Container truck scheduling method, system and equipment based on cluster dynamic adaptation

Abstract

The invention provides a container truck scheduling method, system and equipment based on cluster dynamic adaptation, and is applied to the technical field of port horizontal transportation scheduling. Firstly, a refined container truck classification information base is constructed according to the size bearing capacity and function qualification of container trucks; based on the classification information base and the preset distribution rule, the container trucks are automatically matched and assigned for the transportation tasks, finally, the system state is monitored in real time in the task execution process, and the established task-container truck matching relation is dynamically readjusted based on the same information base, so that intellectualization, elasticity and efficient cooperation of port horizontal transportation are achieved, and the transportation efficiency is improved. The invalid mileage of the container truck is obviously reduced, the collaborative operation capability of port multi-machine equipment is improved, and the stability of a port operation system is guaranteed.

Description

Technical Field

[0001] This application relates to the field of port horizontal transport scheduling technology, specifically to a truck scheduling method, system, and equipment based on cluster dynamic adaptation. Background Technology

[0002] In recent years, the continuous increase in throughput at automated container terminals has placed higher demands on port operational efficiency. Horizontal transport, as a core component of port production operations, significantly impacts the overall operational efficiency of the port.

[0003] However, in the current port operation process, there are still problems such as rough vehicle management, low operating efficiency and difficulty in scheduling decisions in the horizontal transportation link of the port. These problems not only affect the efficiency of horizontal transportation operations, but also seriously restrict the continuity and smoothness of the entire loading and unloading operation process, resulting in additional time and energy cost inputs.

[0004] Therefore, a new truck scheduling scheme is needed. Summary of the Invention

[0005] In view of this, the embodiments of this specification provide a method, system and equipment for scheduling container trucks based on cluster dynamic adaptation. By dynamically adjusting and adapting container trucks to tasks in clusters, the efficiency of horizontal transportation at ports is significantly improved, ineffective operating costs are effectively reduced, and the problems of extensive vehicle management and slow scheduling response in the traditional "one truck, one container" mode are solved.

[0006] The embodiments in this specification provide the following technical solutions: This specification provides an embodiment of a cluster-based dynamic adaptation method for scheduling clustered cards, including: The container trucks are classified according to their dimensions to establish a container truck classification information database; wherein, the dimensions include at least: size, load-bearing capacity, and functional qualifications. Based on the truck classification information database and the preset allocation rules, trucks are matched and assigned to perform container transportation tasks. During the execution of the container transportation operation, the real-time status of the monitoring system is monitored, and the assigned tasks and container trucks are dynamically adapted and adjusted based on the container truck classification information database. The dynamic adaptation adjustment includes: flexible exchange of work tasks, and / or dynamic adjustment of transport vehicles.

[0007] This specification also provides a cluster-based dynamic adaptation truck scheduling system, the truck scheduling system comprising: The classification module is used to classify the container trucks based on their dimensions and establish a container truck classification information database; wherein, the dimensions include at least: size, load-bearing capacity, and functional qualifications; The task scheduling module is used to match and assign container trucks to container transport operation tasks based on the container truck classification information database and preset allocation rules. The dynamic optimization module is used to monitor the real-time status of the system during the execution of the container transportation operation task, and to dynamically adapt and adjust the assigned tasks and container trucks based on the container truck classification information database. The dynamic adaptation adjustment includes: flexible exchange of work tasks, and / or dynamic adjustment of transport vehicles.

[0008] This specification also provides an electronic device, including: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform: the aforementioned cluster-based dynamic adaptation card scheduling method.

[0009] Compared with the prior art, the beneficial effects that at least one technical solution adopted in the embodiments of this specification can achieve include at least: This application achieves intelligent and precise scheduling and real-time optimization of port truck capacity through a multi-dimensional and refined truck classification and dynamic adaptation mechanism. When dealing with sudden working conditions, operational conflicts, or idle resources, it can proactively perform task exchange and vehicle adaptation adjustment, significantly improving truck utilization and operational efficiency, effectively reducing truck empty runs and resource waiting time, and solving the problems of rough truck management and insufficient operational resilience in automated container terminals. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a cluster-based dynamic adaptation card scheduling method in this application; Figure 2 This is a flowchart of the steps for flexible task exchange in a cluster-based dynamic adaptation card scheduling method according to this application. Figure 3 This is a flowchart of the dynamic adaptation steps of a cluster card scheduling method based on cluster dynamic adaptation in this application. Figure 4 A schematic diagram of the electronic device in this application. Detailed Implementation

[0012] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0013] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0015] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0016] Additionally, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that practice can be carried out without these specific details.

[0017] Traditional truck dispatching typically operates on a "one truck, one container" model, meaning each truck transports only one container, leading to wasted capacity and high transportation costs. Meanwhile, the port environment is dynamic and unpredictable, with frequent unexpected events such as vehicle breakdowns and shipping delays. Traditional dispatching systems often respond passively, heavily relying on manual intervention from dispatchers, resulting in low efficiency and a high risk of errors. This not only affects the efficiency of horizontal transport operations but also severely restricts the continuity of the entire loading and unloading process, causing additional time and energy costs. Therefore, improving port horizontal transport efficiency while reducing ineffective costs has become a crucial research topic in automated port operations.

[0018] In view of this, the inventors, through research and exploration, discovered that the current mainstream port horizontal transport scheduling methods mainly suffer from the following bottlenecks: First, the management is rough, treating container trucks as the same, undifferentiated transport units, resulting in the idle and wasted special transport capacity or the forced matching of ordinary transport capacity, causing resource mismatch and potential risks. Secondly, most existing methods rely on fixed plans established before the operation. Once changes occur on site, they heavily depend on manual intervention and cannot be automatically adjusted, which can easily lead to response delays or operation interruptions. Third, there is insufficient coordination. The lack of real-time linkage between tasks, vehicles and loading and unloading equipment leads to a high empty-running rate of container trucks and long ineffective waiting time, making it difficult to achieve efficient and seamless connection of multiple equipment such as quay cranes, yard cranes and container trucks.

[0019] The aforementioned problems not only severely restrict the operational efficiency of horizontal transport itself, but also affect the continuity of the entire loading and unloading process, leading to a series of chain problems such as extended ship port time, increased energy consumption, and rising operating costs.

[0020] Based on this, the embodiments of this specification propose a truck scheduling method based on dynamic cluster adaptation: The overall idea is as follows: First, a refined truck classification information database is constructed according to the size, carrying capacity, and functional qualifications of the trucks. Then, based on this classification information database and preset allocation rules, trucks are automatically matched and assigned to transportation tasks. Finally, the system status is monitored in real time during task execution, and the established "task-truck" matching relationship is dynamically readjusted based on the same information database. This achieves intelligent, flexible, and efficient collaboration in port horizontal transportation, significantly reduces the invalid mileage of trucks, improves the collaborative operation capability of multi-machine equipment in the port, and ensures the stability of the port operation system.

[0021] The technical solutions provided by the various embodiments of this application are described below with reference to the accompanying drawings.

[0022] like Figure 1 As shown in the embodiments of this specification, a cluster-based dynamic adaptation method for scheduling collection cards is provided, including: Step S100: Classify the trucks based on their dimensions and establish a truck classification information database; wherein, the dimensions include at least: size and load-bearing capacity and functional qualifications.

[0023] During implementation, based on the physical attributes and functional qualifications of each container truck, the container trucks are systematically subdivided into different types from at least two core dimensions.

[0024] First, size and load-bearing capacity: determine the specifications and quantity of containers that the truck can carry, such as distinguishing between 20-foot single-unit, 40-foot single-unit, or double 20-foot container types; second, functional qualifications: determine the types of containers that the truck can carry based on whether it is equipped with special equipment or holds the corresponding transportation permits, such as classifying them as refrigerated container qualifications, high-cube container qualifications, dangerous goods qualifications, or general container qualifications, and clarify the usage scenarios and technical characteristics corresponding to different sub-types of trucks.

[0025] Through this classification process, a truck classification information database is built and dynamically maintained, transforming the traditionally crudely managed truck resources into data with clear attributes and precise accessibility, thereby providing a reliable data foundation for subsequent scheduling decisions.

[0026] Step S200: Based on the truck classification information database and the preset allocation rules, match and assign trucks to the container transportation operation tasks.

[0027] In practice, after receiving a container transportation task to be performed, the task's requirements are analyzed, including at least the size specifications of the container to be transported (e.g., 20-foot or 40-foot) and the container type (e.g., general container, refrigerated container, or dangerous goods container). Then, based on the established truck classification information database and the preset allocation rules, the optimal target truck that meets both size and load-bearing capacity and functional qualification requirements is selected from the current truck cluster. A dispatch instruction containing task details and route guidance is then issued to the target truck to complete the task allocation.

[0028] Step S300: During the execution of the container transportation operation task, monitor the real-time status of the system and dynamically adapt and adjust the assigned tasks and container trucks based on the container truck classification information database. The dynamic adaptation adjustment includes: flexible exchange of work tasks, and / or dynamic adjustment of transport vehicles.

[0029] During implementation, the real-time status of the monitoring system is monitored during container transport operations, including vehicle location, load status, and equipment progress. When unexpected situations such as deviations from the plan, operational efficiency bottlenecks, or vehicle malfunctions are detected, the system immediately performs dynamic adjustments to the bound "task-truck" execution relationships based on the truck classification information database. Specifically: A flexible task exchange mechanism can be used, such as dynamically exchanging containers carried by two trucks that are currently in operation, to optimize routes or respond to unplanned events. Alternatively, a dynamic adjustment mechanism for transport vehicles can be used, such as quickly matching suitable alternative trucks, thereby achieving flexible adaptation of the transportation process and continuous optimization of overall efficiency.

[0030] For example: An automated terminal is performing an unloading task involving the following two containers: Container A: 20-foot refrigerated container, container area α, located on the upper deck of the ship; Container B: 20-foot standard container, container area α, located on the lower deck of the ship.

[0031] Using the truck scheduling method of this application, the final plan is to first load truck X onto the upper layer A, then load it onto the lower layer B, and transport it to container area α to complete unloading, with the entire process taking approximately 25 minutes.

[0032] As a comparison, using the traditional "one vehicle, one container" mode, the time required to complete the task under the same operating conditions is approximately 42 minutes.

[0033] The comparison shows that, under the same test case, the truck scheduling method proposed in this application can save about 40% of the operation time, significantly improving the operation efficiency.

[0034] In summary, by dynamically adjusting the clusters of container trucks and flexibly adjusting container operation tasks, the problems of rough management and insufficient operational resilience of container trucks in automated container terminals are solved. This effectively reduces the invalid mileage of container trucks and enhances the collaborative operation capabilities of multiple machines in the port, ensuring the stability of the port operation system.

[0035] In some embodiments, the container trucks matched and assigned to perform container transport tasks include: automatic allocation and proactive intervention; The automatic allocation includes: scanning the task pool to be done in each preset period and automatically generating scheduling instructions; and / or, triggering scheduling tasks associated with the corresponding task events based on preset job events; The proactive intervention includes manually assigning container trucks to perform container transport operations when preset intervention conditions are met.

[0036] In practice, when matching and assigning container trucks to container transport operations, the system integrates automatic decision-making with human experience and judgment, forming a hybrid decision-making mechanism that is "mainly based on system intelligence and supplemented by human control," including automatic allocation and proactive intervention.

[0037] Specifically, the automatic allocation mode is executed fully automatically by the system according to preset logic, without human intervention. It is an automatic allocation method for container transportation operations and includes two driving mechanisms: First, timed triggering: Scan the task pool to be done within each fixed period and automatically generate scheduling instructions, such as executing once every 30 seconds, to realize the periodic and batch automatic dispatch of tasks and ensure the continuous and stable output of task instructions.

[0038] Secondly, event triggering: Real-time monitoring of key event nodes in the terminal's production operation chain. When a specific operation event occurs, such as the completion of unloading and hoisting by the quay crane, the completion of container stacking by the yard crane, or the arrival of a container truck at a designated location, a scheduling task strongly associated with the event is immediately triggered, thereby achieving precise synchronization and seamless connection between tasks and operation processes.

[0039] The proactive intervention mode is mainly used to handle special or emergency scenarios, such as expedited port clearance due to shipping delays, system failures, or abnormal operating conditions that require comprehensive human judgment. In this mode, dispatchers take over the highest control authority and manually assign container transportation tasks, thereby ensuring the flexibility and reliability of dispatching decisions in extreme situations.

[0040] This embodiment, through the synergy of the two modes mentioned above, can achieve efficient and accurate automated scheduling under normal conditions, while maintaining the system's operational flexibility and risk control capabilities in emergency situations, thereby comprehensively ensuring the robustness and intelligence level of the port's horizontal transportation scheduling system under various working conditions.

[0041] In some embodiments, the flexible exchange of job tasks includes: According to the preset exchange rules, the container transportation tasks carried by two or more trucks are exchanged during the task execution process. The exchange includes at least one of the following exchange methods: single box for single box, single box for double box, double box for single box, and double box for double box.

[0042] Specifically, flexible task exchange is a vehicle-swapping strategy during container transport tasks. It operates in the stage where container transport tasks have started but not yet been completed. It involves real-time and dynamic online reallocation of tasks among multiple trucks. Specifically, when performing flexible task exchange, it fully considers multiple factors such as real-time vehicle location, loading status, task progress, and operational efficiency. Based on this, the ongoing transportation process is reorganized online to flexibly adapt to diverse on-site operation scenarios and optimization goals, such as adjustments to loading and unloading sequences, resolution of route conflicts, and sudden changes in priority.

[0043] Interchange operations include at least one of the following exchange methods: Single container for single container: Two trucks, each capable of carrying only one container, exchange their duties. Single container to double container swap: A truck carrying a single container and a truck carrying two containers exchange all their tasks. Double container to single container swap: A truck carrying two containers exchanges all its tasks with a truck carrying a single container. Double container for double container: Two trucks, each carrying two containers, exchange all their tasks.

[0044] In some embodiments, executing the exchange rules includes: Determine whether the container sent by the equipment matches the target container in the current scheduling instruction. If they match, maintain the current scheduling. If there is a discrepancy, determine whether the truck in question is in an "empty state" or "unbound state". If so, the work tasks are directly exchanged. If not, determine whether the carrying capacity of the trucks involved is the same. If they are the same, adjust the task according to the exchange method of single container to single container or double container to double container. If they are different, the task will be adjusted according to the exchange method of exchanging a single container for a double container or a double container for a single container.

[0045] Specifically, such as Figure 2 As shown, it determines whether the container actually dispatched by the terminal loading and unloading equipment (such as quay crane or yard crane) is consistent with the target container in the current scheduling instruction. If they are consistent, the scheduling proceeds normally, the original scheduling instruction is maintained, and the exchange process is not triggered.

[0046] If there is a discrepancy, it indicates a planned deviation event such as a box dispatching error, and the exchange process will be initiated. Determine whether the vehicle performing the task is empty or unavailable. If so, directly switch the task to achieve a quick and low-cost adjustment.

[0047] It should be noted that "whether the vehicle is empty" means that the system has assigned a task but the vehicle has not yet loaded the container and is on its way to the receiving point; "whether the vehicle is unavailable" means that a task has not yet been bound to a specific vehicle in the system, for example, the task has just been generated and is still in the allocation pool; or the vehicle originally scheduled to execute the task cannot be bound due to failure, scheduling conflict or other reasons, resulting in the task being in a "no vehicle available" suspended state.

[0048] If one of the trucks performing the task is not empty or has no truck, then it is determined whether the container carrying capacity of the two trucks is the same. If the carrying capacity is the same, the task is adjusted in an equivalent manner of "single container for single container" or "double container for double container". If the carrying capacity is different, the task will be adjusted in a cross-processing manner of "single container to double container" or "double container to single container".

[0049] This embodiment, through a hierarchical decision-making mechanism, effectively controls the complexity and cost of the adjustment operation itself while ensuring real-time response, achieving the best balance between response speed, adjustment cost and global optimization effect.

[0050] In some embodiments, the transport vehicle is dynamically adjusted upon detection of an emergency. The unforeseen conditions include at least one of the following: vehicle power system failure, special equipment failure, mismatch between truck function qualifications and task requirements, mismatch between truck size and load-bearing capacity and task requirements, and changes in the work task.

[0051] Specifically, during the execution of container transportation tasks, the status of vehicles, equipment, and tasks is continuously monitored in real time. When any of the following types of emergencies are detected, dynamic adjustments to the vehicles are immediately triggered: A malfunction in the vehicle's powertrain, such as a fault in the engine or transmission system, causes the vehicle to lose its ability to drive normally. Specialized equipment malfunctions, such as the failure of the refrigeration unit in a refrigerated container, can cause a vehicle to lose its special operation capabilities. The truck's qualifications and the task requirements are not matched. For example, a truck without dangerous goods qualifications was dispatched to transport dangerous goods containers; or a 20-foot truck was dispatched to transport a 40-foot container. Changes in operational tasks, such as adjustments to shipping schedules, changes in customers, or rescheduling of terminal operations, can fundamentally alter the destination, priority, or cargo attributes of a transport task already assigned to a particular truck, rendering the original truck unable or no longer suitable to perform the task.

[0052] In some embodiments, the transport vehicles are dynamically adjusted according to the task operation rules; The task operation rules include: replacement of the same type of truck, replacement of downgraded and adapted trucks, and task splitting and reorganization.

[0053] Specifically, the task operation rules mainly include the following three adjustment strategies: Replacement of the same type of container truck: When the original container truck is withdrawn due to failure or incompatibility, a vehicle with the same size, load capacity and functional qualifications as the original container truck is found in the available vehicle group and directly replaced one-to-one. This maintains the original operation plan to the greatest extent, achieves rapid and seamless replacement, has low adjustment costs and minimal disruption to the overall process.

[0054] Downgraded Adaptation Truck Replacement: Under the premise of ensuring size and load-bearing capacity matching, conditional downgrade of functional qualifications is allowed, that is, selecting a partially compatible truck for "downgraded" replacement.

[0055] The compatibility rules for functional qualifications are as follows: Trucks with refrigerated container certification can be downgraded to transport ordinary containers and high cube containers; however, they cannot transport dangerous goods containers. Trucks with hazardous materials certification can be downgraded to transport general containers, high cube containers, and hazardous materials containers; however, they cannot transport refrigerated containers. Trucks with general container qualifications can transport high cube containers of the same size; in emergency situations, after being temporarily modified through specific procedures and with permission, they can temporarily transport refrigerated containers; however, they cannot transport dangerous goods containers. Trucks with high-capacity container qualifications can be downgraded to transport ordinary containers; however, they cannot transport dangerous goods containers or refrigerated containers.

[0056] For example, when a truck transporting ordinary containers breaks down, a truck with refrigerated container qualifications can be used as a downgraded replacement to ensure the continuity of the mission.

[0057] And task splitting and reorganization: structurally modifying the transportation task itself or the relationship between transportation tasks.

[0058] In some embodiments, when performing the dynamic adjustment of the transport vehicle, the following adjustments are performed sequentially according to the priority order of the task operation rules, including: Determine if there is a usable container truck that perfectly matches the task to be adjusted in terms of size, load-bearing capacity, and functional qualifications. If so, select that container truck to replace it with a container truck of the same type. If not, determine if there is a usable truck that matches the task to be adjusted in terms of size and load capacity and is partially compatible with the functional qualifications. If so, select the truck for downgrade and adaptation replacement. If not, the task to be adjusted will be split and reorganized to match available truck capacity.

[0059] In conjunction with the above embodiments, such as Figure 3 As shown, when performing the dynamic adjustment of transport vehicles, the principle of "minimizing disruption to the existing work plan and maximizing adjustment efficiency" is followed by three strategies in sequence: replacement of trucks of the same type, replacement of trucks with downgraded and adapted versions, and task splitting and reorganization. First, a replacement strategy for the same type of truck is implemented. Based on the existing truck classification information database, it is determined whether there are any trucks among all available trucks that are completely matched with the task to be adjusted in terms of size, load capacity and functional qualifications. If such a truck is found, it is selected for one-to-one direct replacement, which can completely replace the original operation plan without changing the task attributes or route, achieving seamless replacement and minimizing the operation interruption time and scheduling change costs.

[0060] If no perfectly matching truck of the same type is found, a downgrade and adaptation replacement strategy is adopted to find available trucks that are fully compatible with the task to be adjusted in terms of size and load capacity, but partially compatible in terms of functional qualifications. If a suitable vehicle is found, a downgrade and adaptation replacement is performed. That is, by making limited use of the surplus capacity of high-qualification vehicles (for example, assigning trucks with refrigerated container qualifications to perform ordinary container tasks), the transportation operation can continue with limited resources while ensuring size and load capacity.

[0061] If downgrading and adaptation still cannot find suitable trucks, a task splitting and reorganization strategy is implemented. In this case, the task itself is structurally modified or reorganized to adapt to the current limited transportation capacity. Even in complex scenarios with extremely scarce resources, the continuous operation of the work chain can still be maintained.

[0062] For example, a traditional terminal needs to transport a 40-foot container (C). The original dispatched truck was a 40-foot single container + refrigerated container qualified truck (Y). However, before the operation, truck Y suddenly broke down and needed to be replaced.

[0063] The dynamic vehicle adjustment strategy in this embodiment first attempts to replace the highest-priority truck of the same type, that is, to find another "40-foot single-container + refrigerated container qualified" truck among the available vehicles. However, such a special vehicle cannot be matched in the short term. Then, the downgraded matching truck replacement strategy is automatically executed, and finally a "40-foot single-container + ordinary container qualified" truck Z is matched. According to the preset qualification compatibility rules, the ordinary container qualified truck can temporarily transport specific types of refrigerated containers under emergency scenarios. Finally, truck Z safely transports container C to the designated container area. The entire operation is only delayed by about 5 minutes from the original plan and no safety risks are introduced. Thus, in the event of a sudden shortage of special transport capacity, existing ordinary transport capacity can be used to achieve rapid and safe downgrade matching, effectively ensuring the continuity of operations and minimizing the impact of sudden failures on the overall operation plan.

[0064] In some embodiments, the task splitting and reorganization includes: The transportation task of "one truck with multiple containers" is broken down into multiple sub-tasks of "one truck with one container", and a new truck is matched for each sub-task. And / or, perform a global task allocation logic reorganization on multiple related transportation tasks to adapt to the capacity of currently available trucks.

[0065] In conjunction with the above embodiments, when a suitable truck cannot be found through the truck replacement strategy, the task structure or the logical relationships between tasks are actively restructured to maintain the continuous and normal operation of the work chain. Specifically, this includes: The transportation task of transporting multiple containers in a single trip is broken down into multiple "one truck, one container" sub-tasks. Then, based on the truck classification information database, the most suitable truck is matched for each sub-task individually. For example, the task of transporting two ordinary containers on a disabled truck is split into two independent tasks, which are matched to an ordinary qualified truck and a high-cube qualified truck, respectively. This solves the problem of a single truck being unable to carry multiple containers or the conflicting qualifications.

[0066] And / or, to collaboratively optimize and reorganize the allocation logic of multiple related tasks, including but not limited to: single-container swaps, single-container swaps, double-container swaps, double-container swaps, and so on. For example: a refrigerated container truck T1 is currently transporting a regular container C1. At this time, a newly created refrigerated container C2 is unavailable. In this case, the regular container C1 can be swapped to a regular container truck, thereby freeing up truck T1, which has refrigerated container qualifications, to take over the refrigerated container C2. Similar logic also applies to the release and matching of capacity for special qualifications such as dangerous goods and high-cube containers.

[0067] It should be noted that, as Figure 3 As shown, task splitting and reorganization typically follow the following logical hierarchy: when no truck can meet the "one truck, multiple containers" requirement, task splitting is prioritized, breaking down the "one truck, multiple containers" transportation task into multiple "one truck, one container" sub-tasks, so that each container enters the matching process separately; if there are still sub-tasks that cannot be matched after splitting, global task reorganization is performed, and the task allocation relationship is reconstructed through methods such as exchange and merging, to further match truck capacity, thereby ensuring the continuity of tasks.

[0068] Based on the same inventive concept, this application also provides a truck scheduling system based on dynamic cluster adaptation, the truck scheduling system comprising: The classification module is used to classify the container trucks based on their dimensions and establish a container truck classification information database; wherein, the dimensions include at least: size, load-bearing capacity, and functional qualifications; The task scheduling module is used to match and assign container trucks to container transport operation tasks based on the container truck classification information database and preset allocation rules. The dynamic optimization module is used to monitor the real-time status of the system during the execution of the container transportation operation task, and to dynamically adapt and adjust the assigned tasks and container trucks based on the container truck classification information database. The dynamic adaptation adjustment includes: flexible exchange of work tasks, and / or dynamic adjustment of transport vehicles.

[0069] Based on the same inventive concept, this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform: a cluster dynamic adaptation-based card scheduling method as described in any embodiment of this application.

[0070] like Figure 4 As shown in the figure, the present application also provides a schematic diagram of the structure of an electronic device 500. The electronic device 500 is merely an example and should not be construed as limiting the function and scope of use of the embodiments of the present invention.

[0071] The electronic device 500 may include: at least one processor 510; and a memory 520 communicatively connected to the at least one processor; wherein the memory 520 stores instructions executable by the at least one processor 510, the instructions being executed by the at least one processor 510 to enable the at least one processor 510 to execute: the cluster-based dynamic adaptation card scheduling method according to any embodiment of this application.

[0072] It should be noted that the electronic device 500 can be represented in the form of a general-purpose computing device, such as a server device.

[0073] In practice, the components of electronic device 500 may include, but are not limited to: at least one processor 510, at least one memory 520, and a bus 530 connecting different system components (including memory 520 and processor 510), wherein bus 530 may include a data bus, an address bus, and a control bus.

[0074] In implementation, memory 520 may include volatile memory, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include read-only memory (ROM) 5203.

[0075] The memory 520 may also include a program tool 5205 having a set (at least one) of program modules 5204, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0076] The processor 510 performs various functional applications and data processing by running computer programs stored in the memory 520.

[0077] Electronic device 500 can also communicate with one or more external devices 540 (e.g., keyboard, pointing device, etc.). This communication can be performed through input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 560, which communicates with other modules in electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0078] Based on the same inventive concept, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the cluster-based dynamic adaptation card scheduling method provided in any embodiment of this application.

[0079] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0080] In a possible implementation, the present invention can also be implemented as a program product comprising program code, wherein when the program product is run on a terminal device, the program code is used to cause the terminal device to execute the cluster-based dynamic adaptation card scheduling method provided in any embodiment of the present application.

[0081] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0082] In this specification, the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the descriptions of the embodiments described later are relatively simple, and relevant parts can be referred to the descriptions of the foregoing embodiments.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dynamically adapting the scheduling of trailers based on clustering, characterized in that, The method comprises the following steps: classifying the container trucks based on dimensions, and establishing a container truck classification information database; wherein the dimensions at least include size carrying capacity and functional qualification; matching and assigning container trucks for container transport operation tasks based on the container truck classification information database and preset allocation rules; monitoring system real-time state during the execution of the container transport operation tasks, and dynamically adapting and adjusting the assigned tasks and container trucks based on the container truck classification information database; wherein the dynamic adaptation and adjustment includes flexible exchange of operation tasks and / or dynamic adjustment of transport vehicles.

2. The method of claim 1, wherein, The matching and assigning of container trucks for container transport operation tasks includes automatic allocation and active intervention; the automatic allocation includes scanning the task pool to be operated within each preset period, and automatically generating scheduling instructions; and / or triggering scheduling tasks associated with corresponding operation events based on preset operation events; the active intervention includes manually assigning container trucks for container transport operation tasks when the preset intervention conditions are met.

3. The method of claim 1, wherein, The flexible exchange of operation tasks includes: exchanging container transport tasks carried by two or more container trucks during task execution according to preset exchange rules; wherein the exchange includes at least one of the following exchange modes: single container for single container, single container for double containers, double containers for single container, and double containers for double containers.

4. The method of claim 3, wherein, Executing the exchange rules includes: determining whether the container sent by the device is consistent with the target container in the current scheduling instruction, if consistent, maintaining the current scheduling; if not, determining whether the involved container truck is in "empty state" or "no task binding state", if so, directly exchanging operation tasks; if not, determining whether the carrying capacity of the involved container truck is the same, if so, adjusting the tasks according to the exchange mode of single container for single container or double containers for double containers; if not, adjusting the tasks according to the exchange mode of single container for double containers or double containers for single container.

5. The method of claim 1, wherein, Triggering the dynamic adjustment of transport vehicles when a sudden condition is detected; the sudden condition includes at least one of the following: vehicle power system failure, special equipment failure, mismatch between container truck functional qualification and task requirements, mismatch between container truck size carrying capacity and task requirements, and change of operation task.

6. The method of claim 5, wherein, According to the task operation rules, the dynamic adjustment of transport vehicles is carried out; the task operation rules include same type container truck replacement, downgraded adaptive container truck replacement, and task splitting and reorganization.

7. The method of claim 6, wherein, When the dynamic adjustment of transport vehicles is executed, the following adjustments are carried out in order according to the priority order of the task operation rules, including: determining whether there is an available container truck that completely matches the task to be adjusted in size carrying capacity and functional qualification, if so, selecting the container truck for same type container truck replacement; if not, determining whether there is an available container truck that matches the task to be adjusted in size carrying capacity and partially compatible in functional qualification, if so, selecting the container truck for downgraded adaptive replacement; if not, performing task splitting and reorganization on the task to be adjusted to match the available container truck carrying capacity.

8. The method of claim 6 or 7, wherein, The task splitting and reorganization includes: Splitting the transport task of "one truck with multiple containers" into multiple sub-tasks of "one truck with one container", and re-matching a truck for each sub-task; And / or, reorganizing the global task allocation logic for multiple associated transport tasks to adapt to the current available truck capacity.

9. A cluster-based dynamic adaptation of a container scheduling system, characterized in that, The truck scheduling system comprises: A classification module for classifying the trucks based on their dimensions, establishing a truck classification information library; wherein the dimensions at least include size carrying capacity and functional qualification; A task scheduling module for matching and assigning the trucks to perform the container transport operation tasks based on the truck classification information library and the preset allocation rules; A dynamic optimization module for monitoring the system real-time state during the execution of the container transport operation tasks, and dynamically adapting and adjusting the allocated tasks and trucks based on the truck classification information library; Wherein, the dynamic adaptation and adjustment includes flexible exchange of operation tasks and / or dynamic adjustment of transport vehicles.

10. An electronic device, comprising: Comprise: At least one processor; And a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform: the truck scheduling method based on cluster dynamic adaptation as claimed in any one of claims 1-8.

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