A fan multi-source defect aggregation and centralized alarm system, method and program product

Abstract

The application relates to the technical field of wind farm management, and discloses a wind turbine multi-source defect collection and centralized alarm system, a method and a program product, the system comprising a data source layer, a data access and convergence layer, a data storage layer and an application display layer, wherein the data access and convergence layer comprises a multi-source defect convergence engine. Therefore, by implementing the application, by building a four-layer integrated collaborative architecture, the data barriers of multiple independent monitoring systems of a wind farm are broken through, single wind turbine full-dimension defect automatic collection, standardized storage, intelligent risk research and automatic work order issuing are realized. Further, the problems of multi-system switching viewing of operation and maintenance personnel, alarm fragmentation, slow fault positioning and complicated disposal process are completely solved, the station monitoring human load is greatly reduced, and the fault early warning confidence and operation and maintenance disposal efficiency are simultaneously improved.

Description

Technical Field

[0001] This invention relates to the field of wind farm management technology, specifically to a system, method, and program product for summarizing and centrally alarming multi-source defects in wind turbines. Background Technology

[0002] Existing wind farms typically have multiple independent wind turbine monitoring subsystems that do not share data. Each system can only collect and display single-dimensional equipment anomaly information independently, and the operation, alarm, and storage systems of each subsystem are completely isolated. Specifically, these include four types of independent monitoring systems: Vibration monitoring system: It only independently collects and displays vibration fault data of the wind turbine drivetrain, and the data output channel is closed, making it impossible to synchronize vibration defect information to the outside world; SCADA data acquisition and monitoring control system: It only independently monitors and pushes alarms for wind turbine electrical parameters and overall machine operating conditions, and the defect data is only stored within this system; Video intelligent analysis system: It can only retrieve on-site video playback independently, and the visual defect identification results cannot be linked with other monitoring systems to push alarms; Fire alarm system: It only realizes local independent audible and visual alarms, and fire defect data cannot be synchronized to other equipment monitoring platforms.

[0003] However, the existing monitoring systems in wind farms are isolated from each other, have inconsistent coding standards, and lack the ability to automatically collect, clean, store, and intelligently correlate and analyze multi-source defects. Maintenance personnel need to manually compare scattered alarms across platforms, which can easily lead to the omission of high-risk faults with multiple sources coupled together. Furthermore, it is impossible to automatically generate maintenance work orders that integrate complete defect information, resulting in low fault handling efficiency and a tendency to cause equipment failures to escalate. Summary of the Invention

[0004] This invention provides a system, method, and program product for summarizing and centralizing multi-source defects in wind turbines. It addresses the problems of existing wind farm monitoring systems being isolated from each other, having inconsistent coding standards, lacking automatic collection, cleaning, unified storage, and intelligent correlation analysis capabilities for multi-source defects, requiring maintenance personnel to manually compare scattered alarms across platforms, easily overlooking high-risk faults with multiple coupled sources, and failing to automatically generate maintenance work orders that integrate complete defect information, resulting in low fault handling efficiency and the potential for equipment failure to escalate.

[0005] In a first aspect, the present invention provides a wind turbine multi-source defect aggregation and centralized alarm system, the system comprising: a data source layer, a data access and aggregation layer, a data storage layer, and an application display layer, wherein the data access and aggregation layer includes a multi-source defect aggregation engine; The data source layer monitors the vibration, operation, and location of multiple wind turbines, acquires initial multi-source defect datasets for these turbines, and sends these datasets to the multi-source defect aggregation engine. The multi-source defect aggregation engine filters the initial multi-source defect datasets from multiple wind turbines according to a target mapping table and a preset time window, obtaining the original multi-source defect datasets belonging to the same target wind turbine. Furthermore, the multi-source defect aggregation engine classifies and deduplicates the original multi-source defect datasets for each target wind turbine, obtaining the defect type and target multi-source defect dataset for each wind turbine, and then aggregates the defect data for each target wind turbine. The defect type and target multi-source defect dataset are sent to the data storage layer. The target multi-source defect dataset is used to reflect the defect status of the target wind turbine. The data storage layer is used to perform profiling on the defect type and target multi-source defect dataset of each target wind turbine according to the preset data structure template, so as to obtain the wind turbine defect profile of each target wind turbine and build the wind turbine defect profile database. The application display layer is used to retrieve the wind turbine defect profile of each target wind turbine in the wind turbine defect profile database, and generate high-risk combination alarm prompts for each target wind turbine using preset defect association prompt rules, and generate maintenance work orders for each target wind turbine based on the high-risk combination alarm prompts for each target wind turbine.

[0006] The wind turbine multi-source defect aggregation and centralized alarm system provided by this invention synchronously aggregates abnormal information from four independent monitoring systems—wind turbine vibration, operation, on-site environment, and fire protection—at the data source layer. This connects the data output channels of multiple isolated monitoring systems, enabling unified input of original fault information across all dimensions and eliminating the underlying problem of fragmented data sources from multiple systems. Furthermore, the multi-source defect aggregation engine locks all abnormal data within the same wind turbine time series at a fixed time interval, avoiding cross-device and cross-time period alarm information mixing, thus enabling precise segmentation of fault datasets specific to each wind turbine. Furthermore, by classifying and deduplicating the original multi-source defect datasets belonging to the same target wind turbine, defect information can be organized according to the equipment category to which the fault belongs, reducing redundant alarm noise. Finally, the data storage layer standardizes and integrates all dimensions of defect information for a single wind turbine to form a structured profile, thereby uniformly storing and managing the fault archives of the entire field, providing a standardized and quickly retrieved data foundation for upper-level display and risk assessment. Finally, the application presentation layer automatically associates multi-source coupled faults and outputs high-risk warnings, thereby directly generating work orders with complete fault information. This eliminates the need for maintenance personnel to sift through fault clues across systems, achieving one-stop output of handling guidance. Therefore, by implementing this invention and building a four-layer integrated collaborative architecture, the data barriers between multiple independent monitoring systems in a wind farm are broken down, enabling automatic collection, standardized storage, intelligent risk assessment, and automatic work order issuance of all-dimensional defects for a single wind turbine. Furthermore, it completely solves the problems of maintenance personnel switching between multiple systems, fragmented alarms, slow fault location, and cumbersome handling processes, significantly reducing the manpower load for site monitoring and simultaneously improving the confidence level of fault warnings and the efficiency of maintenance handling.

[0007] In one alternative implementation, the data source layer includes: The vibration monitoring system is used to monitor the vibration of multiple wind turbines and acquire multiple vibration defect data of multiple wind turbines; the data acquisition and monitoring control system is used to monitor the operation of multiple wind turbines and acquire multiple abnormal operation defect data of multiple wind turbines; the video intelligent analysis system is used to monitor the environment at the site of multiple wind turbines and acquire multiple visual defect data of multiple wind turbines; the fire alarm system is used to acquire multiple fire alarm defect data at the site of multiple wind turbines.

[0008] The wind turbine multi-source defect aggregation and centralized alarm system provided by this invention, by clearly defining the division of labor of four complementary monitoring data sources, can fully cover four major fault scenarios of wind turbines: mechanical, electrical, on-site vision, and fire safety. It realizes full coverage collection of abnormal information of wind turbines in all scenarios, ensuring that the back-end aggregation engine can obtain complete and multi-dimensional original fault materials, which helps to support subsequent multi-source cross-verification and judgment.

[0009] In one optional implementation, the multi-source defect aggregation engine is also used to obtain multiple unique identifiers of multiple wind turbines and point codes of each system in the data source layer, and to establish a target mapping table based on the multiple unique identifiers and multiple point codes.

[0010] The wind turbine multi-source defect aggregation and centralized alarm system provided by this invention establishes a binding mapping relationship based on the unique identifier of the wind turbine and the location code of each subsystem. This can unify the equipment indexing standards of different monitoring systems, eliminate the problem of data ownership confusion caused by the incompatibility of independent codes of each subsystem, and help to realize the automatic aggregation of cross-system data according to the wind turbine dimension.

[0011] In one alternative implementation, the multi-source defect aggregation engine is also used to acquire a new initial multi-source defect dataset for multiple wind turbines and update the target multi-source defect dataset for each target wind turbine based on the new initial multi-source defect dataset.

[0012] The wind turbine multi-source defect aggregation and centralized alarm system provided by this invention uses a multi-source defect aggregation engine to continuously receive new abnormal data from each subsystem and synchronously update the target defect dataset of a single wind turbine. This enables automatic synchronization of new faults, elimination of recovered alarms, and upgrade of fault alarm levels, ensuring that the information in the wind turbine defect profile database is updated in real time. It avoids problems such as delayed alarm information, long-term retention of failed alarms, and failure risk levels not being updated synchronously, ensuring that the alarm information viewed by maintenance personnel is always consistent with the actual equipment status on site.

[0013] In one alternative implementation, the application presentation layer includes: a defect association rule engine and a confirmation and work order generation module; The defect association rule engine is used to match the defect types recorded in the defect profile of each target wind turbine with the preset defect association prompt rules, generate a high-risk combination alarm prompt for each target wind turbine, and send the high-risk combination alarm prompt for each target wind turbine to the confirmation and work order generation module; the confirmation and work order generation module is used to generate a maintenance work order for each target wind turbine based on the high-risk combination alarm prompt for each target wind turbine.

[0014] The wind turbine multi-source defect aggregation and centralized alarm system provided by this invention utilizes a defect association rule engine. By automatically matching multi-dimensional defects of the wind turbine with preset risk rules, it can identify high-risk fault combinations caused by multi-system coupling and output specific alerts. Furthermore, the confirmation and work order generation module generates maintenance work orders for each target wind turbine based on the high-risk combination alarm alerts, eliminating the need for maintenance personnel to manually aggregate fault information and simplifying the maintenance dispatch process. Therefore, by implementing this invention, intelligent autonomous fault risk assessment and automated maintenance work order output are achieved, simplifying the pre-fault handling process and shortening the overall time from fault discovery to work order dispatch.

[0015] In one alternative implementation, the application presentation layer further includes: a wind turbine card interface, used to identify and display key data recorded in the wind turbine defect profile of each target wind turbine, wherein the identification is used to distinguish the severity of defects of each target wind turbine.

[0016] The wind turbine multi-source defect aggregation and centralized alarm system provided by this invention uses a single wind turbine card interface as an independent display unit, structurally displaying the core information of the wind turbine defect profile, and distinguishing alarm severity levels through differentiated identifiers. It can intuitively present all fault summary information of a single wind turbine, realizing centralized visual display on a single screen. Maintenance personnel only need to view a single interface to grasp all faults and risk levels of a single wind turbine, without the need to switch between multiple monitoring screens, significantly reducing the human monitoring burden caused by simultaneous operation of multiple systems, and avoiding the omission of alarms from a single system.

[0017] Secondly, the present invention provides a method for summarizing and centrally alarming multi-source defects in wind turbines, used in the wind turbine multi-source defect summarizing and centrally alarming system of the first aspect or any corresponding embodiment thereof; the method includes: The data source layer collects initial multi-source defect datasets from multiple wind turbines and sends these datasets to the multi-source defect aggregation engine. The multi-source defect aggregation engine obtains multiple unique identifiers for each wind turbine and the location codes for each system in the data source layer, and establishes a target mapping table based on these identifiers and location codes. Based on the target mapping table, the multi-source defect aggregation engine filters the initial multi-source defect datasets from multiple wind turbines according to a preset time window, obtaining the original multi-source defect datasets belonging to the same target wind turbine. The multi-source defect aggregation engine then classifies and deduplicates the original multi-source defect datasets for each target wind turbine, obtaining the datasets for each target wind turbine. The system retrieves the defect types and target multi-source defect datasets for each target wind turbine and sends them to the data storage layer. The data storage layer then performs profiling on the defect types and multi-source defect datasets for each target wind turbine according to a pre-defined data structure template, obtaining a wind turbine defect profile for each target wind turbine and constructing wind turbine defect profile data. The application presentation layer retrieves the wind turbine defect profiles for each target wind turbine from the wind turbine defect profile database and, using pre-defined defect association prompting rules, generates high-risk combination alarm prompts for each target wind turbine, as well as maintenance work orders for each target wind turbine based on these high-risk combination alarm prompts.

[0018] The method for summarizing and centrally alarming multi-source defects in wind turbines provided by this invention processes multi-source wind turbine fault data through a standardized and automated end-to-end process, forming a complete closed-loop process from data acquisition, unified aggregation, standardized storage to intelligent early warning and work order issuance. Furthermore, it implements multi-system alarm fusion and centralized management capabilities at the operational level, systematically solving the problems of independent operation and maintenance of multiple monitoring systems in wind farms, fragmented alarms, and low efficiency in fault handling, thus achieving less-staffed and intelligent operation and maintenance management of wind farms.

[0019] Thirdly, the present invention provides an electronic device, including: a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine multi-source defect aggregation and centralized alarm method corresponding to the second aspect above.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the wind turbine multi-source defect aggregation and centralized alarm method corresponding to the second aspect above.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause the computer to execute the wind turbine multi-source defect aggregation and centralized alarm method corresponding to the second aspect above. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a structural block diagram of a wind turbine multi-source defect aggregation and centralized alarm system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the method for summarizing and centrally alarming multi-source defects in wind turbines according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a wind turbine multi-source defect aggregation and centralized alarm system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] The existing independent monitoring architecture in wind farms has many problems: 1. The problem of data silos in multiple systems is prominent. There is no unified data aggregation and collection mechanism. The point coding and equipment index standards of each subsystem are not uniform. Vibration, operation, vision and fire protection defect data of the same target fan are scattered and stored on different platforms, and cannot be automatically collected to the same equipment dimension. 2. Lack of unified defect data processing capabilities, multiple systems synchronously generate a large number of duplicate and same-source alarms, there are no automated classification, deduplication, and time-series filtering methods, redundant alarm information is mixed and interferes with fault identification, and the defect status cannot be updated in real time. 3. There is no standardized data storage carrier for wind turbine defects. The data storage structure for defects in various systems is not uniform, making it impossible to build a complete, structured, multi-dimensional defect file for a single wind turbine, which makes it difficult to support upper-level risk analysis and access. 4. Lack of intelligent correlation analysis and centralized visualization capabilities for defects. Maintenance personnel need to monitor multiple systems and switch between multiple display screens to manually summarize defect information, resulting in a high degree of dispersion of manpower and energy. There is no automatic correlation prompt mechanism for multi-source coupled defects. For example, when abnormal fan vibration is accompanied by smoke and fire in the on-site video, it is entirely up to maintenance personnel to manually compare multiple defect clues across systems. 5. Without alarms, the automated workflow of work orders requires manual processing of all defect information and manual creation of maintenance work orders after identifying high-risk faults, which is a cumbersome process. Manual cross-system alarm screening is prone to missing high-risk combined alarms triggered simultaneously by multiple systems, delaying the opportunity to deal with faults, causing the wind turbine faults to continue to deteriorate, the equipment damage to expand, and increasing the operation and maintenance costs and downtime losses of the site.

[0028] This invention provides a multi-source defect aggregation and centralized alarm system for wind turbines. By building a four-layer integrated collaborative architecture, it breaks down the data barriers between multiple independent monitoring systems in a wind farm, enabling automatic aggregation of all-dimensional defects of a single wind turbine, standardized storage, intelligent risk assessment, and automatic work order issuance. This significantly reduces the manpower load for site monitoring and simultaneously improves the confidence level of fault warnings and the efficiency of operation and maintenance.

[0029] As an optional application scenario of this invention, a system for summarizing and centrally alarming multi-source defects in wind turbines is provided, such as... Figure 1 As shown, the wind turbine multi-source defect aggregation and centralized alarm system 1 includes a data source layer 2, a data access and aggregation layer 3, a data storage layer 4, and an application display layer 5.

[0030] Among them, the data access and aggregation layer 3 includes a multi-source defect aggregation engine 31.

[0031] Optionally, the data source layer 2 is used to monitor the vibration, operation and location of multiple wind turbines, acquire the initial multi-source defect dataset of multiple wind turbines, and send the initial multi-source defect dataset of multiple wind turbines to the multi-source defect aggregation engine 31.

[0032] In one optional embodiment, the initial multi-source defect dataset represents original defect records that are not uniformly collected across four dimensions: vibration, operating conditions, on-site visual data, and fire situation. Each data record includes the corresponding subsystem location code, anomaly description, trigger time, and risk level original identifier.

[0033] In an optional embodiment, the data source layer 2 includes a vibration monitoring system 21, a data acquisition and monitoring control system 22, a video intelligent analysis system 23, and a fire alarm system 24.

[0034] First, the vibration monitoring system 21 is used to monitor the vibration of multiple wind turbines and obtain multiple wind turbine vibration defect data.

[0035] In one optional embodiment, the vibration monitoring system refers to a system that uses sensors, data acquisition and analysis technology to collect, process and diagnose vibration signals of mechanical equipment (especially rotating machinery) in real time or periodically. In this embodiment, the vibration monitoring system 21 is an independent online monitoring subsystem deployed on key components of the transmission chain such as wind turbine gearboxes, main shafts, and generators, and uses vibration sensors to collect mechanical vibration signals and built-in fault diagnosis algorithms to identify wear and damage faults in transmission components.

[0036] Furthermore, the multiple wind turbine vibration defect data represent the raw alarm data for mechanical faults collected by the vibration monitoring system for each wind turbine drivetrain component. These data may include abnormal records such as excessive vibration amplitude, sudden changes in vibration trend, bearing / gearbox fault characteristics, and vibration signal interruption. Additionally, the wind turbine vibration defect data is linked to the vibration channel number.

[0037] In one optional embodiment, rotating machinery failures can alter the vibration spectrum characteristics, and bearing and gear wear can generate characteristic impact signals. By monitoring the vibration of multiple fans and comparing them in real time with a standard vibration baseline, early mechanical failures can be detected in advance, and vibration dimension alarms can be generated independently.

[0038] For example, vibration acquisition sensors are installed at the bearing locations of the wind turbine main shaft, gearbox, and generator, and then the vibration monitoring system 21 continuously acquires vibration time-domain and frequency-domain data at a fixed sampling frequency.

[0039] Furthermore, the vibration monitoring system 21 has a built-in fault algorithm that can compare real-time vibration characteristics with the normal reference threshold of the equipment, and then identify amplitude exceeding the standard, abnormal trend, and component damage based on the comparison results.

[0040] Furthermore, when amplitude exceeding the standard, abnormal trend, or component damage is identified, a vibration defect record is generated that is bound to the corresponding vibration channel number, labeled with the fault location, severity level, and trigger time. Then, all wind turbine vibration defect data is sent in batches to the multi-source defect aggregation engine 31.

[0041] Secondly, the data acquisition and monitoring control system 22 is used to monitor the operation of multiple wind turbines and obtain data on multiple wind turbine operation abnormalities and defects.

[0042] In an alternative embodiment, the data acquisition and monitoring control (SCADA) system 22 represents a computer-based production process control and scheduling automation system that can monitor and control on-site operating equipment.

[0043] Furthermore, multiple wind turbine operational anomaly defect data represent the original records of abnormal wind turbine electrical and overall operating conditions output by the data acquisition and monitoring control system 22, which may include information such as excessive temperature, power deviation from the rated range, abnormal speed, communication interruption, grid voltage / frequency deviation, and pitch and yaw faults. Furthermore, the wind turbine operational anomaly defect data are bound to SCADA point codes.

[0044] In one optional embodiment, faults in the electrical and transmission components of the wind turbine will be directly reflected in the parameters of temperature, power, speed, and communication links. Deviating from the rated range of parameters indicates that the equipment has an operational fault.

[0045] For example, by combining the wind turbine's built-in PLC module with various operating condition sensors such as temperature, power, speed, and voltage, operating parameters such as wind turbine nacelle temperature, power generation, rotor speed, pitch status, and equipment communication links can be continuously collected in seconds.

[0046] Furthermore, the data acquisition and monitoring control system 22 compares real-time parameters with the equipment's rated safe range in real time. Furthermore, in the event of operating conditions such as excessive temperature, power deviation from normal values, communication interruption, or abnormal speed, the data acquisition and monitoring control system 22 generates an operational defect record that binds SCADA point codes and records the abnormal parameter values ​​and fault types.

[0047] Finally, the data acquisition and monitoring control system 22 sends all abnormal operation defect data of all wind turbines in the field to the multi-source defect aggregation engine 31 in batches.

[0048] Then, the video intelligent analysis system 23 is used to monitor the environment at the sites of multiple wind turbines and obtain multiple visual defect data of multiple wind turbines.

[0049] In one optional embodiment, the Intelligent Video Analysis System is an intelligent platform that integrates computer vision, deep learning, and pattern recognition technologies to automatically parse, understand, and extract structured information from video streams. In this embodiment, the Intelligent Video Analysis System 23 is a site security and appearance inspection subsystem equipped with high-definition camera equipment and AI visual recognition algorithms.

[0050] Furthermore, multiple visual defect data represent anomaly records of the wind turbine's on-site appearance and security issues output by the video intelligent analysis system 23 through AI image recognition. These can include identification results such as smoke and open flames, blade cracks and leaks, unauthorized personnel intrusion, and oil stains and damage to the tower. Furthermore, the visual defect data is linked to the camera number and the number of the wind turbine it covers.

[0051] In one optional embodiment, high-definition camera equipment is deployed on the wind turbine tower, outside the nacelle, and in key areas of the wind farm, and then the real-time video data stream is continuously transmitted back to the video intelligent analysis system 23 through the camera.

[0052] Furthermore, the video intelligent analysis system 23 can use its built-in deep learning AI model to analyze video frames one by one and automatically identify abnormal targets in the frame, such as smoke and open flames, equipment oil leaks and damage, unauthorized personnel unauthorized entry, and blade cracks.

[0053] Furthermore, once the identification confidence level reaches a preset threshold, a visual defect record is generated, which includes the camera number, the corresponding covered wind turbine, and a brief description of the abnormal content. All wind turbine visual defect data is then transmitted to the multi-source defect aggregation engine 31.

[0054] Finally, the fire alarm system 24 was used to obtain multiple fire alarm defect data at the locations of multiple wind turbines.

[0055] In one optional embodiment, the fire alarm system refers to a system that uses automated means to detect fire characteristics at an early stage, issue audible and visual alarms, and coordinate with fire-fighting facilities to ensure safe evacuation of personnel and reduce property damage. In this embodiment, the fire alarm system 24 is a dedicated fire monitoring subsystem for wind turbine nacelles and towers.

[0056] Furthermore, multiple fire alarm defect data represent raw fire-related alarm data triggered by the fire alarm system's sensor units, which may include records of smoke concentration exceeding standards, temperature rise exceeding limits, manual alarm button triggering, and flame detection alarms. Furthermore, this data is linked to the detector number and the corresponding fan / area code.

[0057] In one optional embodiment, smoke, temperature and flame composite detectors are installed inside the wind turbine nacelle, electrical control cabinet and tower. Then, various fire protection sensing devices continuously collect on-site smoke concentration, ambient temperature and infrared flame spectrum signals.

[0058] Furthermore, when the monitored parameters exceed the fire safety threshold, or when the on-site manual alarm button is pressed by someone, the fire alarm system 24 generates a fire defect record that binds the detector number, marks the alarm location and fire type, and then sends the fire alarm defect data corresponding to all fans in the field to the multi-source defect aggregation engine 31.

[0059] Optionally, the multi-source defect aggregation engine 31 is used to filter the initial multi-source defect datasets of multiple wind turbines according to a preset time window based on the target mapping table, to obtain the original multi-source defect datasets belonging to the same target wind turbine, to classify and deduplicate the original multi-source defect datasets of each target wind turbine, to obtain the defect type and target multi-source defect dataset of each target wind turbine, and to send the defect type and target multi-source defect dataset of each target wind turbine to the data storage layer.

[0060] In one alternative embodiment, the target multi-source defect dataset is used to reflect the defect status of the target wind turbine.

[0061] First, the multi-source defect aggregation engine 31 obtains multiple unique identifiers of multiple wind turbines and point codes of each system in the data source layer 2, and establishes a target mapping table based on the multiple unique identifiers and multiple point codes.

[0062] In one alternative embodiment, multiple unique identifiers (IDs) represent equipment numbers that are exclusive to each wind turbine and unique across the entire site.

[0063] Furthermore, the location code represents an independent code within each subsystem, which can include vibration channel number, SCADA signal location number, camera number, and fire detector number. Moreover, the coding rules for different systems are not interchangeable.

[0064] In one optional embodiment, each subsystem can only identify its own location code and cannot directly associate it with the corresponding wind turbine. By assigning all collected points to the corresponding wind turbine ID through a static mapping relationship, it is possible to quickly locate the wind turbine to which any alarm data belongs, providing a matching basis for the aggregation of defects of the same wind turbine.

[0065] For example, the multi-source defect aggregation engine 31 reads the wind farm equipment ledger and extracts the unique wind turbine ID corresponding to each wind turbine in the entire field in batches to complete the initialization of the wind turbine identifier library.

[0066] Furthermore, it connects to the interfaces of vibration monitoring system 21, data acquisition and monitoring control system 22, video intelligent analysis system 23, and fire alarm system 24 respectively, and reads all point codes inside each system, which may include vibration channel number, SCADA acquisition point number, camera device number, and smoke / heat detector number.

[0067] Furthermore, by utilizing the site equipment deployment ledger, a one-to-one binding relationship is established between the point code and the fan ID. For example, vibration channel A05 is bound to fan F05, SCADA point B03 is bound to fan F05, camera C12 is bound to fan F05, and fire detector D08 is bound to fan F05.

[0068] Furthermore, all binding relationships are stored and a structured target mapping table is generated. This target mapping table is then permanently cached locally in the aggregation engine, allowing subsequent multi-source defect data to be matched to the corresponding wind turbine via table lookup.

[0069] Furthermore, if new wind turbines or monitoring points are added to the site, the multi-source defect aggregation engine 31 can automatically update the mapping table content to ensure that the index matching relationship is accurate in real time.

[0070] Secondly, the multi-source defect aggregation engine 31 filters the initial multi-source defect datasets of multiple wind turbines according to the target mapping table and a preset time window to obtain the original multi-source defect datasets belonging to the same target wind turbine.

[0071] In one optional embodiment, the preset time window represents a time-series statistical interval pre-configured by the user, typically set to 1 hour, to limit the time range for collecting defect data.

[0072] In one optional embodiment, the initial multi-source defect dataset contains alarms from all wind turbines across the entire site, covering all time periods, without any distinction between equipment affiliations. Further, by matching the wind turbine IDs corresponding to the location codes using a mapping table and extracting alarm records within a preset time window, it is possible to separate all original alarms belonging only to a single wind turbine within the valid time sequence.

[0073] For example, data source layer 2 continuously pushes real-time initial multi-source defect datasets to multi-source defect aggregation engine 31. Furthermore, each defect record includes the corresponding subsystem location code, alarm generation time, and anomaly description.

[0074] Furthermore, the multi-source defect aggregation engine 31 reads the pre-stored target mapping table and extracts the location code carried by each defect record one by one. Then, by combining the target mapping table, it queries and matches to obtain the unique identifier of the wind turbine to which the alarm belongs.

[0075] Furthermore, the multi-source defect aggregation engine 31 system presets a 1-hour time window threshold, and uses the current system time as the endpoint to count backwards by 1 hour to define the effective data period.

[0076] Furthermore, a time-series verification is performed on each defect record that has completed wind turbine matching, retaining only records whose alarm generation time falls within a preset time window, and removing historical alarms that have expired outside the window.

[0077] Furthermore, the data is grouped and categorized according to the wind turbine ID, and then all original alarms that have passed the time-series verification under the same wind turbine ID are integrated to form the original multi-source defect dataset belonging to the same target wind turbine. The original defect data of different wind turbines are isolated from each other and do not mix with each other.

[0078] Finally, the multi-source defect aggregation engine 31 classifies and deduplicates the original multi-source defect datasets of each target wind turbine to obtain the defect type and target multi-source defect dataset of each target wind turbine, and sends the defect type and target multi-source defect dataset of each target wind turbine to the data storage layer.

[0079] In one optional embodiment, the same fault in the same fan can trigger simultaneous alarms from multiple systems, including vibration, SCADA, video, and fire protection systems. Multiple independent alarm messages repeatedly pointing to the same fault can create information noise. This embodiment simplifies alarm information and provides a clearer picture of the fault by classifying and organizing fault dimensions and deduplicating and merging alarms from the same source.

[0080] For example, the multi-source defect aggregation engine 31 classifies the defect types in the original multi-source defect dataset according to the data source, which may include vibration transmission defects, SCADA electrical operation defects, video visual environment defects, and fire-related defects. Furthermore, each original alarm tag is assigned a corresponding defect classification label.

[0081] Furthermore, the multi-source defect convergence engine 31 identifies multiple alarms that are causally related in timing and point to the same equipment fault, and determines them to be duplicate alarms from the same source. For example, gearbox wear sequentially triggers four independent records: excessive vibration, increased oil temperature, smoke detection, and smoke alarm.

[0082] Furthermore, multiple alarms originating from the same source are merged into a single aggregate record, and after retaining all alarm details from all subsystems, a unified aggregate main fault title is generated. Simultaneously, abnormal parameters, fault descriptions, and risk levels from each system are integrated.

[0083] Furthermore, after classification and deduplication are completed, a target multi-source defect dataset containing complete aggregated alarm information and classification labels is generated. Moreover, the generated target multi-source defect dataset contains no duplicate or redundant alarms and fully records all dimensions of anomalies for the wind turbine.

[0084] Furthermore, the multi-source defect aggregation engine 31 synchronously transmits the defect classification label corresponding to the wind turbine and the processed target multi-source defect dataset to the data storage layer 4.

[0085] Optionally, the data storage layer 4 is used to perform profiling on the defect type and target multi-source defect dataset of each target wind turbine according to a preset data structure template, so as to obtain the wind turbine defect profile of each target wind turbine and construct the wind turbine defect profile database.

[0086] In one optional embodiment, a pre-defined standardized structured data storage specification is represented by a preset data structure template. This specification uniformly defines all fields required for storing wind turbine defect profiles, their uses, and data entry formats. These fields may include wind turbine ID, vibration defect, SCADA defect, video defect, fire protection defect, overall rating, and update time.

[0087] For example, the preset data structure template is as follows: Field → Sample Value; Fan ID → F05; Vibration defects → Gearbox tooth surface wear (severe); SCADA defect → Generator bearing temperature is too high (normal). Video defect → Suspected smoke at the bottom of the tower (to be confirmed); Fire safety defects → None; Overall Level → Emergency; Updated on 2025-05-08 at 10:23:00.

[0088] In one optional embodiment, the data fields output by the multi-source defect aggregation engine are scattered and lack a unified storage standard, making it impossible to directly support visualization and risk rule matching. In this embodiment, multi-source defect information is organized and encapsulated using a preset fixed template, integrating scattered mechanical, electrical, visual, and fire protection defects into a complete wind turbine file. Through unified storage, it is possible to quickly retrieve all fault information by wind turbine ID, while the unified field structure facilitates batch reading and parsing of data at the application layer.

[0089] For example, the data storage layer 4 continuously receives the target wind turbine defect types and the deduplicated target multi-source defect dataset transmitted by the multi-source defect aggregation engine 31, and caches the temporarily received data for structured processing.

[0090] Furthermore, data storage layer 4 retrieves the pre-configured preset data structure template from the system and matches the target wind turbine data with the template fields to complete the population: ① Enter the unique identifier of the current wind turbine into the "Wind Turbine ID" field; ② Extract the classified vibration fault descriptions from the target multi-source defect dataset and fill them into the "Vibration Defect" field. If there is no vibration abnormality, fill in "None". ③ Extract the electrical anomaly information identified by the data acquisition and monitoring control system 22 and fill it into the "SCADA Defect" field. If there is no operational anomaly, fill in "None". ④ Extract the smoke, personnel, and equipment appearance abnormalities output by the video intelligent analysis system 23 and fill them into the "Video Defects" field. If there are no visual abnormalities, fill in "None". ⑤ Extract the smoke, heat and manual alarm records pushed by the fire alarm system 24 hours a day and fill them into the "Fire Defects" field. If there is no fire alarm, fill in "None". ⑥ Determine the overall risk level of the machine based on the severity of the four types of defects, classifying them into three levels: emergency, general, and pending confirmation, and fill in the "overall level" field; ⑦ Capture the standard timestamp of the data being processed by the current system, fill it into the "Update Time" field, and record the last refresh time of the wind turbine defect profile; Furthermore, once all fields are filled in, a complete and structured defect profile of the target wind turbine can be generated. Moreover, a single profile can comprehensively include all dimensions of fault information for the wind turbine, helping to eliminate the fragmentation problem of multi-source data.

[0091] Furthermore, the generated wind turbine defect profiles are stored in the database using the wind turbine ID as a unique index, and new wind turbine files are automatically distinguished from existing wind turbine files. New wind turbine files are directly created and stored in the database; existing wind turbine files are overwritten using the newly generated defect profiles.

[0092] Furthermore, the system batch stores defect profiles for all wind turbines in the field and completes the construction and real-time updating of the wind turbine defect profile database. Moreover, all wind turbine files within the database follow the same preset data structure template, with standardized and uniform fields, which helps the application presentation layer 5 quickly retrieve and access complete defect profile data for any wind turbine using its wind turbine ID.

[0093] Optionally, the application presentation layer 5 is used to retrieve the wind turbine defect profile of each target wind turbine from the wind turbine defect profile database, and generate a high-risk combination alarm prompt for each target wind turbine using preset defect association prompt rules, and generate a maintenance work order for each target wind turbine based on the high-risk combination alarm prompt for each target wind turbine.

[0094] The application presentation layer 5 includes: a defect association rule engine 51, a confirmation and work order generation module 52, and a wind turbine card interface 53.

[0095] Optionally, the defect association rule engine 51 is used to match the defect types recorded in the defect profile of each target wind turbine with the preset defect association prompt rules, generate a high-risk combination alarm prompt for each target wind turbine, and send the high-risk combination alarm prompt for each target wind turbine to the confirmation and work order generation module 52.

[0096] In one optional embodiment, a single defect represents only a minor local anomaly. When two or more types of defects, such as vibration, video, fire protection, and SCADA, occur simultaneously, it often indicates a serious cascading failure of the equipment. In this embodiment, the judgment rules are preset based on the historical experience of site operation and maintenance, and the cross-verification of multi-source defects is used to automatically predict the risk of major failures. This can replace the manual cross-dimensional comparison of defects and avoid the problems of manual omission and delayed judgment.

[0097] For example, the defect association rule engine 51 retrieves the complete defect profile of a single wind turbine in the application presentation layer 5, and extracts the four types of fault texts and their corresponding severity levels for vibration defects, SCADA defects, video defects, and fire protection defects in the profile.

[0098] Furthermore, the system retrieves a complete set of pre-entered, configurable, preset defect association prompt rules from the backend. These rules can include trigger conditions and corresponding risk warning text. Examples include: R01 excessive vibration + video smoke; R02 abnormal gearbox vibration + high oil temperature; R03 video personnel intrusion + no work order.

[0099] Furthermore, all possible combinations of defects in the current wind turbine are matched and verified one by one with the triggering conditions of each rule. If a combination of defects completely matches the triggering conditions of a certain rule, the wind turbine is determined to have a complex high-risk fault. If no rule is matched, only the basic alarm for an independent single defect is retained, and no special high-risk warning is generated.

[0100] Furthermore, for successfully matched rules, corresponding preset prompts can be retrieved to generate a high-risk combination alarm specifically for that wind turbine. The prompts clearly indicate the type of fault combination and corresponding handling suggestions.

[0101] Furthermore, the wind turbine ID, complete wind turbine defect profile, and generated high-risk combination alarm prompts can be packaged into a set of associated data and sent to the confirmation and work order generation module 52.

[0102] Optionally, the confirmation and work order generation module 52 is used to generate a maintenance work order for each target wind turbine based on the high-risk combination alarm prompts for each target wind turbine.

[0103] In one optional embodiment, under the traditional operation and maintenance mode, operation and maintenance personnel need to manually enter work orders by extracting various fault information from vibration, electrical, video, and fire protection systems, which is cumbersome and prone to missing fault details. In this embodiment, the confirmation and work order generation module 52 automatically fills in all the contents of the work order by directly reusing the collected full-dimensional defect data, which can realize the one-click conversion of alarm information into work orders and simplify the pre-fault handling process.

[0104] For example, the work order generation module 52 continuously monitors the data push interface of the defect association rule engine 3 and receives a complete set of data including wind turbine ID, wind turbine defect profile, and high-risk combination alarm prompts.

[0105] Furthermore, it was confirmed that the work order generation module 52 can retrieve the unified maintenance work order standard template of the site. This template has reserved fields for filling in the wind turbine number, comprehensive fault level, multi-source fault details, risk handling suggestions, alarm generation time, etc.

[0106] Furthermore, by combining the retrieved standard template for maintenance work orders, it was confirmed that the work order generation module 52 can automatically complete the filling of all fields in the work order: ① Enter the fan ID into the equipment number field of the work order; ② Extract the overall level from the defect profile and mark the urgency of the work order; ③ Separate the four categories of defects—vibration, SCADA, video, and fire protection—and fill in each one in the fault details column; ④ Enter the high-risk combination alarm prompts generated by the engine into the risk reminder area of ​​the work order; ⑤ Automatically write the current system time as the work order creation time; Furthermore, a simplified work order can be generated for a single, independent defect, while a complete comprehensive work order can be generated for all defects of the entire wind turbine. Maintenance personnel can then select the appropriate option based on their actual needs.

[0107] Furthermore, once a work order is generated, the confirmation and work order generation module 52 supports online preview, one-click confirmation and archiving, and direct delivery to the on-site maintenance personnel's terminal. Moreover, the work orders generated in the confirmation and work order generation module 52 can permanently retain all fault traceability information, eliminating the need for manual supplementation of fault details.

[0108] Optionally, the wind turbine card interface 53 is used to mark and display the key data recorded in the wind turbine defect profile of each target wind turbine, wherein the mark is used to distinguish the severity of the defect of each target wind turbine.

[0109] In one optional embodiment, multiple independent systems were previously used to display defects on separate screens, requiring maintenance personnel to switch between multiple screens and manually summarize the same wind turbine faults. In this embodiment, the wind turbine card uses the wind turbine as the smallest display unit, which can integrate defects of all dimensions and distinguish alarm levels by color and icon. There is no need to switch between systems, which greatly reduces the manpower burden of monitoring and maintenance, while avoiding the omission of alarms from a single system.

[0110] For example, the wind turbine card interface 53 receives a full-field wind turbine defect profile from the application presentation layer 5, and independently generates a visual wind turbine card for each individual wind turbine. Furthermore, a unique wind turbine ID is displayed at the top of each card.

[0111] Furthermore, the comprehensive level range within the defect profile is read, and hierarchical visual labeling rules are configured. For example, emergency faults are labeled in red, general faults in orange, and minor defects awaiting confirmation in yellow, with the corresponding risk level icon displayed simultaneously at the top of the card.

[0112] Furthermore, key fault data within the profile can be displayed in zones according to defect classification. For example, it can be divided into four fixed display areas: vibration fault area, SCADA operation fault area, video visual anomaly area, and fire alarm area.

[0113] Furthermore, each area is directly filled with the corresponding defect text. If there are no abnormalities, "none" is displayed uniformly. If there are multi-source coupling faults, the high-risk warning text output by the defect association rule engine can be displayed in a prominent position on the card.

[0114] Furthermore, the cards support click-to-expand / collapse functionality. Specifically, in the collapsed state, only the turbine ID, overall risk level, and total number of faults are displayed; in the expanded state, the full details of the four types of defects, alarm update time, and multiple alarm sources can be displayed.

[0115] Furthermore, by displaying all wind turbine cards on a unified central monitoring screen and mobile terminal page for operation and maintenance, operation and maintenance personnel can simultaneously grasp the complete fault status of each wind turbine in the entire site by simply viewing all wind turbine cards on a single page, without having to switch between multiple monitoring systems.

[0116] Optionally, the multi-source defect aggregation engine 31 is also used to obtain a new initial multi-source defect dataset for multiple wind turbines and update the target multi-source defect dataset for each target wind turbine based on the new initial multi-source defect dataset.

[0117] In one optional embodiment, the wind turbine fault is in a dynamic process, exhibiting three states: new faults arise, faults self-eliminate, and faults continuously worsen and escalate. Furthermore, by continuously accessing new defect data and dynamically iterating the target multi-source defect dataset, database retention failures and delayed alarms can be avoided.

[0118] For example, the data source layer 2 continuously sends the initial multi-source defect datasets newly identified by each subsystem to the multi-source defect aggregation engine 31. Then, the multi-source defect aggregation engine 31 uses the target mapping table to match the wind turbine to which the newly added defect belongs, and compares it with the target multi-source defect dataset currently stored for that wind turbine.

[0119] Furthermore, based on the comparison results, dynamic updates can be performed in three scenarios: ① New fault: If there is no historical matching record for the new defect record, it will be directly appended to the multi-source defect dataset of the wind turbine target; ② Fault elimination: The subsystem synchronously pushes the fault recovery signal, the engine retrieves the corresponding historical aggregated alarms, and marks the defect as invalid; ③ Fault escalation: When a higher risk dimension alarm is added for the same fault, the overall risk level of the aggregated alarm is automatically increased, and the newly added anomaly details are added simultaneously; Furthermore, once the update is complete, the multi-source defect aggregation engine 31 will resend the iterated new target multi-source defect dataset to the data storage layer 4, overwriting the original old data, and simultaneously refresh the corresponding wind turbine file in the wind turbine defect profile database.

[0120] The wind turbine multi-source defect aggregation and centralized alarm system provided in this embodiment breaks down data barriers between multiple independent monitoring systems in a wind farm by building a four-layer integrated collaborative architecture. This enables automatic aggregation, standardized storage, intelligent risk assessment, and automatic work order distribution of all-dimensional defects from a single wind turbine. Furthermore, it completely solves the problems of maintenance personnel switching between multiple systems, fragmented alarms, slow fault location, and cumbersome handling procedures, significantly reducing the manpower load for site monitoring and simultaneously improving the confidence level of fault warnings and the efficiency of maintenance and handling.

[0121] According to an embodiment of the present invention, a method for summarizing and centrally alarming multi-source defects in wind turbines is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0122] This embodiment provides a method for summarizing and centrally alarming multi-source defects in wind turbines, which can be used in the aforementioned wind turbine multi-source defect summarization and centralized alarm system 1. Figure 2 This is a flowchart of a method for summarizing and centrally alarming multi-source defects in wind turbines according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: The data source layer collects the initial multi-source defect dataset of multiple wind turbines and sends the initial multi-source defect dataset of multiple wind turbines to the multi-source defect aggregation engine.

[0123] For details on the specific process, please refer to the functional description of data source layer 2 and the interaction process between data source layer 2 and multi-source defect aggregation engine 31. These will not be elaborated here.

[0124] In step S202, the multi-source defect aggregation engine obtains multiple unique identifiers of multiple wind turbines and point codes of each system in the data source layer, and establishes a target mapping table based on the multiple unique identifiers and multiple point codes.

[0125] For details on the process, please refer to the functional description of the multi-source defect aggregation engine 31 mentioned above; it will not be repeated here.

[0126] In step S203, the multi-source defect aggregation engine filters the initial multi-source defect datasets of multiple wind turbines according to the target mapping table and a preset time window to obtain the original multi-source defect datasets belonging to the same target wind turbine.

[0127] For details on the process, please refer to the functional description of the multi-source defect aggregation engine 31 mentioned above; it will not be repeated here.

[0128] In step S204, the multi-source defect aggregation engine classifies and deduplicates the original multi-source defect dataset of each target wind turbine to obtain the defect type and target multi-source defect dataset of each target wind turbine, and sends the defect type and target multi-source defect dataset of each target wind turbine to the data storage layer.

[0129] For details on the process, please refer to the functional description of the multi-source defect aggregation engine 31 and the interaction process between the multi-source defect aggregation engine 31 and the data storage layer 4. These details will not be repeated here.

[0130] In step S205, the data storage layer performs profiling on the defect type and target multi-source defect dataset of each target wind turbine according to the preset data structure template, thereby obtaining the wind turbine defect profile of each target wind turbine and constructing wind turbine defect profile data.

[0131] For the specific process, please refer to the functional description of data storage layer 4 above, which will not be repeated here.

[0132] Step S206: The application display layer retrieves the wind turbine defect profile of each target wind turbine from the wind turbine defect profile database, and uses preset defect association prompt rules to generate a high-risk combination alarm prompt for each target wind turbine, and generates a maintenance work order for each target wind turbine based on the high-risk combination alarm prompt for each target wind turbine.

[0133] For details on the process, please refer to the functional description of application presentation layer 5 and the interaction process between application presentation layer 5 and data storage layer 4. These details will not be repeated here.

[0134] The method for summarizing and centrally alarming multi-source defects in wind turbines provided in this embodiment processes multi-source wind turbine fault data through standardized and automated end-to-end processing. It forms a complete closed-loop process from data acquisition, unified aggregation, standardized storage to intelligent early warning and work order issuance. Furthermore, it implements multi-system alarm fusion and centralized management capabilities at the operational level, systematically solving the problems of independent operation and maintenance of multiple monitoring systems in wind farms, fragmented alarms, and low efficiency in fault handling, thus achieving less-staffed and intelligent operation and maintenance management of the wind farm.

[0135] In one example, a multi-source defect aggregation and centralized alarm system for wind turbines is provided. Its system architecture consists of four layers, such as... Figure 3As shown, it specifically includes: 1. The first layer is the data access layer, as shown in Table 1 below.

[0136] Table 1. Data Access Layer

[0137] 2. The second-layer multi-source defect convergence has the following functions: Function 1: Unified index for wind turbine IDs. Specifically, a mapping table is established: Location of each subsystem → Unified wind turbine ID. Example: Vibration channel A05 → Fan F05; SCADA point B03 → Fan F05; Camera C12 → Fan F05; Smoke detector D08 → Fan F05.

[0138] Function 2: Multi-source defect aggregation.

[0139] Collect all defects for the same wind turbine ID by time window (e.g., the most recent 1 hour); Classified by defect type: transmission, electrical, environmental, and security; Deduplication (merging alarms triggered by the same device at different times when they are captured by multiple subsystems).

[0140] For example, the evolution of a real wind turbine failure is shown in Table 2 below: Table 2. Evolution of a real wind turbine failure

[0141] As shown in Table 2, at time T4, one wind turbine simultaneously triggered alarms from four systems: vibration, SCADA, video, and fire protection. Without deduplication, the monitoring interface would display four independent alarms, meaning maintenance personnel would see: Vibration alarm: Gearbox malfunction; SCADA alarm: High oil temperature; Video alarm: Smoke detected; Fire alarm: Smoke detector triggered.

[0142] At this point, the maintenance personnel's first reaction is that they need to spend time comparing the four alarms back and forth between different systems to determine the correlation between them and confirm that they are caused by the same gearbox failure. In other words, if deduplication is not performed, problems such as information fragmentation, increased cognitive burden, and response delay will occur.

[0143] Furthermore, after deduplication, the display shows one aggregated alarm and one record, which can be expanded to show four sources. Therefore, deduplication enables multi-source confirmation, resulting in higher confidence, faster response, and immediate identification and handling of the fault cause. Simultaneously, alarm issues from four systems can be confirmed with a single click, eliminating the need for repeated operations.

[0144] For example, the typical display method after deduplication (within the wind turbine card) is as follows: Fan F15 → Emergency; [Severe Gearbox Failure] (Main Title) Vibration: Tooth surface wear (severe); SCADA: Oil temperature exceeds the limit (+15℃); Video: Smoke detection at the base of the tower; Fire safety: Smoke alarm.

[0145] Function 3: Defect status update.

[0146] New defect → Add new record; Defect disappears → automatically eliminated or eliminated after manual confirmation; Defect escalation → Change alarm level.

[0147] 3. Third-layer wind turbine defect profiling database. Specifically, the data structure template is as follows: Field → Sample Value; Fan ID → F05; Vibration defects → Gearbox tooth surface wear (severe); SCADA defect → Generator bearing temperature is too high (normal). Video defect → Suspected smoke at the bottom of the tower (to be confirmed); Fire safety defects → None; Overall Level → Emergency; Updated on 2025-05-08 at 10:23:00.

[0148] 4. Fourth layer: centralized alarm and interaction layer.

[0149] 4.1 The wind turbine card interface displays key data for maintenance and inspection. 4.2 Defect association prompts (rule engine), as shown in Table 3 below.

[0150] Table 3. Defect Association Rules

[0151] 4.3 Confirmation and Work Order Generation: Supports one-click generation of maintenance work orders for a single defect or the entire fan; the work order automatically carries all defect information of the fan (reducing information search time).

[0152] Furthermore, based on the above system, its specific implementation process is as follows: 1. Unified indexing by fan ID: Map the entry address of the monitoring object of all subsystems to a unified fan ID; vibration channel number, SCADA point, video channel, and fire detector are all bound to the corresponding fan.

[0153] 2. Multi-source defect summary (extracting result-oriented and guidance-oriented defects from different systems).

[0154] Vibration: Characteristics exceeding limits, trends abnormal; SCADA: Temperature exceeding limits, abnormal power, communication interruption; Video analytics: smoke detection, intrusion detection, and visual anomalies; Fire protection: Smoke / heat alarm.

[0155] 3. Centralized alarm interface: The centralized monitoring station performs cluster management, with the smallest unit being a single wind turbine. It also provides a terminal display screen for maintenance personnel, which facilitates a comprehensive understanding of the on-site wind turbine status during maintenance work. The display scheme is categorized by defect type (red / orange / yellow three levels), and details can be expanded to view them.

[0156] 4. Defect association prompt: When the same fan shows excessive vibration and visible smoke in the video at the same time, it will be automatically marked "urgent review recommended"; specific rules can be configured as needed according to on-site management requirements.

[0157] The wind turbine multi-source defect aggregation and centralized alarm system provided in this example has the following beneficial effects: 1. Maintenance personnel only need to focus on one interface, reducing the monitoring burden by more than 70%; 2. Multiple system defects of the same fan are displayed together to avoid omissions; 3. Association rules automatically highlight high-risk combinations, improving the accuracy of early warnings; 4. Shorten fault location time and improve handling efficiency.

[0158] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0159] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0160] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0161] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the wind turbine multi-source defect aggregation and centralized alarm method of the embodiments of the present invention.

[0162] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0163] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the wind turbine multi-source defect aggregation and centralized alarm method shown in the above embodiments is implemented.

[0164] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0165] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A system for summarizing and centrally alarming multi-source defects in wind turbines, characterized in that, The system includes: a data source layer, a data access and aggregation layer, a data storage layer, and an application display layer. The data access and aggregation layer includes a multi-source defect aggregation engine. The data source layer is used to monitor the vibration, operation, and location of multiple wind turbines, acquire the initial multi-source defect dataset of multiple wind turbines, and send the initial multi-source defect dataset of multiple wind turbines to the multi-source defect aggregation engine. The multi-source defect aggregation engine is used to filter the initial multi-source defect datasets of multiple wind turbines according to a preset time window based on the target mapping table, so as to obtain the original multi-source defect datasets belonging to the same target wind turbine. The multi-source defect aggregation engine is also used to classify and deduplicate the original multi-source defect dataset of each target wind turbine to obtain the defect type and target multi-source defect dataset of each target wind turbine, and send the defect type and target multi-source defect dataset of each target wind turbine to the data storage layer. The target multi-source defect dataset is used to reflect the defect status of the target wind turbine. The data storage layer is used to perform profiling processing on the defect type and the target multi-source defect dataset of each target wind turbine according to a preset data structure template, so as to obtain a wind turbine defect profile of each target wind turbine and construct a wind turbine defect profile database. The application presentation layer is used to retrieve the wind turbine defect profile of each target wind turbine from the wind turbine defect profile database, and generate a high-risk combination alarm prompt for each target wind turbine using preset defect association prompt rules, and generate a maintenance work order for each target wind turbine based on the high-risk combination alarm prompt for each target wind turbine.

2. The system according to claim 1, characterized in that, The data source layer includes: The vibration monitoring system is used to monitor the vibration of multiple wind turbines and acquire vibration defect data of multiple wind turbines. The data acquisition and monitoring control system is used to monitor the operation of multiple wind turbines and acquire data on multiple wind turbine operation abnormalities and defects. The video intelligent analysis system is used to monitor the environment at the sites of multiple wind turbines and acquire multiple visual defect data of multiple wind turbines; The fire alarm system is used to acquire multiple fire alarm defect data at the locations of multiple wind turbines.

3. The system according to claim 1, characterized in that, The multi-source defect aggregation engine is also used to obtain multiple unique identifiers of multiple wind turbines and point codes of each system in the data source layer, and to establish the target mapping table based on the multiple unique identifiers and multiple point codes.

4. The system according to claim 1, characterized in that, The multi-source defect aggregation engine is also used to acquire a new initial multi-source defect dataset for multiple wind turbines, and to update the target multi-source defect dataset for each target wind turbine based on the new initial multi-source defect dataset.

5. The system according to claim 1, characterized in that, The application presentation layer includes: a defect association rule engine and a confirmation and work order generation module; The defect association rule engine is used to match the defect type recorded in the defect profile of each target wind turbine with the preset defect association prompt rule, generate the high-risk combination alarm prompt for each target wind turbine, and send the high-risk combination alarm prompt for each target wind turbine to the confirmation and work order generation module. The confirmation and work order generation module is used to generate the maintenance work order for each target wind turbine based on the high-risk combination alarm prompts for each target wind turbine.

6. The system according to claim 1, characterized in that, The application presentation layer also includes: The wind turbine card interface is used to mark and display the key data recorded in the wind turbine defect profile of each target wind turbine. The markings are used to distinguish the severity of the defects of each target wind turbine.

7. A method for summarizing and centrally alarming multi-source defects in wind turbines, characterized in that, The method is used for the wind turbine multi-source defect aggregation and centralized alarm system according to any one of claims 1 to 6; the method includes: The data source layer collects the initial multi-source defect datasets of multiple wind turbines and sends the initial multi-source defect datasets of multiple wind turbines to the multi-source defect aggregation engine; The multi-source defect aggregation engine acquires multiple unique identifiers of multiple wind turbines and point codes of each system in the data source layer, and establishes a target mapping table based on the multiple unique identifiers and multiple point codes; The multi-source defect aggregation engine filters the initial multi-source defect datasets of multiple wind turbines according to the target mapping table and a preset time window to obtain the original multi-source defect datasets belonging to the same target wind turbine. The multi-source defect aggregation engine classifies and deduplicates the original multi-source defect dataset for each target wind turbine to obtain the defect type and target multi-source defect dataset for each target wind turbine, and sends the defect type and target multi-source defect dataset for each target wind turbine to the data storage layer. The data storage layer performs profiling on the defect type and the target multi-source defect dataset of each target wind turbine according to a preset data structure template, thereby obtaining a wind turbine defect profile for each target wind turbine and constructing wind turbine defect profile data. The application presentation layer retrieves the wind turbine defect profile of each target wind turbine from the wind turbine defect profile database, and uses preset defect association prompt rules to generate a high-risk combination alarm prompt for each target wind turbine, and generates a maintenance work order for each target wind turbine based on the high-risk combination alarm prompt.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to perform the wind turbine multi-source defect aggregation and centralized alarm method as described in claim 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the wind turbine multi-source defect aggregation and centralized alarm method as described in claim 7.

10. A computer program product, characterized in that, It includes computer instructions, which are used to cause the computer to execute the wind turbine multi-source defect aggregation and centralized alarm method as described in claim 7.

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