An unmanned container truck dispatching method, system and medium for changing the operating position of a bridge crane

Abstract

The present invention relates to a method, system, and medium for scheduling unmanned container trucks for changing operating positions of gantry cranes. The method comprises: M1. receiving operating tasks issued by a TOS, obtaining track point data information of the unmanned container truck's historical operating tasks, and obtaining the data information of the unmanned container truck's location points and the location point data information of the quay crane replacement operation in real time; M2. constructing a track point model for the unmanned container truck's operating tasks based on the data information of the unmanned container truck's location points and the location point data information of the quay crane replacement operation, predicting the unmanned container truck's driving track points, and obtaining the predicted driving track point data information of the unmanned container truck. The present invention not only realizes automated scheduling when the quay crane changes operating positions, but also effectively reduces the number of manual interventions, lowers the risk of safety accidents, shortens the single-turn operation time of the container truck, and improves the efficiency of sorting operations.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned container trucks, and in particular to an unmanned container truck scheduling method, system and medium for changing the operating position of a bridge crane. Background Art

[0002] During actual operations, the bridge crane equipment may change its operating position. When the bridge crane changes its operating position, the container truck cannot sense the change and will continue to the original operating position. When the container truck arrives at the operating position, on-site personnel need to manually adjust the container truck to the correct bridge crane operating position. For container trucks that have not yet reached the operating position while driving, the operator needs to manually issue slow-down commands to the trucks one by one. After the operating position change is completed, the trucks need to re-route and release the slow-down command to proceed to the correct operating position.

[0003] During busy operations, when there are many operating vehicles, manual adjustment of the container truck will affect the movement of other vehicles, which not only increases the single-circuit operation time of the unmanned container truck, but also affects the port's operating efficiency. Summary of the Invention

[0004] In view of the above problems, the present invention provides an unmanned container truck scheduling method, system and medium for changing the operating position of a bridge crane. It can not only realize the automated scheduling when the quay crane changes its operating position, but also effectively reduce the number of manual interventions, reduce the risk of safety accidents, reduce the single-circuit operation time of the container truck, and improve the sorting efficiency.

[0005] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions:

[0006] An unmanned container truck dispatching method for changing the operating position of a bridge crane, the method comprising:

[0007] M1. Receives the operation tasks issued by TOS, obtains the track point data information of the historical operation tasks of the unmanned container truck, and obtains the location data information of the unmanned container truck and the location data of the quay crane replacement operation in real time;

[0008] M2. Based on the data information of the location point of the unmanned container truck and the location point data information of the quay crane replacement operation, a trajectory point model of the unmanned container truck operation task is constructed, and the driving trajectory point of the unmanned container truck is predicted to obtain the predicted driving trajectory point data information of the unmanned container truck;

[0009] M3. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the unmanned container truck's historical operation tasks, the driving trajectory points of the unmanned container truck are optimized using a salp optimization algorithm based on multidimensional scaling analysis to obtain optimized driving trajectory point data information of the unmanned container truck;

[0010] M4. Based on the optimized driving trajectory data information of the unmanned truck, a simulation scheduling function P of the unmanned truck is established to control and adjust the scheduling of the unmanned truck to obtain the scheduling data information of the unmanned truck.

[0011] Furthermore, in step M2, constructing a trajectory point model for the unmanned container truck operation task includes:

[0012] M21. Based on the location data information of the quay crane replacement operation, establish a dynamic programming function Q for the quay crane replacement operation.

[0013]

[0014] Among them, x is the location point data information of the quay crane replacement operation, α1, α2, and α3 are the dynamic planning factors of the quay crane replacement operation, which characterize the dynamic change law of the location point of the quay crane replacement operation and obtain the location point data information of the quay crane replacement operation after dynamic planning;

[0015] M22. Based on the position point data information of the quay crane replacement operation after dynamic planning and the position point data information of the unmanned container truck, establish an unmanned container truck driving trajectory point prediction function W,

[0016]

[0017] Among them, y1 is the location point data information of the quay crane replacement operation after dynamic planning, y2 is the location point data information of the unmanned container truck, β1, β2 and β3 are the prediction factors of the driving trajectory points of the unmanned container truck;

[0018] M23. Based on the driving trajectory point prediction function W of the unmanned container truck, the driving trajectory points of the unmanned container truck are predicted to obtain the predicted driving trajectory point data information of the unmanned container truck.

[0019] Furthermore, the dynamic planning factors α1, α2 and α3 of the quay crane replacement operation are:

[0020] Among them, x is the location point data information of the quay crane replacement operation.

[0021] Furthermore, the constraints of the prediction factors β1, β2 and β3 of the driving trajectory points of the unmanned container truck are:

[0022]

[0023] Furthermore, in step M3, the optimization of the driving trajectory points of the unmanned container truck using the salp optimization algorithm based on multidimensional scaling analysis includes:

[0024] M31. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the historical operation task of the unmanned container truck, a multidimensional scaling analysis function R of the driving trajectory point of the unmanned container truck is established.

[0025]

[0026] Among them, z1 is the predicted driving trajectory point data information of the unmanned container truck, z2 is the trajectory point data information of the unmanned container truck's historical operation tasks, δ1, δ2, and δ3 are the regression constant parameters of the unmanned container truck's driving trajectory points. The driving trajectory points of the unmanned container truck are fused to obtain the fused dot matrix data information of the unmanned container truck's driving trajectory points;

[0027] M32. Based on the fused dot matrix data of the driving trajectory of the unmanned truck, the salp population is initialized, the population parameters are determined, and the initialized salp population data is obtained;

[0028] M33. Based on the initialized salp population data information, establish a position update function S,

[0029]

[0030] Among them, r is the initialized salp population data information, γ1, γ2 and γ3 are the optimization constant parameters of the population, and the driving trajectory points of the unmanned container truck are optimized to obtain the optimized driving trajectory point data information of the unmanned container truck.

[0031] Furthermore, the constraint function f of the regression constant parameters δ1, δ2 and δ3 of the unmanned truck driving trajectory point is:

[0032]

[0033] Among them, the value range of the constraint function f is (3,5).

[0034] Furthermore, the constraints of the optimization constant parameters γ1, γ2 and γ3 of the population are:

[0035] In order to achieve the above-mentioned and other related objectives, the present invention further provides a system for implementing any of the unmanned container truck dispatching methods for changing the operating position of a bridge crane, the system comprising:

[0036] The data acquisition module is used to obtain the trajectory point data information of the historical operation tasks of the unmanned container truck, and obtain the location point data information of the unmanned container truck and the location point data information of the quay crane replacement operation in real time;

[0037] The unmanned container truck trajectory point prediction module is connected to the data acquisition module and is used to construct a trajectory point model of the unmanned container truck's operation task, predict the unmanned container truck's driving trajectory points, and obtain the predicted unmanned container truck's driving trajectory point data information;

[0038] The optimization module is connected to the trajectory point prediction module of the unmanned container truck and is used to optimize the driving trajectory points of the unmanned container truck using a salp optimization algorithm based on multidimensional scaling analysis; the control and adjustment module is connected to the optimization module and is used to establish a simulation scheduling function P of the unmanned container truck to control and adjust the scheduling of the unmanned container truck.

[0039] Furthermore, the system also includes a display module connected to the control and adjustment module for displaying the driving path of the unmanned container truck in real time.

[0040] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the unmanned container truck scheduling methods for changing the working position of the bridge crane.

[0041] The present invention has the following positive effects:

[0042] 1. The present invention predicts the driving trajectory points of unmanned container trucks by constructing a trajectory point model of the operating tasks of unmanned container trucks, and optimizes the driving trajectory points of unmanned container trucks by combining the Salp Insipid optimization algorithm based on multidimensional scaling analysis to obtain optimized driving trajectory point data information of unmanned container trucks. It can not only dynamically schedule the unmanned container trucks according to the change of the bridge crane operating position, thereby improving the efficiency of the unmanned container truck operation, but also effectively reduce the number of manual interventions, reduce the risk of safety accidents, reduce the single-circuit operation time of the container trucks, and improve the operation efficiency of the sorting.

[0043] 2. The present invention establishes a simulation scheduling function P for unmanned container trucks to control and adjust the scheduling of unmanned container trucks. This not only realizes the automatic scheduling when the quay crane changes its operating position, but also can accurately control and adjust the scheduling of unmanned container trucks, thereby reducing the energy consumption of unmanned container trucks. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Schematic diagram of the method flow of the present invention;

[0045] Figure 2 A schematic diagram of the process of constructing a trajectory point model for an unmanned container truck operation task according to the present invention;

[0046] Figure 3 Schematic diagram of the process of the salp optimization algorithm based on multidimensional scaling analysis of the present invention. DETAILED DESCRIPTION

[0047] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0048] Example 1: Figure 1 As shown, a method for dispatching unmanned container trucks for changing the working position of a bridge crane is provided, the method comprising:

[0049] M1. Receives the operation tasks issued by TOS, obtains the track point data information of the historical operation tasks of the unmanned container truck, and obtains the location data information of the unmanned container truck and the location data of the quay crane replacement operation in real time;

[0050] M2. Based on the data information of the location point of the unmanned container truck and the location point data information of the quay crane replacement operation, a trajectory point model of the unmanned container truck operation task is constructed, and the driving trajectory point of the unmanned container truck is predicted to obtain the predicted driving trajectory point data information of the unmanned container truck;

[0051] M3. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the unmanned container truck's historical operation tasks, the driving trajectory points of the unmanned container truck are optimized using a salp optimization algorithm based on multidimensional scaling analysis to obtain optimized driving trajectory point data information of the unmanned container truck;

[0052] M4. Based on the optimized driving trajectory data information of the unmanned truck, a simulation scheduling function P of the unmanned truck is established to control and adjust the scheduling of the unmanned truck to obtain the scheduling data information of the unmanned truck.

[0053] In this embodiment, if Figure 2 As shown, in step M2, the construction of the trajectory point model of the unmanned container truck operation task includes:

[0054] M21. Based on the location data information of the quay crane replacement operation, establish a dynamic programming function Q for the quay crane replacement operation.

[0055]

[0056] Among them, x is the location point data information of the quay crane replacement operation, α1, α2, and α3 are the dynamic planning factors of the quay crane replacement operation, which characterize the dynamic change law of the location point of the quay crane replacement operation and obtain the location point data information of the quay crane replacement operation after dynamic planning;

[0057] M22. Based on the position point data information of the quay crane replacement operation after dynamic planning and the position point data information of the unmanned container truck, establish an unmanned container truck driving trajectory point prediction function W,

[0058]

[0059] Among them, y1 is the location point data information of the quay crane replacement operation after dynamic planning, y2 is the location point data information of the unmanned container truck, β1, β2 and β3 are the prediction factors of the driving trajectory points of the unmanned container truck;

[0060] M23. Based on the driving trajectory point prediction function W of the unmanned container truck, the driving trajectory points of the unmanned container truck are predicted to obtain the predicted driving trajectory point data information of the unmanned container truck.

[0061] In this embodiment, the dynamic planning factors α1, α2 and α3 of the quay crane replacement operation are:

[0062]

[0063] Among them, x is the location point data information of the quay crane replacement operation.

[0064] In this embodiment, the constraints of the prediction factors β1, β2 and β3 of the driving trajectory points of the unmanned container truck are:

[0065]

[0066] Example 2: Based on the unmanned container truck scheduling method for changing the operating position of a bridge crane in Example 1, the present invention is further illustrated and described below.

[0067] like Figure 1 As shown, a method for dispatching unmanned container trucks for changing the working position of a bridge crane is provided, the method comprising:

[0068] M1. Receives the operation tasks issued by TOS, obtains the track point data information of the historical operation tasks of the unmanned container truck, and obtains the location data information of the unmanned container truck and the location data of the quay crane replacement operation in real time;

[0069] M2. Based on the data information of the location point of the unmanned container truck and the location point data information of the quay crane replacement operation, a trajectory point model of the unmanned container truck operation task is constructed, and the driving trajectory point of the unmanned container truck is predicted to obtain the predicted driving trajectory point data information of the unmanned container truck;

[0070] M3. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the unmanned container truck's historical operation tasks, the driving trajectory points of the unmanned container truck are optimized using a salp optimization algorithm based on multidimensional scaling analysis to obtain optimized driving trajectory point data information of the unmanned container truck;

[0071] M4. Based on the optimized driving trajectory data information of the unmanned truck, a simulation scheduling function P of the unmanned truck is established to control and adjust the scheduling of the unmanned truck to obtain the scheduling data information of the unmanned truck.

[0072] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the driving trajectory points of the unmanned container truck using the salp optimization algorithm based on multidimensional scaling analysis includes:

[0073] M31. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the historical operation task of the unmanned container truck, a multidimensional scaling analysis function R of the driving trajectory point of the unmanned container truck is established.

[0074]

[0075] Among them, z1 is the predicted driving trajectory point data information of the unmanned container truck, z2 is the trajectory point data information of the unmanned container truck's historical operation tasks, δ1, δ2, and δ3 are the regression constant parameters of the unmanned container truck's driving trajectory points. The driving trajectory points of the unmanned container truck are fused to obtain the fused dot matrix data information of the unmanned container truck's driving trajectory points;

[0076] M32. Based on the fused dot matrix data of the driving trajectory of the unmanned truck, the salp population is initialized, the population parameters are determined, and the initialized salp population data is obtained;

[0077] M33. Based on the initialized salp population data information, establish a position update function S,

[0078]

[0079] Among them, r is the initialized salp population data information, γ1, γ2 and γ3 are the optimization constant parameters of the population, and the driving trajectory points of the unmanned container truck are optimized to obtain the optimized driving trajectory point data information of the unmanned container truck.

[0080] In this embodiment, the constraint function f of the regression constant parameters δ1, δ2 and δ3 of the unmanned truck driving trajectory point is:

[0081]

[0082] Among them, the value range of the constraint function f is (3,5).

[0083] In this embodiment, the constraints of the optimization constant parameters γ1, γ2 and γ3 of the population are:

[0084]

[0085] In this embodiment, the present invention provides a system for implementing any of the above-mentioned unmanned container truck scheduling methods for changing the operating position of a bridge crane, the system comprising:

[0086] The data acquisition module is used to obtain the trajectory point data information of the historical operation tasks of the unmanned container truck, and obtain the location point data information of the unmanned container truck and the location point data information of the quay crane replacement operation in real time;

[0087] The unmanned container truck trajectory point prediction module is connected to the data acquisition module and is used to construct a trajectory point model of the unmanned container truck's operation task, predict the unmanned container truck's driving trajectory points, and obtain the predicted unmanned container truck's driving trajectory point data information;

[0088] The optimization module is connected to the trajectory point prediction module of the unmanned container truck and is used to optimize the driving trajectory points of the unmanned container truck using a salp optimization algorithm based on multidimensional scaling analysis; the control and adjustment module is connected to the optimization module and is used to establish a simulation scheduling function P of the unmanned container truck to control and adjust the scheduling of the unmanned container truck.

[0089] In this embodiment, the system further includes a display module connected to the control and regulation module for displaying the driving path of the unmanned container truck in real time.

[0090] In this embodiment, the present invention provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the unmanned container truck scheduling methods for changing the operating position of a bridge crane.

[0091] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0092] In summary, the present invention can not only realize the automated scheduling when the quay crane changes its operating position, but also effectively reduce the number of manual interventions, reduce the risk of safety accidents, shorten the single-lap operation time of the container truck, and improve the efficiency of sorting operations.

[0093] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. An unmanned container truck dispatching method for changing the operating position of a bridge crane, characterized in that: The method comprises: M1. Receives the operation tasks issued by TOS, obtains the track point data information of the historical operation tasks of the unmanned container truck, and obtains the location data information of the unmanned container truck and the location data of the quay crane replacement operation in real time; M2. Based on the data information of the location point of the unmanned container truck and the location point data information of the quay crane replacement operation, a trajectory point model of the unmanned container truck operation task is constructed, and the driving trajectory point of the unmanned container truck is predicted to obtain the predicted driving trajectory point data information of the unmanned container truck; M3. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the unmanned container truck's historical operation tasks, the driving trajectory points of the unmanned container truck are optimized using a salp optimization algorithm based on multidimensional scaling analysis to obtain optimized driving trajectory point data information of the unmanned container truck; M4. Based on the optimized driving trajectory data information of the unmanned truck, a simulation scheduling function P of the unmanned truck is established to control and adjust the scheduling of the unmanned truck to obtain the scheduling data information of the unmanned truck.

2. The unmanned container truck dispatching method for changing the working position of the bridge crane according to claim 1 is characterized in that: In step M2, the construction of the trajectory point model of the unmanned container truck operation task includes: M21. Based on the location data information of the quay crane replacement operation, establish a dynamic programming function Q for the quay crane replacement operation. Among them, x is the location point data information of the quay crane replacement operation, α1, α2, and α3 are the dynamic planning factors of the quay crane replacement operation, which characterize the dynamic change law of the location point of the quay crane replacement operation and obtain the location point data information of the quay crane replacement operation after dynamic planning; M22. Based on the position point data information of the quay crane replacement operation after dynamic planning and the position point data information of the unmanned container truck, establish an unmanned container truck driving trajectory point prediction function W, Among them, y1 is the location point data information of the quay crane replacement operation after dynamic planning, y2 is the location point data information of the unmanned container truck, β1, β2 and β3 are the prediction factors of the driving trajectory points of the unmanned container truck; M23. Based on the driving trajectory point prediction function W of the unmanned container truck, the driving trajectory points of the unmanned container truck are predicted to obtain the predicted driving trajectory point data information of the unmanned container truck.

3. The unmanned container truck dispatching method for changing the working position of a bridge crane according to claim 2 is characterized by: The dynamic planning factors α1, α2 and α3 of the quay crane replacement operation are: Among them, x is the location point data information of the quay crane replacement operation.

4. The unmanned container truck dispatching method for changing the working position of a bridge crane according to claim 2 is characterized by: The constraints of the prediction factors β1, β2 and β3 of the driving trajectory points of the unmanned container truck are:

5. The unmanned container truck dispatching method for changing the working position of a bridge crane according to claim 1 is characterized in that: In step M3, the optimization of the driving trajectory points of the unmanned container truck using the salp optimization algorithm based on multidimensional scaling analysis includes: M31. Based on the predicted driving trajectory point data information of the unmanned container truck and the trajectory point data information of the historical operation task of the unmanned container truck, a multidimensional scaling analysis function R of the driving trajectory point of the unmanned container truck is established. Among them, z1 is the predicted driving trajectory point data information of the unmanned container truck, z2 is the trajectory point data information of the unmanned container truck's historical operation tasks, δ1, δ2, and δ3 are the regression constant parameters of the unmanned container truck's driving trajectory points. The driving trajectory points of the unmanned container truck are fused to obtain the fused dot matrix data information of the unmanned container truck's driving trajectory points; M32. Based on the fused dot matrix data of the driving trajectory of the unmanned truck, the salp population is initialized, the population parameters are determined, and the initialized salp population data is obtained; M33. Based on the initialized salp population data information, establish a position update function S, Among them, r is the initialized salp population data information, γ1, γ2 and γ3 are the optimization constant parameters of the population, and the driving trajectory points of the unmanned container truck are optimized to obtain the optimized driving trajectory point data information of the unmanned container truck.

6. The unmanned container truck dispatching method for changing the working position of a bridge crane according to claim 5 is characterized by: The constraint function f of the regression constant parameters δ1, δ2 and δ3 of the unmanned truck driving trajectory point is: Among them, the value range of the constraint function f is (3,5).

7. The unmanned container truck dispatching method for changing the working position of a bridge crane according to claim 5 is characterized by: The constraints of the optimization constant parameters γ1, γ2 and γ3 of the population are:

8. A system for implementing the unmanned container truck dispatching method for changing the working position of a bridge crane according to any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to obtain the trajectory point data information of the historical operation tasks of the unmanned container truck, and obtain the location point data information of the unmanned container truck and the location point data information of the quay crane replacement operation in real time; The unmanned container truck trajectory point prediction module is connected to the data acquisition module and is used to construct a trajectory point model of the unmanned container truck's operation task, predict the unmanned container truck's driving trajectory points, and obtain the predicted unmanned container truck's driving trajectory point data information; The optimization module is connected to the trajectory point prediction module of the unmanned container truck and is used to optimize the driving trajectory points of the unmanned container truck using a salp optimization algorithm based on multidimensional scaling analysis; the control and adjustment module is connected to the optimization module and is used to establish a simulation scheduling function P of the unmanned container truck to control and adjust the scheduling of the unmanned container truck.

9. The system according to claim 8, characterized in that: The system further comprises a display module connected to the control and regulation module for displaying the travel path of the unmanned container truck in real time.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program that is programmed or configured to execute the unmanned container truck scheduling method for changing the operating position of a bridge crane as described in any one of claims 1 to 7.

Images