture the original data information but also imply the potential relationships and mutual influences between the data, forming node features for each phase. Next, the semantically encoded features of these stages are subjected to principal component attenuation based on node features to reduce the dimensionality and complexity of the data. Principal component analysis (PCA) is an effective data dimensionality reduction technique that retains the most important features while removing redundant and noisy data, making subsequent analysis more efficient. This process highlights the contribution of each stage to the full lifecycle data while eliminating unnecessary details, making the data more focused and intuitive. Temporal information propagation processing dynamically analyzes these reduced features to identify trends and patterns in the data over time. By constructing a time series model, companies can identify changes in the time series between different lifecycle stages and observe the causal relationships and impact between stages. This information propagation processing also captures the dynamic characteristics of data over time, enabling companies to better predict future trends. Finally, by aggregating the features of all stages, a principal component temporal propagation aggregate feature of the product's full lifecycle data is generated. This feature integrates key information from each stage, providing companies with a comprehensive perspective on the product's performance throughout its entire lifecycle.

[0031] Figure 2After semantic coding of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the use phase data and the recycling and settlement phase data in the product life cycle carbon footprint data collection and analysis method according to the embodiment of the present application, the semantic coding features of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the use phase data and the recycling and settlement phase data are processed based on the time series information propagation of the semantic coding features of the node feature principal component attenuation to obtain the flow chart of the time series propagation aggregation features of the principal component of the product life cycle data. Figure 2 As shown, the step S130 includes: S131, semantically encoding the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain the semantic coding features of the raw material acquisition stage data, the semantic coding features of the manufacturing stage data, the semantic coding features of the transportation stage data, the semantic coding features of the usage stage data and the semantic coding features of the recycling and settlement stage data; S132, performing time series information propagation processing based on the principal component attenuation of node features on the semantic coding features of the raw material acquisition stage data, the semantic coding features of the manufacturing stage data, the semantic coding features of the transportation stage data, the semantic coding features of the usage stage data and the semantic coding features of the recycling and settlement stage data to obtain the principal component time series propagation aggregation features of the product life cycle data.

[0032] Specifically, in step S131, the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data, and the recycling and settlement stage data are semantically encoded to obtain semantic encoding features of the raw material acquisition stage data, semantic encoding features of the manufacturing stage data, semantic encoding features of the transportation stage data, semantic encoding features of the usage stage data, and semantic encoding features of the recycling and settlement stage data. It should be understood that by semantically encoding the data at each stage in the product life cycle, it is possible to understand the semantic information from the data at different stages of the product and extract the important semantic features in these stages. This helps to identify and emphasize the important factors that affect the subsequent carbon footprint calculation, such as different production batches, different usage conditions, and different recycling methods that exist in each stage of the product's life cycle, and provides a basis for subsequent carbon emissions correction and calculation tasks.

[0033] More specifically, in an embodiment of the present application, the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data are semantically encoded, including: semantically encoding the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain a raw material acquisition stage data semantic encoding vector as the raw material acquisition stage data semantic encoding feature, a manufacturing stage data semantic encoding vector as the manufacturing stage data semantic encoding feature, a transportation stage data semantic encoding vector as the transportation stage data semantic encoding feature, a usage stage data semantic encoding vector as the usage stage data semantic encoding feature and a recycling and settlement stage data semantic encoding vector as the recycling and settlement stage data semantic encoding feature.

[0034] Specifically, in step S132, the semantic coding features of the data in the raw material acquisition stage, the semantic coding features of the data in the manufacturing stage, the semantic coding features of the data in the transportation stage, the semantic coding features of the data in the use stage, and the semantic coding features of the data in the recycling and settlement stage are subjected to time-series information propagation processing based on the decay of the principal component of the node features to obtain the time-series propagation aggregation features of the principal component of the product life cycle data. It should be understood that a product will undergo various changes at different stages of its life cycle (for example, from raw material acquisition to final recycling and processing). These changes have time-series characteristics and will affect the subsequent calculation of carbon emissions. Therefore, the time-series information propagation processing based on the decay of the principal component of the node features can more comprehensively and accurately understand the interaction and time-series correlation relationship between the data semantics of each stage in the product life cycle, thereby more accurately correcting carbon emissions. It is worth mentioning that the time-series information propagation processing method based on the decay of the principal component of the node features is to capture the trend of the temporal node features of the semantics of each stage over time by combining principal component analysis and gradient decay mechanism, and use these trends to effectively propagate and aggregate information. Specifically, the process of temporal information propagation processing based on the attenuation of node feature principal components is to perform principal component analysis on the node feature vectors at each time node (the semantic coding vector of the raw material acquisition stage data, the semantic coding vector of the manufacturing stage data, the semantic coding vector of the transportation stage data, the semantic coding vector of the usage stage data, and the semantic coding vector of the recycling and settlement stage data) to extract the most representative stage data semantic principal components, and then calculate the principal component change rate and its corresponding attenuation entropy factor between different stage time nodes. After further filtering important information by introducing time span modulation and gating function, a product full life cycle data principal component temporal propagation aggregation vector that can reflect the dynamic changes of the semantic node characteristics of each stage in the time series is finally generated as the product full life cycle data principal component temporal propagation aggregation feature.

[0035] Figure 3 A flowchart of the method for collecting and analyzing carbon footprint data for the entire life cycle of a product according to an embodiment of the present application is provided for performing temporal information propagation processing based on the attenuation of principal components of node features on the semantic coding features of data in the raw material acquisition stage, the manufacturing stage, the transportation stage, the usage stage, and the recycling and settlement stage to obtain the principal component temporal propagation aggregation features of data for the entire life cycle of the product. Figure 4 In the product life cycle carbon footprint data collection and analysis method according to the embodiment of the present application, the semantic coding features of the raw material acquisition stage data, the semantic coding features of the manufacturing stage data, the semantic coding features of the transportation stage data, the semantic coding features of the use stage data and the semantic coding features of the recycling and settlement stage data are processed based on the time series information propagation of the node feature principal component attenuation to obtain the data flow diagram of the principal component time series propagation aggregation feature of the product life cycle data. Figure 3 and Figure 4 As shown, the step S132 includes: S1321, performing principal component extraction on the semantic coding features of the raw material acquisition stage data, the semantic coding features of the manufacturing stage data, the semantic coding features of the transportation stage data, the semantic coding features of the use stage data and the semantic coding features of the recycling and settlement stage data to obtain a sequence of principal component representations of the current stage data semantic coding features and principal component representations of the historical stage data semantic coding features; S1322, performing time-series attenuation dynamic aggregation on the sequence of principal component representations of the current stage data semantic coding features and principal component representations of the historical stage data semantic coding features to obtain the principal component time-series propagation aggregation features of the product life cycle data.

[0036] Specifically, in step S1321, principal component extraction is performed on the semantic coding features of the raw material acquisition stage data, the semantic coding features of the manufacturing stage data, the semantic coding features of the transportation stage data, the semantic coding features of the use stage data, and the semantic coding features of the recycling and settlement stage data to obtain a sequence of principal component representations of the current stage data semantic coding features and principal component representations of the historical stage data semantic coding features. It should be understood that by using principal component analysis (PCA), the most representative features can be extracted from high-dimensional data and the influence of interference factors can be reduced. This helps to focus on the semantic key factors of each stage that truly affect carbon emissions, while reducing the complexity of the data processing process.

[0037] Specifically, in the step S1321, it includes: performing eigenvalue-based principal component extraction on the raw material acquisition stage data semantic coding vector, the manufacturing stage data semantic coding vector, the transportation stage data semantic coding vector and the usage stage data semantic coding vector to obtain the raw material acquisition stage data semantic coding principal component representation vector, the manufacturing stage data semantic coding principal component representation vector, the transportation stage data semantic coding principal component representation vector and the usage stage data semantic coding principal component representation vector as a sequence of the historical stage data semantic coding principal component representation, and performing eigenvalue-based principal component extraction on the recycling processing and settlement stage data semantic coding vector to obtain the recycling processing and settlement stage data semantic coding principal component representation vector as the current stage data semantic coding feature principal component representation.

[0038] Specifically, in the step S1322, the sequence of the principal component representation of the semantic coding feature of the current stage data and the principal component representation of the semantic coding feature of the historical stage data is dynamically aggregated with time series attenuation to obtain the principal component time series propagation aggregation feature of the product life cycle data, including: S1322-1, respectively calculating the absolute factors of the time series propagation attenuation entropy of the semantic coding principal component representation vector of the raw material acquisition stage data, the semantic coding principal component representation vector of the manufacturing stage data, the semantic coding principal component representation vector of the transportation stage data and the semantic coding principal component representation vector of the use stage data relative to the semantic coding principal component representation vector of the recycling and settlement stage data to obtain a sequence of absolute factors of the time series propagation attenuation entropy of the stage data; S1322-2, calculating the absolute factors of the time series propagation attenuation entropy of the stage data based on the time span between the semantic coding principal component representation vector of the raw material acquisition stage data, the semantic coding principal component representation vector of the manufacturing stage data, the semantic coding principal component representation vector of the transportation stage data and the semantic coding principal component representation vector of the use stage data and the semantic coding principal component representation vector of the recycling and settlement stage data. The absolute factors of the temporal propagation attenuation entropy of each stage data in the sequence of factors are modulated in the time dimension to obtain a sequence of the temporal span modulation propagation attenuation entropy factors of the stage data; S1322-3, the sequence of the temporal span modulation propagation attenuation entropy factors of the stage data is input into the information transmission screening module based on the gating function to obtain a sequence of the temporal span modulation propagation attenuation weights of the stage data; S1322-4, based on the sequence of the temporal span modulation propagation attenuation weights of the stage data, the weighted sum of the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data and the semantic encoding principal component representation vector of the use stage data is calculated to obtain the historical stage data semantic encoding principal component significant transmission aggregation representation vector; S1322-5, the positional sum of the historical stage data semantic encoding principal component significant transmission aggregation representation vector and the recycling and settlement stage data semantic encoding principal component representation vector is calculated to obtain the product life cycle data principal component temporal propagation aggregation vector as the product life cycle data principal component temporal propagation aggregation feature.

[0039] Specifically, in step S1322-1, the absolute factors of the time-series propagation decay entropy of the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data, and the semantic encoding principal component representation vector of the usage stage data are calculated relative to the semantic encoding principal component representation vector of the recycling, processing and settlement stage data, to obtain a sequence of absolute factors of the time-series propagation decay entropy of the stage data. It should be understood that in order to further evaluate the degree of change in the time node feature distribution between the semantics of different stages, that is, the semantic association and interaction between each stage within the product lifecycle, the absolute factors of the time-series propagation decay entropy of the semantic encoding principal component representation vector of each stage data relative to the semantic encoding principal component representation vector of the last stage data are calculated. This is a measure of the uncertainty of random variables, used to capture the trend of the semantic features of the product over time and the information loss at each stage.

[0040] Specifically, the step S1322-1 includes: calculating the positional division of the corresponding positions of the semantic coding principal component representation vector of the raw material acquisition stage data and the semantic coding principal component representation vector of the recycling and processing settlement stage data to obtain the stage data semantic coding feature principal component attenuation vector; calculating the logarithmic function value with base two of the absolute value of each eigenvalue of the stage data semantic coding feature principal component attenuation vector to obtain the stage data semantic coding feature principal component attenuation logarithmic vector; calculating the positional dot product of the semantic coding principal component representation vector of the raw material acquisition stage data and the stage data semantic coding feature principal component attenuation logarithmic vector, and then performing positional multiplication on each positional eigenvalue in the obtained dot product vector to obtain the stage data time series propagation attenuation value; calculating the exponential function of the stage data time series propagation attenuation value with the natural constant e as the base to obtain the absolute factor of the stage data time series propagation attenuation entropy.

[0041] Specifically, in step S1322-2, the absolute factors of the phase data temporal propagation attenuation entropy in the sequence of absolute factors of the phase data temporal propagation attenuation entropy are modulated in the time dimension based on the time spans between the semantically encoded principal component representation vectors of the raw material acquisition phase data, the semantically encoded principal component representation vectors of the manufacturing phase data, the semantically encoded principal component representation vectors of the transportation phase data, and the semantically encoded principal component representation vectors of the usage phase data, respectively, and the semantically encoded principal component representation vectors of the recycling processing and settlement phase data, to obtain a sequence of phase data temporal propagation attenuation entropy factors modulated by temporal span. It will be appreciated that to further enhance the model's ability to understand information transmission over long time intervals, the time span factor is subsequently introduced to adjust the aforementioned attenuation entropy factors, forming a sequence of phase data temporal span modulated propagation attenuation entropy factors. This adjustment enables a more comprehensive and rational handling of long-distance temporal dependencies between phases within a product's lifecycle, ensuring that even large differences between widely separated phase data semantic temporal nodes are considered to be within a normal range of variation.

[0042] Specifically, the step S1322-2 includes: subtracting the timestamp of the principal component representation vector of the semantic encoding of the recycling processing settlement stage data from the timestamp of the principal component representation vector of the semantic encoding of the raw material acquisition stage data, and rounding down to obtain the stage data time span value; calculating an exponential function with the natural constant e as the base and the stage data time span value as the exponent to obtain the stage data time span modulation value; dividing the absolute factor of the stage data timing propagation attenuation entropy corresponding to the principal component representation vector of the semantic encoding of the raw material acquisition stage data by the stage data time span modulation value to obtain the stage data timing span modulation propagation attenuation entropy factor.

[0043] Specifically, in step S1322-3, the sequence of phase data time series span modulation propagation attenuation entropy factors is input into a gating function-based information transfer and filtering module to obtain a sequence of phase data time series span modulation propagation attenuation weights. It should be understood that the information transfer and filtering module of the gating mechanism is similar to the design in RNN architectures such as LSTM, adaptively determining which phase data semantic time series nodes should be focused on and expressed based on context. In this way, important information can be filtered out and assigned appropriate weights, ensuring that the factors that have the greatest impact on carbon emissions receive due attention.

[0044] Specifically, the step S1322-3 includes: comparing each stage data timing span modulation propagation attenuation entropy factor in the sequence of the stage data timing span modulation propagation attenuation entropy factors with a predetermined threshold to obtain a sequence of the stage data timing span modulation propagation attenuation weights; wherein, in response to the stage data timing span modulation propagation attenuation entropy factor being greater than the predetermined threshold, the stage data timing span modulation propagation attenuation entropy factor greater than the predetermined threshold is input into a sigmoid function; in response to the stage data timing span modulation propagation attenuation entropy factor being less than or equal to the predetermined threshold, the stage data timing span modulation propagation attenuation entropy factor less than or equal to the predetermined threshold is set to zero.

[0045] Specifically, in step S1322-4, based on the sequence of the stage data time span modulation propagation attenuation weights, the weighted sum of the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data and the semantic encoding principal component representation vector of the use stage data is further calculated to obtain the aggregation characteristics of the semantic principal component significance information transmission of other stage data except the recycling, processing and settlement stage in the entire life cycle of the product.

[0046] Specifically, in step S1322-5, the position-wise sum of the semantically encoded principal component significant transfer aggregation representation vector of the historical stage data and the semantically encoded principal component representation vector of the recycling, processing, and settlement stage data is calculated to obtain the product lifecycle data principal component temporal propagation aggregation vector as the product lifecycle data principal component temporal propagation aggregation feature. It should be understood that the position-wise summation of the semantically encoded principal component significant transfer aggregation representation vector of the historical stage data and the semantically encoded principal component representation vector of the recycling, processing, and settlement stage data yields the final output, namely, the product lifecycle data principal component temporal propagation aggregation vector, as the product lifecycle data principal component temporal propagation aggregation feature. This feature vector integrates the key semantic information and temporal interaction relationships of the principal component data at each stage of the product lifecycle. It reflects the state transition of the product at each stage, facilitates a deep understanding of the temporal semantic features of each product stage, and thus can adapt to the dynamic changes of different product processes and external conditions. It provides a basis for a comprehensive assessment of the environmental impact of a product and a solid foundation for a comprehensive understanding of carbon emissions throughout its lifecycle.

[0047] Specifically, the semantic coding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the use stage data, and the recycling and settlement stage data are subjected to time series information propagation processing based on the decay of the principal component of node features using the following stage semantic time series information propagation formula to obtain the principal component time series propagation aggregation features of the product life cycle data;

[0048] Among them, the stage semantic temporal information propagation formula is:

[0049] X={x1,x2,x3,x4,x5}

[0050] v i =PCA(x i )

[0051]

[0052] V={v1,v2,v3,v4,v5}

[0053]

[0054]

[0055] Among them, x1, x2, x3, x4, and x5 are the semantic coding vectors of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the use stage data, and the recycling and settlement stage data, respectively. X is the sequence of the stage data semantic coding vectors, and x i is the i-th stage data semantic encoding vector in the sequence of the stage data semantic encoding vector, PCA(x i ) is x i Perform principal component extraction based on eigenvalues, v i is x i The corresponding stage data semantic encoding principal component representation vector, is x i The transposed vector of n is the number of eigenvalues ​​in the semantic encoding vector of the data in the stage, C i is x i The corresponding stage data covariance matrix, U i is x i The corresponding stage data principal component orthogonal matrix, For U i The transposed matrix, v i1 ,v i2 ,…,v im is the principal component vector of each stage data in the sequence of the principal component vector of the stage data, Λ i is xi The corresponding stage data diagonal matrix, diag(λ i1 ,λ i2 ,…,λ im ) is the matrix whose diagonal elements are λ i1 ,λ i2 ,…,λ im The diagonal matrix of stage data, λ i1 ,λ i2 ,…,λ im are the weight values ​​of the principal component vectors of the data at each stage, To return the k value corresponding to the maximum value, j is the maximum approximate matching value, V is the sequence of the principal component representation vectors of the semantic encoding of the stage data, v1, v2, v3, v4, v5 are the first, second, third, fourth and fifth principal component representation vectors of the semantic encoding of the stage data in the sequence respectively, v i The eigenvalues ​​at each position in , is the eigenvalue of each position in v5, L is v i The number of eigenvalues ​​in the , log represents the logarithmic function value with base 2, exp(·) represents the exponential function with the natural constant e as the base, d i v i The absolute factor of the temporal propagation attenuation entropy of the stage data between t5 and v5, t5 and t i Represent the timestamps of the principal component representation vectors of the 5th and ith phase data semantic encoding, respectively. For the floor operation, e (i→5) It is v i The stage data between v5 and v5 is modulated by the propagation attenuation entropy factor, mask(·) is the masking process, sigmoid(·) is the sigmoid function, θ is the predetermined threshold, and w i is the timing span modulation propagation attenuation weight of each stage data in the sequence of the stage data timing span modulation propagation attenuation weight, and s is the principal component propagation aggregation representation vector of the semantic timing of the device stage data.

[0056] In the above-mentioned method for collecting and analyzing product lifecycle carbon footprint data, step S140 generates a carbon emission correction factor based on the time-series propagation aggregation features of the principal components of the product lifecycle data, and calculates the total carbon emissions of the product lifecycle. It should be understood that the time-series propagation aggregation features of the principal components of the product lifecycle data provide key indicators and trends for carbon emissions at each stage. These features integrate data from each stage of raw material acquisition, manufacturing, transportation, use, and recycling, forming a comprehensive view that helps companies identify the emission characteristics and relative importance of each stage. The establishment of this aggregation feature allows carbon emissions assessment to be not limited to a single point in time, but to take into account time series changes and the interactions between different stages. Next, by analyzing these aggregation features, a carbon emission correction factor is generated. This factor is an adjustment coefficient used to correct for deviations in emission data caused by external factors (such as climate change). Finally, by applying the calculated correction factor to the carbon emission data of each stage, companies can obtain a more accurate total carbon emissions value for the entire lifecycle.

[0057] Specifically, in step S140, a carbon emission correction factor is generated based on the time-series propagation aggregation characteristics of the principal component of the product's full life cycle data, and the total value of the product's full life cycle carbon emissions is calculated, including: inputting the principal component time-series propagation aggregation vector of the product's full life cycle data into a decoder-based carbon emission correction factor generator to obtain the carbon emission correction factor; multiplying the carbon emission correction factor by the initial total value of carbon emissions to obtain the total value of the product's full life cycle carbon emissions.

[0058] It should be understood that the principal component time series propagation aggregation vector of the product life cycle data is input into the decoder-based carbon emission correction factor generator to obtain the carbon emission correction factor. That is to say, the semantic principal component time series propagation aggregation representation information of each stage of the product life cycle data is used to perform decoding regression to obtain the carbon emission correction factor. Then, the carbon emission correction factor is multiplied by the initial total value of carbon emissions to obtain the total carbon emissions of the product throughout its life cycle. In this way, the differences in each processing process can be more accurately reflected, thereby better adapting to the dynamic changes of different product processes and external conditions, and taking into account the mutual influence between each stage and the importance of time series, to obtain a total carbon emission value of the product throughout its life cycle that is closer to the actual value. This method can not only more accurately reflect the real environmental impact of the product, but also provide companies with more reliable product life cycle carbon footprint data support, helping them to formulate effective emission reduction measures.

[0059] Preferably, when the semantic coding vector of the raw material acquisition stage data, the semantic coding vector of the manufacturing stage data, the semantic coding vector of the transportation stage data, the semantic coding vector of the usage stage data and the semantic coding vector of the recycling and settlement stage data respectively represent the coding semantic features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data, when performing feature time series dynamic propagation aggregation based on the node feature principal component gradient attenuation, the feature principal component gradient attenuation under the differences in the semantic coding patterns of each data will have different propagation directionality, causing the product life cycle data principal component time series propagation aggregation vector obtained by dynamic propagation aggregation to have a complex interactive aggregation space structure. Therefore, when the product life cycle data principal component time series propagation aggregation vector is input into the decoder-based carbon emission correction factor generator, it is expected to improve the decoding regression convergence and generalization effect of the product life cycle data principal component time series propagation aggregation vector under the complex aggregation space structure.

[0060] Therefore, the applicant of this application considers optimizing the principal component time series propagation aggregation vector of the product life cycle data when inputting the principal component time series propagation aggregation vector of the product life cycle data into the decoder-based carbon emission correction factor generator. The optimization process includes: calculating the sum of the absolute values ​​of each eigenvalue of the principal component time series propagation aggregation vector of the product life cycle data to obtain a first principal component time series propagation aggregation spatial structure value of the product life cycle data, and calculating the square root of the sum of the squares of each eigenvalue of the principal component time series propagation aggregation vector of the product life cycle data to obtain a second principal component time series propagation aggregation spatial structure value of the product life cycle data:

[0061]

[0062] Among them, v i Represents the eigenvalues ​​of the principal component time series propagation aggregation vector of the product life cycle data, ∑ represents the sum function, |·| represents the absolute value function, represents a set of real numbers, α represents the spatial structure value of the principal component time series propagation aggregation of the first product's full life cycle data, β represents the spatial structure value of the principal component time series propagation aggregation of the second product's full life cycle data, represents the set of real numbers, L represents the length of the vector;

[0063] Multiply each eigenvalue of the product life cycle data principal component time series propagation aggregation vector by the first product life cycle data principal component time series propagation aggregation space structure value and the second product life cycle data principal component time series propagation aggregation space structure value to obtain the first product life cycle data principal component time series propagation aggregation structure reference value and the second product life cycle data principal component time series propagation aggregation structure reference value corresponding to each eigenvalue; multiply each eigenvalue of the product life cycle data principal component time series propagation aggregation vector by the length of the product life cycle data principal component time series propagation aggregation vector and the square root of the length to obtain the first product life cycle data principal component time series propagation aggregation scale transformation value and the second product life cycle data principal component time series propagation aggregation scale transformation value corresponding to each eigenvalue; multiply the first product life cycle data principal component time series propagation aggregation structure by the length of the product life cycle data principal component time series propagation aggregation vector and the square root of the length; The structure reference value is divided by the difference between the spatial structure value of the first product life cycle data principal component time series propagation aggregation and the scale transformation value of the first product life cycle data principal component time series propagation aggregation to obtain the first product life cycle data principal component time series propagation aggregation transformation adjustment value; the second product life cycle data principal component time series propagation aggregation structure reference value is divided by the difference between the spatial structure value of the second product life cycle data principal component time series propagation aggregation and the scale transformation value of the second product life cycle data principal component time series propagation aggregation to obtain the second product life cycle data principal component time series propagation aggregation transformation adjustment value; and the weighted sum of the first product life cycle data principal component time series propagation aggregation transformation adjustment value and the second product life cycle data principal component time series propagation aggregation transformation adjustment value is calculated to obtain each eigenvalue of the optimized product life cycle data principal component time series propagation aggregation vector.

[0064] Here, the optimization expression of the principal component time series propagation aggregation vector of the product life cycle data, denoted as V, is:

[0065] V'=V1⊕ ( ω⊙V2 )

[0066] exist and In the case of:

[0067]

[0068] v 1i =(α×v i ) / (α-L×v i )

[0069] Among them, v irepresents the eigenvalues ​​of the principal component time series propagation aggregation vector of the product life cycle data, α represents the spatial structure value of the principal component time series propagation aggregation of the first product life cycle data, β represents the spatial structure value of the principal component time series propagation aggregation of the second product life cycle data, v 1i represents the principal component time series propagation aggregation transformation adjustment value of the first product's full life cycle data, v 2i represents the principal component time series propagation aggregation transformation adjustment value of the second product's full life cycle data, V1 represents the principal component time series propagation aggregation transformation adjustment feature vector of the first product's full life cycle data obtained by vectorizing the plurality of principal component time series propagation aggregation transformation adjustment values ​​of the first product's full life cycle data, V2 represents the principal component time series propagation aggregation transformation adjustment feature vector of the second product's full life cycle data obtained by vectorizing the plurality of principal component time series propagation aggregation transformation adjustment values ​​of the second product's full life cycle data, V' represents the optimized principal component time series propagation aggregation vector of the product's full life cycle data, ⊕ represents addition by position, ω represents the principal component time series propagation aggregation adjustment weighted value, ⊙ represents multiplication by position, represents the set of real numbers, and L represents the length of the vector.

[0070] Specifically, the spatial structure information of the feature set of the principal component time-series propagation aggregate vector of the product lifecycle data in high-dimensional space is analyzed by using the class-norm spatial structured representation of the principal component time-series propagation aggregate vector of the product lifecycle data as a reference window to perform a scale-based box transformation on each eigenvalue of the principal component time-series propagation aggregate vector of the product lifecycle data. This is followed by spatial structure-based box attention weight adjustment of each eigenvalue of the principal component time-series propagation aggregate vector of the product lifecycle data. This ensures the spatial transformation invariance of the principal component time-series propagation aggregate vector of the product lifecycle data under feature space interactions, thereby improving the convergence and generalization of the decoding regression of the feature set of the principal component time-series propagation aggregate vector of the product lifecycle data under complex spatial structure representations, and improving the accuracy of the carbon emission correction factor generated when input into the decoder-based carbon emission correction factor generator. This allows for more accurate correction of the initial total carbon emission value, better adapting to the dynamic changes of different product processes and external conditions, and obtaining a more realistic total carbon emission value for the product lifecycle. This provides enterprises with more reliable product lifecycle carbon footprint data support, helping them formulate effective emission reduction measures.

[0071] In summary, a method for collecting and analyzing carbon footprint data for the entire life cycle of a product based on an embodiment of the present application is elucidated, which introduces more sophisticated artificial intelligence data processing technology to perform data analysis and semantic understanding on the data for the entire life cycle of the product, and calculates the initial total carbon emissions of the product at each stage. Then, a carbon emission correction factor is obtained based on the semantic information of each stage and the connection between each other, so as to make corresponding corrections to the initial total carbon emissions, thereby more accurately reflecting the differences in each processing process, better adapting to the dynamic changes of different product processes and external conditions, and obtaining a total carbon emissions value for the entire life cycle of the product that is closer to the actual value. In this way, the real environmental impact of the product can be reflected more accurately, providing enterprises with more reliable product carbon footprint data support for the entire life cycle, and formulating effective emission reduction measures.

[0072] Figure 5 This is a system block diagram of a product life cycle carbon footprint data collection and analysis system according to an embodiment of the present application. Figure 5 As shown, according to the embodiment of the present application, the product life cycle carbon footprint data collection and analysis system 100 includes: a product life cycle data acquisition module 110, which is used to acquire product life cycle data, and the product life cycle data includes raw material acquisition stage data, manufacturing stage data, transportation stage data, use stage data and recycling and settlement stage data; a carbon emission initial total value calculation module 120, which is used to calculate the initial total value of carbon emissions based on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the use stage data and the recycling and settlement stage data; a node feature principal component attenuation time series information propagation processing module 130, which is used to process the raw material acquisition stage data After semantic encoding of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data, the semantic encoding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data are subjected to time series information propagation processing based on the attenuation of the principal component of the node characteristics to obtain the principal component time series propagation aggregation characteristics of the product life cycle data; the total value of carbon emissions of the product throughout its life cycle is calculated as a module 140, which is used to generate a carbon emission correction factor based on the principal component time series propagation aggregation characteristics of the product life cycle data, and calculate the total value of carbon emissions of the product throughout its life cycle.

[0073] Here, those skilled in the art will understand that the specific operations of each step in the above-mentioned product life cycle carbon footprint data collection and analysis system have been referenced above. Figures 1 to 4 The product life cycle carbon footprint data collection and analysis method has been introduced in detail in the description of the product life cycle carbon footprint data collection and analysis method, and therefore, its repeated description will be omitted.

[0074] As described above, the product lifecycle carbon footprint data collection and analysis system 100 according to the embodiment of the present application can be implemented in various terminal devices. In one example, the product lifecycle carbon footprint data collection and analysis system 100 can be integrated into the terminal device as a software module and / or hardware module. For example, the product lifecycle carbon footprint data collection and analysis system 100 can be a software module in the operating system of the terminal device, or it can be an application developed for the terminal device; of course, the product lifecycle carbon footprint data collection and analysis system 100 can also be one of the many hardware modules of the terminal device.

[0075] Alternatively, in another example, the product life cycle carbon footprint data collection and analysis system 100 and the terminal device can also be separate devices, and the product life cycle carbon footprint data collection and analysis system 100 can be connected to the terminal device through a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0076] In summary, a product life cycle carbon footprint data collection and analysis system based on an embodiment of the present application is illustrated, which introduces more sophisticated artificial intelligence data processing technology to perform data analysis and semantic understanding on product life cycle data, and calculates the initial total carbon emissions of each stage of the product. Then, based on the semantic information of each stage and the connection between each other, a carbon emission correction factor is obtained to make corresponding corrections to the initial total carbon emissions, thereby more accurately reflecting the differences in each processing process, better adapting to the dynamic changes of different product processes and external conditions, and obtaining a total carbon emissions value of the product life cycle that is closer to the actual value. In this way, the real environmental impact of the product can be reflected more accurately, providing enterprises with more reliable product life cycle carbon footprint data support, and formulating effective emission reduction measures.

Claims

1. A method for collecting and analyzing carbon footprint data of a product throughout its life cycle, characterized in that: include: Acquire product lifecycle data, including data from the raw material acquisition phase, manufacturing phase, transportation phase, usage phase, and recycling and settlement phase; Calculating an initial total carbon emission value based on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data, and the recycling and settlement stage data; After semantic encoding of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data and the recycling and settlement phase data, the semantic encoding features of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data and the recycling and settlement phase data are subjected to time series information propagation processing based on the decay of the principal component of the node feature to obtain the principal component time series propagation aggregation feature of the product life cycle data, including: principal component extraction of the semantic encoding features of the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data and the recycling and settlement phase data to obtain a sequence of principal component representations of the semantic encoding features of the current phase data and principal component representations of the semantic encoding features of the historical phase data; time series decay dynamic aggregation of the sequences of principal component representations of the semantic encoding features of the current phase data and principal component representations of the semantic encoding features of the historical phase data to obtain the principal component time series propagation aggregation feature of the product life cycle data; Generate a carbon emission correction factor based on the principal component time series propagation aggregation characteristics of the product life cycle data, and calculate the total carbon emissions of the product life cycle; The time-series decay dynamic aggregation of the principal component representation of the semantic coding feature of the current stage data and the principal component representation of the semantic coding feature of the historical stage data is performed to obtain the principal component time-series propagation aggregation feature of the product life cycle data, including: Respectively calculating the absolute factors of the time series propagation attenuation entropy of the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data, and the semantic encoding principal component representation vector of the usage stage data relative to the semantic encoding principal component representation vector of the recycling and settlement stage data to obtain a sequence of the absolute factors of the time series propagation attenuation entropy of the stage data; Based on the time spans between the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data, and the semantic encoding principal component representation vector of the usage stage data and the semantic encoding principal component representation vector of the recycling processing and settlement stage data, each stage data time series propagation attenuation entropy absolute factor in the sequence of the stage data time series propagation attenuation entropy absolute factors is modulated in the time dimension to obtain a sequence of stage data time series span modulated propagation attenuation entropy factors; Inputting the sequence of the phase data timing span modulation propagation attenuation entropy factors into the information transfer screening module based on the gating function to obtain a sequence of the phase data timing span modulation propagation attenuation weights; Based on the sequence of the phase data time series span modulation propagation attenuation weights, a weighted sum of the semantic encoding principal component representation vector of the raw material acquisition phase data, the semantic encoding principal component representation vector of the manufacturing phase data, the semantic encoding principal component representation vector of the transportation phase data, and the semantic encoding principal component representation vector of the usage phase data is calculated to obtain a significant transfer aggregation representation vector of the semantic encoding principal component of the historical phase data; Calculate the positional sum of the semantically encoded principal component significant transfer aggregation representation vector of the historical stage data and the semantically encoded principal component representation vector of the recycling and settlement stage data to obtain the principal component temporal propagation aggregation vector of the product's full life cycle data as the principal component temporal propagation aggregation feature of the product's full life cycle data.

2. The method for collecting and analyzing product life cycle carbon footprint data according to claim 1, characterized in that: Calculating the initial total carbon emissions value based on the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data, and the recycling and settlement phase data includes: Determining the carbon emission value of the raw material acquisition stage based on the raw material acquisition stage data; Determining the carbon emission value of the manufacturing stage based on the manufacturing stage data; Determining the carbon emission value of the transportation stage based on the transportation stage data; Determining a carbon emission value during the use phase based on the use phase data; Determine the carbon emission value of the recycling and processing settlement stage based on the data of the recycling and processing settlement stage; The sum of the carbon emission values ​​of the raw material acquisition stage, the manufacturing stage, the transportation stage, the use stage and the recycling and settlement stage is calculated as the initial total carbon emission value.

3. The method for collecting and analyzing product life cycle carbon footprint data according to claim 2, characterized in that: The raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data are semantically encoded, including: semantically encoding the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain a raw material acquisition stage data semantic encoding vector as the raw material acquisition stage data semantic encoding feature, a manufacturing stage data semantic encoding vector as the manufacturing stage data semantic encoding feature, a transportation stage data semantic encoding vector as the transportation stage data semantic encoding feature, a usage stage data semantic encoding vector as the usage stage data semantic encoding feature and a recycling and settlement stage data semantic encoding vector as the recycling and settlement stage data semantic encoding feature.

4. The method for collecting and analyzing product life cycle carbon footprint data according to claim 3, characterized in that: Principal component extraction is performed on the semantic coding features of the raw material acquisition stage data, the semantic coding features of the manufacturing stage data, the semantic coding features of the transportation stage data, the semantic coding features of the use stage data, and the semantic coding features of the recycling and settlement stage data to obtain a sequence of principal component representations of the semantic coding features of the current stage data and principal component representations of the semantic coding features of the historical stage data, including: Performing principal component extraction based on eigenvalues ​​on the semantic coding vector of the raw material acquisition stage data, the semantic coding vector of the manufacturing stage data, the semantic coding vector of the transportation stage data, and the semantic coding vector of the usage stage data, respectively, to obtain the principal component representation vector of the semantic coding of the raw material acquisition stage data, the principal component representation vector of the semantic coding of the manufacturing stage data, the principal component representation vector of the semantic coding of the transportation stage data, and the principal component representation vector of the semantic coding of the usage stage data as a sequence of principal component representations of the semantic coding of the historical stage data; The principal component extraction based on the eigenvalue is performed on the semantic coding vector of the recycling processing settlement stage data to obtain the principal component representation vector of the semantic coding of the recycling processing settlement stage data as the principal component representation of the semantic coding features of the current stage data.

5. The method for collecting and analyzing product life cycle carbon footprint data according to claim 4, characterized in that: The absolute factors of the time series propagation attenuation entropy of the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data, and the semantic encoding principal component representation vector of the usage stage data are respectively calculated relative to the semantic encoding principal component representation vector of the recycling and settlement stage data to obtain a sequence of the absolute factors of the time series propagation attenuation entropy of the stage data, including: Calculating the positional division of the principal component representation vector of the semantic encoding of the raw material acquisition stage data and the principal component representation vector of the semantic encoding of the recycling processing settlement stage data at corresponding positions to obtain the principal component attenuation vector of the semantic encoding feature of the stage data; Calculating the base-two logarithmic function value of the absolute value of each eigenvalue of the principal component attenuation vector of the semantic coding feature of the stage data to obtain the principal component attenuation logarithmic vector of the semantic coding feature of the stage data; After calculating the positional dot product of the semantic coding principal component representation vector of the raw material acquisition stage data and the attenuation logarithm vector of the semantic coding feature principal component of the stage data, each positional feature value in the obtained dot product vector is multiplied by the position point to obtain the stage data time series propagation attenuation value; An exponential function of the phase data time series propagation attenuation value with the natural constant e as the base is calculated to obtain the phase data time series propagation attenuation entropy absolute factor.

6. The method for collecting and analyzing product life cycle carbon footprint data according to claim 5, characterized in that: Based on the time spans between the semantic encoding principal component representation vector of the raw material acquisition stage data, the semantic encoding principal component representation vector of the manufacturing stage data, the semantic encoding principal component representation vector of the transportation stage data, and the semantic encoding principal component representation vector of the usage stage data and the semantic encoding principal component representation vector of the recycling processing settlement stage data, each stage data time series propagation attenuation entropy absolute factor in the sequence of the stage data time series propagation attenuation entropy absolute factors is modulated in the time dimension to obtain a sequence of stage data time series span modulated propagation attenuation entropy factors, including: Subtracting the timestamp of the semantic encoding principal component representation vector of the recycling settlement stage data from the timestamp of the semantic encoding principal component representation vector of the raw material acquisition stage data and rounding down the result to obtain a stage data time span value; calculating an exponential function with the natural constant e as a base and the time span value of the stage data as an exponent to obtain a modulation value of the time span of the stage data; The stage data timing propagation attenuation entropy absolute factor corresponding to the principal component representation vector of the raw material acquisition stage data semantic encoding is divided by the stage data time span modulation value to obtain the stage data timing span modulation propagation attenuation entropy factor.

7. The method for collecting and analyzing product life cycle carbon footprint data according to claim 6, characterized in that: Inputting the sequence of the stage data timing span modulation propagation attenuation entropy factors into the information transfer screening module based on the gating function to obtain a sequence of the stage data timing span modulation propagation attenuation weights, including: Comparing each stage data timing span modulation propagation attenuation entropy factor in the sequence of stage data timing span modulation propagation attenuation entropy factors with a predetermined threshold to obtain a sequence of stage data timing span modulation propagation attenuation weights; In which, in response to the stage data timing span modulation propagation attenuation entropy factor being greater than the predetermined threshold, the stage data timing span modulation propagation attenuation entropy factor greater than the predetermined threshold is input into the sigmoid function; in response to the stage data timing span modulation propagation attenuation entropy factor being less than or equal to the predetermined threshold, the stage data timing span modulation propagation attenuation entropy factor less than or equal to the predetermined threshold is set to zero.

8. The method for collecting and analyzing product life cycle carbon footprint data according to claim 7, characterized in that: Based on the principal component time series propagation aggregation characteristics of the product life cycle data, a carbon emission correction factor is generated, and the total carbon emission value of the product life cycle is calculated, including: Inputting the principal component time series propagation aggregation vector of the product life cycle data into a decoder-based carbon emission correction factor generator to obtain the carbon emission correction factor; The carbon emission correction factor is multiplied by the initial total carbon emission value to obtain the total carbon emission value of the product over its entire life cycle.

9. A product life cycle carbon footprint data collection and analysis system, used to execute the product life cycle carbon footprint data collection and analysis method according to claim 1, characterized in that: include: A product life cycle data acquisition module is used to acquire product life cycle data, including raw material acquisition stage data, manufacturing stage data, transportation stage data, usage stage data, and recycling and settlement stage data; a carbon emission initial total value calculation module, configured to calculate the carbon emission initial total value based on the raw material acquisition phase data, the manufacturing phase data, the transportation phase data, the usage phase data, and the recycling and settlement phase data; A node feature principal component decay time series information propagation processing module is used to perform semantic encoding on the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data, and then perform time series information propagation processing based on node feature principal component decay on the semantic encoding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain the principal component time series propagation aggregation features of the product life cycle data, including: principal component extraction on the semantic encoding features of the raw material acquisition stage data, the manufacturing stage data, the transportation stage data, the usage stage data and the recycling and settlement stage data to obtain a sequence of principal component representations of the semantic encoding features of the current stage data and principal component representations of the semantic encoding features of the historical stage data; and time series decay dynamic aggregation on the sequence of principal component representations of the semantic encoding features of the current stage data and principal component representations of the semantic encoding features of the historical stage data to obtain the principal component time series propagation aggregation features of the product life cycle data; The module for calculating the total carbon emissions of a product over its entire life cycle is used to generate a carbon emission correction factor based on the principal component time series propagation aggregation characteristics of the product's entire life cycle data, and calculate the total carbon emissions of the product over its entire life cycle.

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