Wind turbine drivetrain monitoring method and system

Abstract

This application provides a method and system for monitoring the drivetrain of a wind turbine. The method includes: acquiring video data and operating condition data of the drivetrain components through a data acquisition module; determining displacement data based on the video data and storing it in association with the operating condition data through a data processing module, thus solving the problem of single monitoring data in traditional methods. The data analysis module utilizes a large language model to directly parse user natural language commands, extract structured requirements, and invoke a data analysis intelligent agent, achieving automated processing from data retrieval to analysis conclusions. This application significantly improves the automation and intelligence level of drivetrain monitoring, achieves effective fusion of multi-source data, lowers the data analysis threshold, and ensures the accuracy and efficiency of monitoring results.

Description

Technical Field

[0001] This application relates to the field of wind power technology, and more specifically, to a method and system for monitoring the transmission chain of a wind turbine. Background Technology

[0002] The transmission chain of wind turbine units (main bearings, gearbox, generator) is a high-risk area for failures, and its vibration and displacement status directly reflects the mechanical health level. With the development of large-scale and remote operation and maintenance of wind farms, there is an urgent need for a highly reliable, low-intrusion, and deeply analytical condition monitoring method to support predictive maintenance and intelligent decision-making.

[0003] Current technologies typically rely on manual inspections and contact sensors for monitoring. Contact sensors involve installing contact vibration monitoring devices on drivetrain components to collect vibration data from the drivetrain, and then monitoring the wind turbine drivetrain based on the vibration data and preset warning thresholds.

[0004] However, manual inspections suffer from high workload, long cycles, and strong subjectivity. Contact sensors need to be installed on transmission chain components, which is inconvenient to install and prone to damage. Furthermore, existing monitoring methods can only trigger alarms based on raw data or warning thresholds, lacking the ability to analyze displacement trends, amplitude characteristics, and correlations with operating parameters such as wind speed / rotation speed, resulting in insufficient intelligence. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for monitoring the transmission chain of wind turbine units, addressing the shortcomings of the prior art, in order to solve the problems of inconvenient installation, difficult maintenance, and difficulty in achieving deep correlation analysis between displacement data and operating condition data of the prior art.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides a method for monitoring the drivetrain of a wind turbine, applied to a wind turbine drivetrain monitoring system, the system comprising: a data acquisition module, a data processing module, and a data analysis module; the method comprising: The data acquisition module acquires video data and operating condition data of the transmission chain components; The data processing module determines the displacement data of each transmission chain component in the wind turbine based on the video data, and stores the displacement data and operating condition data in the database. The data analysis module acquires the instruction to be analyzed, parses the instruction based on a large language model to obtain the structured instructions in the instruction, and calls the data analysis agent to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instruction. The structured instructions include: data analysis capabilities and data types.

[0007] Optionally, each transmission chain component is equipped with a target reflector, and the data processing module determines the displacement data of each transmission chain component in the wind turbine based on the video data, including: The data processing module performs target detection on each video frame in the video data based on the target detection model to obtain the position of the target reflector in each video frame. The data processing module determines the displacement data of each transmission chain component based on the position of the target reflector in each video frame.

[0008] Optionally, storing the displacement data and operating condition data in a database includes: The data processing module associates and stores the displacement data and operating condition data in the database based on the time information of the displacement data and the time information of the operating condition data.

[0009] Optionally, the step of parsing the instruction to be analyzed based on a large language model to obtain the structured instructions in the instruction to be analyzed includes: The command to be analyzed is parsed based on a large language model to obtain multiple target entities in the command to be analyzed. The target entities include: target position, time information, data type and working condition field information. The multiple target entities are encapsulated based on a preset model context protocol to obtain the structured instructions.

[0010] Optionally, the invocation of the data analysis agent retrieves target data from the database according to the structured instructions and performs data analysis to obtain the analysis results corresponding to the instructions to be analyzed, including: The data analysis agent is invoked to retrieve displacement data and working condition data corresponding to the data type from the database according to the structured instructions, and to perform data analysis based on the data analysis capabilities to obtain the analysis results corresponding to the instructions to be analyzed.

[0011] Optionally, the method further includes: Based on the large language model, the analysis results are integrated and processed according to the preset report template to obtain an analysis report, which is then sent to the target terminal.

[0012] Secondly, this application provides a wind turbine drivetrain monitoring system, the system comprising: The data acquisition module is used to acquire video data and operating condition data of the drive train components; The data processing module is used to determine the displacement data of each transmission chain component in the wind turbine based on the video data, and to store the displacement data and operating condition data in the database. The data analysis module is used to acquire the instruction to be analyzed, parse the instruction based on a large language model to obtain the structured instructions in the instruction, and call the data analysis agent to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instruction. The structured instructions include: data analysis capabilities and data types.

[0013] Optionally, the data acquisition module includes an image acquisition unit and a working condition data acquisition unit, wherein the image acquisition unit is installed inside the wind turbine nacelle, and the image acquisition unit acquires the video data by identifying the target reflector on the transmission chain component.

[0014] Optionally, the data processing module includes a data calculation unit and a data storage unit, wherein the data calculation unit is used to determine the displacement data of each transmission chain component in the wind turbine based on the video data, and the data storage unit is used to store the displacement data and operating condition data in a database.

[0015] Optionally, the data analysis module includes: a large language model, a model context protocol server, and a data analysis agent; The large language model is used to parse the instruction to be analyzed to obtain multiple target entities in the instruction to be analyzed. The target entities include: target position, time information, data type and working condition field information. The model context protocol server is used to encapsulate the multiple target entities to obtain the structured instructions; The data analysis agent is used to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instructions to be analyzed.

[0016] The beneficial effects of this application are as follows: displacement data of transmission chain components is obtained through video data parsing, and the data is centrally stored in conjunction with the working condition data to provide a data foundation for analysis; the structured conversion of natural language analysis instructions is achieved by using a large language model, allowing users to easily submit analysis requests, and then the data analysis agent retrieves the target data and completes the analysis, realizing non-contact data acquisition and intelligent data analysis for transmission chain monitoring, improving the convenience of data collection and the parsing efficiency of analysis instructions, while realizing standardized data management and accurate analysis, effectively improving the intelligence level and analysis accuracy of wind turbine transmission chain monitoring.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of the structure of a transmission chain component provided in an embodiment of this application is shown; Figure 2 This paper shows a schematic diagram of the architecture of a wind turbine drivetrain monitoring system provided in an embodiment of this application; Figure 3 A flowchart of a wind turbine drivetrain monitoring method provided in an embodiment of this application is shown; Figure 4 A flowchart illustrating a method for determining displacement data according to an embodiment of this application is shown; Figure 5 This document illustrates a flowchart of a method for generating structured instructions according to an embodiment of this application. Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0022] The wind turbine drivetrain includes multiple components, such as Figure 1 The diagram shows a transmission chain structure for a wind turbine generator, including components such as the main shaft, gearbox, high-speed brake, and generator. To ensure the normal operation of the wind turbine generator, it is necessary to monitor the condition of the transmission chain components.

[0023] In existing technologies, sensors are typically installed on transmission chain components, for example in... Figure 1 Accelerometers are installed on the spindle, gearbox, high-speed brake, and generator to monitor the status of the drivetrain. However, since the drivetrain components are rotating at high speeds during operation, installing sensors on these components presents deployment challenges. Furthermore, the sensors themselves and the wiring costs are high, and the sensors are prone to damage during long-term operation in the harsh engine room environment. Replacement and maintenance require downtime, affecting power generation and significantly increasing the workload for subsequent maintenance.

[0024] In addition, existing wind turbine drive chain monitoring systems typically only provide raw data or simple threshold alarms, lacking the ability to deeply mine data and analyze its correlation with operating parameters (such as wind speed and rotational speed). Their functions are generally limited to safety precautions and post-event traceability, and they cannot achieve intelligent status assessment and trend prediction.

[0025] Based on this, this application proposes a method for monitoring the transmission chain of wind turbine units, applicable to... Figure 2 The wind turbine drivetrain monitoring system shown is illustrated. (Refer to...) Figure 2The wind turbine drivetrain monitoring system includes a data acquisition module, a data processing module, and a data analysis module. The data acquisition module includes an image acquisition unit and a condition data acquisition unit deployed inside the wind turbine nacelle. The condition data acquisition unit can be deployed inside or outside the nacelle and communicate with the wind turbine. The image acquisition unit can be a video acquisition device, such as a camera deployed inside the nacelle. The data processing and data analysis modules can be deployed on electronic devices or on a server. When the data processing and data analysis modules are deployed on the server, maintenance personnel can access the interfaces provided by the server to obtain relevant data and status monitoring information of the wind turbine.

[0026] Next, combine Figure 3 This application describes the wind turbine drivetrain monitoring method. The subject performing this method can be... Figure 2 The wind turbine drivetrain monitoring system shown is as follows: Figure 3 As shown, the method includes: S301, The data acquisition module acquires video data and operating condition data of the transmission chain components.

[0027] The transmission chain refers to the sequence of core mechanical components in a wind turbine generator that converts wind energy captured by the wind turbine into mechanical energy and transmits it to the generator. For example... Figure 1 The transmission chain shown includes components such as the main shaft, gearbox, high-speed brake, and generator. It should be understood that... Figure 1 The transmission chain component shown is only one possible example and should not be taken as a limitation.

[0028] Video data refers to continuous frame visual data obtained by filming drivetrain components, which can reflect the visual operating status of the components. Operating condition data refers to various operating parameter data during the operation of wind turbine units, which can reflect the real-time operating status of the unit, such as wind speed, generator speed, and power.

[0029] Optionally, the data acquisition module, as the data acquisition end, performs dual-type data acquisition operations on the wind turbine drivetrain components. On the one hand, it captures video of the drivetrain components through visual acquisition devices (such as cameras) to obtain video data including the appearance and motion status of the components. On the other hand, it collects operating condition data that reflects the operating status of the unit from the wind turbine monitoring system through the data interaction interface. The acquisition process can achieve synchronous control to ensure the time dimension matching of the two types of data. The acquisition frequency can be set according to the monitoring needs and is not limited here.

[0030] In one possible implementation, when acquiring video data, reflective markers can be set on the transmission chain components, such as target reflectors, and video data can be obtained by acquiring a continuous sequence of images through an optical camera device installed inside the wind turbine nacelle.

[0031] In another possible implementation, supplementary lighting equipment can be added inside the wind turbine to collect video data, which can then be acquired using optical cameras. Alternatively, video data can be acquired using infrared cameras; the specific method is not limited here.

[0032] S302 The data processing module determines the displacement data of each transmission chain component in the wind turbine based on the video data, and stores the displacement data and operating condition data in the database.

[0033] Displacement data refers to the change in position of a transmission chain component relative to its initial position during operation, including axial movement, radial runout, or angular displacement, which is used to determine mechanical loosening and wear.

[0034] After receiving the video data transmitted by the data acquisition module, the data processing module parses and processes the video data using a visual analysis algorithm, identifies the positional changes of the transmission chain components and completes the calculation to obtain the displacement data of each transmission chain component. At the same time, the data processing module standardizes the calculated displacement data and the acquired working condition data and stores them in the database to achieve unified management of the two types of data.

[0035] Optionally, a target-assisted method can be used when calculating displacement data, that is, reflective markers are pasted on the component, and the pixel coordinate changes of the markers in the video frame are tracked and converted into actual physical displacement by combining the camera calibration parameters.

[0036] As another possible implementation, a markerless feature point tracking method can be used to calculate displacement data. Algorithms can be used to automatically identify and track component edges or texture features to obtain displacement data. Alternatively, structured light or laser triangulation principles can be used to assist video analysis. The obtained displacement data is then written into a database along with the operating condition data.

[0037] Optionally, the database can be a time-series database or a relational database. When the database is a relational database, it can also record the timestamp index of the data. For example, the data can be stored in association with the data through the timestamp to ensure that the data is traceable.

[0038] S303. The data analysis module obtains the instruction to be analyzed, parses the instruction based on the large language model, obtains the structured instructions in the instruction to be analyzed, and calls the data analysis agent to obtain the target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instruction to be analyzed. The structured instructions include: data analysis capabilities and data types.

[0039] Among them, the instructions to be analyzed are the analysis requirements submitted by the user for monitoring the wind turbine drivetrain, and these instructions are in natural language. Structured instructions refer to instructions that are directly recognizable and executable by the machine and contain clear analysis requirements.

[0040] A data analysis agent is a program or functional unit capable of data querying and data analysis, which can perform specified data processing operations according to instructions. Target data refers to specific data selected from the database for this analysis based on structured instructions.

[0041] Optionally, the large language model can be deployed on an electronic device that accepts user commands, and the data analysis agent can be deployed on the server. At the same time, the data analysis agent can also communicate with the database that stores the data and call the data in the database for data analysis, so as to reduce the data processing pressure on the electronic device.

[0042] The data analysis module first receives the user's input command to be analyzed, inputs the command into the built-in large language model, and uses the natural language understanding capability of the large language model to parse the unstructured command, extract the core analysis requirements, and transform it into machine-recognizable structured commands. Then, the data analysis module calls the pre-configured data analysis agent, sends the structured commands to the agent, and the data analysis agent filters and queries data in the database according to the requirements in the structured commands, obtains the corresponding target data, and performs the specified data analysis operations on the target data, finally obtaining the analysis results that match the command to be analyzed. The large language model can be selected from various models with natural language parsing capabilities, and the analysis operation logic of the data analysis agent can be pre-configured according to monitoring needs.

[0043] Data analysis capability refers to the specific analytical operations that can be performed on the target data, such as calculating the mean, maximum value, variance, trend fitting, and correlation analysis. Data type refers to the specific category of the target data obtained from the database, including the target location, time information, and operating condition field information. The target location refers to which transmission chain component the data belongs to, the time information is the data acquisition time, and the operating condition field information refers to the field information of the operating condition data that needs to be viewed.

[0044] Structured instructions, as the core machine-executable instructions, include data analysis capabilities and data types. The data analysis capabilities specify the specific analysis operations that the data analysis agent needs to perform on the target data, while the data types specify the specific data categories that the data analysis agent needs to retrieve from the database. This ensures that the data analysis agent can accurately execute analysis operations and fulfill the user's analysis needs without deviation.

[0045] In this embodiment, displacement data of transmission chain components is obtained through video data parsing, and the data is centrally stored in conjunction with the operating condition data to provide a data foundation for analysis. A large language model is used to achieve structured conversion of natural language analysis commands, allowing users to easily submit analysis requests. The data analysis agent then retrieves the target data and completes the analysis, realizing non-contact data acquisition and intelligent data analysis for transmission chain monitoring. This improves the convenience of data collection and the parsing efficiency of analysis commands, while also achieving standardized data management and precise analysis, effectively enhancing the intelligence level and accuracy of wind turbine transmission chain monitoring.

[0046] The following is a further explanation of how the data processing module determines the displacement data of each transmission chain component in the wind turbine based on video data. Each transmission chain component is equipped with a target reflector; the displacement data of the transmission chain component can be determined by detecting the target reflector. Figure 4 As shown, step S302 above includes: S401 The data processing module performs target detection on each video frame in the video data based on the target detection model to obtain the position of the target reflector in each video frame.

[0047] Object detection models can identify the category of a preset target from an image or video frame and locate its position in the frame. For example, models such as YOLO series and Faster R-CNN can be used as object detection models.

[0048] Video data is composed of multiple consecutive video frames. When performing target detection on video data, each video frame can be detected sequentially according to its order to obtain the position of the target reflector in each video frame.

[0049] It should be noted that the interior of the wind turbine nacelle is dark. By setting up reflective targets on the key transmission chain components that need to be inspected, non-contact data acquisition of the transmission chain components can be achieved by collecting video data. Compared with the existing method of deploying sensors, this not only reduces the complexity of deployment but also reduces the difficulty of later maintenance.

[0050] In one possible implementation, the data processing module can first perform frame parsing on the input video data, splitting the continuous video into independent single video frames and preprocessing them (such as noise reduction, contrast enhancement, size normalization, etc., to improve the visual features of the target reflector); then, it calls the deployed target detection model, inputs the preprocessed video frames into the model, and the model matches the target reflector features within the frame through trained feature recognition logic, outputs the pixel position information of the target reflector in the video frame (such as rectangle coordinates, center pixel coordinates, etc.), and temporarily stores this position information for subsequent steps.

[0051] S402, The data processing module determines the displacement data of each transmission chain component based on the position of the target reflector in each video frame.

[0052] Displacement data refers to the change in position of the target reflector relative to the initial or preset reference position, including pixel displacement data and actual physical displacement data. The initial reference position can be the starting position of the target reflector in the video data, while the preset reference position can be a fixed reference position within the transmission chain components.

[0053] The transmission chain components and the target reflector are fixedly connected. The positional change of the target reflector is completely synchronized with the positional change of the transmission chain components. By calculating the positional change of the same target reflector in different video frames, the displacement data of the corresponding transmission chain components can be obtained.

[0054] In one possible implementation, the data processing module can first set a reference position for each target reflector, then extract the position information of the same target reflector in each video frame, calculate the pixel difference between it and the reference position, and obtain pixel displacement data; finally, through pre-defined visual calibration (such as setting a conversion ratio between pixels and actual physical size, which is determined by shooting distance, camera parameters, calibration plate calibration, etc.), the pixel displacement data is converted into actual physical displacement data, which is the displacement data of the corresponding transmission chain component. At the same time, the displacement data can be serialized and organized according to the time dimension to match the time information of the corresponding video frame.

[0055] In this embodiment, the target detection model accurately identifies the position of the reflective element of the target in each video frame. Based on the fixed association between the target and the transmission chain components, the visual positioning result is converted into displacement data of the transmission chain components, realizing non-contact quantitative monitoring of transmission chain displacement and solving the problem of difficult installation and maintenance of contact sensors. The frame-by-frame analysis capability of the target detection model ensures the real-time performance and accuracy of the displacement data, providing effective basic data for subsequent transmission chain vibration state analysis. At the same time, the standardized visual recognition and displacement calculation logic can be adapted to the monitoring needs of different transmission chain components, improving the versatility of the monitoring solution.

[0056] The process of storing displacement data and operating condition data in the database as described above includes: The data processing module associates and stores the displacement data and operating condition data in the database based on the time information of the displacement data and the time information of the operating condition data.

[0057] The generation of displacement data and operating condition data is synchronous in time. Displacement data and operating condition data at the same point in time are logically related (e.g., the displacement of transmission chain components may change under high wind speed conditions). By establishing a relationship through the common dimension of time information, the correspondence between the two types of data can be realized, which facilitates subsequent correlation analysis of the data according to the time dimension.

[0058] In one possible implementation, the data processing module first extracts the time information corresponding to the displacement data (such as the timestamp of the video frame being acquired, or the system time when the displacement is calculated) and the time information corresponding to the operating condition data (such as the timestamp of the sensor acquiring the operating condition parameters). Then, it uses various association methods to implement storage. For example, it sorts the displacement data and operating condition data by time information, adds the same time identifier to data at the same time point or time interval, stores them in the same data table in the database, and creates an index based on the time identifier field. Alternatively, it creates separate displacement data tables and operating condition data tables, both containing time information fields, and establishes inter-table relationships through these time information fields to achieve cross-table matching queries. Another approach is to format the time information, encapsulating the displacement data and operating condition data into key-value pairs, where the key is the timestamp and the value is the corresponding data set, storing them in a database that supports key-value storage, and quickly retrieving the corresponding data by the timestamp key.

[0059] In this embodiment, displacement data and operating condition data are associated and stored through time information, which realizes the time sequence correspondence of the two types of monitoring data and avoids data confusion; the associated storage mode ensures that matching data can be quickly retrieved according to the time dimension, thus improving data retrieval efficiency.

[0060] The following is a further explanation of how the above-mentioned large language model is used to parse the instruction to be analyzed, and obtain the structured instructions in the instruction to be analyzed. Figure 5 As shown, the above step S303 includes: S501. Based on the large language model, the instruction to be analyzed is parsed to obtain multiple target entities in the instruction to be analyzed.

[0061] The target entities include: target location, time information, and operating condition field information.

[0062] Optionally, the large language model can be pre-trained to enable it to identify key information related to transmission chain monitoring in the instructions to be analyzed, thereby transforming ambiguous natural language instructions into clear target entities.

[0063] The target position can be a target position corresponding to the transmission chain component that needs to be monitored, as specified by the user. For example, if the user inputs the command to be analyzed as "monitor the displacement and speed of the generator in the past day", then the target position can be determined as the generator housing target position, the time range is the past 24 hours, and the operating condition fields are displacement data and generator speed data.

[0064] In another example, the instruction to be analyzed can be input into a pre-trained large language model. The model extracts target entities such as target position, time information, data type, and operating condition field information from the instruction through semantic analysis, keyword matching, and contextual logic reasoning. For example, for the instruction to be analyzed, "obtain the displacement data of the main bearing target position and the corresponding power condition from 10:00 to 12:00 on June 1, 2024", the model can accurately extract the target entities: target position (main bearing target position), time information (10:00 to 12:00 on June 1, 2024), data type (displacement data), and operating condition field information (power).

[0065] S502. Encapsulate multiple target entities based on a preset model context protocol to obtain structured instructions.

[0066] The Model Context Protocol (MCP) service is a predefined set of standardized service rules for regulating the organization format of target entities. It clarifies the requirements for field names, data formats, arrangement order, and interaction interfaces of target entities, ensuring that the encapsulated instructions can be directly recognized and parsed by subsequent modules of the system (such as data analysis agents).

[0067] The target entities extracted by the large language model are discrete information units, lacking a unified format specification, and cannot be directly called by subsequent modules of the system. By encapsulating them through a preset model context protocol, discrete entities can be integrated into structured instructions with a unified format, clear logic, and conformity to system interaction standards.

[0068] The data processing module calls the preset MCP service and fills the extracted target entities into the corresponding fields according to the field order, data format, and interface requirements specified by the MCP service. For example, if the MCP service specifies that the structured instructions are in JSON format and the fields include "target_position", "time_info", "data_type", and "condition_field", then the extracted entities are encapsulated in this format as {"target_position": "main bearing target position", "time_info": "2024-06-01 10:00-12:00", "data_type": "displacement data", "condition_field": "power"}, which is the structured instructions.

[0069] In one possible implementation, the MCP service can support custom field extensions. If the user instruction contains additional entities, they can be automatically added to the extended fields of the structured instruction. During the encapsulation process, the MCP service's validation interface can be called to perform format and integrity checks on the target entity. If an entity is missing, a default value can be added according to the MCP service's preset rules to ensure the integrity of the structured instruction.

[0070] In this embodiment, the core target entities in the instruction to be analyzed are accurately extracted by a large language model, and then encapsulated into standardized structured instructions by the MCP service, which realizes the efficient conversion of natural language instructions into system-compatible instructions; it not only reduces the user's operation threshold, but also ensures the uniformity of instruction format and interactive compatibility through the MCP service.

[0071] The following is a further explanation of the above-mentioned data analysis agent, which retrieves target data from the database according to structured instructions, performs data analysis, and obtains the analysis results corresponding to the instructions to be analyzed. Step S303 above includes: The data analysis agent is invoked to retrieve displacement data and operating condition data corresponding to the data type from the database according to the structured instructions, and to perform data analysis based on the data analysis capabilities to obtain the analysis results corresponding to the instructions to be analyzed.

[0072] A data analysis agent is an intelligent module with data retrieval, processing, and analysis capabilities. It can automatically complete data acquisition, analysis, and calculation operations according to preset logic or instruction requirements. A data analysis agent can parse the data type and data analysis capabilities in structured instructions, retrieve matching data through database retrieval, and then perform targeted processing on the data according to specified analytical capabilities.

[0073] The data analysis agent can receive structured instructions and extract search conditions such as "data type", "target position", and "time information". It can then filter the corresponding displacement data and working condition data from the database through the database interface. Next, it can extract the data analysis capabilities from the structured instructions, call the corresponding analysis algorithm built into the agent, and input the acquired target data into the analysis algorithm to complete the calculation and processing and obtain the analysis results.

[0074] In one possible implementation, the data analysis agent can support algorithm plugin extensions, allowing the loading of corresponding third-party algorithm plugins for different analytical capabilities. During data acquisition, a batch retrieval strategy can be employed, filtering time intervals before extracting fields for large datasets to improve retrieval efficiency. During analysis, data preprocessing steps (such as missing value imputation and noise filtering) can be added to optimize the accuracy of the analysis results.

[0075] In this embodiment, a data analysis agent parses structured instructions, acquires target data, and completes analysis according to specified capabilities, thereby achieving automation and targeting of data analysis; standardized instructions can be transformed into effective analysis conclusions without human intervention, thus improving the efficiency of data analysis.

[0076] After the data analysis agent obtains the analysis results, the results can be fed back to the large language model, which then integrates the results and provides them to the operations and maintenance personnel. The method in this application also includes: Based on the large language model, the analysis results are integrated and processed according to the preset report template to obtain an analysis report, which is then sent to the target terminal.

[0077] The preset report template is a predefined framework for analysis report format, which includes the report's chapter structure, content modules, and expression style.

[0078] The large language model can understand the analysis results and combine them with the format requirements of the preset report template to structure and integrate the scattered analysis results, polish the language and organize the logic, form a standardized report, and then send it to the target terminal through the communication interface.

[0079] In one possible implementation, the data processing module can synchronously input the analysis results and the core requirements in the structured instructions into the large language model. The large language model calls the preset report template, fills the analysis results into the corresponding modules according to the template's chapter structure, and optimizes the language of the conclusions (e.g., optimizing "no data anomalies" to "the main bearing target displacement data from 10:00 to 12:00 on June 1, 2024 meets the normal operating threshold range, and no abnormal fluctuations were detected"). Finally, the model outputs the integrated analysis report, which is then sent to the preset target terminal by the system through a communication protocol.

[0080] In this embodiment, the analysis results are integrated into a standardized report by a large language model according to a preset template, making the scattered analysis conclusions more readable and practical; and the report can be accurately pushed to the target terminal, realizing the efficient transmission of analysis results and facilitating relevant personnel to obtain transmission chain monitoring information in a timely manner.

[0081] This application also provides a wind turbine drivetrain monitoring system, referring to... Figure 2 The system includes: The data acquisition module is used to acquire video data and operating condition data of the drive train components.

[0082] The data processing module is used to determine the displacement data of each transmission chain component in the wind turbine based on the video data, and to store the displacement data and operating condition data in the database.

[0083] The data analysis module is used to acquire instructions to be analyzed, parse the instructions based on a large language model to obtain structured instructions, and then call the data analysis agent to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instructions. The structured instructions include data analysis capabilities and data types.

[0084] Optionally, the data acquisition module includes an image acquisition unit and an operating condition data acquisition unit, wherein the image acquisition unit is installed inside the wind turbine nacelle, and the image acquisition unit acquires video data by identifying the target reflector on the transmission chain component.

[0085] The image acquisition unit is a subunit within the data acquisition module responsible for acquiring video data. It can consist of an industrial camera, lens, and supplementary lighting equipment. Alternatively, it can consist only of an industrial camera. The image acquisition unit is located inside the nacelle of the wind turbine and uses the visual characteristics of the target reflector to stably acquire dynamic images of the drivetrain components within the nacelle to obtain video data.

[0086] Optionally, the image acquisition unit uses an industrial high-definition camera, which is installed in the nacelle near the transmission chain components to ensure that the target reflector is within the field of view. The contrast between the reflector and the background is enhanced by supplementary lighting equipment, and video data containing the reflector is continuously acquired. The operating condition data acquisition unit acquires operating condition parameters such as wind speed and rotation speed in real time through sensors deployed at key locations of the unit. The two units work synchronously and transmit data.

[0087] Optionally, the data processing module includes a data calculation unit and a data storage unit, wherein the data calculation unit is used to determine the displacement data of each transmission chain component in the wind turbine based on the video data, and the data storage unit is used to store the displacement data and operating condition data in a database.

[0088] The data processing module is functionally divided, with a dedicated data computing unit responsible for extracting and calculating displacement data, and a data storage unit responsible for standardized data storage. The data computing unit can utilize GPU acceleration to improve video data processing speed, while the data storage unit supports data compression to save storage space and ensure data integrity.

[0089] Optionally, the data analysis module includes: a large language model, a model context protocol server, and a data analysis agent.

[0090] The large language model is used to parse the command to be analyzed, and obtain multiple target entities in the command to be analyzed. The target entities include: target position, time information, data type and working condition field information. The Model Context Protocol (MTP) server is used to encapsulate multiple target entities to obtain structured instructions; Data analysis agents are used to retrieve target data from a database based on structured instructions and perform data analysis to obtain the analysis results corresponding to the instructions to be analyzed.

[0091] The large language model, model context protocol server, and data analysis agent interact with each other through internal interfaces and work together according to a preset process.

[0092] The large language model receives the command to be analyzed and identifies and extracts target entities such as target location, time information, data type, and working condition field information through semantic analysis, keyword matching, and contextual reasoning. The model context protocol server receives multiple target entities output by the large language model, encapsulates the entities according to a preset field order and data format, and generates structured commands containing core information such as data analysis capabilities and data types. After receiving the structured commands, the data analysis agent extracts search conditions such as data type and target location, filters target data from the database, and then calls an algorithm that matches the data analysis capabilities to process the target data and obtain the analysis results.

[0093] Figure 6 This illustration shows a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device can deploy the data processing module and data analysis module of the aforementioned wind turbine drivetrain monitoring system. The electronic device includes a processor 601, a storage medium 602, and a bus 603. The storage medium 602 stores machine-readable instructions executable by the processor 601. When the electronic device runs a wind turbine drivetrain monitoring method as described in the embodiment, the processor 601 communicates with the storage medium 602 via the bus 603. The processor 601 executes the machine-readable instructions, specifically the preamble of the method item, to perform the steps in the aforementioned wind turbine drivetrain monitoring method.

[0094] This application also provides a computer-readable storage medium storing a computer program, which is executed by a processor, wherein the processor performs the steps in the above-described wind turbine drivetrain monitoring method.

[0095] In this embodiment, the computer program, when run by the processor, can also execute other machine-readable instructions to perform other methods as described in the embodiments. For details on the specific execution steps and principles, please refer to the description of the embodiments, which will not be repeated here.

[0096] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0099] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0101] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for monitoring the transmission chain of a wind turbine generator, characterized in that, An application in a wind turbine drivetrain monitoring system, the system comprising: a data acquisition module, a data processing module, and a data analysis module; the method comprising: The data acquisition module acquires video data and operating condition data of the transmission chain components; The data processing module determines the displacement data of each transmission chain component in the wind turbine based on the video data, and stores the displacement data and operating condition data in the database. The data analysis module acquires the instruction to be analyzed, parses the instruction based on a large language model to obtain the structured instructions in the instruction, and calls the data analysis agent to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instruction. The structured instructions include: data analysis capabilities and data types.

2. The method according to claim 1, characterized in that, Each drivetrain component is equipped with a target reflector. The data processing module determines the displacement data of each drivetrain component in the wind turbine based on the video data, including: The data processing module performs target detection on each video frame in the video data based on the target detection model to obtain the position of the target reflector in each video frame. The data processing module determines the displacement data of each transmission chain component based on the position of the target reflector in each video frame.

3. The method according to claim 1, characterized in that, The step of storing the displacement data and operating condition data in the database includes: The data processing module associates and stores the displacement data and operating condition data in the database based on the time information of the displacement data and the time information of the operating condition data.

4. The method according to claim 1, characterized in that, The parsing of the instruction to be analyzed based on a large language model yields structured instructions within the instruction to be analyzed, including: The command to be analyzed is parsed based on a large language model to obtain multiple target entities in the command to be analyzed. The target entities include: target position, time information, data type and working condition field information. The multiple target entities are encapsulated based on a preset model context protocol to obtain the structured instructions.

5. The method according to claim 1, characterized in that, The data analysis agent retrieves target data from the database according to the structured instructions and performs data analysis to obtain the analysis results corresponding to the instructions to be analyzed, including: The data analysis agent is invoked to retrieve displacement data and working condition data corresponding to the data type from the database according to the structured instructions, and to perform data analysis based on the data analysis capabilities to obtain the analysis results corresponding to the instructions to be analyzed.

6. The method according to claim 1, characterized in that, The method further includes: Based on the large language model, the analysis results are integrated and processed according to the preset report template to obtain an analysis report, which is then sent to the target terminal.

7. A wind turbine drivetrain monitoring system, characterized in that, include: The data acquisition module is used to acquire video data and operating condition data of the drive train components; The data processing module is used to determine the displacement data of each transmission chain component in the wind turbine based on the video data, and to store the displacement data and operating condition data in the database. The data analysis module is used to acquire the instruction to be analyzed, parse the instruction based on a large language model to obtain the structured instructions in the instruction, and call the data analysis agent to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instruction. The structured instructions include: data analysis capabilities and data types.

8. The system according to claim 7, characterized in that, The data acquisition module includes an image acquisition unit and an operating condition data acquisition unit. The image acquisition unit is installed inside the wind turbine nacelle, and the image acquisition unit acquires the video data by identifying the target reflector on the transmission chain component.

9. The system according to claim 7, characterized in that, The data processing module includes a data calculation unit and a data storage unit. The data calculation unit is used to determine the displacement data of each transmission chain component in the wind turbine based on the video data, and the data storage unit is used to store the displacement data and operating condition data in a database.

10. The system according to claim 7, characterized in that, The data analysis module includes: a large language model, a model context protocol server, and a data analysis agent; The large language model is used to parse the instruction to be analyzed to obtain multiple target entities in the instruction to be analyzed. The target entities include: target position, time information, data type and working condition field information. The model context protocol server is used to encapsulate the multiple target entities to obtain the structured instructions; The data analysis agent is used to retrieve target data from the database according to the structured instructions and perform data analysis to obtain the analysis results corresponding to the instructions to be analyzed.

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