Smart port loading and unloading planning resource allocation scheduling analysis method

Abstract

The invention discloses an intelligent port loading and unloading planning resource allocation scheduling analysis method, and relates to the technical field of allocation scheduling, and the method comprises the steps: obtaining scheduling analysis data, and obtaining priority loading and unloading parameters through a scheduling analysis method; obtaining optimization analysis data, establishing a digital port model and obtaining a standard scheduling method; the ships in the port are scheduled based on a standard scheduling method; the method is used for solving the problems that in an existing port loading and unloading planning distribution scheduling method, when the number of quay cranes in a port and the number of containers in a ship are large, analysis is conducted on all existing quay crane distribution operations, the analysis data size is large, the obtaining time of a legal solution is long, and the efficiency is high. The obtaining time of the optimal scheduling scheme is prolonged; and the actual port loading and unloading scheduling efficiency is influenced.

Description

Technical Field

[0001] The present invention relates to the field of allocation and scheduling technology, and specifically to a resource allocation and scheduling analysis method for intelligent port loading and unloading planning. Background Art

[0002] In today's context of growing global trade, ports, as important hubs for international trade, are experiencing a continuous increase in their container transport business. The efficient operation of container terminals is crucial to enhancing port competitiveness, and resource allocation and scheduling for loading and unloading planning is a key link. Traditional ports typically rely on manual experience to allocate and schedule resources for container loading and unloading operations, which lacks systematicity and scientificity. For example, in the container sector, with a single ship as the statistical dimension, traditional methods make it difficult to effectively summarize the work plans and execution results of a single ship throughout the day and night, resulting in an inability to accurately compare and analyze plans and execution results. At the same time, there is a lack of in-depth research on the relationship between resource input and container volume results, making it difficult to find a more optimal and appropriate resource input-to-value ratio, which in turn affects the port's operational efficiency and economic benefits. With the rapid development of information technology, the concept of smart ports has emerged. Smart ports aim to improve port management and operational efficiency through information and intelligent means.

[0003] Existing methods for port loading and unloading planning, allocation and scheduling usually randomly explore legal solutions based on existing quay crane allocation operations, and then adjust the obtained legal solutions through subsequent data analysis, so that the obtained scheduling method can improve the operation and management efficiency of the port. Although this improved method can obtain the optimal scheduling plan by obtaining legal solutions and optimizing the legal solutions, when there are a large number of quay cranes in the port and a large number of containers in the ship, randomly exploring legal solutions only through existing quay crane allocation operations will result in a large amount of data to be analyzed, resulting in a long time to obtain legal solutions, thereby extending the time to obtain the optimal scheduling plan and affecting the actual port loading and unloading scheduling efficiency. For example, in the patent application with publication number CN117010649A, a generative berth based on neighborhood optimization is disclosed. And the quay crane allocation method and terminal, this scheme is to randomly explore legal solutions in the legal space by combining the early quay crane allocation operation, and then perform neighborhood disturbance on the legal solution obtained in the early stage to further optimize the optimal solution and obtain the optimal scheduling scheme for berths and quay cranes. Other methods for port loading and unloading planning, allocation and scheduling are usually improvements in reducing the impact of unstable factors on port operations, but still cannot solve the problem that when there are a large number of quay cranes in the port and a large number of containers in the ship, the analysis of all existing quay crane allocation operations will result in a large amount of data to be analyzed, resulting in a long time to obtain legal solutions, thereby extending the time to obtain the optimal scheduling scheme and affecting the actual port loading and unloading scheduling efficiency. In view of this, it is necessary to improve the existing port loading and unloading planning, allocation and scheduling methods. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by proposing a smart port loading and unloading planning resource allocation and scheduling analysis method, which is used to solve the problem in the existing port loading and unloading planning allocation and scheduling method that when there are a large number of quay cranes in the port and a large number of containers in the ship, the analysis of all existing quay crane allocation operations will result in a large amount of analyzed data, resulting in a long time to obtain a legal solution, thereby extending the time to obtain the optimal scheduling plan and affecting the actual port loading and unloading scheduling efficiency.

[0005] To achieve the above objectives, this application provides a smart port loading and unloading planning resource allocation and scheduling analysis method, which includes the following steps: Based on the existing loading and unloading data, the loading and unloading data of a single ship at a port is randomly obtained and recorded as scheduling analysis data. The scheduling analysis method is used to analyze all quay cranes, and the priority loading and unloading parameters of each quay crane are obtained based on the results. Obtain loading and unloading data for a single vessel at multiple ports and record it as optimization analysis data; obtain cargo data for a single vessel from the optimization analysis data and establish a digital port model based on the digital twin; simulate the scheduling of the drawbridge using a preliminary scheduling method based on the priority loading and unloading parameters and cargo data, and optimize the preliminary scheduling method based on the results of the simulation scheduling to obtain a standard scheduling method, where the cargo data includes the weight of each container in the ship and the straight-line distance between each container and all quay cranes while in the ship; All ships to be dispatched in the port are dispatched based on the standard dispatch method.

[0006] Furthermore, the scheduling analysis method includes: Based on the digital twin, the virtual models corresponding to all quay cranes in the port are obtained and recorded as virtual quay crane model XA1 to virtual quay crane model XA c ; For any virtual quay crane model XA v , obtain the location of all containers in the ship and the weight of all containers in the scheduling analysis data, and obtain the virtual quay crane model XA in turn v The time taken to load and unload all containers.

[0007] Furthermore, the scheduling analysis method further includes: Establish a spatial coordinate system and record it as the loading and unloading analysis coordinate system, where the unit of the X axis of the loading and unloading analysis coordinate system is t, the unit of the Y axis is m, and the unit of the Z axis is min; obtain the coordinates of each container and the virtual quay crane model XA based on the location of all containers vThe straight-line distance between the containers and the virtual quay crane model XA is taken as the horizontal coordinate and the weight of all containers is taken as the horizontal coordinate. v The straight-line distance between them is the vertical coordinate and the virtual quay crane model XA v The time when all containers are loaded and unloaded is the Z-axis coordinate. The points corresponding to all containers are obtained in the loading and unloading analysis coordinate system and recorded as loading and unloading points. The curve obtained by fitting all loading and unloading points is recorded as the virtual loading and unloading curve XZ v ; The virtual loading and unloading curves XZ obtained by all virtual quay crane models XA based on the scheduling analysis data are obtained, and all virtual loading and unloading curves are placed in the same loading and unloading analysis coordinate system α.

[0008] Furthermore, the scheduling analysis method further includes: All containers in the scheduling analysis data are recorded as simulated containers, and all simulated containers are subjected to loading and unloading screening processing in turn; the loading and unloading screening processing includes: obtaining the points corresponding to the simulated containers in all virtual loading and unloading curves XZ in the loading and unloading analysis coordinate system α, and recording the point with the smallest Z-axis coordinate among all points as the high-efficiency point of the simulated container.

[0009] Furthermore, the scheduling analysis method further includes: Obtaining high-efficiency points for all simulated containers, and sequentially performing a first parameter acquisition process on all virtual quay crane models XA. The first parameter acquisition process includes: for a virtual loading and unloading curve XZ corresponding to the virtual quay crane model XA, when a high-efficiency point exists in the virtual loading and unloading curve XZ, recording the abscissa and ordinate of the high-efficiency point in the virtual loading and unloading curve XZ as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively; When there are multiple high-efficiency points in the virtual loading and unloading curve XZ, the horizontal coordinate and vertical coordinate of the high-efficiency point with the smallest Z-axis coordinate are recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively, and all high-efficiency points except the high-efficiency point with the smallest Z-axis coordinate are recorded as sub-efficiency points. When there is no high efficiency point in the virtual loading and unloading curve XZ, the virtual quay crane model XA is recorded as the undetermined quay crane model.

[0010] Furthermore, the scheduling analysis method further includes: When the first parameter acquisition process is applied to all virtual quay crane models XA and there is a pending quay crane model, the second parameter acquisition process is applied to all pending quay crane models in sequence. The second parameter acquisition process comprises: obtaining the distances between all sub-efficiency points and the virtual loading and unloading curve XZ corresponding to the pending quay crane model, and recording the sub-efficiency point corresponding to the shortest distance among all distances as the high-efficiency point of the pending quay crane model; The horizontal coordinate and vertical coordinate of the high-efficiency point of the undetermined quay crane model are respectively recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the undetermined quay crane model. When any sub-efficiency point α is recorded as a high-efficiency point, the sub-efficiency point α is eliminated from all sub-efficiency points.

[0011] Furthermore, the scheduling analysis method further includes: The preferred loading and unloading weights and preferred loading and unloading distances corresponding to all virtual quay crane models XA are recorded as the priority loading and unloading parameters of the quay cranes corresponding to all virtual quay crane models XA.

[0012] Furthermore, cargo data of a single ship in the optimization analysis data is obtained, and a digital port model is established based on the digital twin. The drawbridge is simulated and scheduled using a preliminary scheduling method based on the priority loading and unloading parameters and cargo data. The preliminary scheduling method is optimized based on the results of the simulation scheduling, and the standard scheduling method is obtained, including: Obtain the loading and unloading data of a single ship at multiple ports and record it as optimization analysis data; for any optimization analysis data, obtain the scheduling time of all containers in the optimization analysis data and record them as standard scheduling time BD1 to standard scheduling time BD n , the total time of all containers dispatched in the optimization analysis data is recorded as the standard total time, where n is the number of dispatched containers in the ship in the optimization analysis data; Obtain cargo data of a single vessel in the optimization analysis data; Based on the digital twin and the cargo data of a single ship, a model corresponding to a single ship is established, and based on all virtual quay crane models XA, a digital twin model corresponding to the port is established and recorded as a digital port model.

[0013] Furthermore, cargo data of a single ship in the optimization analysis data is obtained, and a digital port model is established based on the digital twin. The drawbridge is simulated and scheduled using a preliminary scheduling method based on the priority loading and unloading parameters and cargo data. The preliminary scheduling method is optimized based on the results of the simulation scheduling. The standard scheduling method also includes: The preliminary scheduling method is as follows: based on the scheduling order, the order in which all containers in a single ship are loaded and unloaded is obtained. For any container scheduled based on the loading and unloading order, the virtual quay crane model XA corresponding to the priority loading and unloading parameters corresponding to all virtual quay crane models XA with the smallest correlation difference with the container's cargo data is obtained and recorded as the priority quay crane of the container, where the correlation difference is |t1-t2|×|l1-l2|, t1 is the weight of the container, l1 is the distance between the container and the virtual quay crane model XA, t2 is the preferred loading and unloading weight in the priority loading and unloading parameters corresponding to the virtual quay crane model XA, and l2 is the preferred loading and unloading distance in the priority loading and unloading parameters corresponding to the virtual quay crane model XA. Get the optimal quay cranes for all containers, and use the optimal quay cranes for each container to simulate loading and unloading scheduling for all containers in turn, and record the scheduling time as simulated scheduling time MD1 to simulated scheduling time MD n , the total time for all containers to be dispatched is recorded as the total simulation time; When the total simulation time is greater than or equal to the standard total time, or the total simulation time is less than the standard total time and the simulated dispatch time MD of any container is greater than the standard dispatch time BD, for any container with a simulated dispatch time MD greater than the standard dispatch time BD, the preferred quay crane of the container is recorded as the quay crane to be adjusted, and the priority loading and unloading parameters corresponding to the quay crane to be adjusted are adjusted to the cargo data of the container; after the priority loading and unloading parameters of all quay cranes to be adjusted are adjusted, a new optimization analysis data is obtained and analyzed; When the total simulation time is less than the standard total time and the simulated scheduling time MD of all containers is less than or equal to the standard scheduling time BD, the acquisition of optimization analysis data will be stopped and the preliminary adjustment method will be recorded as the standard scheduling method.

[0014] Furthermore, scheduling all ships to be scheduled in the port based on the standard scheduling method includes: When there are ships to be dispatched at the port, the ships to be dispatched are dispatched in sequence based on the latest acquired priority loading and unloading parameters of all quay cranes and the standard dispatching method.

[0015] Beneficial effects of the present invention: First, based on the existing loading and unloading data, the present application randomly obtains the loading and unloading data of a port for a single ship, and records it as scheduling analysis data; uses the scheduling analysis method to analyze all quay cranes, and obtains the priority loading and unloading parameters of each quay crane based on the results; then obtains the loading and unloading data of multiple ports for a single ship, and records it as optimization analysis data. The advantage of this is that by obtaining the scheduling analysis data and obtaining the priority loading and unloading parameters, the loading and unloading parameters corresponding to each quay crane can be obtained after analyzing the loading and unloading data of only one port, thereby ensuring that when there are a large number of quay cranes in the port and a large number of containers in the ship, a legal solution, i.e., the priority loading and unloading parameters, can still be obtained at a faster speed, which helps to improve the acquisition speed of the standard scheduling method when the standard scheduling method is subsequently obtained through the optimization analysis data, so as to reduce the acquisition time of the preferred scheduling plan after the execution of the scheduling analysis method and reduce the impact on the actual port loading and unloading scheduling efficiency; This application also obtains the cargo data of a single ship in the optimization analysis data, and establishes a digital port model based on the digital twin; based on the priority loading and unloading parameters and cargo data, the preliminary scheduling method is used to simulate the scheduling of the drawbridge, and the preliminary scheduling method is optimized based on the results of the simulated scheduling to obtain a standard scheduling method; finally, all ships to be scheduled in the port are scheduled based on the standard scheduling method. The advantage of this is that by establishing a digital port model and using the preliminary scheduling method to simulate the scheduling of the drawbridge, it helps to improve the optimization efficiency when optimizing the priority loading and unloading parameters of each quay crane, thereby improving the optimization efficiency of the preliminary scheduling method, and at the same time, it can ensure that the subsequent standard scheduling method can be used to schedule ships in the port. The standard scheduling method can fit the scheduling capabilities of the quay crane in the actual port, effectively improving the loading and unloading efficiency and scheduling efficiency of the quay crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the steps of the method of the present invention; Figure 2 A schematic diagram of obtaining a high efficiency point according to the present invention; Figure 3 This is a schematic diagram of obtaining the preferred loading and unloading weight and preferred loading and unloading distance of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, the present application provides a smart port loading and unloading planning resource allocation and scheduling analysis method, including the following steps: Step S1: Based on the existing loading and unloading data, randomly obtain the loading and unloading data of a single ship at a port and record it as scheduling analysis data; use the scheduling analysis method to analyze all quay cranes, and obtain the priority loading and unloading parameters of each quay crane based on the results; The scheduling analysis method includes: step S101, obtaining virtual models corresponding to all quay cranes in the port based on digital twins, and recording them as virtual quay crane model XA1 to virtual quay crane model XA c ; Step S102: for any virtual quay crane model XA v , obtain the location of all containers in the ship and the weight of all containers in the scheduling analysis data, and obtain the virtual quay crane model XA in turn vThe time taken to load and unload all containers; In the specific implementation process, by obtaining the virtual quay crane model XA v The time of loading and unloading operations for all containers can be used to obtain the virtual quay crane model XA v The dispatching data of containers at different locations and weights will help to obtain the most suitable virtual quay crane model XA in subsequent analysis. v The weight and distance of the container being dispatched, so that the priority loading and unloading parameters are more accurate; Step S103: establish a spatial coordinate system and record it as the loading and unloading analysis coordinate system, where the unit of the X axis of the loading and unloading analysis coordinate system is t, the unit of the Y axis is m, and the unit of the Z axis is min; obtain the coordinates of each container and the virtual quay crane model XA based on the positions of all containers. v The straight-line distance between the containers and the virtual quay crane model XA is taken as the horizontal coordinate and the weight of all containers is taken as the horizontal coordinate. v The straight-line distance between them is the vertical coordinate and the virtual quay crane model XA v The time when all containers are loaded and unloaded is the Z-axis coordinate. The points corresponding to all containers are obtained in the loading and unloading analysis coordinate system and recorded as loading and unloading points. In the specific implementation process, for example, during a data analysis, if the weight of a container A in the scheduling analysis data is 4 tons, the distance between container A and the virtual quay crane model XA is 20 meters, and the loading and unloading time of container A by the virtual quay crane model XA is 5 minutes, then (4, 20, 5) can be recorded as the corresponding loading and unloading point of container A. By obtaining all loading and unloading points and obtaining the virtual loading and unloading curve XZ, it is helpful to more intuitively obtain the priority loading and unloading parameters corresponding to each quay crane in subsequent analysis; Step S104: The curve obtained by fitting all loading and unloading points is recorded as the virtual loading and unloading curve XZ v ; Step S105, obtaining virtual loading and unloading curves XZ obtained by all virtual quay crane models XA based on the scheduling analysis data, and placing all virtual loading and unloading curves into the same loading and unloading analysis coordinate system α; Step S106: All containers in the scheduling analysis data are recorded as simulated containers, and loading and unloading screening processing is performed on all simulated containers in sequence. The loading and unloading screening processing includes: obtaining points corresponding to the simulated containers in all virtual loading and unloading curves XZ in the loading and unloading analysis coordinate system α, and recording the point with the smallest Z-axis coordinate among all points as a high-efficiency point of the simulated container; In the specific implementation process, for example, during a data analysis, a simulated container is dispatched by five virtual quay crane models XA. The vertical coordinates of all the points obtained are 4 minutes, 5 minutes, 7 minutes, 10 minutes, and 2 minutes respectively. The point corresponding to 2 minutes can be recorded as the high-efficiency point of the simulated container, which is the point where the simulated container can be loaded and unloaded at the fastest speed. In the specific implementation process, for example, during a data analysis, the loading and unloading analysis coordinate system α is obtained as follows: Figure 2 As shown, curves ZF1 to ZF5 are all virtual loading and unloading curves XZ, points Z1 to Z5 are points corresponding to a simulated container in all virtual loading and unloading curves XZ, and through analysis, it is found that the vertical coordinate of Z3 is the smallest, so Z3 can be used as the high-efficiency point of the simulated container; Step S107: Acquire high-efficiency points for all simulated containers, and sequentially perform a first parameter acquisition process on all virtual quay crane models XA. The first parameter acquisition process includes: for a virtual loading and unloading curve XZ corresponding to the virtual quay crane model XA, when a high-efficiency point exists in the virtual loading and unloading curve XZ, record the abscissa and ordinate of the high-efficiency point in the virtual loading and unloading curve XZ as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively. In a specific implementation, when a high-efficiency point exists in the virtual loading and unloading curve XZ, it indicates that the virtual quay crane model XA corresponding to the virtual loading and unloading curve XZ has a container that can be loaded and unloaded at the fastest speed. Therefore, the weight corresponding to the container and the distance between the container and the virtual quay crane model XA, that is, the horizontal coordinate and the vertical coordinate of the high-efficiency point, can be recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively, to obtain the preferred loading and unloading parameters of the quay crane corresponding to the virtual quay crane model XA. When multiple high-efficiency points exist in the virtual loading and unloading curve XZ, the multiple high-efficiency points can be screened to obtain the preferred loading and unloading parameters of the quay crane corresponding to the virtual loading and unloading curve XZ. Step S108: When there are multiple high-efficiency points in the virtual loading and unloading curve XZ, the horizontal coordinate and vertical coordinate of the high-efficiency point with the smallest Z-axis coordinate are recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively. All high-efficiency points other than the high-efficiency point with the smallest Z-axis coordinate are recorded as sub-efficiency points. In the specific implementation process, for example, during a data analysis, the loading and unloading analysis coordinate system α is obtained as follows: Figure 3As shown in the figure, curve ZF0 is the virtual loading and unloading curve XZ, and points ZZ1 and ZZ2 in the curve ZF0 are both high-efficiency points. Through data processing, the Z-axis coordinate of point ZZ2 is the smallest, which means that the virtual quay crane model XA corresponding to curve ZF0 has a higher loading and unloading efficiency for the simulated container corresponding to point ZZ2. Therefore, the horizontal coordinate and vertical coordinate corresponding to point ZZ2 can be recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively. Step S109: When there is no high-efficiency point in the virtual loading and unloading curve XZ, the virtual quay crane model XA is recorded as a pending quay crane model; Step S110: When the first parameter acquisition process is applied to all virtual quay crane models XA and there is a pending quay crane model, a second parameter acquisition process is sequentially applied to all pending quay crane models. The second parameter acquisition process comprises: obtaining the distances between all sub-efficiency points and the virtual loading and unloading curve XZ corresponding to the pending quay crane model, and recording the sub-efficiency point corresponding to the shortest distance among all distances as the high-efficiency point of the pending quay crane model; Step S111: Record the horizontal coordinate and vertical coordinate of the high-efficiency point of the proposed quay crane model as the preferred loading and unloading weight and preferred loading and unloading distance of the proposed quay crane model, respectively. When any sub-efficiency point α is recorded as a high-efficiency point, the sub-efficiency point α is eliminated from all sub-efficiency points. Step S112: Record the preferred loading and unloading weights and preferred loading and unloading distances corresponding to all virtual quay crane models XA as the priority loading and unloading parameters of the quay cranes corresponding to all virtual quay crane models XA; In the specific implementation process, by obtaining priority loading and unloading parameters, it is helpful to improve the acquisition speed of the standard scheduling method when the standard scheduling method is obtained through subsequent optimization analysis data, so as to reduce the time of obtaining the preferred scheduling plan after the execution of the scheduling analysis method and reduce the impact on the actual port loading and unloading scheduling efficiency.

[0019] Step S2: Obtain loading and unloading data for a single ship from multiple ports and record it as optimization analysis data; obtain cargo data for a single ship from the optimization analysis data, and establish a digital port model based on the digital twin; simulate the scheduling of the drawbridge using a preliminary scheduling method based on the priority loading and unloading parameters and cargo data, and optimize the preliminary scheduling method based on the results of the simulation scheduling to obtain a standard scheduling method, where the cargo data includes the weight of each container in the ship and the straight-line distance between each container and all quay cranes when it is in the ship; Step S2 includes: Step S201, obtaining the loading and unloading data of a single ship at multiple ports and recording it as optimization analysis data; for any optimization analysis data, obtaining the scheduling time of all containers in the optimization analysis data and recording them as standard scheduling time BD1 to standard scheduling time BD n, the total time of all containers dispatched in the optimization analysis data is recorded as the standard total time, where n is the number of dispatched containers in the ship in the optimization analysis data; Step S202, obtaining cargo data of a single ship in the optimization analysis data; Step S203: A model corresponding to a single ship is established based on the digital twin and the cargo data of the single ship, and a digital twin model corresponding to the port is established based on all virtual quay crane models XA, and recorded as a digital port model; Step S204, the preliminary scheduling method is as follows: Step S2041, based on the scheduling order, the order of loading and unloading of all containers in a single ship is obtained. For any container scheduled based on the loading and unloading order: the virtual quay crane model XA corresponding to the priority loading and unloading parameter with the smallest correlation difference with the cargo data of the container among the priority loading and unloading parameters corresponding to all virtual quay crane models XA is obtained, and recorded as the priority quay crane of the container, where the correlation difference is |t1-t2|×|l1-l2|, t1 is the weight of the container, l1 is the distance between the container and the virtual quay crane model XA, t2 is the preferred loading and unloading weight among the priority loading and unloading parameters corresponding to the virtual quay crane model XA, and l2 is the preferred loading and unloading distance among the priority loading and unloading parameters corresponding to the virtual quay crane model XA; In the specific implementation process, for example, during a data processing, the weight of a container and the distance between it and the virtual quay crane model XA are 5t and 20m respectively. The priority loading and unloading parameters corresponding to the virtual quay crane model XA are 4t and 10m. Then, through calculation, it can be obtained that the correlation difference is 10. When the smallest correlation difference corresponds to multiple virtual quay crane models XA, the virtual quay crane model XA obtained first is used as the priority quay crane for the container based on the time sequence. Step S2042: Obtain the preferred quay cranes for all containers, and use the preferred quay cranes for each container to simulate loading and unloading scheduling for all containers in turn, and record the scheduling time as simulated scheduling time MD1 to simulated scheduling time MD n , the total time for all containers to be dispatched is recorded as the total simulation time; Step S205: When the total simulation time is greater than or equal to the standard total time, or when the total simulation time is less than the standard total time and the simulated dispatch time MD of any container is greater than the standard dispatch time BD, the preferred quay crane of the container for any container whose simulated dispatch time MD is greater than the standard dispatch time BD is recorded as the quay crane to be adjusted, and the priority loading and unloading parameters corresponding to the quay crane to be adjusted are adjusted to the cargo data of the container; after the priority loading and unloading parameters of all quay cranes to be adjusted are adjusted, a new optimization analysis data is obtained and analyzed; In the specific implementation process, for example, during a data analysis, a simulation total time of 60 minutes and a standard total time of 70 minutes are obtained. In addition, there is a container B with a simulation scheduling time of 12 minutes and a standard scheduling time of 10 minutes. This indicates that the preferred loading and unloading parameters of the preferred quay crane corresponding to container B need to be optimized. Therefore, the priority loading and unloading parameters corresponding to the quay crane to be adjusted can be adjusted to the cargo data of the container to improve the scheduling efficiency of the quay crane to be adjusted when scheduling containers with the same cargo data as container B. Step S206: When the total simulation time is less than the standard total time and the simulated dispatch time MD of all containers is less than or equal to the standard dispatch time BD, the acquisition of optimization analysis data is stopped and the preliminary adjustment method is recorded as the standard dispatch method; During the specific implementation process, after the preliminary scheduling method is recorded as the standard scheduling method, the simulated loading and unloading scheduling in the standard scheduling method is adjusted to the actual loading and unloading scheduling to realize the subsequent scheduling of ships to be scheduled in the port.

[0020] Step S3, scheduling all ships to be scheduled in the port based on the standard scheduling method; Step S3 includes: when there are ships to be dispatched at the port, the ships to be dispatched are dispatched in sequence based on the latest acquired priority loading and unloading parameters of all quay cranes and the standard dispatching method.

[0021] Example 2. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by the computer, the computer can execute a smart port loading and unloading planning resource allocation and scheduling analysis method provided by the above methods. The method includes: first, based on the existing loading and unloading data, randomly obtain the loading and unloading data of a port for a single ship, and record it as scheduling analysis data; use the scheduling analysis method to analyze all quay cranes, and obtain the priority loading and unloading parameters of each quay crane based on the results; then obtain the loading and unloading data of multiple ports for a single ship, and record it as optimization analysis data; obtain the cargo data of a single ship in the optimization analysis data, and establish a digital port model based on digital twins; use the preliminary scheduling method to simulate the scheduling of the drawbridge based on the priority loading and unloading parameters and cargo data, optimize the preliminary scheduling method based on the results of the simulated scheduling to obtain a standard scheduling method; finally, schedule all ships to be scheduled in the port based on the standard scheduling method.

[0022] Example 3. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by the processor, the steps in the above-mentioned smart port loading and unloading planning resource allocation and scheduling analysis method are executed to achieve the following functions: first, based on the existing loading and unloading data, the loading and unloading data of a port for a single ship are randomly obtained, and recorded as scheduling analysis data; all quay cranes are analyzed using the scheduling analysis method, and the priority loading and unloading parameters of each quay crane are obtained based on the results; then the loading and unloading data of multiple ports for a single ship are obtained, and recorded as optimization analysis data; the cargo data of a single ship in the optimization analysis data is obtained, and a digital port model is established based on the digital twin; the suspension bridge is simulated and scheduled using the preliminary scheduling method based on the priority loading and unloading parameters and cargo data, and the preliminary scheduling method is optimized based on the results of the simulated scheduling to obtain a standard scheduling method; finally, all ships to be scheduled in the port are scheduled based on the standard scheduling method.

[0023] Through the description of the above embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the essence of the above technical solutions or the portion that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments or certain portions of the embodiments.

[0024] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0025] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A resource allocation and scheduling analysis method for intelligent port loading and unloading planning, characterized by: The steps include: Based on the existing loading and unloading data, the loading and unloading data of a single ship at a port is randomly obtained and recorded as scheduling analysis data. The scheduling analysis method is used to analyze all quay cranes, and the priority loading and unloading parameters of each quay crane are obtained based on the results. Obtain loading and unloading data of a single ship at multiple ports and record it as optimization analysis data; obtain cargo data of a single ship in the optimization analysis data, and establish a digital port model based on digital twins; use a preliminary scheduling method to simulate the scheduling of the drawbridge based on priority loading and unloading parameters and cargo data, and optimize the preliminary scheduling method based on the results of the simulation scheduling to obtain a standard scheduling method, where the cargo data includes the weight of each container in the ship and the straight-line distance between each container and all quay cranes when it is in the ship.

2. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 1 is characterized in that: Scheduling analysis methods include: Based on the digital twin, the virtual models corresponding to all quay cranes in the port are obtained and recorded as virtual quay crane model XA1 to virtual quay crane model XA c ; For any virtual quay crane model XA v , obtain the location of all containers in the ship and the weight of all containers in the scheduling analysis data, and obtain the virtual quay crane model XA in turn v The time taken to load and unload all containers.

3. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 2 is characterized in that: Scheduling analysis methods also include: Establish a spatial coordinate system and record it as the loading and unloading analysis coordinate system, where the unit of the X axis of the loading and unloading analysis coordinate system is t, the unit of the Y axis is m, and the unit of the Z axis is min; obtain the coordinates of each container and the virtual quay crane model XA based on the location of all containers v The straight-line distance between the containers and the virtual quay crane model XA is taken as the horizontal coordinate and the weight of all containers is taken as the horizontal coordinate. v The straight-line distance between them is the vertical coordinate and the virtual quay crane model XA v The time when all containers are loaded and unloaded is the Z-axis coordinate. The points corresponding to all containers are obtained in the loading and unloading analysis coordinate system and recorded as loading and unloading points. The curve obtained by fitting all loading and unloading points is recorded as the virtual loading and unloading curve XZ v ; The virtual loading and unloading curves XZ obtained by all virtual quay crane models XA based on the scheduling analysis data are obtained, and all virtual loading and unloading curves are placed in the same loading and unloading analysis coordinate system α.

4. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 3 is characterized in that: Scheduling analysis methods also include: All containers in the scheduling analysis data are recorded as simulated containers, and all simulated containers are subjected to loading and unloading screening processing in turn; the loading and unloading screening processing includes: obtaining the points corresponding to the simulated containers in all virtual loading and unloading curves XZ in the loading and unloading analysis coordinate system α, and recording the point with the smallest Z-axis coordinate among all points as the high-efficiency point of the simulated container.

5. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 4 is characterized in that: Scheduling analysis methods also include: Obtaining high-efficiency points for all simulated containers, and sequentially performing a first parameter acquisition process on all virtual quay crane models XA. The first parameter acquisition process includes: for a virtual loading and unloading curve XZ corresponding to the virtual quay crane model XA, when a high-efficiency point exists in the virtual loading and unloading curve XZ, recording the abscissa and ordinate of the high-efficiency point in the virtual loading and unloading curve XZ as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively; When there are multiple high-efficiency points in the virtual loading and unloading curve XZ, the horizontal coordinate and vertical coordinate of the high-efficiency point with the smallest Z-axis coordinate are recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the virtual quay crane model XA, respectively, and all high-efficiency points except the high-efficiency point with the smallest Z-axis coordinate are recorded as sub-efficiency points. When there is no high efficiency point in the virtual loading and unloading curve XZ, the virtual quay crane model XA is recorded as the undetermined quay crane model.

6. A smart port loading and unloading planning resource allocation and scheduling analysis method according to claim 5, characterized in that: Scheduling analysis methods also include: When the first parameter acquisition process is applied to all virtual quay crane models XA and there is a pending quay crane model, the second parameter acquisition process is applied to all pending quay crane models in sequence. The second parameter acquisition process comprises: obtaining the distances between all sub-efficiency points and the virtual loading and unloading curve XZ corresponding to the pending quay crane model, and recording the sub-efficiency point corresponding to the shortest distance among all distances as the high-efficiency point of the pending quay crane model; The horizontal coordinate and vertical coordinate of the high-efficiency point of the undetermined quay crane model are respectively recorded as the preferred loading and unloading weight and preferred loading and unloading distance of the undetermined quay crane model. When any sub-efficiency point α is recorded as a high-efficiency point, the sub-efficiency point α is eliminated from all sub-efficiency points.

7. A smart port loading and unloading planning resource allocation and scheduling analysis method according to claim 6, characterized in that: Scheduling analysis methods also include: The preferred loading and unloading weights and preferred loading and unloading distances corresponding to all virtual quay crane models XA are recorded as the priority loading and unloading parameters of the quay cranes corresponding to all virtual quay crane models XA.

8. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 7 is characterized in that: Obtain cargo data for a single vessel from the optimization analysis data and establish a digital port model based on the digital twin. Use a preliminary scheduling method to simulate the drawbridge scheduling based on priority loading and unloading parameters and cargo data. Optimize the preliminary scheduling method based on the results of the simulation scheduling, and obtain the standard scheduling method, including: Obtain the loading and unloading data of a single ship at multiple ports and record it as optimization analysis data; for any optimization analysis data, obtain the scheduling time of all containers in the optimization analysis data and record them as standard scheduling time BD1 to standard scheduling time BD n , the total time of all containers dispatched in the optimization analysis data is recorded as the standard total time, where n is the number of dispatched containers in the ship in the optimization analysis data; Obtain cargo data of a single vessel in the optimization analysis data; Based on the digital twin and the cargo data of a single ship, a model corresponding to a single ship is established, and based on all virtual quay crane models XA, a digital twin model corresponding to the port is established and recorded as a digital port model.

9. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 8 is characterized in that: Obtain cargo data for a single vessel from the optimization analysis data and establish a digital port model based on the digital twin. Use a preliminary scheduling method to simulate the drawbridge scheduling based on priority loading and unloading parameters and cargo data. Optimize the preliminary scheduling method based on the results of the simulation scheduling. The standard scheduling method also includes: The preliminary scheduling method is as follows: based on the scheduling order, the order in which all containers in a single ship are loaded and unloaded is obtained. For any container scheduled based on the loading and unloading order, the virtual quay crane model XA corresponding to the priority loading and unloading parameters corresponding to all virtual quay crane models XA with the smallest correlation difference with the container's cargo data is obtained and recorded as the priority quay crane of the container, where the correlation difference is |t1-t2|×|l1-l2|, t1 is the weight of the container, l1 is the distance between the container and the virtual quay crane model XA, t2 is the preferred loading and unloading weight in the priority loading and unloading parameters corresponding to the virtual quay crane model XA, and l2 is the preferred loading and unloading distance in the priority loading and unloading parameters corresponding to the virtual quay crane model XA. Get the optimal quay cranes for all containers, and use the optimal quay cranes for each container to simulate loading and unloading scheduling for all containers in turn, and record the scheduling time as simulated scheduling time MD1 to simulated scheduling time MD n , the total time for all containers to be dispatched is recorded as the total simulation time; When the total simulation time is greater than or equal to the standard total time, or the total simulation time is less than the standard total time and the simulated dispatch time MD of any container is greater than the standard dispatch time BD, for any container with a simulated dispatch time MD greater than the standard dispatch time BD, the preferred quay crane of the container is recorded as the quay crane to be adjusted, and the priority loading and unloading parameters corresponding to the quay crane to be adjusted are adjusted to the cargo data of the container; after the priority loading and unloading parameters of all quay cranes to be adjusted are adjusted, a new optimization analysis data is obtained and analyzed; When the total simulation time is less than the standard total time and the simulated scheduling time MD of all containers is less than or equal to the standard scheduling time BD, the acquisition of optimization analysis data will be stopped and the preliminary adjustment method will be recorded as the standard scheduling method.

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