A heuristic interpolation automatic scheduling method for multi-dimensional data cross-correlation

Abstract

The present invention discloses a trial interpolation automatic scheduling method for multi-dimensional data cross-correlation, including: Step S1, screening and selecting job tasks to be scheduled according to conditions, where the selected job tasks belong to one or more of the same project; Step S2, preparing basic scheduling data to obtain team, ship, and task data, as well as calculating the obtained team window period data and ship window period data; Step S3, transmitting the obtained team, ship, and task data, as well as the calculated team window period data and ship window period data to the AI scheduling algorithm for calculation until all tasks are calculated and the scheduling ends; Step S4, after the scheduling result comes out, manually intervene to adjust the scheduling result, end the scheduling, and the user confirms the scheduling result to generate a scheduling plan. The present invention improves the efficiency and quality of ship scheduling.

Description

Technical Field

[0001] The present invention relates to a trial interpolation automatic scheduling method for multi-dimensional data cross-correlation. Background Art

[0002] In the field of ocean engineering, the scheduling management of offshore construction vessels is a key task, which is directly related to project progress, cost control and resource utilization. With the development of ocean resources and the increase of ocean engineering projects, how to schedule vessels efficiently has become the focus of the industry. Currently, vessel scheduling methods are mainly divided into two categories: manual scheduling and automatic scheduling. Manual scheduling methods mainly rely on human experience for vessel scheduling, mainly based on the experience and intuition of schedulers, and optimize vessel scheduling through a series of rules and guiding principles. Automatic scheduling methods use computer technology and optimization algorithms for vessel scheduling. The main algorithms include: genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, linear programming and integer programming, etc.

[0003] The existing scheduling methods have the following deficiencies:

[0004] Manual scheduling:

[0005] Low efficiency: The process is cumbersome and time-consuming, and it is difficult to handle large-scale vessel scheduling.

[0006] Strong subjectivity: It depends on the experience and intuition of schedulers and is easily affected by personal preferences and cognitive limitations.

[0007] Limited optimization: It is difficult to find the global optimal solution and often can only obtain a local optimal or satisfactory solution.

[0008] Automatic scheduling:

[0009] High algorithm complexity: Some optimization algorithms are computationally complex and the solution time is long, making it difficult to meet the requirements of real-time scheduling.

[0010] Poor model adaptability: Existing models are difficult to fully consider various constraint conditions and dynamic change factors in actual scheduling.

[0011] Strong data dependence: It requires a large amount of accurate and real-time data support, and the data quality directly affects the scheduling effect.

[0012] No consideration of meteorological factors: Meteorological data plays a very important role in ocean engineering. Algorithms that do not consider meteorological factors are incomplete.

[0013] The current offshore construction vessel scheduling methods still have certain deficiencies in terms of practicality, optimization degree and adaptability. Therefore, a trial interpolation automatic scheduling method for multi-dimensional data cross-correlation is provided. Summary of the Invention

[0014] The purpose of the present invention is to overcome the existing defects and provide a multi-dimensional data cross-correlation heuristic interpolation automatic scheduling method to improve the efficiency and quality of ship scheduling.

[0015] The technical solution to achieve the above purpose is:

[0016] A multi-dimensional data cross-correlation heuristic interpolation automatic scheduling method, comprising:

[0017] Step S1, filtering and selecting the tasks to be scheduled according to the conditions, wherein the selected tasks belong to one or more tasks in the same project;

[0018] Step S2, prepare basic scheduling data, obtain team, ship, task data, and calculated team window period data and ship window period data;

[0019] Step S3: The obtained team, ship, and task data, as well as the calculated team window period data and ship window period data, are transmitted to the AI scheduling algorithm for calculation until all tasks are calculated and the scheduling is terminated.

[0020] Step S4: After the scheduling result comes out, the user manually adjusts the scheduling result to end the scheduling. The user confirms the scheduling result and generates a scheduling plan.

[0021] Preferably, in step S1, the operation task includes all information of the operation, including but not limited to task name, priority, task start time, latest completion time, expected working time, expected docking time, work team, expected number of maintenance personnel, whether to use a return ship, whether to dock, selected ship, selected terminal, and destination.

[0022] Preferably, the step S2 includes:

[0023] Step 21: Read the team data, terminal geographic information, meteorological data, and ship data of all tasks from the meteorological information time series database and the business database;

[0024] Step S22: Calculate the work window of the team based on the team data and the existing resource occupancy, and set the terminal geographic information data;

[0025] Step S23, calculating the ship's weather window period according to the ship's weather threshold and the weather forecast data of the destination.

[0026] Preferably, step S3 includes:

[0027] Step S31: suppose there are x tasks in total, sort the x tasks according to their priorities, and calculate the task sorting information in sequence;

[0028] Step S32, calculating the schedule according to the ship crew database into three situations, including having a ship and a crew, having / not having a ship but no crew, and having a crew but no ship, and performing interpolation operations and calculations for each of the three situations;

[0029] Step S33: If there is a ship and a crew, a crew and a ship are selected for the task with the highest priority, and the task is marked as successfully scheduled.

[0030] Step S34, record the timeline of the occupied teams and ships in the outbound, working, and return periods, and then calculate the next task;

[0031] Step S35: If there is / is no ship and no crew, then the timeline of the crew required for the task is traversed and a trial interpolation operation is performed without considering the ship;

[0032] Step S36: If the calculation fails, continue to calculate the scheduling information of the next task;

[0033] Step S37: If the interpolation is successful, a trial interpolation operation is then performed in the ship timeline to insert the round trip distance to the new mission location. If the interpolation operation is successful, a mission is marked as successfully scheduled and the next mission is scheduled. If it fails, the next mission is also scheduled.

[0034] Step S38: If there is a team but no ship, all ships are traversed and the ships with redundant carrying capacity are arranged and combined to obtain all possible ships that can transport along the way;

[0035] Step S39: If there is no possible ship for transportation, the schedule of the next task is calculated;

[0036] Step S310: If there is a ship that is transporting goods along the way, a trial interpolation operation is performed on the ship that is transporting goods along the way to insert the voyage to and from the new team's task location. If successful, the task scheduling is marked as successful and the next task is scheduled.

[0037] Step S311: If the original task is to wait for a ship to dock, the trial inserted voyage is from the dock to the new task location and then to the original task location;

[0038] Step S312: After all x tasks are scheduled, execute the return scheduling subtask.

[0039] Preferably, the step S312 includes:

[0040] Step S3121, using the scheduling results of the ship team database, traverse all ships in sequence;

[0041] Step S3122, searching for the current location and task completion time of the outbound team;

[0042] Step S3123, sort by team task completion time and send a ship to pick up the personnel;

[0043] Step S3124, determining whether the dispatched ship is a return transport ship;

[0044] Step S3125: If not, continue to sort by team task completion time and send a ship to pick up the people;

[0045] Step S3126: If yes, send a ship again and determine whether the ship sent is a return ship.

[0046] Step S3127: If not, continue to sort by team task completion time and send a ship to pick up the people;

[0047] Step S3128: If yes, cancel the ship scheduling task, backtrack and remove the voyage from the ship timeline, add a pick-up voyage for the next day, and proceed to determine the next task;

[0048] Step S3129, until all teams have received the task and returned to the port, all tasks are calculated and the scheduling is ended.

[0049] The beneficial effects of the present invention are: the present invention combines meteorological data, engineering task data, ship data, geographic information data, and human resources data to schedule and optimize offshore construction ships, taking into account the actual working modes of ships such as turning back, waiting, and transporting along the way, using meteorological forecast data, combined with the meteorological threshold defined for ship construction, to calculate the available window period of the ship in the next 7 days, ensuring that the ship only operates when meteorological conditions permit, ensuring construction safety, solving the problem of real-time data accuracy, using Internet of Things technology to obtain the real-time position of the ship, and providing improved calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for automatic scheduling of multi-dimensional data cross-correlation by tentative interpolation according to the present invention;

[0051] Figure 2 This is a specific flow chart of preparing basic scheduling data, obtaining team, ship, and task data, and calculating team window period data and ship window period data in the present invention;

[0052] Figure 3 This is a specific flow chart of the present invention for transmitting the obtained team, ship, and task data, as well as the calculated team window period data and ship window period data, to the AI scheduling algorithm for calculation;

[0053] Figure 4 This is a specific flow chart of executing the return scheduling subtask after all tasks are scheduled and completed in the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0055] The present invention will be further described below with reference to the accompanying drawings.

[0056] like Figure 1 As shown, a multi-dimensional data cross-correlation heuristic interpolation automatic scheduling method includes:

[0057] Step S1: screening and selecting the job tasks to be scheduled according to conditions, wherein the selected job tasks belong to one or more of the same project.

[0058] In the embodiment, the operation task includes all information of the operation, including but not limited to the task name, priority, task start time, latest completion time, expected working time, expected docking time, work team, expected number of maintenance personnel, whether to use a return ship, whether to dock, selected ship, selected terminal, and destination (wind farm wind turbine, booster station, ship).

[0059] Step S2, prepare basic scheduling data, obtain team, ship, task data, and calculated team window period data and ship window period data.

[0060] like Figure 2 As shown, step S2 includes:

[0061] Step 21, read the team data, terminal geographic information, meteorological data, and ship data of all tasks from the meteorological information time series database and the business database.

[0062] Step S22: Calculate the work window of the team based on the team data and the existing resource occupancy, and set the terminal geographic information data.

[0063] Step S23, calculating the ship's weather window period according to the ship's weather threshold and the weather forecast data of the destination.

[0064] In step S3, the obtained team, ship, and task data, as well as the calculated team window period data and ship window period data, are transmitted to the AI scheduling algorithm for calculation until all tasks are calculated and the scheduling is ended.

[0065] like Figure 3 As shown, step S3 includes:

[0066] In step S31 , assuming that there are x tasks in total, the x tasks are sorted according to their priorities, and task sorting information is calculated in sequence.

[0067] Step S32, calculating the schedule according to the ship crew database into three situations, including having a ship and a crew, having / not having a ship but not having a crew, and having a crew but not having a ship, and performing interpolation operations and calculations for each of the three situations.

[0068] Step S33: If there is a ship and a team, a team and a ship are selected for the task with the highest priority, and a task scheduling is marked as successful. Among them, there is a ship and a team means that the ship and the team are at the dock and can depart at any time.

[0069] Step S34, record the timeline of the occupied teams and ships in the outbound, working and return time periods, and then calculate the next task.

[0070] Step S35: If there is / is no ship and no crew, then the timeline of the crew required for the task is traversed and a tentative interpolation operation is performed without considering the ship. The case of there being / is no ship and no crew means that the crew is working at sea and the ship may or may not be at the dock.

[0071] Step S36: If failed, continue to calculate the scheduling information of the next task.

[0072] Step S37, if the interpolation is successful, then perform a trial interpolation operation in the ship timeline to insert the round trip distance to the new mission location. If the interpolation operation is successful, a mission flag is scheduled successfully and the next mission is scheduled. If it fails, the next mission is also scheduled.

[0073] Step S38: If there is a team but no ship, all ships are traversed and the ships with redundant carrying capacity are arranged and combined to obtain all possible ships that can transport along the way. Among them, if there is a team but no ship, the team and the ship have already set out from the dock and are working at sea.

[0074] Step S39: If there is no possible ship that can transport the goods along the way, the schedule of the next task is calculated.

[0075] In step S310, if there is a ship that is transporting the goods along the way, a trial interpolation operation is performed on the ship that is transporting the goods along the way to insert the voyage to and from the new team's task location. If successful, the task scheduling is marked as successful and the next task is scheduled.

[0076] Step S311: If the original task is to wait for a ship to dock, the trial inserted voyage is from the dock to the new task location and then to the original task location.

[0077] Step S312: After all x tasks are scheduled, execute the return scheduling subtask.

[0078] like Figure 4 As shown, step S312 includes:

[0079] Step S3121, using the scheduling results of the ship team library, traverse all ships in sequence.

[0080] Step S3122, search for the current location and task completion time of the outbound team.

[0081] Step S3123, sort by team task completion time and send a ship to pick up the people.

[0082] Step S3124, determine whether the dispatched ship is a return transport ship.

[0083] Step S3125: If not, continue to sort by team task completion time and send a ship to pick up the people.

[0084] Step S3126: If yes, send a ship again and determine whether the ship sent is a return ship.

[0085] Step S3127: If not, continue to sort by team task completion time and send a ship to pick up the people;

[0086] Step S3128: If yes, cancel the ship scheduling task, backtrack and remove the voyage from the ship timeline, add a pick-up voyage for the next day, and proceed to determine the next task;

[0087] Step S3129, until all teams have received the task and returned to the port, all tasks are calculated and the scheduling is ended.

[0088] Step S4: After the scheduling result comes out, the user manually adjusts the scheduling result to end the scheduling. The user confirms the scheduling result and generates a scheduling plan.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A trial interpolation automatic scheduling method for multi-dimensional data cross-correlation, characterized in that include: Step S1, filtering and selecting the tasks to be scheduled according to the conditions, wherein the selected tasks belong to one or more tasks in the same project; Step S2, prepare basic scheduling data, obtain team, ship, task data, and calculated team window period data and ship window period data; Step S3: The obtained team, ship, and task data, as well as the calculated team window period data and ship window period data, are transmitted to the AI scheduling algorithm for calculation until all tasks are calculated and the scheduling is terminated. Step S4: After the scheduling result comes out, the user manually adjusts the scheduling result to end the scheduling. The user confirms the scheduling result and generates a scheduling plan. The step S3 comprises: Step S31: suppose there are x tasks in total, sort the x tasks according to their priorities, and calculate the task sorting information in sequence; Step S32, calculating the schedule according to the ship crew database into three situations, including having a ship and a crew, having / not having a ship but no crew, and having a crew but no ship, and performing interpolation operations and calculations for each of the three situations; Step S33: If there is a ship and a crew, a crew and a ship are selected for the task with the highest priority, and the task is marked as successfully scheduled. Step S34, record the timeline of the occupied teams and ships in the outbound, working, and return periods, and then calculate the next task; Step S35: If there is / is no ship and no crew, then the timeline of the crew required for the task is traversed and a trial interpolation operation is performed without considering the ship; Step S36: If the calculation fails, continue to calculate the scheduling information of the next task; Step S37: If the interpolation is successful, a trial interpolation operation is then performed in the ship timeline to insert the round trip distance to the new mission location. If the interpolation operation is successful, a mission is marked as successfully scheduled and the next mission is scheduled. If it fails, the next mission is also scheduled. Step S38: If there is a team but no ship, all ships are traversed and the ships with redundant carrying capacity are arranged and combined to obtain all possible ships that can transport along the way; Step S39: If there is no ship transporting the goods along the way, the schedule of the next task is calculated; Step S310: If there is a ship that is transporting goods along the way, a trial interpolation operation is performed on the ship that is transporting goods along the way to insert the voyage to and from the new team's task location. If successful, the task scheduling is marked as successful and the next task is scheduled. Step S311: If the original task is to wait for a ship to dock, the trial inserted voyage is from the dock to the new task location and then to the original task location; Step S312: After all x tasks are scheduled, execute the return scheduling subtask.

2. The trial interpolation automatic scheduling method for multi-dimensional data cross-correlation according to claim 1, wherein In step S1, the operation task contains all the information of the operation, including task name, priority, task start time, latest completion time, expected working time, expected docking time, work team, expected number of maintenance personnel, whether to use a return ship, whether to dock, selected ship, selected terminal, and destination.

3. The automatic scheduling method of trial interpolation for multi-dimensional data cross-correlation according to claim 1, wherein The step S2 comprises: Step 21: Read the team data, terminal geographic information, meteorological data, and ship data of all tasks from the meteorological information time series database and the business database; Step S22: Calculate the work window of the team based on the team data and the existing resource occupancy, and set the terminal geographic information data; Step S23, calculating the ship's weather window period according to the ship's weather threshold and the weather forecast data of the destination.

4. A trial interpolation automatic scheduling method for multi-dimensional data cross-correlation according to claim 1, characterized in that The step S312 includes: Step S3121, using the scheduling results of the ship team database, traverse all ships in sequence; Step S3122, searching for the current location and task completion time of the outbound team; Step S3123, sort by team task completion time and send a ship to pick up the personnel; Step S3124, determining whether the dispatched ship is a return transport ship; Step S3125: If not, continue to sort by team task completion time and send a ship to pick up the people; Step S3126: If yes, send a ship again and determine whether the ship sent is a return ship. Step S3127: If not, continue to sort by team task completion time and send a ship to pick up the people; Step S3128: If yes, cancel the ship scheduling task, backtrack and remove the voyage from the ship timeline, add a pick-up voyage for the next day, and proceed to determine the next task; Step S3129, until all teams have received the task and returned to the port, all tasks are calculated and the scheduling is ended.

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