Logistics management system based on time-sharing prediction and intelligent scheduling
By adopting time-sharing prediction and intelligent scheduling technology in the logistics management system, the problem of complex and low accuracy of transportation time prediction in the existing technology is solved, and more efficient transportation time prediction and vehicle scheduling are achieved, thereby improving vehicle usage rate.
Patent Information
- Application Number
- CN202411820638.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-11
- Publication Date
- 2025-05-13
AI Technical Summary
The existing transportation time prediction methods are complex and have low accuracy, making it difficult to effectively optimize logistics transportation management.
The logistics management system based on time-sharing prediction and intelligent scheduling is adopted to obtain time-sharing prediction requests and scheduling requests through the input module. The time-sharing prediction module uses multiple algorithms and models to predict the time-sharing prediction of the transportation process time, and the intelligent scheduling module uses scheduling algorithms for vehicle scheduling.
It improves the accuracy of transportation time prediction and the rationality of vehicle scheduling, thereby improving vehicle usage.
Smart Images
Figure CN119990386A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of logistics and transportation management, and in particular to a logistics management system based on time-sharing prediction and intelligent scheduling. Background Art
[0002] With the continuous development of the global economy and the continuous expansion of enterprises, the logistics process has become more and more complicated. Logistics management systems can help enterprises reduce costs and improve operational efficiency in this process, so as to better adapt to market needs. At present, with the continuous development and improvement of the Internet, logistics-based cargo transportation has been more widely used. Only by formulating a reasonable competition model and improving the sense of responsibility of carriers can we gain the recognition of cargo owners and achieve a virtuous circle.
[0003] Transportation time prediction refers to the estimation of the time required for goods or personnel to reach the destination from the departure point through analysis and calculation of various factors. It is extremely important in many fields such as logistics and transportation.
[0004] For logistics companies, accurate transportation time prediction can help optimize delivery plans. For example, express delivery companies can inform customers of the estimated arrival time of packages in advance, improving customer satisfaction. At the same time, reasonable transportation time prediction can help companies arrange transportation resources, such as vehicle scheduling and personnel arrangements, to reduce operating costs. In the field of transportation, transportation time prediction can provide a reference for travelers, helping them choose the appropriate travel mode and time.
[0005] At present, the factors that affect the transportation time are mainly:
[0006] (1) Traffic conditions
[0007] The degree of road congestion is a key factor affecting transportation time. In urban traffic, during peak hours in the morning and evening, the road traffic is heavy and congestion is prone to occur, which will greatly increase transportation time. For example, during the morning and evening rush hours on weekdays in large cities, the speed of vehicles may be reduced to half of the usual speed or even lower.
[0008] Traffic accidents can also have a serious impact on traffic conditions. Once a traffic accident occurs, traffic control and lane closures may occur on the relevant road sections, causing vehicles to queue up to pass through, extending the transportation time. Road construction should not be ignored either. Construction sections may implement single-lane traffic and speed limits to slow down vehicle speeds.
[0009] (2) Transportation distance
[0010] The longer the transportation distance, the longer the transportation time required at normal speed. For example, the transportation time of intra-city express delivery is generally within 1-2 days, while long-distance transportation across provinces may take 3-5 days or even longer. Different modes of transportation will also have different effects on the time due to differences in speed. For example, airplane transportation is fast and has obvious advantages in long-distance transportation; while water transportation is relatively slow and takes a long time for long-distance water transportation.
[0011] (3) Transportation method
[0012] Common modes of transportation include road transport, rail transport, air transport, and water transport. Road transport is highly flexible, but its speed is slower than that of air and rail transport, and it is greatly affected by traffic conditions. Air transport is the fastest, but it may be restricted by factors such as flight schedules and airport take-off and landing conditions. Rail transport is fast and relatively punctual, and is suitable for medium and long-distance large-volume transportation. Water transport is the slowest, but its transportation volume is large, and it is suitable for transporting bulk goods, such as coal, ore, etc.
[0013] (4) Cargo characteristics
[0014] The weight and volume of your cargo will affect the length of time it takes to ship. Heavier or larger cargo may require special transport equipment, such as large trucks or dedicated cargo planes, and will also take time to load and unload. For example, transporting large machinery requires specialized lifting equipment for loading and unloading, which increases the time the cargo spends at both the origin and destination.
[0015] The nature of the cargo is also important. If it is perishable goods, such as fresh food, it may need to be transported in refrigerated conditions and within strict transit times to ensure the quality of the goods.
[0016] The existing transportation time prediction methods are relatively complex and have low accuracy. Summary of the invention
[0017] The purpose of the embodiments of the present invention is to provide a logistics management system based on time-sharing prediction and intelligent scheduling to improve the accuracy of transportation time prediction and the rationality of vehicle scheduling.
[0018] To achieve the above objectives, an embodiment of the present invention provides a logistics management system based on time-sharing prediction and intelligent scheduling, including:
[0019] An input module is used to obtain a duration prediction request, wherein the duration prediction request includes order type, day of the week, time period, cargo name, loading and unloading status, shipping location, unloading location, and GPS real-time positioning information;
[0020] A time-sharing prediction module is used to use multiple algorithms and models to construct a time-sharing prediction model for targeted waiting time according to the transportation process duration prediction request, and to calculate the transportation process duration based on different dates and time periods according to the time-sharing prediction model; the transportation process duration includes the queuing time outside the shipping plant, the loading queuing time, the loading time, the transportation time, the queuing time outside the receiving plant, the unloading queuing time and the unloading time;
[0021] The output module is used to output duration information and vehicle information; the duration information includes the estimated duration to the loading location, the estimated total loading time, the estimated transportation time, and the estimated total unloading time; the vehicle information includes the estimated number of vehicles queuing at the loading location and the estimated number of vehicles queuing at the unloading location.
[0022] As a specific implementation of the present application, the time-sharing prediction module is specifically used for:
[0023] When the carrier dispatches an order, the vehicle and driver are selected. If the vehicle is online, the vehicle location is obtained in real time, and the map interface is called to predict the time it takes for the vehicle to reach the loading location.
[0024] When the driver grabs the order and confirms the departure dispatch order, the vehicle location is obtained in real time, and the map interface is called to predict how long it will take for the vehicle to reach the loading location.
[0025] Furthermore, as a preferred implementation method of the present application, the input module is also used to input scheduling requests, including demand quantity, vehicle dispatch start time, vehicle dispatch end time, available vehicle license plate numbers, available vehicle cargo capacity, available start time of each vehicle, available end time of each vehicle, historical completion times of each vehicle, historical completion percentage of each vehicle model, time-sharing transportation time, and time-sharing loading and unloading time.
[0026] Furthermore, as a preferred implementation method of the present application, the system also includes an intelligent scheduling module, which is used to perform point-to-point general intelligent scheduling and short-distance intelligent scheduling using an intelligent scheduling model according to the scheduling request; wherein the intelligent scheduling model adopts a scheduling algorithm, which schedules according to the required transportation volume and available vehicles according to the end-to-end predicted loading and unloading time given by the time-sharing forecast.
[0027] As a specific implementation of the present application, the intelligent scheduling module is specifically used for:
[0028] When a carrier receives a transportation task, intelligent ordering is implemented based on ordering rules; the ordering rules are: based on the manually formulated phased periodic delivery plan quantity, as well as the determined number of transportation companies, the number of available vehicles, the quota ratio of transportation companies, the delivery and receipt time limit requirements, average distribution, cost priority and other strategies, orders are automatically placed with the corresponding transportation companies.
[0029] The implementation of the embodiment of the present invention provides a logistics management system based on time-sharing prediction and intelligent scheduling, wherein the input module is used to obtain duration prediction requests and scheduling requests, the time-sharing prediction module adopts a time-sharing prediction model to estimate the duration of the transportation process based on different dates and time periods, and the intelligent scheduling module adopts a scheduling model to perform vehicle scheduling; compared with the prior art, the logistics management system of the embodiment of the present invention can improve the accuracy of transportation duration prediction and the rationality of vehicle scheduling, thereby improving vehicle utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art are briefly introduced below.
[0031] Figure 1 It is a structural diagram of a logistics management system based on time-sharing prediction and intelligent scheduling provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.
[0033] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.
[0034] It should be noted that the embodiments of the present invention mainly involve two models: a time-sharing prediction model and an intelligent scheduling model.
[0035] (I) Time-sharing prediction model
[0036] 1. Model Description
[0037] Time-sharing prediction is a method that uses multiple algorithms and models to model the waiting time at the loading and unloading sites and make inferences based on the models. For the transportation time of a route, the map interface is called to calculate the length of the entire route from loading to transportation to unloading, and these inferences are based on different dates (considering weekdays / weekends / holidays) and time periods (commuting hours / non-commuting hours), and are updated in real time based on the vehicle location. Waiting time modeling is implemented based on historical data and specific business.
[0038] 2. Usage scenarios
[0039] Time-sharing prediction, point-to-point general intelligent scheduling, short-distance intelligent scheduling
[0040] The predicted duration of the transportation process includes: queuing time outside the shipping factory → queuing time for loading → loading time → transportation time (first trip, delivery, return trip) → queuing time outside the receiving factory → queuing time for unloading → unloading time. The relevant processes and corresponding instructions are shown in Table 1:
[0041]
[0042]
[0043]
[0044]
[0045] Table 1
[0046] 3. Model input content
[0047] Order type (sales order, purchase order, transfer order), day of the week (Monday to Sunday), time period (hourly), cargo name, loading and unloading status, shipping location, unloading location, GPS real-time positioning
[0048] 4. Strategy / Rules
[0049] The transportation time is estimated by taking into account the road congestion at different times and on different dates.
[0050] 5. Model Output
[0051] Estimated time to travel to the loading location, estimated number of vehicles queuing at the loading location, estimated total loading time, estimated transportation time, estimated number of vehicles queuing at the unloading location, and estimated total unloading time.
[0052] 2. Point-to-point intelligent scheduling model
[0053] 1. Model Description
[0054] The scheduling algorithm is based on the required transportation volume and available vehicles, and the end-to-end loading and unloading time forecast given by the time-sharing forecast. This schedule also takes into account the working hours of each truck and the working hours of the loading and unloading stations. The optimization goal is to shorten the waiting time in queues as much as possible and maximize the efficiency of truck utilization.
[0055] 2. Usage scenarios
[0056] Point-to-point general intelligent dispatching, short-distance intelligent dispatching
[0057] When the cargo owner has a transportation demand, he assigns the transportation task to the carrier. After the carrier receives the transportation task order, it decomposes the task order and makes a vehicle dispatch task. After that, the vehicle arrives at the place of shipment and enters the factory for loading. The highlight of the present invention patent is that when the carrier receives the transportation task, the received waybill can realize intelligent ordering.
[0058] Order rule 1: Automatically place orders with corresponding transport companies based on manually formulated phased periodic delivery plan quantities, as well as determined transport companies, available vehicles, transport company quota ratios, delivery and receipt time requirements, average distribution, cost priority and other strategies.
[0059] 3. Input content
[0060] Demand, dispatch start time, dispatch end time, available vehicle license plate number, available vehicle cargo capacity, available start time of each vehicle, available end time of each vehicle, historical completion times of each vehicle, historical completion percentage of each vehicle model, time-sharing transportation time, and time-sharing loading and unloading time.
[0061] 4. Constraints: Truck transport volume cannot exceed truck capacity.
[0062] 5. Strategy / Rules
[0063] Arrange the best haulage capacity for each truck, reduce the risk of material inventory depletion and warehouse explosion, improve the utilization rate of carrier vehicles, and ensure that the dispatch volume is as close to the optimal haulage capacity as possible.
[0064] 6. Model output:
[0065] (1) Remaining tasks
[0066] (2) Amount of tasks completed
[0067] (3) Specific information of each vehicle, including the number of transport trips, the start time of each trip, the time of arrival at the loading point, the time of arrival at the unloading point, and the time of completion of unloading; the total transport volume of each vehicle, the total time taken by each vehicle, and the license plate number of each vehicle.
[0068] Please refer to Figure 1 , is a logistics management system based on time-sharing prediction and intelligent scheduling provided by an embodiment of the present invention, including an input module, a time-sharing prediction module, a scheduling module and an output module.
[0069] The input module is used to obtain a duration prediction request, which includes order type, day of the week, time period, cargo name, loading and unloading status, shipping location, unloading location, and GPS real-time positioning information;
[0070] A time-sharing prediction module is used to use multiple algorithms and models to construct a time-sharing prediction model for targeted waiting time according to the transportation process duration prediction request, and to calculate the transportation process duration based on different dates and time periods according to the time-sharing prediction model; the transportation process duration includes the queuing time outside the shipping plant, the loading queuing time, the loading time, the transportation time, the queuing time outside the receiving plant, the unloading queuing time and the unloading time;
[0071] The output module is used to output duration information and vehicle information; the duration information includes the estimated duration to the loading location, the estimated total loading time, the estimated transportation time, and the estimated total unloading time; the vehicle information includes the estimated number of vehicles queuing at the loading location and the estimated number of vehicles queuing at the unloading location.
[0072] Through the cooperation of the above three modules, the transportation process duration can be predicted. For more specific processes, constraints and rules, please refer to the above table 1.
[0073] Furthermore, the input module, the scheduling module and the output module can cooperate to complete intelligent scheduling, specifically:
[0074] The input module is also used to input dispatch requests, including demand, dispatch start time, dispatch end time, available vehicle license plate number, available vehicle cargo capacity, available start time of each vehicle, available end time of each vehicle, historical completion times of each vehicle, historical completion percentage of each vehicle model, time-sharing transportation time, and time-sharing loading and unloading time;
[0075] An intelligent scheduling module, for performing point-to-point general intelligent scheduling and short-haul intelligent scheduling using an intelligent scheduling model according to the scheduling request; wherein the intelligent scheduling model adopts a scheduling algorithm, which schedules according to the required transportation volume and available vehicles and the end-to-end predicted loading and unloading time given by the time-sharing prediction;
[0076] The output module is also used to output the following information:
[0077] The amount of tasks remaining;
[0078] Amount of tasks completed;
[0079] Specific information of each vehicle, including the number of transport trips, the starting time of each trip, the time of arrival at the loading point, the time of arrival at the unloading point, and the time of completion of unloading; the total amount of transportation carried by each vehicle, the total time taken by each vehicle, and the license plate number of each vehicle.
[0080] From the above description, it can be known that the implementation of the embodiment of the present invention provides a logistics management system based on time-sharing prediction and intelligent scheduling, in which the input module is used to obtain duration prediction requests and scheduling requests, the time-sharing prediction module adopts a time-sharing prediction model to calculate the duration of the transportation process based on different dates and time periods, and the intelligent scheduling module adopts a scheduling model to perform vehicle scheduling; compared with the prior art, the logistics management system of the embodiment of the present invention can improve the accuracy of transportation duration prediction and the rationality of vehicle scheduling, thereby improving vehicle utilization rate.
[0081] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0082] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.
[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.
[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.
[0086] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.
Claims
1. A logistics management system based on time-sharing forecasting and intelligent scheduling, characterized in that: include: An input module is used to obtain a duration prediction request, wherein the duration prediction request includes order type, day of the week, time period, cargo name, loading and unloading status, shipping location, unloading location, and GPS real-time positioning information; A time-sharing prediction module is used to use multiple algorithms and models to construct a time-sharing prediction model for targeted waiting time according to the transportation process duration prediction request, and to calculate the transportation process duration based on different dates and time periods according to the time-sharing prediction model; the transportation process duration includes the queuing time outside the shipping plant, the loading queuing time, the loading time, the transportation time, the queuing time outside the receiving plant, the unloading queuing time and the unloading time; The output module is used to output duration information and vehicle information; the duration information includes the estimated duration to the loading location, the estimated total loading time, the estimated transportation time, and the estimated total unloading time; the vehicle information includes the estimated number of vehicles queuing at the loading location and the estimated number of vehicles queuing at the unloading location.
2. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 1 is characterized in that: The time-sharing prediction module is specifically used for: When the carrier dispatches an order, the vehicle and driver are selected. If the vehicle is online, the vehicle location is obtained in real time, and the map interface is called to predict the time it takes for the vehicle to reach the loading location. When the driver grabs the order and confirms the departure dispatch order, the vehicle location is obtained in real time, and the map interface is called to predict how long it will take for the vehicle to reach the loading location.
3. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 1, characterized in that: The time-sharing prediction model is based on the following rules: transportation duration is calculated, taking into account road congestion conditions at different times and dates.
4. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 1 is characterized in that: The input module is also used to input scheduling requests, including demand quantity, vehicle dispatch start time, vehicle dispatch end time, available vehicle license plate number, available vehicle cargo capacity, each vehicle's available start time, each vehicle's available end time, each vehicle's historical completion times, each vehicle's historical completion ratio, time-sharing transportation time, and time-sharing loading and unloading time.
5. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 4 is characterized in that: The system also includes an intelligent scheduling module, which is used to perform point-to-point general intelligent scheduling and short-distance intelligent scheduling using an intelligent scheduling model according to the scheduling request; wherein the intelligent scheduling model adopts a scheduling algorithm, which schedules according to the required transportation volume and available vehicles and the end-to-end predicted loading and unloading time given by the time-sharing forecast.
6. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 5 is characterized in that: The constraint used by the intelligent scheduling model is that the truck transportation volume cannot exceed the truck capacity.
7. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 5 is characterized in that: The intelligent scheduling model is based on the following rules: arranging the optimal haulage capacity for each truck, reducing the risk of material inventory depletion and warehouse explosion, and improving the utilization rate of carrier vehicles and the dispatch volume as close to the optimal haulage capacity as possible.
8. The logistics management system based on time-sharing forecasting and intelligent scheduling as claimed in claim 5, characterized in that: The intelligent scheduling module is specifically used for: When a carrier receives a transportation task, intelligent ordering is implemented based on ordering rules; the ordering rules are: based on the manually formulated phased periodic delivery plan quantity, as well as the determined number of transportation companies, the number of available vehicles, the quota ratio of transportation companies, the delivery and receipt time limit requirements, average distribution, cost priority and other strategies, orders are automatically placed with the corresponding transportation companies.
9. The logistics management system based on time-sharing forecasting and intelligent scheduling according to any one of claims 5 to 8, characterized in that: The output module is also used to output the following information: The amount of tasks remaining; Amount of tasks completed; Specific information of each vehicle, including the number of transport trips, the starting time of each trip, the time of arrival at the loading point, the time of arrival at the unloading point, and the time of completion of unloading; the total amount of transportation carried by each vehicle, the total time taken by each vehicle, and the license plate number of each vehicle.