A wind farm sensing optical fiber abnormal event alarm method and related device

Abstract

The application provides a wind farm sensing optical fiber abnormal event alarm method and related device, and belongs to the technical field of abnormal event alarm. The application obtains vibration signals and temperature signals generated by sensing optical fibers arranged along a wind farm optical fiber route; extracts features of the vibration signals and temperature signals generated by the sensing optical fibers arranged along the wind farm optical fiber route to obtain feature vectors; inputs the feature vectors into a trained abnormal event classification model for abnormal event identification, or compares the feature vectors with a preset event feature fingerprint library to obtain an abnormal event; determines a type and a confidence level of the abnormal event; geolocates the abnormal event to obtain a geographic location where the abnormal event occurs; and obtains hierarchical alarm information based on the type and the confidence level of the abnormal event and the geographic location where the abnormal event occurs. The application solves the problem of low accuracy of wind farm sensing optical fiber abnormal event alarm.

Description

Technical Field

[0001] This invention belongs to the field of abnormal event alarm technology, specifically relating to a method and related device for alarming abnormal events in fiber optic sensing in wind power plants. Background Technology

[0002] In wind farms, the fiber optic communication cable connecting the wind turbine generators and the main control center is the "nerve center" of the wind farm's control and data network. Currently, this fiber optic cable is generally installed by direct burial or through ordinary protective conduits, which has the following significant drawbacks: 1. The protection method is relatively passive, the alarm accuracy is poor, and it is difficult to achieve early warning. Existing armored optical cables or corrugated pipes only provide limited passive physical protection. They are prone to missed alarms and low accuracy in the face of damage such as external construction damage or animal bites that have not yet caused communication interruption. They cannot issue alarms before the above threats (external construction damage, animal bites) cause actual interruption.

[0003] 2. Time consumed in fault location and recovery Once an interruption occurs, relying on traditional OTDR (Optical Time Domain Reflectometer) for coarse fault location (with an accuracy typically at the hundred-meter level), combined with manual troubleshooting, is a time-consuming and lengthy process, resulting in prolonged unplanned shutdowns of wind turbine generators and significant power generation losses.

[0004] 3. Extensive operation and maintenance model Relying on regular inspections and post-incident repairs, it lacks the ability to perceive the real-time status of hidden assets (buried optical cables), which is a passive and experience-based operation and maintenance method, resulting in high operation and maintenance costs and low fault handling efficiency.

[0005] 4. Poor maintainability and scalability When traditional buried fiber optic cables or pipelines are damaged, repair or upgrades require large-scale excavation, resulting in significant construction damage and high costs. It is also difficult to deploy new monitoring functions without impacting existing operations.

[0006] Therefore, there is an urgent need in this field for a technical solution that can achieve proactive alarm, accurate positioning, intelligent diagnosis, and is easy to construct and maintain. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related device for alarming abnormal events of fiber optic sensing in wind power plants, in order to solve the problem of low accuracy of alarms for abnormal events of fiber optic sensing in wind power plants in the prior art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for alarming abnormal events in fiber optic sensing at wind power plants, comprising the following steps: Acquire vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm. Feature vectors are obtained by extracting features from vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm. The feature vector is input into a trained abnormal event classification model to identify abnormal events, or the feature vector is compared with a preset event feature fingerprint database to obtain abnormal events. Determine the type and confidence level of the anomalous event; Geographically locate the abnormal event to obtain the geographical location where the abnormal event occurred; Based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred, hierarchical alarm information is obtained.

[0009] A further improvement of this invention is that the acquisition of vibration and temperature signals generated by the sensing optical fiber deployed along the optical fiber route of the wind power plant specifically involves: Vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm are acquired through distributed optical fiber sensing units.

[0010] A further improvement of the present invention is that, before extracting features from the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind power plant to obtain the feature vector, noise filtering is performed on the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind power plant.

[0011] A further improvement of this invention is that the step of geolocating the abnormal event to obtain the geographical location of the abnormal event specifically involves: Based on the principle of optical time-domain reflectometry, the abnormal event is geolocated to obtain the geographical location where the abnormal event occurred.

[0012] A further improvement of the present invention is that the graded alarm information is divided into three levels: early warning, alarm, and emergency alarm.

[0013] A further improvement of this invention is that, after obtaining the hierarchical alarm information, an operation and maintenance work order is generated based on the hierarchical alarm information.

[0014] A further improvement of this invention is that, after generating an operation and maintenance work order, the work order guides the operation and maintenance personnel to the geographical location where the abnormal event occurred to investigate potential hazards on site.

[0015] Secondly, the present invention provides a wind power plant fiber optic sensing abnormal event alarm system, comprising: The data acquisition module is used to acquire vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm. The feature extraction module is used to extract features from the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm to obtain feature vectors. The abnormal event determination module is used to input feature vectors into a trained abnormal event classification model for abnormal event identification, or to compare feature vectors with a preset event feature fingerprint database to obtain abnormal events. The event attribute information determination module is used to determine the type and confidence level of abnormal events; The geolocation module is used to geolocate abnormal events and obtain the geographical location where the abnormal event occurred. The alarm information determination module is used to obtain hierarchical alarm information based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred.

[0016] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for alarming abnormal events of sensing optical fibers in wind power plants.

[0017] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for alarming abnormal events of sensing optical fibers in wind power farms.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The wind power plant fiber optic sensing anomaly alarm method proposed in this invention acquires vibration and temperature signals generated by the sensing fibers deployed along the wind power plant's fiber optic route. This not only overcomes the incompleteness of information obtained from a single dimension but also enriches the anomaly discrimination criteria from the data source, effectively improving the accuracy of subsequent anomaly alarms. Furthermore, it determines the type and confidence level of the anomaly, distinguishing between false and true anomalies, eliminating invalid alarms with low confidence, and ensuring the authenticity and validity of subsequent alarm content (tiered alarm information). Moreover, this invention obtains tiered alarm information based on the type, confidence level, and geographical location of the anomaly, demonstrating that it considers multiple factors, including the type and confidence level of the anomaly, as well as its geographical location, when generating tiered alarm information. Attached Figure Description

[0019] Figure 1 This is a flowchart of the wind power plant fiber optic sensing abnormal event alarm method of the present invention; Figure 2 This is a schematic diagram of the wind power plant fiber optic sensing abnormal event alarm system of the present invention. Figure 3This is a schematic diagram of the overall architecture of the wind power plant fiber optic sensing abnormal event alarm system in Embodiment 4 of the present invention; Figure 4 This is a schematic diagram of the cross-sectional structure of the composite protective conduit unit (flexible form) in Embodiment 4 of the present invention; Figure 5 This is a schematic diagram of the internal structure of the junction box in Embodiment 4 of the present invention; Figure 6 This is a functional logic block diagram of the data fusion and analysis platform in Embodiment 4 of the present invention; Figure 7 This is a structural schematic diagram of the prefabricated modular slot box (rigid form) in Embodiment 4 of the present invention; Figure 8 This is a flowchart of the wind power plant sensing fiber optic abnormal event alarm method in Embodiment 4 of the present invention; Figure 9 This is a schematic diagram of the structure of the electronic device of the present invention; In the diagram: 1. Inner liner; 2. Sensing channel; 3. Main channel; 4. Mechanical armor layer; 5. Integrated protective layer; 6. Composite reinforcement layer; 7. Outer protective layer; 8. Main tank body; 9. Reinforcing rib wiring hole; 10. Reserved microtube bundle compartment; 11. Fixed sensor compartment; 12. Main communication optical cable compartment; 13. Auxiliary equipment compartment; 14. First top cover; 15. Connecting device; 16. Drain hole; 17. Second top cover; 18. Cable fixing and stress relief area; 19. Sensing fiber optic channel; 20. Communication optical cable splice box; 21. Management board; 22. Main power supply and backup power supply compartment; 23. Sensor cluster; 24. Edge computing compartment; 25. Cable inlet; 26. Cable outlet. Detailed Implementation

[0020] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0021] Example 1: The flowchart of the wind power plant sensing fiber optic abnormal event alarm method of the present invention is as follows: Figure 1 As shown, the wind power plant fiber optic sensing abnormal event alarm method of the present invention includes the following steps: S1. Acquire vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm; S2. Extract features from the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm to obtain feature vectors; S3. Input the feature vector into the trained abnormal event classification model to identify abnormal events, or compare the feature vector with the preset event feature fingerprint database to obtain abnormal events; S4. Determine the type and confidence level of the abnormal event; S5. Geographically locate the abnormal event to obtain the geographical location where the abnormal event occurred; S6. Based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred, hierarchical alarm information is obtained.

[0022] Example 2: A schematic diagram of the wind power plant fiber optic sensing abnormal event alarm system of the present invention is shown below. Figure 2 As shown, the wind power farm fiber optic sensing abnormal event alarm system of the present invention includes: The data acquisition module is used to acquire vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm. The feature extraction module is used to extract features from the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm to obtain feature vectors. The abnormal event determination module is used to input feature vectors into a trained abnormal event classification model for abnormal event identification, or to compare feature vectors with a preset event feature fingerprint database to obtain abnormal events. The event attribute information determination module is used to determine the type and confidence level of abnormal events; The geolocation module is used to geolocate abnormal events and obtain the geographical location where the abnormal event occurred. The alarm information determination module is used to obtain hierarchical alarm information based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred.

[0023] Example 3: The wind power plant fiber optic sensing anomaly alarm method of the present invention includes the following steps: S1. Acquire vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind power plant.

[0024] This step involves acquiring vibration and temperature signals generated by sensing optical fibers deployed along the fiber optic route of the wind farm. Specifically: Vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm are acquired through distributed optical fiber sensing units.

[0025] Specifically, vibration and temperature signals generated by sensing optical fibers deployed along the fiber optic route of the wind farm are acquired through distributed optical fiber sensing units (specifically including DAS (Distributed Acoustic Sensing) and DTS (Distributed Temperature Sensing).

[0026] S2. Extract features from the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind power plant to obtain feature vectors.

[0027] In this step, before extracting the features of the vibration and temperature signals generated by the sensing optical fibers deployed along the fiber optic route of the wind farm to obtain the feature vector, noise filtering is performed on the vibration and temperature signals generated by the sensing optical fibers deployed along the fiber optic route of the wind farm.

[0028] S3. Input the feature vector into the trained abnormal event classification model to identify abnormal events, or compare the feature vector with the preset event feature fingerprint database to obtain abnormal events.

[0029] S4. Determine the type and confidence level of the abnormal event.

[0030] S5. Geographically locate the abnormal event to obtain the geographical location where the abnormal event occurred.

[0031] This step involves geolocating the abnormal event to obtain its geographical location. Specifically: Based on the principle of optical time-domain reflectometry, the abnormal event is geolocated to obtain the geographical location where the abnormal event occurred.

[0032] S6. Based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred, hierarchical alarm information is obtained.

[0033] In this step, the alarm information is divided into three levels: early warning, alarm, and emergency alarm.

[0034] After obtaining the tiered alarm information in this step, an operation and maintenance work order is generated based on the tiered alarm information.

[0035] After generating the maintenance work order in this step, the maintenance work order guides the maintenance personnel to the geographical location where the abnormal event occurred to investigate potential hazards on site.

[0036] Example 4: The wind power plant fiber optic sensing anomaly event alarm method of this invention is specifically implemented through a wind power plant fiber optic sensing anomaly event alarm method (hereinafter referred to as the alarm system). The overall architecture diagram of the alarm system is shown below. Figure 3 As shown below, the alarm system is explained: The alarm system comprises a four-layer architecture: application layer, network and computing layer, sensing and demodulation layer, and physical protection and perception layer. The application layer is equipped with a data fusion and analysis platform (also known as a comprehensive monitoring and operation and maintenance platform), the sensing and demodulation layer is equipped with distributed fiber optic sensing units (also known as distributed fiber optic sensing hosts), and the physical protection and perception layer is equipped with composite protection conduit units and modular connection units.

[0037] The following describes the composite protective conduit unit, the distributed optical fiber sensing unit, the modular connection unit, and the data fusion and analysis platform: A. Composite protective conduit unit A schematic diagram of the cross-sectional structure of the composite protective conduit unit (flexible form) is shown below. Figure 4 As shown, this serves as the main protective structure, laid underground. The composite protective conduit unit is a multi-layered composite design, comprising, from the inside out: an inner lining layer 1 (for reducing micro-bending loss), a sensing channel 2 (for fixing the sensing optical fiber), a main channel 3 (for threading the communication optical cable), a mechanical armor layer 4 (for pressure and impact resistance), an integrated protective layer 5 (including biological protective materials such as rodent repellents), a composite reinforcement layer 6 (including metal tracer wires), and an outer protective layer 7 (weather-resistant and corrosion-resistant). The core feature of the composite protective conduit unit is that the conduit contains physically isolated main channels for the communication optical cable and sensing optical fibers.

[0038] In other embodiments, the inner liner 1 may also be a buffer layer, and the composite reinforcement layer 6 may also be a composite marking layer.

[0039] B. Distributed fiber optic sensing unit The distributed fiber optic sensing unit includes at least one sensing fiber deployed within the sensing fiber optic channel, and a distributed acoustic sensing host and a distributed temperature sensing host optically connected to it. The distributed fiber optic sensing unit transforms the entire sensing fiber into a continuous array of vibration and temperature sensors.

[0040] C. Modular connection unit Modular connection units are used to connect various sections of the protective conduit. Each modular connection unit includes a junction box. This junction box features a multi-layered sealing structure, a built-in standard fiber optic splicing and coiling mechanism, and integrates environmental sensors (such as temperature and humidity sensors) and a unique identification code. A schematic diagram of the internal structure of the junction box is shown below. Figure 5 As shown, the junction box specifically includes a second upper cover 17, a cable fixing and stress relief area 18 (optical cable anchoring point), a sensing fiber optic channel 19 (independent low-loss path), a communication optical cable splice box 20, a management board 21, a main power supply and backup power supply pool 22, a sensor cluster 23, an edge computing compartment 24, an inlet 25 (equipped with a three-level sealing structure, namely a primary sealing ring, a secondary gel, and a tertiary heat shrink tubing), and an outlet 26. In this embodiment, the junction box is designed to be openable for easy maintenance.

[0041] As another implementation, the edge computing module 24 can also be a communication module module 24.

[0042] 4. Data Fusion and Analysis Platform The functional logic module diagram of the data fusion and analysis platform is as follows: Figure 6As shown, the data fusion and analysis platform serves as the central processing and decision-making center of the alarm system, communicating with the distributed fiber optic sensing unit and the modular connection unit. The functions implemented by the data fusion and analysis platform are as follows: Feature extraction and anomaly event identification: Vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm are extracted to obtain feature vectors; the feature vectors are input into a trained anomaly event classification model for anomaly event identification, or the feature vectors are compared with a preset event feature fingerprint database to identify anomalies.

[0043] Determining the type and confidence level of anomalous events: Determine the type (including mechanical excavation, human knocking, animal biting, and cable overheating, etc.) and confidence level of anomalous events.

[0044] Geolocation: Based on the principle of optical time domain reflectance, the identified abnormal events are geolocated with meter-level accuracy to obtain the geographical location of the abnormal event.

[0045] Tiered Alarms: Based on the type and confidence level of the abnormal event, as well as the geographical location of the abnormal event, tiered alarm information is generated (tiered alarm information is divided into three levels: early warning, alarm and emergency alarm).

[0046] Operation and maintenance closed-loop management: Based on the hierarchical alarm information, the data fusion and analysis platform automatically or assistedly generates operation and maintenance work orders. The operation and maintenance work orders guide the operation and maintenance personnel to the geographical location where the abnormal event occurred, investigate the potential hazards on site, and feed back the handling results (investigation results) to the data fusion and analysis platform to update the asset status database and optimize the abnormal event classification model.

[0047] The alarm system in this embodiment also includes an electronic warning tape laid above the protective conduit, and a positioning marker stake set on the ground and bound to the geographic information system of the data fusion and analysis platform.

[0048] This embodiment of the composite protective conduit unit has two forms: a flexible conduit form (suitable for complex terrain) and a prefabricated modular slot form (suitable for straight trunk routes, featuring rapid assembly and openable maintenance). A structural diagram of the prefabricated modular slot (rigid form) is shown below. Figure 7 As shown, the prefabricated modular slot box includes a main slot body 8 (integratedly molded from FRP and high-strength modified PP), reinforcing rib wiring holes 9, a reserved microtube bundle compartment 10 (for capacity expansion), a fixed sensor compartment 11 (with pre-installed sensor cables), a main communication optical cable compartment 12 (for housing communication optical cables), an auxiliary equipment compartment 13 (including sensors, power supplies, and wireless modules), a first top cover 14 (with sealing colloid), a connecting device 15, and a drainage hole 16. The two can be connected via an adapter connector.

[0049] The alarm system in this embodiment supports a flexible sensor signal backhaul architecture. For sections undergoing local modifications, the sensor data can be reliably backhauled to the data fusion and analysis platform by using existing spare fiber cores for "jumping," laying micro-optical cables for "flying wires," or deploying solar-powered wireless relay nodes.

[0050] The flowchart of the wind power plant sensing fiber optic abnormal event alarm method of the present invention is as follows: Figure 8 As shown, the wind power plant fiber optic sensing abnormal event alarm method of the present invention includes the following steps: S1: Systematic laying: Along the planned route, after excavating and treating the trenches, lay composite protective conduits, and run sensing optical fibers through the composite protective conduit channels, and run communication optical cables through the main channels.

[0051] S2: Modular connection: Modular connection units are used to connect each conduit segment, complete fiber optic splicing and connection, ensure physical sealing and optical performance, and record the identity information and coordinates of all nodes.

[0052] S3: All-time sensing: Through distributed optical fiber sensing units, vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm are continuously collected.

[0053] S4: Diagnosis and Alarms: In the data fusion and analysis platform, perform the following steps: a. Noise filtering and feature extraction are performed on vibration and temperature signals generated by sensing optical fibers continuously collected along the fiber optic route of the wind farm. b. Compare the extracted feature vector with the preset event feature fingerprint database, or input the extracted feature vector into the trained abnormal event classification model to identify abnormal events and obtain abnormal events; c. Determine the type and confidence level of the abnormal event; d. Geographically locate the identified anomalous events to obtain their geographical locations; e. Generate tiered alarm information based on the type of abnormal event, the geographical location of the abnormal event, and the confidence level of the abnormal event.

[0054] S5: Precise Operation and Maintenance Closed Loop: Based on the hierarchical alarm information, the data fusion and analysis platform automatically or assistedly generates operation and maintenance work orders, guides operation and maintenance personnel to the geographical location where the abnormal event occurred, investigates potential hazards on site, and feeds back the processing results (investigation results) to the data fusion and analysis platform to update the asset status database and optimize the abnormal event classification model.

[0055] Compared with the prior art, the method of the present invention has the following advantages: a. Turn passive into active DAS technology can provide early warnings before physical disruption occurs due to external forces (such as when an excavator approaches or when personnel begin digging), thus achieving a fundamental shift from "post-failure repair" to "pre-event intervention".

[0056] b. Precision in diagnosis and localization By combining multi-dimensional data from DAS and DTS with AI (Artificial Intelligence) algorithms, it is possible not only to locate abnormal events with meter-level accuracy, but also to determine the nature of the abnormal events (what is causing the damage), greatly improving the efficiency of operation and maintenance response and the accuracy of decision-making.

[0057] c. Multi-functional fiber, full-state sensing The sensing fiber optic cable monitors both external threats and the cable's own health (breakpoints, overheating, water leakage), enabling transparent management of the asset throughout its entire lifecycle and in all its states.

[0058] d. The alarm system is highly maintainable and scalable. The modular design (conduit, junction box) supports partial replacement and repair; the sensing fiber optic channel is independent of the communication system and does not interfere with each other, and can be easily expanded with other sensing functions in the future.

[0059] e. Full-process digital closed loop From physical layer protection to data layer perception, and then to platform layer analysis and operation and maintenance management, a complete digital closed loop of "perception-diagnosis-decision-execution" has been formed, which significantly improves the operation and maintenance level of wind power plants.

[0060] Example 5: Please see Figure 9 As shown, the present invention also provides an electronic device 100 for a method of alarming abnormal events of sensing optical fiber in wind power plants; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0061] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the wind power plant sensing fiber optic abnormal event alarm method described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0062] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0063] The memory 101 in the electronic device 100 stores multiple instructions to implement a wind power farm sensing fiber optic abnormal event alarm method, and the processor 102 can execute the multiple instructions to achieve the following: Acquire vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm. Feature vectors are obtained by extracting features from vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm. The feature vector is input into a trained abnormal event classification model to identify abnormal events, or the feature vector is compared with a preset event feature fingerprint database to obtain abnormal events. Determine the type and confidence level of the anomalous event; Geographically locate the abnormal event to obtain the geographical location where the abnormal event occurred; Based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred, hierarchical alarm information is obtained.

[0064] Example 6: If the modules / units integrated in the electronic device 100 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0065] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for alarming abnormal events in fiber optic sensing in a wind power plant, characterized in that, Includes the following steps: Acquire vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm. Feature vectors are obtained by extracting features from vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm. The feature vector is input into a trained abnormal event classification model to identify abnormal events, or the feature vector is compared with a preset event feature fingerprint database to obtain abnormal events. Determine the type and confidence level of the anomalous event; Geographically locate the abnormal event to obtain the geographical location where the abnormal event occurred; Based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred, hierarchical alarm information is obtained.

2. The method for alarming abnormal events in fiber optic sensing in wind power plants according to claim 1, characterized in that, The acquisition of vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm specifically involves: Vibration and temperature signals generated by sensing optical fibers deployed along the optical fiber route of the wind farm are acquired through distributed optical fiber sensing units.

3. The method for alarming abnormal events in fiber optic sensing in wind power plants according to claim 1, characterized in that, Before extracting the feature vectors from the vibration and temperature signals generated by the sensing optical fibers deployed along the fiber optic route of the wind farm, noise filtering is performed on the vibration and temperature signals generated by the sensing optical fibers deployed along the fiber optic route of the wind farm.

4. The method for alarming abnormal events in fiber optic sensing in wind power plants according to claim 1, characterized in that, The process of geolocating the abnormal event to obtain the geographical location where the abnormal event occurred is specifically as follows: Based on the principle of optical time-domain reflectometry, the abnormal event is geolocated to obtain the geographical location where the abnormal event occurred.

5. The method for alarming abnormal events in fiber optic sensing in wind power plants according to claim 1, characterized in that, The tiered alarm information is divided into three levels: early warning, alarm, and emergency alarm.

6. The method for alarming abnormal events in fiber optic sensing in wind power plants according to claim 1, characterized in that, After obtaining the tiered alarm information, an operation and maintenance work order is generated based on the tiered alarm information.

7. The method for alarming abnormal events in fiber optic sensing in wind power plants according to claim 6, characterized in that, After a maintenance work order is generated, the work order guides maintenance personnel to the geographical location where the abnormal event occurred to investigate potential hazards on site.

8. A fiber optic sensing anomaly event alarm system for wind power farms, characterized in that, include: The data acquisition module is used to acquire vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm. The feature extraction module is used to extract features from the vibration and temperature signals generated by the sensing optical fibers deployed along the optical fiber route of the wind farm to obtain feature vectors. The abnormal event determination module is used to input feature vectors into a trained abnormal event classification model for abnormal event identification, or to compare feature vectors with a preset event feature fingerprint database to obtain abnormal events. The event attribute information determination module is used to determine the type and confidence level of abnormal events; The geolocation module is used to geolocate abnormal events and obtain the geographical location where the abnormal event occurred. The alarm information determination module is used to obtain hierarchical alarm information based on the type and confidence level of the abnormal event, as well as the geographical location where the abnormal event occurred.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the wind power plant sensing fiber optic abnormal event alarm method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wind power plant sensing fiber optic abnormal event alarm method as described in any one of claims 1 to 7.

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