Scheduling method, device and equipment for port operation, storage medium and program product

Abstract

The invention discloses a port operation scheduling method, device and equipment, a storage medium and a program product, and the method comprises the steps: determining the state information of a to-be-scheduled port, and the state information comprises a ship unloading operation task table, real-time traffic flow data, container truck operation information, shore bridge operation information and field bridge operation information; and inputting the state information into a container truck-quay crane-field crane collaborative scheduling model, determining scheduling information of the port to be scheduled through the collaborative scheduling model, and performing collaborative operation scheduling of the container truck, the quay crane and the field crane according to the scheduling information. Therefore, due to the fact that the operation information of the container trucks, the quay cranes, the field cranes and the like is considered at the same time, the operation efficiency of the container trucks, the quay cranes and the field cranes can be maximized through the scheduling information obtained through the collaborative scheduling model, and then the transportation cost of port operation is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, and in particular to a scheduling method and device for port operation, equipment, a storage medium and a program product. BACKGROUND

[0002] In related technologies, the existing scheduling method for port operation mainly builds a scheduling model from the aspects of minimizing the total time of empty running of unmanned trucks or balancing the number of unmanned trucks entering the same intersection, gate, wharf parking lot and other spatial nodes at the same time node. However, the existing scheduling model has a single optimization target and is not comprehensive in terms of consideration, for example, only considers improving the operation efficiency of unmanned trucks, and cannot realize how to improve the efficiency of shore crane operation and yard crane operation. SUMMARY

[0003] The present application provides a scheduling method and device for port operation, equipment, a storage medium and a program product, which can maximize the operation efficiency of trucks, shore cranes and yard cranes, and reduce the transportation cost of port operation.

[0004] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows:

[0005] In a first aspect, the present application provides a scheduling method for port operation, which comprises:

[0006] determining state information of a to-be-scheduled port, the state information comprising a unloading operation task table, real-time traffic flow data, truck operation information, shore crane operation information and yard crane operation information;

[0007] inputting the state information into a coordinated scheduling model of trucks-shore cranes-yard cranes, determining scheduling information of the to-be-scheduled port through the coordinated scheduling model, and performing coordinated operation scheduling of trucks, shore cranes and yard cranes according to the scheduling information.

[0008] In a second aspect, the present application provides a scheduling device for port operation, which comprises a determining unit and a scheduling unit, wherein:

[0009] The determining unit is configured to determine state information of a to-be-scheduled port, the state information comprising a unloading operation task table, real-time traffic flow data, truck operation information, shore crane operation information and yard crane operation information;

[0010] The scheduling unit is configured to input the state information into a coordinated scheduling model of trucks-shore cranes-yard cranes, determine scheduling information of the to-be-scheduled port through the coordinated scheduling model, and perform coordinated operation scheduling of trucks, shore cranes and yard cranes according to the scheduling information.

[0011] In a third aspect, an embodiment of the present application provides a scheduling device, the scheduling device comprising a memory and a processor, wherein:

[0012] a memory for storing a computer program capable of running on the processor;

[0013] the processor is configured to execute the method according to the first aspect when running the computer program.

[0014] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being executed by at least one processor to implement the method according to the first aspect.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, the computer program product comprising a computer program or instructions, the computer program or instructions being executed by a processor to implement the method according to the first aspect.

[0016] The method, device, equipment, storage medium and program product for scheduling port operations provided by the embodiments of the present application first determine the state information of the port to be scheduled, and the state information comprises an unloading operation task table, real-time traffic flow data, container truck operation information, shore crane operation information and yard crane operation information. Then, the state information is input to a container truck-shore crane-yard crane collaborative scheduling model, the scheduling information of the port to be scheduled is determined through the collaborative scheduling model, and the collaborative operation scheduling of the container truck, the shore crane and the yard crane is performed according to the scheduling information. In this way, the operation information of the container truck, the shore crane and the yard crane is fully considered in combination with the real-time traffic flow data in the port, so that the scheduling information obtained through the collaborative scheduling model can maximize the operation efficiency of the container truck, the shore crane and the yard crane, thereby reducing the transportation cost of the port operation. In addition, the waiting operation condition of the container truck at the shore crane or the yard crane and the waiting condition of the shore crane and the yard crane for the container truck are considered in the operation information of the container truck, the shore crane and the yard crane, so that the final scheduling information conforms to the actual situation of the port operation, which is beneficial to improving the efficiency of the port operation. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 Flowchart of the method for scheduling port operations provided by an embodiment of the present application Figure One ;

[0018] Figure 2 Flowchart of the method for scheduling port operations provided by an embodiment of the present application Figure Two ;

[0019] Figure 3 Flowchart of the method for scheduling port operations provided by an embodiment of the present application Figure Three ;

[0020] Figure 4 A flowchart of a port operation scheduling method provided by an embodiment of the present application Figure Four

[0021] Figure 5 An application framework diagram of a port operation scheduling method provided by an embodiment of the present application

[0022] Figure 6 A diagram showing the relationship between the number of trucks and the total unloading operation time provided by an embodiment of the present application

[0023] Figure 7 A block diagram of a port operation scheduling device provided by an embodiment of the present application

[0024] Figure 8 A diagram showing the specific hardware structure of a scheduling device provided by an embodiment of the present application

[0025] Figure 9 A diagram showing the communication process of a port operation scheduling method provided by an embodiment of the present application DETAILED DESCRIPTION

[0026] In order to enable a person skilled in the art to better understand the features and technical contents of the embodiments of the present application, the implementation of the embodiments of the present application will be described in detail below with reference to the accompanying drawings, which are only used for reference and are not intended to limit the embodiments of the present application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0028] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0029] It should also be noted that the terms "first", "second", "third" used in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that "first", "second", "third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0030] Before the technical solutions of the embodiments of the present application are described in detail, the related technical terms are first simply described.

[0031] ​The port refers to a region with functions of ship entering and exiting, berthing, passenger boarding and alighting, cargo loading and unloading, transfer and storage, and corresponding wharf facilities, and a certain range of water area and road area. Among them, the container terminal is a hub station of sea-land intermodal transportation, and plays a very important role in the globalized economy. In the container terminal operation process, reasonable allocation of the loading and unloading resources of the terminal can improve the overall efficiency of the container terminal and reduce the operation cost of the terminal, shipping company and cargo owner.

[0032] The port operation refers to the operation of dispatching, loading and unloading cargo, and removing obstacles of the ship entering and exiting the port. The port dispatching is the most important link in the container terminal operation process. Through reasonable allocation of the unloading operation task table and implementation of automatic unmanned transportation management, the transportation time of the unmanned truck can be greatly reduced, the port cargo transportation efficiency can be improved, and the total port transportation cost can be reduced.

[0033] In the related art, the existing scheduling method of the port operation mainly builds a scheduling model from the aspects of minimizing the total time of the unmanned truck empty running or balancing the number of the unmanned trucks entering the same intersection, gate, terminal parking lot and other spatial nodes at the same time node. However, the optimization target of the existing scheduling model is relatively single, only considers improving the operation efficiency of the unmanned truck, and the influence of the efficiency limitation of the quay crane operation and the yard crane operation on the unmanned truck scheduling is not considered, and the problem of how to improve the efficiency of the quay crane operation and the yard crane operation is not considered.

[0034] In a possible implementation, the scheme first establishes a mathematical model with the total time of the unmanned truck empty running as the objective function, and does not consider improving the operation efficiency of the quay crane and the yard crane, and the optimization target is relatively single. Secondly, the scheme does not consider the case that the unmanned truck waits for operation at the quay crane or the yard crane in the model building, and defaults that the unmanned truck operates as soon as it arrives, which does not conform to the actual situation of the port operation. Finally, the distance between the operation task calculated by the scheme and the berth of the yard crane is only the straight-line distance, which does not conform to the actual situation of the unmanned truck operation.

[0035] Based on this, the embodiment of the present application provides a scheduling method of port operation, specifically a method for determining the optimal number of trucks and coordinating the quay crane and the yard crane. In the embodiment of the present application, the operation information of the truck, the quay crane and the yard crane is fully considered in combination with the real-time traffic flow data in the port, so that the operation efficiency of the truck, the quay crane and the yard crane can be maximized through the scheduling information obtained by the coordination scheduling model, thereby reducing the transportation cost of the port operation. In addition, the case that the truck waits for operation at the quay crane or the yard crane and the case that the quay crane and the yard crane wait for the truck are considered in the operation information of the truck, the quay crane and the yard crane, so that the final scheduling information conforms to the actual situation of the port operation and is beneficial to improving the efficiency of the port operation.

[0036] The various embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0037] In an embodiment of the present application, Figure 1 A flowchart of a port operation scheduling method provided by an embodiment of the present application is shown in Figure One As shown in Figure 1 , the method can include:

[0038] S101: Determine the state information of the port to be scheduled, the state information including the unloading operation task table, real-time traffic flow data, container truck operation information, shore crane operation information and yard crane operation information.

[0039] In an embodiment of the present application, the port to be scheduled is a port that needs to be scheduled, and the state information of the port to be scheduled can reflect the state of the port in real time. Here, the state information such as the unloading operation task table, real-time traffic flow data, container truck operation information, shore crane operation information and yard crane operation information can be obtained through the terminal operation system (TOS), the sensing device of the port, and the vehicle terminal of the container truck.

[0040] Illustratively, according to the unloading operation task table given by the TOS, the tasks in the unloading operation task table are then numbered from 1 to n, and one task corresponds to the whole process of a container from being loaded on a ship to being unloaded on a container truck in a yard. The whole process can include six time nodes, such as the time when the container truck arrives at the shore crane to prepare for loading, the time when the container truck starts loading, the time when the container truck completes loading, the time when the container truck arrives at the yard crane, the time when the container truck starts unloading, and the time when the container truck completes unloading.

[0041] Illustratively, sensing devices (the sensing devices include sensing modules and fusion modules, so the sensing devices can be referred to as fusion sensing sides) are arranged in the port, the positions and speeds of all container trucks in the whole port are obtained through the sensing devices, and the traffic flow rate and traffic flow of all road nodes of the high-precision map of the whole port (i.e., real-time traffic flow data) can be calculated therefrom. In addition, the container truck reports its operation-related information in real time through the vehicle terminal, such as being on the road from the shore crane to the yard crane at a certain time, waiting for operation at the shore crane at a certain time, etc. The shore crane and the yard crane report their operation task information, such as starting the unloading task of a certain container number at a certain time, completing the loading task of a certain container number at a certain time, etc. (i.e., container truck operation information, shore crane operation information and yard crane operation information).

[0042] That is, in an embodiment of the present application, the state information of the port to be scheduled can be obtained by the above-mentioned manner.

[0043] In addition, in the embodiment of the present application, the truck can be referred to as an unmanned truck. The unmanned truck is a kind of unmanned container truck, which mainly consists of a drive-by-wire chassis and sensors. Such a vehicle is used for transporting goods between ships and yards in the port. The unmanned truck is considered to be an advanced productive force in the construction of a smart port, and its technical level needs to meet high standards of perception accuracy, scene positioning accuracy, decision control rationality, system fault tolerance and fault handling capability, and the cooperativity of the "man-car-road-cloud" system. The unmanned truck can be seamlessly connected with the port scheduling system, intelligent rubber-tired gantry crane, shore-based crane, etc. through perception fusion, cooperative computing and instant communication, and can realize all-weather and full-process unmanned operation.

[0044] S102: input the state information into the collaborative scheduling model of the truck-shore-based crane-yard crane, determine the scheduling information of the to-be-scheduled port through the collaborative scheduling model, and perform collaborative operation scheduling of the truck, the shore-based crane and the yard crane according to the scheduling information.

[0045] In the embodiment of the present application, the state information is input into the collaborative scheduling model, and the scheduling information of the to-be-scheduled port is determined through the collaborative scheduling model. For the output result (i.e., the scheduling information) of the collaborative scheduling model, both the optimal number of trucks that makes the required time length of the entire unloading operation tend to a minimum value and the time nodes of the unmanned truck, the shore-based crane and the yard crane in each task can be given, so as to realize the maximum operation efficiency of the unmanned truck, the shore-based crane and the yard crane.

[0046] In the embodiment of the present application, the scheduling information can include the operation task allocation of the truck and the time nodes of the operation tasks of the truck, the shore-based crane and the yard crane; and the collaborative operation scheduling of the truck, the shore-based crane and the yard crane according to the scheduling information can maximize the operation efficiency of the truck, the shore-based crane and the yard crane, thereby reducing the transportation cost of the port operation.

[0047] In another embodiment of the present application, for the establishment of the collaborative scheduling model, as shown in Figure 2 the method comprises:

[0048] S201: establish an unloading operation total time equation, a queuing waiting time equation of the truck at the shore-based crane, a queuing waiting time equation of the truck at the yard crane, a shore-based crane operation total time equation and a yard crane operation total time equation.

[0049] In the embodiments of the present application, a collaborative scheduling model between the container trucks, the quay cranes and the yard cranes can be built according to the actual business logic of the container truck, quay crane and yard crane operations in the port horizontal transportation, such as traffic flow data, container truck operation information, quay crane operation information and yard crane operation information and the like. It can be understood that in the embodiments of the present application, the container truck, quay crane operation and yard crane operation are fully considered in combination with the actual operation situation of the port, and accordingly the total unshipping operation time equation, the queuing waiting time equation of the container truck at the quay crane, the queuing waiting time equation of the container truck at the yard crane, the total operation time equation of the quay crane and the total operation time equation of the yard crane are established.

[0050] Here, the symbols appearing in the embodiments of the present application are defined first.

[0051] In x kij =1, the kth container truck is assigned the ith container task to the jth container task;

[0052] In x kij =0, the kth container truck is not assigned the ith container task to the jth container task;

[0053] In y ki =1, the ith container task is executed by the kth container truck;

[0054] In y ki =0, the ith container task is not executed by the kth container truck.

[0055] It should be noted that each task is in order, and through y ki it can be known that the kth container truck has done the ith container task, and through x kij it can be known that the operation order of the kth container truck, i.e., it is known that after the kth container truck has finished the ith container task, it needs to do the jth container task.

[0056] t ki is the start container loading time of the kth container truck executing the ith container task, and if the kth container truck does not execute the ith container task, t ki =0; h ki is the start container unloading time of the kth container truck executing the ith container task, and if the kth container truck does not execute the ith container task, h ki =0; m ki is the yard crane arrival time of the kth container truck executing the ith container task, and if the kth container truck does not execute the ith container task, m ki =0; c ki is the container loading completion time of the kth container truck executing the ith container task, and if the kth container truck does not execute the ith container task, c ki =0; f kiThe unloading completion time of the kth container truck performing the ith container task, if the kth container truck does not perform the ith container task, f ki = 0; T ij is the time required from the ith container task corresponding to the shore crane to the jth container task corresponding to the yard crane; N is a set of container tasks, such as 1 to 10; n is the maximum value in the set N; N' is a set of virtual starting points and container tasks, such as 0 to 10; M is a set of all container trucks, such as 1 to 5; m is the maximum value in the set M; a ki is the arrival time of the kth container truck at the shore crane for performing the ith container task, if the kth container truck does not perform the ith container task, a ki = 0; S is the unloading ship task start time, such as 9:00, which is converted into a relative time of 0 minutes (min); w1 is the average time required for the shore crane to complete one loading task, such as 2 min; w2 is the average time required for the yard crane to complete one unloading task, such as 3 min; M1 is the total queuing waiting time of the container truck at the shore crane; M2 is the total queuing waiting time of the container truck at the yard crane; R1 is the operation efficiency of the shore crane; R2 is the operation efficiency of the yard crane; H is the total unloading ship operation time; E k is the end time of the last task completed by the kth container truck at the end of the last round of scheduling; U ki is the time interval from the end time of the last task completed by the kth container truck at the end of the last round of scheduling to the arrival time of the kth container truck at the shore crane for the ith container task in the current round; q1 is the end time of the last task completed by the shore crane at the end of the last round of scheduling; q2 is the end time of the last task completed by the yard crane at the end of the last round of scheduling. Here, R1 can also be referred to as the total operation time of the shore crane; R2 is the total operation time of the yard crane. Here, k, i, and j are positive integers.

[0057] In a specific embodiment, the total unloading ship operation time equation is:

[0058]

[0059] wherein a k1 is the arrival time of the kth container truck at the shore crane for performing the first container task, if the kth container truck does not perform the first container task, a k1 = 0.

[0060] The queuing waiting time equation of the container truck at the shore crane is:

[0061]

[0062] The queuing waiting time equation of the container truck at the yard crane is:

[0063]

[0064] The total operation time equation of the shore crane is:

[0065]

[0066] wherein, t kn is the start time of the kth container truck to perform the nth container task, if the kth container truck does not perform the nth container task, then t kn = 0; t k1 is the start time of the kth container truck to perform the 1st container task, if the kth container truck does not perform the 1st container task, then t k1 = 0.

[0067] Here, because the quay crane operation is in order, the quay crane operation total time is the completion time of the last task minus the start time of the first task. In addition, the quay crane operation order must correspond to the task number.

[0068] The yard crane operation total time equation is:

[0069]

[0070] Here, because the yard crane operation has no order, the last unloading completion time and the first unloading start time are determined, that is, the yard crane operation total time equation is obtained.

[0071] S202: According to the unloading operation total time equation, the queuing waiting time equation of the container truck at the quay crane, the queuing waiting time equation of the container truck at the yard crane, the quay crane operation total time equation and the yard crane operation total time equation, a model objective function is constructed.

[0072] In the embodiment of the present application, the model objective function is established by minimizing the unloading operation total time, the queuing waiting time of the container truck at the quay crane, the queuing waiting time of the container truck at the yard crane, the quay crane operation total time and the yard crane operation total time.

[0073] In a specific embodiment, the model objective function is:

[0074] minαH+βM1+δM2+λR1+γR2(6)

[0075] Wherein, α, β, δ, λ, γ are the weights of each optimization index function, for example, they can be constants preset according to the importance of the above five indexes (i.e. the unloading operation total time H, the queuing waiting time of the container truck at the quay crane M1, the queuing waiting time of the container truck at the yard crane M2, the quay crane operation total time R1 and the yard crane operation total time R2), the value range is between 0 and 1, and α+β+δ+λ+γ=1.

[0076] S203: According to the model objective function and the third constraint condition, a collaborative scheduling model is determined.

[0077] In the embodiments of the present application, the third constraint condition is a common constraint condition for establishing the static scheduling model and for establishing the dynamic scheduling model.

[0078] In some embodiments, the third constraint condition can include a basic constraint condition, a time constraint condition, a virtual starting point constraint condition and a constraint condition between decision variables. Accordingly, the method can further include:

[0079] Based on the fact that each container task is served by only one truck, the operation task of the truck is continuous and the corresponding two tasks of the truck cannot be the same, a basic constraint condition is constructed;

[0080] Based on the relationship between the loading completion time, the loading start time, the unloading completion time, the unloading start time, the time when the truck arrives at the quay crane, the time when the truck arrives at the yard crane, the average time required by the quay crane to complete one container loading task and the average time required by the yard crane to complete one container unloading task corresponding to each container task, a time constraint condition is constructed;

[0081] Based on the relationship between the loading start time of the kth truck performing the ith container task, the unloading start time of the kth truck performing the ith container task and whether the ith container task is performed by the kth truck, a constraint condition between decision variables is constructed; wherein k and i are positive integers;

[0082] Based on the fact that all trucks must return to the virtual starting point after completing the last task, a virtual starting point constraint condition is constructed.

[0083] In a specific embodiment, for the basic constraint condition in the third constraint condition, it can include:

[0084] If the jth container task is delivered by the kth truck, the kth truck must arrive at the jth container task from the ith container task, that is:

[0085]

[0086] wherein, when y kj =1, it means that the jth container task is performed by the kth truck, and when y kj =0, it means that the jth container task is not performed by the kth truck.

[0087] If the ith container task is performed by the kth truck, the kth truck must arrive at the jth container task from the ith container task, that is:

[0088]

[0089] Each container task is served by only one truck, that is:

[0090]

[0091]

[0092] Each task of each truck must be consecutive, i.e.:

[0093]

[0094] where x kih = 1 means the kth truck is assigned the task from ith container to hth container, x kih = 0 means the kth truck is not assigned the task from ith container to hth container; x khj = 1 means the kth truck is assigned the task from hth container to jth container, x khj = 0 means the kth truck is not assigned the task from hth container to jth container.

[0095] The two tasks before and after each truck must not be the same, i.e.:

[0096]

[0097] where x kii = 0 means the kth truck is not assigned the task from ith container to ith container, i.e. the two tasks before and after must not be the same.

[0098] In a specific embodiment, for the time constraint in the third constraint condition, it can include:

[0099] For each container task, the truck loading completion time c ki is equal to the loading start time t ki plus the average time required for the quay crane to complete a container task, i.e.:

[0100]

[0101] For each container task, the truck unloading completion time f ki is equal to the unloading start time h ki plus the average time required for the yard crane to complete a container task, i.e.:

[0102]

[0103] For each container task, the truck arrival time at the quay crane a ki is less than the loading start time t ki , i.e.:

[0104]

[0105] For each container shipment, the arrival time m of the truck at the yard bridge ki Both must be less than the time h at which unloading begins. ki ,Right now:

[0106]

[0107] The time m when the kth truck arrives at the depot bridge ki The time c for completing the packing of the kth truck ki Add the time T required for the k-th container truck to travel from the quay crane of the i-th container task to the yard crane of the i-th container task. ii ,Right now:

[0108]

[0109] Start of loading time t ki There is a sequence, and each loading time t ki The difference should be greater than w1, which is the average time w1 required for the quay crane to complete one container loading task, i.e.:

[0110]

[0111] Start unloading time h ki No order, and each loading time h ki The difference should be greater than w2, which is the average time w2 required for a yard crane to complete one unloading task, i.e.:

[0112]

[0113] Among them, h kj Let h be the start time for the k-th container truck to perform the j-th container task. If the k-th container truck does not perform the j-th container task, then... kj =0.

[0114] There are specific order requirements for unloading cargo by quay cranes, namely:

[0115]

[0116] In one specific embodiment, the virtual starting point constraint in the third constraint condition may include:

[0117] After all truck operations complete their last task, they must return to the virtual starting point, that is:

[0118]

[0119] Where, x ki0= 1 indicates that the kth container truck completes the ith container task and needs to return to the virtual starting point after completing the ith task, and the ith container task is the last task of the kth container truck.

[0120] In a specific embodiment, for the inter-decision variable constraint in the third constraint condition, the following can be included:

[0121] In order to ensure that when y ki = 0, t ki and h ki are both 0, the following constraint is given:

[0122]

[0123]

[0124] wherein Q represents a large weight.

[0125] That is, in the embodiments of the present application, the third constraint condition is constructed by the basic constraint condition, the time constraint condition, the virtual starting point constraint condition and the inter-decision variable constraint condition; then the collaborative scheduling model is constructed according to the third constraint condition and the model objective function, and the collaborative scheduling model can maximize the operation efficiency of the container truck, the quay crane and the yard crane.

[0126] In some embodiments, the method can further include: establishing a static scheduling model according to the collaborative scheduling model and the first constraint condition; and establishing a dynamic scheduling model according to the collaborative scheduling model and the second constraint condition.

[0127] In the embodiments of the present application, additional constraint conditions are added to the collaborative scheduling model to construct the static scheduling model and the dynamic scheduling model. Here, the static scheduling model is established according to the collaborative scheduling model and the first constraint condition, and the static scheduling model can be used to determine the optimal number of container trucks (or referred to as the target number of container trucks); the dynamic scheduling model is established according to the collaborative scheduling model and the second constraint condition, and the dynamic scheduling model can be used to determine the operation task of each container truck and the time node (or referred to as the scheduling information) of each operation task of the quay crane and the yard crane. That is, since the operation information of the container truck, the quay crane and the yard crane is fully considered, the optimal number of container trucks output by the static scheduling model and the scheduling information output by the dynamic scheduling model can maximize the operation efficiency of the container truck, the quay crane and the yard crane, thereby reducing the transportation cost of port operation.

[0128] It should be noted that in the application embodiment, the first constraint condition is different from the second constraint condition. Specifically, the first constraint condition is a constraint condition formulated for a static scheduling model, and specifically is a constraint condition for determining the target number of container trucks; the second constraint condition is a constraint condition formulated for a dynamic scheduling model, and specifically is a constraint condition for determining the to-be-scheduled port information.

[0129] In some embodiments, for the first constraint condition, the following can be included:

[0130] When the kth container truck is assigned from the ith container task to the jth container task, the time when the kth container truck arrives at the quay crane is equal to the sum of the kth container truck unloading completion time and the time required for the kth container truck to travel from the field bridge of the ith container task to the quay crane of the jth container task;

[0131] When the first m tasks are respectively executed by the first container truck to the mth container truck, the time when the pth container truck arrives at the quay crane is equal to the sum of the unloading task start time and the time required for the quay crane to complete one container loading task on average (p-1) times; wherein k, i, j, p, m are positive integers, and 1≤p≤m;

[0132] When the first m tasks are respectively executed by the first container truck to the mth container truck, the container truck must start from the virtual starting point.

[0133] In a specific embodiment, if x kij =1, the time when the kth container truck arrives at the quay crane a kj is the kth container truck unloading completion time f ki plus the time T ij required for the kth container truck to travel from the field bridge of the ith container task to the quay crane of the jth container task, that is:

[0134]

[0135] wherein a kj is the time when the kth container truck arrives at the quay crane to execute the jth container task, and if the kth container truck does not execute the jth container task, a kj =0.

[0136] In a specific embodiment, if the first m tasks are respectively executed by the first container truck to the mth container truck, and the time when the first container truck arrives at the quay crane a 11 is the unloading task start time, then the times when the following (m-1) container trucks arrive at the quay crane are sequentially pushed back according to the time required for the quay crane to complete one container loading task on average, that is:

[0137]

[0138] a kk =tkk = S + (k - 1)w1 k e M (26)

[0139] wherein x kkj = 1 means the kth container truck is assigned the kth container task to the jth container task, x kkj = 0 means the kth container truck is not assigned the kth container task to the jth container task; a kk is the time of the kth container truck arriving at the quay crane for performing the kth container task, if the kth container truck does not perform the kth container task, then a kk = 0; t kk is the time of the kth container truck starting loading at the quay crane for performing the kth container task, if the kth container truck does not perform the kth container task, then t kk = 0; S is the time of the unloading task starting.

[0140] In a specific embodiment, when the first m tasks are respectively performed by the first container truck to the mth container truck, the container truck must start from a virtual starting point, i.e.:

[0141]

[0142] wherein x k0k = 1 means the kth container truck is assigned the kth container task, which needs to start from the virtual starting point; x k0k = 0 means the kth container truck is not assigned the kth container task, which does not need to start from the virtual starting point.

[0143] In some embodiments, the second constraint condition can comprise:

[0144] when the kth container truck is assigned from the ith container task to the jth container task, the time of the kth container truck arriving at the quay crane is equal to the time of the kth container truck completing the unloading and the time required for the kth container truck to move from the yard crane of the ith container task to the quay crane of the jth container task;

[0145] when the kth container truck is assigned from the 0th container task to the ith container task, the time of the kth container truck arriving at the quay crane is equal to the time of the kth container truck completing the last task in the last round of scheduling and the time required for the kth container truck to move from the yard bay of the last task in the last round of scheduling to the quay crane of the ith container task in the current round of scheduling;

[0146] the time of the first task of the quay crane starting work in the current round must be greater than or equal to the time of the last task of the quay crane completing work in the last round of scheduling.

[0147] In a specific embodiment, if x kij = 1, then the time of the kth container truck arriving at the quay crane a kjThe time f at which the kth truck finishes unloading ki plus the time T required for the kth truck to travel from the yard bridge of the ith container task to the quay crane of the jth container task ij That is,

[0148]

[0149] In a specific embodiment, if x k0i = 1, the time a at which the kth truck arrives at the quay crane is ki the time E at which the kth truck finishes the last task of the previous scheduling shift k plus the time U required for the kth truck to travel from the yard bay of the last task of the previous scheduling shift to the quay crane of the ith container task of the current scheduling shift ki That is,

[0150]

[0151] wherein x k0i = 1 indicates that the kth truck is assigned the ith container task and needs to start from the virtual starting point; and x k0k = 0 indicates that the kth truck is not assigned the ith container task and does not need to start from the virtual starting point.

[0152] In a specific embodiment, the time at which the first task of the quay crane begins operation in the current scheduling shift must be greater than or equal to the time at which the last task of the previous scheduling shift finishes quay crane operation, because the quay crane operation has an order, so the first task of the quay crane in the current scheduling shift is the first task, that is,

[0153]

[0154] wherein t k1 is the time at which the kth truck starts loading the first container task, and if the kth truck does not perform the first container task, t k1 = 0.

[0155] The time at which the first task of the yard crane begins operation in the current scheduling shift must be greater than or equal to the time at which the last task of the previous scheduling shift finishes yard crane operation.

[0156] In a specific embodiment, the time at which the first task of the yard crane begins operation in the current scheduling shift must be greater than or equal to the time at which the last task of the previous scheduling shift finishes yard crane operation, that is, the time of each task of the yard crane in the current scheduling shift should be greater than or equal to the time at which the last task of the previous scheduling shift finishes yard crane operation, that is,

[0157]

[0158] In the embodiment of the present application, in the dynamic scheduling model, the TOS given unloading operation task table needs to be segmented and intercepted, for example, when there are N unloading tasks, it can be intercepted into s segments, each end n tasks. n=N / s, and each segment task table is renumbered from 1 to n, and one segment task is one round task.

[0159] In the embodiment of the present application, in the second constraint condition, each road node on the high-precision map is taken as a node of a directed graph, and the length of the road node is divided by the real-time traffic flow rate of the current road node to construct a directed graph, and a set algorithm is used to calculate the required time from the yard bay number where the last round task of the container truck is arranged to the quay crane of the current round task, and a time matrix from the yard bay number where the last round task of the container truck is arranged to the quay crane of the current round task is constructed. Here, the set algorithm can be an A * (A-star) algorithm, which is not specifically limited for the preset algorithm.

[0160] Here, the A-star algorithm is a kind of most effective direct search method for solving the shortest path, and is also a common heuristic algorithm for many other problems. The A-star algorithm is actually obtained by optimizing and modifying the Dijkstra algorithm, and has a certain efficiency in the process of path finding and graph traversal.

[0161] The embodiment of the present application provides a scheduling method for port operation, which fully considers the container truck, quay crane operation, yard crane operation, etc. in combination with real-time traffic flow data in the port, and after establishing a container truck-quay crane-yard crane collaborative scheduling model for minimizing the total unloading operation time, the queuing waiting time of unmanned container trucks at the quay crane and the yard crane, and the total operation time of the quay crane and the yard crane, the scheduling information output by the collaborative scheduling model can maximize the operation efficiency of the container truck, the quay crane and the yard crane, thereby reducing the transportation cost of the port operation. In addition, the waiting operation of the container truck at the quay crane or the yard crane and the waiting of the quay crane and the yard crane for the container truck are considered, so that the finally obtained scheduling information also conforms to the actual situation of the port operation, which is beneficial to improving the efficiency of the port operation.

[0162] In another embodiment of the present application, Figure 3 A flowchart of a scheduling method for port operation provided by the embodiment of the present application is shown in Figure Three . As Figure 3 shown, the method can include:

[0163] S301: inputting the state information to the static scheduling model, and determining the target container truck quantity by the static scheduling model.

[0164] In the embodiment of the present application, the state information is input to the static scheduling model, and the target container truck quantity corresponding to the minimum unloading operation total time can be obtained. Specifically, for the determination of the target container truck quantity, refer toFigure 4 The method can comprise:

[0165] S401: determining, according to a static scheduling model, a container truck function when the total time of unloading operations tends to be minimum.

[0166] In the embodiments of the present application, the static scheduling model is a mixed integer linear programming, a preset algorithm can be used to solve the static scheduling model to determine the value of the total time of unloading operations in the optimization target; and the container truck function is obtained when the total time of unloading operations tends to be minimum. Here, the preset algorithm can be a heuristic algorithm or a commercial solver, which is not limited specifically.

[0167] Here, the linear programming problem in which the values of some decision variables are integers is called a mixed integer linear programming problem, and the solving algorithms of the mixed integer linear programming model mainly include two types of exact algorithms and heuristic algorithms, wherein the exact algorithms include branch and bound method, cutting plane method, etc., and the heuristic algorithms include genetic algorithm, ant colony algorithm, particle swarm algorithm, simulated annealing algorithm, etc.

[0168] S402: determining a target parameter value of the unloading operation task according to the state information, inputting the target parameter value into the container truck function for calculation to obtain a function value.

[0169] In the embodiments of the present application, the target parameter value can include the average time required for a shore crane to complete one container loading task, the average time required for a yard crane to complete one container unloading task, the average time interval of the shore crane to the yard berth in the all unloading task table, and the set of container tasks, etc.

[0170] In a specific embodiment, the container truck function is

[0171] g(w1, w2, t) = min(N, h(w1, w2, t)) (32)

[0172]

[0173] wherein t is the average time interval of the shore crane to the yard berth in all operation tasks.

[0174] S403: determining the target container truck quantity according to the function value.

[0175] In the embodiments of the present application, if the function value is an integer, the function value is determined as the target container truck quantity; if the function value is not an integer, the function value can be rounded up or rounded down, that is, the integer near the function value is taken, and the total time of unloading operations is analyzed according to the rounded value to determine the minimum total time of unloading operations, so as to obtain the target container truck quantity.

[0176] S302: input the state information and the target number of containers to the dynamic scheduling model, and determine the scheduling information of the to-be-scheduled port through the dynamic scheduling model.

[0177] In the embodiment of the present application, the scheduling information includes the task allocation of the containers and the time nodes of each task of the containers, the quayside cranes and the yard cranes. The collaborative operation scheduling of the containers, the quayside cranes and the yard cranes according to the scheduling information can maximize the operation efficiency of the containers, the quayside cranes and the yard cranes, and reduce the transportation cost of the port operation.

[0178] In some embodiments, for determining the scheduling information of the to-be-scheduled port, the target number of containers and the state information can be input to the dynamic scheduling model, and a preset algorithm is used to solve the dynamic scheduling model to determine the scheduling information of the to-be-scheduled port.

[0179] In the embodiment of the present application, the scheduling information includes the task allocation of the containers and the time nodes of each task of the containers, the quayside cranes and the yard cranes.

[0180] In the embodiment of the present application, the real-time traffic flow data is used as the input of the dynamic scheduling model, which is beneficial to the solving of the dynamic scheduling model by the preset algorithm, and the task allocation of the containers and the operation time nodes of the containers, the quayside cranes and the yard cranes under the current real-time traffic flow condition are obtained, which can maximize the operation efficiency of the containers, the quayside cranes and the yard cranes. In addition, the real-time traffic situation of all road sections in the entire port is obtained in real time through the perception fusion technology, and is used as the weight of the directed graph to calculate the optimal path from each quayside crane to each berth in real time, which is used as the input of the dynamic scheduling model. This conforms to the actual business process of the horizontal transportation operation of the port, can maximize the operation efficiency of the containers, the quayside cranes and the yard cranes, and further reduce the transportation cost of the port operation. That is, the collaborative scheduling of the unmanned containers, the quayside cranes and the yard cranes through the scheduling information to control the operation time nodes can reduce the transportation cost of the port operation.

[0181] The embodiment of the present application provides a scheduling method for port operation. The state information is input to a static scheduling model to determine a target number of containers. Then, the state information and the target number of containers are input to a dynamic scheduling model to determine scheduling information of a to-be-scheduled port. In this way, the target number of containers when the total duration of the unloading operation tends to be the minimum value is determined through the static scheduling model, and then the time nodes of the containers, the quayside cranes and the yard cranes in each task are obtained through the dynamic scheduling model, so that the operation efficiency of the containers, the quayside cranes and the yard cranes can be maximized, and the final scheduling information conforms to the actual situation of the port operation.

[0182] In another embodiment of the present application, based on the scheduling method of port operation of the foregoing embodiment, the embodiment of the present application takes minimizing the total time of unloading operation, the queuing waiting time of the container truck at the shore crane and the yard crane, and the total time of the shore crane and the yard crane waiting for the container truck as the objective function, establishes a collaborative scheduling model of the unmanned container truck, the shore crane and the yard crane, so that the container truck, the shore crane and the yard crane can maximize the operation efficiency; then a static scheduling model is established to calculate the target number of container trucks that can minimize the total time of unloading operation; then a dynamic scheduling model is established to obtain the container truck operation task allocation and operation time node under the current real-time traffic flow condition by taking the real-time traffic flow data as the input of the model; finally, the unmanned container truck, the shore crane and the yard crane are collaboratively scheduled by controlling the operation time node. That is, the embodiment of the present application fully considers the operation information of the container truck, the shore crane and the yard crane by combining the real-time traffic flow data in the port, so that the model result obtained by the collaborative scheduling model not only gives the target number of container trucks that can minimize the total time of unloading operation, but also gives the time node of the container truck, the shore crane and the yard crane in each task, so as to maximize the operation efficiency of the container truck, the shore crane and the yard crane, which is conducive to improving the efficiency of port operation and further reducing the transportation cost of port operation.

[0183] In the embodiment of the present application. Figure 5 An application framework schematic diagram of the scheduling method of port operation provided by the embodiment of the present application is shown in FIG. 6. Figure 5 As shown in the figure, the application framework can be divided into four parts. The first part a1 is used for data acquisition, which can be specifically: obtaining the unloading task operation table from the TOS, obtaining the real-time traffic flow data from the fusion perception side, and obtaining the operation information reported by the container truck, the shore crane and the yard crane from the OBU and other devices; the second part a2 is used for building a collaborative scheduling model to obtain a collaborative scheduling model of the container truck, the shore crane and the yard crane; the third part a3 is used for building a static scheduling model based on the collaborative scheduling model to determine the optimal number of container trucks; and the fourth part a4 is used for building a dynamic scheduling model based on the collaborative scheduling model to determine the container truck operation task (box truck matching relationship) and the time node of the container truck, the shore crane and the yard crane operation. That is, the first part a1 is a data acquisition and preprocessing module for the input data of the collaborative scheduling model; the second part a2 is a collaborative scheduling model of the container truck, the shore crane and the yard crane built according to the actual business logic of port horizontal transportation; the third part a3 is a static scheduling model built based on the second part a2 to determine the optimal number of container trucks; and the second part a2 is a dynamic scheduling model built based on the second part a2 to determine the container truck operation task and the time node of the shore crane and the yard crane operation in each task. In combination with Figure 5 The scheduling method of port operation of the present application is specifically described as follows:

[0184] In the embodiments of the present application, the cooperative scheduling model can be referred to as a cooperative scheduling optimization model, the static scheduling model can be referred to as a static scheduling optimization model, and the dynamic scheduling model can be referred to as a dynamic scheduling optimization model. In addition, the application of the summation formula in the embodiments of the present application is mainly used to determine that the kth container truck has completed the ith container task. Here, the kth container truck belongs to any one of all container trucks, and the ith container task belongs to any one task in the unloading task table.

[0185] The first part is data acquisition.

[0186] 1. Obtain the unloading operation task table through the TOS.

[0187] In the embodiments of the present application, the container unloading operation task table of the whole ship in the berth is obtained from the TOS, wherein each unloading task mainly includes a container number, a shore crane number and a yard berth number. After obtaining the unloading operation task table from the TOS, the tasks in the task table are numbered from 1 to n, and one task corresponds to one container from the whole process of loading on the container truck to unloading at the yard, which includes six time nodes of the time when the container truck arrives at the shore crane to prepare for loading, the time when the container truck starts loading, the time when the container truck completes loading, the time when the container truck arrives at the shore crane, the time when the container truck starts unloading, and the time when the container truck completes unloading.

[0188] 2. Real-time traffic flow data acquisition.

[0189] In the embodiments of the present application, the position and speed information of all container trucks in the whole port are obtained through the sensing device arranged in the port, that is, the fusion sensing side, and the traffic flow rate and traffic flow of all road nodes of the high-precision map of the whole port can be calculated.

[0190] 3. Obtain the real-time operation information reported by the unmanned container truck and the reported information of the shore crane and the yard crane.

[0191] The unmanned container truck reports its operation related information in real time through the vehicle terminal, such as the on-board unit (OBU) and the like, such as being on the road from the shore crane to the yard crane at a certain time, waiting for operation at the shore crane at a certain time, etc. The shore crane and the yard crane report their operation task information, such as starting the unloading task of a certain container number at a certain time, completing the loading task of a certain container number at a certain time, etc.

[0192] The second part is to build a cooperative scheduling model.

[0193] 1. Calculate the time matrix T between each shore crane and yard.

[0194] The nodes of the directed graph are each road node on the high-precision map, and the weight of the edge of the directed graph is the length of the road node divided by the real-time traffic flow rate of the road node. The A-star algorithm is used to calculate the time interval T between the i th container task and the container yard ii and the time interval T between the i th container task and the j th container yard ij , to form the time matrix T between each container yard and the container yard. Wherein, i is greater than j.

[0195] 2. Decision variable.

[0196] In the embodiment of the present application, the decision variable is x kij , t ki and h ki .

[0197] 3. Building a model objective function.

[0198] The total time equation of the unloading operation, i.e. the above formula (1), the queuing waiting time equation of the container truck at the container yard, i.e. the above formula (2), the queuing waiting time equation of the container truck at the container yard, i.e. the above formula (3), the total time equation of the container yard, i.e. the above formula (4), and the total time equation of the container yard, i.e. the above formula (5),

[0199] The model objective function is established, i.e. the above formula (6).

[0200] 4. Third constraint condition.

[0201] 4.1. Basic constraint condition.

[0202] If the j th container task is delivered by the k th container truck, the k th container truck must arrive at the i th container task to the j th container task, i.e. the above formula (7);

[0203] If the i th container task is executed by the k th container truck, the k th container truck must arrive at the i th container task to the j th container task, i.e. the above formula (8);

[0204] Each container task is served by only one container truck, i.e. the above formulas (9) and (10);

[0205] The task of each container truck operation must be continuous, i.e. the above formulas (11) and (12).

[0206] 4.2. Time constraint condition.

[0207] For each container task, the container truck loading completion time c ki is equal to the start loading time t ki plus the average time required for the container yard to complete one container task, i.e. the above formula (13);

[0208] For each container task, the time f when the truck unloading is completed ki is equal to the time h when the unloading starts ki plus the average time w2 that the yard crane needs to complete one unloading task, i.e. equation (14) above;

[0209] For each container task, the time a when the truck arrives at the quay crane ki is less than the time t when the loading starts ki i.e. equation (15) above;

[0210] For each container task, the time m when the truck arrives at the yard crane ki is less than the time h when the unloading starts ki i.e. equation (16) above;

[0211] The time m when the kth truck arrives at the yard crane ki is the time c when the kth truck completes the loading ki plus the time T that the kth truck needs to travel from the quay crane of the ith container task to the yard crane of the ith container task ii i.e. equation (17) above;

[0212] The time t when the loading starts ki is sequential, and the difference between each loading time t ki should be greater than the average time w1 that the quay crane needs to complete one loading task, i.e. equation (18) above;

[0213] The time h when the unloading starts ki is not sequential, and the difference between each loading time h ki should be greater than the average time w2 that the yard crane needs to complete one unloading task, i.e. equation (19) above;

[0214] The unloading of the quay crane is sequential, i.e. equation (20) above.

[0215] 4.3, Virtual starting point constraint condition.

[0216] When all the trucks have completed the last task, they must return to the virtual starting point, i.e. equation (21) above.

[0217] 4.4, Constraint condition between decision variables.

[0218] In order to ensure that when y ki = 0, t ki and h ki are both 0, the following constraints are given, i.e. equations (22) and (23) above.

[0219] Third part, determine the target number of trucks through the static scheduling model.

[0220] 1. Static scheduling model construction.

[0221] Based on the above-mentioned second part of the container truck-ship-to-shore crane-coordination scheduling model, the first constraint condition of the coordination scheduling model in the second part is supplemented to construct the static scheduling model.

[0222] 1.1. First constraint condition.

[0223] (a) Time constraint condition.

[0224] If x kij = 1, the time a kj at which the kth container truck arrives at the ship-to-shore crane is the completion time f ki of the kth container truck unloading plus the time T ij required for the kth container truck to travel from the i th container task of the yard crane to the j th container task of the ship-to-shore crane, i.e., the above-mentioned formula (24).

[0225] (b) Fixed task point combination condition.

[0226] If the first m tasks are executed by the first m container trucks respectively, and the arrival time a 11 of the first container truck at the ship-to-shore crane is the start time of the unloading task, then the arrival times of the following (m-1) container trucks at the ship-to-shore crane are sequentially pushed back according to the average time required for the ship-to-shore crane to complete one container loading task, i.e., the above-mentioned formulas (25) and (26).

[0227] (c) Virtual starting point constraint condition.

[0228] If the first m tasks are executed by the first m container trucks respectively, then the container trucks must start from the virtual starting point, i.e., the above-mentioned formula (27).

[0229] 2. Solution of static scheduling model.

[0230] The model is a mixed integer linear programming, which can be solved by heuristic algorithm, or solved by commercial solvers such as Cplex or Gurobi. After obtaining the solution of the model, the value of the total unloading operation time H in the optimization objective is calculated.

[0231] 2.1. Selection of target container truck number.

[0232] (a) Analysis of the influence of container truck number m on the total unloading operation time H

[0233] Based on the static scheduling model, a large number of numerical experiments are conducted by setting different parameters w1, w2, N, T ii and T ij It is found that with the continuous increase of the number of container trucks m, the total unloading operation time H first shows a sharp downward trend, and then tends to be flat at a certain point.

[0234] In the embodiment of the present application, the time w1 required for the quay crane to complete one container loading task is 2 minutes, the time w2 required for the yard crane to complete one container unloading task is 3 minutes, the total number of container tasks is 200, and the time distance T between each quay crane and yard crane is ii and the time distance T between each quay crane and yard crane is ij The average value is 8 minutes, and the values of a, b, d, l, g in the objective function are all taken as 0.2 for case analysis.

[0235] Here, the number of trucks is taken as 0 to 20, and the value of the total unloading time H in the optimization objective is calculated by using the above static scheduling model, Figure 6 which is a schematic diagram of the relationship between the number of trucks and the total unloading time provided by the embodiment of the present application. As shown in the figure, Figure 6 from 1 truck to 7 trucks, the total unloading time decreases significantly with the increase of the number of trucks, but from 7 trucks to 20 trucks, the total unloading time decreases very slowly with the increase of the number of trucks, and the entire broken line tends to be flat. Therefore, the efficiency of 7 trucks is the highest.

[0236] (b) Quickly find the target number of trucks.

[0237] In the step (a) of 2.1 in the third part, the minimum unloading time when the number of trucks is 1 to 20 is obtained by running the model 20 times and then summarizing, but it takes a long time to solve the model. Therefore, according to the solving results of a large number of static scheduling functions, the number of trucks when the total unloading time tends to be the minimum value is determined, that is, the above formula (32).

[0238] In the embodiment of the present application, the function value of g(h(w1, w2, t)) is calculated by substituting the parameters w1, w2, t, N of the unloading task into the calculation. If the function value is an integer, the target number of trucks is the function value; if the function value is not an integer, the target number of trucks for the unloading task is obtained by taking the number of trucks near the function value and performing the step (a) of 2.1 in the third part.

[0239] Here, taking the same parameter values as the step (a) of 2.1 in the third part as an example, w1=2, w2=3, t=8, N=200 are substituted into formula (32) to calculate the function value of g(h(w1, w2, t)) as 7, which is consistent with the analysis of the step (a) of 2.1 in the third part, so the function value can represent the target number of trucks.

[0240] Fourth part, determine the scheduling information.

[0241] 1. Determine the number of assigned trucks for the unloading operation task list.

[0242] In the embodiments of the present application, the number of trucks m in the dynamic scheduling model adopts the target number of trucks determined by the static scheduling model in the third part.

[0243] 2. Obtain the unloading operation task list.

[0244] In the embodiments of the present application, the unloading operation task list is segmented according to the TOS. The task amount of each segment should be greater than the target number of trucks determined by the static scheduling model in the third part. For example, when there are N unloading tasks in total, they can be segmented into J segments, each of which contains e tasks, i.e. J = N / e, and each segment of the task list is renumbered from 1 to e. That is, the unloading task list is divided into J rounds of tasks.

[0245] 3. Obtain the state of the last task scheduling of each truck, quay crane and yard crane.

[0246] Through the real-time operation information reported by the truck in 3 of the first part, the time E at which the kth truck completes the last task of the last task scheduling is obtained k , the yard location number L where it is located k ; through the reported information of the quay crane and yard crane in 3 of the first part, the time q1 and q2 at which the quay crane and yard crane complete the last task of the last task scheduling are obtained.

[0247] 4. Calculate the time matrix U between the last task scheduling of the truck and the quay crane of the current task.

[0248] The nodes of the directed graph are the nodes of each road node on the high-precision map, and the weight of the directed graph edge is the length of the road node divided by the current real-time traffic flow rate. The A-star algorithm is used to calculate the time interval U ki from the yard location number where the kth truck is located at the end of the last task scheduling to the quay crane of the i th container task in the current round, which constitutes the time matrix U from the yard location number where the truck is located at the end of the last task scheduling to the quay crane of the current task.

[0249] 5. Dynamic scheduling model construction.

[0250] On the basis of the collaborative scheduling model of unmanned trucks, quay cranes and yard cranes built in the second part, the second constraint condition customized for the dynamic scheduling model is added to build the dynamic scheduling model of unmanned trucks, quay cranes and yard cranes.

[0251] 5.1. Second constraint condition.

[0252] If x kij = 1, the time a kjThe time f for the kth container truck to complete unloading ki The time T for the kth container truck to move from the yard bridge of the ith container task to the quay crane of the jth container task ij That is, the above formula (28);

[0253] If x k0i = 1, the time a for the kth container truck to arrive at the quay crane ki The time E for the kth container truck to complete the last task in the last round of scheduling k The time for the kth container truck to move from the yard bay where the last task in the last round of scheduling is completed to the quay crane of the ith container task in the current round of scheduling, that is, the above formula (29);

[0254] The time for the first task in the current round of quay crane operation must be greater than or equal to the time for the last task in the last round of scheduling to be completed, because quay crane operation has a sequence, so the first task in the current round of quay crane operation is the first task, that is, the above formula (30);

[0255] The time for the first task in the current round of yard bridge operation must be greater than or equal to the time for the last task in the last round of scheduling to be completed, that is, the time for each task in the current round of yard bridge operation should be greater than or equal to the time for the last task in the last round of scheduling to be completed, that is, the above formula (31).

[0256] 6. Solution of the dynamic scheduling model.

[0257] The dynamic scheduling model established in the above steps is a mixed integer linear programming, which can be solved by a heuristic algorithm, or the above mathematical model can be solved by using a commercial solver such as Cplex or Gurobi.

[0258] In the embodiments of the present application, the practical significance of the output result of the model is that if the decision variable x kij is equal to 1, it indicates that the kth container truck first operates the ith container task, then operates the jth container task, and in the whole process of operating the ith container task, the quay crane operates the kth container truck at the time t ki , and the yard bridge operates the kth container truck at the time h ki .

[0259] In the embodiments of the present application, the scheduling method of port operation in the present application is analyzed by taking a case.

[0260] Exemplarily, 200 container tasks n in the static scheduling model are intercepted into 20 segments, 10 tasks per segment, and each segment of task table is renumbered from 1 to 10. The average time w1 required for a quay crane to complete a container loading task is 2 min, the average time w2 required for a yard crane to complete a container unloading task is 3 min, the time distance T ii and the time distance T ij between each quay crane and yard crane are calculated according to the path planning algorithm module, and the number of unmanned trucks m is 3, and the values of α, β, δ, λ and γ in the objective function are all 0.2.

[0261] Suppose that the time E1=51, E1=48 and E1=52 at which the three unmanned trucks complete the last task in the last round of task scheduling are obtained through the real-time reporting of the 3 unmanned trucks in the first part, and the time q1=52 and q2=56 at which the quay crane and the yard crane complete the last task in the last round of task scheduling are obtained through the reporting information of the 3 quay cranes and yard cranes in the first part, and the time distance U ki between the berth number of the yard in which the three unmanned trucks are located at the end of the last round of task scheduling and the quay crane of the 10 tasks in the current round is calculated according to the path planning algorithm module. Table 1 is the solution result of the dynamic scheduling model. The solution result is shown in Table 1 by solving the dynamic scheduling model established above.

[0262] Table 1

[0263]

[0264] According to the dynamic scheduling model, the time at which the truck waits for the quay crane, the time at which the truck waits for the yard crane, the time at which the quay crane waits for the truck and the time at which the yard crane waits for the truck in each task are calculated and shown in Table 2. As shown in Table 2, the time at which the truck waits for the quay crane, the time at which the truck waits for the yard crane, the time at which the quay crane waits for the truck and the time at which the yard crane waits for the truck in each task have reached a small value.

[0265] Table 2

[0266]

[0267] In the embodiments of the present application, numerical simulation is performed according to the fixed truck matching relationship mode, and data comparison is performed with the present technical solution. In the fixed truck matching relationship mode, the 10 tasks are arranged for truck 1, truck 2 and truck 3 in turn to obtain the solution result of the fixed truck matching relationship mode as shown in Table 3. Then, the time at which the truck waits for the quay crane, the time at which the truck waits for the yard crane, the time at which the quay crane waits for the truck and the time at which the yard crane waits for the truck in each task in the fixed truck matching relationship mode are calculated and shown in Table 4.

[0268] Table 3

[0269]

[0270] Table 4

[0271]

[0272] In the embodiment of the present application, compared with the fixed container truck matching relationship mode, the scheduling result obtained by the dynamic scheduling model established in the embodiment of the present application is reduced by 92.66% in the total time of the container truck waiting for the quay crane, has no change in the total time of the container truck waiting for the yard crane, is reduced by 21.83% in the total time of the quay crane waiting for the container truck, is reduced by 24.60% in the total time of the yard crane waiting for the container truck, and is reduced by 9.67% in the total unloading time. Therefore, it can be seen that the establishment of the collaborative scheduling model among the unmanned container truck, the quay crane and the yard crane in the embodiment of the present application can significantly improve the operation efficiency of the unmanned container truck, the quay crane and the yard crane.

[0273] In the embodiment of the present application, the specific implementation of the foregoing embodiment is described in detail through the foregoing embodiment, and it can be seen that the scheduling method proposed in the embodiment of the present application simultaneously optimizes and improves the efficiency of the unmanned container truck, the quay crane and the yard crane; and the situation that the unmanned container truck waits for operation at the quay crane or the yard crane and the situation that the quay crane and the yard crane wait for the unmanned container truck are fully considered, which conforms to the actual situation of port operation; in addition, in the embodiment of the present application, the distance between the quay crane and the yard berth in each operation task calculated through the high-precision map and the A-star algorithm is more in line with the actual situation of the unmanned container truck operation.

[0274] Based on the same inventive concept as the foregoing embodiment, Figure 7 A component structure schematic diagram of a port operation scheduling device provided in the embodiment of the present application is shown in FIG. 7. Figure 7 As shown in FIG. 7, the port operation scheduling device 70 can include a determination unit 701 and a scheduling unit 702, wherein:

[0275] The determination unit 701 is configured to determine the state information of the port to be scheduled, and the state information includes an unloading operation task table, real-time traffic flow data, container truck operation information, quay crane operation information and yard crane operation information;

[0276] The scheduling unit 702 is configured to input the state information to the collaborative scheduling model of the container truck-quay crane-yard crane, determine the scheduling information of the port to be scheduled through the collaborative scheduling model, and perform collaborative operation scheduling of the container truck, the quay crane and the yard crane according to the scheduling information.

[0277] In some embodiments, the determining unit 701 is further configured to input the state information into a static scheduling model, determine a target number of the container trucks through the static scheduling model, and input the state information and the target number of the container trucks into a dynamic scheduling model, determine scheduling information of the to-be-scheduled port through the dynamic scheduling model; the static scheduling model is established according to the collaborative scheduling model and a first constraint condition, and the dynamic scheduling model is established according to the collaborative scheduling model and a second constraint condition; the first constraint condition is a constraint condition for determining the target number of the container trucks, and the second constraint condition is a constraint condition for determining the scheduling information of the to-be-scheduled port.

[0278] In some embodiments, the first constraint condition includes: when the kth container truck is assigned from the ith container task to the jth container task, a time when the kth container truck arrives at the quay crane is equal to a sum of an unloading completion time of the kth container truck and a time required for the kth container truck to move from a yard crane of the ith container task to the quay crane of the jth container task; when the first m tasks are respectively performed by the first container truck to the mth container truck, a time when the pth container truck arrives at the quay crane is equal to a sum of a beginning time of the unloading task and a time required for the quay crane to complete a container loading task once on average for (p-1) times; where k, i, j, p, and m are positive integers, and 1≤p≤m; when the first m tasks are respectively performed by the first container truck to the mth container truck, the container trucks must all start from a virtual starting point.

[0279] In some embodiments, the second constraint condition includes: when the kth container truck is assigned from the ith container task to the jth container task, a time when the kth container truck arrives at the quay crane is equal to a sum of an unloading completion time of the kth container truck and a time required for the kth container truck to move from a yard crane of the ith container task to the quay crane of the jth container task; when the kth container truck is assigned from the 0th container task to the ith container task, a time when the kth container truck arrives at the quay crane is equal to a sum of a time when the kth container truck completes a last task in a last scheduling round and a time required for the kth container truck to move from a yard berth of the last task in the last scheduling round to the quay crane of the ith container task in the current scheduling round; a time when a first task of a current quay crane starts operation must be greater than or equal to a time when a last task of a last scheduling round completes quay crane operation; a time when a first task of a current yard crane starts operation must be greater than or equal to a time when a last task of a last scheduling round completes yard crane operation.

[0280] In some embodiments, the determining unit 701 is further configured to determine a container truck function when a total unloading operation time tends to a minimum value according to the static scheduling model, determine a target parameter value of the unloading operation task according to the state information, input the target parameter value into the container truck function for calculation, and obtain a function value; and determine the target number of the container trucks according to the function value.

[0281] In some embodiments, the determining unit 701 is further configured to input the target number of container trucks and the state information into a dynamic scheduling model, and solve the dynamic scheduling model by using a preset algorithm to determine scheduling information of the port to be scheduled; wherein the scheduling information comprises: assignment of the container truck to a work task, and time nodes of each work task of the container truck, the quay crane and the yard crane.

[0282] In some embodiments, the determining unit 701 is further configured to establish a total time equation of the unloading work, a queuing waiting time equation of the container truck at the quay crane, a queuing waiting time equation of the container truck at the yard crane, a total time equation of the work of the quay crane, and a total time equation of the work of the yard crane; and construct a model objective function according to the total time equation of the unloading work, the queuing waiting time equation of the container truck at the quay crane, the queuing waiting time equation of the container truck at the yard crane, the total time equation of the work of the quay crane, and the total time equation of the work of the yard crane; the determining unit 701 is further configured to determine the collaborative scheduling model according to the model objective function and a third constraint condition; wherein the third constraint condition is a common constraint condition for establishing the static scheduling model and for establishing the dynamic scheduling model.

[0283] In some embodiments, the third constraint condition comprises: a basic constraint condition, a time constraint condition, a virtual starting point constraint condition, and a constraint condition between decision variables; accordingly, the determining unit 701 is further configured to construct the basic constraint condition based on that each container task is served by only one container truck, the work task of the container truck is continuous, and the corresponding first and second tasks of the container truck cannot be the same; construct the time constraint condition based on the relationship between the container truck loading completion time, the container truck loading start time, the container truck unloading completion time, the container truck unloading start time, the container truck arrival time at the quay crane, the container truck arrival time at the yard crane, the quay crane average time required to complete one container loading task, and the yard crane average time required to complete one container unloading task; construct the virtual starting point constraint condition based on that all container trucks must return to the virtual starting point after completing the last task; and construct the constraint condition between decision variables based on the relationship between the start loading time of the kth container truck performing the ith container task, the start unloading time of the kth container truck performing the ith container task, and whether the ith container task is performed by the kth container truck; wherein k and i are positive integers.

[0284] It can be understood that, in the present embodiment, the "unit" can be a part of circuit, a part of processor, a part of program or software, etc., and of course can also be a module, and can also be non-modular. Moreover, the components in the present embodiment can be integrated in one processing unit, or can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function module.

[0285] If the integrated unit is implemented as a software functional module and not 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 this embodiment, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the method described in this embodiment. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Therefore, this embodiment provides a computer-readable storage medium storing a computer program, which, when executed by at least one processor, implements the steps of the method described in any of the foregoing embodiments.

[0286] Based on the composition of the port operation scheduling device 70 and the computer-readable storage medium described above, Figure 8 This is a schematic diagram of the specific hardware structure of a scheduling device provided in an embodiment of this application. For example... Figure 8 As shown, the scheduling device 80 may include: a communication interface 801, a memory 802, and a processor 803; the various components are coupled together through a bus system 804. It is understood that the bus system 804 is used to implement communication between these components. In addition to a data bus, the bus system 804 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 8 The various buses are all labeled as bus system 804. Among them, the communication interface 801 is used for receiving and sending signals during the process of sending and receiving information with other external network elements;

[0287] Memory 802 is used to store computer programs that can run on processor 803;

[0288] Processor 803, when running the computer program, performs the following:

[0289] The status information of the port to be dispatched is determined, including the unloading operation task list, real-time traffic flow data, truck operation information, quay crane operation information, and yard crane operation information. The status information is input into the truck-quay crane-yard crane collaborative dispatch model. The dispatch information of the port to be dispatched is determined through the collaborative dispatch model, and the collaborative operation dispatch of trucks, quay cranes, and yard cranes is carried out according to the dispatch information.

[0290] It is to be understood that the memory 802 in embodiments of the present application can be volatile or nonvolatile memory, or can include both volatile and nonvolatile memory. In one embodiment, nonvolatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as external cache memory. By way of example, and not limitation, many forms of RAM are available, for example, static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchBurst DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The memory 802 of the system and method described herein are intended to include, without being limited to, these and any other suitable types of memory.

[0291] The processor 803 can be an integrated circuit chip including a processing unit that is configured to process signals. In implementation, the steps of the above-described method can be completed by the integrated logic circuit of the processor 803 or the instructions in the form of software. The processor 803 described above can be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed by the processor 803. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present application can be directly embodied as hardware code of the processor or a combination of hardware and software modules in the processor. The software module can reside in the storage media of the memory 802, the flash memory, the read-only memory (ROM), the programmable read-only memory (PROM), the electrically programmable read-only memory (EPROM), the electrically erasable programmable read-only memory (EEPROM), the register, or other forms of the storage media in the art. The storage media is located in the memory 802, and the processor 803 reads information in the memory 802 and combines the hardware to complete the steps of the above-described method.

[0292] It can be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing units can be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSP Devices), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.

[0293] For software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes can be stored in the memory and executed by the processor. The memory can be implemented within the processor or external to the processor.

[0294] Optionally, as another embodiment, the processor 803 is further configured to execute the method according to any one of the preceding embodiments when the computer program is run.

[0295] In some embodiments, the embodiments of the present application also provide a scheduling device 80, which can at least include the port operation scheduling apparatus 70 according to any one of the preceding embodiments.

[0296] In the embodiments of the present application, for the scheduling device 80, the real-time traffic flow data in the port is fully considered in combination with the container trucks, the quay crane operation, the yard crane operation, etc., and accordingly, a container truck-quay crane-yard crane collaborative scheduling model is established, which can maximize the operation efficiency of the container trucks, the quay cranes and the yard cranes, and thus reduce the transportation cost of the port operation; in addition, since the state information also considers the situation that the container trucks wait for operation at the quay cranes or the yard cranes and the situation that the quay cranes and the yard cranes wait for the container trucks, the scheduling information finally obtained conforms to the actual situation of the port operation, which is beneficial to improving the efficiency of the port operation.

[0297] In still another embodiment of the present application, Figure 9 A communication process schematic diagram of the port operation scheduling method provided by the embodiments of the present application is shown in FIG. 8. Figure 9 As shown in FIG. 8, the scheduling device 80 can obtain state information b1, which can be specifically obtained from the TOS to obtain the unloading task operation table, from the fusion perception side to obtain the real-time traffic flow data, and from the OBU and other devices to obtain the operation information reported by the unmanned container trucks, the quay cranes and the yard cranes; then the scheduling device 80 obtains scheduling information through the state information, which can be specifically obtained through the container truck-quay crane-yard crane collaborative scheduling model to obtain the time nodes of the quay crane and yard crane operations, the operation tasks (box-truck matching relationship) of each container truck and the time nodes of the container truck operations. The scheduling device 80 distributes the scheduling information to the quay crane system, the yard crane system and the vehicle management system through the Equipment Control System (ECS) b2, so that the quay cranes, the yard cranes and the container trucks perform tasks according to the scheduling information. In this way, the scheduling according to the scheduling information can maximize the operation efficiency of the container trucks, the quay cranes and the yard cranes, and thus reduce the transportation cost of the port operation.

[0298] In still another embodiment of the present application, the embodiments of the present application also provide a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed by a processor to implement the steps of the method according to any one of the preceding embodiments.

[0299] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, apparatus, device, or computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, optical memory, and the like) embodying computer readable program code.

[0300] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure One one or more functions specified in the flowchart illustrations and / or block diagrams. Figure One one or more functions specified in the flowchart illustrations and / or block diagrams.

[0301] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure One one or more functions specified in the flowchart illustrations and / or block diagrams. Figure One one or more functions specified in the flowchart illustrations and / or block diagrams.

[0302] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure One one or more functions specified in the flowchart illustrations and / or block diagrams. Figure One one or more functions specified in the flowchart illustrations and / or block diagrams.

[0303] It should be noted that, in this application, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0304] The above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent advantages or disadvantages of the embodiments.

[0305] The methods disclosed in the several method embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments.

[0306] The features disclosed in the several product embodiments of the present application can be combined arbitrarily without conflict to obtain new product embodiments.

[0307] The features disclosed in the several method or device embodiments of the present application can be combined arbitrarily without conflict to obtain new method or device embodiments.

[0308] The above-mentioned is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of scheduling port operations, characterized by, The method comprises: determining state information of a port to be scheduled, the state information comprising a ship unloading task list, real-time traffic flow data, container truck operation information, quay crane operation information and yard crane operation information; inputting the state information into a container truck-quay crane-yard crane collaborative scheduling model, determining scheduling information of the port to be scheduled through the collaborative scheduling model, and performing collaborative operation scheduling of the container truck, quay crane and yard crane according to the scheduling information.

2. The method of claim 1, wherein, The inputting the state information into a container truck-quay crane-yard crane collaborative scheduling model, determining scheduling information of the port to be scheduled through the collaborative scheduling model, comprises: inputting the state information into a static scheduling model, and determining a target container truck quantity through the static scheduling model; inputting the state information and the target container truck quantity into a dynamic scheduling model, and determining the scheduling information of the port to be scheduled through the dynamic scheduling model; The static scheduling model is established according to the collaborative scheduling model and a first constraint condition, and the dynamic scheduling model is established according to the collaborative scheduling model and a second constraint condition; the first constraint condition is a constraint condition for determining the target container truck quantity, and the second constraint condition is a constraint condition for determining the scheduling information of the port to be scheduled.

3. The method of claim 2, wherein, The first constraint condition comprises: when the kth container truck is assigned from the ith container task to the jth container task, the time for the kth container truck to arrive at the quay crane is equal to the sum of the unloading completion time of the kth container truck and the time required for the kth container truck to travel from the yard crane of the ith container task to the quay crane of the jth container task; when the first m tasks are performed by the first container truck to the mth container truck respectively, the time for the pth container truck to arrive at the quay crane is equal to the sum of the unloading task start time and the time required for the quay crane to complete one container loading task m times; wherein k, i, j, p and m are positive integers, and 1≤p≤m; when the first m tasks are performed by the first container truck to the mth container truck respectively, the container trucks must all start from a virtual starting point.

4. The method of claim 2, wherein, The second constraint condition comprises: when the kth container truck is assigned from the ith container task to the jth container task, the time for the kth container truck to arrive at the quay crane is equal to the sum of the unloading completion time of the kth container truck and the time required for the kth container truck to travel from the yard crane of the ith container task to the quay crane of the jth container task; when the kth container truck is assigned from the 0th container task to the ith container task, the time for the kth container truck to arrive at the quay crane is equal to the sum of the time at which the kth container truck completes the last task in the last round of scheduling and the time required for the kth container truck to travel from the yard berth of the last task in the last round of scheduling to the quay crane of the ith container task in the current round of scheduling; the time of the first task in the current round of quay crane operation must be greater than or equal to the time at which the last task in the last round of task scheduling is completed; the time of the first task in the current round of yard crane operation must be greater than or equal to the time at which the last task in the last round of task scheduling is completed.

5. The method of claim 2, wherein, The method further comprises: According to the state information, a container ship function is determined when the total unloading time tends to be minimum; According to the state information, a target parameter value of the unloading task is determined, and the target parameter value is input into the container ship function for calculation to obtain a function value; According to the function value, the target number of container ships is determined.

6. The method of claim 2, wherein, The method further comprises: The target number of container ships and the state information are input into the dynamic scheduling model, and a preset algorithm is used to solve the dynamic scheduling model to determine the scheduling information of the to-be-scheduled port; The scheduling information includes the task allocation of the container ship, and the time nodes of the tasks of the container ship, the quay crane and the yard crane.

7. The method according to any one of claims 2 to 6, characterized in that, Before the state information is input into the container ship-quay crane-yard crane collaborative scheduling model, the method further comprises: An unloading total time equation, a container ship queuing waiting time equation at a quay crane, a container ship queuing waiting time equation at a yard crane, a quay crane total operation time equation and a yard crane total operation time equation are established; A model objective function is constructed according to the unloading total time equation, the container ship queuing waiting time equation at the quay crane, the container ship queuing waiting time equation at the yard crane, the quay crane total operation time equation and the yard crane total operation time equation; The collaborative scheduling model is determined according to the model objective function and a third constraint condition; wherein the third constraint condition is a common constraint condition for establishing the static scheduling model and the dynamic scheduling model.

8. The method of claim 7, wherein, The third constraint condition includes a basic constraint condition, a time constraint condition, a virtual starting point constraint condition and a constraint condition between decision variables; Correspondingly, the method further comprises: A basic constraint condition is constructed based on the fact that each container task is served by only one container ship, the operation task of the container ship is continuous, and the corresponding two tasks of the container ship cannot be the same; A time constraint condition is constructed based on the relationship between the loading completion time, the loading start time, the unloading completion time, the unloading start time, the container ship arrival time at the quay crane, the container ship arrival time at the yard crane, the average time required for the quay crane to complete one container loading task and the average time required for the yard crane to complete one container unloading task of each container task corresponding to the container ship; A virtual starting point constraint condition is constructed based on the fact that all container ships must return to the virtual starting point after completing the last task; A constraint condition between decision variables is constructed based on the relationship between the loading start time of the kth container ship performing the ith container task, the unloading start time of the kth container ship performing the ith container task and whether the ith container task is performed by the kth container ship; wherein k and i are positive integers.

9. A device for scheduling port operations, characterized in that The scheduling device for the port operation comprises a determination unit and a scheduling unit. The determining unit is configured to determine state information of the to-be-scheduled port, the state information including a ship unloading task list, real-time traffic flow data, container truck operation information, quay crane operation information, and yard crane operation information; The scheduling unit is configured to input the state information into a container truck-quay crane-yard crane collaborative scheduling model, determine scheduling information of the to-be-scheduled port through the collaborative scheduling model, and perform collaborative operation scheduling of the container truck, the quay crane, and the yard crane according to the scheduling information.

10. A dispatching device characterized by comprising: The scheduling device includes a memory and a processor, wherein: The memory is configured to store a computer program capable of running on the processor; The processor is configured to execute the method according to any one of claims 1 to 8 when the computer program is run.

11. A computer readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the method according to any one of claims 1 to 8.

12. A computer program product comprising computer programs or instructions, characterized in that, The computer program or the instructions are executed by the processor to implement the method according to any one of claims 1 to 8.

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