Recommendation method for road and bridge design optimization based on carbon emission data

Abstract

The present invention discloses a road and bridge design optimization and recommendation method based on carbon emission data, comprising: constructing a standard design library based on several road and bridge design schemes; collecting actual environmental data of road and bridge construction projects, and dividing the road and bridge construction projects into several stages based on the actual environmental data; using a carbon emission prediction model to estimate carbon emissions for several stages, obtaining estimated carbon emissions for several stages, and summing these to obtain a predicted total carbon emission; modifying the standard design library based on the actual environmental data for several stages to obtain an applied design library; and optimizing the current construction scheme based on the predicted total carbon emission and the applied design library. This invention relates to the field of carbon emission technology for engineering projects and solves the technical problem that existing road and bridge design recommendation methods are not rational and do not meet the requirements of sustainable development.

Description

Technical Field

[0001] The present invention belongs to the field of engineering carbon emissions and relates to scheme optimization technology, specifically a road and bridge design optimization recommendation method based on carbon emission data. Background Art

[0002] As a key component of modern transportation infrastructure construction, road and bridge projects require scientific, rational, and sustainable design. A well-designed road and bridge project not only meets traffic flow requirements and ensures safe and smooth driving, but also effectively reduces construction costs, mitigates negative environmental impacts, and promotes regional economic development and social progress. Therefore, a scientific and efficient road and bridge design method recommendation system is crucial for ensuring successful project implementation, achieving optimal resource allocation, and promoting the development of the entire industry.

[0003] However, existing road and bridge design recommendation methods suffer from numerous inefficiencies. For one thing, most traditional methods offer limited consideration of environmental factors during the design recommendation process. They often focus solely on basic information such as topography and landforms, while ignoring the comprehensive impact of environmental factors such as geological conditions, hydrological conditions, and land use on road and bridge design. Furthermore, with increasing global attention to environmental protection and sustainable development, carbon emissions during the construction and operation of road and bridge projects are receiving increasing attention. However, traditional design recommendation methods rarely incorporate carbon emissions into their evaluation metrics during proposal formulation and recommendation. This leaves design solutions with significant deficiencies in carbon emission control, making them difficult to meet the requirements of current green development.

[0004] In addition, faced with the ever-increasing amount of historical road and bridge project data and complex and ever-changing actual environmental data, traditional methods are unable to carry out efficient data integration, in-depth mining, and precise analysis. As a result, in the design recommendation process, historical experience and data patterns cannot be fully utilized to provide designers with comprehensive and accurate reference basis, thus affecting the quality and feasibility of the design plan. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a road and bridge design optimization recommendation method based on carbon emission data, which is used to solve the technical problems that the existing road and bridge design recommendation method is not reasonable and does not meet the requirements of sustainable development.

[0006] To achieve the above objectives, the present invention provides a road and bridge design optimization and recommendation method based on carbon emission data, comprising:

[0007] S1, build a standard scheme design library based on several road and bridge design schemes;

[0008] S2, collecting actual environmental data of the road and bridge construction project, and dividing the road and bridge construction project into several stages based on the actual environmental data;

[0009] S3, using a carbon emission prediction model to predict carbon emissions for several stages, obtaining estimated carbon emissions for the several stages, and summing the estimated carbon emissions to obtain a total predicted carbon emission; wherein the carbon emission prediction model is constructed based on a deep learning algorithm;

[0010] S4, modifying the standard scheme design library according to the actual environmental data of several stages to obtain the application design library;

[0011] S5, optimize the current construction plan based on the predicted total carbon emissions and the application design library.

[0012] Based on the above technical process, the present invention achieves precise optimization and recommendation of road and bridge design schemes. By constructing a standard scheme design library and integrating a large amount of historical key data, it provides rich references for design optimization. At the same time, combined with actual environmental data, the project is divided into several stages, and the characteristics and requirements of each stage are accurately analyzed. Then, a carbon emission prediction model is used to make high-precision predictions of carbon emissions in each stage, providing a quantitative basis for carbon emission control and scheme screening. The standard scheme design library is then revised using actual environmental data to enhance the feasibility and adaptability of the design scheme. Finally, based on the predicted total carbon emissions and design library data, the construction plan is optimized, taking into account both cost and carbon emissions, and the optimal design scheme combination is screened. This provides a set of efficient optimization and recommendation methods for road and bridge design, which helps to improve the overall quality and sustainable development capabilities of road and bridge projects.

[0013] S1, build a standard scheme design library based on several road and bridge design schemes;

[0014] Furthermore, the construction of a standard scheme design library based on a number of road and bridge design schemes includes:

[0015] S1-1, collect environmental data, carbon emission data, and cost data of historical road and bridge project design schemes to obtain several road and bridge design schemes;

[0016] S1-2, determining whether there are duplicated schemes among the road and bridge design schemes; if so, obtaining standard data based on the environmental data, carbon emission data, and cost data of the duplicated schemes; if not, marking the environmental data, carbon emission data, and cost data of the road and bridge design schemes as standard data;

[0017] S1-3, dividing the plurality of road and bridge design schemes into a plurality of highway design schemes and a plurality of bridge design schemes, and constructing a standard scheme design library based on corresponding standard data.

[0018] Furthermore, obtaining standard data based on the environmental data, carbon emission data and cost data of the repeated scheme includes:

[0019] The environmental data of the repeated schemes are averaged according to several indicator categories to obtain standard environmental data;

[0020] The carbon emission data and cost data of the repeated schemes are averaged to obtain standard carbon emission data and standard cost data;

[0021] It should be noted that the environmental data in the present invention include but are not limited to topographic conditions, geological conditions and hydrological conditions, among which the indicator categories of topographic conditions include but are not limited to average altitude, average slope, terrain undulation, and surface cutting depth; the indicator categories of geological conditions include but are not limited to foundation bearing capacity, soil moisture content, and rock compressive strength; the indicator categories of hydrological conditions include but are not limited to average water level, average water flow velocity, and river sediment content.

[0022] By collecting and processing a large amount of historical data, the Standard Design Library provides a rich reference for new projects, helping designers quickly compare and draw on the experience of similar projects, and accurately optimize them based on the specific needs of the current project. At the same time, the data in the library is filtered and standardized to remove duplication and redundant information, ensuring high data quality and reliability, thereby improving the scientific nature and accuracy of design solutions. Furthermore, the data in the Standard Design Library covers a variety of environmental conditions as well as carbon emissions and cost data for different projects, helping designers better understand design requirements in different environments and design solutions that are more tailored to the actual project environment.

[0023] Furthermore, the road and bridge construction project is divided into several stages according to the actual environmental data, including:

[0024] S2-1, based on the scope of the road and bridge construction project, obtain corresponding high-resolution remote sensing satellite images and basic geographic information data and pre-process them to obtain actual environmental data; basic geographic information data includes topographic data, geological data, hydrological data, and land use data;

[0025] S2-2, using the spatial analysis function of GIS software to analyze the terrain data in the actual environmental data and obtain the terrain analysis results;

[0026] S2-3, based on the terrain analysis results, combined with geological data, hydrological data and land use data, the road and bridge construction project is manually segmented into several stages.

[0027] Furthermore, the carbon emission prediction model is constructed based on a deep learning algorithm, including:

[0028] S3-1: Collect environmental data, carbon emission data, and BIM models of historical road and bridge projects, and extract the required material types and quantities from the BIM models to obtain a historical data set;

[0029] S3-2, preprocessing the historical data set and dividing the preprocessed data set according to a preset ratio to obtain a training set, a validation set, and a test set;

[0030] S3-3, building a neural network model based on a deep learning algorithm, and setting training parameters of the neural network model to obtain an initial model;

[0031] S3-4, input the training set and validation set into the initial model for iterative training and optimization update to obtain the model with the best validation accuracy;

[0032] S3-5: Input the test set into the model with the best verification accuracy for model evaluation to obtain the test accuracy and determine whether the test accuracy is greater than the preset accuracy threshold. If yes, mark the model with the best verification accuracy as the carbon emission prediction model; if no, jump to S3-3.

[0033] It should be noted that the input data of the carbon emission prediction model includes environmental data, material type and material quantity, and the output data is the predicted total carbon emissions.

[0034] Furthermore, the data correction of the standard scheme design library based on the actual environmental data of several stages includes:

[0035] S4-1, use the analytic hierarchy process to determine the weight of each indicator category in the environmental data, and obtain the weight vector W = [w1, w2, ..., w n ]; where n represents the number of indicator categories in the environmental data;

[0036] S4-2, the standard environmental data of several design schemes in the standard scheme design library are expressed as E p std =[e std_1 ,e std_2 ,…,e std_n ], the actual environmental data of several stages are expressed as E q real =[e real_1 ,e real_2 ,…,e real_n ]; among them, E p std represents the standard environmental data of the p-th design scheme, E q real Represents the actual environmental data of the qth stage;

[0037] S4-3, according to formula S pq=1-√[∑ n i＝1 w i 2 (e std_i -e real_i ) 2 ] Calculate the environmental similarity Spq between the standard environmental data of the p-th design scheme and the actual environmental data of the q-th stage; where i represents the indicator category index;

[0038] S4-4, calculate the carbon emission correction number and cost correction number according to the correction formula;

[0039] S4-5, the carbon emission correction number, the cost correction number, the design scheme and the several stages are matched one by one to obtain the application design library.

[0040] Furthermore, the carbon emission correction number and the cost correction number calculated according to the correction formula include:

[0041] The carbon emission correction formula is: C 1_mod =C 1_std ×[1+k(1-S)]; where C 1_mod Indicates the carbon emission correction number, C 1_std represents the standard carbon emission data, S represents the environmental similarity, and k represents the carbon emission correction constant, which is obtained by fitting historical data;

[0042] The cost correction formula is: C 2_mod =C 2_std ×[1+m(1-S)]; where C 2_mod Indicates the cost correction number, C 2_std Represents standard cost data, and m represents the cost correction constant, which is obtained by fitting historical data.

[0043] The actual environments of road and bridge construction projects often differ from the historical data in the standard design library. Directly applying this data can lead to mismatches between the design and the project's actual conditions. Furthermore, the carbon emissions and cost data in the standard design library are based on historical projects and may not directly reflect the actual needs of the current project. Therefore, a modified formula needs to be combined with actual environmental data to more accurately predict carbon emissions and costs at each stage.

[0044] S5, optimize the current construction plan based on the predicted total carbon emissions and the application design library.

[0045] Furthermore, the optimization of the current construction plan based on the predicted total carbon emissions and the application of the design library includes:

[0046] S5-1, traversing the scheme data of several stages in the application design library, eliminating the schemes whose carbon emission correction values are greater than the estimated carbon emissions of the current stage, and obtaining the scheme library of the current road and bridge construction project;

[0047] S5-2, using a cyclic algorithm to permutate and combine the solution libraries of several stages to obtain several solution groups;

[0048] S5-3, calculating the total carbon emissions and total costs of each plan group to obtain a number of preset carbon emissions and preset total costs;

[0049] S5-4, eliminating the scheme groups whose preset carbon emissions are greater than the target carbon emissions or whose preset total costs are greater than the target total costs, to obtain several available scheme groups;

[0050] S5-5, screen several available solution groups according to construction requirements to obtain the optimal solution group.

[0051] By traversing the solution data in the application design library and eliminating solutions that do not meet carbon emission requirements, the optimization scope can be quickly narrowed, avoiding interference from invalid solutions, thereby improving optimization efficiency. In addition, the optimization process not only considers carbon emissions, but also conducts a comprehensive assessment based on total costs to ensure that the optimized solution meets project requirements in terms of both carbon emissions and costs, thereby improving the overall benefits of the solution.

[0052] Furthermore, the method of screening several available solution groups according to the construction requirements to obtain the optimal solution group includes:

[0053] If the construction requirement is to minimize carbon emissions, the preset carbon emissions of several available scheme groups are ranked, and the scheme group with the lowest preset carbon emissions is selected as the optimal scheme group;

[0054] If the construction requirement is to have the lowest total cost, the preset total costs of several available scheme groups are sorted, and the scheme group with the lowest preset total cost is selected as the optimal scheme group.

[0055] Based on different construction requirements, the available solution groups are screened in a targeted manner to meet the needs of projects with different focuses, making the optimized solutions more practical and flexible.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This invention builds a standard scheme design library, integrates key data of historical road and bridge design schemes, and standardizes them, providing designers with a rich reference basis. It also modifies standard schemes based on actual environmental data to generate an application design library, allowing design schemes to accurately meet the specific needs of the project, enhancing the adaptability and feasibility of the schemes, reducing design changes and construction risks caused by environmental differences, and providing a strong guarantee for the smooth implementation of the project.

[0058] This invention uses a carbon emission prediction model based on a deep learning algorithm, combined with actual environmental data and material information, to accurately predict carbon emissions at each stage of the project. It dynamically adjusts carbon emission and cost data through a correction formula, and optimizes construction plans in combination with an application design library, effectively screening out low-carbon and high-efficiency solution combinations. The optimization process not only considers carbon emissions but also conducts a comprehensive assessment based on total costs, ensuring that the optimized plan meets project needs while minimizing carbon emissions, meeting the requirements of low-carbon construction and environmental protection, and providing strong support for the sustainable development of the project.

[0059] The optimization method of the present invention can flexibly adjust the screening strategy according to different construction requirements, quickly generate the optimal solution group, and meet diverse construction needs. In addition, the present invention divides the road and bridge construction project into several stages and conducts independent analysis and optimization for each stage, which can fully consider the characteristics and requirements of each stage and ensure the overall optimality of the design solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A schematic diagram of the technical process of the road and bridge design optimization recommendation method based on carbon emission data provided by the present invention;

[0062] Figure 2 This is a schematic diagram of the technical process for optimizing the current construction plan provided by the present invention. DETAILED DESCRIPTION

[0063] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] See also Figure 1 - Figure 2 The first embodiment of the present invention provides a road and bridge design optimization and recommendation method based on carbon emission data, comprising:

[0065] S1, build a standard scheme design library based on several road and bridge design schemes;

[0066] S2, collecting actual environmental data of the road and bridge construction project, and dividing the road and bridge construction project into several stages based on the actual environmental data;

[0067] S3, using a carbon emission prediction model to predict carbon emissions for several stages, obtaining estimated carbon emissions for the several stages, and summing the estimated carbon emissions to obtain a total predicted carbon emission; wherein the carbon emission prediction model is constructed based on a deep learning algorithm;

[0068] S4, modifying the standard scheme design library according to the actual environmental data of several stages to obtain the application design library;

[0069] S5, optimize the current construction plan based on the predicted total carbon emissions and the application design library.

[0070] Road and bridge projects operate in complex environments, encompassing diverse topography, such as mountains, plains, and hills; complex geological conditions, such as soft soil and rock foundations; and varying hydrological conditions, including rivers, lakes, and varying groundwater levels. These diverse environmental factors significantly impact road and bridge design. For example, mountainous terrain may require more bridges and tunnels, soft soil may require specialized foundation treatment measures, and complex hydrological conditions may impact bridge foundation design. Failure to optimize recommended methods can lead to design solutions that are mismatched with the actual environment, increasing construction difficulty and costs, and even compromising project quality and safety.

[0071] Therefore, in order to effectively deal with the complex environmental factors of road and bridge projects and ensure that the design scheme accurately matches the actual environment, in step S1, a standard scheme design library is constructed based on several road and bridge design schemes. Specifically, the following steps are included:

[0072] S1-1, collect environmental data, carbon emission data, and cost data of historical road and bridge project design schemes to obtain several road and bridge design schemes;

[0073] S1-2, determining whether there are duplicated schemes among the road and bridge design schemes; if so, calculating the average value of the environmental data of the duplicated schemes according to several indicator categories to obtain standard environmental data;

[0074] Similarly, the carbon emission data and cost data of the repeated schemes are averaged to obtain the standard carbon emission data and standard cost data;

[0075] If not, the environmental data, carbon emission data, and cost data of the road and bridge design plan will be marked as standard data;

[0076] S1-3, dividing the plurality of road and bridge design schemes into a plurality of highway design schemes and a plurality of bridge design schemes, and constructing a standard scheme design library based on corresponding standard data.

[0077] It should be noted that the environmental data in this embodiment includes but is not limited to terrain conditions, geological conditions and hydrological conditions, among which the indicator categories of terrain conditions include but are not limited to average altitude, average slope, terrain undulation, and surface cutting depth; the indicator categories of geological conditions include but are not limited to foundation bearing capacity, soil moisture content, and rock compressive strength; the indicator categories of hydrological conditions include but are not limited to average water level, average water flow velocity, and river sediment content, etc.

[0078] By collecting environmental, carbon emission, and cost data from historical road and bridge project design schemes, a standard scheme design library is constructed. The scattered multi-source data is integrated, and the data of repeated schemes are averaged to obtain standard data. This makes the data standardized and consistent, facilitates comparison and analysis between different projects, and provides a reliable reference benchmark for new project design.

[0079] Step S2: collecting actual environmental data of the road and bridge construction project, and dividing the road and bridge construction project into several stages according to the actual environmental data.

[0080] In one embodiment, when executing step S2, the following steps may be included:

[0081] S2-1, data acquisition and preprocessing:

[0082] First, satellite remote sensing technology is used to obtain high-resolution satellite imagery within the project area. Simultaneously, basic geographic information data within the project area is collected, including topographic data (such as digital elevation model data, which reflects the ups and downs of the terrain), geological data (such as geological survey reports, which contain information on stratigraphic structure and rock and soil properties), hydrological data (such as river distribution and water level change records), and land use data (such as land type classification maps, which distinguish different land use types such as cultivated land, forest land, and construction land).

[0083] Then, the acquired high-resolution remote sensing satellite images and basic geographic information data are pre-processed, including operations such as radiometric correction and geometric correction of satellite images, and data format conversion and data integration of basic geographic information data.

[0084] S2-2, terrain data analysis:

[0085] Import the pre-processed terrain data from the actual environmental data into GIS (Geographic Information System) software with spatial analysis capabilities, and use spatial analysis tools to analyze the slope, aspect, and terrain undulations of the project area to understand the complexity and characteristic distribution of the terrain;

[0086] S2-3, manual segmentation processing:

[0087] Combine terrain analysis results with geological data, hydrological data, and land use data for comprehensive analysis. For example, consider areas with large terrain fluctuations, which may have complex geological structures and variable hydrological conditions. Combined with land use types, determine whether the area has special construction requirements (such as whether it involves ecological protection areas or important buildings).

[0088] Then, according to the characteristics and construction requirements of the road and bridge construction project, a reasonable principle of manual segmentation is formulated:

[0089] For example, areas with significantly different terrain undulations can be divided into different stages based on the characteristics of terrain changes; areas with large differences in geological types can be separated based on geological conditions; parts spanning rivers, lakes and other different hydrological environments can be distinguished based on hydrological conditions; and land use conditions can be taken into account to avoid excessive division in areas where land use types change frequently or where there are special restrictions on construction.

[0090] Finally, according to the established segmentation principles, the road and bridge construction project was manually segmented to ensure that each segment had relatively consistent environmental characteristics, facilitating the targeted design and implementation of subsequent construction plans. Ultimately, a number of stages with clear environmental characteristics and construction requirements were obtained.

[0091] The actual environment along road and bridge projects is complex and diverse, with significant variations in topography, geology, hydrology, and land use across different regions. Dividing the project into phases allows for the development of more targeted construction plans tailored to the specific environmental characteristics of each phase. For example, in phases with undulating terrain, specialized road slope adjustment plans or bridge and tunnel construction plans can be designed; in phases with soft soil, specialized foundation treatment techniques can be employed. This phased analysis improves the adaptability of construction plans to the actual environment and ensures smooth construction.

[0092] Step S3: Use a carbon emission prediction model to predict carbon emissions in several stages, obtain the estimated carbon emissions in several stages, and sum them up to obtain the predicted total carbon emissions; wherein the carbon emission prediction model is constructed based on a deep learning algorithm.

[0093] In one embodiment, the construction of the carbon emission prediction model in step S3 may include the following steps:

[0094] S3-1: Collect environmental data, carbon emission data, and BIM models of historical road and bridge projects, and extract the required material types and quantities from the BIM models to obtain a historical data set;

[0095] S3-2, preprocessing the historical data set and dividing the preprocessed data set according to a preset ratio to obtain a training set, a validation set, and a test set;

[0096] S3-3, building a neural network model based on a deep learning algorithm, and setting training parameters of the neural network model to obtain an initial model;

[0097] S3-4, input the training set and validation set into the initial model for iterative training and optimization update to obtain the model with the best validation accuracy;

[0098] S3-5: Input the test set into the model with the best verification accuracy for model evaluation to obtain the test accuracy and determine whether the test accuracy is greater than the preset accuracy threshold. If yes, mark the model with the best verification accuracy as the carbon emission prediction model; if no, jump to S3-3.

[0099] It should be noted that the input data of the carbon emission prediction model includes environmental data, material type and material quantity, and the output data is the predicted total carbon emissions.

[0100] To accurately predict carbon emissions from road and bridge projects and provide a scientific basis for their green and sustainable development, this example constructs a carbon emissions prediction model based on historical road and bridge project data using a deep learning algorithm. This model leverages the complex relationships between environmental factors, material usage, and carbon emissions in historical data. By leveraging deep learning's powerful feature learning and pattern recognition capabilities, it accurately predicts carbon emissions at different stages. This provides strong support for developing reasonable carbon emission control strategies during the planning, design, and construction phases of road and bridge projects, helping projects meet engineering requirements while minimizing environmental impact and driving the road and bridge construction industry towards a green and low-carbon future.

[0101] Step S4, modifying the standard solution design library according to the actual environmental data of several stages to obtain the application design library.

[0102] In order to make the standard scheme design library better adapt to the actual environment of road and bridge construction projects and improve the practicality and feasibility of design schemes, this embodiment corrects the data of the standard scheme design library according to the actual environment data to obtain an application design library that is more in line with the actual situation.

[0103] Specifically, the data correction process includes the following steps:

[0104] (1) Determine the indicator weights of environmental data:

[0105] Based on the various indicator categories in the actual environmental data, clearly define the indicators involved in the evaluation. These indicator categories include but are not limited to terrain conditions (average altitude, average slope, terrain relief, surface incision depth, etc.), geological conditions (foundation bearing capacity, soil moisture content, rock compressive strength, etc.), and hydrological conditions (average water level, average water flow velocity, river sediment content, etc.);

[0106] Then, the decision-making objectives (determining the weights of each indicator category) are placed at the top level, and the various indicators of environmental data are placed at the bottom level. The hierarchical structure is constructed using the analytic hierarchy process. Through expert judgment or historical data analysis, the relative importance of different indicator categories is compared to construct a judgment matrix.

[0107] Then, the maximum eigenvalue of the judgment matrix and its corresponding eigenvector are solved by using the calculation method of the hierarchical analysis method, and the eigenvector is normalized to obtain the weight vector W = [w1, w2, ..., w n ], which reflects the relative importance of different environmental indicator categories in the calculation of environmental similarity; where n represents the number of indicator categories in the environmental data;

[0108] (2) Data representation and extraction:

[0109] Extract the standard environmental data of several design schemes from the standard scheme design library and express it as E p std =[e std_1 ,e std_2 ,…,e std_n ]; each e std_i Represents the standard data of the p-th design scheme under the i-th environmental indicator category;

[0110] The actual environmental data of several stages in the road and bridge construction project are expressed as E q real =[e real_1 ,e real_2 ,…,e real_n ]; each e real_i represents the actual data of the qth stage under the i-th environmental indicator category;

[0111] (3) Calculate the environment similarity:

[0112] The above weight vector W and the extracted standard environment data E p std And the actual environmental data E q real Substitute into formula S pq =1-√[∑ n i＝1 w i 2 (e std_i -e real_i ) 2 ], calculate the environmental similarity S between the standard environmental data of the p-th design scheme and the actual environmental data of the q-th stage pq ;

[0113] (4) Calculate the correction number:

[0114] First, the carbon emission correction constant k and the cost correction constant m are determined by fitting and analyzing historical data. The fitting process can use statistical analysis software or programming tools to perform regression analysis on the environmental similarity in the historical data, standard carbon emission (cost) data, and actual carbon emission (cost) data to obtain appropriate k and m values.

[0115] Then, according to the carbon emission correction formula C 1_mod =C 1_std ×[1+k(1-S)], the standard carbon emission data C of the p-th design scheme 1_std and the calculated environment similarity S pq Substitute into the formula to calculate the carbon emission correction number C of the p-th design scheme in the q-th stage 1_mod ;

[0116] Similarly, according to the cost correction formula C 2_mod =C 2_std ×[1+m(1-S)], the standard cost data C of the p-th design scheme 2_std Similarity to the environment S pq Substitute into the formula and calculate the cost correction number C of the p-th design scheme in the q-th stage 2_mod ;

[0117] (5) Build application design library:

[0118] Finally, the calculated carbon emission correction numbers and cost correction numbers are matched one-to-one with the corresponding design schemes and several stages. This information is integrated together to obtain an application design library, which contains the carbon emission and cost correction information of each design scheme in the standard scheme design library under the actual environmental data at different stages, making the design scheme more in line with the actual situation of the current road and bridge construction project.

[0119] By determining the weights of environmental data indicators, accurately calculating environmental similarity, and calculating correction factors based on historical data, designers can consider the impact of the actual environment on carbon emissions and costs while referencing standard solutions. In this way, during the subsequent solution selection and optimization process, the application design library will provide more accurate and realistic data support for decision-making, helping to avoid design deviations caused by environmental differences. While taking into account both economic costs and environmental benefits, the design solution achieves low-carbon and economical results while meeting the project's functional requirements, promoting the sustainable development of road and bridge projects.

[0120] Step S5: Optimize the current construction plan based on the predicted total carbon emissions and the application design library.

[0121] In one embodiment, optimizing the current construction plan by applying the design library may include the following steps:

[0122] S5-1, traversing the scheme data of several stages in the application design library, eliminating the schemes whose carbon emission correction values are greater than the estimated carbon emissions of the current stage, and obtaining the scheme library of the current road and bridge construction project;

[0123] S5-2, using a cyclic algorithm to permutate and combine the solution libraries of several stages to obtain several solution groups;

[0124] S5-3, calculating the total carbon emissions and total costs of each plan group to obtain a number of preset carbon emissions and preset total costs;

[0125] S5-4, based on the basic objectives of “carbon emissions ≤ target total; cost ≤ cost budget”, eliminate the scheme groups whose preset carbon emissions are greater than the target carbon emissions or whose preset total costs are greater than the target total costs, and obtain several available scheme groups;

[0126] S5-5: When the scheme group meets the basic requirements, a scheme recommendation will be made based on the specific construction requirements:

[0127] If the construction requirement is to minimize carbon emissions, the preset carbon emissions of several available scheme groups are ranked, and the scheme group with the lowest preset carbon emissions is selected as the optimal scheme group;

[0128] If the construction requirement is to minimize the total cost, the preset total costs of several available scheme groups are sorted, and the scheme group with the lowest preset total cost is selected as the optimal scheme group;

[0129] Finally, the optimal solution group is used as output to obtain the optimized recommended solution for this construction project.

[0130] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0131] Working principle of the present invention:

[0132] First, by building a standard scheme design library and integrating a large amount of historical key data, a rich reference is provided for design optimization. At the same time, combined with actual environmental data, the project is divided into several stages, and the characteristics and needs of each stage are accurately analyzed. Then, the carbon emission prediction model is used to make high-precision predictions of carbon emissions in each stage, providing a quantitative basis for carbon emission control and scheme screening. The standard scheme design library is revised through actual environmental data to enhance the feasibility and adaptability of the design scheme. Finally, based on the predicted total carbon emissions and design library data, the construction scheme is optimized, taking into account both cost and carbon emissions, and the optimal design scheme combination is screened out, providing a set of efficient optimization recommendation methods for road and bridge design.

[0133] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A road and bridge design optimization recommendation method based on carbon emission data, characterized by: include: S1, build a standard scheme design library based on several road and bridge design schemes; S2, collecting actual environmental data of the road and bridge construction project, and dividing the road and bridge construction project into several stages based on the actual environmental data; S3, using a carbon emission prediction model to predict carbon emissions for several stages, obtaining estimated carbon emissions for the several stages, and summing the estimated carbon emissions to obtain a total predicted carbon emission; wherein the carbon emission prediction model is constructed based on a deep learning algorithm; S4, modifying the standard scheme design library according to the actual environmental data of several stages to obtain the application design library; The data of the standard scheme design library is modified according to the actual environmental data of several stages, including: S4-1, use the hierarchical analysis method to determine the weight of each indicator category in the environmental data, and obtain the weight vector W=[w1,w2,…,w n ]; where n represents the number of indicator categories in the environmental data, w i ∈W, represents the weight of the i-th indicator category in the environmental data; S4-2, the standard environmental data of several design schemes in the standard scheme design library are expressed as E p std =[e std_1 ,e std_2 ,…,e std_n ], the actual environmental data of several stages are expressed as E q real =[e real_1 ,e real_2 ,…,e real_n ]; among them, E p std Represents the standard environmental data of the p-th design scheme, and each e std_i represents the standard data of the pth design scheme under the i-th environmental indicator category, E q real represents the actual environmental data of the qth stage, and each e real_i represents the actual data of the qth stage under the i-th environmental indicator category; S4-3, calculate the environmental similarity S between the standard environmental data of the p-th design scheme and the actual environmental data of the q-th stage according to the environmental similarity formula pq ; S4-4, calculate the carbon emission correction number and cost correction number according to the correction formula; S4-5, matching the carbon emission correction number and the cost correction number with the design scheme and several stages one by one to obtain an application design library; The carbon emission correction number and cost correction number calculated according to the correction formula include: The carbon emission correction formula is: C 1_mod =C 1_std ×[1+k(1-S)]; where C 1_mod Indicates the carbon emission correction number, C 1_std represents the standard carbon emission data, S represents the environmental similarity, and k represents the carbon emission correction constant, which is obtained by fitting historical data; The cost correction formula is: C 2_mod =C 2_std ×[1+m(1-S)]; where C 2_mod Indicates the cost correction number, C 2_std represents standard cost data, and m represents the cost modification constant; S5, optimize the current construction plan based on the predicted total carbon emissions and the application design library.

2. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 1 is characterized in that: The standard scheme design library is constructed based on a number of road and bridge design schemes, including: S1-1, collect environmental data, carbon emission data, and cost data of historical road and bridge project design schemes to obtain several road and bridge design schemes; S1-2, determining whether there are duplicated schemes among the road and bridge design schemes; if so, obtaining standard data based on the environmental data, carbon emission data, and cost data of the duplicated schemes; if not, marking the environmental data, carbon emission data, and cost data of the road and bridge design schemes as standard data; S1-3, dividing the plurality of road and bridge design schemes into a plurality of highway design schemes and a plurality of bridge design schemes, and constructing a standard scheme design library based on corresponding standard data.

3. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 1 is characterized in that: The road and bridge construction project is divided into several stages based on actual environmental data, including: S2-1, based on the scope of the road and bridge construction project, obtain corresponding high-resolution remote sensing satellite images and basic geographic information data and pre-process them to obtain actual environmental data; basic geographic information data includes topographic data, geological data, hydrological data, and land use data; S2-2, using the spatial analysis function of GIS software to analyze the terrain data in the actual environmental data and obtain the terrain analysis results; S2-3, based on the terrain analysis results, combined with geological data, hydrological data and land use data, the road and bridge construction project is manually segmented into several stages.

4. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 1 is characterized in that: The carbon emission prediction model is built based on a deep learning algorithm and includes: S3-1: Collect environmental data, carbon emission data, and BIM models of historical road and bridge projects, and extract the required material types and quantities from the BIM models to obtain a historical data set; S3-2, preprocessing the historical data set and dividing the preprocessed data set according to a preset ratio to obtain a training set, a validation set, and a test set; S3-3, building a neural network model based on a deep learning algorithm, and setting training parameters of the neural network model to obtain an initial model; S3-4, input the training set and validation set into the initial model for iterative training and optimization update to obtain the model with the best validation accuracy; S3-5, input the test set into the model with the best verification accuracy for model evaluation, obtain the test accuracy, and determine whether the test accuracy is greater than the preset accuracy threshold; if yes, mark the model with the best verification accuracy as the carbon emission prediction model; if not, jump to S3-3.

5. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 1 is characterized in that: The environmental similarity formula is: S pq =1-√[∑ n i=1 w i 2 (e std_i -e real_i ) 2 ]; where i represents the indicator category index.

6. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 1 is characterized in that: The optimization of the current construction plan based on the predicted total carbon emissions and the application of the design library includes: S5-1, traversing the scheme data of several stages in the application design library, eliminating the schemes whose carbon emission correction values are greater than the estimated carbon emissions of the current stage, and obtaining the scheme library of the current road and bridge construction project; S5-2, using a cyclic algorithm to permutate and combine the solution libraries of several stages to obtain several solution groups; S5-3, calculating the total carbon emissions and total costs of each plan group to obtain a number of preset carbon emissions and preset total costs; S5-4, eliminating the scheme groups whose preset carbon emissions are greater than the target carbon emissions or whose preset total costs are greater than the target total costs, to obtain several available scheme groups; S5-5, screen several available solution groups according to construction requirements to obtain the optimal solution group.

7. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 6 is characterized in that: The method of screening several available solution groups according to the construction requirements to obtain the optimal solution group includes: If the construction requirement is to minimize carbon emissions, the preset carbon emissions of several available scheme groups are ranked, and the scheme group with the lowest preset carbon emissions is selected as the optimal scheme group; If the construction requirement is to have the lowest total cost, the preset total costs of several available scheme groups are sorted, and the scheme group with the lowest preset total cost is selected as the optimal scheme group.

8. The road and bridge design optimization and recommendation method based on carbon emission data according to claim 2 is characterized in that: The standard data obtained based on the environmental data, carbon emission data and cost data of the repeated scheme includes: The environmental data of the repeated schemes are averaged according to several indicator categories to obtain standard environmental data; The carbon emission data and cost data of the repeated schemes are averaged to obtain standard carbon emission data and standard cost data.

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