Railway infrastructure materialization stage carbon emission monitoring method and system

Abstract

The invention relates to the technical field of carbon emission monitoring, and particularly discloses a railway infrastructure materialization stage carbon emission monitoring method and system, after a materialization stage material list uploaded by a user is received, material data in the materialization stage material list is analyzed by adopting a data processing technology based on deep learning, and the material data is analyzed by adopting a data processing technology based on deep learning; according to the method, context semantic association perception is carried out on various material data, abnormity diagnosis of a material list is realized, and after it is confirmed that the material data is correct, the total carbon emission value of the materialization stage is calculated based on the unit mass carbon emission corresponding to each material; and carrying out corresponding carbon emission early warning prompt according to the offset of the total carbon emission value in the materialization stage relative to the expected value. According to the method, accurate accounting of the railway infrastructure materialization stage carbon emission can be realized, carbon emission calculation deviation caused by inaccurate data is effectively avoided, and powerful support is provided for low-carbon sustainable development of the railway industry.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission monitoring, and more specifically, to a method and system for monitoring carbon emissions during the materialization phase of railway infrastructure. Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing greenhouse gas emissions has become an important issue of common concern to the international community. In this context, railway infrastructure construction is one of the key areas of national economic development and environmental protection, and carbon emission management during its construction and operation is particularly important. In the construction of railway infrastructure, the materialization stage is the period with the most concentrated resource investment, involving the use of a large number of different types of materials. Accurate monitoring of carbon emissions during this stage is crucial to achieving low-carbon and sustainable development in the railway industry. The material list is a key document that records the use of materials in the materialization stage, and the accuracy of its data directly affects the reliability of carbon emission calculations.

[0003] Traditionally, carbon emission monitoring during the materialization phase of railway infrastructure relies on manual statistics and calculations, which is not only time-consuming and laborious, but also prone to large deviations in assessment results due to inaccurate or missing data. In addition, with the continuous expansion of the scale of railway construction projects and the increasing variety of materials, the amount of data in the bill of materials has increased exponentially, which further exacerbates the difficulty of verifying the bill of materials data. If erroneous material data is not discovered and corrected, it will lead to deviations in the total carbon emissions calculated based on it, which will not only fail to provide an accurate basis for project decision-making, but may also mislead the subsequent formulation of carbon emission management strategies.

[0004] Therefore, an optimized carbon emission monitoring method and system for the materialization stage of railway infrastructure is needed to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, this application is proposed.

[0006] According to one aspect of the present application, a railway infrastructure materialization phase carbon emission monitoring system is provided, which includes: A materialization stage list uploading module is used to receive a material list of the materialization stage uploaded by a user, wherein the material list of the materialization stage includes material names and usage amounts of each material, wherein the materialization stage list uploading module is also used to perform material analysis on the material list of the materialization stage to realize abnormality diagnosis of the list; A regional emission factor acquisition module is used to obtain the regional emission factors corresponding to each material from a public database, where the regional emission factors are carbon emissions per unit mass; A carbon emission total value calculation module, used to calculate the total carbon emission value of the physical and chemical stage based on the regional emission factors corresponding to the various materials and the physical and chemical stage material list; The carbon emission total target setting module is used to extract the expected value of carbon emission total in the chemical stage from the background database; A carbon emission monitoring module is used to calculate the difference between the total carbon emission value of the materialization stage and the expected value of the total carbon emission of the materialization stage to obtain a total carbon emission target offset value, and then determine whether to generate a carbon emission warning prompt based on the comparison between the total carbon emission target offset value and a preset threshold value; Specifically, the materialization stage inventory upload module includes: A material list structured coding unit, used for performing structured coding on the material list of the physicalization stage to obtain a set of material property-amount semantic description coding vectors; A material context dynamic walk unit, used for performing causally triggered material context dynamic walk semantic association coding on the set of material property-usage semantic description coding vectors to obtain a material context dynamic walk semantic coding vector; The abnormality diagnosis unit is used to determine the diagnosis result based on the context dynamic walk semantic coding vector between the materials, and the diagnosis result is used to indicate whether there is an abnormality in the material list in the materialization stage.

[0007] Specifically, the bill of materials structured coding unit is used to: Performing semantic embedding coding on each material in the material list of the materialization stage to obtain a set of material semantic description coding vectors; The amount of the corresponding material is added to the end of each material semantic description coding vector in the set of material semantic description coding vectors to obtain the set of material property-amount semantic description coding vectors.

[0008] Specifically, the context dynamic walking unit between materials includes: An implicit feature mining subunit is used to perform implicit feature mining on each material property-usage semantic description coding vector in the set of material property-usage semantic description coding vectors to obtain a set of material property-usage deep implicit semantic feature coding vectors; A causal association topological feature extraction subunit is used to extract the semantic causal association topological features between the sets of material property-usage deep implicit semantic feature encoding vectors to obtain a semantic causal association topological feature matrix between material data; The context semantic association coding subunit is used to perform multi-level dynamic wandering semantic association coding on the set of material property-amount semantic description coding vectors based on the semantic causal association topological feature matrix between the material data to obtain the context dynamic wandering semantic coding vector between the materials.

[0009] Specifically, the causal association topological feature extraction subunit includes: A causal association factor calculation secondary subunit is used to calculate the material data semantic causal association factor between any two material property-usage depth implicit semantic feature coding vectors in the set of material property-usage depth implicit semantic feature coding vectors to obtain a material data semantic causal association topological matrix composed of multiple material data semantic causal association factors; The causal triggering secondary subunit is used to input the semantic causal association topological matrix between the material data into the causal triggering network based on the gated activation function to obtain the semantic causal association topological feature matrix between the material data.

[0010] Specifically, the causal correlation factor calculation secondary subunit is used to: Performing semantic association coding on any two material property-usage depth implicit semantic feature coding vectors in the set of material property-usage depth implicit semantic feature coding vectors to obtain a set of semantic association coding matrices between material data; Based on the statistical characteristics of each semantic association coding matrix between material data in the set of semantic association coding matrices between material data, the corresponding semantic causal association factors between material data are calculated to obtain the multiple semantic causal association factors between material data, wherein the statistical characteristics include the characteristic variance, maximum eigenvalue, characteristic mean and causal association energy bias term of the semantic association coding matrix between material data.

[0011] Specifically, if the feature variance of the semantic association coding matrix between the material data is greater than or equal to a preset threshold, the average weighted value of the Euclidean distance between each two material property-usage depth implicit semantic feature coding vectors in the set of the material property-usage depth implicit semantic feature coding vectors is calculated as the causal association energy bias term; If the feature variance of the semantic association coding matrix between the material data is less than a preset threshold, a weighted value of the feature mean of the semantic association coding matrix between the material data is calculated as the causal association energy bias item.

[0012] Specifically, the context semantic association encoding subunit is used to: Inputting the semantic causal association topological feature matrix between the material data and the set of the material property-usage semantic description encoding vectors into a dynamic walking encoder based on a graph convolutional neural network model to obtain a surface context dynamic walking semantic encoding vector between the material data; Inputting the semantic causal association topological feature matrix between the material data and the set of the material property-usage deep implicit semantic feature encoding vectors into the dynamic walking encoder based on the graph convolutional neural network model to obtain the hidden layer context dynamic walking semantic encoding vector between the material data; The hidden layer context dynamic wandering semantic coding vector between the material data and the surface layer context dynamic wandering semantic coding vector between the material data are fused to obtain the context dynamic wandering semantic coding vector between the materials.

[0013] Specifically, the abnormality diagnosis unit is used to: The context dynamic walk semantic encoding vector between materials is input into a classifier-based inventory diagnostician to obtain the diagnostic result.

[0014] According to another aspect of the present application, a method for monitoring carbon emissions during the materialization phase of railway infrastructure is provided, comprising: Receive a material list of the physical and chemical stage uploaded by a user, wherein the material list of the physical and chemical stage includes material names and usage amounts of each material, wherein the material list uploading module of the physical and chemical stage is further used to perform material analysis on the material list of the physical and chemical stage to realize abnormality diagnosis of the list; Obtain the regional emission factor corresponding to each material from a public database, where the regional emission factor is the carbon emission per unit mass; Calculate the total carbon emission value of the physical and chemical stage based on the regional emission factors corresponding to each material and the list of materials in the physical and chemical stage; Extract the expected value of total carbon emissions in the chemical process from the backend database; The difference between the total carbon emission value in the materialization stage and the expected value of the total carbon emission in the materialization stage is calculated to obtain the total carbon emission target offset value, and then based on the comparison between the total carbon emission target offset value and the preset threshold, it is determined whether to generate a carbon emission warning prompt.

[0015] Specifically, material analysis is performed on the material list of the physical and chemical stage to achieve abnormality diagnosis of the material list, including: Performing structured coding on the material list of the physicalization stage to obtain a set of material property-amount semantic description coding vectors; Performing causally triggered dynamic walking semantic association coding between materials on the set of material property-usage semantic description coding vectors to obtain contextual dynamic walking semantic coding vectors between materials; Based on the contextual dynamic walk semantic coding vector between the materials, a diagnosis result is determined, and the diagnosis result is used to indicate whether there is an abnormality in the material list in the materialization stage.

[0016] This application has at least the following technical effects: Compared with the prior art, the method and system for monitoring carbon emissions in the materialization stage of railway infrastructure provided in the present application, after receiving the material list of the materialization stage uploaded by the user, adopts data processing technology based on deep learning to analyze the material data in the material list of the materialization stage, and realizes abnormal diagnosis of the material list by perceiving the contextual semantic association of each material data. After confirming that the material data is correct, the total value of carbon emissions in the materialization stage is calculated based on the unit mass carbon emissions corresponding to each material, and corresponding carbon emission early warning prompts are given according to the offset of the total value of carbon emissions in the materialization stage relative to the expected value. This can realize accurate accounting of carbon emissions in the materialization stage of railway infrastructure, effectively avoid carbon emission calculation deviations caused by inaccurate data, and provide strong support for the low-carbon and sustainable development of the railway industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 4 is a block diagram of a carbon emission monitoring system for railway infrastructure materialization stage according to an embodiment of the present application.

[0019] Figure 2 It is a block diagram of a materialization phase inventory uploading module in a railway infrastructure materialization phase carbon emission monitoring system according to an embodiment of the present application.

[0020] Figure 3 This is a schematic diagram of the data flow of the materialization phase inventory in the railway infrastructure materialization phase carbon emission monitoring system according to an embodiment of the present application.

[0021] Figure 4 It is a block diagram of a dynamic context walking unit between materials in a carbon emission monitoring system for the materialization stage of railway infrastructure according to an embodiment of the present application.

[0022] Figure 5 It is a block diagram of a causal correlation topological feature extraction subunit in a railway infrastructure materialization phase carbon emission monitoring system according to an embodiment of the present application.

[0023] Figure 6Flow chart of a method for monitoring carbon emissions during the materialization phase of railway infrastructure according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0025] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0026] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0027] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0028] It should be noted that all data acquisition actions in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization given by the owner of the corresponding device.

[0029] Figure 1 FIG. 1 is a block diagram of a carbon emission monitoring system for railway infrastructure materialization stage according to an embodiment of the present application. Figure 1As shown, the railway infrastructure materialization stage carbon emission monitoring system 100 includes: a materialization stage inventory upload module 110, which is used to receive a material list of the materialization stage uploaded by a user, the material list of the materialization stage includes the material name and the amount of each material, wherein the materialization stage inventory upload module is also used to perform material analysis on the material list of the materialization stage to realize inventory abnormality diagnosis; a regional emission factor acquisition module 120, which is used to obtain the regional emission factor corresponding to each material from a public database, the regional emission factor is the carbon emission per unit mass; a total carbon emission value calculation module 130, which is used to calculate the total carbon emission value of the materialization stage based on the regional emission factor corresponding to each material and the material list of the materialization stage; a total carbon emission target setting module 140, which is used to extract the expected value of the total carbon emission of the materialization stage from the background database; a carbon emission monitoring module 150, which is used to calculate the difference between the total carbon emission value of the materialization stage and the expected value of the total carbon emission of the materialization stage to obtain the total carbon emission target offset value, and then determine whether to generate a carbon emission warning prompt based on the comparison between the total carbon emission target offset value and the preset threshold.

[0030] In the above-mentioned railway infrastructure materialization stage carbon emission monitoring system, the materialization stage list upload module 110 is used to receive the material list of the materialization stage uploaded by the user, and the material list of the materialization stage includes the material name and the amount of each material, wherein the materialization stage list upload module is also used to perform material analysis on the material list of the materialization stage to realize the list abnormality diagnosis. It should be understood that the material list contains detailed information on various materials used in the materialization stage of railway infrastructure, such as material type, specification, quantity, etc., which is the cornerstone data for calculating carbon emissions in the materialization stage, and its data accuracy plays a decisive role in the accuracy of subsequent calculation of total carbon emissions. Any erroneous or unreasonable data entry, such as miswriting of material usage, confusion of material types, etc., may lead to serious deviations in the final carbon emission calculation results. Therefore, after receiving the physical and chemical stage material list uploaded by the user, the physical and chemical stage list upload module further uses deep learning technology to analyze the material data in the physical and chemical stage material list to identify whether there are any abnormalities in the material list, thereby avoiding the waste of subsequent computing resources, reducing repeated verification and correction work caused by data errors, and improving the efficiency and credibility of the entire carbon emission monitoring process. Figure 2 It is a block diagram of a materialization phase inventory uploading module in a railway infrastructure materialization phase carbon emission monitoring system according to an embodiment of the present application. Figure 3 This is a schematic diagram of the data flow of the materialization phase inventory in the railway infrastructure materialization phase carbon emission monitoring system according to an embodiment of the present application. Figure 2 and Figure 3As shown, the materialization stage list upload module 110 includes: a material list structured encoding unit 111, which is used to perform structured encoding on the materialization stage material list to obtain a set of material property-usage semantic description encoding vectors; a material context dynamic walk unit 112, which is used to perform causal-triggered material dynamic walk semantic association encoding on the set of material property-usage semantic description encoding vectors to obtain a material context dynamic walk semantic encoding vector; an abnormality diagnosis unit 113, which is used to determine a diagnosis result based on the material context dynamic walk semantic encoding vector, and the diagnosis result is used to indicate whether there is an abnormality in the material list of the material stage.

[0031] Specifically, in a specific example of the present application, the material list structured coding unit 111 is used to: semantically embed the various materials in the material list of the materialization stage to obtain a set of material semantic description coding vectors. Specifically, in order to use a deep learning algorithm to perform material data analysis and abnormality identification on the material list of the materialization stage, it is necessary to convert the various material data into a numerical vector form that can be understood and processed by a computer. Based on this, in the present application, for the text information such as the name and specification of each material in the material list of the materialization stage, a pre-trained semantic embedding model is used to perform semantic embedding coding on it, so as to convert the text information into a numerical representation in a high-dimensional vector space, and obtain a set of material semantic description coding vectors. In an embodiment of the present application, the Word2Vec model is used to perform semantic embedding coding on the various material information in the material list of the materialization stage. The Word2Vec model has been trained with a large-scale corpus and has a strong semantic representation capability. It can accurately convert material information into a vector form and map similar words to similar positions in the vector space, thereby retaining the semantic association between materials. In this way, even when faced with material information that is expressed differently but is essentially the same, it can be accurately identified and characterized accordingly, thereby improving the accuracy of the analysis of material data in the bill of materials and the accuracy of anomaly identification.

[0032] Specifically, in a specific example of the present application, the bill of materials structured coding unit 111 is further used to: add the amount of the corresponding material to the tail of each material semantic description coding vector in the set of material semantic description coding vectors to obtain the set of material property-amount semantic description coding vectors. It should be understood that since material usage is an important dimension to measure the contribution of materials to carbon emissions, its data accuracy is the key to ensuring the reliability of carbon emission calculation results. Therefore, in the present application, the usage information of each material in the physical and chemical stage bill of materials is further appended to the tail of the corresponding material semantic description coding vector in numerical form to form a composite vector representation that combines material properties and usage, and obtain a set of material property-amount semantic description coding vectors, thereby ensuring that subsequent data analysis can consider both the type and amount of materials at the same time, so as to more accurately judge the data rationality of the physical and chemical stage bill of materials.

[0033] Specifically, the inter-material context dynamic walking unit 112 is used to perform inter-material context dynamic walking semantic association encoding based on causal triggering on the set of the material property-usage semantic description coding vector to obtain the inter-material context dynamic walking semantic coding vector. Specifically, in the actual application scenario, there is a certain causal relationship between the various material data in the material list of the materialization stage. For example, in the construction of railway bridges, the usage of materials such as steel, concrete, and admixtures will have a certain range and correlation due to factors such as design standards and construction technology; in the process of laying railway tracks, the usage of rails will be causally related to the usage of fasteners, pads and other materials. Based on this, the present application proposes a dynamic walking semantic association coding method between materials based on causal triggering, which simulates the dynamic walking process of material data on the causal chain by analyzing the causal association strength between each material data, and performs contextual dynamic walking coding on the set of material property-amount semantic description coding vectors, so as to have a deeper understanding of the internal logic between each data in the bill of materials, such as the reasonable ratio range of steel and concrete usage under a certain construction process, and the corresponding relationship between the amount of admixture and the concrete strength grade, etc., so as to obtain the contextual dynamic walking semantic coding vector between materials that integrates the dynamic association information between materials. In this way, the potential relationship and abnormal pattern between each data in the bill of materials can be captured more accurately, and the unreasonable aspects in the data, such as materials with abnormal usage and materials that do not meet the specifications, can be identified, thereby providing a more reliable data basis for the subsequent abnormal diagnosis of the bill of materials.

[0034] Figure 4 FIG. 1 is a block diagram of a dynamic contextual walk unit between materials in a carbon emission monitoring system for the materialization stage of railway infrastructure according to an embodiment of the present application. Figure 4As shown, the inter-material context dynamic walking unit 112 includes: an implicit feature mining subunit 1121, which is used to perform implicit feature mining on each material property-usage semantic description coding vector in the set of the material property-usage semantic description coding vector to obtain a set of material property-usage deep implicit semantic feature coding vectors; a causal association topological feature extraction subunit 1122, which is used to extract the semantic causal association topological features between the set of the material property-usage deep implicit semantic feature coding vectors to obtain a semantic causal association topological feature matrix between material data; a context semantic association coding subunit 1123, which is used to perform multi-level dynamic walking semantic association coding on the set of the material property-usage semantic description coding vectors based on the semantic causal association topological feature matrix between the material data to obtain the inter-material context dynamic walking semantic coding vector.

[0035] More specifically, the implicit feature mining subunit 1121 is expressed as follows: ; ; ; in, Represents a set of material property-quantity semantic description encoding vectors, , , and They are respectively represented as the first, second, and third vectors in the set of material property-amount semantic description encoding vectors. and A material property-amount semantic description encoding vector, Represents the number of vectors in the set of material properties-quantity semantic description encoding vectors, represents a 1×1 convolution operation, represents the activation function, A collection of material property-usage deep implicit semantic feature encoding vectors, , , and Respectively , , and The corresponding material property-usage deep implicit semantic feature encoding vector.

[0036] Specifically, due to the complex relationship between railway construction materials, it is difficult to discover potential rules only from surface data. Therefore, this application generates a more extensive semantic representation, namely, a material property-amount deep implicit semantic feature encoding vector, by extracting deep features in the material property-amount semantic description encoding vector, thereby providing richer data information for subsequent accurate analysis of the causal relationship between materials, making subsequent analysis more accurate and comprehensive.

[0037] Figure 5 FIG. 1 is a block diagram of a causal correlation topological feature extraction subunit in a railway infrastructure materialization phase carbon emission monitoring system according to an embodiment of the present application. Figure 5 As shown, the causal association topological feature extraction subunit 1122 includes: a causal association factor calculation secondary subunit 11221, which is used to calculate the material data semantic causal association factor between any two material property-usage deep implicit semantic feature coding vectors in the set of material property-usage deep implicit semantic feature coding vectors to obtain a material data semantic causal association topological matrix composed of multiple material data semantic causal association factors; a causal triggering secondary subunit 11222, which is used to input the material data semantic causal association topological matrix into a causal triggering network based on a gated activation function to obtain the material data semantic causal association topological feature matrix.

[0038] In a specific example of the present application, the causal correlation factor calculation secondary subunit 11221 is used to: perform semantic association encoding on any two material property-usage depth implicit semantic feature coding vectors in the set of material property-usage depth implicit semantic feature coding vectors to obtain a set of semantic association coding matrices between material data; based on the statistical characteristics of each material data semantic association coding matrix in the set of material data semantic association coding matrices, calculate the corresponding material data semantic causal correlation factor to obtain the multiple material data semantic causal correlation factors, wherein the statistical characteristics include the characteristic variance, maximum eigenvalue, characteristic mean and causal correlation energy bias term of the material data semantic association coding matrix. The above-mentioned causal correlation factor calculation secondary subunit 11221 is expressed by the formula: ; ; in, The first in the set of material property-amount deep implicit semantic feature encoding vectors material property-usage deep implicit semantic feature encoding vector, represents the transpose of a vector, express and The corresponding semantic association encoding matrix between material data, represents the characteristic variance of the semantic association encoding matrix between material data, represents the eigenmean of the matrix, represents the causal energy bias term, Indicates taking the maximum value, express and The semantic causal correlation factor between the corresponding material data, Represents the semantic causal relationship topology matrix between material data, , , and They respectively represent the semantic causal correlation factors between the material data between the corresponding two material property-usage deep implicit semantic feature coding vectors in the set of the material property-usage deep implicit semantic feature coding vectors.

[0039] Specifically, semantic association encoding is performed on any two material property-usage deep implicit semantic feature coding vectors, a semantic association coding matrix between material data is constructed, and a causal association energy measurement function is used to perform feature distribution analysis on the semantic association coding matrix between material data, so as to achieve explicit quantitative coding expression of the causal association between any two material property-usage deep implicit semantic feature coding vectors in the set of material property-usage deep implicit semantic feature coding vectors, and obtain the corresponding semantic causal association factor between material data.

[0040] In particular, in a preferred example of the present application, if the feature variance of the semantic association coding matrix between the material data is greater than or equal to a preset threshold, the average weighted value of the Euclidean distance between each two material property-usage depth implicit semantic feature coding vectors in the set of the material property-usage depth implicit semantic feature coding vectors is calculated as the causal association energy bias item; if the feature variance of the semantic association coding matrix between the material data is less than the preset threshold, the weighted value of the feature mean of the semantic association coding matrix between the material data is calculated as the causal association energy bias item, which is expressed by the formula: ; in, represents a predetermined threshold, Expressed as the Euclidean distance metric function, and Represent different weight parameters respectively. for The length of , that is, the number of vectors in the set of material property-usage deep implicit semantic feature encoding vectors.

[0041] Specifically, by considering the low-level causal association in the semantic association encoding matrix between the material data as a molecular-level relationship inferred based on statistical correlation, the intervention prediction of the causal association energy can be further performed on the basis of the global fine-grained statistical association representation, so as to study the causal association fine-grained structure and dynamic regulation of the semantic association encoding matrix between the material data based on the high-dimensional and heterogeneous representation of causal relationship omics. Among them, when the aggregation distribution representation of the causal graph (i.e., the semantic association encoding matrix between the material data) is greater than a predetermined threshold, the source data integration is biased based on the matrix graph node effect representation of the semantic causal association factor between the material data, and when the aggregation distribution representation of the causal graph is less than a predetermined threshold, the condensed structure modeling can be directly performed through the feature pattern integration compression. In this way, not only can the causal association energy in the semantic association encoding matrix between the material data be encoded and described, but also the implicit causal intervention prediction results can be condensed, so as to obtain a more efficient disclosure of key causal associations.

[0042] In a specific example of the present application, the causal triggering secondary subunit 11222 is expressed by the formula: ; in, represents the normalized exponential function, represents a causal triggering network, represents the gating threshold, Represents the topological feature matrix of semantic causal relationships between material data.

[0043] Specifically, the semantic causal association topological matrix between material data is input into a causal trigger network based on a gated activation function, so as to utilize the gating mechanism to highlight important causal association information, weaken or remove irrelevant or interfering information, and generate a semantic causal association topological feature matrix between material data, making the causal association features more significant and more accurately reflecting the causal relationship between materials, thus providing more effective data for subsequent analysis.

[0044] More specifically, the context semantic association encoding subunit 1123 is used to: input the semantic causal association topological feature matrix between the material data and the set of the material property-amount semantic description encoding vectors into a dynamic walk encoder based on a graph convolutional neural network model to obtain the surface context dynamic walk semantic encoding vector between the material data, which is expressed as follows: ; in, represents a graph convolutional neural network, Represents the semantic encoding vector of the dynamic wandering of surface context between material data.

[0045] Specifically, after using the causal trigger network to deeply optimize the correlation topological structure between material data, the graph convolutional neural network is further used to extract the contextual semantic association between the material property-usage semantic description encoding vectors. By propagating the semantic description features of each material data in the causal correlation topological structure, the explicit semantics between nodes are gradually recursively aggregated from local to global, and the surface context dynamic wandering semantic encoding vector between material data is generated.

[0046] More specifically, the context semantic association encoding subunit 1123 is further used to: input the semantic causal association topological feature matrix between the material data and the set of the material property-usage depth implicit semantic feature encoding vector into the dynamic walking encoder based on the graph convolutional neural network model to obtain the hidden layer context dynamic walking semantic encoding vector between the material data, which is expressed by the formula: ; in, Represents the semantic encoding vector of the hidden context dynamic walk between material data.

[0047] Among them, the material property-amount deep implicit semantic feature coding vector contains deeper implicit semantic information of material data compared to the original material property-amount semantic description coding vector. By inputting the semantic causal association topological feature matrix between the material data and the set of the material property-amount deep implicit semantic feature coding vector into the graph convolutional neural network model, the implicit contextual semantic associations between the material data can be further mined to reveal more subtle and deeper semantic connections between the material data. Similarly, the graph convolutional neural network gradually merges local features into global features by transmitting and aggregating node information layer by layer, thereby generating a hidden context dynamic wandering semantic coding vector between material data.

[0048] More specifically, the context semantic association encoding subunit 1123 is further used to: fuse the hidden context dynamic wandering semantic encoding vector between the material data and the surface context dynamic wandering semantic encoding vector between the material data to obtain the context dynamic wandering semantic encoding vector between the materials, which is expressed by the formula: ; in, represents the fusion weight parameter, Represents the contextual dynamic walk semantic encoding vector between materials.

[0049] Specifically, considering that the characteristic dimensions of the surface context dynamic wandering semantic coding vector between material data and the hidden context dynamic wandering semantic coding vector between material data are complementary, the present application adjusts the importance distribution of the surface context dynamic wandering semantic coding vector between material data and the hidden context dynamic wandering semantic coding vector between material data in the fusion process through a weighted summation fusion strategy to ensure that the generated context dynamic wandering semantic coding vector between materials has stronger distinguishing ability and representation integrity.

[0050] Specifically, in a specific example of the present application, the abnormal diagnosis unit 113 is used to: input the context dynamic walk semantic encoding vector between materials into a list diagnostic device based on a classifier to obtain the diagnosis result, and the diagnosis result is used to indicate whether there is an abnormality in the material list of the physical stage. Specifically, the classifier learns the classification boundary between the normal material list data distribution pattern and the abnormal material list distribution data by training on a large amount of known normal and abnormal material list data, so that after receiving the context dynamic walk semantic encoding vector between materials, it can accurately determine whether it deviates from the normal data distribution range by learning and identifying its feature pattern, and output the corresponding diagnosis result. If the diagnosis result shows that there is an abnormality in the material list of the physical stage, the system will immediately send a corresponding abnormal prompt or suggestion to the user, reminding the user to check and correct the erroneous data in the material list of the physical stage.

[0051] In the above-mentioned railway infrastructure materialization stage carbon emission monitoring system, the regional emission factor acquisition module 120 is used to obtain the regional emission factors corresponding to each material from the public database, and the regional emission factors are the carbon emissions per unit mass. Specifically, after confirming that the material list data of the materialization stage is correct, the carbon emissions can be calculated using the material list data of the materialization stage. Here, considering that there are significant differences in energy structure, production process, environmental protection standards, etc. in different regions, the carbon emissions of the same material in the production process in different regions will be different. For example, in areas where coal is the main energy source, carbon emissions in the cement production process are relatively high; while in areas with abundant hydropower resources, cement production uses clean electricity, so carbon emissions are lower. Therefore, this application further obtains the regional emission factors corresponding to each material from an authoritative public database, that is, the carbon emissions per unit mass of the material in the production process of the corresponding region, to ensure that the carbon emission calculation results can truly reflect the carbon emissions in the material production process.

[0052] Specifically, the material list needs to record in detail all the materials used in the project and their corresponding amounts. At the same time, for each material, obtain its unit mass carbon emissions in a specific area from a reliable source. These emission factors reflect the greenhouse gas emissions generated by different materials throughout the entire production, transportation and other materialization processes. Since the production process and energy consumption pattern of the same material in different regions may be different, it is particularly important to select the emission factor that best matches the project location. This step involves analyzing the differences between different regions. For example, the carbon emissions from cement production may be significantly different in China's eastern coastal areas and western inland areas because the two places differ in fuel efficiency, power supply structure and other aspects. Therefore, when collecting emission factor data, regional factors must be taken into account, and the data source closest to the project location should be selected as much as possible.

[0053] After determining the specific emission factors of the required materials, the next task is to integrate this data into the system. In this process, attention should be paid to the consistency and compatibility of the data format. Since the data formats from different sources may vary, the raw data needs to be preprocessed to ensure that all data can be smoothly imported into the system's database. This includes but is not limited to unifying the measurement units (such as converting all carbon emissions into tons of CO2 / ton of material), standardizing data field names, eliminating duplicate records, and other operations. Only in this way can it be guaranteed that the subsequent calculation modules can correctly read and use these data.

[0054] In view of technological progress, the production technology and energy consumption patterns of materials may change over time, and the corresponding emission factors also need to be updated regularly. To this end, an automated search tool can be set up to regularly scan the selected public database to find the latest emission factor data and compare it with the data in the existing database. If new and more accurate data is found, the old data should be replaced in a timely manner to ensure that the system always operates based on the most accurate information.

[0055] In addition, in order to improve the quality and practicality of the data, an expert review mechanism can be introduced. Experts in fields such as materials science and environmental engineering can be invited to review the collected emission factor data, especially those data points that may have large uncertainties or disputes. Experts can verify the rationality and accuracy of the data by consulting the latest research results, experimental data, etc., and give suggestions for improvement. This approach can not only improve the credibility of the data, but also promote interdisciplinary cooperation and exchanges and promote the development of related fields.

[0056] Since emission factors are directly related to the environmental performance evaluation results of enterprises, strict security measures must be taken to prevent data leakage. Using encryption technology to store and transmit data is an effective method; at the same time, detailed data access rights rules are formulated to allow only authorized personnel to access data at the corresponding level according to work needs. In addition, regular data backup is also essential to prevent accidental loss of important information.

[0057] In the above-mentioned carbon emission monitoring system for the materialization stage of railway infrastructure, the total carbon emission value calculation module 130 is used to calculate the total carbon emission value of the materialization stage based on the regional emission factors corresponding to each material and the material list of the materialization stage. Specifically, according to the law of conservation of matter, the carbon emissions of each material in the materialization process such as production and transportation are proportional to the amount of material used. The carbon emissions of each material can be obtained by multiplying the amount of each material used by its corresponding unit mass carbon emissions (i.e., regional emission factors). Then, the carbon emissions of all materials are added up to obtain the total carbon emission value of the materialization stage.

[0058] To ensure the efficiency and accuracy of the calculation process, automated tools can be used to assist. By writing specialized algorithm scripts or leveraging existing software platforms, large amounts of data can be processed quickly. This is particularly useful for large projects where the bill of materials may contain thousands of different materials and their quantities. Automated tools can automatically extract data from the bill of materials based on preset rules and pair them with the corresponding emission factors for calculation. At the same time, these tools can also detect and mark outliers or inconsistencies in real time for manual review and correction.

[0059] After calculating the carbon emissions of each material, the next step is to aggregate the results of all the individual calculations, that is, to add up the carbon emissions of all materials to get the total carbon emissions value for the entire materialization stage. In this process, special attention must be paid to the consistency of the data format. For example, the carbon emissions of all materials should be uniformly converted into the same unit of measurement to facilitate accurate accumulation. In addition, it is necessary to consider how to handle the values ​​after the decimal point to ensure that the final result is neither distorted nor reflects the actual situation. Generally speaking, retaining an appropriate number of decimal places helps to improve the accuracy of the results.

[0060] In the above-mentioned carbon emission monitoring system for the materialization stage of railway infrastructure, the total carbon emission target setting module 140 is used to extract the expected value of the total carbon emission in the materialization stage from the background database. Here, the expected value of the total carbon emission in the materialization stage is the carbon emission target set by the project in the planning stage based on its own sustainable development goals, industry standards, and experience data of similar projects and other factors. It provides an accurate and reliable benchmark value for the comparative analysis of the total carbon emissions, which helps the project team to better understand the carbon emissions of the project, timely discover the potential risks of excessive carbon emissions, and take corresponding measures to adjust and optimize, so as to ensure that the project can successfully achieve the established carbon emission targets.

[0061] Specifically, in the early stages of project planning, the project team sets carbon emission targets based on its own sustainable development goals, industry standards, and experience data from similar projects. For example, when setting carbon emission targets for railway infrastructure construction projects, it is necessary to comprehensively consider factors such as environmental policy requirements, construction technical conditions, material selection, and corresponding carbon emissions in the region where the project is located. In addition, it is necessary to refer to the actual carbon emission data of similar projects at home and abroad, and by analyzing these data, determine a challenging and feasible total carbon emission expectation.

[0062] Next, these carefully weighed expected total carbon emissions are entered into the backend database. To ensure the accuracy and completeness of the data, the entry must be completed by personnel with professional knowledge and must go through a rigorous review process. In this process, not only the specific values ​​must be recorded, but also the source of each data point, the calculation method, and related assumptions must be detailed. This not only improves the transparency of the data, but also facilitates the traceability and verification of the data in the future.

[0063] In the background database, a data structure is constructed specifically for storing and managing these expected values ​​of total carbon emissions. This structure should have good scalability and flexibility to adapt to the changing needs of different types of projects. Considering that different projects may involve differences in the types of materials, construction processes, etc., it is necessary to design a data model that can be adjusted dynamically. For example, for some projects that use new environmentally friendly materials or innovative construction technologies, corresponding fields can be reserved in the data model to facilitate the flexible addition of new types of carbon emission data. At the same time, in order to facilitate management and query, it is also necessary to establish a classification system to classify and organize data according to dimensions such as project type, geographical location, and implementation time.

[0064] When it is necessary to extract the expected total carbon emissions in the chemical phase from the background database, the system will automatically locate the correct data record based on the relevant parameters entered by the user. The parameters here include but are not limited to basic information such as the unique identifier of the project, the region to which it belongs, and the expected start time. By accurately matching these parameters, the expected total carbon emissions of the corresponding project can be found quickly and accurately. It is worth noting that in this process, the system must also have a certain fault tolerance capability, that is, when encountering a situation where some parameters are missing or incompletely matched, it can provide reasonable alternatives or prompt users to supplement the necessary information.

[0065] In order to ensure that the expected value of the total carbon emissions extracted is always kept up to date, it is essential to regularly update the data in the background database. With changes in the external environment, such as the upgrading of industry standards, the originally set carbon emission targets may no longer apply. At this time, it is necessary to adjust the relevant records in the database in a timely manner. To this end, a special working group can be set up to track the latest policy trends and technological advances, and revise the data in the database accordingly. In addition, automated tools can be introduced to regularly scan relevant policy documents and industry reports, and once new information that affects carbon emission targets is found, the data update process is immediately triggered.

[0066] Under the premise of ensuring data security, authorized users are allowed to access the expected value of total carbon emissions in the background database. Given that these data are crucial to the successful implementation of the project, strict security measures must be taken to prevent unauthorized access or data leakage. Specific practices include but are not limited to: using encryption technology to protect the security of data transmission; setting detailed permission control rules to ensure that only authenticated users can view or modify specific data records; regularly backing up the database to prevent accidental loss of important information. At the same time, a complete audit mechanism should be established to record every data access and modification behavior for future tracking and review.

[0067] In the above-mentioned carbon emission monitoring system for the materialization stage of railway infrastructure, the carbon emission monitoring module 150 is used to calculate the difference between the total carbon emission value of the materialization stage and the expected value of the total carbon emission of the materialization stage to obtain the total carbon emission target offset value, and then determine whether to generate a carbon emission warning prompt based on the comparison between the total carbon emission target offset value and the preset threshold. Specifically, by calculating the difference between the actual total carbon emission value of the materialization stage and the expected value, the degree of deviation between the actual carbon emission and the expected target is quantified to obtain the total carbon emission target offset value. At the same time, the offset value is further compared with the preset threshold to determine whether the total carbon emission exceeds a reasonable range. The preset threshold is determined comprehensively based on factors such as the risk tolerance of the project, the strictness of carbon emission control, and industry standards. If the offset value is greater than the preset threshold, it means that the actual carbon emission deviates too much from the expected target, and a carbon emission warning prompt is automatically generated to attract the attention of the project team, timely analyze the reasons and take corresponding emission reduction measures, including but not limited to optimizing material selection, reducing the use of high-carbon emission materials, improving production processes, improving energy efficiency, and exploring and utilizing low-carbon technologies and clean energy. On the contrary, if the deviation value is within the preset threshold range, it means that the actual carbon emissions are basically consistent with the expected target, and the project team can continue to manage and control carbon emissions according to the established plan.

[0068] In summary, the carbon emission monitoring system for the materialization stage of railway infrastructure based on the embodiment of the present application is explained. After receiving the list of materials for the materialization stage uploaded by the user, it uses a data processing technology based on deep learning to analyze the material data in the list of materials for the materialization stage, and realizes abnormal diagnosis of the list of materials by perceiving the contextual semantic association of each material data. After confirming that the material data is correct, the total value of carbon emissions in the materialization stage is calculated based on the carbon emissions per unit mass corresponding to each material, and a corresponding carbon emission early warning prompt is given according to the offset of the total value of carbon emissions in the materialization stage relative to the expected value. In this way, accurate accounting of carbon emissions in the materialization stage of railway infrastructure can be achieved, effectively avoiding carbon emission calculation deviations caused by inaccurate data, and providing strong support for the low-carbon and sustainable development of the railway industry.

[0069] Furthermore, a method for monitoring carbon emissions during the materialization phase of railway infrastructure is also provided.

[0070] Figure 6 FIG. 1 is a flow chart of a method for monitoring carbon emissions during the materialization phase of railway infrastructure according to an embodiment of the present application. Figure 6As shown, the carbon emission monitoring method for the materialization stage of railway infrastructure includes the following steps: S1, receiving a material list of the materialization stage uploaded by a user, the material list of the materialization stage including the material name and the amount of each material, wherein the materialization stage list uploading module is also used to perform material analysis on the material list of the materialization stage to realize abnormal diagnosis of the list; S2, obtaining the regional emission factor corresponding to each material from a public database, the regional emission factor being the carbon emission per unit mass; S3, calculating the total carbon emission value of the materialization stage based on the regional emission factor corresponding to each material and the material list of the materialization stage; S4, extracting the expected value of the total carbon emission of the materialization stage from the background database; S5, calculating the difference between the total carbon emission value of the materialization stage and the expected value of the total carbon emission of the materialization stage to obtain the total carbon emission target offset value, and then determining whether to generate a carbon emission early warning prompt based on the comparison between the total carbon emission target offset value and a preset threshold.

[0071] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0072] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0073] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0074] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0075] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A carbon emission monitoring system for railway infrastructure in the materialization stage, characterized in that: include: A materialization stage list uploading module is used to receive a material list of the materialization stage uploaded by a user, wherein the material list of the materialization stage includes material names and usage amounts of each material, wherein the materialization stage list uploading module is also used to perform material analysis on the material list of the materialization stage to realize abnormality diagnosis of the list; A regional emission factor acquisition module is used to obtain the regional emission factors corresponding to each material from a public database, where the regional emission factors are carbon emissions per unit mass; A carbon emission total value calculation module, used to calculate the total carbon emission value of the physical and chemical stage based on the regional emission factors corresponding to the various materials and the physical and chemical stage material list; The carbon emission total target setting module is used to extract the expected value of carbon emission total in the chemical stage from the background database; A carbon emission monitoring module, used to calculate the difference between the total carbon emission value of the materialization stage and the expected value of the total carbon emission of the materialization stage to obtain a total carbon emission target offset value, and then determine whether to generate a carbon emission warning prompt based on the comparison between the total carbon emission target offset value and a preset threshold value; The materialization stage list upload module includes: A material list structured coding unit, used for performing structured coding on the material list of the physicalization stage to obtain a set of material property-amount semantic description coding vectors; A material context dynamic walk unit, used for performing causally triggered material context dynamic walk semantic association coding on the set of material property-usage semantic description coding vectors to obtain a material context dynamic walk semantic coding vector; The abnormality diagnosis unit is used to determine the diagnosis result based on the context dynamic walk semantic coding vector between the materials, and the diagnosis result is used to indicate whether there is an abnormality in the material list in the materialization stage.

2. The railway infrastructure materialization stage carbon emission monitoring system according to claim 1 is characterized in that: The bill of materials structured coding unit is used to: Performing semantic embedding coding on each material in the material list of the materialization stage to obtain a set of material semantic description coding vectors; The amount of the corresponding material is added to the end of each material semantic description coding vector in the set of material semantic description coding vectors to obtain the set of material property-amount semantic description coding vectors.

3. The railway infrastructure materialization stage carbon emission monitoring system according to claim 2 is characterized in that: The inter-material context dynamic walking unit includes: An implicit feature mining subunit is used to perform implicit feature mining on each material property-usage semantic description coding vector in the set of material property-usage semantic description coding vectors to obtain a set of material property-usage deep implicit semantic feature coding vectors; A causal association topological feature extraction subunit is used to extract the semantic causal association topological features between the sets of material property-usage deep implicit semantic feature encoding vectors to obtain a semantic causal association topological feature matrix between material data; The context semantic association coding subunit is used to perform multi-level dynamic wandering semantic association coding on the set of material property-quantity semantic description coding vectors based on the semantic causal association topological feature matrix between the material data to obtain the context dynamic wandering semantic coding vector between the materials.

4. The railway infrastructure materialization stage carbon emission monitoring system according to claim 3 is characterized in that: The causal association topological feature extraction subunit includes: A causal association factor calculation secondary subunit is used to calculate the material data semantic causal association factor between any two material property-usage depth implicit semantic feature coding vectors in the set of material property-usage depth implicit semantic feature coding vectors to obtain a material data semantic causal association topological matrix composed of multiple material data semantic causal association factors; The causal triggering secondary subunit is used to input the semantic causal association topological matrix between the material data into the causal triggering network based on the gated activation function to obtain the semantic causal association topological feature matrix between the material data.

5. The railway infrastructure materialization stage carbon emission monitoring system according to claim 4 is characterized in that: The causal correlation factor calculation secondary subunit is used to: Performing semantic association coding on any two material property-usage depth implicit semantic feature coding vectors in the set of material property-usage depth implicit semantic feature coding vectors to obtain a set of semantic association coding matrices between material data; Based on the statistical characteristics of each semantic association coding matrix between material data in the set of semantic association coding matrices between material data, the corresponding semantic causal association factors between material data are calculated to obtain the multiple semantic causal association factors between material data, wherein the statistical characteristics include the characteristic variance, maximum eigenvalue, characteristic mean and causal association energy bias term of the semantic association coding matrix between material data.

6. The railway infrastructure materialization stage carbon emission monitoring system according to claim 5 is characterized in that: If the feature variance of the semantic association coding matrix between the material data is greater than or equal to a preset threshold, the average weighted value of the Euclidean distance between each two material property-usage depth implicit semantic feature coding vectors in the set of the material property-usage depth implicit semantic feature coding vectors is calculated as the causal association energy bias term; If the feature variance of the semantic association coding matrix between the material data is less than a preset threshold, a weighted value of the feature mean of the semantic association coding matrix between the material data is calculated as the causal association energy bias item.

7. The railway infrastructure materialization stage carbon emission monitoring system according to claim 6 is characterized in that: The context semantic association encoding subunit is used to: Inputting the semantic causal association topological feature matrix between the material data and the set of the material property-usage semantic description encoding vectors into a dynamic walking encoder based on a graph convolutional neural network model to obtain a surface context dynamic walking semantic encoding vector between the material data; Inputting the semantic causal association topological feature matrix between the material data and the set of the material property-usage deep implicit semantic feature encoding vectors into the dynamic walking encoder based on the graph convolutional neural network model to obtain the hidden layer context dynamic walking semantic encoding vector between the material data; The hidden layer context dynamic wandering semantic coding vector between the material data and the surface layer context dynamic wandering semantic coding vector between the material data are fused to obtain the context dynamic wandering semantic coding vector between the materials.

8. The railway infrastructure materialization stage carbon emission monitoring system according to claim 7 is characterized in that: The abnormality diagnosis unit is used for: The context dynamic walk semantic encoding vector between materials is input into a classifier-based inventory diagnostician to obtain the diagnostic result.

9. A method for monitoring carbon emissions during the materialization phase of railway infrastructure, characterized in that: include: receiving a material list of the physical and chemical stage uploaded by a user, wherein the material list of the physical and chemical stage includes material names and usage amounts of each material, and performing material analysis on the material list of the physical and chemical stage to realize abnormality diagnosis of the material list; Obtaining the regional emission factor corresponding to each material from a public database, wherein the regional emission factor is the carbon emission per unit mass; Calculate the total carbon emission value of the physical and chemical stage based on the regional emission factors corresponding to each material and the list of materials in the physical and chemical stage; Extract the expected value of total carbon emissions in the chemical process from the backend database; The difference between the total carbon emission value in the materialization stage and the expected value of the total carbon emission in the materialization stage is calculated to obtain the total carbon emission target offset value, and then based on the comparison between the total carbon emission target offset value and the preset threshold, it is determined whether to generate a carbon emission warning prompt.

10. The method for monitoring carbon emissions during the materialization phase of railway infrastructure according to claim 9, characterized in that: Perform material analysis on the material list of the physical and chemical stage to achieve abnormality diagnosis of the material list, including: Performing structured coding on the material list of the physicalization stage to obtain a set of material property-amount semantic description coding vectors; Performing causally triggered dynamic walking semantic association coding between materials on the set of material property-usage semantic description coding vectors to obtain contextual dynamic walking semantic coding vectors between materials; Based on the contextual dynamic walk semantic coding vector between the materials, a diagnosis result is determined, and the diagnosis result is used to indicate whether there is an abnormality in the material list in the materialization stage.

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