A smart port loading and unloading planning resource allocation and scheduling analysis method

Abstract

The application discloses a kind of wisdom port loading and unloading planning resource allocation scheduling analysis method, it is related to allocation scheduling technical field, including: obtaining scheduling analysis data, and using scheduling analysis method to obtain priority loading and unloading parameter;Optimization analysis data are obtained, digital port model is established and standard scheduling method is obtained;Ship in port is scheduled based on standard scheduling method;The present application is used to solve the problem in the prior art port loading and unloading planning allocation scheduling method, when the number of shore cranes in the port and the number of containers in the ship are large, analyzing all existing shore crane allocation operations can lead to a large amount of data analyzed, resulting in a longer time to obtain a legal solution, which in turn prolongs the time to obtain the optimal scheduling solution and affects the actual port loading and unloading scheduling efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of distribution scheduling, in particular to a smart port loading and unloading planning resource distribution scheduling analysis method. BACKGROUND

[0002] Under the background of the continuous development of globalization trade, as an important hub of international trade, the port's container transportation volume continues to grow. The efficient operation of the container terminal is crucial to improving the competitiveness of the port, and the loading and unloading planning resource distribution scheduling is one of the key links. In the traditional port, the resource distribution scheduling is usually based on manual experience, which lacks systematicness and scientificity. For example, in the container block, the traditional method is difficult to effectively summarize the day and night work plans and execution results of each single ship, which leads to the inability to accurately compare and analyze the plans and execution results. At the same time, there is a lack of in-depth study on the relationship between resource input and box volume results, making it difficult to find a more optimal and suitable resource input value ratio, thereby affecting the operation efficiency and economic benefits of the port. With the rapid development of information technology, the concept of smart port has emerged. The smart port aims to improve the management level and operation efficiency of the port through informationization and intelligentization.

[0003] The existing method for port loading and unloading planning distribution scheduling is usually based on existing quay crane distribution operation to randomly explore legal solutions, 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 scheme by obtaining the legal solution and optimizing the legal solution, when the number of quay cranes in the port is large and the number of containers in the ship is large, randomly exploring legal solutions based on existing quay crane distribution operations will result in a large amount of analyzed data, thereby causing a long time to obtain legal solutions, and further prolonging the time to obtain the optimal scheduling scheme and affecting the actual port loading and unloading scheduling efficiency. For example, in the patent application with the publication number CN117010649A, a method for generating berth and quay crane distribution based on neighborhood optimization and a terminal are disclosed. This scheme is to randomly explore legal solutions within the legal space by combining the quay crane distribution operation in the early stage, and to further optimize the optimal scheme by perturbing the legal solutions obtained in the early stage, so as to obtain the optimal scheduling scheme of the berth and the quay crane. Other methods for port loading and unloading planning distribution scheduling are usually improved in terms of reducing the influence of unstable factors on port operations, but still cannot solve the problem that when the number of quay cranes in the port is large and the number of containers in the ship is large, analyzing all existing quay crane distribution operations will result in a large amount of analyzed data, thereby causing a long time to obtain legal solutions, and further prolonging the time to obtain the optimal scheduling scheme and affecting the actual port loading and unloading scheduling efficiency. Therefore, it is necessary to improve the existing port loading and unloading planning distribution scheduling method. SUMMARY

[0004] The present application aims to at least partly solve one of the technical problems in the prior art by proposing a smart port loading and unloading planning resource allocation and scheduling analysis method, which is used to solve the problem in the prior art that when there are a large number of quaysides and a large number of containers in a ship in a port, analyzing all existing quayside allocation operations will result in a large amount of data to be analyzed, thereby causing a long time to obtain a legal solution, and further prolonging the time to obtain an optimal scheduling scheme and affecting the actual port loading and unloading scheduling efficiency.

[0005] To achieve the above-mentioned purpose, the present application provides a smart port loading and unloading planning resource allocation and scheduling analysis method, comprising the following steps:

[0006] Based on the existing loading and unloading data, randomly obtain the loading and unloading data of a single ship in a port, and mark it as scheduling analysis data; use a scheduling analysis method to analyze all quaysides, and obtain the priority loading and unloading parameters of each quayside based on the results;

[0007] Obtain the loading and unloading data of a single ship in multiple ports, and mark 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 twinning; use a preliminary scheduling method to simulate the scheduling of the quayside based on the priority loading and unloading parameters and the cargo data, optimize the preliminary scheduling method based on the results of the simulation scheduling, and obtain a standard scheduling method, wherein the cargo data includes the weight of each container in the ship and the straight-line distance between each container in the ship and all quaysides;

[0008] Based on the standard scheduling method, schedule all ships to be scheduled in the port.

[0009] Further, the scheduling analysis method comprises:

[0010] Based on digital twinning, obtain the virtual models of all quaysides in the port, and mark them as virtual quayside model XA1 to virtual quayside model XA c ;

[0011] For any virtual quayside model XA v , obtain the positions of all containers in the ship and the weights of all containers in the scheduling analysis data, and sequentially obtain the time for the virtual quayside model XA v to perform loading and unloading operations on all containers.

[0012] Further, the scheduling analysis method further comprises:

[0013] A space coordinate system is established and is denoted as a loading and unloading analysis coordinate system, wherein 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; the straight-line distance between each container and the virtual quay crane model XA v is obtained based on the position of all the containers, and the weight of all the containers is taken as the abscissa, the straight-line distance between all the containers and the virtual quay crane model XA v is taken as the ordinate, and the virtual quay crane model XA v is taken as the Z axis coordinate, the points corresponding to all the containers are obtained in the loading and unloading analysis coordinate system, and are denoted as loading and unloading points;

[0014] a curve obtained by fitting all the loading and unloading points is denoted as a virtual loading and unloading curve XZ v ;

[0015] all the virtual quay crane models XA are obtained based on the virtual loading and unloading curve XZ obtained based on the scheduling analysis data, and all the virtual loading and unloading curves are placed in the same loading and unloading analysis coordinate system α.

[0016] Further, the scheduling analysis method further comprises:

[0017] all the containers in the scheduling analysis data are denoted as simulation containers, and the loading and unloading screening processing is sequentially performed on all the simulation containers; the loading and unloading screening processing comprises: obtaining the points corresponding to the simulation containers in all the virtual loading and unloading curves XZ in the loading and unloading analysis coordinate system α, and taking the point with the minimum Z axis coordinate in all the points as the high-efficiency point of the simulation container.

[0018] Further, the scheduling analysis method further comprises:

[0019] the high-efficiency points of all the simulation containers are obtained, and the first parameter obtaining processing is sequentially performed on all the virtual quay crane models XA; the first parameter obtaining processing comprises: for the virtual loading and unloading curve XZ corresponding to the virtual quay crane model XA: when there is one high-efficiency point in the virtual loading and unloading curve XZ, the abscissa and the ordinate of the high-efficiency point in the virtual loading and unloading curve XZ are taken as the preferred loading and unloading weight and the preferred loading and unloading distance of the virtual quay crane model XA, respectively;

[0020] when there are multiple high-efficiency points in the virtual loading and unloading curve XZ, the abscissa and the ordinate of the high-efficiency point with the minimum Z axis coordinate are taken as the preferred loading and unloading weight and the preferred loading and unloading distance of the virtual quay crane model XA, respectively, and the high-efficiency points other than the high-efficiency point with the minimum Z axis coordinate are all taken as secondary-efficiency points;

[0021] when there is no high-efficiency point in the virtual loading and unloading curve XZ, the virtual quay crane model XA is taken as a to-be-determined quay crane model.

[0022] Further, the scheduling analysis method further comprises:

[0023] When the first parameter acquisition process is used for all virtual quay crane models XA and there is a pending quay crane model, sequentially perform a second parameter acquisition process on all pending quay crane models, and the second parameter acquisition process is: respectively acquiring the distances between all secondary efficiency points and the virtual loading and unloading curve XZ corresponding to the pending quay crane model, and recording the secondary efficiency point corresponding to the shortest distance in all distances as the high efficiency point of the pending quay crane model;

[0024] The abscissa and ordinate of the high efficiency point of the pending quay crane model are recorded as the preferred loading and unloading weight and the preferred loading and unloading distance of the pending quay crane model, respectively. When any one secondary efficiency point α is recorded as a high efficiency point, the secondary efficiency point α is excluded from all secondary efficiency points.

[0025] Further, the scheduling analysis method further comprises:

[0026] The preferred loading and unloading weight and the preferred loading and unloading distance corresponding to all virtual quay crane models XA are recorded as the priority loading and unloading parameters of the quay crane corresponding to all virtual quay crane models XA.

[0027] Further, the cargo data of a single ship in the optimization analysis data is acquired, and a digital port model is established based on digital twinning; a preliminary scheduling method is used to simulate scheduling of the gantry crane based on the priority loading and unloading parameters and the cargo data, the preliminary scheduling method is optimized based on the results of the simulation scheduling, and a standard scheduling method is obtained, comprising:

[0028] The loading and unloading data of a plurality of ports for a single ship is acquired and recorded as optimization analysis data; for any one optimization analysis data, the scheduling time of all containers in the optimization analysis data is acquired and recorded as standard scheduling time BD1 to standard scheduling time BD n The total time of all containers in the optimization analysis data is recorded as the standard total time, wherein n is the number of containers in the ship in the optimization analysis data.

[0029] The cargo data of a single ship in the optimization analysis data is acquired;

[0030] A model corresponding to a single ship is established based on digital twinning and the cargo data of a single ship, and a digital port model corresponding to a port is established based on all virtual quay crane models XA, and is recorded as a digital port model.

[0031] Further, the cargo data of a single ship in the optimization analysis data is acquired, and a digital port model is established based on digital twinning; a preliminary scheduling method is used to simulate scheduling of the gantry crane based on the priority loading and unloading parameters and the cargo data, the preliminary scheduling method is optimized based on the results of the simulation scheduling, and a standard scheduling method is obtained, further comprising:

[0032] The preliminary scheduling method is: based on the scheduling sequence, the sequence of all containers of a single ship being loaded and unloaded is obtained, for any container scheduled based on the loading and unloading sequence: obtain the virtual quay crane model XA corresponding to the priority loading and unloading parameter with the smallest associated difference value of the cargo data of the container in the priority loading and unloading parameter, and record it as the priority quay crane of the container, wherein the associated difference value 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 parameter corresponding to the virtual quay crane model XA, and l2 is the preferred loading and unloading distance in the priority loading and unloading parameter corresponding to the virtual quay crane model XA;

[0033] Obtain the preferred quay crane of all containers, and sequentially use the preferred quay crane of each container to simulate the loading and unloading scheduling of all containers, and record the scheduling time as simulation scheduling time MD1 to simulation scheduling time MD n Record the total time of all containers being scheduled as simulation total time;

[0034] When the simulation total time is greater than or equal to the standard total time, or the simulation total time is less than the standard total time and there is any one container whose simulation scheduling time MD is greater than the standard scheduling time BD, for any container whose simulation scheduling time MD is greater than the standard scheduling time BD, record the preferred quay crane of the container as the to-be-adjusted quay crane, and adjust the priority loading and unloading parameter corresponding to the to-be-adjusted quay crane to the cargo data of the container; after adjusting the priority loading and unloading parameter of all to-be-adjusted quay cranes, re-obtain an optimization analysis data and perform analysis;

[0035] When the simulation total time is less than the standard total time and the simulation scheduling time MD of all containers is less than or equal to the standard scheduling time BD, stop obtaining the optimization analysis data, and record the preliminary adjustment method as the standard scheduling method.

[0036] Further, based on the standard scheduling method, scheduling all to-be-scheduled ships in the port includes:

[0037] When there are to-be-scheduled ships in the port, based on the latest obtained priority loading and unloading parameters of all quay cranes and the standard scheduling method, the to-be-scheduled ships are sequentially scheduled.

[0038] The application first obtains a port's loading and unloading data of a single ship randomly based on existing loading and unloading data, and records the data as scheduling analysis data; analyzes all quays using a scheduling analysis method, and obtains the priority loading and unloading parameters of each quay based on the result; then obtains the loading and unloading data of multiple ports for a single ship, and records the data as optimization analysis data, which has the advantage that, by obtaining the scheduling analysis data and the priority loading and unloading parameters, the loading and unloading parameters corresponding to each quay can be obtained after analyzing only the loading and unloading data of one port, so that when the number of quays in the port is large and the number of containers in the ship is large, the legal solution, i.e., the priority loading and unloading parameters, can still be obtained at a relatively fast speed, which helps to improve the acquisition speed of the standard scheduling method when the standard scheduling method is obtained by optimizing the analysis data, so as to reduce the acquisition time of the optimal scheduling scheme after the scheduling analysis method is executed and reduce the influence on the actual port loading and unloading scheduling efficiency;

[0039] The application also obtains the cargo data of a single ship in the optimization analysis data, and establishes a digital port model based on digital twinning; simulates the scheduling of the quays using a preliminary scheduling method based on the priority loading and unloading parameters and the cargo data, optimizes the preliminary scheduling method based on the result of the simulation scheduling, and obtains the standard scheduling method; finally, schedules all the ships to be scheduled in the port based on the standard scheduling method, which has the advantage that, by establishing the digital port model and simulating the scheduling of the quays using the preliminary scheduling method, the optimization efficiency of the priority loading and unloading parameters of each quay can be improved when the priority loading and unloading parameters are optimized, so as to improve the optimization efficiency of the preliminary scheduling method, and at the same time, the standard scheduling method obtained subsequently can be ensured to be able to adapt to the scheduling capacity of the quays in the actual port when the quays in the port are scheduled, so as to effectively improve the loading and unloading efficiency and the scheduling efficiency of the quays. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The step flow chart of the method of the application;

[0041] Figure 2 The acquisition schematic diagram of the high-efficiency point of the application;

[0042] Figure 3 The acquisition schematic diagram of the preferred loading and unloading weight and the preferred loading and unloading distance of the application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0044] Embodiment 1, please refer to Figure 1 As shown in the figure, the application provides a smart port loading and unloading planning resource allocation and scheduling analysis method, including the following steps:

[0045] Step S1, based on the existing loading and unloading data, randomly obtain a port's loading and unloading data for a single ship, and mark it as scheduling analysis data; use the scheduling analysis method to analyze all quays, and obtain the priority loading and unloading parameters of each quay based on the results;

[0046] The scheduling analysis method includes: step S101, based on digital twinning, obtain the virtual models corresponding to all quays in the port, and mark them as virtual quay model XA1 to virtual quay model XA c ;

[0047] Step S102, for any virtual quay model XA v , obtain the positions of all containers in the ship and the weights of all containers in the scheduling analysis data, and sequentially obtain the time when the virtual quay model XA v performs loading and unloading operations on all containers;

[0048] In the specific implementation process, by obtaining the time when the virtual quay model XA v performs loading and unloading operations on all containers, the scheduling data when the virtual quay model XA v schedules containers of different positions and different weights can be obtained, which helps to obtain the weight and distance of the containers most suitable for scheduling by the virtual quay model XA v in subsequent analysis, so that the obtained priority loading and unloading parameters are more accurate;

[0049] Step S103, establish a space coordinate system, and mark it as a loading and unloading analysis coordinate system, wherein 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 straight line distance between each container and the virtual quay model XA v based on the position of all containers, and take the weight of all containers as the horizontal coordinate, the straight line distance between all containers and the virtual quay model XA v as the vertical coordinate, and the time when the virtual quay model XA v performs loading and unloading operations on all containers as the Z-axis coordinate, obtain the points corresponding to all containers in the loading and unloading analysis coordinate system, and mark them as loading and unloading points;

[0050] In the implementation process, such as in a data analysis, the weight of a container A in the analysis data is 4t, the distance between container A and virtual quay crane model XA is 20m, and the loading and unloading time of container A by virtual quay crane model XA is 5min, then (4, 20, 5) can be recorded as the loading and unloading point corresponding to container A; by obtaining all loading and unloading points and obtaining 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;

[0051] Step S104, the curve obtained by fitting all loading and unloading points is recorded as virtual loading and unloading curve XZ v ;

[0052] Step S105, obtain the virtual loading and unloading curve XZ of all virtual quay crane models XA based on the scheduling analysis data, and put all virtual loading and unloading curves into the same loading and unloading analysis coordinate system α;

[0053] Step S106, all containers in the scheduling analysis data are recorded as simulation containers, and loading and unloading screening processing is performed on all simulation containers in turn; the loading and unloading screening processing includes: obtaining the points corresponding to the simulation 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 in all points as the high-efficiency point of the simulation container;

[0054] In the implementation process, such as in a data analysis, a simulation container is scheduled by 5 virtual quay crane models XA, and the corresponding longitudinal coordinates of all points are 4min, 5min, 7min, 10min and 2min respectively, then the point corresponding to 2min can be recorded as the high-efficiency point of the simulation container, that is, the point for the fastest speed loading and unloading of the simulation container;

[0055] In the implementation process, such as in a data analysis, the obtained loading and unloading analysis coordinate system α is as shown in Figure 2 , wherein the curves ZF1 to ZF5 are virtual loading and unloading curves XZ, the points Z1 to Z5 are the points corresponding to a simulation container in all virtual loading and unloading curves XZ, and Z3 is obtained by analysis with the smallest longitudinal coordinate, then Z3 can be taken as the high-efficiency point of the simulation container;

[0056] Step S107, obtain the high-efficiency points of all simulation containers, and perform first parameter obtaining processing on all virtual quay crane models XA in turn, the first parameter obtaining processing includes: for the virtual loading and unloading curve XZ corresponding to the virtual quay crane model XA: when there is a high-efficiency point in the virtual loading and unloading curve XZ, the horizontal coordinate and the longitudinal coordinate of the high-efficiency point in the virtual loading and unloading curve XZ are recorded as the preferred loading and unloading weight and the preferred loading and unloading distance of the virtual quay crane model XA respectively;

[0057] In the implementation process, when there is an efficient point 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, and thus the weight of the container and the distance between the virtual quay crane model XA, that is, the abscissa and the ordinate of the efficient point, can be recorded as the preferred loading and unloading weight and the preferred loading and unloading distance of the virtual quay crane model XA, so as to obtain the preferred loading and unloading parameters of the quay crane corresponding to the virtual quay crane model XA; and when there are multiple efficient points in the virtual loading and unloading curve XZ, the multiple efficient 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.

[0058] In step S108, when there are multiple efficient points in the virtual loading and unloading curve XZ, the abscissa and the ordinate of the efficient point with the minimum Z-axis coordinate are recorded as the preferred loading and unloading weight and the preferred loading and unloading distance of the virtual quay crane model XA, and the efficient points other than the efficient point with the minimum Z-axis coordinate are recorded as sub-efficient points.

[0059] In the implementation process, for example, in one data analysis, the obtained loading and unloading analysis coordinate system a is as shown in FIG. 8. Figure 3 As shown in FIG. 8, the curve ZF0 is the virtual loading and unloading curve XZ, and the points ZZ1 and ZZ2 in the curve ZF0 are both efficient points. Through data processing, it is obtained that the Z-axis coordinate of the point ZZ2 is the minimum, and thus it indicates that the virtual quay crane model XA corresponding to the curve ZF0 has a higher loading and unloading efficiency for the simulation container corresponding to the point ZZ2, and thus the abscissa and the ordinate of the point ZZ2 can be recorded as the preferred loading and unloading weight and the preferred loading and unloading distance of the virtual quay crane model XA.

[0060] In step S109, when there is no efficient point in the virtual loading and unloading curve XZ, the virtual quay crane model XA is recorded as a to-be-determined quay crane model.

[0061] In step S110, when the first parameter acquisition processing is performed on all virtual quay crane models XA and there is a to-be-determined quay crane model, the second parameter acquisition processing is performed on all to-be-determined quay crane models in sequence. The second parameter acquisition processing is to respectively acquire the distances between all sub-efficient points and the virtual loading and unloading curve XZ corresponding to the to-be-determined quay crane model, and record the sub-efficient point corresponding to the shortest distance among all distances as the efficient point of the to-be-determined quay crane model.

[0062] In step S111, the abscissa and the ordinate of the efficient point of the to-be-determined quay crane model are recorded as the preferred loading and unloading weight and the preferred loading and unloading distance of the to-be-determined quay crane model, and when any one sub-efficient point a is recorded as the efficient point, the sub-efficient point a is excluded from all sub-efficient points.

[0063] Step S112, record the preferred loading and unloading weight and distance corresponding to all virtual quay crane models XA as the priority loading and unloading parameters of the quay crane corresponding to all virtual quay crane models XA;

[0064] In the specific implementation process, by obtaining the priority loading and unloading parameters, it is helpful to improve the acquisition speed of the standard scheduling method when subsequently obtaining the standard scheduling method through optimization analysis data, so as to reduce the acquisition time of the preferred scheduling scheme after the scheduling analysis method is executed and reduce the influence on the actual port loading and unloading scheduling efficiency.

[0065] Step S2, obtain the loading and unloading data of a plurality of 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 twinning; simulate the scheduling of the crane bridge based on the priority loading and unloading parameters and the cargo data using a preliminary scheduling method, optimize the preliminary scheduling method based on the results of the simulation scheduling, and obtain a standard scheduling method, wherein the cargo data includes the weight of each container in the ship and the straight-line distance between each container in the ship and all quay cranes;

[0066] Step S2 includes: step S201, obtaining the loading and unloading data of a plurality of ports for a single ship, and recording it as optimization analysis data; for any one optimization analysis data, obtaining the scheduling time of all containers in the optimization analysis data, and recording it as standard scheduling time BD1 to standard scheduling time BDn respectively; n Record the total time of all containers in the optimization analysis data as the standard total time, wherein n is the number of containers in the ship in the optimization analysis data that are scheduled;

[0067] Step S202, obtain the cargo data of a single ship in the optimization analysis data;

[0068] Step S203, based on digital twinning and the cargo data of a single ship, establish a model corresponding to a single ship, and based on all virtual quay crane models XA, establish a digital twinning model corresponding to a port, and record it as a digital port model;

[0069] Step S204, the preliminary scheduling method is: step S2041, based on the scheduling sequence, the sequence of all containers in a single ship being loaded and unloaded is obtained, for any one container based on the loading and unloading sequence: obtain the virtual shore crane model XA corresponding to the priority loading and unloading parameter with the smallest associated difference value of the cargo data of the container, and record it as the priority shore crane of the container, wherein the associated difference value is |t1-t2|*|l1-l2|, t1 is the weight of the container, l1 is the distance between the container and the virtual shore crane model XA, t2 is the preferred loading and unloading weight in the priority loading and unloading parameter corresponding to the virtual shore crane model XA, and l2 is the preferred loading and unloading distance in the priority loading and unloading parameter corresponding to the virtual shore crane model XA;

[0070] In the specific implementation process, for example, in one data processing, the weight of one container and the distance between the container and the virtual shore crane model XA are 5t and 20m respectively, and the priority loading and unloading parameters corresponding to the virtual shore crane model XA are 4t and 10m, then by calculation, the associated difference value is 10; when the smallest associated difference value corresponds to multiple virtual shore crane models XA, the first obtained virtual shore crane model XA is taken as the priority shore crane of the container based on the time sequence;

[0071] Step S2042, obtain the preferred shore crane of all containers, and use the preferred shore crane of each container to simulate the loading and unloading scheduling of all containers in turn, and record the scheduling time as simulation scheduling time MD1 to simulation scheduling time MD n The total time of all containers being scheduled is recorded as the simulation total time;

[0072] Step S205, when the simulation total time is greater than or equal to the standard total time, or the simulation total time is less than the standard total time and there is any one container whose simulation scheduling time MD is greater than the standard scheduling time BD, for any one container whose simulation scheduling time MD is greater than the standard scheduling time BD, record the preferred shore crane of the container as the adjusted shore crane, and adjust the priority loading and unloading parameter corresponding to the adjusted shore crane to the cargo data of the container; after adjusting the priority loading and unloading parameter of all adjusted shore cranes, reacquire an optimization analysis data and analyze it;

[0073] In the specific implementation process, for example, in one data analysis, the simulation total time obtained is 60min, the standard total time is 70min, and there is one container B whose simulation scheduling time is 12min and the standard scheduling time is 10min, which means that the preferred loading and unloading parameter of the preferred shore crane corresponding to the container B needs to be optimized, so the priority loading and unloading parameter corresponding to the adjusted shore crane can be adjusted to the cargo data of the container to improve the scheduling efficiency when the adjusted shore crane schedules the containers with the same cargo data as the container B;

[0074] Step S206, when the simulation total time is less than the standard total time and the simulation scheduling time MD of all containers is less than or equal to the standard scheduling time BD, the acquisition of the optimization analysis data is stopped, and the preliminary adjustment method is recorded as the standard scheduling method.

[0075] In the specific implementation process, after the preliminary scheduling method is recorded as the standard scheduling method, the simulation loading and unloading scheduling in the standard scheduling method is adjusted to the actual loading and unloading scheduling, so as to realize the subsequent scheduling of the ships to be scheduled in the port.

[0076] Step S3, scheduling all the ships to be scheduled in the port based on the standard scheduling method;

[0077] Step S3 includes: when there are ships to be scheduled in the port, the ships to be scheduled are sequentially scheduled based on the latest acquired priority loading and unloading parameters of all the quayside cranes and the standard scheduling method.

[0078] In embodiment 2, the application further provides a computer program product, which comprises a computer program stored on a computer readable storage medium, and the computer program comprises program instructions, when the program instructions are executed by a computer, the computer can execute the intelligent port loading and unloading planning resource allocation and scheduling analysis method provided by each method, and the method comprises the following steps: first, based on the existing loading and unloading data, randomly acquiring the loading and unloading data of a single ship of a port, and recording as scheduling analysis data; using the scheduling analysis method to analyze all the quayside cranes, and acquiring the priority loading and unloading parameters of each quayside crane based on the result; then, acquiring the loading and unloading data of a single ship of multiple ports, and recording as optimization analysis data; acquiring the cargo data of the single ship in the optimization analysis data, and establishing a digital port model based on digital twinning; using a preliminary scheduling method to simulate the scheduling of the quayside cranes based on the priority loading and unloading parameters and the cargo data, optimizing the preliminary scheduling method based on the result of the simulation scheduling, and obtaining a standard scheduling method; finally, scheduling all the ships to be scheduled in the port based on the standard scheduling method.

[0079] In the embodiment 3, the application further provides a computer readable storage medium, and the application provides a storage medium having a computer program stored thereon, the computer program is executed by a processor to run the steps of the above-mentioned intelligent port loading and unloading planning resource allocation and scheduling analysis method to realize the following functions: first, based on the existing loading and unloading data, a port's loading and unloading data of a single ship is randomly obtained and recorded as scheduling analysis data; all quays are analyzed by using the scheduling analysis method, and the priority loading and unloading parameters of each quay are obtained based on the results; then, the loading and unloading data of a plurality of ports for a single ship is obtained and recorded as optimization analysis data; the cargo data of the single ship in the optimization analysis data is obtained, and a digital port model is established based on digital twinning; the hoist bridge is simulated and scheduled by using the preliminary scheduling method based on the priority loading and unloading parameters and the cargo data, the preliminary scheduling method is optimized based on the results of the simulation scheduling, and a standard scheduling method is obtained; finally, all the ships to be scheduled in the port are scheduled based on the standard scheduling method.

[0080] Through the description of the above embodiments, the embodiments of the application can be provided as a method, a system or a computer program product. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method described in each embodiment or some parts of the embodiment.

[0081] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are only illustrative. For example, the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of modules or units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed elements can be indirect coupling or communication connection through some communication interfaces, systems, modules or units, which can be electrical, mechanical or other forms.

[0082] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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 smart 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 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; 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; 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 ; Obtain the virtual loading and unloading curves XZ obtained by all virtual quay crane models XA based on the scheduling analysis data, and put all virtual loading and unloading curves into the same loading and unloading analysis coordinate system α; Scheduling analysis methods also include: All containers in the scheduling analysis data are recorded as simulated containers, and loading and unloading screening is performed on all simulated containers in sequence. The loading and unloading screening process includes: obtaining all points corresponding to the simulated containers in the virtual loading and unloading curve 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; 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; 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 proposed quay crane model are recorded 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. 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.

2. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 1 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.

3. The method for resource allocation and scheduling analysis of intelligent port loading and unloading planning according to claim 2 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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