Efficient optimization method for multi-vessel coordinated offshore wind power operation and maintenance scheduling, and related apparatus

Abstract

Provided in the present application are an efficient optimization method for multi-vessel coordinated offshore wind power operation and maintenance scheduling, and a related apparatus. A constructed multi-vessel coordinated operation and maintenance scheduling model is decomposed into a plurality of single-vessel operation and maintenance optimization scheduling sub-models which meet constraint conditions of the multi-vessel coordinated operation and maintenance scheduling model. After the single-vessel operation and maintenance optimization scheduling sub-models are separately solved, the solutions thereof are combined to obtain the solution of the multi-vessel coordinated operation and maintenance scheduling model, which serves as an optimization strategy for offshore wind power operation and maintenance scheduling. The efficient solution of a multi-vessel coordinated optimization scheduling model for an offshore wind farm is realized, thereby assisting a wind power operator and a third-party operation and maintenance service provider in improving the operation and maintenance task arrangement efficiency, and reducing risks and operation and maintenance costs. Even a large-scale multi-vessel coordinated operation and maintenance scheduling model can be quickly solved to obtain an optimization strategy, achieving high efficiency and computational accuracy.

Description

Efficient optimization method and related device for multi-vessel coordinated offshore wind power operation and maintenance scheduling Technical Field

[0001] The present application relates to an operation and maintenance scheduling optimization method, and specifically to an efficient optimization method and related devices for multi-vessel coordinated offshore wind power operation and maintenance scheduling. Background Art

[0002] Offshore wind farm maintenance scheduling and route planning are core scenarios for intelligent offshore wind farm operations and maintenance (O&M). In practice, O&M tasks must be executed based on the results of scheduling and route planning. Crew Travel Vessels (CTVs) are used to transport technicians and supplies between O&M bases and wind farms. They are widely used due to their lower cost compared to other types of vessels. However, CTVs cannot remain at sea overnight and can only carry lightweight maintenance tools and spare parts. Once maintenance is completed, they must return to the O&M base the same day. Due to the relatively long distance between the O&M base and the wind farm, a vessel typically only makes one round trip per day. Due to the high cost of SOVs, the majority of daily offshore wind farm O&M tasks are currently performed by CTVs. Offshore wind farms have numerous maintenance nodes, while a single CTV can only handle a limited amount of tasks. Therefore, in practical applications, multiple CTVs are often deployed to collaborate to accelerate O&M tasks and address the tight O&M window.

[0003] Offshore wind power operation and maintenance scheduling models are usually MILP (Mixed Integer Linear Programming) problems. There are three main existing methods for solving offshore wind power operation and maintenance scheduling models: exhaustive method, branch and bound method, and heuristic method. Since the computational complexity of solving MILP problems increases exponentially with the scale of the problem, the exhaustive method is only suitable for solving very small-scale operation and maintenance scheduling models and is difficult to adapt to the needs of multi-vessel collaborative operations in offshore wind farm clusters. Commercial solvers are usually based on the branch and bound method, which has much higher solution efficiency than the exhaustive method and can obtain an accurate solution to the original problem. However, when the problem scale is large and the solution time is limited, the branch and bound method still has difficulty returning a satisfactory solution, and only a longer solution time can obtain a better feasible solution. As a compromise, heuristic methods are used to solve the problem approximate solution. But even so, achieving an optimization is still time-consuming and labor-intensive. For example, precise solving techniques like the commercial Gurobi solver can take dozens of hours to solve a multi-ship collaborative optimization problem, while metaheuristic methods like the Large Neighborhood Scheme (LNS) can still take anywhere from tens of minutes to several hours. Furthermore, any changes in external conditions necessitate re-running this time-consuming and labor-intensive optimization process.

[0004] Summary of the Invention

[0005] The purpose of this application is to solve the problems in the prior art and provide an efficient optimization method and related devices for multi-vessel coordinated offshore wind power operation and maintenance scheduling.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] First, this application proposes an efficient optimization method for multi-vessel coordinated offshore wind power operation and maintenance scheduling, including:

[0008] Build a multi-vessel collaborative operation and maintenance scheduling model;

[0009] Construct a multi-vessel collaborative operation and maintenance scheduling decomposition model;

[0010] According to the multi-ship collaborative operation and maintenance scheduling decomposition model, the multi-ship collaborative operation and maintenance scheduling model is decomposed into a plurality of single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the operation and maintenance scheduling model;

[0011] Solve each sub-model of single-ship operation and maintenance optimization scheduling in turn to obtain the solution of each sub-model of single-ship operation and maintenance optimization scheduling;

[0012] The solutions of the single-ship operation and maintenance optimization scheduling sub-models are merged to obtain the solution of the multi-ship collaborative operation and maintenance scheduling model, which is used as the offshore wind power operation and maintenance scheduling optimization strategy.

[0013] Preferably, the objective function of the multi-vessel collaborative operation and maintenance scheduling decomposition model includes:

[0014] Among them, I represents the set of units that need maintenance, V represents the set of ships, and x iv =1 or x iv =0,x iv =1 means that the unit i to be repaired is being repaired by ship v, x iv =0 means that the unit i to be repaired has not been repaired by the ship v, x jv =1 or x jv =0,x jv =1 means that the unit j to be repaired is being repaired by ship v, x jv =0 means that the unit j to be repaired has not been repaired by the ship v, d max =max i,j∈I d ij , represents the maximum distance between the units to be repaired; I(·) is the indicative function, when x iv =x jv When established, I(x iv =x jv )=1, otherwise, I(x iv =x jv )=0;dij Represents the distance from maintenance node i to maintenance node j.

[0015] Preferably, the constraints of the multi-vessel collaborative operation and maintenance scheduling decomposition model include:

[0016] Maintenance task allocation uniqueness constraints:

[0017] Minimum crew mission constraints:

[0018] Limitation of the number of passengers on board a ship:

[0019] Among them, ρ ip represents the number of technicians of type p required for unit i to be repaired, Indicates the maximum number of people that the ship v can carry;

[0020] Weight constraints for operation and maintenance vessels:

[0021] Among them, w i represents the weight of spare parts and maintenance equipment required for unit i to be repaired, Indicates the maximum spare parts weight that the vessel v can carry;

[0022] Ship unit matching constraints:

[0023] Among them, I v represents the set of units that can be repaired by ship v;

[0024] Variable integer constraints:

[0025] Preferably, according to the multi-ship collaborative operation and maintenance scheduling decomposition model, the multi-ship collaborative operation and maintenance scheduling model is decomposed into a plurality of single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the multi-ship collaborative operation and maintenance scheduling model, including:

[0026] The CP-SAT solver is used to solve the decomposition model through the constraint programming method. According to the solution of the decomposition model, the multi-ship collaborative operation and maintenance scheduling model is decomposed into multiple single-ship operation and maintenance optimization scheduling sub-models, and multiple single-ship operation and maintenance optimization scheduling sub-models all meet the constraints of the multi-ship collaborative operation and maintenance scheduling model.

[0027] Preferably, the solution of each single-ship operation and maintenance optimization scheduling sub-model includes: single-ship path planning and operation and maintenance task scheduling.

[0028] Preferably, the merging of the solutions of the single-vessel operation and maintenance optimization scheduling sub-models further includes a conflict check:

[0029] The solutions of each single-ship operation and maintenance optimization scheduling sub-model are checked to see whether the operation and maintenance task allocations overlap, whether the operation and maintenance time conflicts, and whether the time to complete the operation and maintenance tasks is within the given time window. If all the requirements are met, the solutions of each single-ship operation and maintenance optimization scheduling sub-model are merged. Otherwise, the multi-ship collaborative operation and maintenance scheduling model is re-decomposed.

[0030] Secondly, this application proposes a multi-vessel collaborative offshore wind power operation and maintenance scheduling and optimization system, including:

[0031] The first building module is used to build a multi-vessel collaborative operation and maintenance scheduling model;

[0032] The second building block is used to construct a multi-vessel collaborative operation and maintenance scheduling decomposition model;

[0033] a decomposition module, configured to decompose the multi-ship collaborative operation and maintenance scheduling model into a plurality of single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the multi-ship collaborative operation and maintenance scheduling model according to the multi-ship collaborative operation and maintenance scheduling decomposition model;

[0034] A solution module is used to solve the sub-models of the optimization and scheduling of the operation and maintenance of each single ship in turn to obtain the solution of the sub-models of the optimization and scheduling of the operation and maintenance of each single ship;

[0035] The merging module is used to merge the solutions of the single-ship operation and maintenance optimization scheduling sub-models to obtain the solution of the multi-ship collaborative operation and maintenance scheduling model as the offshore wind power operation and maintenance scheduling optimization strategy.

[0036] Preferably, it further includes an inspection module;

[0037] The solutions of each single-ship operation and maintenance optimization scheduling sub-model include: single-ship path planning and operation and maintenance task scheduling;

[0038] The checking module is used to check whether the operation and maintenance task allocations of each single-ship operation and maintenance optimization scheduling sub-model overlap, whether the operation and maintenance time conflicts, and whether the time for completing the operation and maintenance tasks is within a given time window in the solutions of each single-ship operation and maintenance optimization scheduling sub-model before the merging module merges the solutions of each single-ship operation and maintenance optimization scheduling sub-model. If all the requirements are met, the solutions of each single-ship operation and maintenance optimization scheduling sub-model are merged; otherwise, the multi-ship collaborative operation and maintenance scheduling model is re-decomposed.

[0039] In a third aspect, the present application provides an electronic device, comprising:

[0040] Memory for storing computer programs;

[0041] A processor is configured to implement the steps of the above-mentioned method for efficient optimization of offshore wind power operation and maintenance scheduling with multi-vessel collaboration when executing the computer program.

[0042] In a fourth aspect, the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned method for efficient optimization of offshore wind power operation and maintenance scheduling with multi-vessel collaboration.

[0043] Compared with the prior art, this application has the following beneficial effects:

[0044] This application proposes an efficient optimization method for multi-ship collaborative offshore wind power operation and maintenance scheduling, which decomposes the constructed multi-ship collaborative operation and maintenance scheduling model into multiple single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the multi-ship collaborative operation and maintenance scheduling model. After solving them separately, the solutions of each single-ship operation and maintenance optimization scheduling sub-model are merged to obtain the solution of the multi-ship collaborative operation and maintenance scheduling model as an offshore wind power operation and maintenance scheduling optimization strategy. The efficient solution of the multi-ship collaborative optimization scheduling model of offshore wind farms is achieved, helping wind power operators and third-party operation and maintenance service providers to improve the efficiency of arranging operation and maintenance tasks, reduce risks, and reduce operation and maintenance costs. Even in the face of large-scale multi-ship collaborative operation and maintenance scheduling models, the solution can be quickly completed to obtain an optimization strategy with high efficiency and high calculation accuracy.

[0045] This application also proposes a multi-ship collaborative offshore wind power operation and maintenance scheduling efficient optimization system, electronic equipment and computer-readable storage medium, which can realize the above-mentioned multi-ship collaborative offshore wind power operation and maintenance scheduling efficient optimization method through different hardware forms, has broad promotion value, and has all the advantages of the above-mentioned multi-ship collaborative offshore wind power operation and maintenance scheduling efficient optimization method. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0047] FIG1 is a schematic diagram of the first process of the multi-vessel coordinated offshore wind power operation and maintenance scheduling efficient optimization method of the present application;

[0048] FIG2 is a schematic diagram of a second process flow of the multi-vessel coordinated offshore wind power operation and maintenance scheduling efficient optimization method of the present application;

[0049] FIG3 is a comparison chart of the solution speeds of three solution methods in an embodiment of the present application;

[0050] FIG4 is a comparison diagram of the advantages and disadvantages of the solutions obtained by the three solving methods in the embodiment of the present application;

[0051] FIG5 is a connection diagram of an embodiment of an efficient optimization system for offshore wind power operation and maintenance scheduling with multi-vessel collaboration in the present application. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0054] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0055] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the accompanying drawings, or the orientation or position relationship in which the product of the invention is usually placed when in use. This is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, it should not be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0056] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0057] In the description of the embodiments of this application, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art can understand the specific meanings of the above terms in this application based on specific circumstances.

[0058] Offshore wind farms have numerous maintenance nodes and fleeting maintenance windows. In the event of an emergency, timely adjustments and rapid replanning of the operation and maintenance plan are essential, placing extremely high demands on the performance of the optimization and scheduling solution. Currently, combinatorial optimization methods based on operations research are commonly used to establish operation and maintenance scheduling models, and commercial solvers are used to solve the models. However, the computational complexity of this approach increases exponentially with the scale of the problem, making it difficult to adapt to the requirements of multi-vessel collaborative operations in offshore wind farm clusters.

[0059] At present, there are three main common methods for solving the multi-vessel collaborative offshore wind power operation and maintenance scheduling problem: exhaustive method, branch and bound method and heuristic method.

[0060] 1) Exhaustive search method. Also known as brute force search or exhaustive search, it is a basic algorithm for solving optimization problems. The basic idea is to find the optimal solution or a set of optimal solutions to the problem by trying all possible solutions. The main advantage of the exhaustive method is that it can guarantee the global optimal solution to the problem because all possible solutions are considered. However, the main disadvantage of the exhaustive method is that the computational cost may be very high when the solution space is large or the problem complexity is high because a large number of combinations need to be considered. Since the computational cost of solving the MILP problem increases exponentially with the expansion of the problem scale, the exhaustive method can only be applied to solving very small-scale operation and maintenance scheduling models, and it is difficult to adapt to the needs of multi-ship collaborative operations in offshore wind farm clusters.

[0061] 2) Branch and bound method. It is an algorithm for solving mathematical optimization and combinatorial optimization problems, which aims to find the optimal solution to the problem or the optimal solution that meets specific conditions. By dividing the solution space of the problem into multiple subspaces and searching in each subspace, the range of potential solutions is gradually narrowed and the optimal solution is determined. Commercial solvers represented by Gurobi and CPLEX use the branch and bound method as a key component of their mixed integer programming problem solving, and combine other advanced optimization techniques such as pruning, heuristics, linear programming relaxation, polynomial algorithms, etc. to improve performance and efficiency. The solution efficiency of commercial solvers based on the branch and bound method is much higher than that of the exhaustive method. However, when the problem scale is large and the solution time is limited, the branch and bound method still finds it difficult to return a satisfactory solution, and only a longer solution time can obtain a better feasible solution.

[0062] 3) Heuristic method. It is a strategy for solving optimization problems, usually used to solve complex combinatorial optimization problems. Based on experience, rules or heuristic information, it finds approximate optimal solutions or high-quality solutions by quickly exploring the solution space of the problem. The heuristic method is an approximate algorithm. Its main feature is that it does not guarantee to find the global optimal solution during the solution process, but seeks to find a solution close to the optimal solution within a reasonable time. Among them, the Large Neighborhood Search (LNS) algorithm is a heuristic search method specifically used to solve combinatorial optimization problems. Its core idea is to select and explore a large neighborhood space during the search process to find high-quality solutions. The LNS method is usually used to deal with complex problems, where the solution space of the problem is very large, and traditional search methods are difficult to find the optimal solution or high-quality solution efficiently. However, due to the huge solution scale of the multi-vessel collaborative operation and maintenance scheduling model of offshore wind farms, even if the LNS method is used, achieving a single optimization is still time-consuming and labor-intensive, and the optimality of the solution cannot be guaranteed.

[0063] Based on the problems of the above-mentioned existing methods, this application proposes an efficient optimization method and related devices for multi-ship collaborative offshore wind power operation and maintenance scheduling, which decomposes the collaborative optimization problem into multiple single-ship scheduling problems. At the same time, it ensures that the decomposed single-ship scheduling problems meet the constraints of the original problem, greatly improving the solution efficiency of the offshore wind farm operation and maintenance scheduling model.

[0064] The present application is further described in detail below with reference to the embodiments and drawings:

[0065] As shown in FIG1 , a first flow chart of an efficient optimization method for multi-vessel coordinated offshore wind power operation and maintenance scheduling in this application may include:

[0066] S101, build a multi-vessel collaborative operation and maintenance scheduling model.

[0067] It should be noted that the multi-vessel collaborative operation and maintenance scheduling model is a mathematical model used to describe and solve the problem of multi-vessel collaborative operation and maintenance scheduling. Its goal is generally to optimize the effectiveness and efficiency of operation and maintenance scheduling while satisfying various constraints. For example, the multi-vessel collaborative operation and maintenance scheduling model can include ship navigation status, task allocation, time scheduling, resource utilization, and other factors. Offshore wind farms typically have multiple operation and maintenance vessels, which require reasonable scheduling to ensure efficient maintenance and repair of wind turbine equipment. Using the multi-vessel collaborative operation and maintenance scheduling model, the optimal vessel scheduling solution can be found, improving the operational efficiency and economic benefits of the wind farm.

[0068] S102: According to the multi-ship collaborative operation and maintenance scheduling decomposition model, decompose the multi-ship collaborative operation and maintenance scheduling model into a plurality of single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the multi-ship collaborative operation and maintenance scheduling model.

[0069] S103, solving each single-ship operation and maintenance optimization scheduling sub-model in sequence to obtain a solution for each single-ship operation and maintenance optimization scheduling sub-model.

[0070] S104: merging the solutions of the single-vessel operation and maintenance optimization scheduling sub-models to obtain the solution of the multi-vessel collaborative operation and maintenance scheduling model as the offshore wind power operation and maintenance scheduling optimization strategy.

[0071] The core of the efficient optimization method for offshore wind power operation and maintenance scheduling with multi-ship collaboration proposed in this application is a DivideFusion method, which includes constructing a decomposition model, a solution method for the decomposition model, solving the decomposed sub-problems, and a solution merging process. This application decomposes the multi-ship collaborative optimization scheduling problem into two layers of optimization problems for hierarchical solution. The first layer of optimization problem decomposes the multi-ship collaborative optimization scheduling model into a single-ship operation and maintenance scheduling problem, while ensuring that the decomposed single-ship scheduling problem meets the constraints of the multi-ship collaborative optimization scheduling problem, i.e., the original problem; the second layer of problem solves the decomposed multiple single-ship problems in turn, and summarizes the solution results to obtain the solution to the multi-ship collaborative optimization scheduling problem. The efficient solution of the multi-ship collaborative optimization scheduling model for offshore wind farms has been successfully achieved, helping wind power operators and third-party operation and maintenance service providers to improve the efficiency of scheduling operation and maintenance tasks, reduce risks, and reduce operation and maintenance costs.

[0072] As shown in FIG2 , a second flow chart of a multi-vessel coordinated offshore wind power operation and maintenance scheduling efficient optimization method of the present application may include:

[0073] S201, build a multi-vessel collaborative operation and maintenance scheduling model.

[0074] The objective function consists of two parts, as shown in formula (1). min T m +α C ·Z C +α P ·Z P (1)

[0075] Among them, T m Indicates the time required to complete the maintenance task; Z C represents the number of corrective maintenance tasks that are not scheduled into the maintenance plan, as shown in formula (1); Z P represents the number of preventive maintenance tasks that are not scheduled in the maintenance plan, as shown in formula (3), α C and α P is the weight coefficient of the target. Since corrective maintenance has a higher weight, α C >α P .

[0076] The objective function contains three different but related objectives. As the number of tasks scheduled into the maintenance plan increases, that is, Z C and Z P When the time to complete the maintenance is reduced, T m On the contrary, when the number of units scheduled for maintenance decreases, that is, Z C and Z P When the time to complete the maintenance increases, the time T m In order to arrange as many unit maintenance tasks as possible and reduce the overall operation and maintenance time by optimizing the maintenance path, the target Z C and Z P A larger weight forces the model to prioritize more unit maintenance tasks and prioritize corrective maintenance tasks. The weight α C Should be greater than α P And is much greater than 1, that is, α C >α P >> 1. When the number of maintenance tasks for the unit cannot be increased, the total maintenance time can be reduced through path optimization.

[0077] The constraints are shown in equations (4) to (32). Constraint (4) ensures that the time to complete maintenance is not less than the sum of the arrival time of any node and the time to transport technicians and equipment. Constraint (5) ensures that the sending node and the receiving node of each unit can only be visited once. Constraint (6) ensures that the operation and maintenance ship of each unit can only go from the sending node to the receiving node, that is, it cannot return from the receiving node to the sending node. Constraints (7) and (8) ensure that the ship can only visit the sending node and the receiving node of the operation and maintenance base once each. Constraint (9) ensures that the ship returns from the receiving node. Constraint (10) ensures that the number of entries to node j and the number of exits from node j are the same, that is, if a ship visits a node, then the ship must also leave this node. Constraint (11) ensures that the number of visits to the sending node and the receiving node of each unit is the same. Constraint (12) is the ship load capacity constraint. Constraints (13), (14) and (15) are constraints on the number of people on board the ship, which together ensure that the number of technicians on board does not exceed the maximum number of people on board the ship. The number of technicians on board is tracked by constraints (16) and (17). Constraints (18) and (19) are operation and maintenance time window constraints, which ensure that the time a ship performs operation and maintenance tasks is within its allowed time window. Constraint (20) ensures that the time taken for maintenance and navigation during the process of the ship moving from the current node to the next node is compatible with the time it takes to reach the next node. Constraint (21) ensures that the time interval between the ship picking up and dropping off people is greater than the time required to perform maintenance. Constraint (22) is a constraint on the number of technicians available at the operation and maintenance base, which ensures that the sum of the number of technicians on all ships is less than or equal to the number of technicians available at the operation and maintenance base. If the ship needs to be present during the execution of the maintenance task, constraint (23) ensures that the ship only travels directly from the delivery node to the pickup node, that is, waits for the operation and maintenance task to be performed at the same location. Constraints (24)-(27) ensure that the flow of the receiving and sending nodes of the operation and maintenance base is conserved while eliminating invalid node transfers. Constraints (28)-(29) ensure that specific maintenance tasks need to be repaired by specific ships. Constraints (30)-(32) indicate the types of decision variables.

[0078] S202, construct a multi-vessel collaborative operation and maintenance scheduling decomposition model.

[0079] Define the parameters, decision variables, objective function, and constraints of the decomposition model.

[0080] (1) Define the decision variable x of the decomposition model iv Among them, x iv =1, it means that the unit i to be repaired is being repaired by ship v, x iv =0, it means that the unit i to be repaired has not been repaired by the ship v.

[0081] (2) Define the parameters of the decomposition model. V represents the set of ships, v is the ship index, and the total number of ships is n. v =|V|; I represents the set of units that need maintenance, i is the index, and the total number of units that need maintenance is n=|I|; I v represents the set of units that can be repaired by ship v; d ij represents the distance from maintenance node i to maintenance node j; P represents the set of technician types; w i represents the weight of spare parts and maintenance equipment required for unit i to be repaired; ρ ip represents the number of technicians of type p required by unit i; Indicates the maximum number of people that the ship v can carry; Indicates the maximum spare parts weight that the vessel v can carry.

[0082] (3) Establish the objective function:

[0083] Among them, d max =max i,j∈I d ij , represents the maximum distance between the units to be repaired; I(·) is the indicative function; when x iv =x jv When established, I(x iv =x jv )=1, otherwise, I(x iv =x jv )=0.

[0084] The objective function aims to maximize the minimum distance between the maintenance units covered by different maintenance vessels. Specifically, the objective is to find a method for partitioning a set of maintenance tasks so that units that are close to each other are assigned to the same maintenance vessel, while units covered by different maintenance vessels are as far apart as possible.

[0085] (4) Establish the following constraints:

[0086] Establish a unique constraint on maintenance task allocation, that is, the maintenance task of each wind turbine can only be assigned to a unique operation and maintenance ship:

[0087] Establish a minimum unit task constraint, that is, each operation and maintenance ship performs at least one unit's operation and maintenance:

[0088] Establish constraints on the number of people on board a ship:

[0089] Establish the weight constraint for the operation and maintenance vessel:

[0090] Establish ship unit matching constraints, that is, the operation and maintenance ship must support the operation and maintenance tasks of the selected unit:

[0091] Create integer constraints on variables:

[0092] In step S203, the CP-SAT solver of OR-Tools is used to solve the decomposition model through the constraint programming (CP) method, and the multi-ship collaborative optimization scheduling model is decomposed into multiple single-ship operation and maintenance optimization scheduling sub-models.

[0093] In practical applications, OR-Tools (Operations Research Tools) is an open-source operations research and optimization tool designed to find the optimal solution from a large number of possible solutions. OR-Tools provides a variety of solvers, including Gurobi, CPLEX, SCIP, GLPK, GLOP, and CP-SAT. These solvers can handle different types of optimization problems, and you can choose the appropriate solver based on the characteristics and requirements of the problem.

[0094] The CP-SAT solver is a constraint satisfaction problem solver that can solve optimization problems with constraints. During the solution process, it is necessary to define the variables, constraints, and objective function for each sub-model and select an appropriate solution algorithm for calculation.

[0095] When decomposing multiple single-ship operation and maintenance optimization scheduling sub-models, the following methods can be used:

[0096] S203-1, initialize the CP-SAT solver and set an appropriate search time limit according to the problem scale. The solution time should not be too short, and the solver should be able to completely find the global optimal solution of the decomposition model.

[0097] S203-2, select an appropriate search strategy and continuously check whether the constraints are satisfied. If the assignment of a variable causes the constraint to be unsatisfied, the solver can backtrack to the previous state and try other assignments.

[0098] S203-3, continuously evaluate the value of the objective function to determine the quality of the current solution.

[0099] S203-4, when the solver completes the search, it outputs the optimal solution.

[0100] In practical applications, the solver can find multiple feasible optimal solutions. In this case, only one solution needs to be output.

[0101] S203-5. When the solution is completed, check whether the solution state is globally optimal. If it is globally optimal, end the search. If only a feasible solution is returned instead of a globally optimal solution, try adjusting the solver parameters or search strategy, and repeat the process from step S203-1 to step S203-4.

[0102] S203-6, the decision variable x in the solution obtained according to the decomposition model iv By assigning all maintenance tasks to individual maintenance vessels, while ensuring that the constraints of the multi-vessel collaborative operation and maintenance scheduling model are met, this process achieves model decoupling. At this point, for each maintenance vessel's maintenance tasks, combined with the same objective function and constraints as the multi-vessel collaborative operation and maintenance scheduling model, a set of single-vessel operation and maintenance optimization scheduling sub-models can be derived. These single-vessel operation and maintenance optimization scheduling sub-models are uncoupled and can be solved independently. Since a single maintenance vessel is responsible for a limited number of tasks, the single-vessel scheduling model can be solved very quickly.

[0103] S204, solving each single-ship operation and maintenance optimization scheduling sub-model in turn, and merging the solutions to obtain the solution of the entire multi-ship collaborative operation and maintenance scheduling model.

[0104] Considering only one maintenance vessel, the single-vessel operation and maintenance optimization scheduling sub-model applies the solution process from steps S203-1 to S203-4 one by one to obtain a solution for that vessel, including the vessel's path planning and maintenance task scheduling. The same method is applied to each maintenance vessel until solutions are obtained for all vessels.

[0105] Before merging the solutions for each vessel, a conflict check can be performed. This includes checking for overlap in maintenance task assignments, conflicts in maintenance timelines, and whether the maintenance tasks are completed within a given time window. If conflicts are found, the multi-vessel collaborative maintenance scheduling model can be refactored.

[0106] S205, merging the conflict-free single-vessel solutions to obtain the offshore wind power operation and maintenance scheduling optimization strategy for the entire multi-vessel collaborative operation and maintenance scheduling model.

[0107] Offshore wind power operation and maintenance scheduling optimization strategies can include operation and maintenance task allocation, route planning, operation and maintenance time scheduling, spare parts arrangement, and maintenance personnel arrangement under multi-vessel collaboration.

[0108] It should be noted that, in other embodiments of the present application, after obtaining the solution of the multi-vessel collaborative operation and maintenance scheduling model, the feasibility of the solution may be checked again to ensure the availability of the final output result.

[0109] In order to fully evaluate the solution speed and optimization degree of the efficient optimization method for multi-vessel collaborative offshore wind power operation and maintenance scheduling proposed in this application, a set of comparative examples were designed. For different numbers of ships and units, the method proposed in this application was compared with the open source constraint programming solver OR-Tools and the commercial solver Gurobi for directly solving the multi-vessel collaborative model. The main evaluation was made on the solution speed and optimality of the solutions of the three solution methods.

[0110] The example setup included 12 offshore wind turbine maintenance scheduling model tasks, ranging from a single ship with five turbines to four ships with 16 turbines. The scale of the tasks increased exponentially. It should be noted that, under a typical PC environment, the scale of the four-ship, 16-turbine problem far exceeds the optimal solution that can be found using the CP-SAT and Gurobi solvers. Therefore, a time limit of 2.78 hours (10,000 seconds) was set. If no optimal solution is found after this time limit, the solver will return the best feasible solution found so far.

[0111] Figure 3 shows a comparison of the solution speeds of the three solution methods. "DivideFusion" in Figure 3 represents the method proposed in this application. As can be seen from Figure 3, the solution time consumed by the Gurobi solver and the OR-Tools solver increases exponentially with the increase in problem size, quickly reaching the set solution time limit (10,000 seconds), while the solution time of the method proposed in this application never exceeds 10 seconds, resulting in a significant difference in solution performance. Furthermore, the problem size of 16 maintenance units on four ships is far from reaching the upper limit of the solution capability of the method proposed in this application. It is worth noting that the solution time of the OR-Tools solver on the last task experienced an abnormally sharp drop. This may be because the OR-Tools solver found a relatively good solution within a few hundred seconds of starting the solution, but was unable to find a better solution in the subsequent time, thus returning a shorter solution time.

[0112] As shown in Figure 4, it is a comparison chart of the advantages and disadvantages of the solutions obtained by the three solving methods. It can be seen that the objective function value of the feasible solution obtained by the method of the present application is very close to that of the other two exact solution methods, especially when solving problems of a relatively large scale. This also proves that the solution found by the method of the present application within 10 seconds has a similar degree of optimization as the best feasible solution obtained by the exact solution algorithm within 10,000 seconds. In addition, based on the settings of the above examples, it can also be explained that, under the premise of similar solution accuracy, the solution speed of the method proposed in this application is nearly 1,000 times that of the commercial / open source solver based on the branch and bound method. While solving the problem of solving the problem of collaborative operation and maintenance scheduling of multiple ships in offshore wind farms, it is expected to significantly improve the efficiency of operation and maintenance task scheduling of wind power operators and third-party operation and maintenance service providers, reduce operation and maintenance costs, and improve the competitiveness of offshore wind power.

[0113] The method proposed in this application can achieve a second-level solution to the multi-ship collaborative operation and maintenance scheduling problem of an offshore wind farm, greatly improving the efficiency of solving large-scale operation and maintenance scheduling problems without losing the optimality of the solution. While improving the solution efficiency, it is expected to significantly improve the efficiency of the operation and maintenance task scheduling of wind power operators and third-party operation and maintenance service providers, reduce operation and maintenance costs, and improve the competitiveness of offshore wind power. It has brought higher sustainability and competitiveness to the offshore wind power industry, and is expected to promote the effective development and utilization of more wind power resources. In practical applications, the efficient optimization method for offshore wind power operation and maintenance scheduling with multi-ship collaborative operation and maintenance proposed in this application can also be used to solve other optimization problems with the same structure as the offshore wind power multi-ship collaborative operation and maintenance scheduling model.

[0114] As shown in FIG5 , based on the above-mentioned multi-vessel collaborative offshore wind power operation and maintenance scheduling efficient optimization method, this application also proposes a multi-vessel collaborative offshore wind power operation and maintenance scheduling efficient optimization system, including:

[0115] The first building module is used to build a multi-vessel collaborative operation and maintenance scheduling model;

[0116] The second model is used to build a multi-vessel collaborative operation and maintenance scheduling decomposition model;

[0117] a decomposition module configured to decompose the multi-ship collaborative operation and maintenance scheduling model into a plurality of single-ship operation and maintenance optimization scheduling sub-models that satisfy the constraints of the multi-ship collaborative operation and maintenance scheduling model according to the multi-ship collaborative operation and maintenance scheduling decomposition model; the objective function of the plurality of single-ship operation and maintenance optimization scheduling sub-models is to maximize the minimum distance between maintenance units covered by different operation and maintenance ships;

[0118] A solution module is used to solve the sub-models of the optimization and scheduling of the operation and maintenance of each single ship in turn to obtain the solution of the sub-models of the optimization and scheduling of the operation and maintenance of each single ship;

[0119] The merging module is used to merge the solutions of the single-ship operation and maintenance optimization scheduling sub-models to obtain the solution of the multi-ship collaborative operation and maintenance scheduling model as the offshore wind power operation and maintenance scheduling optimization strategy.

[0120] In other embodiments of the multi-ship collaborative offshore wind power operation and maintenance scheduling efficient optimization system of the present application, an inspection module may also be included. The solution of each single-ship operation and maintenance optimization scheduling sub-model includes: single-ship path planning and operation and maintenance task scheduling. The inspection module is used to check whether the operation and maintenance task allocation overlaps, whether the operation and maintenance time conflicts, and whether the time to complete the operation and maintenance task is within a given time window in the solution of each single-ship operation and maintenance optimization scheduling sub-model before the merging module merges the solutions of each single-ship operation and maintenance optimization scheduling sub-model. If all the requirements are met, the solutions of each single-ship operation and maintenance optimization scheduling sub-model are merged. Otherwise, the multi-ship collaborative operation and maintenance scheduling model is re-decomposed.

[0121] It should be noted that in other embodiments of the multi-ship collaborative offshore wind power operation and maintenance scheduling efficient optimization system of the present application, the specific functions of each module can also adopt the embodiments of the aforementioned multi-ship collaborative offshore wind power operation and maintenance scheduling efficient optimization method, which will not be repeated here.

[0122] It should be noted that in the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The modules described as separate components may or may not be physically separated. The components displayed as modules may be one physical unit or multiple physical units, that is, they may be located in one place, or they may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0123] In addition, the modules in the various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0124] An electronic device provided in an embodiment of the present application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the efficient optimization method for multi-vessel collaborative offshore wind power operation and maintenance scheduling as described in any of the above embodiments.

[0125] Another electronic device provided in an embodiment of the present application may further include: an input port connected to the processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processing results of the processor to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanning display, etc.; the communication method adopted by the communication module includes but is not limited to mobile high-definition link technology (HML), universal serial bus (USB), high-definition multimedia interface (HDMI), wireless connection: wireless fidelity technology (WiFi), Bluetooth communication technology, low-power Bluetooth communication technology, and communication technology based on IEEE802.11s.

[0126] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the efficient optimization method for multi-vessel collaborative offshore wind power operation and maintenance scheduling described in any of the above embodiments are implemented.

[0127] The computer-readable storage medium involved in this application includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field.

[0128] For descriptions of the relevant portions of the multi-vessel coordinated offshore wind power operation and maintenance scheduling efficient optimization system, electronic device, and computer-readable storage medium provided in the embodiments of this application, please refer to the detailed description of the corresponding portions of the multi-vessel coordinated offshore wind power operation and maintenance scheduling efficient optimization method provided in the embodiments of this application, and will not be repeated here. In addition, portions of the above-mentioned technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0129] The above are merely preferred embodiments of the present application and are not intended to limit the present application. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. An efficient optimization method for offshore wind power operation and maintenance scheduling with multi-ship collaboration, characterized in that, including: Constructing a multi-ship collaborative operation and maintenance scheduling model; Constructing a multi-ship collaborative operation and maintenance scheduling decomposition model; According to the multi-ship collaborative operation and maintenance scheduling decomposition model, decomposing the multi-ship collaborative operation and maintenance scheduling model into multiple single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the operation and maintenance scheduling model; Solving each single-ship operation and maintenance optimization scheduling sub-model in sequence to obtain the solutions of each single-ship operation and maintenance optimization scheduling sub-model; Combining the solutions of each single-ship operation and maintenance optimization scheduling sub-model to obtain the solution of the multi-ship collaborative operation and maintenance scheduling model, which is used as the optimization strategy for offshore wind power operation and maintenance scheduling.

2. The efficient optimization method for the offshore wind power operation and maintenance scheduling with multi-ship collaboration according to claim 1, characterized in that The objective function of the multi-vessel collaborative operation and maintenance scheduling decomposition model includes: where $I$ represents the set of units to be repaired, $V$ represents the set of ships, and $x$ iv $= 1$ or $x$ iv $= 0$, $x$ iv $= 1$ means that the unit $i$ to be repaired is repaired by the ship $v$, $x$ iv $= 0$ means that the unit $i$ to be repaired is not repaired by the ship $v$, $x$ jv $= 1$ or $x$ jv $= 0$, $x$ jv $= 1$ means that the unit $j$ to be repaired is repaired by the ship $v$, $x$ jv $= 0$ means that the unit $j$ to be repaired is not repaired by the ship $v$, $d$ max $= \max$ i,j∈I $d$ ij represents the maximum distance between the units to be repaired; $I(\cdot)$ is an indicator function, when $x$ iv $= x$ jv holds, $I(x$ iv $= x$ jv ) $= 1$, otherwise, $I(x$ iv $= x$ jv ) $= 0$; $d$ ij represents the distance from the repair node $i$ to the repair node $j$.

3. The efficient optimization method for offshore wind power operation and maintenance scheduling with multi-vessel collaboration according to claim 2, wherein The constraints of the multi-ship collaborative operation and maintenance scheduling decomposition model include: Uniqueness constraint for maintenance task assignment: Minimum Crew Mission Constraint: Restriction on the number of passengers carried by the ship: Among them, ρ ip represents the number of technicians of type p required for the unit i to be repaired, Indicating the maximum number of people that ship v can carry; Operation and maintenance ship deadweight constraint: Among them, w i represents the weight of spare parts and maintenance equipment required for the unit i to be repaired, Indicating the maximum spare part weight that ship v can carry; Matching constraints of ship units: Among them, I v represents the set of units that can be repaired by the ship v; Variable integer constraint:

4. The efficient optimization method for the offshore wind power operation and maintenance scheduling with multi-vessel collaboration according to claim 3, wherein, The step of decomposing the multi-ship collaborative operation and maintenance scheduling model into multiple single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the multi-ship collaborative operation and maintenance scheduling model according to the multi-ship collaborative operation and maintenance scheduling decomposition model includes: Using a CP-SAT solver to solve the decomposition model through constraint programming. According to the solution of the decomposition model, decomposing the multi-ship collaborative operation and maintenance scheduling model into multiple single-ship operation and maintenance optimization scheduling sub-models, and all the multiple single-ship operation and maintenance optimization scheduling sub-models meet the constraints of the multi-ship collaborative operation and maintenance scheduling model.

5. The highly efficient optimization method for offshore wind power operation and maintenance scheduling with multi-ship collaboration according to claim 4, wherein, The solutions of each single-ship operation and maintenance optimization scheduling sub-model include: path planning and operation and maintenance task scheduling of a single ship.

6. The efficient optimization method for the offshore wind power operation and maintenance scheduling with multi-vessel collaboration according to claim 5, characterized in that, Before combining the solutions of each single-ship operation and maintenance optimization scheduling sub-model, conflict checking is also included: Checking whether the operation and maintenance task assignments overlap, whether the operation and maintenance times conflict, and whether the time to complete the operation and maintenance tasks is within the given time window in the solutions of each single-ship operation and maintenance optimization scheduling sub-model. If all requirements are met, then combine the solutions of each single-ship operation and maintenance optimization scheduling sub-model; otherwise, re-decompose the multi-ship collaborative operation and maintenance scheduling model.

7. An efficient optimization system for offshore wind power operation and maintenance scheduling with multi-vessel collaboration, characterized in that, including: A first construction module for constructing a multi-ship collaborative operation and maintenance scheduling model; A second construction module for constructing a multi-ship collaborative operation and maintenance scheduling decomposition model; A decomposition module for decomposing the multi-ship collaborative operation and maintenance scheduling model into multiple single-ship operation and maintenance optimization scheduling sub-models that meet the constraints of the multi-ship collaborative operation and maintenance scheduling model according to the multi-ship collaborative operation and maintenance scheduling decomposition model; A solving module for solving each single-ship operation and maintenance optimization scheduling sub-model in sequence to obtain the solutions of each single-ship operation and maintenance optimization scheduling sub-model; A combining module for combining the solutions of each single-ship operation and maintenance optimization scheduling sub-model to obtain the solution of the multi-ship collaborative operation and maintenance scheduling model, which is used as the optimization strategy for offshore wind power operation and maintenance scheduling.

8. The highly efficient optimization system for offshore wind power operation and maintenance scheduling with multi-vessel collaboration according to claim 7, characterized in that, It also includes an inspection module; The solutions of each single-ship operation and maintenance optimization scheduling sub-model include: path planning and operation and maintenance task scheduling of a single ship; The inspection module is used to check whether the operation and maintenance task assignments overlap, whether the operation and maintenance times conflict, and whether the time to complete the operation and maintenance tasks is within the given time window in the solutions of each single-ship operation and maintenance optimization scheduling sub-model before the combining module combines the solutions of each single-ship operation and maintenance optimization scheduling sub-model. If all requirements are met, then combine the solutions of each single-ship operation and maintenance optimization scheduling sub-model; otherwise, re-decompose the multi-ship collaborative operation and maintenance scheduling model.

9. An electronic device, characterized in that, including: A memory for storing computer programs; A processor, which is used to implement the steps of the method for efficiently optimizing the offshore wind power operation and maintenance scheduling with multi-vessel collaboration as described in any one of claims 1 to 6 when executing the computer program. The steps of the method for efficiently optimizing the offshore wind power operation and maintenance scheduling with multi-vessel collaboration as described in any one of claims 1 to 6 are implemented when the computer program stored in the computer-readable storage medium is executed by the processor.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and the steps of the method for efficiently optimizing the offshore wind power operation and maintenance scheduling with multi-vessel collaboration as described in any one of claims 1 to 6 are implemented when the computer program is executed by the processor.

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