Automobile consignment line intelligent distribution system and method

Abstract

The invention discloses an automobile consignment line intelligent distribution system and method, and belongs to the technical field of line intelligent distribution. According to the invention, the automatic path planning module and the consignment demand prediction module are arranged, the automatic path planning module can perform automatic path planning according to some existing consignment demands, a relatively perfect consignment path can be obtained, and the consignment demand prediction module can predict future consignment demands according to some historical information. The system and the method are combined with each other, consignment scheduling can be effectively optimized, automation, high efficiency and intelligentization of consignment services are guaranteed, reasonable utilization of vehicles, reduction of the empty driving rate and improvement of the transportation efficiency can be achieved, and a dynamic adjustment scheduling function is set in the system, so that the transportation efficiency can be improved after line distribution. According to some emergencies, the scheduling situation is quickly adjusted in real time, so that the scheme is more adaptive to the current situation, and the flexibility is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent route allocation, and in particular relates to an intelligent route allocation system and method for automobile consignment. Background Art

[0002] As the name suggests, automobile shipping is the use of automobiles to ship goods. Automobile shipping can achieve rapid transfer of goods, ensure resource utilization between different industries, and improve the economic circulation effect of society. When conducting automobile shipping, in order to ensure the smooth flow of automobile shipping routes, it is necessary to apply a line distribution system to ensure the effectiveness of automobile shipping.

[0003] Although the current shipping route allocation system can also realize the allocation of shipping routes, it generally only allocates shipping routes based on customer needs. There is no shipping demand prediction module in the system, and it is unable to predict demand based on historical data. As a result, it is impossible to pre-allocate routes when the number of orders is small. As a result, when the number of orders accumulates, it is impossible to quickly allocate routes, resulting in low application efficiency and poor results. In order to solve this problem, there is an urgent need for an intelligent allocation system and method for automobile shipping routes. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that although the current consignment route allocation system can also realize the allocation of consignment routes, it generally only allocates consignment routes according to customer needs. There is no consignment demand prediction module in the system, and it is impossible to predict demand based on historical data. As a result, when the number of orders is small, it is impossible to pre-allocate routes. As a result, when the number of orders accumulates, it is impossible to quickly allocate routes, resulting in low application efficiency and poor results. A smart allocation system and method for automobile consignment routes are proposed.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: an intelligent distribution system for automobile consignment routes, comprising a data collection module and a data processing module, the output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the path automatic planning module, the path automatic planning module is used for automatic path planning, the output end of the path automatic planning module is connected to the input end of the consignment demand prediction module, the consignment demand prediction module is used for fixed-point prediction of consignment demand, and the output end of the consignment demand prediction module is connected to the input end of the intelligent scheduling optimization module.

[0006] As a further description of the above technical solution:

[0007] The output end of the intelligent scheduling optimization module is connected to the input end of the scheduling dynamic adjustment module, and the intelligent scheduling optimization module is used to generate automatic optimization of the scheduling plan. The output end of the scheduling dynamic adjustment module is connected to the input end of the cost-benefit analysis module, and the output end of the cost-benefit analysis module is connected to the input end of the route evaluation allocation module.

[0008] The present invention also discloses a method for intelligently allocating automobile consignment routes, comprising the following steps:

[0009] S1. Conduct data collection;

[0010] S2, perform data processing;

[0011] S3, perform automatic path planning model;

[0012] S4. Carry out shipping demand forecast;

[0013] S5. Perform intelligent scheduling optimization;

[0014] S6. Perform dynamic adjustment and scheduling;

[0015] S7. Conduct cost-benefit analysis and route evaluation

[0016] As a further description of the above technical solution:

[0017] In said S1, data collection is performed, and the acquired data includes vehicle status data, consignment route information, consignment date information, consignment weather information, customer demand information and consignment route historical consignment data.

[0018] As a further description of the above technical solution:

[0019] In S2, data processing is performed, and the specific steps are: first, the collected data is sorted, then the data is cleaned, the data is denoised, important information is marked, important information and data are extracted, the format of the extracted data is converted, the extracted data is missing value processed, the data outliers are detected, and finally the data is standardized.

[0020] As a further description of the above technical solution:

[0021] In S3, automatic path planning is performed, and the processed data is used to automatically generate the optimal shipping route using mathematical tools. The mathematical tools used are one or more of the Dijkstra algorithm and the Floyd-Warshall algorithm. The optimal shipping route needs to take into account shipping costs, time costs, traffic conditions and traffic restrictions.

[0022] As a further description of the above technical solution:

[0023] In S4, shipping demand forecasting is performed, specifically by collecting historical shipping data for 10-15 days and current market conditions, forecasting future shipping demand, and formulating a reasonable shipping plan. The forecasting method uses time series analysis, regression analysis, and machine learning algorithms.

[0024] As a further description of the above technical solution:

[0025] In S5, intelligent scheduling optimization is performed, specifically: based on the route automatically planned in S3 and the prediction of consignment demand in S4, intelligent scheduling of consignment routes, consignment drivers and consignment vehicles is carried out to achieve rational use of vehicles, reduce empty driving rate and improve transportation efficiency.

[0026] As a further description of the above technical solution:

[0027] In S6, dynamic adjustment and scheduling are performed, specifically: dynamic adjustment and scheduling of consignment routes, consignment drivers and consignment vehicles are performed according to changes in traffic conditions, weather conditions and actual customer needs, to ensure the flexibility and adaptability of the transportation process and improve the reliability and safety of transportation.

[0028] As a further description of the above technical solution:

[0029] In S7, a cost-benefit analysis and route evaluation are performed, specifically: the optimal transportation plan is determined by comprehensively evaluating the transportation cost and transportation benefit, wherein the transportation cost includes vehicle expenses, fuel costs and labor costs, and the transportation benefit includes customer satisfaction, cargo damage rate and transportation time.

[0030] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0031] In the present invention, by providing an automatic path planning module and a consignment demand prediction module, the automatic path planning module can automatically plan the path according to some existing consignment demands, and can obtain a relatively perfect consignment route. The consignment demand prediction module can predict future consignment demands based on some historical information. The combination of the two can effectively optimize consignment scheduling, ensure the automation, efficiency and intelligence of consignment business, and can also realize the rational use of vehicles, reduce empty driving rate, and improve transportation efficiency. The system is also equipped with a dynamic adjustment scheduling function, which can quickly adjust the scheduling situation in real time according to some emergencies after the route is allocated, so that the plan is more suitable for the current situation and has high flexibility. This system method is of great significance for improving transportation efficiency, reducing costs and enhancing customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of the module structure of an intelligent allocation system for automobile consignment routes.

[0033] Figure 2 This is a flowchart of a method for intelligently allocating automobile shipping routes. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] Example 1

[0036] See also Figure 1 The present invention provides a technical solution: an intelligent allocation system for automobile consignment routes, comprising a data collection module and a data processing module, wherein the output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the path automatic planning module, the path automatic planning module is used for automatic path planning, the output end of the path automatic planning module is connected to the input end of the consignment demand prediction module, the consignment demand prediction module is used for fixed-point prediction of consignment demand, the output end of the consignment demand prediction module is connected to the input end of the intelligent scheduling optimization module, the output end of the intelligent scheduling optimization module is connected to the input end of the scheduling dynamic adjustment module, the intelligent scheduling optimization module is used to generate automatic optimization of the scheduling plan, the output end of the scheduling dynamic adjustment module is connected to the input end of the cost-benefit analysis module, and the output end of the cost-benefit analysis module is connected to the input of the route evaluation and allocation module.

[0037] See also Figure 2 The present invention also discloses a method for intelligently allocating automobile consignment routes, comprising the following steps:

[0038] S1. Collect data, including vehicle status data, shipping route information, shipping date information, shipping weather information, customer demand information, and historical shipping data of the shipping route;

[0039] S2. Data processing, the specific steps of which are: first, organize the collected data, then clean the data, perform noise reduction on the data, mark important information, extract important information and data, convert the format of the extracted data, process missing values for the extracted data, detect outliers in the data, and finally perform data standardization;

[0040] S3. Automatically plan the route, using the processed data and mathematical tools such as the Dijkstra algorithm to automatically generate the optimal shipping route. The optimal shipping route needs to take into account shipping costs, time costs, traffic conditions, and traffic restrictions.

[0041] S4. Forecast shipping demand, specifically by collecting 15 days of historical shipping data and current market conditions, forecasting future shipping demand, and developing a reasonable shipping plan. The forecasting methods use time series analysis, regression analysis, and machine learning algorithms.

[0042] S5: Perform intelligent dispatch optimization, specifically: Based on the routes automatically planned in S3 and the forecast of consignment demand in S4, intelligent dispatch of consignment routes, consignment drivers, and consignment vehicles is carried out to achieve rational utilization of vehicles, reduce idle driving rates, and improve transportation efficiency;

[0043] S6. Dynamically adjust and dispatch, specifically: dynamically adjust and dispatch shipping routes, shipping drivers, and shipping vehicles based on changes in traffic conditions, weather conditions, and actual customer needs, to ensure flexibility and adaptability of the transportation process and improve transportation reliability and safety;

[0044] S7. Conduct a cost-benefit analysis and route evaluation. Specifically, determine the optimal transportation plan by comprehensively evaluating transportation costs and transportation benefits. Transportation costs include vehicle fees, fuel costs, and labor costs, while transportation benefits include customer satisfaction, cargo damage rate, and transportation time.

[0045] In this embodiment, by providing an automatic path planning module and a consignment demand prediction module, the automatic path planning module can automatically plan the path according to some existing consignment demands, and can obtain a relatively perfect consignment route. The consignment demand prediction module can predict future consignment demands based on some historical information. The combination of the two can effectively optimize consignment scheduling, ensure the automation, efficiency and intelligence of consignment business, and also realize the rational use of vehicles, reduce empty driving rate and improve transportation efficiency.

[0046] Example 2

[0047] See also Figure 1The present invention provides a technical solution: an intelligent allocation system for automobile consignment routes, comprising a data collection module and a data processing module, wherein the output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the path automatic planning module, the path automatic planning module is used for automatic path planning, the output end of the path automatic planning module is connected to the input end of the consignment demand prediction module, the consignment demand prediction module is used for fixed-point prediction of consignment demand, the output end of the consignment demand prediction module is connected to the input end of the intelligent scheduling optimization module, the output end of the intelligent scheduling optimization module is connected to the input end of the scheduling dynamic adjustment module, the intelligent scheduling optimization module is used to generate automatic optimization of the scheduling plan, the output end of the scheduling dynamic adjustment module is connected to the input end of the cost-benefit analysis module, and the output end of the cost-benefit analysis module is connected to the input of the route evaluation and allocation module.

[0048] See also Figure 2 The present invention also discloses a method for intelligently allocating automobile consignment routes, comprising the following steps:

[0049] S1. Collect data, including vehicle status data, shipping route information, shipping date information, shipping weather information, customer demand information, and historical shipping data of the shipping route;

[0050] S2. Data processing, the specific steps of which are: first, organize the collected data, then clean the data, perform noise reduction on the data, mark important information, extract important information and data, convert the format of the extracted data, process missing values for the extracted data, detect outliers in the data, and finally perform data standardization;

[0051] S3. Automatically plan the route, using the processed data and mathematical tools such as the Dijkstra algorithm to automatically generate the optimal shipping route. The optimal shipping route needs to take into account shipping costs, time costs, traffic conditions, and traffic restrictions.

[0052] S4. Forecast shipping demand, specifically by collecting 10 days of historical shipping data and current market conditions, forecasting future shipping demand, and developing a reasonable shipping plan. The forecasting methods used are time series analysis, regression analysis, and machine learning algorithms.

[0053] S5: Perform intelligent dispatch optimization, specifically: Based on the routes automatically planned in S3 and the forecast of consignment demand in S4, intelligent dispatch of consignment routes, consignment drivers, and consignment vehicles is carried out to achieve rational utilization of vehicles, reduce idle driving rates, and improve transportation efficiency;

[0054] S6. Dynamically adjust and dispatch, specifically: dynamically adjust and dispatch shipping routes, shipping drivers, and shipping vehicles based on changes in traffic conditions, weather conditions, and actual customer needs, to ensure flexibility and adaptability of the transportation process and improve transportation reliability and safety;

[0055] S7. Conduct a cost-benefit analysis and route evaluation. Specifically, determine the optimal transportation plan by comprehensively evaluating transportation costs and transportation benefits. Transportation costs include vehicle fees, fuel costs, and labor costs, while transportation benefits include customer satisfaction, cargo damage rate, and transportation time.

[0056] In this embodiment, the system is provided with a dynamic adjustment scheduling function, which can quickly adjust the scheduling situation in real time according to some emergencies after the route is allocated, so that the plan is more suitable for the current situation and has high flexibility. This system method is of great significance for improving transportation efficiency, reducing costs and enhancing customer satisfaction.

[0057] Example 3

[0058] See also Figure 1 The present invention provides a technical solution: an intelligent allocation system for automobile consignment routes, comprising a data collection module and a data processing module, wherein the output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the path automatic planning module, the path automatic planning module is used for automatic path planning, the output end of the path automatic planning module is connected to the input end of the consignment demand prediction module, the consignment demand prediction module is used for fixed-point prediction of consignment demand, the output end of the consignment demand prediction module is connected to the input end of the intelligent scheduling optimization module, the output end of the intelligent scheduling optimization module is connected to the input end of the scheduling dynamic adjustment module, the intelligent scheduling optimization module is used to generate automatic optimization of the scheduling plan, the output end of the scheduling dynamic adjustment module is connected to the input end of the cost-benefit analysis module, and the output end of the cost-benefit analysis module is connected to the input of the route evaluation and allocation module.

[0059] See also Figure 2 The present invention also discloses a method for intelligently allocating automobile consignment routes, comprising the following steps:

[0060] S1. Collect data, including vehicle status data, shipping route information, shipping date information, shipping weather information, customer demand information, and historical shipping data of the shipping route;

[0061] S2. Data processing, the specific steps of which are: first, organize the collected data, then clean the data, perform noise reduction on the data, mark important information, extract important information and data, convert the format of the extracted data, process missing values for the extracted data, detect outliers in the data, and finally perform data standardization;

[0062] S3. Automatically plan the route, using the processed data and mathematical tools such as the Dijkstra algorithm to automatically generate the optimal shipping route. The optimal shipping route needs to take into account shipping costs, time costs, traffic conditions, and traffic restrictions.

[0063] S4. Forecast shipping demand, specifically by collecting 12 days of historical shipping data and current market conditions, forecasting future shipping demand, and developing a reasonable shipping plan. The forecasting methods used include time series analysis, regression analysis, and machine learning algorithms.

[0064] S5: Perform intelligent dispatch optimization, specifically: Based on the routes automatically planned in S3 and the forecast of consignment demand in S4, intelligent dispatch of consignment routes, consignment drivers, and consignment vehicles is carried out to achieve rational utilization of vehicles, reduce idle driving rates, and improve transportation efficiency;

[0065] S6. Dynamically adjust and dispatch, specifically: dynamically adjust and dispatch shipping routes, shipping drivers, and shipping vehicles based on changes in traffic conditions, weather conditions, and actual customer needs, to ensure flexibility and adaptability of the transportation process and improve transportation reliability and safety;

[0066] S7. Conduct a cost-benefit analysis and route evaluation. Specifically, determine the optimal transportation plan by comprehensively evaluating transportation costs and transportation benefits. Transportation costs include vehicle fees, fuel costs, and labor costs, while transportation benefits include customer satisfaction, cargo damage rate, and transportation time.

[0067] In this embodiment, by providing an automatic path planning module and a consignment demand prediction module, the automatic path planning module can automatically plan the path according to some existing consignment demands, and can obtain a relatively perfect consignment route. The consignment demand prediction module can predict future consignment demands based on some historical information. The combination of the two can effectively optimize consignment scheduling, ensure the automation, efficiency and intelligence of consignment business, and can also realize the rational use of vehicles, reduce empty driving rate, and improve transportation efficiency. The system is also equipped with a dynamic adjustment scheduling function, which can quickly adjust the scheduling situation in real time according to some emergencies after the route is allocated, so that the plan is more suitable for the current situation and has high flexibility. This system method is of great significance for improving transportation efficiency, reducing costs and enhancing customer satisfaction.

[0068] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An intelligent automobile consignment route allocation system, comprising a data collection module and a data processing module, characterized in that: The output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the path automatic planning module, the path automatic planning module is used for automatic path planning, the output end of the path automatic planning module is connected to the input end of the consignment demand prediction module, the consignment demand prediction module is used for fixed-point prediction of consignment demand, and the output end of the consignment demand prediction module is connected to the input end of the intelligent scheduling optimization module.

2. The intelligent automobile consignment route allocation system according to claim 1 is characterized in that: The output end of the intelligent scheduling optimization module is connected to the input end of the scheduling dynamic adjustment module, and the intelligent scheduling optimization module is used to generate automatic optimization of the scheduling plan. The output end of the scheduling dynamic adjustment module is connected to the input end of the cost-benefit analysis module, and the output end of the cost-benefit analysis module is connected to the input end of the route evaluation allocation module.

3. A method for intelligently allocating automobile shipping routes, characterized in that: The steps include: S1. Conduct data collection; S2, perform data processing; S3, perform automatic path planning model; S4. Carry out shipping demand forecast; S5. Perform intelligent scheduling optimization; S6. Perform dynamic adjustment and scheduling; S7. Conduct cost-benefit analysis and perform route evaluation.

4. The method for intelligently allocating automobile shipping routes according to claim 3, characterized in that: In said S1, data collection is performed, and the acquired data includes vehicle status data, consignment route information, consignment date information, consignment weather information, customer demand information and consignment route historical consignment data.

5. The method for intelligently allocating automobile consignment routes according to claim 3, characterized in that: In S2, data processing is performed, and the specific steps are: first, the collected data is sorted, then the data is cleaned, the data is denoised, important information is marked, important information and data are extracted, the format of the extracted data is converted, the extracted data is missing value processed, the data outliers are detected, and finally the data is standardized.

6. The method for intelligently allocating automobile shipping routes according to claim 3, characterized in that: In S3, automatic path planning is performed, and the processed data is used to automatically generate the optimal shipping route using mathematical tools. The mathematical tools used are one or more of the Dijkstra algorithm and the Floyd-Warshall algorithm. The optimal shipping route needs to take into account shipping costs, time costs, traffic conditions and traffic restrictions.

7. The method for intelligently allocating automobile shipping routes according to claim 3, characterized in that: In S4, shipping demand forecasting is performed, specifically by collecting historical shipping data for 10-15 days and current market conditions, forecasting future shipping demand, and formulating a reasonable shipping plan. The forecasting method uses time series analysis, regression analysis, and machine learning algorithms.

8. The method for intelligently allocating automobile shipping routes according to claim 3, characterized in that: In S5, intelligent scheduling optimization is performed, specifically: based on the route automatically planned in S3 and the prediction of consignment demand in S4, intelligent scheduling of consignment routes, consignment drivers and consignment vehicles is carried out to achieve rational use of vehicles, reduce empty driving rate and improve transportation efficiency.

9. The method for intelligently allocating automobile shipping routes according to claim 3, characterized in that: In S6, dynamic adjustment and scheduling are performed, specifically: dynamic adjustment and scheduling of consignment routes, consignment drivers and consignment vehicles are performed according to changes in traffic conditions, weather conditions and actual customer needs, to ensure the flexibility and adaptability of the transportation process and improve the reliability and safety of transportation.

10. The method for intelligently allocating automobile shipping routes according to claim 3, characterized in that: In S7, a cost-benefit analysis and route evaluation are performed, specifically: the optimal transportation plan is determined by comprehensively evaluating the transportation cost and transportation benefit, wherein the transportation cost includes vehicle expenses, fuel costs and labor costs, and the transportation benefit includes customer satisfaction, cargo damage rate and transportation time.

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