Comprehensive generation system and method for road transport cost optimization budget

Abstract

The present invention discloses a comprehensive generation system and method for optimizing a road transport cost budget. The system and method utilize a cost optimization module to obtain an optimization result of a road transport cost budget according to an optimization algorithm, and a budget scheme generation module to generate a feasible road transport budget scheme based on the optimization result. The system and method are characterized in that the system includes a historical cost data collection module, a cost model construction module, a cost optimization module, and a budget scheme generation module. The historical cost data collection module establishes a signal connection with the cost model construction module, the cost model construction module establishes a signal connection with the cost optimization module, and the cost optimization module establishes a signal connection with the budget scheme generation module. The historical cost data collection module is used to collect historical cost data of road transport, enterprise vehicle configuration information, cargo information, etc., to ensure the integrity, accuracy, and timely updating of the data.

Description

Technical Field

[0001] The present invention relates to a comprehensive budget generation system and method for optimizing a road transport cost, which can reduce an enterprise's road transport costs. The system belongs to the field of road transport technology, and particularly relates to a comprehensive budget generation system and method for optimizing a road transport cost budget, which can reduce an enterprise's road transport costs by obtaining an optimization result of the road transport cost budget according to an optimization algorithm through a cost optimization module, and generating a feasible road transport budget plan according to the optimization result through a budget plan generation module. Background Art

[0002] Road transport is one of the main modes of modern transportation. Due to its small impact on climate and natural conditions, large cargo capacity, low transportation costs, and the ability to use a variety of different types of transport vehicles, road transport can carry almost any commodity. Therefore, it is widely used in supply chain management, international trade, logistics distribution, and inventory management. In order to deliver goods from the origin to the destination at a lower road transport cost, it is necessary to budget the road transport costs to plan the optimal road transport plan, maximize cost-effectiveness, improve transportation efficiency, and enhance competitiveness. The existing road transport cost budgeting method is mainly to manually prepare budgets based on past experience to plan appropriate road transport plans. However, manual budgeting is easily affected by subjective factors, lacks intelligence and flexibility, and cannot adapt to changes quickly, resulting in high road transport cost budgets.

[0003] Publication No. CN109242154A discloses a method for freight transportation route planning, comprising: obtaining the quantity of goods; obtaining the direct freight cost of the goods from the shipping address to the delivery address; calculating the average direct freight cost of the goods from the shipping address to the delivery address; planning a transfer route for the goods from the shipping address to the delivery address via an intermediate address; obtaining the transfer freight cost of the goods from the shipping address to the delivery address via an intermediate address; calculating the average transfer freight cost of the goods from the shipping address to the delivery address via an intermediate address; comparing the average direct freight cost and the average transfer freight cost; if the average direct freight cost is greater than the average transfer freight cost, planning a corresponding transfer route; otherwise, planning a direct route, thereby reducing the average freight cost and improving service quality. The above-mentioned freight transportation route planning method only selects a transportation route with a lower average freight cost when the quantity of goods transported is small, thereby reducing the average freight cost. However, it is not applicable to situations where the quantity of goods transported is large. Moreover, it only optimizes the transportation route and cannot optimize the vehicle configuration, labor costs, and daily maintenance costs of the vehicles used to transport the goods. It lacks intelligence and flexibility, and the freight transportation cost is high. Summary of the Invention

[0004] In order to improve the above situation, the road transport cost optimization budget comprehensive generation system and generation method of the present invention provides a budget comprehensive generation system and generation method that obtains the optimization result of the road transport cost budget according to the optimization algorithm through a cost optimization module, and generates a feasible road transport budget plan according to the optimization result. It can reduce the road transport cost of the enterprise.

[0005] The road transport cost optimization budget comprehensive generation system and generation method of the present invention are implemented as follows: the road transport cost optimization budget comprehensive generation system of the present invention includes a historical cost data collection module, a cost model construction module, a cost optimization module and a budget scheme generation module, and is characterized in that:

[0006] The historical cost data collection module and the cost model construction module establish a signal connection,

[0007] The cost model building module and the cost optimization module establish a signal connection,

[0008] The cost optimization module and the budget solution generation module establish a signal connection,

[0009] The historical cost data collection module is used to collect historical cost data of road transportation, enterprise vehicle configuration information, cargo information, etc., to ensure the integrity, accuracy and timely update of the data, and provide data support for the subsequent cost model construction module.

[0010] Preferably, the historical cost data of road transport is a record of various costs and expenses related to road transport within a preset time period, including fuel costs, tolls, labor costs, daily maintenance costs of vehicles, etc. The enterprise vehicle configuration information is relevant information about the vehicles owned by the enterprise, including model, number, year, usage status, carrying capacity, etc. The cargo information is relevant information about the cargo that can be transported by the enterprise's vehicles, including cargo type, quantity, weight, volume, value, etc.

[0011] Preferably, the historical cost data collection module realizes the sharing and automatic collection of the historical cost data of the enterprise's road transportation, the enterprise's vehicle configuration information, and the cargo information by sharing data with enterprise partners and establishing a data sharing mechanism and data interface.

[0012] Preferably, the historical cost data collection module collects historical cost data of road transportation, enterprise vehicle configuration information, and cargo information by manual entry, or directly imports the data after obtaining relevant data through communication with the enterprise.

[0013] Preferably, the historical cost data collection module includes a data cleaning module, which is used to clean, integrate and store the collected historical data, remove invalid data, repair erroneous data, and supplement missing data to ensure the accuracy and completeness of the data, unify the data format and data structure of data from different enterprises, and store the cleaned and integrated data in the database for subsequent cost model construction.

[0014] The cost model building module is used to decompose the historical cost of road transportation through the collected historical cost data of road transportation in order to better understand and manage costs in different aspects, enable enterprises to identify the main expenditure items in the cost structure, find potential opportunities for cost control and optimization, and provide data support for subsequent cost optimization modules.

[0015] The historical cost of road transportation includes two parts: floating cost and fixed cost. The floating cost is the cost that cannot be determined, including fuel cost and tolls, of which fuel cost accounts for about 35%-40% and tolls account for about 15%-30%. The fixed cost is the cost that can be determined, including labor cost and daily maintenance cost of vehicles, of which labor cost accounts for about 5%-10% and daily maintenance cost of vehicles accounts for about 10%.

[0016] Preferably, the factors affecting the fuel cost and toll include vehicle configuration, cargo load, transportation route, weather conditions and driver's operation mode, among which, optimized vehicle configuration, such as the design of the head and trailer, can reduce air resistance and reduce fuel consumption, thereby saving fuel expenses; the greater the cargo load, the greater the fuel consumption of the vehicle, and it is necessary to select the most appropriate cargo load; by planning the most economical and efficient transportation route, the vehicle's fuel cost and toll expenses can be reduced.

[0017] Preferably, the labor costs include driver's salary, training, social insurance and other expenses, and the daily maintenance costs of vehicles include depreciation, maintenance, repair, insurance and other expenses, which are essential expenses for maintaining the normal operation of transportation business.

[0018] Cost Optimization Module: This module uses an optimization algorithm to optimize the road transport cost budget while meeting road transport safety and service level requirements, providing optimization results for the subsequent budget plan generation module.

[0019] Preferably, the optimization algorithm selects the best vehicle configuration according to the type and weight of the goods to be transported, making full use of the vehicle capacity; selects the best transportation route based on the multiple routes between the origin and the destination, reducing mileage, saving fuel and tolls, and shortening transportation time; installs a reminder device to issue voice or text reminders when the driver has irregular driving operations, such as excessive acceleration, sudden braking or long idling, and violates traffic regulations, so as to reduce vehicle wear and tear and casualties, and reduce daily vehicle maintenance costs; reasonably arranges personnel working hours and task allocation, improves work efficiency, and reduces labor cost expenditures,

[0020] Preferably, the optimization algorithm obtains weather and traffic condition data in real time and uses the optimization algorithm to re-plan the best transportation route to avoid slippery roads caused by rain or snow, congested or dangerous roads, improve transportation efficiency, reduce delays and accident risks, further optimize road transportation costs, and enhance the flexibility and adaptability of road transportation.

[0021] Budget plan generation module: It is used to generate feasible road transport budget plans based on the optimization results of the cost optimization module for decision makers to choose, including vehicle configuration, cargo load, transportation route, etc. At the same time, the budget plan generation module can also provide enterprises with personalized budget plans based on their actual transportation conditions.

[0022] Preferably, the budget plan generation module can help enterprises understand the budget plan generation process by building a decision tree, and make corresponding plan suggestions and decision support according to different situations.

[0023] Preferably, the budget solution generation module can simulate and optimize different budget solutions by establishing a mathematical model and using corresponding algorithms, and find the optimal budget solution.

[0024] The present invention also relates to a method for generating a comprehensive road transport cost optimization budget, which is characterized by utilizing a comprehensive road transport cost optimization budget generation system for optimization support, and the specific steps include:

[0025] (1) The historical cost data collection module collects historical cost data of road transportation, enterprise vehicle configuration information, cargo information, etc., to ensure the integrity, accuracy and timely update of the data. The historical cost data collection module transmits the collected data to the cost model construction module;

[0026] (2) The cost model building module decomposes the historical cost of road transportation through the collected historical cost data of road transportation in order to better understand and manage costs in different aspects, enable enterprises to identify the main expenditure items in the cost structure, and find potential opportunities for cost control and optimization. The cost model building module transmits the decomposed historical cost of road transportation to the cost optimization module;

[0027] (3) The cost optimization module uses an optimization algorithm to optimize the road transport cost budget while meeting the road transport safety and service level requirements. The cost optimization module transmits the optimization results to the budget plan generation module;

[0028] (4) The budget plan generation module generates a feasible road transport budget plan based on the optimization results of the cost optimization module for decision makers to choose, including vehicle configuration, cargo load, transportation route, etc. At the same time, the budget plan generation module can also provide enterprises with personalized budget plans based on their actual transportation conditions.

[0029] Beneficial effects

[0030] 1. The cost optimization module obtains the optimization result of the road transportation cost budget according to the optimization algorithm. The budget plan generation module generates a feasible road transportation budget plan based on the optimization result, which can reduce the company's road transportation costs.

[0031] 2. It has good scalability and customizability, and can provide enterprises with personalized budget plans based on their actual transportation conditions to meet the transportation needs of different types of enterprises. DETAILED DESCRIPTION

[0032] Example 1:

[0033] The road transport cost optimization budget comprehensive generation system of the present invention includes a historical cost data collection module, a cost model construction module, a cost optimization module and a budget scheme generation module, and is characterized in that:

[0034] The historical cost data collection module and the cost model construction module establish a signal connection,

[0035] The cost model building module and the cost optimization module establish a signal connection,

[0036] The cost optimization module and the budget solution generation module establish a signal connection,

[0037] The historical cost data collection module is used to collect historical cost data of road transportation, enterprise vehicle configuration information, cargo information, etc., to ensure the integrity, accuracy and timely update of the data, and provide data support for the subsequent cost model construction module.

[0038] Preferably, the historical cost data of road transport is a record of various costs and expenses related to road transport within a preset time period, including fuel costs, tolls, labor costs, daily maintenance costs of vehicles, etc. The enterprise vehicle configuration information is relevant information about the vehicles owned by the enterprise, including model, number, year, usage status, carrying capacity, etc. The cargo information is relevant information about the cargo that can be transported by the enterprise's vehicles, including cargo type, quantity, weight, volume, value, etc.

[0039] Preferably, the historical cost data collection module realizes the sharing and automatic collection of the historical cost data of the enterprise's road transportation, the enterprise's vehicle configuration information, and the cargo information by sharing data with enterprise partners and establishing a data sharing mechanism and data interface.

[0040] Preferably, the historical cost data collection module collects historical cost data of road transportation, enterprise vehicle configuration information, and cargo information by manual entry, or directly imports the data after obtaining relevant data through communication with the enterprise.

[0041] Preferably, the historical cost data collection module includes a data cleaning module, which is used to clean, integrate and store the collected historical data, remove invalid data, repair erroneous data, and supplement missing data to ensure the accuracy and completeness of the data, unify the data format and data structure of data from different enterprises, and store the cleaned and integrated data in the database for subsequent cost model construction.

[0042] The cost model building module is used to decompose the historical cost of road transportation through the collected historical cost data of road transportation in order to better understand and manage costs in different aspects, enable enterprises to identify the main expenditure items in the cost structure, find potential opportunities for cost control and optimization, and provide data support for subsequent cost optimization modules.

[0043] The historical cost of road transportation includes two parts: floating cost and fixed cost. The floating cost is the cost that cannot be determined, including fuel cost and tolls, of which fuel cost accounts for about 35%-40% and tolls account for about 15%-30%. The fixed cost is the cost that can be determined, including labor cost and daily maintenance cost of vehicles, of which labor cost accounts for about 5%-10% and daily maintenance cost of vehicles accounts for about 10%.

[0044] Preferably, factors affecting fuel costs and tolls include vehicle configuration, cargo load, transportation route, weather conditions, and driver's operating methods. Optimized vehicle configuration, such as the design of the head and trailer, can reduce air resistance and fuel consumption, thereby saving fuel costs. The greater the cargo load, the greater the vehicle's fuel consumption, so it is necessary to select the most appropriate cargo load. By planning the most economical and efficient transportation route, fuel costs and tolls can be reduced.

[0045] Preferably, labor costs include driver's salary, training, social insurance and other expenses, and daily vehicle maintenance costs include depreciation, maintenance, repair, insurance, etc., which are essential expenses to maintain the normal operation of transportation business.

[0046] Cost Optimization Module: This module uses an optimization algorithm to optimize the road transport cost budget while meeting road transport safety and service level requirements, providing optimization results for the subsequent budget plan generation module.

[0047] Preferably, the optimization algorithm can select the optimal vehicle configuration according to the type and weight of the goods to be transported, and make full use of the vehicle capacity; select the best transportation route based on the multiple routes between the origin and the destination, reduce mileage, save fuel and tolls, and shorten transportation time; install a reminder device to issue a voice or text reminder when the driver has irregular driving operations, such as excessive acceleration, sudden braking or long idling, and violates traffic regulations, so as to reduce vehicle wear and tear and casualties, and reduce daily vehicle maintenance costs; reasonably arrange personnel working hours and task allocation, improve work efficiency, and reduce labor costs.

[0048] Preferably, the optimization algorithm can obtain weather and traffic data in real time and use the optimization algorithm to re-plan the best transportation route to avoid slippery roads caused by rain or snow, congested or dangerous roads, improve transportation efficiency, reduce delays and accident risks, further optimize road transportation costs, and enhance the flexibility and adaptability of road transportation.

[0049] Budget plan generation module: It is used to generate feasible road transport budget plans based on the optimization results of the cost optimization module for decision makers to choose, including vehicle configuration, cargo load, transportation route, etc. At the same time, the budget plan generation module can also provide enterprises with personalized budget plans based on their actual transportation conditions.

[0050] Preferably, the budget plan generation module can help enterprises understand the budget plan generation process by building a decision tree, and make corresponding plan suggestions and decision support according to different situations.

[0051] Preferably, the budget solution generation module can simulate and optimize different budget solutions by establishing a mathematical model and using corresponding algorithms, and find the optimal budget solution.

[0052] The method for comprehensively generating an optimized budget for road transport costs of the present invention is characterized in that: a comprehensive generation system for optimizing the budget for road transport costs is used for optimization support, and the specific steps include:

[0053] (1) The historical cost data collection module collects historical cost data of road transportation, enterprise vehicle configuration information, cargo information, etc., to ensure the integrity, accuracy and timely update of the data. The historical cost data collection module transmits the collected data to the cost model construction module;

[0054] (2) The cost model building module decomposes the historical cost of road transportation through the collected historical cost data of road transportation in order to better understand and manage costs in different aspects, enable enterprises to identify the main expenditure items in the cost structure, and find potential opportunities for cost control and optimization. The cost model building module transmits the decomposed historical cost of road transportation to the cost optimization module;

[0055] (3) The cost optimization module uses an optimization algorithm to optimize the road transport cost budget while meeting the road transport safety and service level requirements. The cost optimization module transmits the optimization results to the budget plan generation module;

[0056] Preferably, the specific steps of the optimization algorithm adopted by the cost optimization module include:

[0057] 1) Select vehicle configuration based on historical data, choosing the optimal vehicle head and trailer configuration based on the type and weight of the cargo to be transported, fully utilizing vehicle capacity, reducing air resistance, lowering fuel consumption, and saving fuel expenses;

[0058] 2) Based on the origin and destination of road transport, a topological map between the origin and destination is constructed and divided into multiple transport nodes;

[0059] 3) Based on historical data, the historical cost of road transportation per mile between two transportation nodes is constructed. The historical cost set of transportation per mile between two transportation nodes is:

[0060] Where: i represents the first transport node, j represents the second transport node, Indicates unit mileage, represents the historical cost of road transportation per unit mileage between two transportation nodes,

[0061] 4) Based on historical data, a cost optimization model is constructed between two transportation nodes. The set of transportation nodes is: , the transport node set includes the origin A and the destination D, and the transport node set Set to a single element set, the first single element is S A , construct the first single-element transport network map:

[0062] S A → ,

[0063] Based on the minimum cost function Perform the first single element transportation network optimization, the minimum cost function satisfy:

[0064]

[0065] in, represents the topological distance from the origin A to each transportation node j,

[0066]

[0067] in, Indicates the origin location, represents the loop step length, n represents the loop increment, and the point where the minimum value of the first single element transportation network is obtained is calculated as the first single element optimal solution;

[0068] 5) According to the transport node collection , repeat steps 2) and 3) to obtain the second single element optimal solution and the third single element optimal solution, until the optimal solutions for all transportation nodes are completed;

[0069] 6) Determine the road transport cost based on each transport node in the transport network and output the road transport budget;

[0070] (4) The budget plan generation module generates feasible road transport budget plans based on the optimization results of the cost optimization module for decision makers to choose from, including vehicle configuration, cargo load, transportation route, etc. At the same time, the budget plan generation module can also provide enterprises with personalized budget plans based on their actual transportation conditions;

[0071] The historical cost data collection module realizes the design of sharing and automatic collection of historical cost data of enterprise road transportation, enterprise vehicle configuration information, and cargo information by sharing data with enterprise partners and establishing a data sharing mechanism and data interface. It can reduce manual intervention and human errors, improve the speed and accuracy of data acquisition, and can update data in a timely manner to ensure the timeliness and accuracy of historical cost data information.

[0072] The budget scheme generation module simulates and optimizes different budget schemes by establishing mathematical models and using corresponding algorithms, and finds the design of the optimal budget scheme. It can quickly simulate and analyze different budget schemes, save time and cost, improve decision-making efficiency, provide enterprises with the optimal road transportation budget scheme, and improve operational efficiency and cost control capabilities.

[0073] The purpose is to obtain the optimization result of the road transport cost budget through the cost optimization module according to the optimization algorithm, and the budget plan generation module generates a feasible road transport budget plan according to the optimization result, thereby reducing the company's road transport costs.

[0074] After considering the specification and practicing the invention disclosed herein, those skilled in the art will easily think of other similar embodiments of the present invention. This application is intended to cover any modified uses or adaptive changes of the present invention. These modifications or uses, applicability changes follow the general principles of the present invention and include common knowledge or customary technical means in the technical field not disclosed in the present invention.

[0075] It should be noted that, in the specific implementation mode of the present invention, for the sake of simplicity of description, the data processing process of the controller is described as a series of action combinations. However, those skilled in the art should know that the present invention is not limited to the described actions, because according to the present invention, certain steps can be performed sequentially or simultaneously. Secondly, those skilled in the art should also know that the actions described and involved in the specification are not necessarily necessary for the present invention. The said content is only a preferred implementation case of the present invention and cannot be considered to limit the scope of implementation of the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for comprehensively generating an optimized budget for road transport costs, characterized by: The road transport cost optimization budget comprehensive generation system is used for optimization support. The specific steps include: (1) the historical cost data collection module collects the historical cost data of road transport, enterprise vehicle configuration information and cargo information, and the historical cost data collection module transmits the collected data to the cost model construction module; (2) the cost model construction module decomposes the historical cost of road transport through the collected historical cost data of road transport, and the cost model construction module transmits the decomposed historical cost of road transport to the cost optimization module; (3) the cost optimization module uses the optimization algorithm to optimize the road transport cost budget under the premise of meeting the road transport safety and service level requirements, and the cost optimization module transmits the optimization results to the budget plan generation module; (4) the budget plan generation module generates a feasible road transport budget plan for decision makers to choose based on the optimization results of the cost optimization module, including vehicle configuration, cargo load and transportation route. At the same time, the budget plan generation module provides personalized budget plans for enterprises based on the actual transportation situation of the enterprise; the road transport cost optimization budget comprehensive generation system includes a historical cost data collection module, a cost model construction module, a cost optimization module and a budget plan generation module. The historical cost data collection module and the cost model construction module establish a signal connection. The cost model building module and the cost optimization module establish a signal connection, and the cost optimization module and the budget plan generation module establish a signal connection; the historical cost data of road transportation are records of various expenses and expenditures related to road transportation within a preset time, including fuel costs, tolls, labor costs and daily maintenance costs of vehicles; the enterprise vehicle configuration information is relevant information about the vehicles owned by the enterprise, including model, quantity, year, usage status and carrying capacity; the cargo information is relevant information about the cargo that can be transported by the enterprise's vehicles, including cargo type, quantity, weight, volume and value; the historical cost data collection module is through Share data with corporate partners, establish data sharing mechanisms and data interfaces to achieve sharing and automated collection of historical cost data on corporate road transportation, corporate vehicle configuration information, and cargo information. The historical road transportation costs include floating costs and fixed costs. The floating costs are uncertain costs, including fuel costs and tolls, of which fuel costs account for 35%-40% and tolls account for 15%-30%. The fixed costs are determinable costs, including labor costs and daily vehicle maintenance costs, of which labor costs account for 5%-10% and daily vehicle maintenance costs account for 10%.

2. A method for comprehensively generating an optimized budget for road transport costs according to claim 1, characterized in that The specific steps of the optimization algorithm adopted by the cost optimization module include: 1) selecting vehicle configuration based on historical data, selecting the optimal head and trailer configuration according to the type and weight of the goods to be transported, making full use of vehicle capacity, reducing air resistance, reducing fuel consumption, and saving fuel expenses; 2) constructing a topological map between the origin and destination of road transportation and dividing it into multiple transportation nodes; 3) constructing the historical cost of unit mileage road transportation between two transportation nodes based on historical data. The historical cost set of unit mileage transportation between two transportation nodes is: ; Where: i represents the first transport node, j represents the second transport node, Indicates unit mileage, represents the historical cost of road transportation per mile between two transportation nodes. 4) Based on the historical data, a cost optimization model between the two transportation nodes is constructed. The set of transportation nodes is: , the transport node set includes the origin A and the destination D, and the transport node set Set to a single element set, the first single element is S A , construct the first single element transportation network mapping: S A → , based on the minimum cost function Perform the first single element transportation network optimization, the minimum cost function satisfy: ;in, represents the topological distance from the origin A to each transportation node j, ;in, Indicates the origin location, Represents the loop step, n represents the loop increment, and the point where the minimum value of the first single element transportation network is calculated is taken as the first single element optimal solution; 5) According to the transportation node set Repeat steps 2) and 3) to obtain the second single element optimal solution and the third single element optimal solution until the optimal solution for all transportation nodes is completed; 6) Determine the road transportation cost based on each transportation node in the transportation network and output the road transportation budget.

3. A method for comprehensively generating an optimized budget for road transport costs according to claim 1, characterized in that The historical cost data collection module collects historical cost data of road transportation, enterprise vehicle configuration information, and cargo information through manual entry, or directly imports the data after obtaining relevant data through communication with the enterprise. The historical cost data collection module includes a data cleaning module, which is used to clean, integrate and store the collected historical data, remove invalid data, repair erroneous data, and supplement missing data, unify the data format and data structure of data from different enterprises, and store the cleaned and integrated data in the database for subsequent cost model construction.

4. A method for comprehensively generating an optimized budget for road transport costs according to claim 1, characterized in that The factors affecting fuel costs and tolls include vehicle configuration, cargo load, transportation route, weather conditions and driver's operating method. Among them, optimized vehicle configuration, such as the front and trailer design, can reduce air resistance and reduce fuel consumption, thereby saving fuel expenses; the greater the cargo load, the greater the vehicle's fuel consumption, and it is necessary to select the most appropriate cargo load; by planning the most economical and efficient transportation route, the vehicle's fuel costs and toll expenses can be reduced. The labor costs include the driver's salary, training and social insurance expenses, and the daily maintenance costs of the vehicle include depreciation, maintenance, repairs and insurance.

5. A method for comprehensively generating an optimized budget for road transport costs according to claim 1, characterized in that The optimization algorithm selects the optimal vehicle configuration based on the type and weight of the goods to be transported, fully utilizing the vehicle capacity; selects the best transportation route based on the multiple routes between the origin and destination, reducing mileage, saving fuel and tolls, and shortening transportation time; Install reminder devices to provide voice or text reminders when the driver makes irregular driving operations, such as excessive acceleration, sudden braking, or idling for a long time, or violates traffic rules, so as to reduce vehicle wear and tear, casualties, and reduce daily vehicle maintenance costs; reasonably arrange personnel working hours and task allocation, improve work efficiency, and reduce labor costs.

6. A method for comprehensively generating an optimized budget for road transport costs according to claim 5, characterized in that The optimization algorithm obtains weather and traffic condition data in real time and uses the optimization algorithm to re-plan the best transportation route to avoid slippery roads caused by rain or snow, and congested or dangerous roads.

7. A method for comprehensively generating an optimized budget for road transport costs according to claim 1, characterized in that The budget plan generation module helps enterprises understand the budget plan generation process by constructing a decision tree, and makes corresponding plan recommendations and decision support according to different situations. The budget plan generation module simulates and optimizes and analyzes different budget plans by establishing mathematical models and using corresponding algorithms, and finds the optimal budget plan.