Intercity cargo splicing transportation path optimization algorithm based on large model optimization

Abstract

The invention relates to the technical field of inter-city cargo splicing and loading, in particular to an inter-city cargo splicing and loading transportation path optimization algorithm based on large model optimization, and solves the problems that in the prior art, an optimization algorithm based on a large model generally relates to a complex calculation process and needs a large amount of calculation resources and time, and especially in a real-time path planning scene, the calculation process is complicated. And the calculation delay can influence the transportation efficiency. The invention discloses an inter-city cargo splicing transportation path optimization algorithm based on large model optimization. The algorithm comprises data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, and algorithm deployment and application. According to the method, various possible transportation paths are analyzed and compared through an algorithm, the optimal path is selected, unnecessary detour and waiting time is reduced, so that the transportation efficiency is improved, and the algorithm can perform intelligent scheduling and reasonable arrangement of transportation vehicles and routes according to real-time traffic conditions, cargo demands, vehicle states and other information.

Description

Technical Field

[0001] The present invention relates to the technical field of inter-city cargo consolidation, and in particular to an inter-city cargo consolidation transportation path optimization algorithm based on large model optimization. Background Art

[0002] Intercity cargo consolidation transport route is a complex issue involving multiple aspects. Intercity cargo consolidation transport route refers to the route planning of consolidating goods from different sources and destinations but with similar transport routes between cities to optimize transportation resources, reduce transportation costs and improve transportation efficiency. The characteristics of this mode of transportation are its flexibility and efficiency. It can be dynamically adjusted according to the actual situation of the goods and transportation needs. When planning intercity cargo consolidation transport routes, the main optimization goals include: shortest transportation distance: through reasonable route planning, reduce the driving distance during transportation, thereby reducing fuel consumption and vehicle wear. Minimum transportation time: on the premise of ensuring the safety of the goods, shorten the transportation time as much as possible and improve transportation efficiency. Minimum transportation cost: through consolidation transportation and route optimization, reduce transportation costs and improve the economic benefits of enterprises. Cargo information: including the weight, volume, fragility, perishability and other characteristics of the goods, as well as the origin and destination of the goods. Transport vehicle: including parameters such as vehicle type, load capacity, driving performance, as well as vehicle availability and scheduling. Road network: including the length, direction, quality standards, traffic flow, road condition information, etc. of the road, as well as possible no-traffic areas, restrictions on large vehicles and other special restrictions. Data analysis method: by collecting and analyzing historical data on freight transportation, problems and potential optimization opportunities in the transportation process are discovered. Using data mining and analysis techniques, in-depth analysis is conducted on factors such as the type, quantity, transportation distance, transportation time, and transportation cost of goods to provide data support for the formulation of optimization plans. Simulation method: by establishing a model to predict the effects of different transportation plans. For example, traffic simulation software can be used to simulate and analyze the transportation route to find the optimal transportation route. Genetic algorithm: This is an optimization method based on biological evolution theory, which is often used to solve complex combinatorial optimization problems. In the optimization of freight transportation plans, genetic algorithms can find the best transportation routes and vehicle allocation plans by imitating the process of natural selection and inheritance.

[0003] The optimization algorithms based on large models in the existing technology usually involve complex calculation processes and require a lot of computing resources and time, which may be a considerable expense for logistics companies, especially in real-time path planning scenarios. Computational delays will affect transportation efficiency. The training of large models requires a lot of data and computing resources, and the training process is complicated. In addition, the effectiveness of the model is also affected by multiple factors such as data quality, model architecture, and hyperparameter selection, which increases the difficulty and uncertainty of model training. The performance of the algorithm depends to a large extent on the accuracy, completeness and real-time performance of the input data. However, in actual applications, due to problems such as diverse data sources, different data formats, and uneven data quality, the algorithm performance is reduced or even fails. There are many uncertain factors in the inter-city freight transportation process, such as traffic congestion, weather changes, vehicle failures, etc. The existing algorithms may not be flexible and robust enough when dealing with these uncertain factors, resulting in path planning results deviating from the actual situation. Summary of the invention

[0004] The purpose of the present invention is to provide an inter-city cargo loading and unloading transportation path optimization algorithm based on large model optimization, which solves the problem that the optimization algorithm based on large model in the prior art usually involves complex calculation process and requires a lot of computing resources and time, which may be a considerable expense for logistics companies, especially in real-time path planning scenarios, where calculation delays will affect transportation efficiency.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: An inter-city cargo loading and unloading transport path optimization algorithm based on large model optimization, including data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, algorithm deployment and application, collects cargo information, vehicle information, road network information, traffic flow information, weather forecast and other data from logistics management systems, traffic information systems, meteorological forecast systems and other sources, selects a suitable deep learning model architecture according to actual needs, adopts convolutional neural networks, recurrent neural networks, and graph neural networks, uses validation set data to verify the trained model, evaluates the generalization ability of the model, inputs the relevant data of the inter-city cargo loading and unloading transport path to be optimized into the trained model, selects suitable evaluation indicators to evaluate the optimization results, such as transportation time, transportation cost, and vehicle utilization, and deploys the optimized algorithm to the logistics management system or related application platform.

[0006] Preferably, data is collected to obtain detailed information on the goods from the logistics management system, including the type, weight, volume, loading and unloading location, and loading and unloading time of the goods; road network information is obtained from the traffic information system, including road length, capacity, speed limit, and toll station location; weather forecast data is obtained from the meteorological forecast system, including temperature, humidity, precipitation probability, wind speed, etc., to consider the impact of weather on transportation; historical transportation data can be collected to analyze transportation patterns and predict future transportation demand; duplicate records can be removed to ensure that each piece of data is unique; the integrity of data fields can be checked, missing values ​​can be filled or incomplete records can be deleted, and errors in the data can be corrected, such as incorrect location names, unreasonable cargo weights, etc.; data standardization: data in different formats are uniformly converted into a format acceptable to the algorithm, such as date format, numerical format, etc.; data are normalized or standardized to eliminate dimensional differences between data and improve algorithm performance.

[0007] Preferably, select a model architecture and choose a suitable deep learning model according to the complexity of the problem and the data type. Model design: design the input layer of the model, determine the dimension and format of the input data, design the hidden layer, select the appropriate activation function and number of layers to extract the characteristics of the data, design the output layer, determine the dimension and format of the output data, such as the node sequence of the transportation path, transportation cost, etc. Data partitioning: randomly divide the data set into training set, validation set and test set, usually in a ratio of 7:2:1 or 6:2:2, for model training, validation and testing. Model training: use the training set data to iteratively train the model, calculate the loss through forward propagation, update the model parameters through back propagation, monitor the loss function value during training, and stop training when the loss function value no longer decreases significantly.

[0008] Preferably, hyperparameter tuning: try different hyperparameter combinations within the preset parameter range through methods such as grid search or random search, and select the combination that optimizes the model performance. Model verification: use the verification set data to verify the model, calculate the model's accuracy, recall rate, F1 score and other indicators, and evaluate the model's generalization ability. Algorithm improvement: analyze the shortcomings of the model based on the verification results, such as overfitting, underfitting, etc., and improve the model architecture, loss function, optimization algorithm, etc.

[0009] Preferably, input data: input the data related to the inter-city cargo loading and transportation route to be optimized, such as cargo information, vehicle information, road network information, etc., into the trained model; path calculation: the model calculates the optimal transportation route plan through forward propagation based on the input data and learned knowledge, including cargo loading sequence, transportation vehicle selection, transportation route planning, etc.; result output: output the calculated optimal path plan in a visual or text form to facilitate logistics management personnel to understand and execute.

[0010] Preferably, evaluation indicators: select appropriate evaluation indicators to evaluate the optimization results, such as transportation time, transportation cost, vehicle utilization, customer satisfaction, result comparison, compare and analyze the optimization results with the actual situation or the optimization results of other algorithms, evaluate the performance and advantages of the algorithm, feedback and adjustment: according to the evaluation results and comparative analysis results, provide feedback and adjustment to the algorithm, such as adjusting model parameters and optimizing algorithm processes.

[0011] The present invention has at least the following beneficial effects: The invention discloses an inter-city cargo loading and unloading transportation path optimization algorithm based on large model optimization to improve transportation efficiency and optimize path selection: the algorithm analyzes and compares multiple possible transportation paths, selects the best path, reduces unnecessary detours and waiting time, thereby improving transportation efficiency; the algorithm can perform intelligent scheduling based on real-time traffic conditions, cargo demand, vehicle status and other information, reasonably arranges transportation vehicles and routes, avoids resource waste and congestion, reduces transportation costs, and reduces fuel consumption: the optimized path is usually shorter and smoother, reduces vehicle mileage and fuel consumption, thereby reducing transportation costs, and optimizes cargo loading: the algorithm can perform intelligent loading based on factors such as cargo weight, volume, loading and unloading location, etc., improve vehicle loading rate and utilization rate, further reduce transportation costs, and improve customer satisfaction. By optimizing routes and intelligent scheduling, the on-time delivery rate of goods can be improved and the customer's delivery time requirements can be met. The algorithm can provide personalized services based on customer needs and preferences, such as providing a variety of transportation options for customers to choose from, thereby improving the customer's service experience. The optimization algorithm based on the large model is one of the important directions for the intelligent development of the logistics industry. Its research and application will promote technological innovation and progress in the logistics industry. By applying optimization algorithms, logistics companies can improve transportation efficiency, reduce transportation costs, and improve service quality, thereby enhancing the company's market competitiveness and profitability. Optimized transportation routes and intelligent scheduling can reduce vehicle empty driving and congestion, alleviate urban traffic pressure, and reduce carbon emissions in the logistics transportation process by reducing fuel consumption and vehicle mileage, which is conducive to environmental protection and sustainable development. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Example

[0013] It includes data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, algorithm deployment and application. It collects cargo information, vehicle information, road network information, traffic flow information, weather forecast and other data from logistics management systems, traffic information systems, meteorological forecast systems and other sources. It selects the appropriate deep learning model architecture according to actual needs, uses convolutional neural networks, recurrent neural networks, and graph neural networks, and uses validation set data to verify the trained model and evaluate the generalization ability of the model. It inputs the relevant data of the inter-city cargo loading and unloading transportation path to be optimized into the trained model, selects appropriate evaluation indicators to evaluate the optimization results, such as transportation time, transportation cost, and vehicle utilization, and deploys the optimized algorithm to the logistics management system or related application platform.

[0014] The algorithm analyzes and compares multiple possible transportation routes, selects the optimal route, reduces unnecessary detours and waiting time, and thus improves transportation efficiency. The algorithm can perform intelligent scheduling based on real-time traffic conditions, cargo demand, vehicle status and other information, and reasonably arrange transportation vehicles and routes to avoid waste of resources and congestion. Example

[0015] Collect data, obtain detailed information about the goods from the logistics management system, including the type, weight, volume, loading and unloading location, and loading and unloading time of the goods; obtain road network information from the traffic information system, including road length, capacity, speed limit, and toll station location; obtain weather forecast data from the meteorological forecast system, including temperature, humidity, precipitation probability, wind speed, etc., to consider the impact of weather on transportation; collect historical transportation data to analyze transportation patterns and predict future transportation demand; remove duplicate records to ensure that each piece of data is unique; check the integrity of data fields, fill in missing values ​​or delete incomplete records; correct errors in the data, such as incorrect location names, unreasonable cargo weights, etc.; data standardization: convert data in different formats into a format acceptable to the algorithm, such as date format, numerical format, etc.; normalize or standardize the data to eliminate dimensional differences between data and improve algorithm performance.

[0016] The optimized route is usually shorter and smoother, reducing vehicle mileage and fuel consumption, thereby reducing transportation costs. The algorithm can perform intelligent loading and unloading based on factors such as the weight, volume, and loading and unloading locations of the goods, thereby improving the vehicle's loading rate and utilization rate, and further reducing transportation costs. Example

[0017] Select the model architecture and choose a suitable deep learning model according to the complexity of the problem and the data type. Model design: design the input layer of the model, determine the dimension and format of the input data, design the hidden layer, select the appropriate activation function and number of layers to extract the characteristics of the data, design the output layer, determine the dimension and format of the output data, such as the node sequence of the transportation path, transportation cost, etc. Data partitioning: randomly divide the data set into training set, validation set and test set, usually in a ratio of 7:2:1 or 6:2:2, for model training, validation and testing, model training: use the training set data to iteratively train the model, calculate the loss through forward propagation, update the model parameters through back propagation, monitor the loss function value during training, and stop training when the loss function value no longer decreases significantly.

[0018] By optimizing routes and intelligent scheduling, the on-time delivery rate of goods can be improved, the customer's delivery time requirements can be met, and the service quality can be improved. The algorithm can provide personalized services based on customer needs and preferences, such as providing a variety of transportation options for customers to choose from, thereby improving customers' service experience. Example

[0019] Hyperparameter tuning: Try different hyperparameter combinations within the preset parameter range through grid search or random search, and select the combination that optimizes the model performance. Model verification: Use the verification set data to verify the model, calculate the model's accuracy, recall rate, F1 score and other indicators, and evaluate the model's generalization ability. Algorithm improvement: Analyze the shortcomings of the model based on the verification results, such as overfitting, underfitting, etc., and improve the model architecture, loss function, optimization algorithm, etc. Example

[0020] Input data: The data related to the inter-city cargo loading and transportation route to be optimized, such as cargo information, vehicle information, road network information, etc., are input into the trained model. Path calculation: The model calculates the optimal transportation path plan through forward propagation based on the input data and learned knowledge, including cargo loading sequence, transportation vehicle selection, transportation route planning, etc. Result output: The calculated optimal path plan is output in a visual or text form to facilitate logistics management personnel to understand and execute. Example

[0021] Evaluation indicators: Select appropriate evaluation indicators to evaluate the optimization results, such as transportation time, transportation cost, vehicle utilization, customer satisfaction, and compare the results. Compare and analyze the optimization results with the actual situation or the optimization results of other algorithms to evaluate the performance and advantages of the algorithm. Feedback and adjustment: Based on the evaluation results and comparative analysis results, provide feedback and adjustments to the algorithm, such as adjusting model parameters and optimizing algorithm processes.

[0022] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An inter-city freight loading and unloading transportation path optimization algorithm based on large model optimization, including data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, algorithm deployment and application, collect cargo information, vehicle information, road network information, traffic flow information, weather forecast and other data from logistics management systems, traffic information systems, meteorological forecast systems and other sources, select a suitable deep learning model architecture according to actual needs, use convolutional neural networks, recurrent neural networks, and graph neural networks, use validation set data to verify the trained model, evaluate the generalization ability of the model, input the relevant data of the inter-city freight loading and unloading transportation path to be optimized into the trained model, select suitable evaluation indicators to evaluate the optimization results, transportation time, transportation cost, and vehicle utilization, and deploy the optimized algorithm to the logistics management system or related application platform.

2. According to the large model optimization based inter-city cargo loading transportation path optimization algorithm of claim 1, it is characterized in that: The data collected can obtain detailed information about the goods from the logistics management system, including the type, weight, volume, loading and unloading location, and loading and unloading time of the goods; obtain road network information from the traffic information system, including road length, capacity, speed limit, and toll station location; obtain weather forecast data from the meteorological forecast system, including temperature, humidity, precipitation probability, wind speed, etc., to consider the impact of weather on transportation; collect historical transportation data for analyzing transportation patterns and predicting future transportation demand; remove duplicate records to ensure that each piece of data is unique; check the integrity of data fields, fill in missing values ​​or delete incomplete records; correct errors in the data, such as incorrect location names, unreasonable cargo weights, etc.; data standardization: convert data in different formats into a format acceptable to the algorithm, such as date format, numerical format, etc.; normalize or standardize the data to eliminate dimensional differences between data and improve algorithm performance.

3. The inter-city cargo consolidation transport path optimization algorithm based on large model optimization according to claim 1 is characterized in that: The described model architecture is selected, and a suitable deep learning model is selected according to the complexity of the problem and the data type. Model design: design the input layer of the model, determine the dimension and format of the input data, design the hidden layer, select the appropriate activation function and number of layers to extract the characteristics of the data, design the output layer, determine the dimension and format of the output data, such as the node sequence of the transportation path, the transportation cost, etc. Data partitioning: randomly divide the data set into training set, validation set and test set, usually in a ratio of 7:2:1 or 6:2:2, for model training, validation and testing, model training: use the training set data to iteratively train the model, calculate the loss through forward propagation, update the model parameters through back propagation, monitor the loss function value during training, and stop training when the loss function value no longer decreases significantly.

4. According to the large model optimization based inter-city cargo loading transportation path optimization algorithm of claim 1, it is characterized in that: The hyperparameter tuning is as follows: different hyperparameter combinations are tried within the preset parameter range through grid search or random search, and the combination with the best model performance is selected; model validation is as follows: the model is validated using validation set data, and the model's accuracy, recall rate, F1 score and other indicators are calculated to evaluate the generalization ability of the model; algorithm improvement is as follows: the deficiencies of the model are analyzed according to the validation results, such as overfitting, underfitting, etc., and the model architecture, loss function, optimization algorithm and the like are improved.

5. The inter-city cargo consolidation transport path optimization algorithm based on large model optimization according to claim 1 is characterized in that: The input data: the data related to the inter-city cargo loading and transportation route to be optimized, such as cargo information, vehicle information, road network information, etc., are input into the trained model; path calculation: the model calculates the optimal transportation path plan through forward propagation based on the input data and the learned knowledge, including cargo loading sequence, transportation vehicle selection, transportation route planning, etc.; result output: the calculated optimal path plan is output in a visual or text form to facilitate logistics management personnel to understand and execute.

6. The inter-city cargo consolidation transport path optimization algorithm based on large model optimization according to claim 1 is characterized in that: The evaluation indicators: select appropriate evaluation indicators to evaluate the optimization results, such as transportation time, transportation cost, vehicle utilization, customer satisfaction, and compare the results. Compare and analyze the optimization results with the actual situation or the optimization results of other algorithms to evaluate the performance and advantages of the algorithm. Feedback and adjustment: provide feedback and adjustment to the algorithm based on the evaluation results and comparative analysis results, such as adjusting model parameters and optimizing algorithm processes.