Intercity railway construction period material transportation path planning method under carbon emission reduction target

Abstract

The invention provides an inter-city railway construction period material transportation path planning method under a carbon emission reduction target, and the method comprises the steps: collecting the material demand data, transportation network data and carbon emission factor data of an inter-city railway construction period, and carrying out the data cleaning and data standardization processing, and obtaining the standardized data; calculating the carbon emission on each path according to the standardized data; building a multi-objective optimization model by taking minimum carbon emission, minimum transportation cost and timely delivery of materials meeting construction requirements as objective functions; optimizing the multi-objective optimization model by adopting an optimization algorithm to obtain an optimization result; and generating an initial transportation path scheme according to an optimization result, and monitoring traffic conditions and construction requirements in real time in an actual transportation process to dynamically adjust the initial transportation path scheme so as to obtain an optimal transportation path scheme. According to the method, the carbon emission and the cost of material transportation in the inter-city railway construction period can be effectively reduced, and the timeliness of construction requirements can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and particularly to a method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target. Background Art

[0002] With the acceleration of the urbanization process, the construction of intercity railways has become an important means to relieve urban traffic pressure. However, the transportation of a large amount of building materials during the construction of intercity railways not only causes pressure on urban traffic but also generates a large amount of carbon emissions. Traditional methods for planning material transportation paths usually aim to minimize transportation costs or time, ignoring the impact of carbon emissions and making it difficult to meet the current requirements of low-carbon environmental protection.

[0003] Under the carbon emission reduction target, how to optimize the material transportation path to reduce carbon emissions while ensuring the construction progress has become an important issue in the construction management of intercity railways. There is a lack of a path planning method in the existing technology that comprehensively considers carbon emissions, transportation costs, and construction requirements. Therefore, it is of great practical significance to propose a method for planning the material transportation path during the construction period of intercity railways based on the carbon emission reduction target. Summary of the Invention

[0004] In order to overcome the deficiencies of the existing technology, the purpose of the present invention is to provide a method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target, which can not only effectively reduce the carbon emissions and costs of material transportation during the construction period of intercity railways but also ensure the timeliness of meeting construction requirements, with significant economic, environmental, and social benefits, providing an efficient, low-carbon, and intelligent solution for the material transportation path planning of intercity railway construction and other large-scale engineering projects.

[0005] To achieve the above purpose, the present invention provides the following solutions:

[0006] A method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target, comprising:

[0007] Collecting material demand data, transportation network data, and carbon emission factor data during the construction period of intercity railways;

[0008] Performing data cleaning and data standardization processing on the material demand data, the transportation network data, and the carbon emission factor data to obtain standardized data;

[0009] Calculating the carbon emissions on each path according to the standardized data;

[0010] Constructing a multi-objective optimization model with minimizing carbon emissions, minimizing transportation costs, and timely delivery of materials to meet construction requirements as the objective function;

[0011] Optimize the multi-objective optimization model using an optimization algorithm to obtain an optimization result;

[0012] Generate an initial transportation route plan according to the optimization result, and monitor the traffic conditions and construction requirements in real time during the actual transportation process to dynamically adjust the initial transportation route plan to obtain an optimal transportation route plan.

[0013] Preferably, the material demand data includes: material type, material weight, material quantity, and material delivery time; the transportation network data includes: transportation nodes, path distance, road traffic capacity, and traffic conditions.

[0014] Preferably, perform data cleaning and data standardization processing on the material demand data, the transportation network data, and the carbon emission factor data to obtain standardized data, including:

[0015] Group the material demand data, the transportation network data, and the carbon emission factor data according to a preset collection period to obtain multiple data groups;

[0016] Calculate the difference coefficient between the current data group and the previous data group in sequence;

[0017] Judge whether the value of the difference coefficient is within a preset range;

[0018] If the value of the difference coefficient is not within the preset range, remove the corresponding data group;

[0019] If the value of the difference coefficient is within the preset range, retain the corresponding data group until all data groups are traversed to obtain data cleaning data;

[0020] Perform standardization processing on the cleaning data to obtain the standardized data.

[0021] Preferably, the difference coefficient calculation formula is:

[0022]

[0023] where p X,Y is the difference coefficient, cov(X,Y) represents the covariance between the current data group X and the previous data group Y, α X represents the mean of the current data group X, and β Y represents the mean of the previous data group Y.

[0024] Preferably, the carbon emission calculation formula is:

[0025]

[0026] where E is the carbon emission, W iis the weight of the material for the i-th path, D i is the path distance for the i-th path, F i is the carbon emission factor per unit distance of the transportation vehicle, and n is the number of paths.

[0027] Preferably, the constraint conditions of the objective function include:

[0028] The transportation path of each material meets the requirements of the construction site, the road traffic capacity limit, and the transportation time limit.

[0029] Preferably, the formula of the objective function includes:

[0030]

[0031] where x ij indicates whether the transportation vehicle j is selected for path i, and x ij is a binary variable of 0 or 1, W ij is the material transportation weight when using transportation vehicle j on path i, D ij is the transportation distance of path i, F j is the carbon emission factor per unit distance of transportation vehicle j, C is the total transportation cost, C j is the transportation cost per unit distance of transportation vehicle j, T ij is the transportation time when using transportation vehicle j on path i, T deadline is the material delivery time requirement of the construction site, Q k is the total demand for materials required by construction site k, C i is the maximum traffic capacity of path i, V j is the transportation speed of transportation vehicle j.

[0032] Preferably, the optimization algorithm is the particle swarm optimization algorithm.

[0033] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0034] The present invention provides a method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target, including: collecting material demand data, transportation network data, and carbon emission factor data during the construction period of intercity railways; performing data cleaning and data standardization processing on the material demand data, the transportation network data, and the carbon emission factor data to obtain standardized data; calculating the carbon emissions on each path according to the standardized data; constructing a multi-objective optimization model with minimizing carbon emissions, minimizing transportation costs, and ensuring the timely delivery of materials to meet construction requirements as the objective function; using an optimization algorithm to optimize the multi-objective optimization model to obtain an optimization result; generating an initial transportation path plan according to the optimization result, and monitoring the traffic conditions and construction requirements in real time during the actual transportation process to dynamically adjust the initial transportation path plan to obtain an optimal transportation path plan. The present invention can not only effectively reduce the carbon emissions and costs of material transportation during the construction period of intercity railways, but also ensure the timeliness of construction requirements, and has significant economic, environmental, and social benefits, providing an efficient, low-carbon, and intelligent solution for the material transportation path planning of intercity railway construction and other large-scale engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] The purpose of the present invention is to provide a method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target, which can not only effectively reduce the carbon emissions and costs of material transportation during the construction period of intercity railways, but also ensure the timeliness of construction requirements, and has significant economic, environmental, and social benefits, providing an efficient, low-carbon, and intelligent solution for the material transportation path planning of intercity railway construction and other large-scale engineering projects.

[0039] To make the above objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Figure 1 The flowchart of the method provided for the embodiment of the present invention is as Figure 1 shown. The present invention provides a method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target, including:

[0041] Step 100: Collect material demand data, transportation network data, and carbon emission factor data during the construction period of intercity railways;

[0042] Step 200: Perform data cleaning and data standardization processing on the material demand data, transportation network data, and carbon emission factor data to obtain standardized data;

[0043] Step 300: Calculate the carbon emissions on each path according to the standardized data;

[0044] Step 400: Construct a multi-objective optimization model with the goal of minimizing carbon emissions, minimizing transportation costs, and ensuring the timely delivery of materials to meet construction requirements;

[0045] Step 500: Use an optimization algorithm to optimize the multi-objective optimization model to obtain an optimization result;

[0046] Step 600: Generate an initial transportation path plan according to the optimization result, and monitor the traffic conditions and construction requirements in real time during the actual transportation process to dynamically adjust the initial transportation path plan to obtain an optimal transportation path plan.

[0047] Preferably, the material demand data includes: material types, material weights, material quantities, and material delivery times; the transportation network data includes: transportation nodes, path distances, road passing capacities, and traffic conditions.

[0048] Specifically, step 100 of this embodiment includes:

[0049] Step 101: Collection of material demand data

[0050] First of all, in this embodiment, the material demand data of the construction site is collected through the construction plan and material list of the intercity railway construction project. It includes the types of each material (such as steel bars, cement, sand and gravel, etc.), the required weight, quantity, and the delivery time requirements of the materials. These data can be obtained through the material management system of the construction unit or the project schedule, and confirmed with the person in charge of the construction site to ensure the accuracy and integrity of the data. At the same time, in combination with the time arrangement of the construction nodes, the material demand and delivery time in each stage are clarified.

[0051] Step 102: Collection of transportation network data

[0052] The collection of transportation network data mainly includes transportation nodes, path distances, road capacities, and traffic conditions. Transportation nodes can be determined through map services or logistics systems, including the locations of material suppliers, transfer stations, and specific construction sites. Path distances can be calculated through a Geographic Information System (GIS) and accurately measured in combination with the path lengths in the actual road network. Data on road capacities and traffic conditions need to be obtained through public data interfaces of traffic management departments or real-time traffic monitoring systems, including the maximum carrying capacity of roads, traffic restrictions, and real-time congestion conditions.

[0053] Step 103: Collection of carbon emission factor data

[0054] Carbon emission factor data is defined based on the carbon emissions per unit distance of different transportation tools. In this embodiment, by referring to relevant literature, standards, or industry specifications, unit carbon emission factor data for common transportation tools (such as trucks, electric vehicles, trains, etc.) is obtained. If the carbon emission factors of transportation tools vary due to vehicle types or fuel types, further refinement of classification is required. For example, the carbon emission factors of diesel trucks and electric trucks should be calculated separately. In addition, verification can be carried out through data provided by third-party carbon emission accounting tools or relevant research institutions to ensure the accuracy and authority of the carbon emission factors.

[0055] Through the above steps, this embodiment completely collects material requirement data, transportation network data, and carbon emission factor data, providing a reliable data basis for subsequent data cleaning, standardization processing, and path planning optimization.

[0056] Preferably, data cleaning and data standardization processing are performed on the material requirement data, the transportation network data, and the carbon emission factor data to obtain standardized data, including:

[0057] Group the material requirement data, the transportation network data, and the carbon emission factor data according to a preset collection period to obtain multiple data groups;

[0058] Calculate the coefficient of variation between the current data group and the previous data group in sequence;

[0059] Judge whether the value of the coefficient of variation is within a preset range;

[0060] If the value of the coefficient of variation is not within the preset range, remove the corresponding data group;

[0061] If the value of the coefficient of variation is within the preset range, retain the corresponding data group until all data groups are traversed to obtain data cleaning data;

[0062] Standardize the cleaning data to obtain the standardized data.

[0063] Preferably, the formula for the coefficient of variation is:

[0064]

[0065] where p X,Y is the coefficient of variation, cov(X,Y) represents the covariance between the current data set X and the previous data set Y, and α X represents the mean of the current data set X, and β Y represents the mean of the previous data set Y.

[0066] Specifically, step 200 of this embodiment includes:

[0067] Step 201: Data grouping and coefficient of variation calculation

[0068] First, group the material demand data, transportation network data, and carbon emission factor data according to a preset collection period (such as daily, weekly, or monthly) to form multiple data sets. Each data set contains all relevant data collected within that period, such as the material demand on a certain day, the traffic conditions of the transportation route, and the carbon emission factors of the transportation tools. Then, calculate the coefficient of variation between the current data set and the previous data set in sequence to measure the degree of change between the two data sets. The calculation of the coefficient of variation comprehensively considers the covariance and mean change of the data set and can reflect the correlation and consistency between data sets.

[0069] Step 202: Coefficient of variation range judgment and data cleaning

[0070] After calculating the coefficient of variation, compare it with a preset range. If the value of the coefficient of variation exceeds the preset range (for example, the data fluctuates too much or is abnormal), it is considered that there is an abnormality in this data set, which may be caused by collection errors, equipment failures, or external interferences, and this data set needs to be removed from the data set. If the value of the coefficient of variation is within the preset range, it is considered that the data in this data set is normal and reliable, and it is retained. By traversing all data sets, abnormal data is gradually eliminated, and finally a cleaned data set is obtained.

[0071] Step 203: Data standardization processing

[0072] Standardize the cleaned data to eliminate the dimensional differences between different data dimensions. For example, normalize or standardize the material requirement data (such as weight, quantity) and the transportation network data (such as path distance, road capacity) so that their values are within the same range (such as between 0 and 1). Standardization can improve the computational efficiency and accuracy of subsequent optimization models, ensure the comparability and consistency of different types of data in the model, and thus provide high-quality standardized data input for path planning optimization.

[0073] Preferably, the calculation formula for the carbon emissions is:

[0074]

[0075] where E is the carbon emissions, W i is the material weight of the i-th path, D i is the path distance of the i-th path, F i is the carbon emission factor per unit distance of the transportation tool, and n is the number of paths.

[0076] Specifically, step 300 of this embodiment includes:

[0077] Step 301: Preparation for calculating path carbon emissions

[0078] First, according to the standardized data, extract the relevant parameters of each path, including the material weight on the path, the path distance, and the carbon emission factor per unit distance of the selected transportation tool. The material weight can be obtained from the material requirement data, the path distance is provided by the transportation network data, and the carbon emission factor of the transportation tool is from the carbon emission factor data. Ensure that these data are accurate after cleaning and standardization to provide reliable input for calculating the carbon emissions of each path.

[0079] Step 302: Calculate carbon emissions path by path

[0080] Calculate the carbon emissions for each path one by one. Multiply the material weight on the path, the path distance, and the carbon emission factor per unit distance of the transportation tool to obtain the carbon emissions of that path. Accumulate the carbon emissions of all paths to get the total carbon emissions of the entire transportation network. During the calculation process, ensure that the path parameters match the transportation tool type to avoid calculation deviations caused by data errors. Finally, store the carbon emissions of each path as input parameters for the subsequent optimization model for optimizing the solution of path planning.

[0081] Preferably, the constraint conditions of the objective function include:

[0082] The transportation paths of each material meet the requirements of the construction site, the road capacity limit, and the transportation time limit.

[0083] Preferably, the formula of the objective function includes:

[0084] Minimize carbon emissions:

[0085]

[0086] Minimize transportation costs:

[0087]

[0088] Timely delivery of materials to meet construction requirements:

[0089] T ij ≤T deadline

[0090] Meet construction requirements:

[0091]

[0092] Road capacity restrictions:

[0093]

[0094] Transportation time limit:

[0095]

[0096] Path selection constraints:

[0097]

[0098] where x ij is whether to select transportation tool j for path i, x ij is a binary variable of 0 or 1, W ij is the material transportation weight when using transportation tool j on path i, D ij is the transportation distance of path i, F j is the carbon emission factor per unit distance of transportation tool j, C is the total transportation cost, C j is the transportation cost per unit distance of transportation tool j, T ij is the transportation time when using transportation tool j on path i, T deadline is the material delivery time requirement of the construction site, Q k is the total demand for materials required by construction site k, C i is the maximum capacity of path i, V j is the transportation speed of transportation tool j. Only one transportation tool j can be selected for each path i.

[0099] Preferably, the optimization algorithm is the particle swarm optimization algorithm.

[0100] Specifically, step 500 of this embodiment includes:

[0101] Step 501: Particle swarm initialization and multi-objective modeling

[0102] First, transform the multi-objective optimization problem (minimizing carbon emissions, minimizing transportation costs, and timely delivery of materials to meet construction requirements) into a problem solved by the particle swarm optimization algorithm. Each particle represents a transportation route plan, including information such as route selection, transportation tool allocation, and material transportation plan. When initializing the particle swarm, randomly generate a set of initial route plans that meet the constraint conditions, and assign an initial velocity and position to each particle. At the same time, design a fitness function, perform weighted processing on the objective functions of carbon emissions, transportation costs, and delivery time, and comprehensively evaluate the advantages and disadvantages of each particle to ensure the balance of multi-objective optimization.

[0103] Step 502: Combining dynamic adjustment and global search

[0104] During the iteration process of the particle swarm, combine the dynamic adjustment mechanism and the global search strategy to improve the optimization ability of the algorithm. The dynamic adjustment mechanism dynamically updates the constraint conditions and fitness function of the particles according to the changes in real-time traffic conditions and construction requirements, enabling the algorithm to adapt to the changes in the actual environment. The global search strategy enhances the global search ability by introducing hybrid optimization methods (such as simulated annealing or genetic algorithms). When the particle swarm falls into a local optimum, it jumps out of the local optimum solution through random perturbation or crossover mutation operations, thereby improving the quality of the optimization results.

[0105] Step 503: Adaptive adjustment of multi-objective weights

[0106] To further enhance the intelligence of the optimization algorithm, introduce an adaptive adjustment mechanism for multi-objective weights. During the iteration process of the particle swarm, dynamically adjust the weights of carbon emissions, transportation costs, and delivery time according to the distribution of the current solution and the convergence speed of the objective function. For example, when the optimization effect of carbon emissions tends to be stable, appropriately increase the weights of transportation costs or delivery time to prompt the algorithm to achieve dynamic balance among different objectives. Finally, through multiple iterations, the particle swarm converges to the optimal solution, and the optimization results are output, including the optimal route plan, transportation tool allocation, and material delivery plan.

[0107] Furthermore, step 600 of this embodiment includes:

[0108] Step 601: Generate an initial transportation route plan

[0109] Generate an initial transportation route plan based on the optimization results output by the optimization algorithm. The optimization results include the selection of transportation routes for each material, the allocation of transportation tools, the transportation time arrangement, as well as the corresponding carbon emissions and transportation costs. Combine the material requirements and delivery time requirements of the construction site, and transform the optimization results into a specific transportation plan, specifying the transportation tasks, transportation tools, and time nodes for each route. The initial plan needs to be verified to ensure that it meets the construction requirements, road traffic capacity limitations, and transportation time constraints.

[0110] Step 602: Establish a real-time monitoring system

[0111] During the actual transportation process, establish a real-time monitoring system for dynamically tracking traffic conditions, construction requirements, and transportation status. Traffic condition monitoring obtains real-time data through the traffic management system or map services, including road congestion, closures, and accident information. Construction requirement monitoring obtains the latest changes in material requirements through the material management system of the construction site, such as increased demand, earlier delivery time, or urgent needs. Transportation status monitoring uses GPS devices or logistics management systems to track the location, transportation progress, and material status of transportation vehicles in real time.

[0112] Step 603: Trigger dynamic adjustment conditions

[0113] The real-time monitoring system will determine whether it is necessary to adjust the initial transportation route plan based on the collected data. The conditions for triggering dynamic adjustment include: the current route is unavailable due to traffic congestion or closure; the material requirements at the construction site have changed (such as earlier or increased); the transportation vehicle cannot complete the task as planned due to a breakdown or delay. Once any of these conditions is met, the system will trigger the dynamic adjustment mechanism to re-plan the transportation route.

[0114] Step 604: Dynamically adjust the transportation route plan

[0115] After triggering the dynamic adjustment, adjust the initial transportation route plan according to the real-time data. First, combine the real-time traffic conditions, select alternative routes from the transportation network, recalculate the transportation time, transportation cost, and carbon emissions to ensure that the adjusted route plan still tries to meet the optimization objectives. Second, if the construction requirements change, adjust the priority of material transportation and re-allocate transportation tools and routes for materials with higher priorities. Finally, if there is a problem with the transportation tool, call in a standby vehicle or re-schedule other transportation tools to ensure that the transportation tasks are completed on time.

[0116] Step 605: Verify the adjusted route plan

[0117] The dynamically adjusted path plan needs to be verified to ensure that it meets all the constraints. The verification content includes: whether the adjusted path meets the material requirements of the construction site; whether the transportation time is within the construction requirements; whether the adjusted carbon emissions and transportation costs are close to the initial optimization goal. If the adjusted plan passes the verification, it will be executed as the new transportation path plan; if it fails the verification, further adjustment is required until all conditions are met.

[0118] Step 606: Generate the optimal transportation path plan

[0119] During the actual transportation process, the dynamically adjusted path plan will be continuously optimized to finally form the optimal transportation path plan. The optimal plan not only meets the construction requirements but also achieves a balance between carbon emissions and transportation costs. Record the optimal plan as the reference data for subsequent transportation path planning. At the same time, the traffic conditions, construction requirements, and transportation status data accumulated during the dynamic adjustment process can be used to improve the optimization algorithm, further enhancing the efficiency and intelligence level of path planning.

[0120] Exemplarily, the optimization result of this embodiment is the output data obtained by solving the multi-objective optimization model through a multi-objective optimization algorithm (such as the particle swarm optimization algorithm), reflecting the comprehensive optimal solution that minimizes carbon emissions, minimizes transportation costs, and enables timely delivery of materials under the premise of meeting the constraints. The optimization result includes the following content: the transportation path selection for each material (such as the specific path from the supplier to the construction site), the allocation of transportation tools (such as the selection of trucks, electric vehicles, etc.), the transportation time arrangement for each path, and corresponding indicators such as carbon emissions and transportation costs. The optimization result is the theoretically optimal solution, generated based on static data and model assumptions, providing the basis for subsequent generation of the initial transportation path plan.

[0121] The initial path plan is a specific transportation plan generated based on the optimization result, including the detailed arrangements for the actual transportation tasks. It transforms the path selection, transportation tool allocation, and time arrangement in the optimization result into an executable transportation plan. For example, it clarifies the transportation path for each material, the specific model of the transportation tool, the transportation time nodes, and the types and quantities of materials transported on each path. The initial path plan also needs to be verified to ensure that it meets the material requirements of the construction site, the road traffic capacity limit, and the transportation time constraints. The initial path plan is the initial execution plan for the transportation task, but due to uncertain factors such as traffic congestion and changes in construction requirements during the actual transportation process, the initial plan needs to be dynamically adjusted during actual execution.

[0122] The optimal transportation route plan is the final plan obtained by dynamically adjusting the initial route plan through real-time monitoring of traffic conditions, construction requirements, and transportation status during the actual transportation process. The optimal plan not only meets the material requirements and time requirements of the construction site but also achieves a dynamic balance between carbon emissions and transportation costs. For example, when a certain route becomes unavailable due to traffic congestion, the optimal plan will select an alternative route and reallocate transportation tools while ensuring that the adjusted plan still approaches the optimization goal as much as possible. The optimal transportation route plan is the final implementation plan generated by combining the actual situation and the dynamic adjustment mechanism, with higher flexibility and adaptability, and is an optimization and improvement of the initial route plan.

[0123] The beneficial effects of the present invention are as follows:

[0124] (1) By constructing a carbon emission calculation model and a multi-objective optimization model, the present invention takes carbon emissions as one of the optimization objectives, significantly reducing the carbon emissions during the material transportation process in the construction period of intercity railways, meeting the requirements of green and low-carbon development.

[0125] (2) When optimizing the route planning, the present invention comprehensively considers the transportation cost. By reasonably selecting the transportation route and transportation tools, it reduces unnecessary transportation distances and resource waste, thereby effectively reducing the transportation cost and improving the economic benefits.

[0126] (3) In the present invention, taking the timely delivery of materials that meet the construction requirements as one of the optimization objectives, it ensures that the materials required by the construction site can be delivered on time, avoiding the problem of construction progress lag caused by material delays.

[0127] (4) During the actual transportation process, by real-time monitoring of traffic conditions and construction requirements and dynamically adjusting the initial transportation route plan, the present invention can effectively cope with uncertain factors such as traffic congestion, road closures, and changes in construction requirements, ensuring the flexibility and reliability of the transportation plan.

[0128] (5) The present invention uses an optimization algorithm (such as the particle swarm optimization algorithm) to solve the multi-objective optimization model, which can quickly generate an efficient transportation route plan and is applicable to the route planning problem of large-scale complex transportation networks.

[0129] (6) By performing data cleaning and standardization processing on the material demand data, transportation network data, and carbon emission factor data, the present invention eliminates data noise and outliers, ensuring the accuracy and consistency of the input data and providing a reliable data basis for subsequent optimization.

[0130] (7) The present invention simultaneously considers three objectives: carbon emissions, transportation cost, and construction requirements, can achieve a balance among multiple objectives, provides a comprehensive optimal plan, and avoids other problems caused by single-objective optimization (such as only optimizing the cost may lead to an increase in carbon emissions).

[0131] (8) The present invention conforms to the current concepts of green construction and sustainable development. By optimizing the material transportation route, it reduces energy consumption and environmental pollution, providing technical support for the low-carbon transformation of the intercity railway construction industry.

[0132] (9) Through the functions of real-time monitoring and dynamic adjustment, combined with intelligent optimization algorithms, the present invention realizes the intelligentization and automation of transportation route planning, reduces manual intervention, and improves management efficiency.

[0133] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0134] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target, characterized in that, Including: Collecting material demand data, transportation network data, and carbon emission factor data during the construction period of the intercity railway; Performing data cleaning and data standardization processing on the material demand data, the transportation network data, and the carbon emission factor data to obtain standardized data; Calculating the carbon emissions on each path according to the standardized data; Constructing a multi-objective optimization model with minimizing carbon emissions, minimizing transportation costs, and ensuring the timely delivery of materials to meet construction requirements as the objective function; Using an optimization algorithm to optimize the multi-objective optimization model to obtain an optimization result; Generating an initial transportation route plan according to the optimization result, and monitoring the traffic conditions and construction requirements in real time during the actual transportation process to dynamically adjust the initial transportation route plan to obtain an optimal transportation route plan.

2. The method for planning the material transportation route during the construction period of intercity railways under the carbon emission reduction target according to claim 1, wherein The material demand data includes: material type, material weight, material quantity, and material delivery time; the transportation network data includes: transportation nodes, path distance, road capacity, and traffic conditions.

3. The method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target according to claim 1, wherein Performing data cleaning and data standardization processing on the material demand data, the transportation network data, and the carbon emission factor data to obtain standardized data, including: Grouping the material demand data, the transportation network data, and the carbon emission factor data according to a preset collection period to obtain multiple data groups; Calculating the difference coefficient between the current data group and the previous data group in sequence; Judging whether the value of the difference coefficient is within a preset range; If the value of the difference coefficient is not within the preset range, removing the corresponding data group; If the value of the difference coefficient is within the preset range, retaining the corresponding data group until all data groups are traversed to obtain data cleaning data; Performing standardization processing on the cleaning data to obtain the standardized data.

4. The method for planning the material transportation route during the construction period of intercity railways under the carbon emission reduction target according to claim 3, wherein The formula for the difference coefficient is: where p X,Y is the coefficient of difference, cov(X, Y) represents the covariance between the current data set X and the previous data set Y, α X represents the mean of the current data set X, and β Y represents the mean of the previous data set Y.

5. The method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target according to claim 2, characterized in that, The formula for the carbon emissions is: Among them, E is the carbon emission, W i is the material weight of the i-th path, D i is the path distance of the i-th path, F i is the carbon emission factor per unit distance of the transportation vehicle, and n is the number of paths.

6. The method for planning the material transportation route during the construction period of intercity railways under the carbon emission reduction target according to claim 5, wherein The constraint conditions of the objective function include: The transportation route of each material meets the requirements of the construction site, road capacity restrictions, and transportation time restrictions.

7. The method for planning the material transportation route during the construction period of intercity railways under the carbon emission reduction target according to claim 6, characterized in that The formula of the objective function includes: T ij ≤T deadline Among them, x ij represents whether transportation vehicle j is selected for path i, and x ij is a binary variable of 0 or 1. W ij is the material transportation weight when using transportation vehicle j on path i. D ij is the transportation distance of path i. F j is the carbon emission factor per unit distance of transportation vehicle j. C is the total transportation cost. C j is the transportation cost per unit distance of transportation vehicle j. T ij is the transportation time when using transportation vehicle j on path i. T deadline is the material delivery time requirement of the construction site. Q k is the total demand for materials required by construction site k. C i is the maximum traffic capacity of path i. V j is the transportation speed of transportation vehicle j.

8. The method for planning the material transportation path during the construction period of intercity railways under the carbon emission reduction target according to claim 1, wherein The optimization algorithm is the particle swarm optimization algorithm.

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