A method for planning a multi-platform helicopter delivery task at sea

Abstract

The application discloses a kind of offshore multi-platform helicopter delivery task planning method, it is related to offshore aircraft task planning technical field, the method includes obtaining the task parameter information of task entity to be planned;According to task parameter information, with the minimum task completion total time as target to build task planning model;Task planning model is solved using tabu search algorithm, and task planning scheme is obtained.The application can effectively integrate offshore multi-platform helicopter resources by building task planning model, using optimization methods such as tabu search algorithm, realize cross-platform collaborative planning, significantly improve the rationality and efficiency of delivery task planning, reduce task completion total time, improve offshore delivery efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marine aircraft mission planning, in particular to a marine multi-platform helicopter delivery mission planning method. BACKGROUND

[0002] In today's many fields such as marine operations, military operations and emergency rescue, the importance of marine multi-platform cooperative delivery is increasingly prominent. With the continuous development and utilization of marine resources, the increasing frequency of marine military activities and the increasing demand for marine emergency disaster rescue, marine multi-platform plays an indispensable role in ensuring personnel safety, material supply and task execution. These platforms are distributed in a wide ocean area and need efficient cooperation among each other to complete various complex delivery tasks. Helicopters, with their vertical take-off and landing, flexibility and other advantages, have become the key tool for personnel transportation, material supply and other delivery tasks between marine multi-platforms. The rationality of its mission planning is directly related to whether the delivery task can be efficiently and accurately completed, thereby affecting the effectiveness of the entire marine operation, military operation or emergency rescue.

[0003] However, the related art mostly adopts a single-platform independent scheduling mode. In this mode, each platform only schedules according to its own helicopter resources and task requirements, resulting in "information silos" of helicopter resources among each platform. When a platform faces an emergency delivery task and its own capacity is insufficient, it cannot fully utilize the capacity of the remaining platforms to achieve emergency and nearby delivery of personnel / resources, thereby leading to low delivery efficiency and difficulty in meeting complex and changing marine delivery needs.

[0004] Therefore, there is an urgent need for a marine multi-platform helicopter delivery mission planning method to effectively improve delivery efficiency. SUMMARY

[0005] The purpose of the present application is to provide a marine multi-platform helicopter delivery mission planning method, which can effectively integrate the helicopter resources of each platform at sea, realize cross-platform capacity sharing and global optimization, quickly respond to various complex and changing marine delivery needs, significantly improve delivery efficiency, reduce delivery task execution time, avoid resource waste, ensure that delivery tasks can be completed in time in emergency situations, and seize the best rescue or action opportunity.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a marine multi-platform helicopter delivery mission planning method, comprising:

[0008] obtaining task parameter information of a task entity to be planned; the task entity to be planned includes a marine platform, a helicopter and a delivery point;

[0009] construct a task planning model according to the task parameter information, with the objective of minimizing total task completion time;

[0010] solving the task planning model by using a tabu search algorithm to obtain a task planning scheme; the task planning scheme comprises a helicopter task sequence of each helicopter to be planned, a helicopter arrival time, a helicopter departure time and a helicopter delivery capacity of each delivery point to be planned, and a loading start time and a loading end time of each offshore platform to be planned; the helicopter task sequence comprises a delivery point sequence and an offshore platform node sequence.

[0011] In a second aspect, the present application provides a device for planning a helicopter delivery task for offshore platforms, comprising:

[0012] an information acquisition module configured to acquire task parameter information of task entities to be planned, wherein the task entities to be planned comprise offshore platforms, helicopters and delivery points;

[0013] a model construction module configured to construct a task planning model according to the task parameter information, with the objective of minimizing total task completion time;

[0014] a task planning scheme generation module configured to solve the task planning model by using a tabu search algorithm to obtain a task planning scheme; the task planning scheme comprises a helicopter task sequence of each helicopter to be planned, a helicopter arrival time, a helicopter departure time and a helicopter delivery capacity of each delivery point to be planned, and a loading start time and a loading end time of each offshore platform to be planned; the helicopter task sequence comprises a delivery point sequence and an offshore platform node sequence.

[0015] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0016] The application provides a marine multi-platform helicopter delivery task planning method and device, which obtains task parameter information of a task entity to be planned, solves the problem of unreasonable task planning and insufficient resource utilization caused by incomplete or inaccurate information acquisition in the prior art, and realizes comprehensive control of marine multi-platform helicopter delivery task information, laying a foundation for efficient planning. According to the obtained task parameter information, a task planning model is constructed with the minimum total task completion time as the target. This step fully considers the high requirement of marine multi-platform helicopter delivery task on time efficiency, takes the minimum total task completion time as the planning target, and makes the constructed task planning model more in line with actual demand. The problem of low delivery efficiency caused by lack of clear target orientation in the prior art is solved, and the marine multi-platform helicopter delivery task is scientifically planned with the minimum total task completion time as the target, improving the pertinence and effectiveness of task planning. A tabu search algorithm is used to solve the task planning model to obtain a task planning scheme, solving the problem of unreasonable task planning scheme and difficult improvement of delivery efficiency caused by improper solving method in the prior art, realizing a scientific and reasonable task planning scheme, ensuring that the helicopter can be delivered according to the optimal task sequence and time arrangement, greatly improving the efficiency of marine multi-platform helicopter delivery, and realizing effective integration and collaborative use of helicopter resources of each platform. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 An application environment diagram of a marine multi-platform helicopter delivery task planning method in an embodiment of the present application.

[0019] Figure 2 A flowchart of a marine multi-platform helicopter delivery task planning method provided by an embodiment of the present application.

[0020] Figure 3 A flowchart of a marine multi-platform helicopter delivery task planning method provided by another embodiment of the present application.

[0021] Figure 4 A cross-helicopter task sequence exchange delivery node diagram provided by an embodiment of the present application.

[0022] Figure 5 A marine platform re-arrangement diagram in a helicopter sequence provided by an embodiment of the present application.

[0023] Figure 6 A schematic diagram of forward insertion of a delivery point in a helicopter sequence is provided for an embodiment of the present application.

[0024] Figure 7 A schematic diagram of reverse order of delivery points in a delivery batch is provided for an embodiment of the present application.

[0025] Figure 8 A schematic diagram of a tabu search algorithm flow is provided for an embodiment of the present application.

[0026] Figure 9 A schematic diagram of a target function convergence curve is provided for an embodiment of the present application.

[0027] Figure 10 A task planning Gantt chart is provided for an embodiment of the present application.

[0028] Figure 11 A functional module schematic diagram of a marine multi-platform helicopter delivery task planning device is provided for an embodiment of the present application.

[0029] Figure 12 A structural schematic diagram of a computer device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0030] Firstly, some technical terms involved in the embodiments of the present application are introduced.

[0031] The helicopter offshore platform delivery task can be expressed as: the offshore platform carries multiple types of helicopters, the helicopter can carry a certain number of personnel / resources, and after the helicopter delivers the carried personnel / resources to the designated delivery point, it needs to return to the platform for personnel / resource replenishment, and then continue to deliver to the original delivery point or other delivery points. The marine multi-platform helicopter delivery task is to consider multiple offshore platforms that can carry helicopters and can replenish personnel / resources during the delivery task, and the helicopters can cross-platform for personnel / resource replenishment.

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application in combination with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0033] In order to make the above purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail in combination with the accompanying drawings and specific embodiments.

[0034] The offshore multi-platform helicopter delivery task planning method provided in the embodiments of the present application can be applied in the application environment as shown in Figure 1 The terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be separately arranged, integrated on the server 104, 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 receiving the task parameter information of the task entity to be planned, the server 104 constructs a task planning model according to the task parameter information, with the goal of minimizing the total task completion time. The server 104 solves the task planning model by using a tabu search algorithm to obtain a task planning scheme. The task planning scheme includes a helicopter task sequence of each helicopter to be planned, a helicopter arrival time, a helicopter departure time and a helicopter delivery capacity of each delivery point to be planned, and a loading start time and a loading end time of each offshore platform to be planned. The helicopter task sequence includes a delivery point sequence and an offshore platform node sequence. The server 104 can feed back the obtained task planning scheme to the terminal 102. In addition, in some embodiments, the offshore multi-platform helicopter delivery task planning method can also be implemented by the server 104 or the terminal 102 alone, 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.

[0035] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0036] In an exemplary embodiment, as shown in Figure 2 An offshore multi-platform helicopter delivery task planning method is provided, which is executed by a computer device, specifically by a terminal or a server or the like, and can be executed by the terminal and the server together. In the embodiments of the present application, the method is applied to the server 104 in Figure 1 The method includes the following steps 201 to 203. Wherein:

[0037] In step 201, task parameter information of a task entity to be planned is acquired; the task entity to be planned comprises offshore platforms, helicopters and delivery points. The task parameter information of the offshore platforms comprises quantity information of the offshore platforms, position coordinate information of each offshore platform, resource capacity information of each offshore platform and distance information between the offshore platforms. The task parameter information of the helicopters comprises type information, quantity information, resource delivery capacity information, maximum flight time information and flight speed information of the helicopters. The task parameter information of the delivery points comprises position coordinate information and resource demand quantity information of the delivery points.

[0038] In step 202, a task planning model is constructed according to the task parameter information, with the objective of minimizing total task completion time.

[0039] In step 203, a task planning scheme is obtained by solving the task planning model by using a tabu search algorithm; the task planning scheme comprises a helicopter task sequence of each helicopter to be planned, helicopter arrival time, helicopter departure time and helicopter delivery capacity of each delivery point to be planned, and loading start time and loading end time of each offshore platform to be planned; the helicopter task sequence comprises a delivery point sequence and an offshore platform node sequence.

[0040] By implementing the above steps 201 to 203, the present application can break the status quo of "information island" of resources of each platform in offshore multi-platform helicopter delivery tasks, realize effective integration and collaborative use of multi-platform helicopter resources, and further significantly improve efficiency and accuracy of offshore multi-platform helicopter delivery tasks.

[0041] In another exemplary embodiment of the present application, in step 203, the task planning model is solved by using a tabu search algorithm to obtain a task planning scheme, which specifically comprises:

[0042] All the delivery points are traversed, each delivery point is assigned a delivery helicopter and an offshore platform, an initial task planning scheme is obtained, and a target function value corresponding to the initial task planning scheme is taken as a current optimal solution.

[0043] Based on the initial task planning scheme, a hierarchical field structure method is used to obtain a neighborhood solution set; the hierarchical field structure method comprises design of a cross-helicopter collaborative neighborhood structure and a neighborhood structure for optimization of a single-helicopter delivery path.

[0044] If the number of neighborhood solutions in the neighborhood solution set is less than or equal to a preset number threshold, all the neighborhood solutions in the neighborhood solution set are taken 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 of the preset number threshold are randomly selected from the neighborhood solution set according to a preset proportion to obtain the candidate solution set.

[0045] The candidate solution set is traversed, if the current candidate solution satisfies a preset condition, the current candidate solution is taken as a current optimal solution, and a tabu list is updated; until the candidate solution set is traversed completely, the current optimal solution is taken as an optimal objective function value, and a task planning scheme is obtained; the preset condition includes that the current candidate solution does not violate a tabu feature in the tabu list or satisfies a pardon criterion; the pardon criterion is that an objective function value corresponding to the current candidate solution is less than an objective function value corresponding to the current optimal solution.

[0046] In another exemplary embodiment of the present application, the traversing all delivery points comprises: assigning a delivery helicopter and a sea platform to each delivery point to obtain an initial task planning scheme, specifically comprising:

[0047] The helicopter closest to the current delivery point is taken as the delivery helicopter of the current delivery point.

[0048] A first timing parameter of the delivery helicopter arriving at the current delivery point is updated; the first timing parameter includes a first arrival time of the delivery helicopter arriving at the current delivery point, a departure time, a remaining delivery capacity on the helicopter after delivery according to a maximum demand, and position coordinate information.

[0049] When the delivery capacity on the delivery helicopter of the current delivery point is equal to 0, a sea platform closest to the position of the delivery helicopter and having a resource capacity greater than a preset capacity threshold is taken as the sea platform of the current delivery point.

[0050] A second timing parameter of the delivery helicopter arriving at the sea platform is updated; the second timing parameter includes a loading start time of the delivery helicopter arriving at the sea platform, a loading end time, a delivery capacity on the helicopter after loading is completed, and position coordinate information.

[0051] In another exemplary embodiment of the present application, the cross-helicopter coordinated neighborhood structure is constructed based on exchanging delivery nodes in a cross-helicopter task sequence and re-arranging sea platforms in a helicopter sequence.

[0052] The exchanging delivery nodes in the cross-helicopter task sequence specifically comprises:

[0053] The helicopter task sequences in the initial task planning scheme are combined two by two to obtain a plurality of task sequence combinations.

[0054] All the task sequence combinations are traversed, one delivery task point in two helicopter task sequences in a current task sequence combination is randomly selected for exchange, a neighborhood solution is generated and added to a neighborhood solution set, until all the task sequence combinations are traversed; the first tabu feature includes a neighborhood structure name and sorted two helicopter numbers.

[0055] The re-arranging sea platforms in the helicopter sequence specifically comprises:

[0056] Traverse the helicopter task sequence in the initial task planning scheme, generate a neighborhood solution and add it to the neighborhood solution set through the following steps until all offshore platforms in all helicopter task sequences are traversed:

[0057] For the current helicopter task sequence, select an offshore platform as the current offshore platform in turn.

[0058] Replace the current offshore platform with an offshore platform closest to the next delivery point among the offshore platforms other than the current offshore platform to obtain a neighborhood solution and add it to the neighborhood solution set.

[0059] Take the next offshore platform as the current offshore platform and return to the step of "replacing the current offshore platform with an offshore platform closest to the next delivery point among the offshore platforms other than the current offshore platform to obtain a neighborhood solution and add it to the neighborhood solution set" until all offshore platforms in the current helicopter task sequence are traversed; the second tabu feature includes the neighborhood structure name, the selected helicopter number, and the offshore platform label after the exchange.

[0060] In another exemplary embodiment of the application, the neighborhood structure of the single-helicopter delivery path optimization is constructed based on forward insertion of delivery points and reversal of delivery point order within a delivery batch.

[0061] The forward insertion of delivery points specifically includes:

[0062] For each helicopter in the initial task planning scheme, the response importance of each delivery point in the corresponding helicopter task sequence is calculated respectively.

[0063] Insert the path point with the highest response importance into the first position of the corresponding helicopter operation sequence, generate a neighborhood solution, and the third tabu feature includes the neighborhood structure name and the selected helicopter number.

[0064] The reversal of the delivery point order within a delivery batch specifically includes:

[0065] For each helicopter in the initial task planning scheme, traverse each delivery batch of each helicopter respectively.

[0066] For the target delivery batch of the target helicopter, randomly select two delivery points within the target batch; reverse the order of all delivery points between the two delivery points to generate a neighborhood solution; the fourth tabu 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 task planning scheme; the target delivery batch is any delivery batch of the target helicopter.

[0067] In another exemplary embodiment of the application, the calculation formula of the response importance is:

[0068] .

[0069] .

[0070] wherein, represents the response importance of the ith helicopter at the nth delivery point; represents the delivery capacity still needed after the nth delivery point completes delivery; N is the set of all delivery points; is the response waiting time of the ith helicopter at the nth delivery point; represents the delivery amount needed by the nth delivery point at the initial moment; represents the preset response waiting time; represents the arrival time of the ith helicopter to the nth delivery point, represents the moment when the demand of the nth delivery point of the ith helicopter starts; represents the scale coefficient; represents the waiting response coefficient.

[0071] In another exemplary embodiment of the present application, as shown in Figure 3 , a sea multi-platform helicopter delivery task planning method is provided, which is suitable for scenarios such as offshore oil and gas platform groups, offshore wind power clusters, and other scenarios requiring cross-platform personnel / resource transportation. The method specifically includes:

[0072] S1. Construct a task planning model. The constraint conditions of the task planning model include helicopter capacity constraints, flight time constraints, delivery point demand constraints, and operation timing constraints.

[0073] As an optional implementation, the helicopter capacity constraint is as follows:

[0074] .

[0075] .

[0076] .

[0077] wherein, is the set of all helicopters of type k in all offshore platforms; Q i represents the capacity of the ith helicopter; Q k represents the maximum safe capacity of the k type helicopter; represents the number of personnel / resources delivered by the ith helicopter to the nth delivery point; represents the node set (including delivery points and offshore platform locations) of the bth delivery batch of the ith helicopter; is the decision variable indicating 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, = 1; otherwise, = 0; a delivery batch of a helicopter means the process from the helicopter taking off from a sea platform to the helicopter landing on a sea platform after completing the delivery.

[0078] The flight time constraints are as follows:

[0079] .

[0080] .

[0081] wherein, denotes the flight time of the i-th helicopter; denotes the flight time of the i-th helicopter between the delivery points m and n; denotes the maximum flight time of the k-th type of helicopter; the helicopter is in the off state when it is at the delivery point or the sea platform.

[0082] After the helicopter completes a delivery batch, it returns to the sea platform for personnel / resource replenishment and fuel replenishment, and thus the waiting time of the i-th helicopter at the sea platform can be expressed as:

[0083] .

[0084] wherein, denotes the refueling time of the i-th helicopter after completing the b-th delivery batch, denotes the personnel / resource loading time of the i-th helicopter in the b-th delivery batch.

[0085] The delivery point demand constraints are as follows:

[0086] .

[0087] .

[0088] wherein, denotes the delivery amount required by the n-th delivery point at the initial time; S denotes the set of helicopters of all sea platforms, B i denotes the set of all delivery batches of the i-th helicopter; K denotes the set of all helicopter types.

[0089] The operation sequence constraints are as follows:

[0090] The sequence constraints existing in the process of the helicopter arriving at different delivery points in the same delivery batch include:

[0091] .

[0092] wherein, denotes the time consumption of the i-th helicopter to unload personnel / resources at the n-th delivery point; denotes the time when the i-th helicopter arrives at the n-th delivery point; denotes the time when the i-th helicopter leaves the n-th delivery point; denotes the set of all delivery points of the i-th helicopter in the b-th delivery batch; S denotes the set of all helicopters of offshore platforms, satisfying .

[0093] S2. According to the characteristics of the model, an optimization objective function is proposed.

[0094] In order to complete the delivery task as soon as possible, the objective function of the delivery task planning is set as minimizing the task completion time F, and the objective function of the task planning model is:

[0095] .

[0096] .

[0097] .

[0098] wherein, F denotes the objective function value; denotes the maximum completion time of all delivery tasks of the i-th helicopter; S denotes the set of all helicopters of offshore platforms; denotes the completion time of the delivery task of the i-th helicopter in the b-th delivery batch; B i denotes the set of delivery batches of the i-th helicopter; n denotes the n-th delivery point; denotes the set of all delivery points of the i-th helicopter in the b-th delivery batch; denotes the time when the i-th helicopter leaves the n-th delivery point.

[0099] S3. A tabu search algorithm is designed. Specifically, the following steps are included:

[0100] S31. Initial solution construction.

[0101] As an optional implementation, S31 specifically includes the following steps:

[0102] S311. Loop through all delivery points, and select the closest helicopter i to the delivery point n as the delivery carrier of the delivery point.

[0103] S312. Update the timing parameters of the helicopter i after arriving at the delivery point n, including the arrival time, the leaving time, the remaining delivery personnel / resources on the helicopter after delivering according to the maximum demand, and the location of the helicopter.

[0104] S313. When the number of personnel / resources on the helicopter i equals 0, go to the nearest offshore platform with sufficient capacity to replenish personnel / resources, update the arrival time, platform waiting time, number of personnel / resources delivered after helicopter loading, and location of the helicopter.

[0105] S314. Check if the demand of all delivery points is satisfied, if yes, complete the initial solution construction, otherwise return to S311. In S314, the initial solution includes the sequence of all delivery points that the helicopter needs to deliver and the sequence of offshore platform nodes, as well as the arrival time, departure time, and number of personnel / resources delivered at each delivery point, and the loading start time and end time of the offshore platform.

[0106] S32. Neighborhood structure design and taboo feature extraction. Neighborhood structure design, using the idea of hierarchical field structure design, proposes 2 layers of 4 neighborhood structures, and extracts the corresponding taboo features, and clearly defines the candidate solution selection method, including:

[0107] S321. First layer: cross-helicopter coordination neighborhood structure, which is based on cross-helicopter task sequence exchange delivery node and re-arranging offshore platforms in the helicopter sequence.

[0108] S3211. Cross-helicopter task sequence exchange delivery node, specifically including: the input of this neighborhood structure is a feasible solution, by combining, selecting two helicopter task sequences in the feasible solution, randomly exchanging one delivery point in each sequence in each helicopter combination, and each helicopter combination generates a neighborhood solution; a cross-helicopter task sequence exchange delivery point diagram is shown in Figure 4 , randomly selecting one delivery point in the delivery point sequence of two helicopters in the feasible solution to exchange.

[0109] The extracted taboo features include: neighborhood structure name, sorted two helicopter numbers.

[0110] S3212. Re-arranging offshore platforms in the helicopter sequence, specifically including: the input of this neighborhood structure is a feasible solution, in which a helicopter task sequence is selected in turn, a offshore platform is selected in turn as a supply platform, by calculation, one of the remaining available offshore platforms is selected as the nearest offshore platform to the next delivery point, to replace the original supply platform, and a neighborhood solution is generated for each replacement of a supply platform. The diagram of re-arranging offshore platforms in the helicopter sequence is shown in Figure 5 .

[0111] The extracted taboo features include: neighborhood structure name, selected helicopter number, and exchanged platform label.

[0112] S322. The second layer: single-helicopter delivery path optimization, which is based on forward insertion of high-priority delivery points, and reverse construction of delivery point order within delivery batch.

[0113] S3221. Forward insertion of delivery points: the input of this neighborhood structure is a feasible solution, in which a helicopter is selected one by one, and the response importance of each delivery point in the delivery sequence is calculated , the most important point in the path is selected, and a more forward position in the path sequence is randomly inserted, and a neighborhood solution is generated for each selected helicopter. The schematic diagram of forward insertion of delivery points in the helicopter sequence is shown in Figure 6 .

[0114] Response importance Importance , is expressed as:

[0115] .

[0116] .

[0117] wherein, represents the response importance of the ith helicopter at the nth delivery point; represents the delivery capacity still needed after the nth delivery point completes delivery; N is the set of all delivery points; is the response waiting time of the ith helicopter at the nth delivery point; represents the delivery amount required by the nth delivery point at the initial time; represents the preset response waiting time; represents the arrival time of the ith helicopter to the nth delivery point, represents the time when the demand of the nth delivery point of the ith helicopter starts; represents the scale coefficient; represents the waiting response coefficient.

[0118] The extracted tabu features include: neighborhood structure name, selected helicopter number.

[0119] S3222. Reverse of delivery point order within delivery batch: the input of this neighborhood structure is a feasible solution, in which a helicopter delivery sequence is selected one by one, and a delivery batch with a delivery point quantity greater than 1 is selected one by one, and the delivery order is reversed to reconstitute a new delivery sequence, and a neighborhood solution is generated for each reversed delivery batch. The schematic diagram of reverse of delivery point order within delivery batch is shown in Figure 7 .

[0120] The extracted tabu features include: neighborhood structure name, selected helicopter number, delivery batch number, and two delivery point numbers involved in the reverse.

[0121] S33. Candidate solution generation.

[0122] The number of neighborhood solutions generated by S32 is very large. In order to improve the calculation efficiency, candidate solutions need to be selected from the neighborhood solutions. The way to do this can be: according to the two-layer neighborhood structure designed in 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, and when the number of neighborhood solutions is greater than the preset number, such as 50, the number of neighborhood solutions generated according to the two-layer neighborhood structure is randomly selected according to the proportion, and the number of candidate solutions does not exceed 50.

[0123] S34. Tabu list update mechanism design, including designing tabu length to update tabu list and designing special pardon criteria to release neighborhood solutions.

[0124] The design of tabu length to update tabu list and the design of special pardon criteria to release neighborhood solutions specifically include:

[0125] Set the length of the tabu list.

[0126] The special pardon criteria is that when the objective function corresponding to the candidate solution is better than the current optimal solution, ignore the restriction of the tabu features in the tabu list, and update the current optimal solution with the better solution.

[0127] S4. Use the proposed tabu search algorithm to optimize the proposed objective function to obtain a helicopter task planning scheme, specifically including task allocation and delivery timing planning scheme, and the implementation process is as shown in Figure 8

[0128] In another exemplary embodiment of the present application, the task parameters are set: the number of offshore platforms is 3, the corresponding coordinates are (100, 0), (50, 0), and (100, 50), each offshore platform is equipped with 2 helicopters of the same model, the speed is 150 km / h, the range is 4 hours, the refueling rate is that each minute of refueling can make the helicopter travel for 20 minutes, the capacity is 5 units of personnel / resources, the loading and unloading speed is 1 unit of personnel / resources per 8 minutes, the number of delivery points is 10, the corresponding coordinates are randomly generated in the area { (x, y) | 0 < x < 100 and y > x}, the demand is randomly generated in the interval (5, 15) and is an integer unit of personnel / resources.

[0129] The algorithm parameters are set: the length of the tabu list is 8, the maximum number of iterations is 100, and the tabu search algorithm is implemented according to the process shown in Figure 8 The algorithm convergence curve is as shown in Figure 9 The task planning scheme is as shown in Figure 10

[0130] Figure 10 ​​In the figure, the dark gray rectangular blocks correspond to the process of the helicopter loading personnel / resources on the offshore platform, the light gray rectangular blocks represent the transfer process of the helicopter between the delivery points and the offshore platform, and the black rectangular blocks represent the process of the helicopter unloading personnel / resources at the delivery point; the dark gray and black rectangular blocks correspond to the corresponding characters in the upper left corner, which represent the offshore platform number and the delivery point number; the vertical coordinate scale represents the helicopter number, and the horizontal coordinate axis scale represents the time process.

[0131] As can be seen from Figure 10 As can be seen from the figure, because the delivery point demand is greater than the helicopter capacity, multiple deliveries are required to meet the delivery demand of the delivery point; at the same time, the helicopter can realize nearby personnel / resource replenishment between different platforms, effectively improving the delivery efficiency.

[0132] The application also provides an application scenario of the offshore multi-platform helicopter delivery task planning method. Specifically, the offshore multi-platform helicopter delivery task planning method provided in the embodiment can be applied in an offshore wind cluster operation and maintenance scenario. The offshore wind cluster operation and maintenance scenario includes a task demand analysis link, a resource scheduling planning link, and a task execution and monitoring link. In the offshore wind cluster operation and maintenance 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 platform, and determine the specific operation and maintenance demand of each platform, for example, the gear box of a certain platform needs to be replaced with lubricating oil, and the electrical equipment of another platform needs to be repaired by professional technicians, etc. These task demands record detailed information such as the required personnel type, the material type and quantity, and the task urgency. After these task demand information enters the resource scheduling planning link, it is processed by the offshore multi-platform helicopter delivery task planning method provided in the embodiment to obtain a scientific and reasonable helicopter task planning scheme. For example, it is determined which helicopters are responsible for which platform operation and maintenance personnel and material transportation tasks, and the flight route of each helicopter, the takeoff and landing time at each platform, etc. After obtaining the helicopter task planning scheme, it enters the task execution and monitoring link. In this link, the helicopter executes the delivery task of the operation and maintenance personnel and materials according to the planning scheme, and at the same time, the operation and maintenance team tracks the flight state of the helicopter and the execution progress of the delivery task in real time through the monitoring system, to ensure that the task can be completed safely and efficiently.

[0133] The offshore multi-platform helicopter delivery task planning method provided in the embodiment belongs to the resource scheduling planning link in the offshore wind cluster operation and maintenance scenario. Through the application of the offshore multi-platform helicopter delivery task planning method provided in the embodiment in the offshore wind cluster operation and maintenance scenario, the deployment efficiency of operation and maintenance resources can be effectively improved, the operation and maintenance cost can be reduced, the stable operation of the offshore wind cluster can be ensured, and the economic benefit and reliability of the offshore wind industry can be improved.

[0134] Based on the same inventive concept, the embodiments of the present application also provide a sea multi-platform helicopter delivery task planning device for implementing the above-mentioned sea multi-platform helicopter delivery task planning method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above-mentioned method, so the specific limitations in one or more sea multi-platform helicopter delivery task planning device embodiments provided below can refer to the limitations of the sea multi-platform helicopter delivery task planning method in the foregoing, which will not be described here again.

[0135] In one exemplary embodiment, as shown in Figure 11 A sea multi-platform helicopter delivery task planning device is provided, which includes:

[0136] An information acquisition module 301 is configured to acquire task parameter information of a task entity to be planned; the task entity to be planned includes a sea platform, a helicopter and a delivery point.

[0137] A model construction module 302 is configured to construct a task planning model according to the task parameter information, with the objective of minimizing the total time for completing the task.

[0138] A task planning scheme generation module 303 is configured to solve the task planning model by using a tabu search algorithm to obtain a task planning scheme; the task planning scheme includes a helicopter task sequence of each helicopter to be planned, a helicopter arrival time, a helicopter departure time and a helicopter delivery capacity of each delivery point to be planned, a loading start time and a loading end time of each sea platform to be planned; the helicopter task sequence includes a delivery point sequence and a sea platform node sequence.

[0139] In an exemplary embodiment, a computer device can be provided, which can be a server or a terminal, and an internal structure diagram of the computer device can be as shown in Figure 12As shown in the figure. The computer device includes a processor, a memory, an Input / Output (I / O) interface, and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store task planning processing data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a multi-platform helicopter launching task planning method on the sea.

[0140] Those skilled in the art can understand that, Figure 12 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the method embodiments described above.

[0141] In one exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to implement the steps in each of the method embodiments described above.

[0142] In one exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to implement the steps in each of the method embodiments described above.

[0143] 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 for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of related data need to comply with relevant regulations.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present 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 storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive 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. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0145] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0146] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0147] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present 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; 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. The collaborative neighborhood structure between helicopters is based on exchanging delivery nodes in the helicopter mission sequence and rearranging the offshore platforms in the helicopter sequence. Exchanging delivery nodes across helicopter task sequences specifically includes: combining helicopter task sequences in the initial task planning scheme in pairs to obtain multiple task sequence combinations; traversing all task sequence combinations, randomly selecting a delivery task point from two helicopter task sequences in the current task sequence combination for exchange, generating a neighborhood solution and adding it to the neighborhood solution set until all task sequence combinations are traversed; the first taboo feature includes the neighborhood structure name and the two sorted helicopter numbers; Rearranging offshore platforms in the helicopter sequence in the nearest order specifically includes: traversing the helicopter mission sequence in the initial mission planning scheme, generating neighborhood solutions and adding them to the neighborhood solution set through the following steps until all offshore platforms in all helicopter mission sequences are traversed: for the current helicopter mission sequence, selecting an offshore platform in turn as the current offshore platform; selecting an offshore platform closest to the next delivery point from 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; taking the next offshore platform as the current offshore platform, returning to the step of "selecting an offshore platform closest to the next delivery point from 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; 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, specifically 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; 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, and 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, specifically including: for each helicopter in the initial mission planning scheme, traversing each delivery batch of each helicopter respectively; for the target delivery batch of the target helicopter, randomly selecting two delivery points within the target batch; reversing the order of all delivery points between the two delivery points to generate a neighborhood solution; the fourth taboo feature includes the neighborhood structure name, the selected helicopter number, the delivery batch number, and the numbers of the two delivery points 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; 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, and if the current candidate solution meets the preset conditions, take the current candidate solution as the current optimal solution and update the taboo table; until the candidate solution set is traversed, take the current optimal solution as the optimal objective function value to 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; the task planning scheme 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.

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 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.

5. The method for planning a multi-platform helicopter delivery mission at sea according to claim 1, 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.

6. 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.

7. 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 the tabu search algorithm to obtain the task planning scheme, 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. The collaborative neighborhood structure between helicopters is based on exchanging delivery nodes in the helicopter mission sequence and rearranging the offshore platforms in the helicopter sequence. Exchanging delivery nodes across helicopter task sequences specifically includes: combining helicopter task sequences in the initial task planning scheme in pairs to obtain multiple task sequence combinations; traversing all task sequence combinations, randomly selecting a delivery task point from two helicopter task sequences in the current task sequence combination for exchange, generating a neighborhood solution and adding it to the neighborhood solution set until all task sequence combinations are traversed; the first taboo feature includes the neighborhood structure name and the two sorted helicopter numbers; Rearranging offshore platforms in the helicopter sequence in the nearest order specifically includes: traversing the helicopter mission sequence in the initial mission planning scheme, generating neighborhood solutions and adding them to the neighborhood solution set through the following steps until all offshore platforms in all helicopter mission sequences are traversed: for the current helicopter mission sequence, selecting an offshore platform in turn as the current offshore platform; selecting an offshore platform closest to the next delivery point from 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; taking the next offshore platform as the current offshore platform, returning to the step of "selecting an offshore platform closest to the next delivery point from 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; 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, specifically 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; 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, and 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, specifically including: for each helicopter in the initial mission planning scheme, traversing each delivery batch of each helicopter respectively; for the target delivery batch of the target helicopter, randomly selecting two delivery points within the target batch; reversing the order of all delivery points between the two delivery points to generate a neighborhood solution; the fourth taboo feature includes the neighborhood structure name, the selected helicopter number, the delivery batch number, and the numbers of the two delivery points 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; 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, and if the current candidate solution meets the preset conditions, take the current candidate solution as the current optimal solution and update the taboo table; until the candidate solution set is traversed, take the current optimal solution as the optimal objective function value to 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; the task planning scheme 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.

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