An integrated device for real-time monitoring of wind turbine blade operating status and its usage method

Abstract

This invention discloses an integrated device and method for real-time monitoring of wind turbine blade operating status, comprising a multi-sensor sensing unit, a redundant data fusion unit, a complex terrain adaptation unit, a SCADA linkage and interaction unit, and a main control unit. The multi-sensor sensing unit heterogeneously integrates four types of modules: millimeter-wave radar, lidar, acoustic vibration monitoring, and load monitoring, forming a full-dimensional redundant sensing network covering blade surface morphology, internal structural damage, and structural loads. The redundant data fusion unit employs a two-level fusion algorithm to achieve redundant arbitration and complementary fusion of multi-source data. The complex terrain adaptation unit performs echo interference compensation and dynamic adjustment of the monitoring area for complex terrains such as cliff sides. This invention significantly improves the accuracy and reliability of blade monitoring under complex terrain conditions, achieves deep integration of the monitoring system and the turbine main control unit, and adapts to the safe operation requirements of mountain wind farms.

Description

Technical Field

[0001] This invention relates to the field of wind power equipment condition monitoring technology, specifically to an integrated device for real-time monitoring of the operating status of wind turbine blades and its usage method. Background Technology

[0002] Wind turbine blades are the core components for capturing wind energy, and their operating status directly affects the power generation efficiency and structural safety of the unit. With the large-scale development of mountain wind farms, wind turbines in complex terrains such as cliffs and lateral slopes face problems such as variable lateral winds, terrain echo interference, and harsh environmental conditions, which place higher demands on the accuracy and reliability of blade status monitoring. Therefore, there is a need for an integrated device and method for real-time monitoring of wind turbine blade operating status.

[0003] Currently used integrated devices and methods for real-time monitoring of wind turbine blade operation status mostly employ single-sensor solutions, such as vibration monitoring, image monitoring, or ordinary radar monitoring. These solutions have the following technical drawbacks: First, a single sensor can only cover a single dimension of the blade's condition, failing to simultaneously monitor surface morphology, internal damage, and structural loads. This leads to blind spots in conditions such as cliff-side terrain obstruction and rainy / foggy weather, resulting in a high rate of missed fault detection. Second, some solutions use simple stacking of multiple sensors, lacking redundancy arbitration and fusion mechanisms. When multiple data sources conflict, valid data cannot be identified, leading to insufficient monitoring accuracy in complex environments. Third, existing monitoring systems are mostly independent offline monitoring devices that only upload alarm signals to the SCADA system. They cannot achieve bidirectional real-time linkage between monitoring data and turbine operation data, cannot dynamically adjust turbine operating parameters based on blade status, and suffer from delayed fault response, making them unsuitable for the safe operation requirements in complex terrain. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated device and method for real-time monitoring of the operating status of wind turbine blades, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An integrated device and method for real-time monitoring of wind turbine blade operating status are disclosed, comprising a multi-sensor sensing unit, a redundant data fusion unit, a complex terrain adaptation unit, a SCADA linkage and interaction unit, and a main control unit. The multi-sensor sensing unit includes a millimeter-wave radar module, a lidar module, an acoustic vibration monitoring module, and a load monitoring module. These four types of sensors are heterogeneously arranged in the nacelle, tower, and blade root of the wind turbine, forming a full-dimensional redundant sensing network covering blade surface morphology, internal structural damage, and structural load. The redundant data fusion unit is connected to the multi-sensor sensing unit and performs hierarchical redundant fusion processing. It first arbitrates the reliability of monitoring data within the same dimension, then performs complementary fusion of cross-dimensional data to output blade operating status parameters. The complex terrain adaptation unit is connected to the redundant data fusion unit and incorporates a terrain compensation model. For complex terrain such as cliff sides, it filters out radar terrain echo interference by combining terrain elevation data and dynamically adjusts the sampling weight and monitoring area of ​​each sensor according to the wind direction. The SCADA linkage and interaction unit is connected to the main control unit and the wind turbine SCADA system to realize the interaction between monitoring data and SCADA. Two-way real-time interaction of operating data: The blade operating status parameters are uploaded to the SCADA system in real time. At the same time, the unit operating parameters of the SCADA system are read to dynamically correct the monitoring threshold. When the blade status is abnormal, control commands are sent to the SCADA system. The main control unit is used to coordinate the timing synchronization and logical operation of each unit.

[0007] Preferably, there are two millimeter-wave radar modules, symmetrically installed on both sides of the front of the nacelle. The radar beams cover the entire swept surface of the rotating blades, and the operating frequency band is 77GHz. These modules are used to collect data on blade spacing, deformation, and rotational speed in rain, fog, and dusty environments. The lidar module is installed on the side of the tower facing the cliff. It uses a single-line laser ranging radar with a sampling frequency of not less than 100Hz. This module is used to collect high-precision data on blade tip deformation, surface defects, and swept trajectory. The acoustic vibration monitoring module includes an acoustic emission sensor and a three-dimensional vibration sensor, arranged at the connection end face between the blade root and the hub. This module is used to collect acoustic vibration characteristic signals corresponding to internal cracks and delamination damage in the blades. The load monitoring module is an integrated load sensor connected in series in the connecting bolt group at the blade root. This module is used to collect real-time load data in the blade flapping and oscillation directions.

[0008] Preferably, the hierarchical redundancy fusion processing of the redundant data fusion unit specifically includes: Level 1 same-dimensional redundancy arbitration: the blade deformation and displacement data collected by millimeter-wave radar and lidar are unified into the same coordinate system, the deviation value of the two sets of data is calculated, when the deviation is less than a set threshold, a weighted average is used for output, when the deviation is greater than the set threshold, historical working condition data is retrieved for credibility verification, and a set of data with higher credibility is output; Level 2 cross-dimensional complementary fusion: the morphology data after same-dimensional arbitration, the damage feature data of the acoustic vibration monitoring module, and the stress data of the load monitoring module are fused at the feature level to construct the blade state feature vector, input to the pre-trained damage identification model, and output the blade damage type, damage degree and load margin parameters.

[0009] Preferably, the terrain compensation model of the complex terrain adaptation unit specifically performs the following steps:

[0010] Pre-store the topographic elevation map of the wind farm and obtain wind direction and wind speed data of the turbines in real time;

[0011] When the wind direction is towards the cliff side, the terrain contour data within the blade sweep range is extracted from the terrain elevation map, and terrain clutter is filtered out from the echo signals of millimeter-wave radar and lidar.

[0012] Based on the angle of the crosswind, the sampling weight of the millimeter-wave radar module is dynamically increased, while the sampling weight of the lidar module is decreased. At the same time, the monitoring angle range of the lidar is narrowed to avoid terrain-obstructed areas.

[0013] Preferably, the SCADA linkage interaction unit uses the OPC UA real-time communication protocol to interface with the SCADA system, with a bidirectional data interaction cycle of no more than 100ms. The unit operating parameters read by the SCADA linkage interaction unit include real-time wind speed, hub speed, pitch angle, and output power. When the blade load is detected to exceed the safety threshold or there is extended damage, the SCADA linkage interaction unit directly sends control commands to the SCADA system for pitch adjustment, power limitation, or shutdown, thereby realizing closed-loop linkage between monitoring and control.

[0014] An integrated device for real-time monitoring of the operating status of wind turbine blades and its usage method, implemented based on the device according to any one of claims 1-5, includes the following steps:

[0015] S1. Synchronous Acquisition: The main control unit triggers the four types of sensors of the multi-sensor sensing unit to acquire radar echo data, acoustic vibration data and load data of the blades synchronously.

[0016] S2. Terrain preprocessing: The complex terrain adaptation unit performs terrain clutter filtering and coordinate correction on radar data based on the current wind direction and terrain data, and performs working condition normalization processing on acoustic vibration and load data.

[0017] S3, Hierarchical Fusion: The redundant data fusion unit first performs first-level same-dimensional data arbitration, and then performs second-level cross-dimensional feature fusion to obtain blade operating status parameters;

[0018] S4. Status identification: Compare the blade operating status parameters with dynamic thresholds to identify the blade's health status, abnormality type, and risk level.

[0019] S5. Two-way linkage: The SCADA linkage interaction unit uploads blade status data to the SCADA system in real time, and reads the unit operating parameters from SCADA to update the monitoring thresholds; if a dangerous state is identified, it directly issues the corresponding control command to SCADA.

[0020] Preferably, in step S2, the normalization process specifically involves converting the acoustic vibration characteristic value and load value to the equivalent value under the rated operating condition based on the current wind speed and rotational speed, thereby eliminating the interference of operating condition fluctuations on the state judgment.

[0021] Preferably, in step S3, when any type of sensor malfunctions or its data becomes invalid, the redundant data fusion unit automatically switches to a degraded operation mode, calls the data from the other three types of sensors for fusion calculation, and maintains the basic functions of the monitoring system.

[0022] Preferably, the dynamic threshold in step S4 is corrected in real time by the unit operating parameters read by the SCADA linkage interaction unit, and the threshold correction coefficient is positively correlated with wind speed and rotational speed.

[0023] Preferably, in step S5, the control command level corresponds one-to-one with the danger level: Level 1 anomaly corresponds to power limiting command, Level 2 anomaly corresponds to propeller adjustment command, and Level 3 anomaly corresponds to emergency shutdown command.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. This invention integrates four types of heterogeneous sensors, covering three dimensions: surface morphology, internal damage, and structural load. Simultaneously, two types of radar form redundant backups in the same dimension. Compared to single-sensor solutions, the monitoring accuracy under complex terrain is improved by more than 35%. When a single sensor fails, the system can automatically degrade its operation while still maintaining over 85% monitoring accuracy, solving the problem of single-point failure in traditional solutions. It incorporates a terrain compensation model to actively filter out terrain echo interference for cliff-side terrain and dynamically adjusts sensor sampling weights and monitoring areas, solving the problem of decreased monitoring accuracy caused by terrain obstruction and echo clutter under lateral winds. It can be directly adapted to complex wind farm scenarios such as mountain cliffs, filling the technological gap in specialized monitoring of complex terrain.

[0026] 2. The OPC UA high-speed protocol is adopted to achieve bidirectional real-time interaction between the monitoring system and the SCADA system. It can not only upload monitoring data, but also read unit operating parameters to dynamically correct monitoring thresholds. In abnormal conditions, control commands can be directly issued. The fault response time is shortened from the traditional second level to less than 100ms. It realizes deep integration of status monitoring and operation control. The two-level fusion architecture of "same-dimensional arbitration + cross-dimensional fusion" effectively solves the problem of multi-source data conflict and improves the accuracy of status identification under complex operating conditions. The algorithm supports degraded operation, adapts to the health status of different sensors, and has strong engineering practicality. Attached Figure Description

[0027] Figure 1 This is a structural block diagram of the integrated device for real-time monitoring of the operating status of wind turbine blades, and its usage method, according to the present invention.

[0028] Figure 2 This is a flowchart of the hierarchical redundancy fusion processing of a real-time monitoring integrated device and method for wind turbine blade operating status according to the present invention.

[0029] Figure 3 This is a flowchart of the real-time monitoring and integration method for the operating status of a wind turbine blade, as described in this invention. Detailed Implementation

[0030] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figure 1 - Figure 3 This invention provides a technical solution for an integrated device and method for real-time monitoring of the operating status of wind turbine blades:

[0032] An integrated device and method for real-time monitoring of wind turbine blade operating status includes a multi-sensor sensing unit, a redundant data fusion unit, a complex terrain adaptation unit, a SCADA linkage and interaction unit, and a main control unit. The multi-sensor sensing unit comprises a millimeter-wave radar module, a lidar module, an acoustic vibration monitoring module, and a load monitoring module. These four types of sensors are heterogeneously arranged in the nacelle, tower, and blade root of the wind turbine, forming a full-dimensional redundant sensing network covering blade surface morphology, internal structural damage, and structural loads. The redundant data fusion unit is connected to the multi-sensor sensing unit and performs hierarchical redundant fusion processing. It first arbitrates the reliability of monitoring data within the same dimension, then performs complementary fusion of cross-dimensional data, outputting blade operating status parameters. The complex terrain adaptation unit is connected to the redundant data fusion unit and incorporates a terrain compensation model. For complex terrain such as cliff sides, it filters out radar terrain echo interference by combining terrain elevation data and dynamically adjusts the sampling weight and monitoring area of ​​each sensor according to the wind direction. The SCADA linkage and interaction unit is connected to both the main control unit and the wind turbine SCADA system to realize the interaction between monitoring data and SCADA. Two-way real-time interaction of operating data: The blade operating status parameters are uploaded to the SCADA system in real time. At the same time, the unit operating parameters of the SCADA system are read to dynamically correct the monitoring threshold. When the blade status is abnormal, control commands are sent to the SCADA system. The main control unit is used to coordinate the timing synchronization and logical operation of each unit.

[0033] Two millimeter-wave radar modules are symmetrically installed on both sides of the front of the nacelle. The radar beams cover the entire swept surface of the rotating blades, and the operating frequency band is 77GHz. They are used to collect data on blade spacing, deformation, and rotational speed in rain, fog, and dusty environments. The lidar module is installed on the side of the tower facing the cliff. It uses a single-line laser ranging radar with a sampling frequency of not less than 100Hz. It is used to collect high-precision data on blade tip deformation, surface defects, and swept trajectory. The acoustic vibration monitoring module includes acoustic emission sensors and triaxial vibration sensors, which are arranged at the connection end face between the blade root and the hub. It is used to collect acoustic vibration characteristic signals corresponding to internal cracks and delamination damage in the blades. The load monitoring module is an integrated load sensor connected in series in the connecting bolt group at the blade root. It is used to collect real-time load data in the blade flapping direction and oscillation direction.

[0034] The hierarchical redundancy fusion processing of the redundant data fusion unit specifically includes: Level 1 same-dimensional redundancy arbitration: the blade deformation and displacement data collected by millimeter-wave radar and lidar are unified into the same coordinate system, the deviation value of the two sets of data is calculated, when the deviation is less than a set threshold, a weighted average is used for output, when the deviation is greater than the set threshold, historical working condition data is retrieved for credibility verification, and a set of data with higher credibility is output; Level 2 cross-dimensional complementary fusion: the morphology data after same-dimensional arbitration, the damage feature data of the acoustic vibration monitoring module, and the stress data of the load monitoring module are fused at the feature level to construct the blade state feature vector, input to the pre-trained damage identification model, and output the blade damage type, damage degree and load margin parameters.

[0035] The terrain compensation model for complex terrain adaptation units specifically performs the following steps:

[0036] Pre-store the topographic elevation map of the wind farm and obtain wind direction and wind speed data of the turbines in real time;

[0037] When the wind direction is towards the cliff side, the terrain contour data within the blade sweep range is extracted from the terrain elevation map, and terrain clutter is filtered out from the echo signals of millimeter-wave radar and lidar.

[0038] Based on the angle of the crosswind, the sampling weight of the millimeter-wave radar module is dynamically increased, while the sampling weight of the lidar module is decreased. At the same time, the monitoring angle range of the lidar is narrowed to avoid terrain-obstructed areas.

[0039] The SCADA linkage interaction unit uses the OPC UA real-time communication protocol to interface with the SCADA system. The bidirectional data interaction cycle is no more than 100ms. The unit reads the unit's operating parameters, including real-time wind speed, hub speed, pitch angle, and output power. When the blade load exceeds the safety threshold or there is extended damage, the SCADA linkage interaction unit directly sends control commands to the SCADA system for pitch adjustment, power limitation, or shutdown, realizing closed-loop linkage between monitoring and control.

[0040] An integrated device for real-time monitoring of the operating status of wind turbine blades and its usage method, implemented based on the device of any one of claims 1-5, includes the following steps:

[0041] S1. Synchronous Acquisition: The main control unit triggers the four types of sensors of the multi-sensor sensing unit to acquire radar echo data, acoustic vibration data and load data of the blades synchronously.

[0042] S2. Terrain preprocessing: The complex terrain adaptation unit performs terrain clutter filtering and coordinate correction on radar data based on the current wind direction and terrain data, and performs working condition normalization processing on acoustic vibration and load data.

[0043] S3, Hierarchical Fusion: The redundant data fusion unit first performs first-level same-dimensional data arbitration, and then performs second-level cross-dimensional feature fusion to obtain blade operating status parameters;

[0044] S4. Status identification: Compare the blade operating status parameters with dynamic thresholds to identify the blade's health status, abnormality type, and risk level.

[0045] S5. Two-way linkage: The SCADA linkage interaction unit uploads blade status data to the SCADA system in real time, and reads the unit operating parameters from SCADA to update the monitoring thresholds; if a dangerous state is identified, it directly issues the corresponding control command to SCADA.

[0046] In step S2, the working condition normalization process specifically involves converting the acoustic vibration characteristic values ​​and load values ​​to equivalent values ​​under rated working conditions based on the current wind speed and rotational speed, thereby eliminating the interference of working condition fluctuations on state judgment.

[0047] In step S3, when any type of sensor malfunctions or its data becomes invalid, the redundant data fusion unit automatically switches to a degraded operation mode, calls the data from the other three types of sensors for fusion calculation, and maintains the basic functions of the monitoring system.

[0048] The dynamic threshold in step S4 is corrected in real time by the unit operating parameters read by the SCADA linkage interaction unit, and the threshold correction coefficient is positively correlated with wind speed and rotational speed.

[0049] In step S5, the control command levels correspond one-to-one with the danger levels: Level 1 anomaly corresponds to the power limiting command, Level 2 anomaly corresponds to the propeller adjustment command, and Level 3 anomaly corresponds to the emergency stop command.

[0050] It should be noted that this invention is an integrated device and method for real-time monitoring of the operating status of wind turbine blades. The main control unit outputs a synchronous trigger signal to control the millimeter-wave radar, lidar, acoustic vibration sensor, and load sensor to synchronously collect data in a unified time sequence, acquiring raw monitoring data. The complex terrain adaptation unit determines whether the current wind direction is lateral to a cliff. If so, it uses terrain elevation data to filter out terrain clutter and correct coordinates for the radar echo. Simultaneously, based on the current wind speed and rotational speed, it normalizes the acoustic vibration characteristic values ​​and load values ​​to convert them into equivalent values ​​under rated wind speed, eliminating interference from operating condition fluctuations. The redundant data fusion unit first performs a first-level same-dimensional data arbitration, processing the topographic data from the millimeter-wave radar and lidar, and then performs a second-level cross-dimensional feature fusion, combining acoustic vibration and load data to output blade operating status parameters. When any type of sensor fails, it automatically switches to a degraded mode, using data fusion from the remaining three types of sensors to maintain basic monitoring functions. The fused status parameters are compared with a dynamic threshold, which is determined by SCADA. The uploaded operating parameters are corrected in real time, identifying four levels of blade health: healthy, sub-healthy, abnormal, and dangerous. Simultaneously, the abnormality type is located, and the SCADA linkage interaction unit uploads blade status data to the SCADA system in real time. If an abnormality or higher level is identified, corresponding control commands are issued to the SCADA system according to the danger level, achieving closed-loop control. Simultaneously, SCADA operating data is continuously read to update the monitoring threshold for the next round. This enables all-weather real-time monitoring of blade status under cliff-side terrain, with a fault identification accuracy of ≥95% and a control response time of ≤80ms. This effectively improves the operational safety of wind turbine units in complex terrain. Thus, an integrated device and method for real-time monitoring of wind turbine blade operating status are completed.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An integrated device for real-time monitoring of the operating status of wind turbine blades and its usage method, characterized in that: The system comprises a multi-sensor sensing unit, a redundant data fusion unit, a complex terrain adaptation unit, a SCADA linkage and interaction unit, and a main control unit. The multi-sensor sensing unit includes a millimeter-wave radar module, a lidar module, an acoustic vibration monitoring module, and a load monitoring module. These four types of sensors are heterogeneously arranged in the nacelle, tower, and blade roots of the wind turbine, forming a full-dimensional redundant sensing network covering blade surface morphology, internal structural damage, and structural loads. The redundant data fusion unit, connected to the multi-sensor sensing unit, performs hierarchical redundant fusion processing. It first arbitrates the reliability of monitoring data within the same dimension, then performs complementary fusion of cross-dimensional data to output blade operating status parameters. The complex terrain adaptation unit, connected to the redundant data fusion unit, incorporates a terrain compensation model to filter radar terrain echo interference from cliff-like lateral complex terrain, combining terrain elevation data, and dynamically adjusts the sampling weights and monitoring areas of each sensor according to the wind direction. The SCADA linkage and interaction unit is connected to both the main control unit and the wind turbine SCADA system, enabling bidirectional real-time interaction between monitoring data and SCADA operating data: uploading blade operating status parameters to SCADA in real time. The system simultaneously reads the unit operating parameters from the SCADA system to dynamically correct the monitoring thresholds, and issues control commands to the SCADA system when the blade condition is abnormal. The main control unit is used to coordinate the timing synchronization and logical operation of each unit.

2. The integrated device for real-time monitoring of wind turbine blade operating status and its usage method according to claim 1, characterized in that: Two millimeter-wave radar modules are symmetrically installed on both sides of the front of the nacelle. The radar beams cover the entire swept surface of the rotating blades, and the operating frequency band is 77GHz. They are used to collect data on blade spacing, deformation, and rotational speed in rain, fog, and dusty environments. The lidar module is installed on the side of the tower facing the cliff. It uses a single-line laser ranging radar with a sampling frequency of not less than 100Hz. It is used to collect high-precision data on blade tip deformation, surface defects, and swept trajectory. The acoustic vibration monitoring module includes an acoustic emission sensor and a three-dimensional vibration sensor, which are arranged at the connection end face between the blade root and the hub. It is used to collect acoustic vibration characteristic signals corresponding to internal cracks and delamination damage in the blade. The load monitoring module is an integrated load sensor connected in series in the connecting bolt group at the blade root. It is used to collect real-time load data in the blade flapping direction and oscillation direction.

3. The integrated device for real-time monitoring of wind turbine blade operating status and its usage method according to claim 1, characterized in that: The hierarchical redundancy fusion processing of the redundant data fusion unit specifically includes: Level 1 same-dimensional redundancy arbitration: the blade deformation and displacement data collected by millimeter-wave radar and lidar are unified into the same coordinate system, the deviation value of the two sets of data is calculated, when the deviation is less than a set threshold, a weighted average is used for output, when the deviation is greater than the set threshold, historical working condition data is retrieved for credibility verification, and a set of data with higher credibility is output; Level 2 cross-dimensional complementary fusion: the morphology data after same-dimensional arbitration, the damage feature data of the acoustic vibration monitoring module, and the stress data of the load monitoring module are fused at the feature level to construct the blade state feature vector, input to the pre-trained damage identification model, and output the blade damage type, damage degree and load margin parameters.

4. The integrated device for real-time monitoring of wind turbine blade operating status and its usage method according to claim 1, characterized in that: The terrain compensation model of the complex terrain adaptation unit specifically performs the following steps: Pre-store the topographic elevation map of the wind farm and obtain wind direction and wind speed data of the turbines in real time; When the wind direction is towards the cliff side, the terrain contour data within the blade sweep range is extracted from the terrain elevation map, and terrain clutter is filtered out from the echo signals of millimeter-wave radar and lidar. Based on the angle of the crosswind, the sampling weight of the millimeter-wave radar module is dynamically increased, while the sampling weight of the lidar module is decreased. At the same time, the monitoring angle range of the lidar is narrowed to avoid terrain-obstructed areas.

5. The integrated device for real-time monitoring of wind turbine blade operating status and its usage method according to claim 1, characterized in that: The SCADA linkage interaction unit uses the OPC UA real-time communication protocol to interface with the SCADA system. The bidirectional data interaction cycle is no more than 100ms. The unit's operating parameters read by the SCADA linkage interaction unit include real-time wind speed, hub speed, pitch angle, and output power. When the blade load exceeds the safety threshold or there is extended damage, the SCADA linkage interaction unit directly sends control commands to the SCADA system for pitch adjustment, power limitation, or shutdown, thereby realizing closed-loop linkage between monitoring and control.

6. A real-time monitoring integrated device for wind turbine blade operating status and its usage method, implemented based on the device described in any one of claims 1-5, characterized in that: Includes the following steps: S1. Synchronous Acquisition: The main control unit triggers the four types of sensors of the multi-sensor sensing unit to acquire radar echo data, acoustic vibration data and load data of the blades synchronously. S2. Terrain preprocessing: The complex terrain adaptation unit performs terrain clutter filtering and coordinate correction on radar data based on the current wind direction and terrain data, and performs working condition normalization processing on acoustic vibration and load data. S3, Hierarchical Fusion: The redundant data fusion unit first performs first-level same-dimensional data arbitration, and then performs second-level cross-dimensional feature fusion to obtain blade operating status parameters; S4. Status identification: Compare the blade operating status parameters with dynamic thresholds to identify the blade's health status, abnormality type, and hazard level. S5. Two-way linkage: The SCADA linkage interaction unit uploads blade status data to the SCADA system in real time, and reads the unit operating parameters from SCADA to update the monitoring thresholds; if a dangerous state is identified, it directly issues the corresponding control command to SCADA.

7. The integrated device for real-time monitoring of wind turbine blade operating status and its method of use according to claim 6, characterized in that, In step S2, the normalization process specifically involves converting the acoustic vibration characteristic value and load value to the equivalent value under the rated operating condition based on the current wind speed and rotational speed, thereby eliminating the interference of operating condition fluctuations on the state judgment.

8. The integrated device for real-time monitoring of wind turbine blade operating status and its method of use according to claim 6, characterized in that, In step S3, when any type of sensor malfunctions or its data becomes invalid, the redundant data fusion unit automatically switches to a degraded operation mode, calls the data from the other three types of sensors for fusion calculation, and maintains the basic functions of the monitoring system.

9. The integrated device for real-time monitoring of wind turbine blade operating status and its method of use according to claim 6, characterized in that, The dynamic threshold in step S4 is corrected in real time by the unit operating parameters read by the SCADA linkage interaction unit, and the threshold correction coefficient is positively correlated with wind speed and rotational speed.

10. The integrated device for real-time monitoring of wind turbine blade operating status and its method of use according to claim 6, characterized in that, In step S5, the control command levels correspond one-to-one with the danger levels: Level 1 anomaly corresponds to the power limiting command, Level 2 anomaly corresponds to the propeller adjustment command, and Level 3 anomaly corresponds to the emergency shutdown command.

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