Marine multi-platform helicopter delivery task planning method

Abstract

The invention discloses a marine multi-platform helicopter delivery task planning method, and relates to the technical field of marine aircraft task planning, and the method comprises the steps: obtaining the task parameter information of a to-be-planned task entity; according to the task parameter information, constructing a task planning model by taking minimization of total task completion time as a target; and solving the task planning model by adopting a tabu search algorithm to obtain a task planning scheme. According to the method, by constructing the task planning model and adopting optimization methods such as a tabu search algorithm, offshore multi-platform helicopter resources can be effectively integrated, cross-platform collaborative planning is realized, the reasonability and efficiency of delivery task planning are remarkably improved, the total task completion time is shortened, and the offshore delivery efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of maritime aircraft mission planning, and in particular to a method for planning maritime multi-platform helicopter delivery missions. Background Art

[0002] The importance of coordinated delivery from multiple platforms at sea is becoming increasingly prominent in today's maritime operations, military operations, and emergency rescue. With the continued development and utilization of marine resources, the increasing frequency of maritime military activities, and the growing demand for maritime disaster relief, multiple platforms at sea play an indispensable role in ensuring personnel safety, material supply, and mission execution. These platforms are spread across vast ocean areas and require efficient coordination to complete complex delivery missions. Helicopters, with their advantages of vertical takeoff and landing (VTOL) and maneuverability, have become a key tool for transporting personnel and supplies between multiple platforms at sea. The rationality of their mission planning directly impacts the efficient and accurate completion of delivery missions, which in turn impacts the effectiveness of the entire maritime operation, military operation, or emergency rescue effort.

[0003] However, related technologies often use a single-platform independent dispatch model. Under this model, each platform dispatches helicopters based solely on its own resources and mission requirements, resulting in "information islands" of helicopter resources. When a platform faces an emergency delivery mission and lacks its own capacity, it cannot fully utilize the capacity of other platforms to deliver personnel / resources to the nearest location. This leads to low delivery efficiency and makes it difficult to meet the complex and ever-changing needs of maritime delivery.

[0004] Therefore, there is an urgent need for a multi-platform helicopter delivery mission planning method at sea to effectively improve the delivery efficiency. Summary of the Invention

[0005] The purpose of this application is to provide a method for planning maritime multi-platform helicopter delivery missions, which can effectively integrate the helicopter resources of various maritime platforms, realize cross-platform capacity sharing and global optimization, quickly respond to various complex and changeable maritime delivery needs, significantly improve delivery efficiency, reduce delivery mission execution time, avoid resource waste, ensure that delivery missions can be completed in a timely manner in emergency situations, and seize the best time for rescue or action.

[0006] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for planning a multi-platform helicopter delivery mission at sea, comprising: Acquiring task parameter information of a task entity to be planned; the task entity to be planned includes an offshore platform, a helicopter, and a delivery point; Building a task planning model based on the task parameter information with the goal of minimizing the total task completion time; A tabu search algorithm is used to solve the task planning model to obtain a task planning scheme; the task planning scheme includes a helicopter task sequence for each helicopter to be planned; a helicopter arrival time, a helicopter departure time, and a helicopter delivery capacity for each delivery point to be planned; and a loading start time and a loading end time for each offshore platform to be planned; the helicopter task sequence includes a delivery point sequence and an offshore platform node sequence.

[0007] In a second aspect, the present application provides a multi-platform helicopter delivery mission planning device at sea, comprising: An information acquisition module is used to obtain task parameter information of a task entity to be planned; the task entity to be planned includes an offshore platform, a helicopter, and a delivery point; A model building module is used to build a task planning model based on the task parameter information with the goal of minimizing the total task completion time; The task planning scheme generation module is used to solve the task planning model using a tabu search algorithm to obtain a task planning scheme; the task planning scheme includes a helicopter task sequence for each helicopter to be planned; the helicopter arrival time, helicopter departure time and helicopter delivery capacity for each delivery point to be planned; the loading start time and loading end time for each offshore platform to be planned; the helicopter task sequence includes a delivery point sequence and an offshore platform node sequence.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a method and device for planning maritime multi-platform helicopter delivery missions. By obtaining the mission parameter information of the mission entity to be planned, it solves the problem in the prior art of unreasonable task planning and insufficient resource utilization due to incomplete or inaccurate information acquisition, and achieves comprehensive control of maritime multi-platform helicopter delivery mission information, laying the foundation for efficient planning. Based on the acquired mission parameter information, a mission planning model is constructed with the goal of minimizing the total time to complete the mission. This step fully considers the high time efficiency requirements of maritime multi-platform helicopter delivery missions, and takes minimizing the total time to complete the mission as the planning goal, so that the constructed mission planning model is more in line with actual needs. It solves the problem in the prior art of lack of clear goal orientation in mission planning, which leads to low delivery efficiency, and achieves scientific planning of maritime multi-platform helicopter delivery missions with the goal of minimizing the total time to complete the mission, thereby improving the pertinence and effectiveness of mission planning. The taboo search algorithm is used to solve the mission planning model and obtain the mission planning scheme, which solves the problem in the existing technology that the mission planning scheme is unreasonable and the delivery efficiency is difficult to improve due to improper solution methods. A scientific and reasonable mission planning scheme is obtained, ensuring that helicopters can be delivered according to the optimal mission sequence and time schedule, greatly improving the efficiency of multi-platform helicopter delivery at sea, and realizing the effective integration and coordinated utilization of helicopter resources on each platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 This is an application environment diagram of a method for planning a multi-platform helicopter delivery mission at sea in one embodiment of the present application.

[0011] Figure 2 A flowchart of a method for planning a multi-platform helicopter delivery mission at sea is provided in accordance with one embodiment of the present application.

[0012] Figure 3 A flowchart of a method for planning a multi-platform helicopter delivery mission at sea is provided as another embodiment of the present application.

[0013] Figure 4 A schematic diagram of exchanging delivery nodes across helicopter mission sequences provided in one embodiment of the present application.

[0014] Figure 5 A schematic diagram of rearranging offshore platforms in a helicopter sequence according to an embodiment of the present application.

[0015] Figure 6 A schematic diagram of inserting a delivery point forward in a helicopter sequence provided in one embodiment of the present application.

[0016] Figure 7 A schematic diagram of the reversal of the order of delivery points within a delivery batch provided in one embodiment of the present application.

[0017] Figure 8 A schematic diagram of the tabu search algorithm flow provided in one embodiment of the present application.

[0018] Figure 9 A schematic diagram of the objective function convergence curve provided in one embodiment of the present application.

[0019] Figure 10 A Gantt chart for task planning is provided in one embodiment of the present application.

[0020] Figure 11 A schematic diagram of the functional modules of a multi-platform offshore helicopter delivery mission planning device provided in one embodiment of the present application.

[0021] Figure 12 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] First, some technical terms involved in the embodiments of this application are introduced.

[0023] A helicopter offshore platform delivery mission can be described as follows: an offshore platform is equipped with multiple types of helicopters, each capable of carrying a certain number of personnel / resources. After delivering these personnel / resources to a designated delivery point, the helicopters must return to the platform for resupply and then continue to the original delivery point or another delivery point. A multi-platform offshore helicopter delivery mission considers multiple offshore platforms capable of carrying helicopters and resupplying personnel / resources, allowing helicopters to resupply personnel / resources across platforms.

[0024] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0025] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0026] The method for planning a multi-platform helicopter delivery mission at sea provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the task parameter information of the task entity to be planned to the server 104. After the server 104 receives the task parameter information of the task entity to be planned, the server 104 constructs a task planning model based on the task parameter information with the goal of minimizing the total time to complete the task; uses the taboo search algorithm to solve the task planning model to obtain a task planning plan; the task planning plan includes the helicopter task sequence of each helicopter to be planned; the helicopter arrival time, helicopter departure time and helicopter delivery capacity of each delivery point to be planned; the loading start time and loading end time of each offshore platform to be planned; the helicopter task sequence includes the delivery point sequence and the offshore platform node sequence. The server 104 can feedback the obtained task planning plan to the terminal 102. In addition, in some embodiments, the offshore multi-platform helicopter delivery mission planning method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform task planning processing on the task parameter information of the task entity to be planned, or the server 104 can obtain the task parameter information of the task entity to be planned from the data storage system and perform task planning processing on the task parameter information of the task entity to be planned.

[0027] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0028] In an exemplary embodiment, Figure 2 As shown, a method for planning a multi-platform helicopter delivery mission at sea is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in FIG. 1 as an example, the method includes the following steps 201 to 203. In which: Step 201: Obtain mission parameter information for the mission entities to be planned. The mission entities to be planned include offshore platforms, helicopters, and delivery points. The mission parameter information for offshore platforms includes: the number of offshore platforms, the location coordinates of each offshore platform, the resource capacity of each offshore platform, and the distance between offshore platforms. The mission parameter information for helicopters includes: helicopter type, number, resource delivery capacity, maximum flight time, and flight speed. The mission parameter information for delivery points includes: the location coordinates of the delivery points and the resource demand.

[0029] Step 202: construct a task planning model based on the task parameter information with the goal of minimizing the total time required to complete the task.

[0030] Step 203, using a tabu search algorithm to solve the task planning model to obtain a task planning scheme; the task planning scheme includes a helicopter task sequence for each helicopter to be planned; a helicopter arrival time, a helicopter departure time, and a helicopter delivery capacity for each delivery point to be planned; and a loading start time and a loading end time for each offshore platform to be planned; the helicopter task sequence includes a delivery point sequence and an offshore platform node sequence.

[0031] By implementing the above steps 201 to 203, this application can break the current situation of "information islands" of resources on each platform in the multi-platform helicopter delivery mission at sea, realize the effective integration and coordinated use of multi-platform helicopter resources, and thus significantly improve the efficiency and accuracy of the multi-platform helicopter delivery mission at sea.

[0032] In another exemplary embodiment of the present application, step 203 uses a tabu search algorithm to solve the task planning model to obtain a task planning solution, which specifically includes: Traverse all delivery points, assign delivery helicopters and offshore platforms to each delivery point, obtain the initial mission planning scheme, and take the objective function value corresponding to the initial mission planning scheme as the current optimal solution.

[0033] Based on the initial mission planning scheme, a hierarchical domain structure method is adopted to obtain a neighborhood solution set; the hierarchical domain structure method includes designing a collaborative neighborhood structure between helicopters and a neighborhood structure for optimizing the delivery path of a single helicopter.

[0034] If the number of neighborhood solutions in the neighborhood solution set is less than or equal to a preset number threshold, all neighborhood solutions in the neighborhood solution set are used as candidate solutions to obtain a candidate solution set; if the number of neighborhood solutions in the neighborhood solution set is greater than the preset number threshold, neighborhood solutions with a preset number threshold are randomly selected from the neighborhood solution set according to a preset proportion as candidate solutions to obtain a candidate solution set.

[0035] Traverse the candidate solution set. If the current candidate solution meets the preset conditions, take the current candidate solution as the current optimal solution and update the taboo table. After the candidate solution set is traversed, take the current optimal solution as the optimal objective function value and obtain the task planning scheme. The preset conditions include that the current candidate solution does not violate the taboo features in the taboo table or meets the amnesty criteria. The amnesty criteria is that the objective function value corresponding to the current candidate solution is less than the objective function value corresponding to the current optimal solution.

[0036] In another exemplary embodiment of the present application, traversing all delivery points, assigning a delivery helicopter and an offshore platform to each delivery point, and obtaining an initial mission planning scheme specifically includes: The helicopter closest to the current delivery point is used as the delivery helicopter for the current delivery point.

[0037] Update the first timing parameters of the delivery helicopter arriving at the current delivery point; the first timing parameters include the first arrival time, departure time, remaining delivery capacity on board after delivery according to the maximum demand, and location coordinate information of the delivery helicopter arriving at the current delivery point.

[0038] When the delivery capacity of the delivery helicopter at the current delivery point is equal to 0, the offshore platform with a resource capacity greater than the preset capacity threshold and closest to the delivery helicopter is used as the offshore platform at the current delivery point.

[0039] Update the second timing parameters of the delivery helicopter arriving at the offshore platform; the second timing parameters include the loading start time, loading end time, on-board delivery capacity after loading is completed, and location coordinate information of the delivery helicopter arriving at the offshore platform.

[0040] In another exemplary embodiment of the present application, the cross-helicopter collaborative neighborhood structure is constructed based on exchanging delivery nodes in cross-helicopter mission sequences and rearranging offshore platforms in the helicopter sequence.

[0041] Exchange delivery nodes across helicopter mission sequences, including: The helicopter task sequences in the initial task planning scheme are combined in pairs to obtain multiple task sequence combinations.

[0042] Traverse all task sequence combinations, randomly select one delivery task point from the two helicopter task sequences in the current task sequence combination for exchange, generate a neighborhood solution and add it to the neighborhood solution set until all task sequence combinations are traversed; the first taboo feature includes the neighborhood structure name and the sorted two helicopter numbers.

[0043] Reposition offshore platforms closer together in helicopter sequences, including: Traverse the helicopter mission sequence in the initial mission planning scheme, generate neighborhood solutions through the following steps and add them to the neighborhood solution set until all offshore platforms in all helicopter mission sequences are traversed: For the current helicopter mission sequence, one offshore platform is selected in turn as the current offshore platform.

[0044] Select an offshore platform closest to the next delivery point from the offshore platforms other than the current offshore platform to replace the current offshore platform, obtain the neighborhood solution and add it to the neighborhood solution set.

[0045] Take the next offshore platform as the current offshore platform and return to the step "select an offshore platform closest to the next delivery point from the offshore platforms other than the current offshore platform to replace the current offshore platform, obtain the neighborhood solution and add it to the neighborhood solution set" until all offshore platforms in the current helicopter mission sequence are traversed; the second taboo feature includes the neighborhood structure name, the selected helicopter number, and the exchanged offshore platform number.

[0046] In another exemplary embodiment of the present application, the neighborhood structure for optimizing the single-helicopter delivery path is constructed based on forward insertion of delivery points and reversal of the order of delivery points within a delivery batch.

[0047] The delivery point is inserted forward, including: For each helicopter in the initial mission planning scheme, the response importance of each delivery point in the corresponding helicopter mission sequence is calculated respectively.

[0048] The path point with the highest response importance is inserted forward to the first position of the corresponding helicopter operation sequence to generate a neighborhood solution. The third taboo feature includes the neighborhood structure name and the selected helicopter number.

[0049] The order of delivery points within a delivery batch is reversed, including: For each helicopter in the initial mission planning scheme, each delivery batch of each helicopter is traversed separately.

[0050] For the target delivery batch of the target helicopter, two delivery points within the target batch are randomly selected; the order of all delivery points between the two delivery points is reversed to generate a neighborhood solution; the fourth taboo feature includes the neighborhood structure name, the selected helicopter number, the delivery batch number, and the two delivery point numbers involved in the reversal; the target helicopter is any helicopter in the initial mission planning scheme; the target delivery batch is any delivery batch of the target helicopter.

[0051] In another exemplary embodiment of the present application, the calculation formula for response importance is: .

[0052] .

[0053] in, represents the response importance of the i-th helicopter at the n-th delivery point; It represents the delivery capacity required after the nth delivery point completes the delivery; N is the set of all delivery points; is the response waiting time of the i-th helicopter at the n-th delivery point; Indicates the delivery quantity required by the nth delivery point at the initial moment; Indicates the preset response waiting time; represents the arrival time of the i-th helicopter at the n-th delivery point, Indicates the time when the nth delivery point demand of the i-th helicopter begins; represents the scale factor; Indicates the waiting response coefficient.

[0054] In another exemplary embodiment of the present application, Figure 3 As shown, a method for planning offshore multi-platform helicopter delivery missions is provided, which is applicable to scenarios such as offshore oil and gas platform clusters and offshore wind power clusters that require cross-platform personnel / resource transportation. The method specifically includes: S1. Construct a mission planning model. The constraints of the mission planning model include helicopter capacity constraints, flight time constraints, delivery point demand constraints, and operation sequence constraints.

[0055] As an optional implementation, the helicopter capacity constraint is as follows: .

[0056] .

[0057] .

[0058] Where, is the set of helicopters of type k on all offshore platforms; Q i represents the capacity of the i-th helicopter; Q k Indicates the maximum safe capacity of type k helicopter; Indicates the number of personnel / resources delivered by the i-th helicopter to the n-th delivery point; The set of nodes representing the bth delivery batch of the i-th helicopter (including the delivery point and the offshore platform location); The decision variable representing whether the i-th helicopter goes to the n-th delivery point in the b-th delivery batch; if the i-th helicopter goes to the n-th delivery point in the b-th delivery batch, then =1; otherwise, =0; a helicopter delivery batch is represented by the process from the helicopter taking off from the offshore platform to the completion of the delivery and returning to an offshore platform for landing.

[0059] The flight time constraints are as follows: .

[0060] .

[0061] Where, represents the flight time of the i-th helicopter; represents the flight time of the i-th helicopter between delivery points m and n; Indicates the maximum flight time of a K-type helicopter; the helicopter is in the shutdown state when at the delivery point and offshore platform.

[0062] After completing a batch of delivery missions, the helicopter returns to the offshore platform to replenish personnel / resources and fuel. Therefore, the waiting time of the i-th helicopter on the offshore platform is , which can be expressed as: .

[0063] Where, represents the refueling time of the i-th helicopter after the b-th delivery batch is completed, represents the personnel / resource loading time of the bth delivery batch of the i-th helicopter.

[0064] The delivery point demand constraints are as follows: .

[0065] .

[0066] Where, represents the delivery volume required at the nth delivery point at the initial moment; S represents the set of helicopters on all offshore platforms, B i represents the set of all delivery batches of the i-th helicopter; K represents the set of all helicopter types.

[0067] The job timing constraints are as follows: The timing constraints for helicopters arriving at different delivery points in the same delivery batch include: .

[0068] Where, It represents the time it takes for the i-th helicopter to unload personnel / resources at the n-th delivery point; represents the time when the i-th helicopter arrives at the n-th delivery point; represents the time when the i-th helicopter leaves the n-th delivery point; represents the set of all delivery points of the i-th helicopter in the b-th delivery batch; S represents the set of helicopters on all offshore platforms, satisfying .

[0069] S2. According to the characteristics of the model, propose an optimization objective function.

[0070] In order to complete the delivery mission as quickly as possible, the objective function of the delivery mission planning is set to minimize the mission completion time F. The objective function of the mission planning model is: .

[0071] .

[0072] .

[0073] Where F represents the objective function value; represents the maximum completion time of all delivery tasks of the i-th helicopter; S represents the set of helicopters of all offshore platforms; B represents the completion time of the delivery mission of the bth delivery batch of the i-th helicopter; i represents the delivery batch set of the i-th helicopter; n represents the n-th delivery point; represents the set of all delivery points of the i-th helicopter in the b-th delivery batch; represents the time when the i-th helicopter leaves the n-th delivery point.

[0074] S3. Design a tabu search algorithm. It specifically includes the following steps: S31. Initial solution construction.

[0075] As an optional implementation, S31 specifically includes the following steps: S311. Loop through all delivery points and select the closest helicopter i to delivery point n as the delivery carrier for that delivery point.

[0076] S312. Update the timing parameters of helicopter i after it arrives at delivery point n, including arrival time, departure time, remaining delivery personnel / resources on board after delivery according to the maximum demand, and the location of the helicopter.

[0077] S313. When the number of personnel / resources on helicopter i is equal to 0, go to the nearest offshore platform with sufficient capacity to replenish personnel / resources, and update the arrival time, platform waiting time, number of personnel / resources delivered after helicopter loading, and the location of the helicopter.

[0078] S314. Check whether all delivery point requirements are met. If so, complete the initial solution construction. Otherwise, return to S311. In S314, the initial solution includes the sequence of all delivery points and the order of offshore platform nodes that the helicopter needs to deliver to, as well as the arrival time, departure time, and number of people delivered to each delivery point, and the start and end time of offshore platform loading.

[0079] S32. Neighborhood structure design and taboo feature extraction. The neighborhood structure design adopts the hierarchical domain structure design idea, proposes 4 neighborhood structures with 2 layers, extracts corresponding taboo features, and clarifies the candidate solution selection method, including: S321. The first layer: a collaborative neighborhood structure between helicopters, which is based on exchanging delivery nodes across helicopter mission sequences and rearranging offshore platforms in the helicopter sequence.

[0080] S3211. Exchanging delivery nodes across helicopter mission sequences specifically includes: the input of the neighborhood structure is a feasible solution, and two helicopter mission sequences in the feasible solution are selected in a combination manner. Each helicopter combination randomly exchanges a delivery point in its own sequence, and each helicopter combination generates a neighborhood solution; a schematic diagram of exchanging delivery points across helicopter mission sequences is shown in FIG. Figure 4 As shown, a delivery point in the delivery point sequence of the two helicopters in the feasible solution is randomly selected and exchanged.

[0081] The extracted taboo features include: neighborhood structure name and the two sorted helicopter numbers.

[0082] S3212. Rearrange the offshore platforms in the helicopter sequence. Specifically, the input of the neighborhood structure is a feasible solution. A helicopter mission sequence is selected in turn. An offshore platform is selected in turn as a supply platform. Through calculation, an offshore platform closest to the next delivery point is selected from the remaining available offshore platforms to replace the original supply platform. Each replacement of a supply platform generates a neighborhood solution. The schematic diagram of rearranging the offshore platforms in the helicopter sequence is shown as follows: Figure 5 shown.

[0083] The extracted taboo features include: neighborhood structure name, selected helicopter number, and swapped platform number.

[0084] S322. Second layer: Single helicopter delivery path optimization, which is based on the forward insertion of high-priority delivery points and the reversal of the order of delivery points within the delivery batch.

[0085] S3221. Delivery point forward insertion: The input of the neighborhood structure is a feasible solution, in which a helicopter is selected in turn, and the response importance of each delivery point in the delivery sequence is calculated. , select the most important path point in the path, randomly select a position closer to the front in the path sequence to insert, and generate a neighborhood solution for each helicopter selected. Figure 6 shown.

[0086] Response Importance Importance , expressed as: .

[0087] .

[0088] in, represents the response importance of the i-th helicopter at the n-th delivery point; It represents the delivery capacity required after the nth delivery point completes the delivery; N is the set of all delivery points; is the response waiting time of the i-th helicopter at the n-th delivery point; Indicates the delivery quantity required by the nth delivery point at the initial moment; Indicates the preset response waiting time; represents the arrival time of the i-th helicopter at the n-th delivery point, Indicates the time when the nth delivery point demand of the i-th helicopter begins; represents the scale factor; Indicates the waiting response coefficient.

[0089] The extracted taboo features include: neighborhood structure name and selected helicopter number.

[0090] S3222. Reversing the order of delivery points within a delivery batch: The input of this neighborhood structure is a feasible solution, in which a helicopter delivery sequence is selected in turn, and a delivery batch with more than 1 delivery points is selected in turn, and the delivery order is reversed to form a new delivery sequence. Each time a delivery batch is reversed, a neighborhood solution is generated. The schematic diagram of reversing the order of delivery points within a delivery batch is as follows: Figure 7 shown.

[0091] The extracted taboo features include: neighborhood structure name, selected helicopter number, delivery batch number, and the numbers of the two delivery points involved in the reversal.

[0092] S33. Candidate solution generation.

[0093] The number of neighborhood solutions generated by S32 is extremely large. To improve the computational efficiency, it is necessary to select candidate solutions from the neighborhood solutions. The method can be as follows: According to the two-layer neighborhood structure designed by S32, when the number of neighborhood solutions is less than or equal to a preset number, such as 50, all neighborhood solutions are set as candidate solutions. When the number of neighborhood solutions is greater than the preset number, such as 50, a candidate solution with a quantity not exceeding 50 is randomly selected according to the proportion of the number of neighborhood solutions generated by the two-layer neighborhood structure.

[0094] S34. Design of the tabu list update mechanism, including designing the tabu length to update the tabu list and designing the amnesty criterion to release neighborhood solutions.

[0095] The design of the tabu length to update the tabu list and the design of the amnesty criterion to release neighborhood solutions specifically include: Set the tabu list length.

[0096] The amnesty criterion is that when the objective function corresponding to the candidate solution is better than the current optimal solution, the restriction of the tabu feature in the tabu list is ignored, and the current optimal solution is updated with a better solution.

[0097] S4. Use the proposed tabu search algorithm to optimize the proposed objective function to obtain a helicopter mission planning scheme, specifically including a mission assignment and a delivery time sequence planning scheme. The implementation process is as Figure 8 shown.

[0098] In another exemplary embodiment of the present application, mission parameters are set: the number of offshore platforms is 3, and the corresponding coordinates are (100, 0), (50, 0), (100, 50). Each offshore platform is equipped with 2 helicopters of the same model, with a speed of 150 kilometers per hour, a range of 4 hours, a refueling rate such that the helicopter can travel for 20 minutes per minute of refueling, a capacity of 5 units of personnel / resources, and a loading and unloading speed of 1 unit of personnel / resources per 8 minutes. The number of delivery points is 10, and the corresponding coordinates are randomly generated within the region {(x, y)|0 < x < 100 and y > x}, and the demand is randomly generated within the interval (5, 15) and is an integer unit of personnel / resources.

[0099] Set algorithm parameters: the tabu list length is 8, the maximum number of iterations is 100, and the tabu search algorithm is implemented according to the Figure 8 shown process. The algorithm convergence curve is as Figure 9 shown, and the mission planning scheme is as Figure 10 shown, including a helicopter mission assignment scheme and a delivery time sequence planning scheme.

[0100] Figure 10In the figure, the dark gray rectangular blocks correspond to the process of helicopter loading personnel / resources on the offshore platform, the light gray rectangular blocks represent the process of helicopter transferring between the delivery point and the offshore platform, and the black rectangular blocks represent the process of helicopter unloading personnel / resources at the delivery point. The characters in the upper left corner of the dark gray and black rectangular blocks represent the offshore platform number and the delivery point number respectively. The vertical axis scale represents the helicopter number, and the horizontal axis scale represents the time process.

[0101] from Figure 10 It can be seen that since the demand for delivery points is greater than the capacity of helicopters, multiple deliveries are required to meet the delivery needs of the delivery points; at the same time, helicopters can realize the supply of personnel / resources from nearby different platforms, effectively improving the delivery efficiency.

[0102] The present application also provides an application scenario, which applies the above-mentioned offshore multi-platform helicopter delivery mission planning method. Specifically: the offshore multi-platform helicopter delivery mission planning method provided in this embodiment can be applied in the offshore wind power cluster operation and maintenance guarantee scenario. The offshore wind power cluster operation and maintenance guarantee scenario includes a task demand analysis link, a resource scheduling planning link, and a task execution and monitoring link. In the offshore wind power cluster operation and maintenance guarantee work, the task demand enters the resource scheduling planning link from the task demand analysis link. In the task demand analysis link, the operation and maintenance team will conduct a comprehensive inspection and evaluation of each offshore wind power platform to clarify the specific operation and maintenance requirements of each platform. For example, the wind turbine gearbox of a certain platform needs to replace the lubricating oil, and the electrical equipment of another platform needs to be repaired by professional technicians, etc. These task requirements record in detail the required personnel type, material type and quantity, task urgency and other information. After these task demand information enters the resource scheduling planning link, it is processed by the offshore multi-platform helicopter delivery mission planning method provided in this embodiment to obtain a scientific and reasonable helicopter task planning plan. For example, the team determines which helicopters will be responsible for transporting maintenance personnel and supplies to which platforms, as well as each helicopter's flight route and takeoff and landing times at each platform. Once the helicopter mission plan is in place, the team enters the mission execution and monitoring phase. During this phase, the helicopters carry out the planned delivery of maintenance personnel and supplies. Simultaneously, the operations team uses a monitoring system to track the helicopters' flight status and mission progress in real time, ensuring the mission is completed safely and efficiently.

[0103] The offshore multi-platform helicopter delivery mission planning method provided in this embodiment belongs to the resource scheduling planning phase in offshore wind power cluster operation and maintenance support scenarios. By applying the offshore multi-platform helicopter delivery mission planning method provided in this embodiment in offshore wind power cluster operation and maintenance support scenarios, it can effectively improve the efficiency of operation and maintenance resource allocation, reduce operation and maintenance costs, ensure the stable operation of offshore wind power clusters, and enhance the economic benefits and reliability of the offshore wind power industry.

[0104] Based on the same inventive concept, the embodiments of the present application also provide a device for planning a multi-platform offshore helicopter delivery mission for implementing the aforementioned method for planning a multi-platform offshore helicopter delivery mission. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more embodiments of the device for planning a multi-platform offshore helicopter delivery mission provided below can be found in the aforementioned limitations of the method for planning a multi-platform offshore helicopter delivery mission, and will not be further elaborated here.

[0105] In an exemplary embodiment, Figure 11 As shown, a multi-platform helicopter delivery mission planning device at sea is provided, comprising: The information acquisition module 301 is used to acquire task parameter information of the task entity to be planned; the task entity to be planned includes an offshore platform, a helicopter, and a delivery point.

[0106] The model building module 302 is used to build a task planning model based on the task parameter information with the goal of minimizing the total time to complete the task.

[0107] The mission planning scheme generation module 303 is used to use the tabu search algorithm to solve the mission planning model and obtain the mission planning scheme; the mission planning scheme includes the helicopter mission sequence of each helicopter to be planned; the helicopter arrival time, helicopter departure time and helicopter delivery capacity of each delivery point to be planned; the loading start time and loading end time of each offshore platform to be planned; the helicopter mission sequence includes the delivery point sequence and the offshore platform node sequence.

[0108] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 12 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store mission planning processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for planning a multi-platform helicopter delivery mission at sea is implemented.

[0109] Those skilled in the art will understand that Figure 12 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0110] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0111] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0112] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0113] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0114] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0115] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for planning a multi-platform helicopter delivery mission at sea, characterized in that: The method for planning a multi-platform helicopter delivery mission at sea includes: Acquiring task parameter information of a task entity to be planned; the task entity to be planned includes an offshore platform, a helicopter, and a delivery point; Building a task planning model based on the task parameter information with the goal of minimizing the total task completion time; A tabu search algorithm is used to solve the task planning model to obtain a task planning scheme; the task planning scheme includes a helicopter task sequence for each helicopter to be planned; a helicopter arrival time, a helicopter departure time, and a helicopter delivery capacity for each delivery point to be planned; and a loading start time and a loading end time for each offshore platform to be planned; the helicopter task sequence includes a delivery point sequence and an offshore platform node sequence.

2. The method for planning a multi-platform helicopter delivery mission at sea according to claim 1, characterized in that: The objective function of the task planning model is: ； ； ； Where F represents the objective function value; represents the maximum completion time of all delivery tasks of the i-th helicopter; S represents the set of helicopters of all offshore platforms; B represents the completion time of the delivery mission of the bth delivery batch of the i-th helicopter; i represents the delivery batch set of the i-th helicopter; n represents the n-th delivery point; represents the set of all delivery points of the i-th helicopter in the b-th delivery batch; represents the time when the i-th helicopter leaves the n-th delivery point.

3. The method for planning a multi-platform helicopter delivery mission at sea according to claim 1, characterized in that: The constraints of the mission planning model include: helicopter capacity constraints, flight time constraints, delivery point demand constraints, and operation timing constraints.

4. The method for planning a multi-platform helicopter delivery mission at sea according to claim 1, characterized in that: The tabu search algorithm is used to solve the task planning model and obtain the task planning solution, which specifically includes: Traverse all delivery points, assign a delivery helicopter and offshore platform to each delivery point, obtain the initial mission planning scheme, and use the objective function value corresponding to the initial mission planning scheme as the current optimal solution; Based on the initial mission planning scheme, a hierarchical domain structure method is used to obtain a neighborhood solution set; the hierarchical domain structure method includes designing a collaborative neighborhood structure between helicopters and a neighborhood structure for optimizing the delivery path of a single helicopter; If the number of neighborhood solutions in the neighborhood solution set is less than or equal to a preset number threshold, all neighborhood solutions in the neighborhood solution set are used as candidate solutions to obtain a candidate solution set; if the number of neighborhood solutions in the neighborhood solution set is greater than the preset number threshold, neighborhood solutions with a preset number threshold are randomly selected from the neighborhood solution set according to a preset ratio as candidate solutions to obtain a candidate solution set; Traverse the candidate solution set. If the current candidate solution meets the preset conditions, take the current candidate solution as the current optimal solution and update the taboo table. After the candidate solution set is traversed, take the current optimal solution as the optimal objective function value and obtain the task planning scheme. The preset conditions include that the current candidate solution does not violate the taboo features in the taboo table or meets the amnesty criteria. The amnesty criteria is that the objective function value corresponding to the current candidate solution is less than the objective function value corresponding to the current optimal solution.

5. The method for planning a multi-platform helicopter delivery mission at sea according to claim 4, characterized in that: The method traverses all delivery points, assigns a delivery helicopter and an offshore platform to each delivery point, and obtains an initial mission planning scheme, specifically including: The helicopter closest to the current delivery point is used as the delivery helicopter for the current delivery point; Updating the first timing parameters of the delivery helicopter arriving at the current delivery point; the first timing parameters include the first arrival time and departure time of the delivery helicopter arriving at the current delivery point, the remaining delivery capacity on board after delivery according to the maximum demand, and the location coordinate information; When the delivery capacity of the delivery helicopter at the current delivery point is equal to 0, the offshore platform with a resource capacity greater than the preset capacity threshold and closest to the delivery helicopter is used as the offshore platform at the current delivery point; Update the second timing parameters of the delivery helicopter arriving at the offshore platform; the second timing parameters include the loading start time, loading end time, on-board delivery capacity after loading is completed, and location coordinate information of the delivery helicopter arriving at the offshore platform.

6. The method for planning a multi-platform helicopter delivery mission at sea according to claim 4, characterized in that: The cross-helicopter collaborative neighborhood structure is constructed based on exchanging delivery nodes in the cross-helicopter mission sequence and rearranging the offshore platforms in the helicopter sequence; Exchange delivery nodes across helicopter mission sequences, including: Combine the helicopter mission sequences in the initial mission planning scheme in pairs to obtain multiple mission sequence combinations; Traverse all task sequence combinations, randomly select one delivery task point from the two helicopter task sequences in the current task sequence combination to exchange, generate a neighborhood solution and add it to the neighborhood solution set until all task sequence combinations are traversed; the first taboo feature includes the neighborhood structure name and the sorted two helicopter numbers; Reposition offshore platforms closer together in helicopter sequences, including: Traverse the helicopter mission sequence in the initial mission planning scheme, generate neighborhood solutions through the following steps and add them to the neighborhood solution set until all offshore platforms in all helicopter mission sequences are traversed: For the current helicopter mission sequence, one offshore platform is selected in turn as the current offshore platform; Select an offshore platform closest to the next delivery point from the offshore platforms other than the current offshore platform to replace the current offshore platform, obtain a neighborhood solution and add it to the neighborhood solution set; The next offshore platform is used as the current offshore platform, and the process returns to step "selecting an offshore platform closest to the next delivery point from the offshore platforms other than the current offshore platform to replace the current offshore platform, obtaining a neighborhood solution, and adding it to the neighborhood solution set" until all offshore platforms in the current helicopter mission sequence are traversed. The second taboo feature includes the neighborhood structure name, the selected helicopter number, and the exchanged offshore platform number.

7. The method for planning a multi-platform helicopter delivery mission at sea according to claim 4, characterized in that: The neighborhood structure for optimizing the single helicopter delivery path is constructed based on forward insertion of delivery points and reversal of the order of delivery points within a delivery batch; The delivery point is inserted forward, including: For each helicopter in the initial mission planning scheme, the response importance of each delivery point of the corresponding helicopter mission sequence is calculated respectively; Insert the path point with the highest response importance into the first position of the corresponding helicopter operation sequence to generate a neighborhood solution. The third taboo feature includes the neighborhood structure name and the selected helicopter number. The order of delivery points within a delivery batch is reversed, including: For each helicopter in the initial mission planning scheme, traverse each delivery batch of each helicopter separately; For the target delivery batch of the target helicopter, two delivery points within the target batch are randomly selected; the order of all delivery points between the two delivery points is reversed to generate a neighborhood solution; the fourth taboo feature includes the neighborhood structure name, the selected helicopter number, the delivery batch number, and the two delivery point numbers involved in the reversal; the target helicopter is any helicopter in the initial mission planning scheme; the target delivery batch is any delivery batch of the target helicopter.

8. The method for planning a multi-platform helicopter delivery mission at sea according to claim 7, characterized in that: The calculation formula for response importance is: ； ； in, represents the response importance of the i-th helicopter at the n-th delivery point; It represents the delivery capacity required after the nth delivery point completes the delivery; N is the set of all delivery points; is the response waiting time of the i-th helicopter at the n-th delivery point; Indicates the delivery quantity required by the nth delivery point at the initial moment; Indicates the preset response waiting time; represents the arrival time of the i-th helicopter at the n-th delivery point, Indicates the time when the nth delivery point demand of the i-th helicopter begins; represents the scale factor; Indicates the waiting response coefficient.

9. The method for planning a multi-platform helicopter delivery mission at sea according to claim 1, characterized in that: The mission parameter information of the offshore platform includes: the number of offshore platforms, the location coordinate information of each offshore platform, the resource capacity information of each offshore platform, and the distance information between the offshore platforms; The mission parameter information of the helicopter includes: helicopter type information, quantity information, resource delivery capacity information, maximum flight time information, and flight speed information; The task parameter information of the delivery point includes: the location coordinate information of the delivery point and the resource demand information.

10. A multi-platform helicopter delivery mission planning device at sea, characterized in that: The offshore multi-platform helicopter delivery mission planning device includes: An information acquisition module is used to obtain task parameter information of a task entity to be planned; the task entity to be planned includes an offshore platform, a helicopter, and a delivery point; A model building module is used to build a task planning model based on the task parameter information with the goal of minimizing the total task completion time; The task planning scheme generation module is used to solve the task planning model using a tabu search algorithm to obtain a task planning scheme; the task planning scheme includes a helicopter task sequence for each helicopter to be planned; the helicopter arrival time, helicopter departure time and helicopter delivery capacity for each delivery point to be planned; the loading start time and loading end time for each offshore platform to be planned; the helicopter task sequence includes a delivery point sequence and an offshore platform node sequence.

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