Intelligent simulation method, device and equipment for offshore wind power simulation operation and medium

Abstract

The application discloses an intelligent simulation method, device and equipment for offshore wind power simulation operation and a medium. The method comprises the following steps: receiving a natural language instruction corresponding to offshore wind power simulation operation, analyzing the natural language instruction based on an analyst intelligent agent, and generating a standardized task instruction conforming to a model context protocol (MCP); connecting the MCP server through a tool calling intelligent agent, calling offshore wind power simulation operation tools encapsulated in the MCP server based on the standardized task instruction, and executing simulation operation to obtain simulation original results; analyzing the simulation original results through a result analysis intelligent agent to obtain simulation analysis results, and adjusting the standardized task instruction based on the simulation analysis results and re-executing the simulation operation to obtain target simulation results in the case that the simulation analysis results are not up to standard. The technical scheme can realize natural language driven full-process automatic simulation, improve simulation efficiency and result reliability, and support multi-tool standardized collaborative calling.

Description

Technical Field

[0001] This invention relates to the field of offshore wind power simulation operation and maintenance technology, and in particular to an intelligent simulation method, device, equipment and medium for offshore wind power simulation operation and maintenance. Background Technology

[0002] As the scale of offshore wind power installations continues to expand, the clustering and complexity of wind farms have significantly increased. The complex marine environment and the characteristics of multi-unit collaborative operation place higher demands on the simulation accuracy, response efficiency, and intelligence level of wind power operation and maintenance. Traditional offshore wind power operation and maintenance relies heavily on human experience and fixed rule bases for decision-making and operation. The overall process depends on manual intervention, has a low degree of automation, and cannot meet the needs of large-scale wind farms for refined, real-time, and intelligent operation and maintenance.

[0003] Currently, the industry widely uses specialized simulation tools such as aerodynamics, structure, hydrodynamics, and control to conduct numerical simulations and dynamic analyses of offshore wind power. However, these tools generally have significant limitations: they are highly specialized, have cumbersome parameter configurations, and high operational barriers, requiring a high level of academic background from users; the interaction methods are mainly scripts and configuration files, lacking lightweight interaction methods such as natural language, resulting in a poor user experience; each simulation tool is relatively independent, lacking unified scheduling and standardized interaction protocols, making it difficult to achieve collaborative simulation across multiple tools and scenarios; at the same time, simulation execution and result analysis are disconnected, lacking a closed-loop optimization mechanism, resulting in low overall operation and simulation efficiency.

[0004] The aforementioned issues have resulted in complex, costly, and limited-scale offshore wind power simulation operation and maintenance processes. The industry urgently needs a simulation operation and maintenance solution that can lower the barriers to entry, simplify the operation process, and achieve intelligent interaction and automatic execution in order to improve overall operation and maintenance efficiency and intelligence level. Summary of the Invention

[0005] This invention provides an intelligent simulation method, device, equipment, and medium for offshore wind power simulation operation and maintenance, so as to realize the automatic configuration and execution of tool-level simulation tasks driven by natural language.

[0006] According to one aspect of the present invention, an intelligent simulation method for offshore wind power simulation operation and maintenance is provided, comprising: Receive natural language commands corresponding to offshore wind power simulation operation and maintenance, and generate standardized task commands that conform to the Model Context Protocol (MCP) based on the analyst agent parsing the natural language commands. The tool calls the intelligent agent to connect to the MCP server, and calls the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server to perform simulation operations based on the standardized task instructions, so as to obtain the original simulation results. The simulation analysis agent analyzes the original simulation results to obtain simulation analysis results. If the simulation analysis results are not up to standard, the standardized task instructions are adjusted based on the simulation analysis results and the simulation operation is re-executed to obtain the target simulation results.

[0007] According to another aspect of the present invention, an intelligent simulation device for offshore wind power simulation operation and maintenance is provided, comprising: The instruction receiving and parsing module is used to receive natural language instructions corresponding to offshore wind power simulation and operation and maintenance, and generate standardized task instructions that conform to the Model Context Protocol (MCP) based on the analysis agent parsing the natural language instructions. The simulation execution module is used to call the intelligent agent to connect to the MCP server through the tool, and to call the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server to perform simulation operations based on the standardized task instructions, so as to obtain the original simulation results. The result analysis and optimization module is used to analyze the original simulation results through a result analysis agent to obtain simulation analysis results. If the simulation analysis results are not up to standard, the module adjusts the standardized task instructions based on the simulation analysis results and re-triggers the simulation execution module to perform simulation operations to obtain the target simulation results.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, which is then executed by at least one processor to enable the at least one processor to execute the intelligent simulation method for offshore wind power simulation operation and maintenance according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the intelligent simulation method for offshore wind power simulation operation and maintenance according to any embodiment of the present invention.

[0010] The technical solution of this invention receives natural language commands corresponding to offshore wind power simulation operation and maintenance, and generates standardized task commands conforming to the Model Context Protocol (MCP) based on the analysis of the natural language commands by the analyst agent. The tool calling agent connects to the MCP server and calls the offshore wind power simulation operation and maintenance tools encapsulated in the MCP server based on the standardized task commands to execute simulation operations and obtain the original simulation results. The result analysis agent analyzes the original simulation results to obtain simulation analysis results. If the simulation analysis results do not meet the preset standards, the standardized task commands are adjusted based on the simulation analysis results, and the simulation operations are re-executed to obtain the target simulation results. This solves the problems of high barriers to entry, high professional requirements, complex tool calls, lack of closed-loop simulation processes, and low intelligence in existing offshore wind power simulation operation and maintenance. It significantly reduces the barriers to use simulation, realizes fully automated simulation driven by natural language, improves simulation efficiency and result reliability, supports standardized collaborative calls of multiple tools, and forms a complete closed loop of simulation execution and result optimization.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating an intelligent simulation method for offshore wind power operation and maintenance provided in this embodiment of the invention; Figure 2 A flowchart of another intelligent simulation method for offshore wind power simulation operation and maintenance provided in this embodiment of the invention; Figure 3 A flowchart illustrating another intelligent simulation method for offshore wind power simulation operation and maintenance provided in this embodiment of the invention; Figure 4 A flowchart of a preferred intelligent simulation method for offshore wind power simulation operation and maintenance is provided in this embodiment of the invention; Figure 5 A schematic diagram of the structure of an intelligent simulation device for offshore wind power simulation operation and maintenance provided in an embodiment of the present invention; Figure 6 A schematic diagram of the electronic device used to implement the intelligent simulation method for offshore wind power simulation operation and maintenance according to embodiments of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Figure 1 This is a flowchart illustrating an intelligent simulation method for offshore wind power simulation operation and maintenance provided in an embodiment of the present invention. This embodiment is applicable to scenarios where non-professionals can complete complex simulation operation and maintenance operations through natural language interaction. The method can be executed by an intelligent simulation device for offshore wind power simulation operation and maintenance, which can be implemented in hardware and / or software and can be configured in a computer device. Figure 1 As shown, the method specifically includes the following steps: S110. Receive natural language instructions corresponding to offshore wind power simulation operation and maintenance, and generate standardized task instructions that conform to the Model Context Protocol (MCP) based on the analyst agent parsing the natural language instructions.

[0017] The technical solution of this invention can be applied to offshore wind farm operation and maintenance scenarios. Based on intelligent agents and the standardized protocol MCP, it can complete the simulation of offshore wind farm operation and maintenance through natural language interaction and execute the entire simulation process.

[0018] Among them, natural language commands can be commands issued by users in natural language form that are related to the needs of offshore wind power simulation. For example, natural language commands can simulate the blade load and tower dynamic response of a 3MW wind turbine at a wind speed of 12m / s, and detect the pile-soil interaction damage of the wind turbine foundation structure.

[0019] In this embodiment of the invention, the analyst agent can be an agent with natural language parsing and task processing capabilities; the Model Context Protocol (MCP) is a unified interaction specification that allows offshore wind power simulation tools from different manufacturers and of different types to receive instructions and return data according to the same rules, achieving interoperability and standardized calls. Standardized task instructions refer to simulation task instructions that conform to the MCP specification, and these standardized task instructions can be recognized by the agent and the MCP server.

[0020] Specifically, the interactive client can receive natural language commands corresponding to offshore wind power simulation operation and maintenance. For example, users can input natural language commands through the dialog box of the interactive client to express simulation requirements in natural language. Then, the analyst agent parses the received natural language commands, such as recognizing and processing the semantics and requirements of the natural language commands, and generating standardized task commands according to the format requirements of MCP.

[0021] S120. The intelligent agent is invoked through the tool to connect to the MCP server, and the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server is invoked to perform simulation operations based on the standardized task instructions, so as to obtain the original simulation results.

[0022] Among them, the tool calling agent can connect to the MCP server, call the simulation tools in the MCP server, and execute the corresponding simulation operations.

[0023] In some possible implementations, the MCP server encapsulates offshore wind power simulation and operation tools, which include at least one of wind turbine dynamics simulation tools, structural finite element analysis tools, and structural damage detection tools.

[0024] Understandably, the MCP server can be a standardized server deployed with offshore wind power simulation and operation tools, enabling the encapsulation and unified invocation of these tools. Within the MCP server, offshore wind power simulation and operation tools, such as wind turbine dynamics simulation tools, structural finite element analysis tools, and structural damage detection tools, are containerized and encapsulated. Simultaneously, a tool semantic database is built to record tool functions, parameter constraints, and invocation dependencies, providing standardized tool invocation interfaces to the outside world.

[0025] In this embodiment of the invention, an intelligent agent can connect to an MCP server via a tool invocation mechanism. Based on generated standardized task instructions, the agent retrieves the corresponding offshore wind power simulation and maintenance tool from the MCP server and executes the simulation operation. After the simulation tool completes its execution, the raw output data is obtained as the original simulation result. This process, based on the standardized encapsulation of the MCP server, enables automated invocation of simulation tools by the intelligent agent, achieves unattended execution of simulation operations, improves simulation execution efficiency, and lowers the technical threshold.

[0026] In some preferred embodiments, to effectively address the issues of inconsistent interfaces, inconsistent parameter formats, and lack of standardized calling among different simulation tools, simulation tool integration and Model Context Protocol (MCP) server construction can be performed through the following process: 1. Collect and organize mainstream offshore wind power simulation and operation and maintenance software to form a tool resource pool. Prioritize simulation tools with industry authority and wide application scenarios, such as OpenFAST (wind turbine dynamics), OpenSees (structural finite element method), and structural damage detection. OpenFAST is used for dynamic response simulation of foundation structures such as monopiles and tripods; OpenSees is used to simulate the nonlinear mechanical behavior of offshore wind power foundation structures under complex loads, such as pile-soil interaction and concrete crack propagation; and structural damage detection is used to detect the existence of structural damage based on user-uploaded data.

[0027] 2. Standardized encapsulation of the MCP server; it can parse the API documentation of various simulation tools, extract the core input parameters, output variables, data formats and error codes of the simulation tools, and organize them into a structured parameter dictionary; then, based on the JSON-LD specification, it defines a unified model context protocol to complete the semantic mapping of heterogeneous fields, such as mapping loc_id to wind turbine location, and assigning a unique identifier to each simulation tool.

[0028] Furthermore, Docker containerization technology is used to encapsulate each simulation tool, and middleware is used to automatically convert parameter formats. A standardized RESTful calling interface is provided to the outside world. At the same time, a tool semantic database is built to record the functional description, parameter constraints and calling dependencies of each tool. This allows the agent to retrieve and match the appropriate simulation tool as needed.

[0029] 3. Server Capability Output: After encapsulation, the MCP server can expose four types of core interfaces to the tool calling agent: tool query interface, parameter verification interface, instruction forwarding interface, and running status feedback interface. This ensures that standardized task instructions can be accurately converted into operations that can be executed by the underlying simulation tool.

[0030] S130. The simulation analysis agent analyzes the original simulation results to obtain simulation analysis results. If the simulation analysis results are not up to standard, the standardized task instructions are adjusted based on the simulation analysis results and the simulation operation is re-executed to obtain the target simulation results.

[0031] In this context, the result analysis agent can be understood as an agent capable of parsing and analyzing simulation data. The simulation analysis result can be structured and interpretable analytical data obtained after analyzing and processing the original simulation results. The target simulation result can be the simulation analysis result that meets the preset evaluation criteria and can be used for offshore wind power operation and maintenance.

[0032] Specifically, the simulation results are parsed and analyzed by a result analysis agent to obtain simulation analysis results that can be directly used for operation and maintenance reference. If the simulation analysis results do not meet the preset evaluation criteria, the standardized task instructions can be adjusted based on the simulation analysis results. After adjustment, the simulation operation is re-executed until the target simulation results that meet the preset evaluation criteria are obtained. Through the closed-loop operation of the above result feedback and instruction adjustment, the effectiveness and usability of the simulation results can be guaranteed, providing a reference for offshore wind power operation and maintenance decisions.

[0033] The technical solution of this invention receives natural language commands corresponding to offshore wind power simulation operation and maintenance, and generates standardized task commands conforming to the Model Context Protocol (MCP) based on the analysis of the natural language commands by the analyst agent. The tool calling agent connects to the MCP server and calls the offshore wind power simulation operation and maintenance tools encapsulated in the MCP server based on the standardized task commands to execute simulation operations and obtain the original simulation results. The result analysis agent analyzes the original simulation results to obtain simulation analysis results. If the simulation analysis results do not meet the preset standards, the standardized task commands are adjusted based on the simulation analysis results, and the simulation operations are re-executed to obtain the target simulation results. This solves the problems of high barriers to entry, high professional requirements, complex tool calls, lack of closed-loop simulation processes, and low intelligence in existing offshore wind power simulation operation and maintenance. It significantly reduces the barriers to use simulation, realizes fully automated simulation driven by natural language, improves simulation efficiency and result reliability, supports standardized collaborative calls of multiple tools, and forms a complete closed loop of simulation execution and result optimization.

[0034] Figure 2 This is a flowchart illustrating another intelligent simulation method for offshore wind power simulation operation and maintenance provided by an embodiment of the present invention. Based on the above embodiments, the generation process of standardized task instructions is further optimized. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 2 As shown, the method specifically includes the following steps: S210: Receive natural language commands corresponding to offshore wind power simulation operation and maintenance.

[0035] In some embodiments, the analyst agent can be a dedicated agent equipped with a fine-tuned large language model for offshore wind power simulation operation and maintenance. The analyst agent includes at least a dialogue module, a task verification module, and a tool extraction module.

[0036] Among them, the fine-tuned large language model in the field of offshore wind power simulation and operation is based on a general large language model and is trained and optimized using professional datasets in the field of offshore wind power simulation and operation. It can identify professional terms and requirements in the field of offshore wind power.

[0037] The aforementioned dialogue module is a functional module in the analyst agent that implements natural language semantic parsing; the task verification module is a functional module in the analyst agent that performs feasibility verification of task execution; and the tool extraction module is a functional module in the analyst agent that implements simulation tool matching and identification acquisition.

[0038] Understandably, the analyst agent is a specialized agent equipped with a large language model fine-tuned using professional datasets from the offshore wind power simulation and operation field. This large language model, trained with domain data, can adapt to the professional scenarios of offshore wind power simulation and operation, improving the accuracy of natural language parsing. The analyst agent integrates at least a dialogue module, a task verification module, and a tool extraction module, which respectively complete natural language parsing, task verification, and tool matching.

[0039] S220. The dialogue module performs semantic parsing on the natural language instructions and completes the ambiguous information in the natural language instructions to obtain the initial task framework.

[0040] The initial task framework refers to the preliminary task information set formed after the dialogue module parses the natural language instructions. It includes the basic information required to complete the simulation task, such as the task type, core parameters, and execution scenario of offshore wind power simulation operation and maintenance. The task type can be the operation type in offshore wind power simulation operation and maintenance, such as wind turbine dynamics simulation, structural damage detection, and power generation simulation. The core parameters are the key technical parameters required to complete the corresponding simulation task. The execution scenario can be the offshore wind power operating environment and working conditions corresponding to the simulation task.

[0041] Specifically, by using the dialogue module in the analyst agent to perform semantic parsing of natural language commands, identifying simulation requirements in the commands, and supplementing ambiguous or missing task information in the commands, an initial task framework can be formed, including the task type, core parameters, and execution scenarios of offshore wind power simulation operation and maintenance, thus transforming the user's ambiguous requirements into specific preliminary task information.

[0042] S230. The initial task framework is executed and verified by the task verification module, and the initial task framework that passes the verification is determined as the verified task framework.

[0043] Among them, the verified task framework can be a task framework that has passed the verification by the task verification module and has the feasibility of execution.

[0044] Specifically, the initial task framework is executed through the task verification module to check whether the task conforms to physical laws, engineering requirements and system execution conditions, and the initial task framework that passes the verification is determined as the verified task framework.

[0045] S240. The tool identifier of the offshore wind power simulation operation and maintenance tool that matches the verified task framework is determined from the MCP server by the tool extraction module.

[0046] The tool identifier can be an identifier assigned by the MCP server to each packaged simulation tool, used to distinguish different simulation tools.

[0047] Specifically, the tool extraction module can determine the matching simulation tool from the MCP server based on the task type and execution scenario information of the verified task framework, and obtain the corresponding tool identifier.

[0048] To further clarify the modules within the analyst agent, the following example illustrates the process: The analyst agent is mainly used to transform unstructured natural language input from users into standardized task instructions that are compliant, executable, and have unique tool matching information. It achieves a complete conversion from natural language to structured tasks and then to tool call parameters. Before task execution, it performs pre-verification of feasibility, compliance, and security to avoid invalid simulation tasks consuming system resources.

[0049] The analyst agent includes at least a dialogue module, a task verification module, and a tool extraction module. These modules work together to complete the entire process of requirement analysis, task verification, and tool matching.

[0050] The dialogue module can perform preliminary parsing of user natural language requests. Based on a large language model fine-tuned using datasets from the offshore wind power simulation and operation and maintenance field, this module decomposes the received natural language requests into tasks, extracts core parameters, and completes fuzzy requirements, generating an initial task plan that includes the task type, configurable parameters, and application scenario. For example, if a user inputs "simulate wind turbine load under high wind speed," the dialogue module can decompose it into a wind turbine dynamics simulation task, extract core parameters such as wind speed, power, and blade length, and complete "high wind speed" into a specific value range of 10-15 m / s according to industry standards, forming structured task information that can be further processed.

[0051] The task verification module performs multi-dimensional safety and compliance verification on the initial task plan to ensure the rationality and reliability of the simulation operation. The verification mainly includes three aspects: first, feasibility verification, determining whether the task parameters conform to physical laws and engineering constraints, such as whether the wind speed is within the wind turbine's design tolerance range and whether the tower height and blade length match; second, compliance verification, checking whether the parameter format, units, and values ​​meet industry standards and the input requirements of the simulation tool; and third, safety verification, predicting whether the task execution may exceed system safety thresholds, such as overspeeding or overload. After successful verification, this module determines the initial task plan as an executable and verified task plan.

[0052] The tool extraction module, based on validated task schemes, completes the matching and identification of the optimal simulation tool. This module interfaces with the tool semantic database in the MCP server, and based on task type, parameter requirements, and execution scenario, uses vector matching to select the uniquely suitable offshore wind power simulation and maintenance tool from the tool resource pool, obtaining the corresponding tool identifier. After tool matching is completed, the analyst agent integrates the task type, tool identifier, and complete parameter list, and generates standardized task instructions that can be directly passed to the tool-calling agent according to the MCP specification requirements.

[0053] S250. Based on the tool identifier, the verified task framework, and the Model Context Protocol (MCP), generate the standardized task instructions.

[0054] Specifically, based on the acquired tool identifier and all information of the verified task framework, standardized task instructions can be generated according to the format and specification requirements of the model context protocol. These instructions can be recognized and executed by the tool's calling agent and the MCP server, achieving accurate conversion from natural language instructions to standardized execution instructions.

[0055] S260. The intelligent agent is invoked through the tool to connect to the MCP server, and the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server is invoked based on the standardized task instructions to perform simulation operations in order to obtain the original simulation results.

[0056] S270. The simulation analysis agent analyzes the original simulation results to obtain simulation analysis results. If the simulation analysis results are not up to standard, the standardized task instructions are adjusted based on the simulation analysis results and the simulation operation is re-executed to obtain the target simulation results.

[0057] The technical solution of this invention, through a complete process of natural language instruction reception, multi-module collaborative parsing, task verification, tool matching, standardized instruction generation, automatic simulation execution, and result closed-loop optimization, can automatically transform users' vague and non-professional natural language requirements into directly executable standardized simulation tasks. Users do not need to have professional simulation tool operation capabilities, thus significantly lowering the usage threshold of offshore wind power simulation operation and maintenance. Secondly, by verifying the feasibility and compliance of the initial task framework through the task verification module, unreasonable and non-compliant simulation tasks can be eliminated in advance, reducing invalid execution and improving system operating efficiency and simulation reliability. Furthermore, based on the MCP server, a unified encapsulation and standardized invocation of various simulation operation and maintenance tools are realized, allowing seamless integration and unified scheduling of simulation tools from different vendors and of different types, improving system compatibility and scalability. Finally, through the result analysis intelligent agent, simulation data is automatically processed, and when the results do not meet the requirements, closed-loop adjustment instructions are made to re-execute the simulation, realizing full-process automation and intelligence from requirement input to target result output, greatly improving the efficiency, accuracy, and practicality of offshore wind power simulation operation and maintenance.

[0058] Figure 3 This is a flowchart illustrating another intelligent simulation method for offshore wind power simulation operation and maintenance provided by an embodiment of the present invention. This embodiment further optimizes the process of the tool calling the intelligent agent to parse standardized task instructions and call the simulation tool. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 3 As shown, the method specifically includes the following steps: S310. Receive natural language instructions corresponding to offshore wind power simulation and maintenance, and generate standardized task instructions that conform to the Model Context Protocol (MCP) based on the analyst agent's parsing of the natural language instructions.

[0059] S320. The tool is used to call the intelligent agent to parse the standardized task instruction and obtain the tool identifier and parameter list in the standardized task instruction.

[0060] Here, the parameter list refers to the set of technical parameters contained in the standardized task instructions, that is, all the technical parameters required to complete the simulation operation.

[0061] Specifically, the standardized task instructions are parsed by the tool invocation agent, and the tool identifier used to invoke the tool and the list of parameters required to complete the simulation operation are extracted from the instructions.

[0062] In some optional implementations, after obtaining the tool identifier and parameter list in the standardized task instruction, the method further includes: intelligently completing the missing parameters in the parameter list, and performing compliance verification on the completed parameter list through the MCP server.

[0063] Missing parameters can be technical parameters that are not explicitly marked in the parameter list of standardized task instructions but are necessary to complete the simulation operation. Intelligent completion refers to the process of automatically supplementing missing parameters based on offshore wind power industry standards, simulation operation and maintenance experience, and historical wind farm data. Compliance verification can be the MCP server's check and verification of the parameter's value range, format, unit, etc., to ensure that the parameters meet the tool's execution requirements and industry standards.

[0064] Specifically, after the tool calling agent obtains the tool identifier and parameter list from the standardized task instructions, it can check the parameter list, identify the missing technical parameters in the list, and intelligently complete the missing parameters based on offshore wind power industry standards, simulation operation and maintenance experience, and historical operation and maintenance data of wind farms to ensure the integrity of the parameter list.

[0065] Furthermore, the completed parameter list is sent to the MCP server. The MCP server performs a comprehensive compliance check on the parameter value range, data format, and unit of measurement to ensure that the parameters meet the execution requirements of the corresponding simulation tool and the industry standards for offshore wind power. This avoids simulation operation failure or simulation result distortion due to missing or non-compliant parameters, thereby improving the success rate of simulation operation.

[0066] S330. The tool is used to call the intelligent agent to connect to the MCP server and query the tool protocol specification and parameter mapping rules corresponding to the tool identifier.

[0067] The tool protocol specification can be understood as the calling rules, input / output requirements, and other standardized information for each simulation tool. This specification can be stored in the MCP server. Parameter mapping rules are the rules that transform general parameters in standardized task instructions into unique parameters that each simulation tool can recognize.

[0068] Specifically, the tool calls the intelligent agent to establish a connection with the MCP server. Based on the extracted tool identifier, the tool protocol specification and parameter mapping rules corresponding to the tool are queried from the MCP server, providing a basis for tool calls and parameter processing.

[0069] S340. Based on the tool protocol specification and the parameter mapping rules, the parameter list is formatted to generate a tool call request that meets the MCP server call requirements.

[0070] Among them, the tool call request is a request information that meets the MCP server call requirements and can trigger the simulation tool to perform simulation operations.

[0071] Specifically, based on the queried tool protocol specifications and parameter mapping rules, the parameter list is formatted and adapted, and the general standardized parameters are transformed into parameter forms that the corresponding simulation tools can recognize, generating tool call requests that meet the MCP server call requirements. Such tool call requests can be recognized and processed by the server.

[0072] S350. Send the tool call request to the MCP server to trigger the corresponding offshore wind power simulation operation and maintenance tool in the MCP server to perform simulation operation, and obtain the original simulation result after the simulation operation is completed.

[0073] Specifically, the generated tool call request is sent to the MCP server. The MCP server triggers the corresponding offshore wind power simulation operation and maintenance tool based on the information in the request, executes the simulation operation according to the parameters in the parameter list, and waits for the simulation operation to be completed before directly obtaining the original simulation results from the server. This realizes the automation and standardization of the simulation operation, and improves the execution efficiency and accuracy of the simulation operation.

[0074] S360. The result analysis agent performs structured analysis on the original simulation results to obtain at least one structured indicator related to offshore wind power simulation operation and maintenance.

[0075] S370. Perform error assessment and compliance analysis on the structured indicators, and generate the simulation analysis results based on the results of the error assessment and compliance analysis.

[0076] S380. If the simulation analysis results are not up to standard, adjust the standardized task instructions based on the simulation analysis results and re-execute the simulation operation to obtain the target simulation results.

[0077] Structured analysis is the process of transforming unformatted simulation results into structured and standardized data. Structured metrics can be offshore wind power simulation operation and maintenance-related indicators obtained after processing the original simulation results, which can be used for analysis and evaluation, including but not limited to dimensions such as power generation, load factor, and failure probability. Error assessment is the process of comparing structured metrics with design values ​​and historical values ​​to analyze data errors. Compliance analysis is the process of checking whether structured metrics meet industry standards and wind turbine design thresholds.

[0078] Specifically, the result analysis agent performs structured analysis on the original simulation results, removes invalid data, and extracts at least one structured indicator related to offshore wind power simulation operation and maintenance. This transforms the messy original simulation data into standardized and analyzable indicator data, improving the readability and usability of the data.

[0079] Furthermore, error evaluation and compliance analysis are performed on the extracted structured indicators. The error range between the indicator data and the design values and historical values is analyzed, and whether the indicators meet the offshore wind power industry standards and the turbine design thresholds is checked. Based on the results of this error evaluation and compliance analysis, a simulation analysis result is generated, transforming the simulation result from raw data into analysis data that can be directly used for operation and maintenance reference, providing clear and professional data support for offshore wind power operation and maintenance decisions.

[0080] In some possible implementation manners, the analyst agent, the tool invocation agent, and the result analysis agent establish a collaborative relationship through the ReAct framework. The method further includes: detecting the running state of the simulation operation through the tool invocation agent; in the case of a running exception in the simulation operation, feedbacking exception information to the analyst agent through the tool invocation agent; and the analyst agent adjusting the standardized task instruction based on the exception information and re-executing the simulation operation to obtain the target simulation result.

[0081] Among them, the ReAct framework is a framework that can implement the thinking-execution-reflection-correction collaboration logic between agents, enabling each agent to dynamically adjust the operation strategy according to the execution result. The running state is the real-time running situation during the execution of the simulation operation by the offshore wind power simulation tool, including states such as normal running, lagging, and interruption. A running exception can be an abnormal running situation such as tool invocation failure, parameter error, and simulation interruption during the execution of the simulation operation, and the exception information is information that records the type, cause, occurrence node, etc. of the running exception.

[0082] It should be understood that the analyst agent, the tool invocation agent, and the result analysis agent establish a collaborative relationship through the ReAct framework, and the three agents form a linkage mechanism, which can dynamically adjust the operation according to the simulation execution situation. The running state of the simulation operation can be detected in real time through the tool invocation agent to timely master the execution situation of the simulation operation. In the case of a running exception in the simulation operation, the tool invocation agent feedbacks the exception information including the exception type and cause to the analyst agent, realizing the rapid transmission of the exception information.

[0083] Then, the analyst agent analyzes the cause of the exception based on the received exception information, makes a targeted adjustment to the standardized task instruction, and re-executes the simulation operation after the adjustment until the target simulation result meeting the preset standard is obtained, realizing the rapid response and correction after the simulation operation exception, and ensuring the continuity and effectiveness of the simulation process.

[0084] In some possible implementations, adjusting the standardized task instruction based on the simulation analysis results includes: using the result analysis agent to determine non-compliance-related information based on the simulation analysis results, determining information to be adjusted in the standardized task instruction based on the non-compliance-related information, and adjusting the information to be adjusted in the standardized task instruction using the analyst agent.

[0085] The non-compliance-related information includes all information related to the simulation analysis results not meeting the preset evaluation standards, including non-compliant indicators, deviation ranges, and causal clues. The information to be adjusted includes at least one or more of tool matching information and parameter configuration information. The information to be adjusted refers to the content in the standardized task instructions that needs modification and optimization. Tool matching information is information in the standardized task instructions related to the matching with offshore wind power simulation and maintenance tools. Parameter configuration information can be configuration-related information such as parameter values ​​and combinations in the standardized task instructions.

[0086] Specifically, the results analysis agent comprehensively analyzes the simulation results, identifies non-compliant correlation information based on the results, and accurately pinpoints the core issues causing the simulation results to fail to meet preset standards. Based on the identified non-compliant correlation information, the causes of the problems are analyzed, and information requiring modification and optimization is determined from the standardized task instructions. Furthermore, the analyst agent makes targeted adjustments to the information requiring adjustment in the standardized task instructions, optimizing tool matching schemes or adjusting parameter configuration values. This makes the adjusted standardized task instructions more suitable for simulation requirements, and upon re-execution, it yields target simulation results that meet preset standards, thus improving the quality and usability of the simulation results.

[0087] The technical solution of this invention, by performing structured analysis of the original simulation results and conducting error assessment and compliance analysis, can quickly output clear and usable simulation analysis results, improving the readability and reference value of simulation data. When the simulation analysis results do not meet requirements, it automatically locates the information to be adjusted and optimizes the instructions. Combined with the ReAct framework, it achieves real-time detection and rapid correction of abnormal states, enabling the entire simulation process to have self-verification, self-correction, and self-optimization capabilities, further improving the stability, execution efficiency, and accuracy of offshore wind power simulation operation and maintenance.

[0088] Figure 5 This is a schematic diagram of the structure of an intelligent simulation device for offshore wind power simulation operation and maintenance, provided as an embodiment of the present invention. Figure 5 As shown, the device includes: The instruction receiving and parsing module 510 is used to receive natural language instructions corresponding to offshore wind power simulation operation and maintenance, and generate standardized task instructions that conform to the Model Context Protocol (MCP) based on the analysis agent parsing the natural language instructions. The simulation execution module 520 is used to call the intelligent agent to connect to the MCP server through the tool, and to call the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server to perform simulation operations based on the standardized task instructions, so as to obtain the original simulation results. The result analysis and optimization module 530 is used to analyze the original simulation results through the result analysis agent to obtain simulation analysis results. If the simulation analysis results are not up to standard, the module adjusts the standardized task instructions based on the simulation analysis results and re-triggers the simulation execution module to perform simulation operations to obtain the target simulation results.

[0089] In some possible implementations, the instruction receiving and parsing module includes: The analyst intelligent agent unit is a dedicated intelligent agent unit equipped with a fine-tuned large language model for offshore wind power simulation operation and maintenance. The analyst intelligent agent unit includes at least a dialogue submodule, a task verification submodule, and a tool extraction submodule.

[0090] In some possible implementations, the dialogue submodule is used to perform semantic parsing on the natural language instructions and complete the ambiguous information in the natural language instructions to obtain an initial task framework, wherein the initial task framework includes the task type, core parameters and execution scenario of offshore wind power simulation operation and maintenance. The task verification submodule is used to perform execution verification on the initial task framework and determine the initial task framework that passes the verification as the verified task framework. The tool extraction submodule is used to determine the tool identifier of the offshore wind power simulation operation and maintenance tool that matches the verified task framework from the MCP server. The analyst agent unit is also used to generate the standardized task instructions based on the tool identifier, the verified task framework, and the Model Context Protocol (MCP).

[0091] In some possible implementations, the MCP server encapsulates offshore wind power simulation and operation tools, which include at least one of wind turbine dynamics simulation tools, structural finite element analysis tools, and structural damage detection tools.

[0092] In some possible implementations, the simulation execution module includes: The instruction parsing submodule is used to call the intelligent agent through the tool to parse the standardized task instruction and obtain the tool identifier and parameter list in the standardized task instruction; The server interface submodule is used to call the intelligent agent to interface with the MCP server through the tool, and query the tool protocol specification and parameter mapping rules corresponding to the tool identifier; The request generation submodule is used to convert the format of the parameter list based on the tool protocol specification and the parameter mapping rules, and generate a tool call request that meets the MCP server call requirements. The simulation operation submodule is used to send the tool call request to the MCP server, trigger the corresponding offshore wind power simulation operation and maintenance tool in the MCP server to perform simulation operation, and obtain the original simulation result after the simulation operation is completed.

[0093] In some possible implementations, the simulation execution module further includes a parameter processing submodule, which is used to intelligently complete the missing parameters in the parameter list after the instruction parsing submodule obtains the tool identifier and parameter list in the standardized task instruction, and to perform compliance verification on the completed parameter list through the MCP server.

[0094] In some possible implementations, the result analysis and optimization module includes: The indicator extraction submodule is used to perform structured parsing of the original simulation results through the result analysis agent to obtain at least one structured indicator associated with offshore wind power simulation operation and maintenance. The analysis and generation submodule is used to perform error assessment and compliance analysis on the structured indicators, and generate the simulation analysis results based on the results of the error assessment and compliance analysis.

[0095] In some possible implementations, the device further includes an agent collaboration module for establishing a ReAct framework collaboration relationship between the analyst agent, the tool invocation agent, and the result analysis agent, and for detecting the running status of the simulation operation through the tool invocation agent; The simulation execution module is also used to, in the event of an operational anomaly in the simulation operation, call the intelligent agent through the tool to report the anomaly information to the analyst intelligent agent; The instruction receiving and parsing module is further configured to adjust the standardized task instruction based on the abnormal information through the analyst agent, and re-trigger the simulation execution module to perform the simulation operation to obtain the target simulation result.

[0096] In some possible implementations, the result analysis and optimization module further includes an instruction adjustment submodule, which is used to determine non-compliance correlation information based on the simulation analysis results through the result analysis agent, and determine the information to be adjusted in the standardized task instruction based on the non-compliance correlation information, wherein the information to be adjusted includes at least one or more of tool matching information and parameter configuration information; The instruction receiving and parsing module is also used to adjust the information to be adjusted in the standardized task instruction through the analyst agent.

[0097] The intelligent simulation device for offshore wind power simulation operation and maintenance provided in this embodiment of the invention can execute the intelligent simulation method for offshore wind power simulation operation and maintenance provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0098] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing the intelligent simulation method for offshore wind power simulation operation and maintenance according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0099] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0101] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as intelligent simulation methods for offshore wind power simulation operation and maintenance.

[0102] In some embodiments, the intelligent simulation method for offshore wind power simulation operation and maintenance can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the intelligent simulation method for offshore wind power simulation operation and maintenance described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the intelligent simulation method for offshore wind power simulation operation and maintenance by any other suitable means (e.g., by means of firmware).

[0103] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0106] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0107] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0108] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0109] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent simulation method for offshore wind power operation and maintenance, characterized in that, include: Receive natural language commands corresponding to offshore wind power simulation operation and maintenance, and generate standardized task commands that conform to the Model Context Protocol (MCP) based on the analyst agent parsing the natural language commands. The tool calls the intelligent agent to connect to the MCP server, and calls the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server to perform simulation operations based on the standardized task instructions, so as to obtain the original simulation results. The simulation analysis agent analyzes the original simulation results to obtain simulation analysis results. If the simulation analysis results are not up to standard, the standardized task instructions are adjusted based on the simulation analysis results and the simulation operation is re-executed to obtain the target simulation results.

2. The method according to claim 1, characterized in that, The analyst agent is a dedicated agent equipped with a fine-tuned large language model for offshore wind power simulation and operation and maintenance. The analyst agent includes at least a dialogue module, a task verification module, and a tool extraction module.

3. The method according to claim 2, characterized in that, The process of parsing the natural language instructions based on the analyst agent to generate standardized task instructions conforming to the Model Context Protocol (MCP) includes: The dialogue module performs semantic parsing on the natural language instructions and completes the ambiguous information in the natural language instructions to obtain an initial task framework, wherein the initial task framework includes the task type, core parameters and execution scenario of offshore wind power simulation operation and maintenance. The initial task framework is executed and verified by the task verification module, and the initial task framework that passes the verification is determined as the verified task framework. The tool extraction module determines the tool identifier of the offshore wind power simulation operation and maintenance tool that matches the verified task framework from the MCP server. Based on the tool identifier, the verified task framework, and the Model Context Protocol (MCP), the standardized task instructions are generated.

4. The method according to claim 1, characterized in that, The MCP server encapsulates offshore wind power simulation and maintenance tools, which include at least one of the following: wind turbine dynamics simulation tools, structural finite element analysis tools, and structural damage detection tools.

5. The method according to claim 1, characterized in that, The process involves using a tool to call an intelligent agent to connect to the MCP server and, based on the standardized task instructions, calling the offshore wind power simulation and maintenance tool encapsulated in the MCP server to perform simulation operations to obtain the original simulation results, including: The tool is used to invoke an intelligent agent to parse the standardized task instructions and obtain the tool identifier and parameter list in the standardized task instructions; The tool is used to invoke the intelligent agent to connect to the MCP server and query the tool protocol specification and parameter mapping rules corresponding to the tool identifier. Based on the tool protocol specification and the parameter mapping rules, the parameter list is formatted to generate a tool call request that meets the MCP server call requirements; The tool call request is sent to the MCP server, triggering the corresponding offshore wind power simulation operation and maintenance tool in the MCP server to perform simulation operations, and the original simulation results are obtained after the simulation operations are completed.

6. The method according to claim 5, characterized in that, After obtaining the tool identifier and parameter list from the standardized task instructions, the method further includes: The missing parameters in the parameter list are intelligently completed, and the completed parameter list is verified for compliance by the MCP server.

7. The method according to claim 1, characterized in that, The process of analyzing the original simulation results through a result analysis agent to obtain simulation analysis results includes: The result analysis agent performs structured analysis on the original simulation results to obtain at least one structured indicator related to offshore wind power simulation operation and maintenance. Error assessment and compliance analysis are performed on the structured indicators, and the simulation analysis results are generated based on the results of the error assessment and compliance analysis.

8. The method according to claim 1, characterized in that, The analyst agent, the tool invocation agent, and the result analysis agent establish a collaborative relationship through the ReAct framework. The method further includes: The tool is used to invoke an intelligent agent to detect the running status of the simulation operation; In the event of an operational anomaly during the simulation, the tool invokes an agent to report the anomaly information to the analyst agent. The analyst agent adjusts the standardized task instructions based on the anomaly information and re-executes the simulation operation to obtain the target simulation result.

9. The method according to claim 1 or 8, characterized in that, The adjustment of the standardized task instructions based on the simulation analysis results includes: The intelligent agent analyzes the results and identifies non-compliant correlation information based on the simulation analysis results. Based on the non-compliance association information, the information to be adjusted in the standardized task instruction is determined, wherein the information to be adjusted includes at least one or more of tool matching information and parameter configuration information; The analyst agent adjusts the information to be adjusted in the standardized task instructions.

10. An intelligent simulation device for offshore wind power simulation operation and maintenance, characterized in that, include: The instruction receiving and parsing module is used to receive natural language instructions corresponding to offshore wind power simulation and operation and maintenance, and generate standardized task instructions that conform to the Model Context Protocol (MCP) based on the analysis agent parsing the natural language instructions. The simulation execution module is used to call the intelligent agent to connect to the MCP server through the tool, and to call the offshore wind power simulation operation and maintenance tool encapsulated in the MCP server to perform simulation operations based on the standardized task instructions, so as to obtain the original simulation results. The result analysis and optimization module is used to analyze the original simulation results through a result analysis agent to obtain simulation analysis results. If the simulation analysis results are not up to standard, the module adjusts the standardized task instructions based on the simulation analysis results and re-triggers the simulation execution module to perform simulation operations to obtain the target simulation results.

11. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the intelligent simulation method for offshore wind power simulation operation and maintenance as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the intelligent simulation method for offshore wind power simulation operation and maintenance as described in any one of claims 1-9.

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