A digital twin management system and method for railway construction

Abstract

The application relates to the technical field of railway construction, and particularly discloses a digital twin management system and method for railway construction, which comprises a model establishing unit, a stage clustering unit, an emission calculation unit and an emission early warning unit.The model establishing unit is used for establishing a digital twin model of railway construction based on railway construction project information, and determining carbon emission prediction values of each stage of railway construction according to simulation results of the digital twin model.The stage clustering unit is used for clustering each construction stage according to the carbon emission prediction values of each stage of railway construction.The emission calculation unit is used for determining carbon emission allowance values according to historical carbon emissions of each construction stage.The emission early warning unit is used for determining carbon emission deviation values according to real-time carbon emissions of a current construction stage, and performing real-time carbon emission early warning based on the carbon emission deviation values.The application realizes real-time monitoring and early warning of carbon emissions of each stage of railway construction based on a digital twin model, and meets the management requirements of green and low-carbon in the railway construction process.

Description

Technical Field

[0001] This application relates to the field of railway construction technology, and more specifically, to a digital twin management system and method for railway construction. Background Technology

[0002] Statistics show that the construction industry accounts for approximately 40% of total carbon emissions, with the construction phase accounting for about 12% of the total carbon emissions throughout the building's lifecycle. Current research on railway carbon emissions largely focuses on its lifecycle carbon emissions and energy consumption, as well as electricity usage during operation and maintenance. However, most studies on high-speed railway carbon emissions are from a lifecycle perspective, with relatively little research on carbon emissions during the construction phase. Yet, the construction phase accounts for a significant portion of carbon emissions, and its carbon reduction potential should not be overlooked. Compared to the operation phase, the construction phase is characterized by substantial energy and resource consumption. Therefore, accurate measurement of carbon emissions during this period and management of carbon emissions throughout the entire lifecycle of railway construction are crucial for achieving energy conservation and emission reduction.

[0003] Digital twin technology integrates cutting-edge technologies such as 5G, big data, cloud computing, AI, and converged communications, organically combining information technology, equipment, and management needs. It maps various information from the physical construction site into a virtual space in real time. This system integrates multiple data acquisition tools, including sensors, Internet of Things (IoT) devices, and drones, to monitor key parameters such as construction progress, equipment status, and environmental conditions in real time. Managers can use this system to comprehensively understand the construction site situation in a virtual environment, promptly identifying and resolving potential problems. Therefore, how to apply digital twin technology to the entire lifecycle of railway construction to achieve carbon emission management throughout the entire railway construction lifecycle has become an urgent technical problem to be solved in this field. Summary of the Invention

[0004] This invention provides a digital twin management system and method for railway construction, addressing the problem in existing technologies of applying digital twin technology to the entire lifecycle of railway construction to achieve carbon emission management throughout the entire lifecycle of railway construction. The system includes:

[0005] The model building unit is used to acquire railway construction project information, build a railway construction digital twin model based on the railway construction project information, determine the carbon emission factor prediction value based on the simulation results of the digital twin model, and determine the carbon emission prediction value for each stage of railway construction based on the carbon emission factor prediction value.

[0006] The stage clustering unit is used to cluster each construction stage based on the predicted carbon emissions of each stage of railway construction, and to divide each construction stage into the corresponding clustering partition based on the clustering results.

[0007] The emissions calculation unit is used to obtain the historical carbon emissions at each construction stage and determine the allowable carbon emissions based on the historical carbon emissions at each construction stage.

[0008] The emission early warning unit is used to obtain the real-time carbon emissions during the current construction phase, determine the carbon emission deviation value based on the real-time carbon emissions during the current construction phase, and issue real-time carbon emission early warnings based on the carbon emission deviation value.

[0009] Furthermore, the model building unit determines the predicted carbon emissions for each stage of railway construction based on the predicted carbon emission factor values, including:

[0010] Obtain historical carbon emission factor change data and corresponding carbon emission amounts during the construction phase, and establish a carbon emission sample set based on the historical carbon emission factor change data and corresponding carbon emission amounts during the construction phase;

[0011] An initial carbon emission prediction model is established based on a carbon emission sample set, and the initial carbon emission prediction model is trained to obtain the final carbon emission prediction model.

[0012] Obtain the predicted carbon emission factors during the construction phase, input the predicted carbon emission factors during the construction phase into the final carbon emission prediction model, and obtain the predicted carbon emission value.

[0013] Furthermore, the model building unit establishes a digital twin model of railway construction based on railway construction project information, and determines the predicted value of carbon emission factors based on the simulation results of the digital twin model, including:

[0014] Obtain railway construction project information and divide the railway construction project information into multiple construction stages according to the preset construction period;

[0015] Obtain construction site data for each construction stage, determine the construction cost of each railway station based on the construction site data, and determine the weight value of each railway station based on the construction cost;

[0016] Obtain the preset central station, construct the construction site topology network based on the minimum tree generation algorithm and the weight values ​​of each railway station, and establish a railway construction digital twin model based on the topological association of the site topology network and the construction site data.

[0017] The construction phases are simulated using a digital twin model of railway construction, and the predicted carbon emission factors for each construction phase are determined based on the simulation results.

[0018] Establish a carbon emission prediction model, and determine the predicted carbon emission values ​​during the construction phase based on the predicted values ​​of carbon emission factors according to the carbon emission prediction model.

[0019] Furthermore, the stage clustering unit clusters each construction stage based on the predicted carbon emissions of each stage of railway construction, and assigns each construction stage to a corresponding clustering partition based on the clustering results, including:

[0020] A carbon emission dataset is established based on the predicted average carbon emissions for each construction phase, and k initial cluster centers are randomly selected from the carbon emission dataset.

[0021] Calculate the Euclidean distance between the average predicted carbon emissions in the carbon emissions dataset and the initial cluster center, and divide each construction stage into the corresponding cluster partition based on the Euclidean distance between the average predicted carbon emissions in the carbon emissions dataset and the initial cluster center.

[0022] Calculate the average of the predicted average carbon emissions in each cluster partition, and recalculate the cluster centers based on the average of the predicted average carbon emissions in each cluster partition;

[0023] Repeat the above steps until the cluster centers no longer change or the number of iterations reaches the preset maximum number of iterations. Based on the clustering results, divide each construction stage into the corresponding final cluster partition.

[0024] Furthermore, the emission calculation unit determines the permissible carbon emission value based on the historical carbon emissions of each construction phase and the corresponding clustering partitions, including:

[0025] Obtain the cluster center values ​​of the cluster partition to which the construction phase belongs, and determine the carbon emission exceedance threshold based on the cluster center values ​​of the cluster partition to which the construction phase belongs;

[0026] Obtain historical carbon emission change data for each construction phase, and plot historical carbon emission change curves based on the historical carbon emission change data;

[0027] Screen out historical carbon emissions that exceed the carbon emission exceedance threshold from the historical carbon emission change curve, and determine the exceedance frequency of historical carbon emissions based on the historical carbon emissions that exceed the carbon emission exceedance threshold.

[0028] The permissible value for carbon emissions is determined based on the frequency of historical carbon emissions exceeding the limit.

[0029] Furthermore, determining the carbon emission exceedance threshold based on the cluster center value of the cluster partition to which the construction phase belongs includes:

[0030] Obtain the sum of cluster center values ​​for all construction stages, calculate the ratio of the cluster center value for each construction stage to the sum of the cluster center values ​​for all construction stages, and obtain the carbon emission ratio coefficient for each construction stage.

[0031] Obtain the preset full-cycle carbon emission exceedance threshold, and multiply the carbon emission ratio coefficient of each construction stage by the preset full-cycle carbon emission exceedance threshold to obtain the carbon emission exceedance threshold for each construction stage.

[0032] Furthermore, determining the permissible carbon emission value based on the frequency of historical carbon emission exceedances includes:

[0033] Based on the frequency of exceeding historical carbon emission limits, a permissible carbon emission value is calculated using a specific formula.

[0034]

[0035] Where P is the permissible value for carbon emissions, P α To preset the initial allowable carbon emissions, C α C represents the preset frequency of exceeding the standard, where C is the frequency of exceeding the standard in historical carbon emissions, and R is the preset range coefficient.

[0036] Furthermore, the emission early warning unit determines the carbon emission deviation value based on the real-time carbon emissions during the current construction phase, including:

[0037] Based on the real-time carbon emission changes during the current construction phase, a real-time carbon emission change curve is plotted, and the real-time carbon emission change curve is divided into several sub-change curves according to a preset sliding time window.

[0038] Calculate the average carbon emissions of each sub-curve, and plot the average value change curve based on the average carbon emissions of each sub-curve.

[0039] Calculate the absolute value of the slope of adjacent average values ​​in the average value change curve, and determine the average value of all absolute slope values ​​based on the absolute value of the slope of adjacent average values ​​in the average value change curve;

[0040] The carbon emission fluctuation coefficient is obtained by normalizing the average of the absolute values ​​of all slopes.

[0041] Calculate the difference between the real-time carbon emissions and the allowable carbon emissions during the current construction phase, and multiply the carbon emission fluctuation coefficient by the corresponding difference between the real-time carbon emissions and the allowable carbon emissions to obtain the carbon emission deviation value.

[0042] Furthermore, the emission early warning unit provides real-time carbon emission early warning based on the carbon emission deviation value, including:

[0043] Establish a pre-set carbon emission information database and obtain the carbon emission level parameters corresponding to each carbon emission deviation range in the carbon emission information database;

[0044] The carbon emission deviation value is matched with each carbon emission deviation range in the carbon emission information database, and the carbon emission level parameter corresponding to the carbon emission deviation value is determined based on the matching result.

[0045] The carbon emission level parameter corresponding to the carbon emission deviation value is compared with the preset carbon emission allowance parameter. If the carbon emission level parameter corresponding to the carbon emission deviation value is greater than the preset carbon emission allowance parameter, a carbon emission warning is issued.

[0046] To achieve the above objectives, the present invention also provides a digital twin management method for railway construction, comprising:

[0047] Obtain railway construction project information, establish a digital twin model of railway construction based on the railway construction project information, determine the predicted value of carbon emission factor based on the simulation results of the digital twin model, and determine the predicted value of carbon emission at each stage of railway construction based on the predicted value of carbon emission factor.

[0048] Based on the predicted carbon emissions of each stage of railway construction, the construction stages are clustered, and each construction stage is assigned to a corresponding cluster partition based on the clustering results.

[0049] Obtain historical carbon emissions for each construction phase and determine permissible carbon emission values ​​based on historical carbon emissions for each construction phase.

[0050] Obtain real-time carbon emissions during the current construction phase, determine carbon emission deviation values ​​based on these values, and issue real-time carbon emission warnings based on these deviation values.

[0051] The beneficial effects of this invention are as follows:

[0052] By applying the above technical solutions, this invention simulates each construction stage of railway construction by establishing a digital twin model, and predicts the carbon emissions of each construction stage based on the simulation results of the digital twin model, effectively improving the accuracy of carbon emission prediction during railway construction. At the same time, by dividing each construction stage into zones, the real-time carbon emissions within each zone are monitored and warned in real time, meeting the green and low-carbon management requirements during railway construction. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This invention presents a schematic diagram of the structure of a digital twin management system for railway construction according to an embodiment of the present invention;

[0055] Figure 2 The diagram shows the overall flowchart of a digital twin management method for railway construction proposed in an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] This application provides a digital twin management system for railway construction, such as... Figure 1 As shown, it includes: a model building unit, used to acquire railway construction project information, build a digital twin model of railway construction based on the railway construction project information, determine the predicted value of carbon emission factors based on the simulation results of the digital twin model, and determine the predicted value of carbon emissions for each stage of railway construction based on the predicted value of carbon emission factors; a stage clustering unit, used to cluster each construction stage based on the predicted value of carbon emissions for each stage of railway construction, and divide each construction stage into the corresponding cluster partition based on the clustering results; an emission calculation unit, used to acquire the historical carbon emissions for each construction stage, and determine the allowable value of carbon emissions based on the historical carbon emissions for each construction stage; and an emission early warning unit, used to acquire the real-time carbon emissions for the current construction stage, determine the carbon emission deviation value based on the real-time carbon emissions for the current construction stage, and issue a real-time carbon emission early warning based on the carbon emission deviation value.

[0058] In this embodiment, a digital twin model of railway construction is used to predict carbon emissions at each stage of railway construction. The predicted carbon emissions are used to divide each construction stage into corresponding clusters, and the allowable carbon emissions value is calculated based on the cluster to which each construction stage belongs. This enables precise differentiation of each construction stage in the railway construction process. The carbon emissions of each construction stage are compared in segments to obtain more accurate carbon emission early warning data. The carbon emission deviation value is obtained by using the current real-time carbon emissions and the allowable carbon emissions value, and carbon emission early warning is based on the carbon emission deviation value. This can effectively meet the green and low-carbon management requirements in the railway construction process.

[0059] In some embodiments of this application, the step of establishing a carbon emission prediction model and determining the predicted carbon emission value during the construction phase based on the predicted carbon emission factor value of the carbon emission prediction model includes: acquiring historical carbon emission factor change data and corresponding carbon emission amounts during the construction phase; establishing a carbon emission sample set based on the historical carbon emission factor change data and corresponding carbon emission amounts during the construction phase; establishing an initial carbon emission prediction model based on the carbon emission sample set and training the initial carbon emission prediction model to obtain a final carbon emission prediction model; acquiring the predicted carbon emission factor value during the construction phase and inputting the predicted carbon emission factor value during the construction phase into the final carbon emission prediction model to obtain the predicted carbon emission value.

[0060] In this embodiment, an initial carbon emission prediction model is established based on a deep learning neural network model using a carbon emission sample set. The initial carbon emission prediction model is then trained using the carbon emission sample set to obtain the final carbon emission prediction model. Accurate prediction of carbon emissions during the railway construction phase is achieved through simulation prediction data of carbon emission factors.

[0061] In some embodiments of this application, the model building unit establishes a digital twin model of railway construction based on railway construction project information, and determines the predicted value of carbon emission factors based on the simulation results of the digital twin model. This includes: acquiring railway construction project information and dividing the railway construction project information into multiple construction stages according to a preset construction period; acquiring construction site data for each construction stage, determining the construction cost of each railway station based on the construction site data, and determining the weight value of each railway station based on the construction cost; acquiring a preset central station, constructing a construction site topology network based on the minimum tree generation algorithm combined with the weight values ​​of each railway station, and establishing a railway construction digital twin model based on the topological association of the site topology network combined with the construction site data; simulating each construction stage based on the railway construction digital twin model, and determining the predicted value of carbon emission factors for each construction stage based on the simulation results; and establishing a carbon emission prediction model, and determining the predicted value of carbon emissions for each construction stage based on the carbon emission factor prediction value using the carbon emission prediction model.

[0062] In this embodiment, the construction site data specifically includes the site layout, production materials, construction machinery, and construction personnel of the railway construction site. The weight value of each railway station is determined by its construction cost; the higher the construction cost, the smaller the corresponding weight value. Based on a minimum tree generation algorithm, starting from the central station, an edge is selected and added to the network each time using a predetermined random strategy. The weight value of each edge is obtained through the weight value corresponding to each railway station; the smaller the weight value, the greater the probability of it being selected. This process is iterated until all railway stations are connected to the network, resulting in a construction site topology network. A digital twin model of railway construction is established by combining the construction site topology network with the construction site data, enabling simulation of each stage of railway construction. This simulates a three-dimensional model of the construction site's structure, electromechanical systems, personnel, and geographical location, creating a digital image of the railway construction site. The material and energy consumption of the construction site is predicted, thereby simulating the carbon emission factors for each construction stage. Based on the carbon emission prediction model, the carbon emission amount for each construction stage is predicted according to the carbon emission factors.

[0063] In some embodiments of this application, the stage clustering unit clusters each construction stage according to the predicted carbon emissions of each stage of railway construction, and divides each construction stage into a corresponding cluster partition according to the clustering results. This includes: establishing a carbon emission dataset based on the average predicted carbon emissions of each construction stage, and randomly selecting k initial cluster centers from the carbon emission dataset; calculating the Euclidean distance from the average predicted carbon emissions in the carbon emission dataset to the initial cluster centers, and dividing each construction stage into a corresponding cluster partition according to the Euclidean distance from the average predicted carbon emissions in the carbon emission dataset to the initial cluster centers; calculating the average value of the average predicted carbon emissions in each cluster partition, and recalculating the cluster centers according to the average value of the average predicted carbon emissions in each cluster partition; repeating the above steps iteratively until the cluster centers no longer change or the number of iterations reaches a preset maximum number of iterations, and dividing each construction stage into a corresponding final cluster partition according to the clustering results. In this embodiment, the value of k is set to 4. Based on the k-means clustering algorithm, the predicted carbon emissions are clustered for each construction stage, and each construction stage is divided into 4 cluster partitions. The allowable carbon emissions value is calculated based on the cluster partition to which each construction stage belongs, so as to achieve accurate differentiation of each construction stage in the railway construction process and segmented comparison of carbon emissions of each construction stage to obtain more accurate carbon emission early warning data.

[0064] In some embodiments of this application, the emission calculation unit determines the allowable carbon emission value based on the historical carbon emissions of each construction stage and the corresponding clustering partition, including: obtaining the cluster center value of the clustering partition to which the construction stage belongs, and determining the carbon emission exceedance threshold based on the cluster center value of the clustering partition to which the construction stage belongs; obtaining historical carbon emission change data for each construction stage, and plotting historical carbon emission change curves based on the historical carbon emission change data; filtering out historical carbon emissions that exceed the carbon emission exceedance threshold from the historical carbon emission change curves, and determining the exceedance frequency of historical carbon emissions based on the historical carbon emissions that exceed the carbon emission exceedance threshold; and determining the allowable carbon emission value based on the exceedance frequency of historical carbon emissions.

[0065] In this embodiment, historical carbon emission change data corresponding to the entire construction phase is obtained and a historical carbon emission change curve is plotted. The number of historical carbon emissions exceeding the carbon emission exceedance threshold in the historical carbon emission change curve is determined as the exceedance frequency of historical carbon emissions.

[0066] In some embodiments of this application, determining the carbon emission exceedance threshold based on the cluster center value of the cluster partition to which the construction stage belongs includes: obtaining the sum of the cluster center values ​​of all construction stages, calculating the ratio of the cluster center value of each construction stage to the sum of the cluster center values ​​of all construction stages, and obtaining the carbon emission proportion coefficient of each construction stage; obtaining a preset full-cycle carbon emission exceedance threshold, and multiplying the carbon emission proportion coefficient of each construction stage by the preset full-cycle carbon emission exceedance threshold to obtain the carbon emission exceedance threshold of each construction stage.

[0067] In this embodiment, the proportion of cluster centers in each construction stage to all cluster centers is obtained by calculating the ratio of the cluster center value of each construction stage to the sum of the cluster center values ​​of all construction stages. The preset full-cycle carbon emission exceedance threshold is then divided into each construction stage by the carbon emission ratio coefficient, thus obtaining the carbon emission exceedance threshold for each construction stage.

[0068] In some embodiments of this application, determining the permissible carbon emission value based on the frequency of historical carbon emission exceedances includes: calculating the permissible carbon emission value based on the frequency of historical carbon emission exceedances using a permissible value calculation formula, wherein the permissible value calculation formula is specifically as follows:

[0069]

[0070] Where P is the permissible value for carbon emissions, P α To preset the initial allowable carbon emissions, C α C represents the preset frequency of exceeding the standard, where C is the frequency of exceeding the standard in historical carbon emissions, and R is the preset range coefficient.

[0071] In this embodiment, the allowable carbon emission value is determined by the frequency of historical carbon emission exceedances. The higher the frequency of historical carbon emission exceedances, the lower the set allowable carbon emission value, and the smaller the tolerance for carbon emissions during the construction phase.

[0072] In some embodiments of this application, the emission early warning unit determines the carbon emission deviation value based on the real-time carbon emissions during the current construction phase, including: drawing a real-time carbon emission change curve based on the changes in real-time carbon emissions during the current construction phase; dividing the real-time carbon emission change curve into several sub-change curves according to a preset sliding time window; calculating the average carbon emissions of each sub-change curve; drawing an average value change curve based on the average carbon emissions of each sub-change curve; calculating the absolute value of the slope of adjacent average values ​​in the average value change curve; determining the average value of all absolute slope values ​​based on the absolute value of the slope of adjacent average values ​​in the average value change curve; normalizing the average value of all absolute slope values ​​to obtain a carbon emission fluctuation coefficient; calculating the difference between the real-time carbon emissions during the current construction phase and the allowable carbon emission value; and multiplying the carbon emission fluctuation coefficient by the corresponding difference between the real-time carbon emissions and the allowable carbon emission value to obtain the carbon emission deviation value.

[0073] In this embodiment, the carbon emission changes during the current construction phase are monitored in real time using a digital twin model of railway construction, and a real-time carbon emission change curve is plotted. By normalizing the average value of all slope absolute values, the range of the average value of all slope absolute values ​​is limited to [1, 2], thereby obtaining the carbon emission fluctuation coefficient. The carbon emission fluctuation coefficient represents the fluctuation of carbon emissions. The larger the carbon emission fluctuation coefficient, the higher the corresponding carbon emission deviation value, which facilitates accurate early warning of real-time carbon emissions.

[0074] In some embodiments of this application, the emission warning unit performs real-time carbon emission warnings based on carbon emission deviation values, including: establishing a preset carbon emission information database and obtaining carbon emission level parameters corresponding to each carbon emission deviation range in the carbon emission information database; matching the carbon emission deviation value with each carbon emission deviation range in the carbon emission information database and determining the carbon emission level parameter corresponding to the carbon emission deviation value based on the matching result; comparing the carbon emission level parameter corresponding to the carbon emission deviation value with a preset carbon emission allowance parameter, and if the carbon emission level parameter corresponding to the carbon emission deviation value is greater than the preset carbon emission allowance parameter, then a carbon emission warning is issued.

[0075] In this embodiment, real-time carbon emission early warning is provided based on the carbon emission deviation value at the current construction stage. This facilitates project managers in taking different emission reduction measures for different warning levels, thus meeting the green and low-carbon management requirements during railway construction.

[0076] Based on the same technological concept, such as Figure 2As shown, the present invention also provides a digital twin management method for railway construction, comprising:

[0077] S101. Obtain railway construction project information, establish a railway construction digital twin model based on the railway construction project information, determine the carbon emission factor prediction value based on the simulation results of the digital twin model, and determine the carbon emission prediction value for each stage of railway construction based on the carbon emission factor prediction value.

[0078] S102, based on the predicted carbon emissions of each stage of railway construction, the construction stages are clustered, and each construction stage is assigned to the corresponding cluster partition based on the clustering results;

[0079] S103, obtain the historical carbon emissions of each construction phase, and determine the allowable carbon emission value based on the historical carbon emissions of each construction phase;

[0080] S104: Obtain the real-time carbon emissions of the current construction phase, determine the carbon emission deviation value based on the real-time carbon emissions of the current construction phase, and issue a real-time carbon emission warning based on the carbon emission deviation value.

[0081] By applying the above technical solutions, this invention employs a model building unit to acquire railway construction project information, establish a digital twin model of railway construction based on the project information, determine the predicted value of carbon emission factors based on the simulation results of the digital twin model, and determine the predicted value of carbon emissions for each stage of railway construction based on the predicted value of carbon emission factors; a stage clustering unit to cluster each construction stage based on the predicted value of carbon emissions for each stage of railway construction, and classify each construction stage into a corresponding cluster partition based on the clustering results; an emission calculation unit to acquire the historical carbon emissions for each construction stage, and determine the allowable value of carbon emissions based on the historical carbon emissions for each construction stage; and an emission early warning unit to acquire the real-time carbon emissions for the current construction stage, determine the carbon emission deviation value based on the real-time carbon emissions for the current construction stage, and provide real-time carbon emission early warning based on the carbon emission deviation value. This invention, based on a digital twin model, provides real-time monitoring and early warning of carbon emissions for each stage of railway construction, meeting the green and low-carbon management requirements in the railway construction process.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A digital twin management system for railway construction, characterized in that, include: The model building unit is used to acquire railway construction project information, build a railway construction digital twin model based on the railway construction project information, determine the carbon emission factor prediction value based on the simulation results of the digital twin model, and determine the carbon emission prediction value for each stage of railway construction based on the carbon emission factor prediction value. The stage clustering unit is used to cluster each construction stage based on the predicted carbon emissions of each stage of railway construction, and to divide each construction stage into the corresponding clustering partition based on the clustering results. The emission calculation unit is used to obtain the historical carbon emissions of each construction phase and determine the allowable carbon emission value based on the historical carbon emissions of each construction phase and the corresponding clustering partition. The emission early warning unit is used to obtain the real-time carbon emissions during the current construction phase, determine the carbon emission deviation value based on the real-time carbon emissions during the current construction phase, and issue real-time carbon emission early warnings based on the carbon emission deviation value.

2. The digital twin management system for railway construction according to claim 1, characterized in that, The model building unit determines the predicted carbon emissions for each stage of railway construction based on the predicted carbon emission factor values, including: Obtain historical carbon emission factor change data and corresponding carbon emission amounts during the construction phase, and establish a carbon emission sample set based on the historical carbon emission factor change data and corresponding carbon emission amounts during the construction phase; An initial carbon emission prediction model is established based on a carbon emission sample set, and the initial carbon emission prediction model is trained to obtain the final carbon emission prediction model. Obtain the predicted carbon emission factors during the construction phase, input the predicted carbon emission factors during the construction phase into the final carbon emission prediction model, and obtain the predicted carbon emission value.

3. The digital twin management system for railway construction according to claim 2, characterized in that, The model building unit establishes a digital twin model of railway construction based on railway construction project information, and determines the predicted value of carbon emission factors based on the simulation results of the digital twin model, including: Obtain railway construction project information and divide the railway construction project information into multiple construction stages according to the preset construction period; Obtain construction site data for each construction stage, determine the construction cost of each railway station based on the construction site data, and determine the weight value of each railway station based on the construction cost of each railway station. Obtain the preset central stations, construct the construction site topology network based on the minimum tree generation algorithm and the weight values ​​of each railway station, and establish a railway construction digital twin model based on the topological associations of the site topology network and the construction site data. The construction phases are simulated using a digital twin model of railway construction, and the simulation results are obtained. Based on the simulation results, the predicted carbon emission factor values ​​for each construction phase are determined. Establish a carbon emission prediction model, and determine the predicted carbon emission values ​​during the construction phase based on the predicted values ​​of carbon emission factors according to the carbon emission prediction model.

4. The digital twin management system for railway construction according to claim 3, characterized in that, The stage clustering unit clusters each construction stage based on the predicted carbon emissions for each stage of railway construction, and assigns each construction stage to a corresponding cluster partition based on the clustering results, including: A carbon emission dataset is established based on the predicted average carbon emissions for each construction phase, and k initial cluster centers are randomly selected from the carbon emission dataset. Calculate the Euclidean distance between the average predicted carbon emissions in the carbon emissions dataset and the initial cluster center, and divide each construction stage into the corresponding cluster partition based on the Euclidean distance between the average predicted carbon emissions in the carbon emissions dataset and the initial cluster center. Calculate the average of the predicted average carbon emissions in each cluster partition, and recalculate the cluster centers based on the average of the predicted average carbon emissions in each cluster partition; Repeat the above steps until the cluster centers no longer change or the number of iterations reaches the preset maximum number of iterations. Based on the clustering results, divide each construction stage into the corresponding final cluster partition.

5. The digital twin management system for railway construction according to claim 4, characterized in that, The emission calculation unit determines the allowable carbon emission value based on the historical carbon emissions of each construction phase and the corresponding clustering partitions, including: Obtain the cluster center values ​​of the cluster partition to which the construction phase belongs, and determine the carbon emission exceedance threshold based on the cluster center values ​​of the cluster partition to which the construction phase belongs; Obtain historical carbon emission change data for each construction phase, and plot historical carbon emission change curves based on the historical carbon emission change data; Screen out historical carbon emissions that exceed the carbon emission exceedance threshold from the historical carbon emission change curve, and determine the exceedance frequency of historical carbon emissions based on the historical carbon emissions that exceed the carbon emission exceedance threshold. The permissible value for carbon emissions is determined based on the frequency of historical carbon emissions exceeding the limit.

6. The digital twin management system for railway construction according to claim 5, characterized in that, The determination of carbon emission exceedance thresholds based on the cluster center values ​​of the cluster partition to which the construction phase belongs includes: Obtain the sum of cluster center values ​​for all construction stages, calculate the ratio of the cluster center value for each construction stage to the sum of the cluster center values ​​for all construction stages, and obtain the carbon emission ratio coefficient for each construction stage. Obtain the preset full-cycle carbon emission exceedance threshold, and multiply the carbon emission ratio coefficient of each construction stage by the preset full-cycle carbon emission exceedance threshold to obtain the carbon emission exceedance threshold for each construction stage.

7. The digital twin management system for railway construction according to claim 6, characterized in that, The determination of permissible carbon emission values ​​based on the frequency of historical carbon emission exceedances includes: Based on the frequency of exceeding historical carbon emission limits, a permissible carbon emission value is calculated using a specific formula. Where P is the permissible value for carbon emissions, P α To preset the initial allowable carbon emissions, C α C represents the preset frequency of exceeding the standard, where C is the frequency of exceeding the standard in historical carbon emissions, and R is the preset range coefficient.

8. The digital twin management system for railway construction according to claim 1, characterized in that, The emission early warning unit determines the carbon emission deviation value based on the real-time carbon emissions during the current construction phase, including: Based on the real-time carbon emission changes during the current construction phase, a real-time carbon emission change curve is plotted, and the real-time carbon emission change curve is divided into several sub-change curves according to a preset sliding time window. Calculate the average carbon emissions of each sub-curve, and plot the average value change curve based on the average carbon emissions of each sub-curve. Calculate the absolute value of the slope of adjacent average values ​​in the average value change curve, and determine the average value of all absolute slope values ​​based on the absolute value of the slope of adjacent average values ​​in the average value change curve; The carbon emission fluctuation coefficient is obtained by normalizing the average of the absolute values ​​of all slopes. Calculate the difference between the real-time carbon emissions and the allowable carbon emissions during the current construction phase, and multiply the carbon emission fluctuation coefficient by the corresponding difference between the real-time carbon emissions and the allowable carbon emissions to obtain the carbon emission deviation value.

9. The digital twin management system for railway construction according to claim 8, characterized in that, The emission early warning unit provides real-time carbon emission early warnings based on carbon emission deviation values, including: Establish a pre-set carbon emission information database and obtain the carbon emission level parameters corresponding to each carbon emission deviation range in the carbon emission information database; The carbon emission deviation value is matched with each carbon emission deviation range in the carbon emission information database, and the carbon emission level parameter corresponding to the carbon emission deviation value is determined based on the matching result. The carbon emission level parameter corresponding to the carbon emission deviation value is compared with the preset carbon emission allowance parameter. If the carbon emission level parameter corresponding to the carbon emission deviation value is greater than the preset carbon emission allowance parameter, a carbon emission warning is issued.

10. A digital twin management method for railway construction, characterized in that, include: Obtain railway construction project information, establish a digital twin model of railway construction based on the railway construction project information, determine the predicted value of carbon emission factor based on the simulation results of the digital twin model, and determine the predicted value of carbon emission at each stage of railway construction based on the predicted value of carbon emission factor. Based on the predicted carbon emissions of each stage of railway construction, the construction stages are clustered, and each construction stage is assigned to a corresponding cluster partition based on the clustering results. Obtain historical carbon emissions for each construction phase and determine permissible carbon emission values ​​based on historical carbon emissions for each construction phase. Obtain real-time carbon emissions during the current construction phase, determine carbon emission deviation values ​​based on these values, and issue real-time carbon emission warnings based on these deviation values.

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