An offshore wind power discrete event simulation optimization event selection method, system, device and medium based on a greedy algorithm
By optimizing the simulation of discrete events in offshore wind power using a greedy algorithm, and combining resource utilization and weather conditions, the problems of resource waste and low simulation efficiency in existing methods are solved, achieving more efficient simulation and improved production efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- POWERCHINA HUADONG ENG CORP LTD
- Filing Date
- 2024-11-28
- Publication Date
- 2026-05-29
AI Technical Summary
Existing discrete event simulation methods for offshore wind power suffer from resource waste and low simulation efficiency in both the logically simple definition method and the highly complex exhaustive method, making it difficult to effectively utilize resources and improve simulation efficiency in complex offshore wind power construction.
A greedy algorithm is adopted, which combines resource utilization and weather conditions to optimize the simulation event selection method. By establishing a discrete event simulation process queue, an executable process queue, and a pre-simulation analysis, the simulation event with the highest resource utilization is selected, and the simulation status is dynamically updated.
It improves resource utilization and simulation efficiency in offshore wind power construction, reduces computational costs, achieves simulation results that are closer to reality, and enhances production efficiency and decision-making accuracy.
Smart Images
Figure CN122114581A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of offshore wind power technology, and particularly relates to the field of offshore wind power discrete event simulation technology, especially to a method, system, equipment and medium for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm. Background Technology
[0002] Discrete Event Simulation (DES) is a dynamic system simulation technique widely used in industries such as industry, transportation, and communications. It studies the behavior and performance of a system under different conditions by simulating the occurrence and processing of discrete events. The core of DES lies in the dynamic scheduling and processing of events, accurately reflecting the complex processes of a system in actual operation. Offshore wind power construction simulation systems are typically characterized by numerous conditions and complex logic. The installation process of each wind turbine involves dozens of procedures, each containing numerous dependencies and resource conflicts.
[0003] To ensure the smooth and orderly execution of discrete event simulations for offshore wind power, the following event selection methods are commonly used:
[0004] 1) Definition method: This involves defining the initial construction sequence of the wind turbines, and the simulation will proceed sequentially according to this order. Although this approach is logically simple, it is rigid and inflexible. Many executable procedures that meet the construction conditions will continue to wait for the procedures ahead of them to complete before their turn in the sequence, resulting in wasted resources and a discrepancy with the actual construction process, leading to low accuracy of the simulation results.
[0005] 2) Exhaustive search: This method exhaustively lists all possible construction scenarios and selects the optimal simulation route. The simulation complexity of this method increases exponentially with the number of procedures, and it is usually only suitable for scenarios with a small number of simulations. Offshore wind power projects typically involve hundreds of wind turbines, each with dozens of procedures. Although a series of search and optimization algorithms can be used to reduce the simulation time complexity, the computational and time costs are still high, the simulation efficiency is low, and it is not suitable for general construction simulation scenarios.
[0006] To accurately reflect real-world construction scenarios, the simulation sequence of offshore wind power construction procedures should consider both resource consumption and interdependencies between procedures. Given the high complexity of construction projects and the need for efficient simulation, researching a method for selecting simulation events to improve resource utilization and construction efficiency is of great significance. Summary of the Invention
[0007] The first objective of this invention is to provide a method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm.
[0008] Therefore, the above-mentioned objective of the present invention is achieved through the following technical solution:
[0009] A greedy algorithm-based method for optimizing event selection in discrete event simulation of offshore wind power, wherein when the current process in discrete event simulation is completed and the next construction event is selected, the greedy algorithm is combined to design subsequent event selection methods and logical rules, specifically including the following steps:
[0010] S1: Establish a discrete event simulation process queue: Based on the content of the offshore wind power construction project, establish a discrete event simulation process queue, select initial processes for simulation, perform discrete event simulation according to the set simulation logic, and dynamically update the impact of completed initial processes on the simulation queue.
[0011] S2: Establish an executable process queue: Select executable processes that are not affected by preceding processes at the current moment of the simulation to form an executable process queue. The process is not constrained by preceding processes at the current simulation stage, including two cases: all preceding processes of the process have been completed or the process has no preceding processes.
[0012] S3: Analyze the list of executable processes under the current step: Perform pre-simulation on all processes in the list of executable processes without updating the simulation state. The pre-simulation needs to meet the resource and weather conditions required when the process was set up. Obtain the simulation results based on its own duration interruption attribute. Finally, record all pre-simulation results in the list of executable processes.
[0013] Resources refer to the specific resources used in the construction process, such as human resources, hoisting resources, or transportation resources.
[0014] Weather conditions refer to external environmental disturbances that need to be addressed during construction processes, such as temperature and rainfall.
[0015] The above two factors work together to make logical judgments in construction simulation prevention.
[0016] S4: Complete the pre-simulation: Compare all pre-simulation results, select the method with the highest resource utilization based on the greedy algorithm to determine the next simulation event for the discrete event simulation, and dynamically update the impact of the process after the simulation of the event ends, in preparation for the next round of simulation.
[0017] While adopting the above technical solutions, the present invention may also adopt or combine the following technical solutions:
[0018] As a preferred technical solution of the present invention: In step S1, the process events are established by the operator based on the characteristics of each stage of the construction project. Each process event includes the construction duration, whether the construction process can be interrupted, and the constraints that restrict the construction.
[0019] The initial sequence of construction processes is determined by the operator when creating the list. When multiple processes can be performed simultaneously, the order of construction will be determined by this sequence.
[0020] Here, the list refers to the initial procedure list created by the operator in step S1, which only contains the initial key information of the construction procedure.
[0021] As a preferred technical solution of the present invention: In step S3, when performing pre-simulation on the executable process queue, the processes are subject to different constraints due to their own characteristics, and require different types of resources, resulting in different pre-simulation results. Selecting a suitable process as the subsequent simulation will directly affect the simulation efficiency and resource utilization.
[0022] As a preferred technical solution of the present invention: when selecting subsequent procedures based on the pre-simulation results, the analysis of the pre-simulation results needs to consider the resource consumption of each procedure in the executable list and the start time of each procedure. Assuming the current simulation time is T0, and the start times of the n procedures in the pre-simulation results are T... n The completion time of the construction process is T. e The resource utilization rate of each pre-simulation process is In this formula, the numerator is the actual construction time of process n, and the denominator is the total duration from the current simulation time to the end of the process; referring to the formula, the start time T of the process is... n The earlier, the higher the resource utilization rate σ. n The higher the T n At time T0, the resource utilization rate reaches its maximum.
[0023] Based on the greedy algorithm, the simulation process with the highest resource utilization rate and the earliest start time in the current state is selected as the subsequent simulation process. If there are cases where the earliest start times are the same, the process with the earlier sequence number is selected for simulation according to the rules, thus completing the selection process for the current round of offshore wind power discrete event simulation.
[0024] As a preferred technical solution of the present invention: In step S4, after the simulation of the current round is completed, the executed process is marked as completed, and the resources consumed by the process and the impact of subsequent processes are updated into the system status; the uncompleted processes in the executable process queue are carried over to the next round of simulation for comparison again, until all process simulations are completed.
[0025] The second objective of this invention is to provide a system for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm.
[0026] Therefore, the above-mentioned objective of the present invention is achieved through the following technical solution:
[0027] A system for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm includes the following modules:
[0028] The discrete event simulation process queue establishment module is used to establish a discrete event simulation process queue based on the content of the offshore wind power construction project, select initial processes for simulation, perform discrete event simulation according to the set simulation logic, and dynamically update the impact of completed initial processes on the simulation queue.
[0029] An executable process queue establishment module is used to select executable processes that are not affected by preceding processes at the current time of the simulation to form an executable process queue. The process is not constrained by preceding processes at the current simulation stage, including two cases: all preceding processes of the process have been completed or the process has no preceding processes.
[0030] The executable process list analysis module is used to perform pre-simulation on all processes in the executable process queue without updating the simulation state. The pre-simulation needs to meet the resource and weather conditions required when the process is set up. The simulation results are obtained based on its own duration interruption attribute. Finally, all pre-simulation results in the executable process list are recorded.
[0031] The optimization module compares all pre-simulation results and selects the next simulation event for discrete event simulation based on a greedy algorithm, using the method with the highest resource utilization. After the simulation of this event ends, the impact of the process is dynamically updated to prepare for the next round of simulation.
[0032] The third objective of this invention is to provide an electronic device comprising a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other via the communication bus. The memory stores computer programs. The processor executes the computer programs stored in the memory to implement the steps of the offshore wind power discrete event simulation optimization event selection method based on the greedy algorithm described above.
[0033] Another objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the offshore wind power discrete event simulation optimization event selection method based on a greedy algorithm as described above.
[0034] This invention provides a method, system, equipment, and medium for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm. The method decomposes each process event in the offshore wind power discrete event simulation process into an algorithmic step. After completing the initial process, it determines the processes that can be carried out on the wind turbine at the current stage based on the dependencies within each process. It then conducts pre-simulations for each process to explore various possibilities for subsequent construction. Based on the greedy algorithm, it obtains the subsequent process with the highest resource utilization rate under the current state, achieving the optimal solution for the current simulation round. This process is then extended sequentially to complete the wind turbine simulation. The method takes into account both the dependencies between processes and the resource utilization at each stage.
[0035] This invention addresses complex offshore wind power construction models by optimizing the selection logic of simulation procedures to improve resource utilization and construction efficiency. The method includes establishing a discrete model of offshore wind power construction procedures, handling procedure dependencies, designing pre-simulation methods for procedures, and analyzing resource utilization based on a greedy algorithm. While ensuring simulation efficiency, it provides reasonable analysis and judgment for various construction sequences. It closely reflects the real-world construction environment, offering flexibility and higher confidence levels. It boasts a fast simulation response speed while requiring relatively low computing power. Attached Figure Description
[0036] Figure 1 The flowchart shows the method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm provided by this invention.
[0037] Figure 2 This is a schematic diagram of the executable process.
[0038] Figure 3 This is a schematic diagram of the pre-simulation results.
[0039] Figure 4 This is a comparison chart of resource utilization rates. Detailed Implementation
[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.
[0041] like Figure 1 As shown, a method for optimizing event selection rules in offshore wind power discrete event simulation is completed through steps such as establishing a discrete model of offshore wind power construction procedures, handling procedure dependencies, designing a procedure pre-simulation method, and analyzing resource utilization based on a greedy algorithm. The method requires the following four designs:
[0042] (1) Establish a discrete model of offshore wind power construction procedures
[0043] Discrete models of construction procedures are the foundation of simulation. These models need to reference real-world factors as much as possible to ensure the realism of the simulation. Resource requirements for each procedure and the immediate dependencies within each procedure are determined by identifying its basic type. Based on the construction experience of on-site personnel at the Mengsong 600MW offshore wind power project, a construction event table for the standard wind turbine (EN171-4.5MW-110HH) was obtained, as detailed in Table 1 below.
[0044] Table 1
[0045]
[0046] The main structural data involved in the construction of a single wind turbine are as follows:
[0047] 1) Four tower sections T1 to T4
[0048] After the multiple tower sections are transported, the relevant construction processes for each tower section are carried out. Each tower section includes two steps: transshipment and hoisting. The transshipment process must be completed before the hoisting process. The resources required for the transshipment process are transport vehicles / ships, and the resources required for the hoisting process are hoisting vehicles / ships. There are also internal logical relationships between the tower sections. The construction of tower section T3 must be completed after the hoisting of tower section T4, the construction of tower section T2 must be completed after the hoisting of tower section T3, and so on, until all tower sections are constructed in sequence.
[0049] 2) Cabin and wheel hubs
[0050] Construction of the nacelle and hub must be completed after all tower construction is finished. The nacelle and hub construction involves two steps: transshipment and hoisting. The transshipment process must be completed before the hoisting process. The transshipment process requires transport vehicles / ships, while the hoisting process requires hoisting vehicles / ships.
[0051] 3) Three blades
[0052] The blades themselves do not have a defined construction sequence, but they cannot be installed simultaneously. Therefore, the prerequisite for subsequent blade installation is the completion of previous blade installations. Each blade involves two steps: transshipment and hoisting. The transshipment process must be completed before the hoisting process. The transshipment process requires transport vehicles / ships, while the hoisting process requires hoisting vehicles / ships. After all transshipment and hoisting are completed, the blade stretching and finishing work is performed.
[0053] (2) Analyze the list of executable procedures in the current state of the system.
[0054] To obtain the optimal solution for each stage of the construction simulation, it is necessary to statistically analyze all executable procedures within the current system state. An executable procedure is defined as one that has no preceding procedures in the current simulation stage, including either all its preceding procedures have been completed or it has no preceding procedures. Figure 2 As shown.
[0055] The process for obtaining the list of executable steps is as follows:
[0056] 1) At the beginning of the wind turbine simulation, all construction procedures without immediate predecessor procedures are selected as the list of executable procedures in the initial state.
[0057] 2) Select the first process to be simulated based on the resource utilization of each process.
[0058] 3) If multiple processes in the list of executable processes have the same resource utilization status, the process with the highest sequence number when each process was set up will be the process for that round of simulation.
[0059] 4) After completing the simulation of this process, update the simulation status of this process to "completed" in the standard wind turbine construction event diagram ( Figure 1 Eliminate the immediate preconditions that are subject to the event in the process.
[0060] 5) Re-search all unfinished executable procedures in the construction process, including those not selected in the first round of procedure list, and new executable procedures that are affected by the first completed procedure and have no preceding procedures.
[0061] 6) Repeat the above process until the fan simulation is complete.
[0062] (3) Design the pre-simulation process
[0063] Design (2) outlines the selection process for the list of executable steps; however, the specific selection method needs to be obtained through pre-simulation of the list. The pre-simulation process is as follows:
[0064] 1) Obtain the current simulation clock position, resource usage information, and list of executable procedures that need to be pre-simulated in the current discrete simulation system.
[0065] 2) Considering all factors such as weather and manpower, perform discrete event simulations on the processes in the list in sequence, and obtain and count the construction start time and resource usage of the process in this simulation.
[0066] 3) It should be noted that since the purpose of pre-simulation is to collect simulation information of all processes under the current system status, the simulation results are only saved after the simulation is completed, and the system status such as resource construction progress is not updated.
[0067] 4) Collect the pre-simulation results for each process to prepare for subsequent analysis of resource usage. After completing the analysis, wait for the next round of executable process list evaluation until the simulation is complete. The pre-simulation results are as follows: Figure 3 As shown.
[0068] (4) Resource utilization analysis based on greedy algorithm
[0069] After collecting the pre-simulation results of the current executable process list, resource utilization analysis needs to be performed on each result to obtain the simulation selection with the highest resource utilization rate under the current state. By maximizing resource utilization in each round, the event selection rule method for offshore wind power discrete event simulation optimization based on greedy algorithm is obtained. The specific calculation process is as follows:
[0070] Assume there are n executable processes in the current list, and each process has a different start time for using resources. The start time of the n processes in the pre-simulation results is T. n The current time of the system simulation clock is T0, and the completion time of each construction process is T. e Based on the above parameters, the resource utilization rate of each process can be calculated as follows: In this formula, the numerator is the actual construction time of process n, and the denominator is the total duration from the current simulation moment to the end of the process. Referring to the formula, the start time T of the process... n The earlier, the higher the resource utilization rate σ. n The higher the T n At time T0, resource utilization reaches its highest level, as shown in Table 2 and... Figure 4 As shown.
[0071] Based on the greedy algorithm, the simulation process with the highest resource utilization and earliest start time in the current state is selected as the subsequent simulation process. If there are cases where the earliest start times are the same, the process with the earlier sequence number is selected for simulation according to the rules, completing the selection process for the current round of offshore wind power discrete event simulation. After the simulation of this process is completed, it is marked as completed, and the resources consumed by this process and the impact of subsequent processes are updated in the system state. Unfinished processes in the executable process queue are carried over to the next round of simulation for comparison again. This continues until all processes are simulated.
[0072] Table 2
[0073] Executable process Start time System simulation time End time resource utilization rate Executable process A 10:05 8:00 18:00 79.17％ Executable process B 12:10 8:00 18:00 58.33％ Executable process C 9:20 8:00 18:00 86.67％ Executable process D 13:50 8:00 18:00 41.67％ Executable process E 8:00 8:00 18:00 100.00％
[0074] In summary, the greedy algorithm-based method for optimizing event selection rules in offshore wind power discrete event simulation provided by this invention closely aligns with actual production conditions. While ensuring system simulation efficiency, it achieves a reasonable arrangement of complex construction procedures for offshore wind power. In this embodiment, the simulation system dynamically breaks down the process selection problem faced at each stage of the simulation. It combines pre-simulation of executable processes with a greedy algorithm approach to maximize overall resource utilization, improve production efficiency, and reduce production costs, thus helping enterprises make better production decisions.
[0075] This invention also provides a system for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm, comprising the following modules:
[0076] The discrete event simulation process queue establishment module is used to establish a discrete event simulation process queue based on the content of the offshore wind power construction project, select initial processes for simulation, perform discrete event simulation according to the set simulation logic, and dynamically update the impact of completed initial processes on the simulation queue.
[0077] An executable process queue establishment module is used to select executable processes that are not affected by preceding processes at the current time of the simulation to form an executable process queue. The process is not constrained by preceding processes at the current simulation stage, including two cases: all preceding processes of the process have been completed or the process has no preceding processes.
[0078] The executable process list analysis module is used to perform pre-simulation on all processes in the executable process queue without updating the simulation state. The pre-simulation needs to meet the resource and weather conditions required when the process is set up. The simulation results are obtained based on its own duration interruption attribute. Finally, all pre-simulation results in the executable process list are recorded.
[0079] The optimization module compares all pre-simulation results and selects the next simulation event for discrete event simulation based on a greedy algorithm, using the method with the highest resource utilization. After the simulation of this event ends, the impact of the process is dynamically updated to prepare for the next round of simulation.
[0080] The present invention also provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus. The memory is used to store computer programs. The processor is used to execute the computer programs stored in the memory to implement the steps of the offshore wind power discrete event simulation optimization event selection method based on greedy algorithm as described above.
[0081] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the offshore wind power discrete event simulation optimization event selection method based on a greedy algorithm as described above.
[0082] The above specific embodiments are used to explain and illustrate the present invention, and are only preferred embodiments of the present invention, not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.
Claims
1. A method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm, characterized in that: Includes the following steps: S1: Establish a discrete event simulation process queue: Based on the content of the offshore wind power construction project, establish a discrete event simulation process queue, select initial processes for simulation, perform discrete event simulation according to the set simulation logic, and dynamically update the impact of completed initial processes on the simulation queue. S2: Establish an executable process queue: Select executable processes that are not affected by preceding processes at the current moment of the simulation to form an executable process queue. The process is not constrained by preceding processes at the current simulation stage, including two cases: all preceding processes of the process have been completed or the process has no preceding processes. S3: Analyze the list of executable processes under the current step: Perform pre-simulation on all processes in the list of executable processes without updating the simulation state. The pre-simulation needs to meet the resource and weather conditions required when the process was set up. Obtain the simulation results based on its own duration interruption attribute. Finally, record all pre-simulation results in the list of executable processes. S4: Complete the pre-simulation: Compare all pre-simulation results, select the method with the highest resource utilization based on the greedy algorithm to determine the next simulation event for the discrete event simulation, and dynamically update the impact of the process after the simulation of the event ends, in preparation for the next round of simulation.
2. The method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm as described in claim 1, characterized in that: In step S1, the process events are established by the operators based on the characteristics of each stage of the construction project. Each process event includes the construction duration, whether the construction process can be interrupted, and the constraints that restrict the construction. The initial sequence of construction processes is determined by the operator when creating the list. When multiple processes can be performed simultaneously, the order of construction will be determined by this sequence.
3. The method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm as described in claim 1, characterized in that: In step S3, when performing pre-simulation on the executable process queue, the processes will have different pre-simulation results due to the different constraints imposed on them by their own characteristics and the different types of resources they require. Selecting the appropriate process as the next step in the simulation will directly affect the simulation efficiency and resource utilization.
4. The method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm as described in claim 3, characterized in that: When selecting subsequent procedures based on the pre-simulation results, the analysis of the pre-simulation results needs to consider the resource consumption of each procedure in the executable list and the start time of each procedure. Assume the current simulation time is T0, and the start times of the n procedures in the pre-simulation results are T... n The completion time of the construction process is T. e The resource utilization rate of each pre-simulation process is In this formula, the numerator is the actual construction time of process n, and the denominator is the total time from the current simulation moment to the end of the process construction; Based on the greedy algorithm, the simulation process with the highest resource utilization rate and the earliest start time in the current state is selected as the subsequent simulation process. If there are cases where the earliest start times are the same, the process with the earlier sequence number is selected for simulation according to the rules, thus completing the selection process for the current round of offshore wind power discrete event simulation.
5. The method for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm as described in claim 1, characterized in that: In step S4, after the simulation of the current round is completed, the executed process is marked as completed, and the resources consumed by the process and the impact of subsequent processes are updated in the system status. Unfinished processes in the executable process queue are carried over to the next round of simulation for comparison again, until all process simulations are completed.
6. A system for optimizing event selection in offshore wind power discrete event simulation based on a greedy algorithm, characterized in that: Includes the following modules: The discrete event simulation process queue establishment module is used to establish a discrete event simulation process queue based on the content of the offshore wind power construction project, select initial processes for simulation, perform discrete event simulation according to the set simulation logic, and dynamically update the impact of completed initial processes on the simulation queue. An executable process queue establishment module is used to select executable processes that are not affected by preceding processes at the current time of the simulation to form an executable process queue. The process is not constrained by preceding processes at the current simulation stage, including two cases: all preceding processes of the process have been completed or the process has no preceding processes. The executable process list analysis module is used to perform pre-simulation on all processes in the executable process queue without updating the simulation state. The pre-simulation needs to meet the resource and weather conditions required when the process is set up. The simulation results are obtained based on its own duration interruption attribute. Finally, all pre-simulation results in the executable process list are recorded. The optimization module compares all pre-simulation results and selects the next simulation event for discrete event simulation based on a greedy algorithm, using the method with the highest resource utilization. After the simulation of this event ends, the impact of the process is dynamically updated to prepare for the next round of simulation.
7. An electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that: The memory, which is used to store computer programs, A processor, wherein the processor is configured to execute a computer program stored in a memory to implement the steps of the offshore wind power discrete event simulation optimization event selection method based on a greedy algorithm as described in any one of claims 1-5.
8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the offshore wind power discrete event simulation optimization event selection method based on a greedy algorithm as described in any one of claims 1-5.